Security: Update .gitignore to exclude runtime data, sessions, and logs

- Add session files exclusion (core/relay/sessions/) - Add log directories exclusion (logs/, *-logs/, intake-logs/) - Add runtime database files exclusion (*.db, *.sqlite*) - Add temporary files exclusion (.cache/, *.tmp, *.temp) - Remove previously tracked session and database files from git These files contain runtime data and should not be version controlled.
tool improvment
2026-01-02 13:41:28 -05:00 · 2025-12-31 22:36:24 -05:00 · 2025-12-29 22:44:47 -05:00 · 2025-12-29 02:10:59 -05:00 · 2025-12-29 01:58:20 -05:00 · 2025-12-26 03:49:20 -05:00
255 changed files with 47830 additions and 853 deletions
@@ -1,11 +1,87 @@
-# Local backend (Ollama) — used by default for most calls.
+# ====================================
-LOCAL_BASE_URL=http://localhost:11434
+# 🌌 GLOBAL LYRA CONFIG
-LOCAL_MODEL=qwen2.5:7b-instruct
+# ====================================
-
+LOCAL_TZ_LABEL=America/New_York
-# Cloud backend (OpenAI) — used for harder reasoning and embeddings.
+DEFAULT_SESSION_ID=default
-OPENAI_API_KEY=
+
-CLOUD_MODEL=gpt-4o-mini
+
-EMBED_MODEL=text-embedding-3-small
+# ====================================
-
+# 🤖 LLM BACKEND OPTIONS
-# Where Lyra stores her memory.
+# ====================================
-LYRA_DB_PATH=data/lyra.db
+# Services choose which backend to use from these options
 # Primary: vLLM on MI50 GPU
 LLM_PRIMARY_PROVIDER=vllm
 LLM_PRIMARY_URL=http://10.0.0.43:8000
 LLM_PRIMARY_MODEL=/model
 # Secondary: Ollama on 3090 GPU
 LLM_SECONDARY_PROVIDER=ollama
 LLM_SECONDARY_URL=http://10.0.0.3:11434
 LLM_SECONDARY_MODEL=qwen2.5:7b-instruct-q4_K_M
 # Cloud: OpenAI
 LLM_CLOUD_PROVIDER=openai_chat
 LLM_CLOUD_URL=https://api.openai.com/v1
 LLM_CLOUD_MODEL=gpt-4o-mini
 OPENAI_API_KEY=sk-proj-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
 # Local Fallback: llama.cpp or LM Studio
 LLM_FALLBACK_PROVIDER=openai_completions
 LLM_FALLBACK_URL=http://10.0.0.41:11435
 LLM_FALLBACK_MODEL=llama-3.2-8b-instruct
 # Global LLM controls
 LLM_TEMPERATURE=0.7
 # ====================================
 # 🗄️ DATABASE CONFIGURATION
 # ====================================
 # Postgres (pgvector for NeoMem)
 POSTGRES_USER=neomem
 POSTGRES_PASSWORD=change_me_in_production
 POSTGRES_DB=neomem
 POSTGRES_HOST=neomem-postgres
 POSTGRES_PORT=5432
 # Neo4j Graph Database
 NEO4J_URI=bolt://neomem-neo4j:7687
 NEO4J_USERNAME=neo4j
 NEO4J_PASSWORD=change_me_in_production
 NEO4J_AUTH=neo4j/change_me_in_production
 # ====================================
 # 🧠 MEMORY SERVICES (NEOMEM)
 # ====================================
 NEOMEM_API=http://neomem-api:7077
 NEOMEM_API_KEY=generate_secure_random_token_here
 NEOMEM_HISTORY_DB=postgresql://neomem:change_me_in_production@neomem-postgres:5432/neomem
 # Embeddings configuration (used by NeoMem)
 EMBEDDER_PROVIDER=openai
 EMBEDDER_MODEL=text-embedding-3-small
 # ====================================
 # 🔌 INTERNAL SERVICE URLS
 # ====================================
 # Using container names for Docker network communication
 INTAKE_API_URL=http://intake:7080
 CORTEX_API=http://cortex:7081
 CORTEX_URL=http://cortex:7081/reflect
 CORTEX_URL_INGEST=http://cortex:7081/ingest
 RAG_API_URL=http://rag:7090
 RELAY_URL=http://relay:7078
 # Persona service (optional)
 PERSONA_URL=http://persona-sidecar:7080/current
 # ====================================
 # 🔧 FEATURE FLAGS
 # ====================================
 CORTEX_ENABLED=true
 MEMORY_ENABLED=true
 PERSONA_ENABLED=false
 DEBUG_PROMPT=true
@@ -0,0 +1,132 @@
 # ============================================================================
 # CORTEX LOGGING CONFIGURATION
 # ============================================================================
 # This file contains all logging-related environment variables for the
 # Cortex reasoning pipeline. Copy this to your .env file and adjust as needed.
 #
 # Log Detail Levels:
 #   minimal  - Only errors and critical events
 #   summary  - Stage completion + errors (DEFAULT - RECOMMENDED FOR PRODUCTION)
 #   detailed - Include raw LLM outputs, RAG results, timing breakdowns
 #   verbose  - Everything including intermediate states, full JSON dumps
 #
 # Quick Start:
 #   - For debugging weak links: LOG_DETAIL_LEVEL=detailed
 #   - For finding performance bottlenecks: LOG_DETAIL_LEVEL=detailed + VERBOSE_DEBUG=true
 #   - For production: LOG_DETAIL_LEVEL=summary
 #   - For silent mode: LOG_DETAIL_LEVEL=minimal
 # ============================================================================
 # -----------------------------
 # Primary Logging Level
 # -----------------------------
 # Controls overall verbosity across all components
 LOG_DETAIL_LEVEL=detailed
 # Legacy verbose debug flag (kept for compatibility)
 # When true, enables maximum logging including raw data dumps
 VERBOSE_DEBUG=false
 # -----------------------------
 # LLM Logging
 # -----------------------------
 # Enable raw LLM response logging (only works with detailed/verbose levels)
 # Shows full JSON responses from each LLM backend call
 # Set to "true" to see exact LLM outputs for debugging weak links
 LOG_RAW_LLM_RESPONSES=true
 # -----------------------------
 # Context Logging
 # -----------------------------
 # Show full raw intake data (L1-L30 summaries) in logs
 # WARNING: Very verbose, use only for deep debugging
 LOG_RAW_CONTEXT_DATA=false
 # -----------------------------
 # Loop Detection & Protection
 # -----------------------------
 # Enable duplicate message detection to prevent processing loops
 ENABLE_DUPLICATE_DETECTION=true
 # Maximum number of messages to keep in session history (prevents unbounded growth)
 # Older messages are trimmed automatically
 MAX_MESSAGE_HISTORY=100
 # Session TTL in hours - sessions inactive longer than this are auto-expired
 SESSION_TTL_HOURS=24
 # -----------------------------
 # NeoMem / RAG Logging
 # -----------------------------
 # Relevance score threshold for NeoMem results
 RELEVANCE_THRESHOLD=0.4
 # Enable NeoMem long-term memory retrieval
 NEOMEM_ENABLED=false
 # -----------------------------
 # Autonomous Features
 # -----------------------------
 # Enable autonomous tool invocation (RAG, WEB, WEATHER, CODEBRAIN)
 ENABLE_AUTONOMOUS_TOOLS=true
 # Confidence threshold for autonomous tool invocation (0.0 - 1.0)
 AUTONOMOUS_TOOL_CONFIDENCE_THRESHOLD=0.6
 # Enable proactive monitoring and suggestions
 ENABLE_PROACTIVE_MONITORING=true
 # Minimum priority for proactive suggestions to be included (0.0 - 1.0)
 PROACTIVE_SUGGESTION_MIN_PRIORITY=0.6
 # ============================================================================
 # EXAMPLE LOGGING OUTPUT AT DIFFERENT LEVELS
 # ============================================================================
 #
 # LOG_DETAIL_LEVEL=summary (RECOMMENDED):
 # ────────────────────────────────────────────────────────────────────────────
 # ✅ [LLM] PRIMARY | 14:23:45.123 | Reply: Based on your question about...
 # 📊 Context | Session: abc123 | Messages: 42 | Last: 5.2min | RAG: 3 results
 # 🧠 Monologue | question | Tone: curious
 # ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 # 📤 Output: 342 characters
 # ────────────────────────────────────────────────────────────────────────────
 #
 # LOG_DETAIL_LEVEL=detailed (FOR DEBUGGING):
 # ────────────────────────────────────────────────────────────────────────────
 # 🚀 PIPELINE START | Session: abc123 | 14:23:45.123
 # 📝 User: What is the meaning of life?
 # ────────────────────────────────────────────────────────────────────────────
 # 🧠 LLM CALL | Backend: PRIMARY | 14:23:45.234
 # ────────────────────────────────────────────────────────────────────────────
 # 📝 Prompt: You are Lyra, a thoughtful AI assistant...
 # 💬 Reply: Based on philosophical perspectives, the meaning...
 # ╭─ RAW RESPONSE ────────────────────────────────────────────────────────────
 # │ {
 # │   "choices": [
 # │     {
 # │       "message": {
 # │         "content": "Based on philosophical perspectives..."
 # │       }
 # │     }
 # │   ]
 # │ }
 # ╰───────────────────────────────────────────────────────────────────────────
 #
 # ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 # ⏱️  Stage Timings:
 #    context        :   150ms ( 12.0%)
 #    identity       :    10ms (  0.8%)
 #    monologue      :   200ms ( 16.0%)
 #    reasoning      :   450ms ( 36.0%)
 #    refinement     :   300ms ( 24.0%)
 #    persona        :   140ms ( 11.2%)
 # ────────────────────────────────────────────────────────────────────────────
 #
 # LOG_DETAIL_LEVEL=verbose (MAXIMUM DEBUG):
 # Same as detailed but includes:
 # - Full 50+ line raw JSON dumps
 # - Complete intake data structures
 # - All intermediate processing states
 # - Detailed traceback on errors
 # ============================================================================
@@ -1,37 +1,83 @@
-# Python
+# =============================
 # 📦 General
 # =============================
 __pycache__/
-*.py[cod]
+*.pyc
-*.egg-info/
+*.log
-.pytest_cache/
+/.vscode/
-.ruff_cache/
+.vscode/
-.mypy_cache/
+# =============================
-build/
+# 🔐 Environment files (NEVER commit secrets!)
-dist/
+# =============================
-
+# Ignore all .env files
 # Virtual environments
 .venv/
 venv/
 env/
 # Env files (never commit secrets)
 .env
 .env.local
 .env.*.local
-!.env.example
+**/.env
 **/.env.local
-# Local data
+# BUT track .env.example templates (safe to commit)
-data/
+!.env.example
 !**/.env.example
 # Ignore backup directory
 .env-backups/
 # =============================
 # 🐳 Docker volumes (HUGE)
 # =============================
 volumes/
 */volumes/
 # =============================
 # 📚 Databases & vector stores
 # =============================
 postgres_data/
 neo4j_data/
 */postgres_data/
 */neo4j_data/
 rag/chromadb/
 rag/*.sqlite3
 rag/chatlogs/
 rag/lyra-chatlogs/
 # =============================
 # 🤖 Model weights (big)
 # =============================
 models/
 *.gguf
 *.bin
 *.pt
 *.safetensors
 # =============================
 # 📦 Node modules (installed via npm)
 # =============================
 node_modules/
 core/relay/node_modules/
 # =============================
 # 💬 Runtime data & sessions
 # =============================
 # Session files (contain user conversation data)
 core/relay/sessions/
 **/sessions/
 *.jsonl
 # Log directories
 logs/
 **/logs/
 *-logs/
 intake-logs/
 # Database files (generated at runtime)
 *.db
 *.sqlite
 *.sqlite3
 neomem_history/
 **/neomem_history/
-# IDE / OS
+# Temporary and cache files
-.vscode/
+.cache/
-.idea/
+*.tmp
-.DS_Store
+*.temp
 # Logs
 *.log
 #lyra Stuff
 /core/relay/sessions/
@@ -0,0 +1,91 @@
 # Deprecated Files - Safe to Delete
 This file lists all deprecated files that can be safely deleted after verification.
 ## Files Marked for Deletion
 ### Docker Compose Files
 #### `/core/docker-compose.yml.DEPRECATED`
 - **Status**: DEPRECATED
 - **Reason**: All services consolidated into main `/docker-compose.yml`
 - **Replaced by**: `/docker-compose.yml` (relay service now has complete config)
 - **Safe to delete**: Yes, after verifying main docker-compose works
 ### Environment Files
 All original `.env` files have been consolidated. Backups exist in `.env-backups/` directory.
 #### Previously Deleted (Already Done)
 - ✅ `/core/.env` - Deleted (redundant with root .env)
 ### Experimental/Orphaned Files
 #### `/core/env experiments/` (entire directory)
 - **Status**: User will handle separately
 - **Contains**: `.env`, `.env.local`, `.env.openai`
 - **Action**: User to review and clean up
 ## Verification Steps Before Deleting
 Before deleting the deprecated files, verify:
 1. **Test main docker-compose.yml works:**
   ```bash
   cd /home/serversdown/project-lyra
   docker-compose down
   docker-compose up -d
   docker-compose ps  # All services should be running
   ```
 2. **Verify relay service has correct config:**
   ```bash
   docker exec relay env | grep -E "LLM_|NEOMEM_|OPENAI"
   docker exec relay ls -la /app/sessions  # Sessions volume mounted
   ```
 3. **Test relay functionality:**
   - Send a test message through relay
   - Verify memory storage works
   - Confirm LLM backend connections work
 ## Deletion Commands
 After successful verification, run:
 ```bash
 cd /home/serversdown/project-lyra
 # Delete deprecated docker-compose file
 rm core/docker-compose.yml.DEPRECATED
 # Optionally clean up backup directory after confirming everything works
 # (Keep backups for at least a few days/weeks)
 # rm -rf .env-backups/
 ```
 ## Files to Keep
 These files should **NOT** be deleted:
 - ✅ `.env` (root) - Single source of truth
 - ✅ `.env.example` (root) - Security template (commit to git)
 - ✅ `cortex/.env` - Service-specific config
 - ✅ `cortex/.env.example` - Security template (commit to git)
 - ✅ `neomem/.env` - Service-specific config
 - ✅ `neomem/.env.example` - Security template (commit to git)
 - ✅ `intake/.env` - Service-specific config
 - ✅ `intake/.env.example` - Security template (commit to git)
 - ✅ `rag/.env.example` - Security template (commit to git)
 - ✅ `docker-compose.yml` - Main orchestration file
 - ✅ `ENVIRONMENT_VARIABLES.md` - Documentation
 - ✅ `.gitignore` - Git configuration
 ## Backup Information
 All original `.env` files backed up to:
 - Location: `/home/serversdown/project-lyra/.env-backups/`
 - Timestamp: `20251126_025334`
 - Files: 6 original .env files
 Keep backups until you're confident the new setup is stable (recommended: 2-4 weeks).
@@ -0,0 +1,178 @@
 # Logging System Migration Complete
 ## ✅ What Changed
 The old `VERBOSE_DEBUG` logging system has been completely replaced with the new structured `LOG_DETAIL_LEVEL` system.
 ### Files Modified
 1. **[.env](.env)** - Removed `VERBOSE_DEBUG`, cleaned up duplicate `LOG_DETAIL_LEVEL` settings
 2. **[cortex/.env](cortex/.env)** - Removed `VERBOSE_DEBUG` from cortex config
 3. **[cortex/router.py](cortex/router.py)** - Replaced `VERBOSE_DEBUG` checks with `LOG_DETAIL_LEVEL`
 4. **[cortex/context.py](cortex/context.py)** - Replaced `VERBOSE_DEBUG` with `LOG_DETAIL_LEVEL`, removed verbose file logging setup
 ## 🎯 New Logging Configuration
 ### Single Environment Variable
 Set `LOG_DETAIL_LEVEL` in your `.env` file:
 ```bash
 LOG_DETAIL_LEVEL=detailed
 ```
 ### Logging Levels
 | Level | Lines/Message | What You See |
 |-------|---------------|--------------|
 | **minimal** | 1-2 | Only errors and critical events |
 | **summary** | 5-7 | Pipeline completion, errors, warnings (production mode) |
 | **detailed** | 30-50 | LLM outputs, timing breakdowns, context (debugging mode) |
 | **verbose** | 100+ | Everything including raw JSON dumps (deep debugging) |
 ## 📊 What You Get at Each Level
 ### Summary Mode (Production)
 ```
 📊 Context | Session: abc123 | Messages: 42 | Last: 5.2min | RAG: 3 results
 🧠 Monologue | question | Tone: curious
 ====================================================================================================
 ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 ====================================================================================================
 📤 Output: 342 characters
 ====================================================================================================
 ```
 ### Detailed Mode (Debugging - RECOMMENDED)
 ```
 ====================================================================================================
 🚀 PIPELINE START | Session: abc123 | 14:23:45.123
 ====================================================================================================
 📝 User: What is the meaning of life?
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 🧠 LLM CALL | Backend: PRIMARY | 14:23:45.234
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 📝 Prompt: You are Lyra, analyzing the user's question...
 💬 Reply: Based on the context provided, here's my analysis...
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 📊 Context | Session: abc123 | Messages: 42 | Last: 5.2min | RAG: 3 results
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 [CONTEXT] Session abc123 | User: What is the meaning of life?
 ────────────────────────────────────────────────────────────────────────────────────────────────────
  Mode: default | Mood: neutral | Project: None
  Tools: RAG, WEB, WEATHER, CODEBRAIN, POKERBRAIN
  ╭─ INTAKE SUMMARIES ────────────────────────────────────────────────
  │ L1  : Last message discussed philosophy...
  │ L5  : Recent 5 messages covered existential topics...
  │ L10 : Past 10 messages showed curiosity pattern...
  ╰───────────────────────────────────────────────────────────────────
  ╭─ RAG RESULTS (3) ──────────────────────────────────────────────
  │ [1] 0.923 | Previous discussion about purpose...
  │ [2] 0.891 | Note about existential philosophy...
  │ [3] 0.867 | Memory of Viktor Frankl discussion...
  ╰───────────────────────────────────────────────────────────────────
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 🧠 Monologue | question | Tone: curious
 ====================================================================================================
 ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 ====================================================================================================
 ⏱️  Stage Timings:
   context        :   150ms ( 12.0%)
   identity       :    10ms (  0.8%)
   monologue      :   200ms ( 16.0%)
   tools          :     0ms (  0.0%)
   reflection     :    50ms (  4.0%)
   reasoning      :   450ms ( 36.0%)  ← BOTTLENECK!
   refinement     :   300ms ( 24.0%)
   persona        :   140ms ( 11.2%)
   learning       :    50ms (  4.0%)
 📤 Output: 342 characters
 ====================================================================================================
 ```
 ### Verbose Mode (Maximum Debug)
 Same as detailed, plus:
 - Full raw JSON responses from LLMs (50-line boxes)
 - Complete intake data structures
 - Stack traces on errors
 ## 🚀 How to Use
 ### For Finding Weak Links (Your Use Case)
 ```bash
 # In .env:
 LOG_DETAIL_LEVEL=detailed
 # Restart services:
 docker-compose restart cortex relay
 ```
 You'll now see:
 - ✅ Which LLM backend is used
 - ✅ What prompts are sent to each LLM
 - ✅ What each LLM responds with
 - ✅ Timing breakdown showing which stage is slow
 - ✅ Context being used (RAG, intake summaries)
 - ✅ Clean, hierarchical structure
 ### For Production
 ```bash
 LOG_DETAIL_LEVEL=summary
 ```
 ### For Deep Debugging
 ```bash
 LOG_DETAIL_LEVEL=verbose
 ```
 ## 🔍 Finding Performance Bottlenecks
 With `detailed` mode, look for:
 1. **Slow stages in timing breakdown:**
   ```
   reasoning      :  3450ms ( 76.0%)  ← THIS IS YOUR BOTTLENECK!
   ```
 2. **Backend failures:**
   ```
   ⚠️  [LLM] PRIMARY failed | 14:23:45.234 | Connection timeout
   ✅ [LLM] SECONDARY | Reply: Based on...  ← Fell back to secondary
   ```
 3. **Loop detection:**
   ```
   ⚠️  DUPLICATE MESSAGE DETECTED | Session: abc123
   🔁 LOOP DETECTED - Returning cached context
   ```
 ## 📁 Removed Features
 The following old logging features have been removed:
 - ❌ `VERBOSE_DEBUG` environment variable (replaced with `LOG_DETAIL_LEVEL`)
 - ❌ File logging to `/app/logs/cortex_verbose_debug.log` (use `docker logs` instead)
 - ❌ Separate verbose handlers in Python logging
 - ❌ Per-module verbose flags
 ## ✨ New Features
 - ✅ Single unified logging configuration
 - ✅ Hierarchical, scannable output
 - ✅ Collapsible data sections (boxes)
 - ✅ Stage timing always shown in detailed mode
 - ✅ Performance profiling built-in
 - ✅ Loop detection and warnings
 - ✅ Clean error formatting
 ---
 **The logging is now clean, concise, and gives you exactly what you need to find weak links!** 🎯
@@ -0,0 +1,176 @@
 # Cortex Logging Quick Reference
 ## 🎯 TL;DR
 **Finding weak links in the LLM chain?**
 ```bash
 export LOG_DETAIL_LEVEL=detailed
 export VERBOSE_DEBUG=true
 ```
 **Production use?**
 ```bash
 export LOG_DETAIL_LEVEL=summary
 ```
 ---
 ## 📊 Log Levels Comparison
 | Level | Output Lines/Message | Use Case | Raw LLM Output? |
 |-------|---------------------|----------|-----------------|
 | **minimal** | 1-2 | Silent production | ❌ No |
 | **summary** | 5-7 | Production (DEFAULT) | ❌ No |
 | **detailed** | 30-50 | Debugging, finding bottlenecks | ✅ Parsed only |
 | **verbose** | 100+ | Deep debugging, seeing raw data | ✅ Full JSON |
 ---
 ## 🔍 Common Debugging Tasks
 ### See Raw LLM Outputs
 ```bash
 export LOG_DETAIL_LEVEL=verbose
 ```
 Look for:
 ```
 ╭─ RAW RESPONSE ────────────────────────────────────
 │ { "choices": [ { "message": { "content": "..." } } ] }
 ╰───────────────────────────────────────────────────
 ```
 ### Find Performance Bottlenecks
 ```bash
 export LOG_DETAIL_LEVEL=detailed
 ```
 Look for:
 ```
 ⏱️  Stage Timings:
   reasoning      :  3450ms ( 76.0%)  ← SLOW!
 ```
 ### Check Which RAG Memories Are Used
 ```bash
 export LOG_DETAIL_LEVEL=detailed
 ```
 Look for:
 ```
 ╭─ RAG RESULTS (5) ──────────────────────────────
 │ [1] 0.923 | Memory content...
 ```
 ### Detect Loops
 ```bash
 export ENABLE_DUPLICATE_DETECTION=true  # (default)
 ```
 Look for:
 ```
 ⚠️  DUPLICATE MESSAGE DETECTED
 🔁 LOOP DETECTED - Returning cached context
 ```
 ### See All Backend Failures
 ```bash
 export LOG_DETAIL_LEVEL=summary  # or higher
 ```
 Look for:
 ```
 ⚠️  [LLM] PRIMARY failed | Connection timeout
 ⚠️  [LLM] SECONDARY failed | Model not found
 ✅ [LLM] CLOUD | Reply: Based on...
 ```
 ---
 ## 🛠️ Environment Variables Cheat Sheet
 ```bash
 # Verbosity Control
 LOG_DETAIL_LEVEL=detailed        # minimal | summary | detailed | verbose
 VERBOSE_DEBUG=false              # true = maximum verbosity (legacy)
 # Raw Data Visibility
 LOG_RAW_CONTEXT_DATA=false       # Show full intake L1-L30 dumps
 # Loop Protection
 ENABLE_DUPLICATE_DETECTION=true  # Detect duplicate messages
 MAX_MESSAGE_HISTORY=100          # Trim history after N messages
 SESSION_TTL_HOURS=24             # Expire sessions after N hours
 # Features
 NEOMEM_ENABLED=false             # Enable long-term memory
 ENABLE_AUTONOMOUS_TOOLS=true     # Enable tool invocation
 ENABLE_PROACTIVE_MONITORING=true # Enable suggestions
 ```
 ---
 ## 📋 Sample Output
 ### Summary Mode (Default - Production)
 ```
 ✅ [LLM] PRIMARY | 14:23:45.123 | Reply: Based on your question...
 📊 Context | Session: abc123 | Messages: 42 | Last: 5.2min | RAG: 5 results
 🧠 Monologue | question | Tone: curious
 ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 📤 Output: 342 characters
 ```
 ### Detailed Mode (Debugging)
 ```
 ════════════════════════════════════════════════════════════════════════════
 🚀 PIPELINE START | Session: abc123 | 14:23:45.123
 ════════════════════════════════════════════════════════════════════════════
 📝 User: What is the meaning of life?
 ────────────────────────────────────────────────────────────────────────────
 ────────────────────────────────────────────────────────────────────────────
 🧠 LLM CALL | Backend: PRIMARY | 14:23:45.234
 ────────────────────────────────────────────────────────────────────────────
 📝 Prompt: You are Lyra, a thoughtful AI assistant...
 💬 Reply: Based on philosophical perspectives...
 📊 Context | Session: abc123 | Messages: 42 | Last: 5.2min | RAG: 5 results
  ╭─ RAG RESULTS (5) ──────────────────────────────
  │ [1] 0.923 | Previous philosophy discussion...
  │ [2] 0.891 | Existential note...
  ╰────────────────────────────────────────────────
 ════════════════════════════════════════════════════════════════════════════
 ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 ════════════════════════════════════════════════════════════════════════════
 ⏱️  Stage Timings:
   context        :   150ms ( 12.0%)
   reasoning      :   450ms ( 36.0%)  ← Largest component
   persona        :   140ms ( 11.2%)
 📤 Output: 342 characters
 ════════════════════════════════════════════════════════════════════════════
 ```
 ---
 ## ⚡ Quick Troubleshooting
 | Symptom | Check | Fix |
 |---------|-------|-----|
 | **Logs too verbose** | Current level | Set `LOG_DETAIL_LEVEL=summary` |
 | **Can't see LLM outputs** | Current level | Set `LOG_DETAIL_LEVEL=detailed` or `verbose` |
 | **Repeating operations** | Loop warnings | Check for `🔁 LOOP DETECTED` messages |
 | **Slow responses** | Stage timings | Look for stages >1000ms in detailed mode |
 | **Missing RAG data** | NEOMEM_ENABLED | Set `NEOMEM_ENABLED=true` |
 | **Out of memory** | Message history | Lower `MAX_MESSAGE_HISTORY` |
 ---
 ## 📁 Key Files
 - **[.env.logging.example](.env.logging.example)** - Full configuration guide
 - **[LOGGING_REFACTOR_SUMMARY.md](LOGGING_REFACTOR_SUMMARY.md)** - Detailed explanation
 - **[cortex/utils/logging_utils.py](cortex/utils/logging_utils.py)** - Logging utilities
 - **[cortex/context.py](cortex/context.py)** - Context + loop protection
 - **[cortex/router.py](cortex/router.py)** - Pipeline stages
 - **[core/relay/lib/llm.js](core/relay/lib/llm.js)** - LLM backend logging
 ---
 **Need more detail? See [LOGGING_REFACTOR_SUMMARY.md](LOGGING_REFACTOR_SUMMARY.md)**
@@ -0,0 +1,352 @@
 # Cortex Logging Refactor Summary
 ## 🎯 Problem Statement
 The cortex chat loop had severe logging issues that made debugging impossible:
 1. **Massive verbosity**: 100+ log lines per chat message
 2. **Raw LLM dumps**: Full JSON responses pretty-printed on every call (1000s of lines)
 3. **Repeated data**: NeoMem results logged 71 times individually
 4. **No structure**: Scattered emoji logs with no hierarchy
 5. **Impossible to debug**: Couldn't tell if loops were happening or just verbose logging
 6. **No loop protection**: Unbounded message history growth, no session cleanup, no duplicate detection
 ## ✅ What Was Fixed
 ### 1. **Structured Hierarchical Logging**
 **Before:**
 ```
 🔍 RAW LLM RESPONSE: {
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "gpt-4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Here is a very long response that goes on for hundreds of lines..."
      }
    }
  ],
  "usage": {
    "prompt_tokens": 123,
    "completion_tokens": 456,
    "total_tokens": 579
  }
 }
 🧠 Trying backend: PRIMARY (http://localhost:8000)
 ✅ Success via PRIMARY
 [STAGE 0] Collecting unified context...
 [STAGE 0] Context collected - 5 RAG results
 [COLLECT_CONTEXT] Intake data retrieved:
 {
  "L1": [...],
  "L5": [...],
  "L10": {...},
  "L20": {...},
  "L30": {...}
 }
 [COLLECT_CONTEXT] NeoMem search returned 71 results
  [1] Score: 0.923 - Memory content here...
  [2] Score: 0.891 - More memory content...
  [3] Score: 0.867 - Even more content...
  ... (68 more lines)
 ```
 **After (summary mode - DEFAULT):**
 ```
 ✅ [LLM] PRIMARY | 14:23:45.123 | Reply: Based on your question about...
 📊 Context | Session: abc123 | Messages: 42 | Last: 5.2min | RAG: 5 results
 🧠 Monologue | question | Tone: curious
 ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 📤 Output: 342 characters
 ```
 **After (detailed mode - for debugging):**
 ```
 ════════════════════════════════════════════════════════════════════════════════════════════════════
 🚀 PIPELINE START | Session: abc123 | 14:23:45.123
 ════════════════════════════════════════════════════════════════════════════════════════════════════
 📝 User: What is the meaning of life?
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 🧠 LLM CALL | Backend: PRIMARY | 14:23:45.234
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 📝 Prompt: You are Lyra, a thoughtful AI assistant...
 💬 Reply: Based on philosophical perspectives, the meaning...
 📊 Context | Session: abc123 | Messages: 42 | Last: 5.2min | RAG: 5 results
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 [CONTEXT] Session abc123 | User: What is the meaning of life?
 ────────────────────────────────────────────────────────────────────────────────────────────────────
  Mode: default | Mood: neutral | Project: None
  Tools: RAG, WEB, WEATHER, CODEBRAIN, POKERBRAIN
  ╭─ INTAKE SUMMARIES ────────────────────────────────────────────────
  │ L1  : Last message discussed philosophy...
  │ L5  : Recent 5 messages covered existential topics...
  │ L10 : Past 10 messages showed curiosity pattern...
  │ L20 : Session focused on deep questions...
  │ L30 : Long-term trend shows philosophical interest...
  ╰───────────────────────────────────────────────────────────────────
  ╭─ RAG RESULTS (5) ──────────────────────────────────────────────
  │ [1] 0.923 | Previous discussion about purpose and meaning...
  │ [2] 0.891 | Note about existential philosophy...
  │ [3] 0.867 | Memory of Viktor Frankl discussion...
  │ [4] 0.834 | Reference to stoic philosophy...
  │ [5] 0.801 | Buddhism and the middle path...
  ╰───────────────────────────────────────────────────────────────────
 ────────────────────────────────────────────────────────────────────────────────────────────────────
 ════════════════════════════════════════════════════════════════════════════════════════════════════
 ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 ════════════════════════════════════════════════════════════════════════════════════════════════════
 ⏱️  Stage Timings:
   context        :   150ms ( 12.0%)
   identity       :    10ms (  0.8%)
   monologue      :   200ms ( 16.0%)
   tools          :     0ms (  0.0%)
   reflection     :    50ms (  4.0%)
   reasoning      :   450ms ( 36.0%)
   refinement     :   300ms ( 24.0%)
   persona        :   140ms ( 11.2%)
 📤 Output: 342 characters
 ════════════════════════════════════════════════════════════════════════════════════════════════════
 ```
 ### 2. **Configurable Verbosity Levels**
 Set via `LOG_DETAIL_LEVEL` environment variable:
 - **`minimal`**: Only errors and critical events
 - **`summary`**: Stage completion + errors (DEFAULT - recommended for production)
 - **`detailed`**: Include raw LLM outputs, RAG results, timing breakdowns (for debugging)
 - **`verbose`**: Everything including full JSON dumps (for deep debugging)
 ### 3. **Raw LLM Output Visibility** ✅
 **You can now see raw LLM outputs clearly!**
 In `detailed` or `verbose` mode, LLM calls show:
 - Backend used
 - Prompt preview
 - Parsed reply
 - **Raw JSON response in collapsible format** (verbose only)
 ```
 ╭─ RAW RESPONSE ────────────────────────────────────────────────────────────────────────────
 │ {
 │   "id": "chatcmpl-123",
 │   "object": "chat.completion",
 │   "model": "gpt-4",
 │   "choices": [
 │     {
 │       "message": {
 │         "content": "Full response here..."
 │       }
 │     }
 │   ]
 │ }
 ╰───────────────────────────────────────────────────────────────────────────────────────────
 ```
 ### 4. **Loop Detection & Protection** ✅
 **New safety features:**
 - **Duplicate message detection**: Prevents processing the same message twice
 - **Message history trimming**: Auto-trims to last 100 messages (configurable via `MAX_MESSAGE_HISTORY`)
 - **Session TTL**: Auto-expires inactive sessions after 24 hours (configurable via `SESSION_TTL_HOURS`)
 - **Hash-based detection**: Uses MD5 hash to detect exact duplicate messages
 **Example warning when loop detected:**
 ```
 ⚠️  DUPLICATE MESSAGE DETECTED | Session: abc123 | Message: What is the meaning of life?
 🔁 LOOP DETECTED - Returning cached context to prevent processing duplicate
 ```
 ### 5. **Performance Timing** ✅
 In `detailed` mode, see exactly where time is spent:
 ```
 ⏱️  Stage Timings:
   context        :   150ms ( 12.0%)  ← Context collection
   identity       :    10ms (  0.8%)  ← Identity loading
   monologue      :   200ms ( 16.0%)  ← Inner monologue
   tools          :     0ms (  0.0%)  ← Autonomous tools
   reflection     :    50ms (  4.0%)  ← Reflection notes
   reasoning      :   450ms ( 36.0%)  ← Main reasoning (BOTTLENECK)
   refinement     :   300ms ( 24.0%)  ← Answer refinement
   persona        :   140ms ( 11.2%)  ← Persona layer
 ```
 **This helps you identify weak links in the chain!**
 ## 📁 Files Modified
 ### Core Changes
 1. **[llm.js](core/relay/lib/llm.js)**
   - Removed massive JSON dump on line 53
   - Added structured logging with 4 verbosity levels
   - Shows raw responses only in verbose mode (collapsible format)
   - Tracks failed backends and shows summary on total failure
 2. **[context.py](cortex/context.py)**
   - Condensed 71-line NeoMem loop to 5-line summary
   - Removed repeated intake data dumps
   - Added structured hierarchical logging with boxes
   - Added duplicate message detection
   - Added message history trimming
   - Added session TTL and cleanup
 3. **[router.py](cortex/router.py)**
   - Replaced 15+ stage logs with unified pipeline summary
   - Added stage timing collection
   - Shows performance breakdown in detailed mode
   - Clean start/end markers with total duration
 ### New Files
 4. **[utils/logging_utils.py](cortex/utils/logging_utils.py)** (NEW)
   - Reusable structured logging utilities
   - `PipelineLogger` class for hierarchical logging
   - Collapsible data sections
   - Stage timing tracking
   - Future-ready for expansion
 5. **[.env.logging.example](.env.logging.example)** (NEW)
   - Complete logging configuration guide
   - Shows example output at each verbosity level
   - Documents all environment variables
   - Production-ready defaults
 6. **[LOGGING_REFACTOR_SUMMARY.md](LOGGING_REFACTOR_SUMMARY.md)** (THIS FILE)
 ## 🚀 How to Use
 ### For Finding Weak Links (Your Use Case)
 ```bash
 # Set in your .env or export:
 export LOG_DETAIL_LEVEL=detailed
 export VERBOSE_DEBUG=false  # or true for even more detail
 # Now run your chat - you'll see:
 # 1. Which LLM backend is used
 # 2. Raw LLM outputs (in verbose mode)
 # 3. Exact timing per stage
 # 4. Which stage is taking longest
 ```
 ### For Production
 ```bash
 export LOG_DETAIL_LEVEL=summary
 # Minimal, clean logs:
 # ✅ [LLM] PRIMARY | 14:23:45.123 | Reply: Based on your question...
 # ✨ PIPELINE COMPLETE | Session: abc123 | Total: 1250ms
 ```
 ### For Deep Debugging
 ```bash
 export LOG_DETAIL_LEVEL=verbose
 export LOG_RAW_CONTEXT_DATA=true
 # Shows EVERYTHING including full JSON dumps
 ```
 ## 🔍 Finding Weak Links - Quick Guide
 **Problem: "Which LLM stage is failing or producing bad output?"**
 1. Set `LOG_DETAIL_LEVEL=detailed`
 2. Run a test conversation
 3. Look for timing anomalies:
   ```
   reasoning      :  3450ms ( 76.0%)  ← BOTTLENECK!
   ```
 4. Look for errors:
   ```
   ⚠️  Reflection failed: Connection timeout
   ```
 5. Check raw LLM outputs (set `VERBOSE_DEBUG=true`):
   ```
   ╭─ RAW RESPONSE ────────────────────────────────────
   │ {
   │   "choices": [
   │     { "message": { "content": "..." } }
   │   ]
   │ }
   ╰───────────────────────────────────────────────────
   ```
 **Problem: "Is the loop repeating operations?"**
 1. Enable duplicate detection (on by default)
 2. Look for loop warnings:
   ```
   ⚠️  DUPLICATE MESSAGE DETECTED | Session: abc123
   🔁 LOOP DETECTED - Returning cached context
   ```
 3. Check stage timings - repeated stages will show up as duplicates
 **Problem: "Which RAG memories are being used?"**
 1. Set `LOG_DETAIL_LEVEL=detailed`
 2. Look for RAG results box:
   ```
   ╭─ RAG RESULTS (5) ──────────────────────────────
   │ [1] 0.923 | Previous discussion about X...
   │ [2] 0.891 | Note about Y...
   ╰────────────────────────────────────────────────
   ```
 ## 📊 Environment Variables Reference
 | Variable | Default | Description |
 |----------|---------|-------------|
 | `LOG_DETAIL_LEVEL` | `summary` | Verbosity: minimal/summary/detailed/verbose |
 | `VERBOSE_DEBUG` | `false` | Legacy flag for maximum verbosity |
 | `LOG_RAW_CONTEXT_DATA` | `false` | Show full intake data dumps |
 | `ENABLE_DUPLICATE_DETECTION` | `true` | Detect and prevent duplicate messages |
 | `MAX_MESSAGE_HISTORY` | `100` | Max messages to keep per session |
 | `SESSION_TTL_HOURS` | `24` | Auto-expire sessions after N hours |
 ## 🎉 Results
 **Before:** 1000+ lines of logs per chat message, unreadable, couldn't identify issues
 **After (summary mode):** 5 lines of structured logs, clear and actionable
 **After (detailed mode):** ~50 lines with full visibility into each stage, timing, and raw outputs
 **Loop protection:** Automatic detection and prevention of duplicate processing
 **You can now:**
 ✅ See raw LLM outputs clearly (in detailed/verbose mode)
 ✅ Identify performance bottlenecks (stage timings)
 ✅ Detect loops and duplicates (automatic)
 ✅ Find failing stages (error markers)
 ✅ Scan logs quickly (hierarchical structure)
 ✅ Debug production issues (adjustable verbosity)
 ## 🔧 Next Steps (Optional Improvements)
 1. **Structured JSON logging**: Output as JSON for log aggregation tools
 2. **Log rotation**: Implement file rotation for verbose logs
 3. **Metrics export**: Export stage timings to Prometheus/Grafana
 4. **Error categorization**: Tag errors by type (network, timeout, parsing, etc.)
 5. **Performance alerts**: Auto-alert when stages exceed thresholds
 ---
 **Happy debugging! You can now see what's actually happening in the cortex loop.** 🎯
@@ -1,21 +1,902 @@
-# Lyra
+# Project Lyra - README v0.9.1
-A persistent, autonomous AI assistant. From-scratch rewrite of an earlier attempt.
+Lyra is a modular persistent AI companion system with advanced reasoning capabilities and autonomous decision-making.
 It provides memory-backed chat using **Relay** + **Cortex** with integrated **Autonomy System**,
 featuring a multi-stage reasoning pipeline powered by HTTP-based LLM backends.
-The design thinking that survives the rewrite lives in [`docs/`](docs/) — start with [`docs/ARCH_v0-6-1.md`](docs/ARCH_v0-6-1.md). The previous implementation is preserved on the `archive` branch.
+**NEW in v0.9.0:** Trilium Notes integration - Search and create notes from conversations
-## Status
+**Current Version:** v0.9.1 (2025-12-29)
-Pre-MVP. Building toward the smallest useful version: chat with persistent memory across sessions.
+> **Note:** As of v0.6.0, NeoMem is **disabled by default** while we work out integration hiccups in the pipeline. The autonomy system is being refined independently before full memory integration.
-## Setup
+## Mission Statement
-```bash
+The point of Project Lyra is to give an AI chatbot more abilities than a typical chatbot. Typical chatbots are essentially amnesic and forget evertything about your project. Lyra helps keep projects organized and remembers everything you have done. Think of her abilities as a notepad/schedule/database/co-creator/collaborator all with its own executive function. Say something in passing, Lyra remembers it then reminds you of it later.
-uv sync
+
-cp .env.example .env
+---
-# fill in ANTHROPIC_API_KEY and point LOCAL_BASE_URL at your Ollama
+
 ## Architecture Overview
 Project Lyra operates as a **single docker-compose deployment** with multiple Docker containers networked together in a microservices architecture. Like how the brain has regions, Lyra has modules:
 ### Core Services
 **1. Relay** (Node.js/Express) - Port 7078
 - Main orchestrator and message router
 - Coordinates all module interactions
 - OpenAI-compatible endpoint: `POST /v1/chat/completions`
 - Internal endpoint: `POST /chat`
 - Dual-mode routing: Standard Mode (simple chat) or Cortex Mode (full reasoning)
 - Server-side session persistence with file-based storage
 - Session management API: `GET/POST/PATCH/DELETE /sessions`
 - Manages async calls to Cortex ingest
 - *(NeoMem integration currently disabled in v0.6.0)*
 **2. UI** (Static HTML) - Port 8081 (nginx)
 - Browser-based chat interface with cyberpunk theme
 - Mode selector (Standard/Cortex) in header
 - Settings modal with backend selection and session management
 - Light/Dark mode toggle (dark by default)
 - **NEW in v0.8.0:** "🧠 Show Work" button for real-time thinking stream
  - Opens popup window with live SSE connection
  - Color-coded events: thinking, tool calls, results, completion
  - Auto-scrolling with animations
  - Session-aware (matches current chat session)
 - Server-synced session management (persists across browsers and reboots)
 - OpenAI-compatible message format
 **3. NeoMem** (Python/FastAPI) - Port 7077 - **DISABLED IN v0.6.0**
 - Long-term memory database (fork of Mem0 OSS)
 - Vector storage (PostgreSQL + pgvector) + Graph storage (Neo4j)
 - RESTful API: `/memories`, `/search`
 - Semantic memory updates and retrieval
 - No external SDK dependencies - fully local
 - **Status:** Currently disabled while pipeline integration is refined
 ### Reasoning Layer
 **4. Cortex** (Python/FastAPI) - Port 7081
 - Primary reasoning engine with multi-stage pipeline and autonomy system
 - **Includes embedded Intake module** (no separate service as of v0.5.1)
 - **Integrated Autonomy System** (NEW in v0.6.0) - See Autonomy System section below
 - **Tool Calling System** (NEW in v0.8.0) - Agentic execution for Standard Mode
  - Sandboxed code execution (Python, JavaScript, Bash)
  - Web search via Tavily API
  - **Trilium knowledge base integration** (NEW in v0.9.0)
  - Multi-iteration autonomous tool use (max 5 iterations)
  - Real-time thinking stream via SSE
 - **Dual Operating Modes:**
  - **Standard Mode** (v0.7.0) - Simple chatbot with context retention + tool calling (v0.8.0)
    - Bypasses reflection, reasoning, refinement stages
    - Direct LLM call with conversation history
    - User-selectable backend (SECONDARY, OPENAI, or custom)
    - **NEW:** Autonomous tool calling for code execution, web search, knowledge queries
    - **NEW:** "Show Your Work" real-time thinking stream
    - Faster responses for coding and practical tasks
  - **Cortex Mode** - Full 4-stage reasoning pipeline
    1. **Reflection** - Generates meta-awareness notes about conversation
    2. **Reasoning** - Creates initial draft answer using context
    3. **Refinement** - Polishes and improves the draft
    4. **Persona** - Applies Lyra's personality and speaking style
 - Integrates with Intake for short-term context via internal Python imports
 - Flexible LLM router supporting multiple backends via HTTP
 - **Endpoints:**
  - `POST /reason` - Main reasoning pipeline (Cortex Mode)
  - `POST /simple` - Direct LLM chat with tool calling (Standard Mode)
  - `GET /stream/thinking/{session_id}` - SSE stream for thinking events **NEW in v0.8.0**
  - `POST /ingest` - Receives conversation exchanges from Relay
  - `GET /health` - Service health check
  - `GET /debug/sessions` - Inspect in-memory SESSIONS state
  - `GET /debug/summary` - Test summarization for a session
 **5. Intake** (Python Module) - **Embedded in Cortex**
 - **No longer a standalone service** - runs as Python module inside Cortex container
 - Short-term memory management with session-based circular buffer
 - In-memory SESSIONS dictionary: `session_id → {buffer: deque(maxlen=200), created_at: timestamp}`
 - Multi-level summarization (L1/L5/L10/L20/L30) produced by `summarize_context()`
 - Deferred summarization - actual summary generation happens during `/reason` call
 - Internal Python API:
  - `add_exchange_internal(exchange)` - Direct function call from Cortex
  - `summarize_context(session_id, exchanges)` - Async LLM-based summarization
  - `SESSIONS` - Module-level global state (requires single Uvicorn worker)
 ### LLM Backends (HTTP-based)
 **All LLM communication is done via HTTP APIs:**
 - **PRIMARY**: llama.cpp server (`http://10.0.0.44:8080`) - AMD MI50 GPU backend
 - **SECONDARY**: Ollama server (`http://10.0.0.3:11434`) - RTX 3090 backend
  - Model: qwen2.5:7b-instruct-q4_K_M
 - **CLOUD**: OpenAI API (`https://api.openai.com/v1`) - Cloud-based models
  - Model: gpt-4o-mini
 - **FALLBACK**: Local backup (`http://10.0.0.41:11435`) - Emergency fallback
  - Model: llama-3.2-8b-instruct
 Each module can be configured to use a different backend via environment variables.
 ### Autonomy System (NEW in v0.6.0)
 **Cortex Autonomy Subsystems** - Multi-layered autonomous decision-making and learning
 - **Executive Layer** [cortex/autonomy/executive/](cortex/autonomy/executive/)
  - High-level planning and goal setting
  - Multi-step reasoning for complex objectives
  - Strategic decision making
 - **Decision Engine** [cortex/autonomy/tools/decision_engine.py](cortex/autonomy/tools/decision_engine.py)
  - Autonomous decision-making framework
  - Option evaluation and selection
  - Coordinated decision orchestration
 - **Autonomous Actions** [cortex/autonomy/actions/](cortex/autonomy/actions/)
  - Self-initiated action execution
  - Context-aware behavior implementation
  - Action logging and tracking
 - **Pattern Learning** [cortex/autonomy/learning/](cortex/autonomy/learning/)
  - Learns from interaction patterns
  - Identifies recurring user needs
  - Adaptive behavior refinement
 - **Proactive Monitoring** [cortex/autonomy/proactive/](cortex/autonomy/proactive/)
  - System state monitoring
  - Intervention opportunity detection
  - Background awareness capabilities
 - **Self-Analysis** [cortex/autonomy/self/](cortex/autonomy/self/)
  - Performance tracking and analysis
  - Cognitive pattern identification
  - Self-state persistence in [cortex/data/self_state.json](cortex/data/self_state.json)
 - **Orchestrator** [cortex/autonomy/tools/orchestrator.py](cortex/autonomy/tools/orchestrator.py)
  - Coordinates all autonomy subsystems
  - Manages tool selection and execution
  - Handles external integrations (with enable/disable controls)
 **Autonomy Architecture:**
 The autonomy system operates in coordinated layers, all maintaining state in `self_state.json`:
 1. Executive Layer → Planning and goals
 2. Decision Layer → Evaluation and choices
 3. Action Layer → Execution
 4. Learning Layer → Pattern adaptation
 5. Monitoring Layer → Proactive awareness
 ---
 ## Data Flow Architecture (v0.7.0)
 ### Standard Mode Flow (NEW in v0.7.0):
 ```
 User (UI) → POST /v1/chat/completions {mode: "standard", backend: "SECONDARY"}
  ↓
 Relay (7078)
  ↓ POST /simple
 Cortex (7081)
  ↓ (internal Python call)
 Intake module → get_recent_messages() (last 20 messages)
  ↓
 Direct LLM call (user-selected backend: SECONDARY/OPENAI/custom)
  ↓
 Returns simple response to Relay
  ↓
 Relay → POST /ingest (async)
  ↓
 Cortex → add_exchange_internal() → SESSIONS buffer
  ↓
 Relay → POST /sessions/:id (save session to file)
  ↓
 Relay → UI (returns final response)
 Note: Bypasses reflection, reasoning, refinement, persona stages
 ```
-## Architecture
+### Cortex Mode Flow (Full Reasoning):
-The long-term target is the cognitive split in `docs/ARCH_v0-6-1.md` — Inner Self as the seat of consciousness, Executive for hard reasoning, Cortex Chat for drafting, Persona for voice. The MVP implements only the chat + memory baseline. Cognitive layers come back one at a time.
+```
 User (UI) → POST /v1/chat/completions {mode: "cortex"}
  ↓
 Relay (7078)
  ↓ POST /reason
 Cortex (7081)
  ↓ (internal Python call)
 Intake module → summarize_context()
  ↓
 Autonomy System → Decision evaluation & pattern learning
  ↓
 Cortex processes (4 stages):
  1. reflection.py → meta-awareness notes (CLOUD backend)
  2. reasoning.py → draft answer (PRIMARY backend, autonomy-aware)
  3. refine.py → refined answer (PRIMARY backend)
  4. persona/speak.py → Lyra personality (CLOUD backend, autonomy-aware)
  ↓
 Returns persona answer to Relay
  ↓
 Relay → POST /ingest (async)
  ↓
 Cortex → add_exchange_internal() → SESSIONS buffer
  ↓
 Autonomy System → Update self_state.json (pattern tracking)
  ↓
 Relay → POST /sessions/:id (save session to file)
  ↓
 Relay → UI (returns final response)
 Note: NeoMem integration disabled in v0.6.0
 ```
 ### Session Persistence Flow (NEW in v0.7.0):
 ```
 UI loads → GET /sessions → Relay → List all sessions from files → UI dropdown
 User sends message → POST /sessions/:id → Relay → Save to sessions/*.json
 User renames session → PATCH /sessions/:id/metadata → Relay → Update *.meta.json
 User deletes session → DELETE /sessions/:id → Relay → Remove session files
 Sessions stored in: core/relay/sessions/
 - {sessionId}.json (conversation history)
 - {sessionId}.meta.json (name, timestamps, metadata)
 ```
 ### Cortex 4-Stage Reasoning Pipeline:
 1. **Reflection** (`reflection.py`) - Cloud LLM (OpenAI)
   - Analyzes user intent and conversation context
   - Generates meta-awareness notes
   - "What is the user really asking?"
 2. **Reasoning** (`reasoning.py`) - Primary LLM (llama.cpp)
   - Retrieves short-term context from Intake module
   - Creates initial draft answer
   - Integrates context, reflection notes, and user prompt
 3. **Refinement** (`refine.py`) - Primary LLM (llama.cpp)
   - Polishes the draft answer
   - Improves clarity and coherence
   - Ensures factual consistency
 4. **Persona** (`speak.py`) - Cloud LLM (OpenAI)
   - Applies Lyra's personality and speaking style
   - Natural, conversational output
   - Final answer returned to user
 ---
 ## Features
 ### Core Services
 **Relay**:
 - Main orchestrator and message router
 - OpenAI-compatible endpoint: `POST /v1/chat/completions`
 - Internal endpoint: `POST /chat`
 - Health check: `GET /_health`
 - **NEW:** Dual-mode routing (Standard/Cortex)
 - **NEW:** Server-side session persistence with CRUD API
 - **NEW:** Session management endpoints:
  - `GET /sessions` - List all sessions
  - `GET /sessions/:id` - Retrieve session history
  - `POST /sessions/:id` - Save session history
  - `PATCH /sessions/:id/metadata` - Update session metadata
  - `DELETE /sessions/:id` - Delete session
 - Async non-blocking calls to Cortex
 - Shared request handler for code reuse
 - Comprehensive error handling
 **NeoMem (Memory Engine)**:
 - Forked from Mem0 OSS - fully independent
 - Drop-in compatible API (`/memories`, `/search`)
 - Local-first: runs on FastAPI with Postgres + Neo4j
 - No external SDK dependencies
 - Semantic memory updates - compares embeddings and performs in-place updates
 - Default service: `neomem-api` (port 7077)
 **UI**:
 - Lightweight static HTML chat interface
 - Cyberpunk theme with light/dark mode toggle
 - **NEW:** Mode selector (Standard/Cortex) in header
 - **NEW:** Settings modal (⚙ button) with:
  - Backend selection for Standard Mode (SECONDARY/OPENAI/custom)
  - Session management (view, delete sessions)
  - Theme toggle (dark mode default)
 - **NEW:** Server-synced session management
  - Sessions persist across browsers and reboots
  - Rename sessions with custom names
  - Delete sessions with confirmation
  - Automatic session save on every message
 - OpenAI message format support
 ### Reasoning Layer
 **Cortex** (v0.7.0):
 - **NEW:** Dual operating modes:
  - **Standard Mode** - Simple chat with context (`/simple` endpoint)
    - User-selectable backend (SECONDARY, OPENAI, or custom)
    - Full conversation history via Intake integration
    - Bypasses reasoning pipeline for faster responses
  - **Cortex Mode** - Full reasoning pipeline (`/reason` endpoint)
    - Multi-stage processing: reflection → reasoning → refine → persona
    - Per-stage backend selection
    - Autonomy system integration
 - Flexible LLM backend routing via HTTP
 - Async processing throughout
 - Embedded Intake module for short-term context
 - `/reason`, `/simple`, `/ingest`, `/health`, `/debug/sessions`, `/debug/summary` endpoints
 - Lenient error handling - never fails the chat pipeline
 **Intake** (Embedded Module):
 - **Architectural change**: Now runs as Python module inside Cortex container
 - In-memory SESSIONS management (session_id → buffer)
 - Multi-level summarization: L1 (ultra-short), L5 (short), L10 (medium), L20 (detailed), L30 (full)
 - Deferred summarization strategy - summaries generated during `/reason` call
 - `bg_summarize()` is a logging stub - actual work deferred
 - **Single-worker constraint**: SESSIONS requires single Uvicorn worker or Redis/shared storage
 **LLM Router**:
 - Dynamic backend selection via HTTP
 - Environment-driven configuration
 - Support for llama.cpp, Ollama, OpenAI, custom endpoints
 - Per-module backend preferences:
  - `CORTEX_LLM=SECONDARY` (Ollama for reasoning)
  - `INTAKE_LLM=PRIMARY` (llama.cpp for summarization)
  - `SPEAK_LLM=OPENAI` (Cloud for persona)
  - `NEOMEM_LLM=PRIMARY` (llama.cpp for memory operations)
 ### Beta Lyrae (RAG Memory DB) - Currently Disabled
 - **RAG Knowledge DB - Beta Lyrae (sheliak)**
  - This module implements the **Retrieval-Augmented Generation (RAG)** layer for Project Lyra.
  - It serves as the long-term searchable memory store that Cortex and Relay can query for relevant context before reasoning or response generation.
  - **Status**: Disabled in docker-compose.yml (v0.5.1)
 The system uses:
 - **ChromaDB** for persistent vector storage
 - **OpenAI Embeddings (`text-embedding-3-small`)** for semantic similarity
 - **FastAPI** (port 7090) for the `/rag/search` REST endpoint
 Directory Layout:
 ```
 rag/
 ├── rag_chat_import.py    # imports JSON chat logs
 ├── rag_docs_import.py    # (planned) PDF/EPUB/manual importer
 ├── rag_build.py          # legacy single-folder builder
 ├── rag_query.py          # command-line query helper
 ├── rag_api.py            # FastAPI service providing /rag/search
 ├── chromadb/             # persistent vector store
 ├── chatlogs/             # organized source data
 │   ├── poker/
 │   ├── work/
 │   ├── lyra/
 │   ├── personal/
 │   └── ...
 └── import.log            # progress log for batch runs
 ```
 **OpenAI chatlog importer features:**
 - Recursive folder indexing with **category detection** from directory name
 - Smart chunking for long messages (5,000 chars per slice)
 - Automatic deduplication using SHA-1 hash of file + chunk
 - Timestamps for both file modification and import time
 - Full progress logging via tqdm
 - Safe to run in background with `nohup … &`
 ---
 ## Docker Deployment
 All services run in a single docker-compose stack with the following containers:
 **Active Services:**
 - **relay** - Main orchestrator (port 7078)
 - **cortex** - Reasoning engine with embedded Intake and Autonomy System (port 7081)
 **Disabled Services (v0.6.0):**
 - **neomem-postgres** - PostgreSQL with pgvector extension (port 5432) - *disabled while refining pipeline*
 - **neomem-neo4j** - Neo4j graph database (ports 7474, 7687) - *disabled while refining pipeline*
 - **neomem-api** - NeoMem memory service (port 7077) - *disabled while refining pipeline*
 - **intake** - No longer needed (embedded in Cortex as of v0.5.1)
 - **rag** - Beta Lyrae RAG service (port 7090) - currently disabled
 All containers communicate via the `lyra_net` Docker bridge network.
 ## External LLM Services
 The following LLM backends are accessed via HTTP (not part of docker-compose):
 - **llama.cpp Server** (`http://10.0.0.44:8080`)
  - AMD MI50 GPU-accelerated inference
  - Primary backend for reasoning and refinement stages
  - Model path: `/model`
 - **Ollama Server** (`http://10.0.0.3:11434`)
  - RTX 3090 GPU-accelerated inference
  - Secondary/configurable backend
  - Model: qwen2.5:7b-instruct-q4_K_M
 - **OpenAI API** (`https://api.openai.com/v1`)
  - Cloud-based inference
  - Used for reflection and persona stages
  - Model: gpt-4o-mini
 - **Fallback Server** (`http://10.0.0.41:11435`)
  - Emergency backup endpoint
  - Local llama-3.2-8b-instruct model
 ---
 ## Version History
 ### v0.9.0 (2025-12-29) - Current Release
 **Major Feature: Trilium Notes Integration**
 - ✅ Added Trilium ETAPI integration for knowledge base access
 - ✅ `search_notes()` tool for searching personal notes during conversations
 - ✅ `create_note()` tool for capturing insights and information
 - ✅ ETAPI authentication with secure token management
 - ✅ Complete setup documentation and API reference
 - ✅ Environment configuration with feature flag (`ENABLE_TRILIUM`)
 - ✅ Automatic parent note handling (defaults to "root")
 - ✅ Connection error handling and user-friendly messages
 **Key Capabilities:**
 - Search your Trilium notes during conversations for context
 - Create new notes from conversation insights automatically
 - Cross-reference information between chat and knowledge base
 - Future: Find duplicates, suggest organization, summarize notes
 **Documentation:**
 - Added [TRILIUM_SETUP.md](TRILIUM_SETUP.md) - Complete setup guide
 - Added [docs/TRILIUM_API.md](docs/TRILIUM_API.md) - Full API reference
 ### v0.8.0 (2025-12-26)
 **Major Feature: Agentic Tool Calling + "Show Your Work"**
 - ✅ Added tool calling system for Standard Mode
 - ✅ Real-time thinking stream visualization
 - ✅ Sandboxed code execution (Python, JavaScript, Bash)
 - ✅ Web search integration via Tavily API
 - ✅ Server-Sent Events (SSE) for live tool execution updates
 ### v0.7.0 (2025-12-21)
 **Major Features: Standard Mode + Backend Selection + Session Persistence**
 - ✅ Added Standard Mode for simple chatbot functionality
 - ✅ UI mode selector (Standard/Cortex) in header
 - ✅ Settings modal with backend selection for Standard Mode
 - ✅ Server-side session persistence with file-based storage
 - ✅ Session management UI (view, rename, delete sessions)
 - ✅ Light/Dark mode toggle (dark by default)
 - ✅ Context retention in Standard Mode via Intake integration
 - ✅ Fixed modal positioning and z-index issues
 - ✅ Cortex `/simple` endpoint for direct LLM calls
 - ✅ Session CRUD API in Relay
 - ✅ Full backward compatibility - Cortex Mode unchanged
 **Key Changes:**
 - Standard Mode bypasses 6 of 7 reasoning stages for faster responses
 - Sessions now sync across browsers and survive container restarts
 - User can select SECONDARY (Ollama), OPENAI, or custom backend for Standard Mode
 - Theme preference and backend selection persisted in localStorage
 - Session files stored in `core/relay/sessions/` directory
 ### v0.6.0 (2025-12-18)
 **Major Feature: Autonomy System (Phase 1, 2, and 2.5)**
 - ✅ Added autonomous decision-making framework
 - ✅ Implemented executive planning and goal-setting layer
 - ✅ Added pattern learning system for adaptive behavior
 - ✅ Implemented proactive monitoring capabilities
 - ✅ Created self-analysis and performance tracking system
 - ✅ Integrated self-state persistence (`cortex/data/self_state.json`)
 - ✅ Built decision engine with orchestrator coordination
 - ✅ Added autonomous action execution framework
 - ✅ Integrated autonomy into reasoning and persona layers
 - ✅ Created comprehensive test suites for autonomy features
 - ✅ Added complete system breakdown documentation
 **Architecture Changes:**
 - Autonomy system integrated into Cortex reasoning pipeline
 - Multi-layered autonomous decision-making architecture
 - Self-state tracking across sessions
 - NeoMem disabled by default while refining pipeline integration
 - Enhanced orchestrator with flexible service controls
 **Documentation:**
 - Added [PROJECT_LYRA_COMPLETE_BREAKDOWN.md](docs/PROJECT_LYRA_COMPLETE_BREAKDOWN.md)
 - Updated changelog with comprehensive autonomy system details
 ### v0.5.1 (2025-12-11)
 **Critical Intake Integration Fixes:**
 - ✅ Fixed `bg_summarize()` NameError preventing SESSIONS persistence
 - ✅ Fixed `/ingest` endpoint unreachable code
 - ✅ Added `cortex/intake/__init__.py` for proper package structure
 - ✅ Added diagnostic logging to verify SESSIONS singleton behavior
 - ✅ Added `/debug/sessions` and `/debug/summary` endpoints
 - ✅ Documented single-worker constraint in Dockerfile
 - ✅ Implemented lenient error handling (never fails chat pipeline)
 - ✅ Intake now embedded in Cortex - no longer standalone service
 **Architecture Changes:**
 - Intake module runs inside Cortex container as pure Python import
 - No HTTP calls between Cortex and Intake (internal function calls)
 - SESSIONS persist correctly in Uvicorn worker
 - Deferred summarization strategy (summaries generated during `/reason`)
 ### v0.5.0 (2025-11-28)
 - ✅ Fixed all critical API wiring issues
 - ✅ Added OpenAI-compatible endpoint to Relay (`/v1/chat/completions`)
 - ✅ Fixed Cortex → Intake integration
 - ✅ Added missing Python package `__init__.py` files
 - ✅ End-to-end message flow verified and working
 ### Infrastructure v1.0.0 (2025-11-26)
 - Consolidated 9 scattered `.env` files into single source of truth
 - Multi-backend LLM strategy implemented
 - Docker Compose consolidation
 - Created `.env.example` security templates
 ### v0.4.x (Major Rewire)
 - Cortex multi-stage reasoning pipeline
 - LLM router with multi-backend support
 - Major architectural restructuring
 ### v0.3.x
 - Beta Lyrae RAG system
 - NeoMem integration
 - Basic Cortex reasoning loop
 ---
 ## Known Issues (v0.7.0)
 ### Temporarily Disabled
 - **NeoMem disabled by default** - Being refined independently before full integration
  - PostgreSQL + pgvector storage inactive
  - Neo4j graph database inactive
  - Memory persistence endpoints not active
 - RAG service (Beta Lyrae) currently disabled in docker-compose.yml
 ### Standard Mode Limitations
 - No reflection, reasoning, or refinement stages (by design)
 - DeepSeek R1 not recommended for Standard Mode (generates reasoning artifacts)
 - No RAG integration (same as Cortex Mode - currently disabled)
 - No NeoMem memory storage (same as Cortex Mode - currently disabled)
 ### Session Management Limitations
 - Sessions stored in container filesystem - requires volume mount for true persistence
 - No session import/export functionality yet
 - No session search or filtering
 - Old localStorage sessions don't automatically migrate to server
 ### Operational Notes
 - **Single-worker constraint**: Cortex must run with single Uvicorn worker to maintain SESSIONS state
  - Multi-worker scaling requires migrating SESSIONS to Redis or shared storage
 - Diagnostic endpoints (`/debug/sessions`, `/debug/summary`) available for troubleshooting
 - Backend selection only affects Standard Mode - Cortex Mode uses environment-configured backends
 ### Future Enhancements
 - Re-enable NeoMem integration after pipeline refinement
 - Full autonomy system maturation and optimization
 - Re-enable RAG service integration
 - Session import/export functionality
 - Session search and filtering UI
 - Migrate SESSIONS to Redis for multi-worker support
 - Add request correlation IDs for tracing
 - Comprehensive health checks across all services
 - Enhanced pattern learning with long-term memory integration
 ---
 ## Quick Start
 ### Prerequisites
 - Docker + Docker Compose
 - At least one HTTP-accessible LLM endpoint (llama.cpp, Ollama, or OpenAI API key)
 ### Setup
 1. Copy `.env.example` to `.env` and configure your LLM backend URLs and API keys:
   ```bash
   # Required: Configure at least one LLM backend
   LLM_PRIMARY_URL=http://10.0.0.44:8080       # llama.cpp
   LLM_SECONDARY_URL=http://10.0.0.3:11434     # Ollama
   OPENAI_API_KEY=sk-...                        # OpenAI
   ```
 2. Start all services with docker-compose:
   ```bash
   docker-compose up -d
   ```
 3. Check service health:
   ```bash
   # Relay health
   curl http://localhost:7078/_health
   # Cortex health
   curl http://localhost:7081/health
   # NeoMem health
   curl http://localhost:7077/health
   ```
 4. Access the UI at `http://localhost:8081`
 ### Using the UI
 **Mode Selection:**
 - Use the **Mode** dropdown in the header to switch between:
  - **Standard** - Simple chatbot for coding and practical tasks
  - **Cortex** - Full reasoning pipeline with autonomy features
 **Settings Menu:**
 1. Click the **⚙ Settings** button in the header
 2. **Backend Selection** (Standard Mode only):
   - Choose **SECONDARY** (Ollama/Qwen on 3090) - Fast, local
   - Choose **OPENAI** (GPT-4o-mini) - Cloud-based, high quality
   - Enter custom backend name for advanced configurations
 3. **Session Management**:
   - View all saved sessions with message counts and timestamps
   - Click 🗑️ to delete unwanted sessions
 4. **Theme Toggle**:
   - Click **🌙 Dark Mode** or **☀️ Light Mode** to switch themes
 **Session Management:**
 - Sessions automatically save on every message
 - Use the **Session** dropdown to switch between sessions
 - Click **➕ New** to create a new session
 - Click **✏️ Rename** to rename the current session
 - Sessions persist across browsers and container restarts
 ### Test
 **Test Standard Mode:**
 ```bash
 curl -X POST http://localhost:7078/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "standard",
    "backend": "SECONDARY",
    "messages": [{"role": "user", "content": "Hello!"}],
    "sessionId": "test"
  }'
 ```
 **Test Cortex Mode (Full Reasoning):**
 ```bash
 curl -X POST http://localhost:7078/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "cortex",
    "messages": [{"role": "user", "content": "Hello Lyra!"}],
    "sessionId": "test"
  }'
 ```
 **Test Cortex /ingest endpoint:**
 ```bash
 curl -X POST http://localhost:7081/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "test",
    "user_msg": "Hello",
    "assistant_msg": "Hi there!"
  }'
 ```
 **Inspect SESSIONS state:**
 ```bash
 curl http://localhost:7081/debug/sessions
 ```
 **Get summary for a session:**
 ```bash
 curl "http://localhost:7081/debug/summary?session_id=test"
 ```
 **List all sessions:**
 ```bash
 curl http://localhost:7078/sessions
 ```
 **Get session history:**
 ```bash
 curl http://localhost:7078/sessions/sess-abc123
 ```
 **Delete a session:**
 ```bash
 curl -X DELETE http://localhost:7078/sessions/sess-abc123
 ```
 All backend databases (PostgreSQL and Neo4j) are automatically started as part of the docker-compose stack.
 ---
 ## Environment Variables
 ### LLM Backend Configuration
 **Backend URLs (Full API endpoints):**
 ```bash
 LLM_PRIMARY_URL=http://10.0.0.44:8080           # llama.cpp
 LLM_PRIMARY_MODEL=/model
 LLM_SECONDARY_URL=http://10.0.0.3:11434         # Ollama
 LLM_SECONDARY_MODEL=qwen2.5:7b-instruct-q4_K_M
 LLM_OPENAI_URL=https://api.openai.com/v1
 LLM_OPENAI_MODEL=gpt-4o-mini
 OPENAI_API_KEY=sk-...
 ```
 **Module-specific backend selection:**
 ```bash
 CORTEX_LLM=SECONDARY         # Use Ollama for reasoning
 INTAKE_LLM=PRIMARY           # Use llama.cpp for summarization
 SPEAK_LLM=OPENAI             # Use OpenAI for persona
 NEOMEM_LLM=PRIMARY           # Use llama.cpp for memory
 UI_LLM=OPENAI                # Use OpenAI for UI
 RELAY_LLM=PRIMARY            # Use llama.cpp for relay
 STANDARD_MODE_LLM=SECONDARY  # Default backend for Standard Mode (NEW in v0.7.0)
 ```
 ### Database Configuration
 ```bash
 POSTGRES_USER=neomem
 POSTGRES_PASSWORD=neomempass
 POSTGRES_DB=neomem
 POSTGRES_HOST=neomem-postgres
 POSTGRES_PORT=5432
 NEO4J_URI=bolt://neomem-neo4j:7687
 NEO4J_USERNAME=neo4j
 NEO4J_PASSWORD=neomemgraph
 ```
 ### Service URLs (Internal Docker Network)
 ```bash
 NEOMEM_API=http://neomem-api:7077
 CORTEX_API=http://cortex:7081
 CORTEX_REASON_URL=http://cortex:7081/reason
 CORTEX_SIMPLE_URL=http://cortex:7081/simple      # NEW in v0.7.0
 CORTEX_INGEST_URL=http://cortex:7081/ingest
 RELAY_URL=http://relay:7078
 ```
 ### Feature Flags
 ```bash
 CORTEX_ENABLED=true
 MEMORY_ENABLED=true
 PERSONA_ENABLED=false
 DEBUG_PROMPT=true
 VERBOSE_DEBUG=true
 ENABLE_TRILIUM=true          # NEW in v0.9.0
 ```
 For complete environment variable reference, see [ENVIRONMENT_VARIABLES.md](ENVIRONMENT_VARIABLES.md).
 ---
 ## Documentation
 - [CHANGELOG.md](CHANGELOG.md) - Detailed version history
 - [PROJECT_SUMMARY.md](PROJECT_SUMMARY.md) - Comprehensive project overview for AI context
 - [ENVIRONMENT_VARIABLES.md](ENVIRONMENT_VARIABLES.md) - Environment variable reference
 - [DEPRECATED_FILES.md](DEPRECATED_FILES.md) - Deprecated files and migration guide
 ---
 ## Troubleshooting
 ### SESSIONS not persisting
 **Symptom:** Intake buffer always shows 0 exchanges, summaries always empty.
 **Solution (Fixed in v0.5.1):**
 - Ensure `cortex/intake/__init__.py` exists
 - Check Cortex logs for `[Intake Module Init]` message showing SESSIONS object ID
 - Verify single-worker mode (Dockerfile: `uvicorn main:app --workers 1`)
 - Use `/debug/sessions` endpoint to inspect current state
 ### Cortex connection errors
 **Symptom:** Relay can't reach Cortex, 502 errors.
 **Solution:**
 - Verify Cortex container is running: `docker ps | grep cortex`
 - Check Cortex health: `curl http://localhost:7081/health`
 - Verify environment variables: `CORTEX_REASON_URL=http://cortex:7081/reason`
 - Check docker network: `docker network inspect lyra_net`
 ### LLM backend timeouts
 **Symptom:** Reasoning stage hangs or times out.
 **Solution:**
 - Verify LLM backend is running and accessible
 - Check LLM backend health: `curl http://10.0.0.44:8080/health`
 - Increase timeout in llm_router.py if using slow models
 - Check logs for specific backend errors
 ---
 ## License
 NeoMem is a derivative work based on Mem0 OSS (Apache 2.0).
 © 2025 Terra-Mechanics / ServersDown Labs. All modifications released under Apache 2.0.
 **Built with Claude Code**
 ---
 ## Integration Notes
 - NeoMem API is compatible with Mem0 OSS endpoints (`/memories`, `/search`)
 - All services communicate via Docker internal networking on the `lyra_net` bridge
 - History and entity graphs are managed via PostgreSQL + Neo4j
 - LLM backends are accessed via HTTP and configured in `.env`
 - Intake module is imported internally by Cortex (no HTTP communication)
 - SESSIONS state is maintained in-memory within Cortex container
 ---
 ## Beta Lyrae - RAG Memory System (Currently Disabled)
 **Note:** The RAG service is currently disabled in docker-compose.yml
 ### Requirements
 - Python 3.10+
 - Dependencies: `chromadb openai tqdm python-dotenv fastapi uvicorn`
 - Persistent storage: `./chromadb` or `/mnt/data/lyra_rag_db`
 ### Setup
 1. Import chat logs (must be in OpenAI message format):
   ```bash
   python3 rag/rag_chat_import.py
   ```
 2. Build and start the RAG API server:
   ```bash
   cd rag
   python3 rag_build.py
   uvicorn rag_api:app --host 0.0.0.0 --port 7090
   ```
 3. Query the RAG system:
   ```bash
   curl -X POST http://127.0.0.1:7090/rag/search \
     -H "Content-Type: application/json" \
     -d '{
       "query": "What is the current state of Cortex?",
       "where": {"category": "lyra"}
     }'
   ```
 ---
 ## Development Notes
 ### Cortex Architecture (v0.6.0)
 - Cortex contains embedded Intake module at `cortex/intake/`
 - Intake is imported as: `from intake.intake import add_exchange_internal, SESSIONS`
 - SESSIONS is a module-level global dictionary (singleton pattern)
 - Single-worker constraint required to maintain SESSIONS state
 - Diagnostic endpoints available for debugging: `/debug/sessions`, `/debug/summary`
 - **NEW:** Autonomy system integrated at `cortex/autonomy/`
  - Executive, decision, action, learning, and monitoring layers
  - Self-state persistence in `cortex/data/self_state.json`
  - Coordinated via orchestrator with flexible service controls
 ### Adding New LLM Backends
 1. Add backend URL to `.env`:
   ```bash
   LLM_CUSTOM_URL=http://your-backend:port
   LLM_CUSTOM_MODEL=model-name
   ```
 2. Configure module to use new backend:
   ```bash
   CORTEX_LLM=CUSTOM
   ```
 3. Restart Cortex container:
   ```bash
   docker-compose restart cortex
   ```
 ### Debugging Tips
 - Enable verbose logging: `VERBOSE_DEBUG=true` in `.env`
 - Check Cortex logs: `docker logs cortex -f`
 - Check Relay logs: `docker logs relay -f`
 - Inspect SESSIONS: `curl http://localhost:7081/debug/sessions`
 - Test summarization: `curl "http://localhost:7081/debug/summary?session_id=test"`
 - List sessions: `curl http://localhost:7078/sessions`
 - Test Standard Mode: `curl -X POST http://localhost:7078/v1/chat/completions -H "Content-Type: application/json" -d '{"mode":"standard","backend":"SECONDARY","messages":[{"role":"user","content":"test"}],"sessionId":"test"}'`
 - Monitor Docker network: `docker network inspect lyra_net`
 - Check session files: `ls -la core/relay/sessions/`
@@ -0,0 +1,163 @@
 # "Show Your Work" - Thinking Stream Feature
 Real-time Server-Sent Events (SSE) stream that broadcasts the internal thinking process during tool calling operations.
 ## What It Does
 When Lyra uses tools to answer a question, you can now watch her "think" in real-time through a parallel stream:
 - 🤔 **Thinking** - When she's planning what to do
 - 🔧 **Tool Calls** - When she decides to use a tool
 - 📊 **Tool Results** - The results from tool execution
 - ✅ **Done** - When she has the final answer
 - ❌ **Errors** - If something goes wrong
 ## How To Use
 ### 1. Open the SSE Stream
 Connect to the thinking stream for a session:
 ```bash
 curl -N http://localhost:7081/stream/thinking/{session_id}
 ```
 The stream will send Server-Sent Events in this format:
 ```
 data: {"type": "thinking", "data": {"message": "🤔 Thinking... (iteration 1/5)"}}
 data: {"type": "tool_call", "data": {"tool": "execute_code", "args": {...}, "message": "🔧 Using tool: execute_code"}}
 data: {"type": "tool_result", "data": {"tool": "execute_code", "result": {...}, "message": "📊 Result: ..."}}
 data: {"type": "done", "data": {"message": "✅ Complete!", "final_answer": "The result is..."}}
 ```
 ### 2. Send a Request
 In parallel, send a request to `/simple` with the same `session_id`:
 ```bash
 curl -X POST http://localhost:7081/simple \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "your-session-id",
    "user_prompt": "Calculate 50/2 using Python",
    "backend": "SECONDARY"
  }'
 ```
 ### 3. Watch the Stream
 As the request processes, you'll see real-time events showing:
 - Each thinking iteration
 - Every tool call being made
 - The results from each tool
 - The final answer
 ## Event Types
 | Event Type | Description | Data Fields |
 |-----------|-------------|-------------|
 | `connected` | Initial connection | `session_id` |
 | `thinking` | LLM is processing | `message` |
 | `tool_call` | Tool is being invoked | `tool`, `args`, `message` |
 | `tool_result` | Tool execution completed | `tool`, `result`, `message` |
 | `done` | Process complete | `message`, `final_answer` |
 | `error` | Something went wrong | `message` |
 ## Demo Page
 A demo HTML page is included at [test_thinking_stream.html](../test_thinking_stream.html):
 ```bash
 # Serve the demo page
 python3 -m http.server 8000
 ```
 Then open http://localhost:8000/test_thinking_stream.html in your browser.
 The demo shows:
 - **Left panel**: Chat interface
 - **Right panel**: Real-time thinking stream
 - **Mobile**: Swipe between panels
 ## Architecture
 ### Components
 1. **ToolStreamManager** (`autonomy/tools/stream_events.py`)
   - Manages SSE subscriptions per session
   - Broadcasts events to all connected clients
   - Handles automatic cleanup
 2. **FunctionCaller** (`autonomy/tools/function_caller.py`)
   - Enhanced with event emission at each step
   - Checks for active subscribers before emitting
   - Passes `session_id` through the call chain
 3. **SSE Endpoint** (`/stream/thinking/{session_id}`)
   - FastAPI streaming response
   - 30-second keepalive for connection maintenance
   - Automatic reconnection on client side
 ### Event Flow
 ```
 Client                 SSE Endpoint           FunctionCaller          Tools
  |                         |                         |                  |
  |--- Connect SSE -------->|                         |                  |
  |<-- connected ----------|                          |                  |
  |                         |                         |                  |
  |--- POST /simple --------|                         |                  |
  |                         |                         |                  |
  |                         |<-- emit("thinking") ---|                  |
  |<-- thinking ------------|                         |                  |
  |                         |                         |                  |
  |                         |<-- emit("tool_call") ---|                  |
  |<-- tool_call -----------|                         |                  |
  |                         |                         |-- execute ------>|
  |                         |                         |<-- result -------|
  |                         |<-- emit("tool_result")--|                  |
  |<-- tool_result ---------|                         |                  |
  |                         |                         |                  |
  |                         |<-- emit("done") --------|                  |
  |<-- done ---------------|                         |                  |
  |                         |                         |                  |
 ```
 ## Configuration
 No additional configuration needed! The feature works automatically when:
 1. `STANDARD_MODE_ENABLE_TOOLS=true` (already set)
 2. A client connects to the SSE stream BEFORE sending the request
 ## Example Output
 ```
 🟢 Connected to thinking stream
 ✓ Connected (Session: thinking-demo-1735177234567)
 🤔 Thinking... (iteration 1/5)
 🔧 Using tool: execute_code
 📊 Result: {'stdout': '12.0\n', 'stderr': '', 'exit_code': 0, 'execution_time': 0.04}
 🤔 Thinking... (iteration 2/5)
 ✅ Complete!
 ```
 ## Use Cases
 - **Debugging**: See exactly what tools are being called and why
 - **Transparency**: Show users what the AI is doing behind the scenes
 - **Education**: Learn how the system breaks down complex tasks
 - **UI Enhancement**: Create engaging "thinking" animations
 - **Mobile App**: Separate tab for "Show Your Work" view
 ## Future Enhancements
 Potential additions:
 - Token usage per iteration
 - Estimated time remaining
 - Tool execution duration
 - Intermediate reasoning steps
 - Visual progress indicators
@@ -0,0 +1,159 @@
 # Trilium ETAPI Integration Setup
 This guide will help you enable Lyra's integration with your Trilium notes using the ETAPI (External API).
 ## What You Can Do with Trilium Integration
 Once enabled, Lyra can help you:
 - 🔍 Search through your notes
 - 📝 Create new notes from conversations
 - 🔄 Find duplicate or similar notes
 - 🏷️ Suggest better organization and tags
 - 📊 Summarize and update existing notes
 ## Prerequisites
 - Trilium Notes installed and running
 - Access to Trilium's web interface
 - Lyra running on the same network as Trilium
 ## Step 1: Generate ETAPI Token in Trilium
 1. **Open Trilium** in your web browser (e.g., `http://10.0.0.2:4292`)
 2. **Navigate to Options**:
   - Click the menu icon (≡) in the top-left corner
   - Select **"Options"** from the menu
 3. **Go to ETAPI Section**:
   - In the Options sidebar, find and click **"ETAPI"**
   - This section manages external API access
 4. **Generate a New Token**:
   - Look for the **"Create New Token"** or **"Generate Token"** button
   - Click it to create a new ETAPI token
   - You may be asked to provide a name/description for the token (e.g., "Lyra Integration")
 5. **Copy the Token**:
   - Once generated, you'll see a long string of characters (this is your token)
   - **IMPORTANT**: Copy this token immediately - Trilium stores it hashed and you won't see it again!
   - The token message will say: "ETAPI token created, copy the created token into the clipboard"
   - Example format: `3ZOIydvNps3R_fZEE+kOFXiJlJ7vaeXHMEW6QuRYQm3+6qpjVxFwp9LE=`
 6. **Save the Token Securely**:
   - Store it temporarily in a secure place (password manager or secure note)
   - You'll need to paste it into Lyra's configuration in the next step
 ## Step 2: Configure Lyra
 1. **Edit the Environment File**:
   ```bash
   nano /home/serversdown/project-lyra/.env
   ```
 2. **Add/Update Trilium Configuration**:
   Find or add these lines:
   ```env
   # Trilium ETAPI Integration
   ENABLE_TRILIUM=true
   TRILIUM_URL=http://10.0.0.2:4292
   TRILIUM_ETAPI_TOKEN=your_token_here
   # Enable tools in standard mode (if not already set)
   STANDARD_MODE_ENABLE_TOOLS=true
   ```
 3. **Replace `your_token_here`** with the actual token you copied from Trilium
 4. **Save and exit** (Ctrl+O, Enter, Ctrl+X in nano)
 ## Step 3: Restart Cortex Service
 For the changes to take effect, restart the Cortex service:
 ```bash
 cd /home/serversdown/project-lyra
 docker-compose restart cortex
 ```
 Or if running with Docker directly:
 ```bash
 docker restart cortex
 ```
 ## Step 4: Test the Integration
 Once restarted, try these example queries in Lyra (using Cortex mode):
 1. **Test Search**:
   - "Search my Trilium notes for topics about AI"
   - "Find notes containing 'project planning'"
 2. **Test Create Note**:
   - "Create a note in Trilium titled 'Meeting Notes' with a summary of our conversation"
   - "Save this to my Trilium as a new note"
 3. **Watch the Thinking Stream**:
   - Open the thinking stream panel (🧠 Show Work)
   - You should see tool calls to `search_notes` and `create_note`
 ## Troubleshooting
 ### "Connection refused" or "Cannot reach Trilium"
 - Verify Trilium is running: `curl http://10.0.0.2:4292`
 - Check that Cortex can access Trilium's network
 - Ensure the URL in `.env` is correct
 ### "Authentication failed" or "Invalid token"
 - Double-check the token was copied correctly (no extra spaces)
 - Generate a new token in Trilium if needed
 - Verify `TRILIUM_ETAPI_TOKEN` in `.env` is set correctly
 ### "No results found" when searching
 - Verify you have notes in Trilium
 - Try a broader search query
 - Check Trilium's search functionality works directly
 ### Tools not appearing in Cortex mode
 - Verify `ENABLE_TRILIUM=true` is set
 - Restart Cortex after changing `.env`
 - Check Cortex logs: `docker logs cortex`
 ## Security Notes
 ⚠️ **Important Security Considerations**:
 - The ETAPI token provides **full access** to your Trilium notes
 - Keep the token secure - do not share or commit to git
 - The `.env` file should be in `.gitignore` (already configured)
 - Consider using a dedicated token for Lyra (you can create multiple tokens)
 - Revoke tokens you no longer use from Trilium's ETAPI settings
 ## Available Functions
 Currently enabled functions:
 ### `search_notes(query, limit)`
 Search through your Trilium notes by keyword or phrase.
 **Example**: "Search my notes for 'machine learning' and show the top 5 results"
 ### `create_note(title, content, parent_note_id)`
 Create a new note in Trilium with specified title and content.
 **Example**: "Create a note called 'Ideas from Today' with this summary: [content]"
 **Optional**: Specify a parent note ID to nest the new note under an existing note.
 ## Future Enhancements
 Potential additions to the integration:
 - Update existing notes
 - Retrieve full note content by ID
 - Manage tags and attributes
 - Clone/duplicate notes
 - Export notes in various formats
 ---
 **Need Help?** Check the Cortex logs or open an issue on the project repository.
@@ -0,0 +1,109 @@
 # Thinking Stream UI Integration
 ## What Was Added
 Added a "🧠 Show Work" button to the main chat interface that opens a dedicated thinking stream window.
 ## Changes Made
 ### 1. Main Chat Interface ([core/ui/index.html](core/ui/index.html))
 Added button to session selector:
 ```html
 <button id="thinkingStreamBtn" title="Show thinking stream in new window">🧠 Show Work</button>
 ```
 Added event listener to open stream window:
 ```javascript
 document.getElementById("thinkingStreamBtn").addEventListener("click", () => {
  const streamUrl = `/thinking-stream.html?session=${currentSession}`;
  const windowFeatures = "width=600,height=800,menubar=no,toolbar=no,location=no,status=no";
  window.open(streamUrl, `thinking_${currentSession}`, windowFeatures);
 });
 ```
 ### 2. Thinking Stream Window ([core/ui/thinking-stream.html](core/ui/thinking-stream.html))
 New dedicated page for the thinking stream:
 - **Header**: Shows connection status with live indicator
 - **Events Area**: Scrollable list of thinking events
 - **Footer**: Clear button and session info
 Features:
 - Auto-reconnecting SSE connection
 - Color-coded event types
 - Slide-in animations for new events
 - Automatic scrolling to latest event
 - Session ID from URL parameter
 ### 3. Styling ([core/ui/style.css](core/ui/style.css))
 Added purple/violet theme for the thinking button:
 ```css
 #thinkingStreamBtn {
  background: rgba(138, 43, 226, 0.2);
  border-color: #8a2be2;
 }
 ```
 ## How To Use
 1. **Open Chat Interface**
   - Navigate to http://localhost:7078 (relay)
   - Select or create a session
 2. **Open Thinking Stream**
   - Click the "🧠 Show Work" button
   - A new window opens showing the thinking stream
 3. **Send a Message**
   - Type a message that requires tools (e.g., "Calculate 50/2 in Python")
   - Watch the thinking stream window for real-time updates
 4. **Observe Events**
   - 🤔 Thinking iterations
   - 🔧 Tool calls
   - 📊 Tool results
   - ✅ Completion
 ## Event Types & Colors
 | Event | Icon | Color | Description |
 |-------|------|-------|-------------|
 | Connected | ✓ | Green | Stream established |
 | Thinking | 🤔 | Light Green | LLM processing |
 | Tool Call | 🔧 | Orange | Tool invocation |
 | Tool Result | 📊 | Blue | Tool output |
 | Done | ✅ | Purple | Task complete |
 | Error | ❌ | Red | Something failed |
 ## Architecture
 ```
 User clicks "Show Work"
        ↓
 Opens thinking-stream.html?session=xxx
        ↓
 Connects to SSE: /stream/thinking/{session}
        ↓
 User sends message in main chat
        ↓
 FunctionCaller emits events
        ↓
 Events appear in thinking stream window
 ```
 ## Mobile Support
 The thinking stream window is responsive:
 - Desktop: Side-by-side windows
 - Mobile: Use browser's tab switcher to swap between chat and thinking stream
 ## Future Enhancements
 Potential improvements:
 - **Embedded panel**: Option to show thinking stream in a split panel within main UI
 - **Event filtering**: Toggle event types on/off
 - **Export**: Download thinking trace as JSON
 - **Replay**: Replay past thinking sessions
 - **Statistics**: Show timing, token usage per step
@@ -0,0 +1,14 @@
 FROM node:18-alpine
 WORKDIR /app
 # install deps
 COPY package.json ./package.json
 RUN npm install --production
 # copy code + config
 COPY persona-server.js ./persona-server.js
 COPY personas.json ./personas.json
 EXPOSE 7080
 CMD ["node", "persona-server.js"]
@@ -0,0 +1,8 @@
 {
  "name": "persona-sidecar",
  "version": "0.1.0",
  "type": "module",
  "dependencies": {
    "express": "^4.19.2"
  }
 }
@@ -0,0 +1,78 @@
 // persona-server.js — Persona Sidecar v0.1.0 (Docker Lyra)
 // Node 18+, Express REST
 import express from "express";
 import fs from "fs";
 const app = express();
 app.use(express.json());
 const PORT = process.env.PORT || 7080;
 const CONFIG_FILE = process.env.PERSONAS_FILE || "./personas.json";
 // allow JSON with // and /* */ comments
 function parseJsonWithComments(raw) {
  return JSON.parse(
    raw
      .replace(/\/\*[\s\S]*?\*\//g, "")   // block comments
      .replace(/^\s*\/\/.*$/gm, "")       // line comments
  );
 }
 function loadConfig() {
  const raw = fs.readFileSync(CONFIG_FILE, "utf-8");
  return parseJsonWithComments(raw);
 }
 function saveConfig(cfg) {
  fs.writeFileSync(CONFIG_FILE, JSON.stringify(cfg, null, 2));
 }
 // GET /persona → active persona JSON
 app.get("/persona", (_req, res) => {
  try {
    const cfg = loadConfig();
    const active = cfg.active;
    const persona = cfg.personas?.[active];
    if (!persona) return res.status(404).json({ error: "Active persona not found" });
    res.json({ active, persona });
  } catch (err) {
    res.status(500).json({ error: String(err.message || err) });
  }
 });
 // GET /personas → all personas
 app.get("/personas", (_req, res) => {
  try {
    const cfg = loadConfig();
    res.json(cfg.personas || {});
  } catch (err) {
    res.status(500).json({ error: String(err.message || err) });
  }
 });
 // POST /persona/select { name }
 app.post("/persona/select", (req, res) => {
  try {
    const { name } = req.body || {};
    if (!name) return res.status(400).json({ error: "Missing 'name'" });
    const cfg = loadConfig();
    if (!cfg.personas || !cfg.personas[name]) {
      return res.status(404).json({ error: `Persona '${name}' not found` });
    }
    cfg.active = name;
    saveConfig(cfg);
    res.json({ ok: true, active: name });
  } catch (err) {
    res.status(500).json({ error: String(err.message || err) });
  }
 });
 // health + fallback
 app.get("/_health", (_req, res) => res.json({ ok: true, time: new Date().toISOString() }));
 app.use((_req, res) => res.status(404).json({ error: "no such route" }));
 app.listen(PORT, () => {
  console.log(`Persona Sidecar listening on :${PORT}`);
 });
@@ -0,0 +1,17 @@
 {
  // v0.1.0 default active persona
  "active": "Lyra",
  // Personas available to the service
  "personas": {
    "Lyra": {
      "name": "Lyra",
      "style": "warm, slyly supportive, collaborative confidante",
      "protocols": ["Project logs", "Confidence Bank", "Scar Notes"]
    }
  }
  // Placeholders for later (commented out for now)
  // "Doyle": { "name": "Doyle", "style": "gritty poker grinder", "protocols": [] },
  // "Mr GPT": { "name": "Mr GPT", "style": "direct, tactical mentor", "protocols": [] }
 }
@@ -0,0 +1,16 @@
 # Ignore node_modules - Docker will rebuild them inside
 node_modules
 npm-debug.log
 yarn-error.log
 *.log
 # Ignore environment files
 .env
 .env.local
 # Ignore OS/editor cruft
 .DS_Store
 *.swp
 *.swo
 .vscode
 .idea
@@ -0,0 +1,18 @@
 # relay/Dockerfile
 FROM node:18-alpine
 # Create app directory
 WORKDIR /app
 # Copy package.json and install deps first (better caching)
 COPY package.json ./
 RUN npm install
 # Copy the rest of the app
 COPY . .
 # Expose port
 EXPOSE 7078
 # Run the server
 CMD ["npm", "start"]
@@ -0,0 +1,73 @@
 // relay/lib/cortex.js
 import fetch from "node-fetch";
 const REFLECT_URL = process.env.CORTEX_URL || "http://localhost:7081/reflect";
 const INGEST_URL  = process.env.CORTEX_URL_INGEST || "http://localhost:7081/ingest";
 export async function reflectWithCortex(userInput, memories = []) {
  const body = { prompt: userInput, memories };
  try {
    const res = await fetch(REFLECT_URL, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify(body),
      timeout: 120000,
    });
    const rawText = await res.text();
 	console.log("🔎 [Cortex-Debug] rawText from /reflect →", rawText.slice(0, 300));
    if (!res.ok) {
      throw new Error(`HTTP ${res.status} — ${rawText.slice(0, 200)}`);
    }
    let data;
    try {
      data = JSON.parse(rawText);
    } catch (err) {
      // Fallback ① try to grab a JSON-looking block
      const match = rawText.match(/\{[\s\S]*\}/);
      if (match) {
        try {
          data = JSON.parse(match[0]);
        } catch {
          data = { reflection_raw: rawText.trim(), notes: "partial parse" };
        }
      } else {
        // Fallback ② if it’s already an object (stringified Python dict)
        try {
          const normalized = rawText
            .replace(/'/g, '"')        // convert single quotes
            .replace(/None/g, 'null'); // convert Python None
          data = JSON.parse(normalized);
        } catch {
          data = { reflection_raw: rawText.trim(), notes: "no JSON found" };
        }
      }
    }
    if (typeof data !== "object") {
      data = { reflection_raw: rawText.trim(), notes: "non-object response" };
    }
    console.log("🧠 Cortex reflection normalized:", data);
    return data;
  } catch (e) {
    console.warn("⚠️ Cortex reflect failed:", e.message);
    return { error: e.message, reflection_raw: "" };
  }
 }
 export async function ingestToCortex(user, assistant, reflection = {}, sessionId = "default") {
  const body = { turn: { user, assistant }, reflection, session_id: sessionId };
  try {
    const res = await fetch(INGEST_URL, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify(body),
      timeout: 120000,
    });
    console.log(`📤 Sent exchange to Cortex ingest (${res.status})`);
  } catch (e) {
    console.warn("⚠️ Cortex ingest failed:", e.message);
  }
 }
@@ -0,0 +1,161 @@
 async function tryBackend(backend, messages) {
  if (!backend.url || !backend.model) throw new Error("missing url/model");
  const isOllama = backend.type === "ollama";
  const isOpenAI = backend.type === "openai";
  const isVllm = backend.type === "vllm";
  const isLlamaCpp = backend.type === "llamacpp";
  let endpoint = backend.url;
  let headers = { "Content-Type": "application/json" };
  if (isOpenAI) headers["Authorization"] = `Bearer ${OPENAI_API_KEY}`;
  // Choose correct endpoint automatically
  if (isOllama && !endpoint.endsWith("/api/chat")) endpoint += "/api/chat";
  if ((isVllm || isLlamaCpp) && !endpoint.endsWith("/v1/completions")) endpoint += "/v1/completions";
  if (isOpenAI && !endpoint.endsWith("/v1/chat/completions")) endpoint += "/v1/chat/completions";
  // Build payload based on backend style
  const body = (isVllm || isLlamaCpp)
    ? {
        model: backend.model,
        prompt: messages.map(m => m.content).join("\n"),
        max_tokens: 400,
        temperature: 0.3,
      }
    : isOllama
    ? { model: backend.model, messages, stream: false }
    : { model: backend.model, messages, stream: false };
  const resp = await fetch(endpoint, {
    method: "POST",
    headers,
    body: JSON.stringify(body),
    timeout: 120000,
  });
  if (!resp.ok) throw new Error(`${backend.key} HTTP ${resp.status}`);
  const raw = await resp.text();
  // 🧩 Normalize replies
  let reply = "";
  let parsedData = null;
  try {
    if (isOllama) {
      // Ollama sometimes returns NDJSON lines; merge them
      const merged = raw
        .split("\n")
        .filter(line => line.trim().startsWith("{"))
        .map(line => JSON.parse(line))
        .map(obj => obj.message?.content || obj.response || "")
        .join("");
      reply = merged.trim();
    } else {
      parsedData = JSON.parse(raw);
 	  reply =
 	    parsedData?.choices?.[0]?.text?.trim() ||
 	    parsedData?.choices?.[0]?.message?.content?.trim() ||
 	    parsedData?.message?.content?.trim() ||
 	    "";
    }
  } catch (err) {
    reply = `[parse error: ${err.message}]`;
  }
  return { reply, raw, parsedData, backend: backend.key };
 }
 // ------------------------------------
 // Structured logging helper
 // ------------------------------------
 const LOG_DETAIL = process.env.LOG_DETAIL_LEVEL || "summary"; // minimal | summary | detailed | verbose
 function logLLMCall(backend, messages, result, error = null) {
  const timestamp = new Date().toISOString().split('T')[1].slice(0, -1);
  if (error) {
    // Always log errors
    console.warn(`⚠️  [LLM] ${backend.key.toUpperCase()} failed | ${timestamp} | ${error.message}`);
    return;
  }
  // Success - log based on detail level
  if (LOG_DETAIL === "minimal") {
    return; // Don't log successful calls in minimal mode
  }
  if (LOG_DETAIL === "summary") {
    console.log(`✅ [LLM] ${backend.key.toUpperCase()} | ${timestamp} | Reply: ${result.reply.substring(0, 80)}...`);
    return;
  }
  // Detailed or verbose
  console.log(`\n${'─'.repeat(100)}`);
  console.log(`🧠 LLM CALL | Backend: ${backend.key.toUpperCase()} | ${timestamp}`);
  console.log(`${'─'.repeat(100)}`);
  // Show prompt preview
  const lastMsg = messages[messages.length - 1];
  const promptPreview = (lastMsg?.content || '').substring(0, 150);
  console.log(`📝 Prompt: ${promptPreview}...`);
  // Show parsed reply
  console.log(`💬 Reply: ${result.reply.substring(0, 200)}...`);
  // Show raw response only in verbose mode
  if (LOG_DETAIL === "verbose" && result.parsedData) {
    console.log(`\n╭─ RAW RESPONSE ────────────────────────────────────────────────────────────────────────────`);
    const jsonStr = JSON.stringify(result.parsedData, null, 2);
    const lines = jsonStr.split('\n');
    const maxLines = 50;
    lines.slice(0, maxLines).forEach(line => {
      console.log(`│ ${line}`);
    });
    if (lines.length > maxLines) {
      console.log(`│ ... (${lines.length - maxLines} more lines - check raw field for full response)`);
    }
    console.log(`╰${'─'.repeat(95)}`);
  }
  console.log(`${'─'.repeat(100)}\n`);
 }
 // ------------------------------------
 // Export the main call helper
 // ------------------------------------
 export async function callSpeechLLM(messages) {
  const backends = [
    { key: "primary",  type: "vllm",     url: process.env.LLM_PRIMARY_URL,  model: process.env.LLM_PRIMARY_MODEL },
    { key: "secondary",type: "ollama",   url: process.env.LLM_SECONDARY_URL,model: process.env.LLM_SECONDARY_MODEL },
    { key: "cloud",    type: "openai",   url: process.env.LLM_CLOUD_URL,    model: process.env.LLM_CLOUD_MODEL },
    { key: "fallback", type: "llamacpp", url: process.env.LLM_FALLBACK_URL, model: process.env.LLM_FALLBACK_MODEL },
  ];
  const failedBackends = [];
  for (const b of backends) {
    if (!b.url || !b.model) continue;
    try {
      const out = await tryBackend(b, messages);
      logLLMCall(b, messages, out);
      return out;
    } catch (err) {
      logLLMCall(b, messages, null, err);
      failedBackends.push({ backend: b.key, error: err.message });
    }
  }
  // All backends failed - log summary
  console.error(`\n${'='.repeat(100)}`);
  console.error(`🔴 ALL LLM BACKENDS FAILED`);
  console.error(`${'='.repeat(100)}`);
  failedBackends.forEach(({ backend, error }) => {
    console.error(`  ${backend.toUpperCase()}: ${error}`);
  });
  console.error(`${'='.repeat(100)}\n`);
  throw new Error("all_backends_failed");
 }
@@ -0,0 +1,16 @@
 {
  "name": "lyra-relay",
  "version": "0.1.0",
  "type": "module",
  "main": "server.js",
  "scripts": {
    "start": "node server.js"
  },
  "dependencies": {
    "cors": "^2.8.5",
    "dotenv": "^16.6.1",
    "express": "^4.21.2",
    "mem0ai": "^2.1.38",
    "node-fetch": "^3.3.2"
  }
 }
@@ -0,0 +1,368 @@
 // relay v0.3.0
 // Core relay server for Lyra project
 // Handles incoming chat requests and forwards them to Cortex services
 import express from "express";
 import dotenv from "dotenv";
 import cors from "cors";
 import fs from "fs/promises";
 import path from "path";
 import { fileURLToPath } from "url";
 dotenv.config();
 // ES module __dirname workaround
 const __filename = fileURLToPath(import.meta.url);
 const __dirname = path.dirname(__filename);
 const SESSIONS_DIR = path.join(__dirname, "sessions");
 const app = express();
 app.use(cors());
 app.use(express.json());
 const PORT = Number(process.env.PORT || 7078);
 // Cortex endpoints
 const CORTEX_REASON = process.env.CORTEX_REASON_URL || "http://cortex:7081/reason";
 const CORTEX_SIMPLE = process.env.CORTEX_SIMPLE_URL || "http://cortex:7081/simple";
 // -----------------------------------------------------
 // Helper request wrapper
 // -----------------------------------------------------
 async function postJSON(url, data) {
  const resp = await fetch(url, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify(data),
  });
  const raw = await resp.text();
  let json;
  try {
    json = raw ? JSON.parse(raw) : null;
  } catch (e) {
    throw new Error(`Non-JSON from ${url}: ${raw}`);
  }
  if (!resp.ok) {
    throw new Error(json?.detail || json?.error || raw);
  }
  return json;
 }
 // -----------------------------------------------------
 // The unified chat handler
 // -----------------------------------------------------
 async function handleChatRequest(session_id, user_msg, mode = "cortex", backend = null) {
  let reason;
  // Determine which endpoint to use based on mode
  const endpoint = mode === "standard" ? CORTEX_SIMPLE : CORTEX_REASON;
  const modeName = mode === "standard" ? "simple" : "reason";
  console.log(`Relay → routing to Cortex.${modeName} (mode: ${mode}${backend ? `, backend: ${backend}` : ''})`);
  // Build request payload
  const payload = {
    session_id,
    user_prompt: user_msg
  };
  // Add backend parameter if provided (only for standard mode)
  if (backend && mode === "standard") {
    payload.backend = backend;
  }
  // Call appropriate Cortex endpoint
  try {
    reason = await postJSON(endpoint, payload);
  } catch (e) {
    console.error(`Relay → Cortex.${modeName} error:`, e.message);
    throw new Error(`cortex_${modeName}_failed: ${e.message}`);
  }
  // Correct persona field
  const persona =
    reason.persona ||
    reason.final_output ||
    "(no persona text)";
  // Return final answer
  return {
    session_id,
    reply: persona
  };
 }
 // -----------------------------------------------------
 // HEALTHCHECK
 // -----------------------------------------------------
 app.get("/_health", (_, res) => {
  res.json({ ok: true });
 });
 // -----------------------------------------------------
 // OPENAI-COMPATIBLE ENDPOINT
 // -----------------------------------------------------
 app.post("/v1/chat/completions", async (req, res) => {
  try {
    const session_id = req.body.session_id || req.body.sessionId || req.body.user || "default";
    const messages = req.body.messages || [];
    const lastMessage = messages[messages.length - 1];
    const user_msg = lastMessage?.content || "";
    const mode = req.body.mode || "cortex"; // Get mode from request, default to cortex
    const backend = req.body.backend || null; // Get backend preference
    if (!user_msg) {
      return res.status(400).json({ error: "No message content provided" });
    }
    console.log(`Relay (v1) → received: "${user_msg}" [mode: ${mode}${backend ? `, backend: ${backend}` : ''}]`);
    const result = await handleChatRequest(session_id, user_msg, mode, backend);
    res.json({
      id: `chatcmpl-${Date.now()}`,
      object: "chat.completion",
      created: Math.floor(Date.now() / 1000),
      model: "lyra",
      choices: [{
        index: 0,
        message: {
          role: "assistant",
          content: result.reply
        },
        finish_reason: "stop"
      }],
      usage: {
        prompt_tokens: 0,
        completion_tokens: 0,
        total_tokens: 0
      }
    });
  } catch (err) {
    console.error("Relay v1 fatal:", err);
    res.status(500).json({
      error: {
        message: err.message || String(err),
        type: "server_error",
        code: "relay_failed"
      }
    });
  }
 });
 // -----------------------------------------------------
 // MAIN ENDPOINT (Lyra-native UI)
 // -----------------------------------------------------
 app.post("/chat", async (req, res) => {
  try {
    const session_id = req.body.session_id || "default";
    const user_msg   = req.body.message || "";
    const mode = req.body.mode || "cortex"; // Get mode from request, default to cortex
    const backend = req.body.backend || null; // Get backend preference
    console.log(`Relay → received: "${user_msg}" [mode: ${mode}${backend ? `, backend: ${backend}` : ''}]`);
    const result = await handleChatRequest(session_id, user_msg, mode, backend);
    res.json(result);
  } catch (err) {
    console.error("Relay fatal:", err);
    res.status(500).json({
      error: "relay_failed",
      detail: err.message || String(err)
    });
  }
 });
 // -----------------------------------------------------
 // SESSION ENDPOINTS (for UI)
 // -----------------------------------------------------
 // Helper functions for session persistence
 async function ensureSessionsDir() {
  try {
    await fs.mkdir(SESSIONS_DIR, { recursive: true });
  } catch (err) {
    console.error("Failed to create sessions directory:", err);
  }
 }
 async function loadSession(sessionId) {
  try {
    const sessionPath = path.join(SESSIONS_DIR, `${sessionId}.json`);
    const data = await fs.readFile(sessionPath, "utf-8");
    return JSON.parse(data);
  } catch (err) {
    // File doesn't exist or is invalid - return empty array
    return [];
  }
 }
 async function saveSession(sessionId, history, metadata = {}) {
  try {
    await ensureSessionsDir();
    const sessionPath = path.join(SESSIONS_DIR, `${sessionId}.json`);
    const metadataPath = path.join(SESSIONS_DIR, `${sessionId}.meta.json`);
    // Save history
    await fs.writeFile(sessionPath, JSON.stringify(history, null, 2), "utf-8");
    // Save metadata (name, etc.)
    await fs.writeFile(metadataPath, JSON.stringify(metadata, null, 2), "utf-8");
    return true;
  } catch (err) {
    console.error(`Failed to save session ${sessionId}:`, err);
    return false;
  }
 }
 async function loadSessionMetadata(sessionId) {
  try {
    const metadataPath = path.join(SESSIONS_DIR, `${sessionId}.meta.json`);
    const data = await fs.readFile(metadataPath, "utf-8");
    return JSON.parse(data);
  } catch (err) {
    // No metadata file, return default
    return { name: sessionId };
  }
 }
 async function saveSessionMetadata(sessionId, metadata) {
  try {
    await ensureSessionsDir();
    const metadataPath = path.join(SESSIONS_DIR, `${sessionId}.meta.json`);
    await fs.writeFile(metadataPath, JSON.stringify(metadata, null, 2), "utf-8");
    return true;
  } catch (err) {
    console.error(`Failed to save metadata for ${sessionId}:`, err);
    return false;
  }
 }
 async function listSessions() {
  try {
    await ensureSessionsDir();
    const files = await fs.readdir(SESSIONS_DIR);
    const sessions = [];
    for (const file of files) {
      if (file.endsWith(".json") && !file.endsWith(".meta.json")) {
        const sessionId = file.replace(".json", "");
        const sessionPath = path.join(SESSIONS_DIR, file);
        const stats = await fs.stat(sessionPath);
        // Try to read the session to get message count
        let messageCount = 0;
        try {
          const data = await fs.readFile(sessionPath, "utf-8");
          const history = JSON.parse(data);
          messageCount = history.length;
        } catch (e) {
          // Invalid JSON, skip
        }
        // Load metadata (name)
        const metadata = await loadSessionMetadata(sessionId);
        sessions.push({
          id: sessionId,
          name: metadata.name || sessionId,
          lastModified: stats.mtime,
          messageCount
        });
      }
    }
    // Sort by last modified (newest first)
    sessions.sort((a, b) => b.lastModified - a.lastModified);
    return sessions;
  } catch (err) {
    console.error("Failed to list sessions:", err);
    return [];
  }
 }
 async function deleteSession(sessionId) {
  try {
    const sessionPath = path.join(SESSIONS_DIR, `${sessionId}.json`);
    const metadataPath = path.join(SESSIONS_DIR, `${sessionId}.meta.json`);
    // Delete session file
    await fs.unlink(sessionPath);
    // Delete metadata file (if exists)
    try {
      await fs.unlink(metadataPath);
    } catch (e) {
      // Metadata file doesn't exist, that's ok
    }
    return true;
  } catch (err) {
    console.error(`Failed to delete session ${sessionId}:`, err);
    return false;
  }
 }
 // GET /sessions - List all sessions
 app.get("/sessions", async (req, res) => {
  const sessions = await listSessions();
  res.json(sessions);
 });
 // GET /sessions/:id - Get specific session history
 app.get("/sessions/:id", async (req, res) => {
  const sessionId = req.params.id;
  const history = await loadSession(sessionId);
  res.json(history);
 });
 // POST /sessions/:id - Save session history
 app.post("/sessions/:id", async (req, res) => {
  const sessionId = req.params.id;
  const history = req.body;
  // Load existing metadata to preserve it
  const existingMetadata = await loadSessionMetadata(sessionId);
  const success = await saveSession(sessionId, history, existingMetadata);
  if (success) {
    res.json({ ok: true, saved: history.length });
  } else {
    res.status(500).json({ error: "Failed to save session" });
  }
 });
 // PATCH /sessions/:id/metadata - Update session metadata (name, etc.)
 app.patch("/sessions/:id/metadata", async (req, res) => {
  const sessionId = req.params.id;
  const metadata = req.body;
  const success = await saveSessionMetadata(sessionId, metadata);
  if (success) {
    res.json({ ok: true, metadata });
  } else {
    res.status(500).json({ error: "Failed to update metadata" });
  }
 });
 // DELETE /sessions/:id - Delete a session
 app.delete("/sessions/:id", async (req, res) => {
  const sessionId = req.params.id;
  const success = await deleteSession(sessionId);
  if (success) {
    res.json({ ok: true, deleted: sessionId });
  } else {
    res.status(500).json({ error: "Failed to delete session" });
  }
 });
 // -----------------------------------------------------
 app.listen(PORT, () => {
  console.log(`Relay is online on port ${PORT}`);
 });
@@ -0,0 +1,39 @@
 // test-llm.js
 import path from "path";
 import { fileURLToPath } from "url";
 import dotenv from "dotenv";
 import { callSpeechLLM } from "./lib/llm.js";
 // ───────────────────────────────────────────────
 // 🔧 Load environment
 // ───────────────────────────────────────────────
 const __filename = fileURLToPath(import.meta.url);
 const __dirname = path.dirname(__filename);
 const envPath = path.join(__dirname, "../.env");
 dotenv.config({ path: envPath });
 console.log("🔧 Using .env from:", envPath);
 console.log("🔧 LLM_FORCE_BACKEND =", process.env.LLM_FORCE_BACKEND);
 console.log("🔧 LLM_PRIMARY_URL  =", process.env.LLM_PRIMARY_URL);
 // ───────────────────────────────────────────────
 // 🧪 Run a simple test message
 // ───────────────────────────────────────────────
 async function testLLM() {
  console.log("🧪 Testing LLM helper...");
  const messages = [
    { role: "user", content: "Say hello in five words or less." }
  ];
  try {
    const { reply, backend } = await callSpeechLLM(messages);
    console.log(`✅ Reply: ${reply || "[no reply]"}`);
    console.log(`Backend used: ${backend || "[unknown]"}`);
  } catch (err) {
    console.error("💥 Test failed:", err.message);
  }
 }
 testLLM();
@@ -0,0 +1,927 @@
 <!DOCTYPE html>
 <html lang="en">
 <head>
  <meta charset="UTF-8" />
  <title>Lyra Core Chat</title>
  <link rel="stylesheet" href="style.css" />
  <!-- PWA -->
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
  <meta name="mobile-web-app-capable" content="yes" />
  <meta name="apple-mobile-web-app-capable" content="yes" />
  <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
  <link rel="manifest" href="manifest.json" />
 </head>
 <body>
  <!-- Mobile Menu Overlay -->
  <div class="mobile-menu-overlay" id="mobileMenuOverlay"></div>
  <!-- Mobile Slide-out Menu -->
  <div class="mobile-menu" id="mobileMenu">
    <div class="mobile-menu-section">
      <h4>Mode</h4>
      <select id="mobileMode">
        <option value="standard">Standard</option>
        <option value="cortex">Cortex</option>
      </select>
    </div>
    <div class="mobile-menu-section">
      <h4>Session</h4>
      <select id="mobileSessions"></select>
      <button id="mobileNewSessionBtn">➕ New Session</button>
      <button id="mobileRenameSessionBtn">✏️ Rename Session</button>
    </div>
    <div class="mobile-menu-section">
      <h4>Actions</h4>
      <button id="mobileThinkingStreamBtn">🧠 Show Work</button>
      <button id="mobileSettingsBtn">⚙ Settings</button>
      <button id="mobileToggleThemeBtn">🌙 Toggle Theme</button>
      <button id="mobileForceReloadBtn">🔄 Force Reload</button>
    </div>
  </div>
  <div id="chat">
    <!-- Mode selector -->
    <div id="model-select">
      <!-- Hamburger menu (mobile only) -->
      <button class="hamburger-menu" id="hamburgerMenu" aria-label="Menu">
        <span></span>
        <span></span>
        <span></span>
      </button>
      <label for="mode">Mode:</label>
      <select id="mode">
        <option value="standard">Standard</option>
        <option value="cortex">Cortex</option>
      </select>
      <button id="settingsBtn" style="margin-left: auto;">⚙ Settings</button>
      <div id="theme-toggle">
        <button id="toggleThemeBtn">🌙 Dark Mode</button>
      </div>
    </div>
    <!-- Session selector -->
    <div id="session-select">
      <label for="sessions">Session:</label>
      <select id="sessions"></select>
      <button id="newSessionBtn">➕ New</button>
      <button id="renameSessionBtn">✏️ Rename</button>
      <button id="thinkingStreamBtn" title="Show thinking stream panel">🧠 Show Work</button>
    </div>
    <!-- Status -->
    <div id="status">
      <span id="status-dot"></span>
      <span id="status-text">Checking Relay...</span>
    </div>
    <!-- Chat messages -->
    <div id="messages"></div>
    <!-- Thinking Stream Panel (collapsible) -->
    <div id="thinkingPanel" class="thinking-panel collapsed">
      <div class="thinking-header" id="thinkingHeader">
        <span>🧠 Thinking Stream</span>
        <div class="thinking-controls">
          <span class="thinking-status-dot" id="thinkingStatusDot"></span>
          <button class="thinking-clear-btn" id="thinkingClearBtn" title="Clear events">🗑️</button>
          <button class="thinking-toggle-btn" id="thinkingToggleBtn">▼</button>
        </div>
      </div>
      <div class="thinking-content" id="thinkingContent">
        <div class="thinking-empty" id="thinkingEmpty">
          <div class="thinking-empty-icon">🤔</div>
          <p>Waiting for thinking events...</p>
        </div>
      </div>
    </div>
    <!-- Input box -->
    <div id="input">
      <input id="userInput" type="text" placeholder="Type a message..." autofocus />
      <button id="sendBtn">Send</button>
    </div>
  </div>
  <!-- Settings Modal (outside chat container) -->
  <div id="settingsModal" class="modal">
    <div class="modal-overlay"></div>
    <div class="modal-content">
      <div class="modal-header">
        <h3>Settings</h3>
        <button id="closeModalBtn" class="close-btn">✕</button>
      </div>
      <div class="modal-body">
        <div class="settings-section">
          <h4>Standard Mode Backend</h4>
          <p class="settings-desc">Select which LLM backend to use for Standard Mode:</p>
          <div class="radio-group">
            <label class="radio-label">
              <input type="radio" name="backend" value="SECONDARY" checked>
              <span>SECONDARY - Ollama/Qwen (3090)</span>
              <small>Fast, local, good for general chat</small>
            </label>
            <label class="radio-label">
              <input type="radio" name="backend" value="PRIMARY">
              <span>PRIMARY - llama.cpp (MI50)</span>
              <small>Local, powerful, good for complex reasoning</small>
            </label>
            <label class="radio-label">
              <input type="radio" name="backend" value="OPENAI">
              <span>OPENAI - GPT-4o-mini</span>
              <small>Cloud-based, high quality (costs money)</small>
            </label>
            <label class="radio-label">
              <input type="radio" name="backend" value="custom">
              <span>Custom Backend</span>
              <input type="text" id="customBackend" placeholder="e.g., FALLBACK" />
            </label>
          </div>
        </div>
        <div class="settings-section" style="margin-top: 24px;">
          <h4>Session Management</h4>
          <p class="settings-desc">Manage your saved chat sessions:</p>
          <div id="sessionList" class="session-list">
            <p style="color: var(--text-fade); font-size: 0.85rem;">Loading sessions...</p>
          </div>
        </div>
      </div>
      <div class="modal-footer">
        <button id="saveSettingsBtn" class="primary-btn">Save</button>
        <button id="cancelSettingsBtn">Cancel</button>
      </div>
    </div>
  </div>
  <script>
    const RELAY_BASE = "http://10.0.0.41:7078";
    const API_URL = `${RELAY_BASE}/v1/chat/completions`;
 	function generateSessionId() {
      return "sess-" + Math.random().toString(36).substring(2, 10);
    }
    let history = [];
 	let currentSession = localStorage.getItem("currentSession") || null;
 	let sessions = []; // Now loaded from server
 	async function loadSessionsFromServer() {
 	  try {
 		const resp = await fetch(`${RELAY_BASE}/sessions`);
 		const serverSessions = await resp.json();
 		sessions = serverSessions;
 		return sessions;
 	  } catch (e) {
 		console.error("Failed to load sessions from server:", e);
 		return [];
 	  }
 	}
 	async function renderSessions() {
 	  const select = document.getElementById("sessions");
 	  const mobileSelect = document.getElementById("mobileSessions");
 	  select.innerHTML = "";
 	  mobileSelect.innerHTML = "";
 	  sessions.forEach(s => {
 		const opt = document.createElement("option");
 		opt.value = s.id;
 		opt.textContent = s.name || s.id;
 		if (s.id === currentSession) opt.selected = true;
 		select.appendChild(opt);
 		// Clone for mobile menu
 		const mobileOpt = opt.cloneNode(true);
 		mobileSelect.appendChild(mobileOpt);
 	  });
 	}
 	function getSessionName(id) {
 	  const s = sessions.find(s => s.id === id);
 	  return s ? (s.name || s.id) : id;
 	}
 	async function saveSessionMetadata(sessionId, name) {
 	  try {
 		await fetch(`${RELAY_BASE}/sessions/${sessionId}/metadata`, {
 		  method: "PATCH",
 		  headers: { "Content-Type": "application/json" },
 		  body: JSON.stringify({ name })
 		});
 		return true;
 	  } catch (e) {
 		console.error("Failed to save session metadata:", e);
 		return false;
 	  }
 	}
    async function loadSession(id) {
 	  try {
 		const res = await fetch(`${RELAY_BASE}/sessions/${id}`);
 		const data = await res.json();
 		history = Array.isArray(data) ? data : [];
 		const messagesEl = document.getElementById("messages");
 		messagesEl.innerHTML = "";
 		history.forEach(m => addMessage(m.role, m.content, false)); // Don't auto-scroll for each message
 		addMessage("system", `📂 Loaded session: ${getSessionName(id)} — ${history.length} message(s)`, false);
 		// Scroll to bottom after all messages are loaded
 		messagesEl.scrollTo({ top: messagesEl.scrollHeight, behavior: "smooth" });
 	  } catch (e) {
 		addMessage("system", `Failed to load session: ${e.message}`);
 	  }
 	}
 	async function saveSession() {
 	  if (!currentSession) return;
 	  try {
 		await fetch(`${RELAY_BASE}/sessions/${currentSession}`, {
 		  method: "POST",
 		  headers: { "Content-Type": "application/json" },
 		  body: JSON.stringify(history)
 		});
 	  } catch (e) {
 		addMessage("system", `Failed to save session: ${e.message}`);
 	  }
 	}
    async function sendMessage() {
      const inputEl = document.getElementById("userInput");
      const msg = inputEl.value.trim();
      if (!msg) return;
      inputEl.value = "";
      addMessage("user", msg);
      history.push({ role: "user", content: msg });
 	  await saveSession(); // ✅ persist both user + assistant messages
      const mode = document.getElementById("mode").value;
 	// make sure we always include a stable user_id
 	let userId = localStorage.getItem("userId");
 	if (!userId) {
 	  userId = "brian"; // use whatever ID you seeded Mem0 with
 	  localStorage.setItem("userId", userId);
 	}
      // Get backend preference for Standard Mode
      let backend = null;
      if (mode === "standard") {
        backend = localStorage.getItem("standardModeBackend") || "SECONDARY";
      }
      const body = {
        mode: mode,
        messages: history,
        sessionId: currentSession
      };
      // Only add backend if in standard mode
      if (backend) {
        body.backend = backend;
      }
      try {
        const resp = await fetch(API_URL, {
          method: "POST",
          headers: { "Content-Type": "application/json" },
          body: JSON.stringify(body)
        });
        const data = await resp.json();
        const reply = data.choices?.[0]?.message?.content || "(no reply)";
        addMessage("assistant", reply);
        history.push({ role: "assistant", content: reply });
 		await saveSession();
      } catch (err) {
        addMessage("system", "Error: " + err.message);
      }
    }
 	function addMessage(role, text, autoScroll = true) {
 	  const messagesEl = document.getElementById("messages");
 	  const msgDiv = document.createElement("div");
 	  msgDiv.className = `msg ${role}`;
 	  msgDiv.textContent = text;
 	  messagesEl.appendChild(msgDiv);
 	  // Auto-scroll to bottom if enabled
 	  if (autoScroll) {
 		// Use requestAnimationFrame to ensure DOM has updated
 		requestAnimationFrame(() => {
 		  messagesEl.scrollTo({ top: messagesEl.scrollHeight, behavior: "smooth" });
 		});
 	  }
 	}
    async function checkHealth() {
      try {
        const resp = await fetch(API_URL.replace("/v1/chat/completions", "/_health"));
        if (resp.ok) {
          document.getElementById("status-dot").className = "dot ok";
          document.getElementById("status-text").textContent = "Relay Online";
        } else {
          throw new Error("Bad status");
        }
      } catch (err) {
        document.getElementById("status-dot").className = "dot fail";
        document.getElementById("status-text").textContent = "Relay Offline";
      }
    }
    document.addEventListener("DOMContentLoaded", () => {
      // Mobile Menu Toggle
      const hamburgerMenu = document.getElementById("hamburgerMenu");
      const mobileMenu = document.getElementById("mobileMenu");
      const mobileMenuOverlay = document.getElementById("mobileMenuOverlay");
      function toggleMobileMenu() {
        mobileMenu.classList.toggle("open");
        mobileMenuOverlay.classList.toggle("show");
        hamburgerMenu.classList.toggle("active");
      }
      function closeMobileMenu() {
        mobileMenu.classList.remove("open");
        mobileMenuOverlay.classList.remove("show");
        hamburgerMenu.classList.remove("active");
      }
      hamburgerMenu.addEventListener("click", toggleMobileMenu);
      mobileMenuOverlay.addEventListener("click", closeMobileMenu);
      // Sync mobile menu controls with desktop
      const mobileMode = document.getElementById("mobileMode");
      const desktopMode = document.getElementById("mode");
      // Sync mode selection
      mobileMode.addEventListener("change", (e) => {
        desktopMode.value = e.target.value;
        desktopMode.dispatchEvent(new Event("change"));
      });
      desktopMode.addEventListener("change", (e) => {
        mobileMode.value = e.target.value;
      });
      // Mobile theme toggle
      document.getElementById("mobileToggleThemeBtn").addEventListener("click", () => {
        document.getElementById("toggleThemeBtn").click();
        updateMobileThemeButton();
      });
      function updateMobileThemeButton() {
        const isDark = document.body.classList.contains("dark");
        document.getElementById("mobileToggleThemeBtn").textContent = isDark ? "☀️ Light Mode" : "🌙 Dark Mode";
      }
      // Mobile settings button
      document.getElementById("mobileSettingsBtn").addEventListener("click", () => {
        closeMobileMenu();
        document.getElementById("settingsBtn").click();
      });
      // Mobile thinking stream button
      document.getElementById("mobileThinkingStreamBtn").addEventListener("click", () => {
        closeMobileMenu();
        document.getElementById("thinkingStreamBtn").click();
      });
      // Mobile new session button
      document.getElementById("mobileNewSessionBtn").addEventListener("click", () => {
        closeMobileMenu();
        document.getElementById("newSessionBtn").click();
      });
      // Mobile rename session button
      document.getElementById("mobileRenameSessionBtn").addEventListener("click", () => {
        closeMobileMenu();
        document.getElementById("renameSessionBtn").click();
      });
      // Sync mobile session selector with desktop
      document.getElementById("mobileSessions").addEventListener("change", async (e) => {
        closeMobileMenu();
        const desktopSessions = document.getElementById("sessions");
        desktopSessions.value = e.target.value;
        desktopSessions.dispatchEvent(new Event("change"));
      });
      // Mobile force reload button
      document.getElementById("mobileForceReloadBtn").addEventListener("click", async () => {
        if (confirm("Force reload the app? This will clear cache and reload.")) {
          // Clear all caches if available
          if ('caches' in window) {
            const cacheNames = await caches.keys();
            await Promise.all(cacheNames.map(name => caches.delete(name)));
          }
          // Force reload from server (bypass cache)
          window.location.reload(true);
        }
      });
      // Dark mode toggle - defaults to dark
      const btn = document.getElementById("toggleThemeBtn");
      // Set dark mode by default if no preference saved
      const savedTheme = localStorage.getItem("theme");
      if (!savedTheme || savedTheme === "dark") {
        document.body.classList.add("dark");
        btn.textContent = "☀️ Light Mode";
        localStorage.setItem("theme", "dark");
      } else {
        btn.textContent = "🌙 Dark Mode";
      }
      btn.addEventListener("click", () => {
        document.body.classList.toggle("dark");
        const isDark = document.body.classList.contains("dark");
        btn.textContent = isDark ? "☀️ Light Mode" : "🌙 Dark Mode";
        localStorage.setItem("theme", isDark ? "dark" : "light");
        updateMobileThemeButton();
      });
      // Initialize mobile theme button
      updateMobileThemeButton();
      // Sessions - Load from server
 	  (async () => {
 		await loadSessionsFromServer();
 		await renderSessions();
 		// Ensure we have at least one session
 		if (sessions.length === 0) {
 		  const id = generateSessionId();
 		  const name = "default";
 		  currentSession = id;
 		  history = [];
 		  await saveSession(); // Create empty session on server
 		  await saveSessionMetadata(id, name);
 		  await loadSessionsFromServer();
 		  await renderSessions();
 		  localStorage.setItem("currentSession", currentSession);
 		} else {
 		  // If no current session or current session doesn't exist, use first one
 		  if (!currentSession || !sessions.find(s => s.id === currentSession)) {
 			currentSession = sessions[0].id;
 			localStorage.setItem("currentSession", currentSession);
 		  }
 		}
 		// Load current session history
 		if (currentSession) {
 		  await loadSession(currentSession);
 		}
 	  })();
 	// Switch session
 	document.getElementById("sessions").addEventListener("change", async e => {
 	  currentSession = e.target.value;
 	  history = [];
 	  localStorage.setItem("currentSession", currentSession);
 	  addMessage("system", `Switched to session: ${getSessionName(currentSession)}`);
 	  await loadSession(currentSession);
 	});
 	// Create new session
 	document.getElementById("newSessionBtn").addEventListener("click", async () => {
 	  const name = prompt("Enter new session name:");
 	  if (!name) return;
 	  const id = generateSessionId();
 	  currentSession = id;
 	  history = [];
 	  localStorage.setItem("currentSession", currentSession);
 	  // Create session on server
 	  await saveSession();
 	  await saveSessionMetadata(id, name);
 	  await loadSessionsFromServer();
 	  await renderSessions();
 	  addMessage("system", `Created session: ${name}`);
 	});
 	// Rename session
 	document.getElementById("renameSessionBtn").addEventListener("click", async () => {
 	  const session = sessions.find(s => s.id === currentSession);
 	  if (!session) return;
 	  const newName = prompt("Rename session:", session.name || currentSession);
 	  if (!newName) return;
 	  // Update metadata on server
 	  await saveSessionMetadata(currentSession, newName);
 	  await loadSessionsFromServer();
 	  await renderSessions();
 	  addMessage("system", `Session renamed to: ${newName}`);
 	});
 	// Thinking Stream button
 	document.getElementById("thinkingStreamBtn").addEventListener("click", () => {
 	  if (!currentSession) {
 		alert("Please select a session first");
 		return;
 	  }
 	  // Open thinking stream in new window
 	  const streamUrl = `http://10.0.0.41:8081/thinking-stream.html?session=${currentSession}`;
 	  const windowFeatures = "width=600,height=800,menubar=no,toolbar=no,location=no,status=no";
 	  window.open(streamUrl, `thinking_${currentSession}`, windowFeatures);
 	  addMessage("system", "🧠 Opened thinking stream in new window");
 	});
      // Settings Modal
      const settingsModal = document.getElementById("settingsModal");
      const settingsBtn = document.getElementById("settingsBtn");
      const closeModalBtn = document.getElementById("closeModalBtn");
      const saveSettingsBtn = document.getElementById("saveSettingsBtn");
      const cancelSettingsBtn = document.getElementById("cancelSettingsBtn");
      const modalOverlay = document.querySelector(".modal-overlay");
      // Load saved backend preference
      const savedBackend = localStorage.getItem("standardModeBackend") || "SECONDARY";
      // Set initial radio button state
      const backendRadios = document.querySelectorAll('input[name="backend"]');
      let isCustomBackend = !["SECONDARY", "PRIMARY", "OPENAI"].includes(savedBackend);
      if (isCustomBackend) {
        document.querySelector('input[name="backend"][value="custom"]').checked = true;
        document.getElementById("customBackend").value = savedBackend;
      } else {
        document.querySelector(`input[name="backend"][value="${savedBackend}"]`).checked = true;
      }
      // Session management functions
      async function loadSessionList() {
        try {
          // Reload from server to get latest
          await loadSessionsFromServer();
          const sessionListEl = document.getElementById("sessionList");
          if (sessions.length === 0) {
            sessionListEl.innerHTML = '<p style="color: var(--text-fade); font-size: 0.85rem;">No saved sessions found</p>';
            return;
          }
          sessionListEl.innerHTML = "";
          sessions.forEach(sess => {
            const sessionItem = document.createElement("div");
            sessionItem.className = "session-item";
            const sessionInfo = document.createElement("div");
            sessionInfo.className = "session-info";
            const sessionName = sess.name || sess.id;
            const lastModified = new Date(sess.lastModified).toLocaleString();
            sessionInfo.innerHTML = `
              <strong>${sessionName}</strong>
              <small>${sess.messageCount} messages • ${lastModified}</small>
            `;
            const deleteBtn = document.createElement("button");
            deleteBtn.className = "session-delete-btn";
            deleteBtn.textContent = "🗑️";
            deleteBtn.title = "Delete session";
            deleteBtn.onclick = async () => {
              if (!confirm(`Delete session "${sessionName}"?`)) return;
              try {
                await fetch(`${RELAY_BASE}/sessions/${sess.id}`, { method: "DELETE" });
                // Reload sessions from server
                await loadSessionsFromServer();
                // If we deleted the current session, switch to another or create new
                if (currentSession === sess.id) {
                  if (sessions.length > 0) {
                    currentSession = sessions[0].id;
                    localStorage.setItem("currentSession", currentSession);
                    history = [];
                    await loadSession(currentSession);
                  } else {
                    const id = generateSessionId();
                    const name = "default";
                    currentSession = id;
                    localStorage.setItem("currentSession", currentSession);
                    history = [];
                    await saveSession();
                    await saveSessionMetadata(id, name);
                    await loadSessionsFromServer();
                  }
                }
                // Refresh both the dropdown and the settings list
                await renderSessions();
                await loadSessionList();
                addMessage("system", `Deleted session: ${sessionName}`);
              } catch (e) {
                alert("Failed to delete session: " + e.message);
              }
            };
            sessionItem.appendChild(sessionInfo);
            sessionItem.appendChild(deleteBtn);
            sessionListEl.appendChild(sessionItem);
          });
        } catch (e) {
          const sessionListEl = document.getElementById("sessionList");
          sessionListEl.innerHTML = '<p style="color: #ff3333; font-size: 0.85rem;">Failed to load sessions</p>';
        }
      }
      // Show modal and load session list
      settingsBtn.addEventListener("click", () => {
        settingsModal.classList.add("show");
        loadSessionList(); // Refresh session list when opening settings
      });
      // Hide modal functions
      const hideModal = () => {
        settingsModal.classList.remove("show");
      };
      closeModalBtn.addEventListener("click", hideModal);
      cancelSettingsBtn.addEventListener("click", hideModal);
      modalOverlay.addEventListener("click", hideModal);
      // ESC key to close
      document.addEventListener("keydown", (e) => {
        if (e.key === "Escape" && settingsModal.classList.contains("show")) {
          hideModal();
        }
      });
      // Save settings
      saveSettingsBtn.addEventListener("click", () => {
        const selectedRadio = document.querySelector('input[name="backend"]:checked');
        let backendValue;
        if (selectedRadio.value === "custom") {
          backendValue = document.getElementById("customBackend").value.trim().toUpperCase();
          if (!backendValue) {
            alert("Please enter a custom backend name");
            return;
          }
        } else {
          backendValue = selectedRadio.value;
        }
        localStorage.setItem("standardModeBackend", backendValue);
        addMessage("system", `Backend changed to: ${backendValue}`);
        hideModal();
      });
      // Health check
      checkHealth();
      setInterval(checkHealth, 10000);
      // Input events
      document.getElementById("sendBtn").addEventListener("click", sendMessage);
      document.getElementById("userInput").addEventListener("keypress", e => {
        if (e.key === "Enter") sendMessage();
      });
      // ========== THINKING STREAM INTEGRATION ==========
      const thinkingPanel = document.getElementById("thinkingPanel");
      const thinkingHeader = document.getElementById("thinkingHeader");
      const thinkingToggleBtn = document.getElementById("thinkingToggleBtn");
      const thinkingClearBtn = document.getElementById("thinkingClearBtn");
      const thinkingContent = document.getElementById("thinkingContent");
      const thinkingStatusDot = document.getElementById("thinkingStatusDot");
      const thinkingEmpty = document.getElementById("thinkingEmpty");
      let thinkingEventSource = null;
      let thinkingEventCount = 0;
      const CORTEX_BASE = "http://10.0.0.41:7081";
      // Load thinking panel state from localStorage
      const isPanelCollapsed = localStorage.getItem("thinkingPanelCollapsed") === "true";
      if (!isPanelCollapsed) {
        thinkingPanel.classList.remove("collapsed");
      }
      // Toggle thinking panel
      thinkingHeader.addEventListener("click", (e) => {
        if (e.target === thinkingClearBtn) return; // Don't toggle if clicking clear
        thinkingPanel.classList.toggle("collapsed");
        localStorage.setItem("thinkingPanelCollapsed", thinkingPanel.classList.contains("collapsed"));
      });
      // Clear thinking events
      thinkingClearBtn.addEventListener("click", (e) => {
        e.stopPropagation();
        clearThinkingEvents();
      });
      function clearThinkingEvents() {
        thinkingContent.innerHTML = '';
        thinkingContent.appendChild(thinkingEmpty);
        thinkingEventCount = 0;
        // Clear from localStorage
        if (currentSession) {
          localStorage.removeItem(`thinkingEvents_${currentSession}`);
        }
      }
      function connectThinkingStream() {
        if (!currentSession) return;
        // Close existing connection
        if (thinkingEventSource) {
          thinkingEventSource.close();
        }
        // Load persisted events
        loadThinkingEvents();
        const url = `${CORTEX_BASE}/stream/thinking/${currentSession}`;
        console.log('Connecting thinking stream:', url);
        thinkingEventSource = new EventSource(url);
        thinkingEventSource.onopen = () => {
          console.log('Thinking stream connected');
          thinkingStatusDot.className = 'thinking-status-dot connected';
        };
        thinkingEventSource.onmessage = (event) => {
          try {
            const data = JSON.parse(event.data);
            addThinkingEvent(data);
            saveThinkingEvent(data); // Persist event
          } catch (e) {
            console.error('Failed to parse thinking event:', e);
          }
        };
        thinkingEventSource.onerror = (error) => {
          console.error('Thinking stream error:', error);
          thinkingStatusDot.className = 'thinking-status-dot disconnected';
          // Retry connection after 2 seconds
          setTimeout(() => {
            if (thinkingEventSource && thinkingEventSource.readyState === EventSource.CLOSED) {
              console.log('Reconnecting thinking stream...');
              connectThinkingStream();
            }
          }, 2000);
        };
      }
      function addThinkingEvent(event) {
        // Remove empty state if present
        if (thinkingEventCount === 0 && thinkingEmpty.parentNode) {
          thinkingContent.removeChild(thinkingEmpty);
        }
        const eventDiv = document.createElement('div');
        eventDiv.className = `thinking-event thinking-event-${event.type}`;
        let icon = '';
        let message = '';
        let details = '';
        switch (event.type) {
          case 'connected':
            icon = '✓';
            message = 'Stream connected';
            details = `Session: ${event.session_id}`;
            break;
          case 'thinking':
            icon = '🤔';
            message = event.data.message;
            break;
          case 'tool_call':
            icon = '🔧';
            message = event.data.message;
            if (event.data.args) {
              details = JSON.stringify(event.data.args, null, 2);
            }
            break;
          case 'tool_result':
            icon = '📊';
            message = event.data.message;
            if (event.data.result && event.data.result.stdout) {
              details = `stdout: ${event.data.result.stdout}`;
            }
            break;
          case 'done':
            icon = '✅';
            message = event.data.message;
            if (event.data.final_answer) {
              details = event.data.final_answer;
            }
            break;
          case 'error':
            icon = '❌';
            message = event.data.message;
            break;
          default:
            icon = '•';
            message = JSON.stringify(event.data);
        }
        eventDiv.innerHTML = `
          <span class="thinking-event-icon">${icon}</span>
          <span>${message}</span>
          ${details ? `<div class="thinking-event-details">${details}</div>` : ''}
        `;
        thinkingContent.appendChild(eventDiv);
        thinkingContent.scrollTop = thinkingContent.scrollHeight;
        thinkingEventCount++;
      }
      // Persist thinking events to localStorage
      function saveThinkingEvent(event) {
        if (!currentSession) return;
        const key = `thinkingEvents_${currentSession}`;
        let events = JSON.parse(localStorage.getItem(key) || '[]');
        // Keep only last 50 events to avoid bloating localStorage
        if (events.length >= 50) {
          events = events.slice(-49);
        }
        events.push({
          ...event,
          timestamp: Date.now()
        });
        localStorage.setItem(key, JSON.stringify(events));
      }
      // Load persisted thinking events
      function loadThinkingEvents() {
        if (!currentSession) return;
        const key = `thinkingEvents_${currentSession}`;
        const events = JSON.parse(localStorage.getItem(key) || '[]');
        // Clear current display
        thinkingContent.innerHTML = '';
        thinkingEventCount = 0;
        // Replay events
        events.forEach(event => addThinkingEvent(event));
        // Show empty state if no events
        if (events.length === 0) {
          thinkingContent.appendChild(thinkingEmpty);
        }
      }
      // Update the old thinking stream button to toggle panel instead
      document.getElementById("thinkingStreamBtn").addEventListener("click", () => {
        thinkingPanel.classList.remove("collapsed");
        localStorage.setItem("thinkingPanelCollapsed", "false");
      });
      // Mobile thinking stream button
      document.getElementById("mobileThinkingStreamBtn").addEventListener("click", () => {
        closeMobileMenu();
        thinkingPanel.classList.remove("collapsed");
        localStorage.setItem("thinkingPanelCollapsed", "false");
      });
      // Connect thinking stream when session loads
      if (currentSession) {
        connectThinkingStream();
      }
      // Reconnect thinking stream when session changes
      const originalSessionChange = document.getElementById("sessions").onchange;
      document.getElementById("sessions").addEventListener("change", () => {
        setTimeout(() => {
          connectThinkingStream();
        }, 500); // Wait for session to load
      });
      // Cleanup on page unload
      window.addEventListener('beforeunload', () => {
        if (thinkingEventSource) {
          thinkingEventSource.close();
        }
      });
    });
  </script>
 </body>
 </html>
@@ -0,0 +1,20 @@
 {
  "name": "Lyra Chat",
  "short_name": "Lyra",
  "start_url": "./index.html",
  "display": "standalone",
  "background_color": "#181818",
  "theme_color": "#181818",
  "icons": [
    {
      "src": "icon-192.png",
      "sizes": "192x192",
      "type": "image/png"
    },
    {
      "src": "icon-512.png",
      "sizes": "512x512",
      "type": "image/png"
    }
  ]
 }
@@ -0,0 +1,909 @@
 :root {
  --bg-dark: #0a0a0a;
  --bg-panel: rgba(255, 115, 0, 0.1);
  --accent: #ff6600;
  --accent-glow: 0 0 12px #ff6600cc;
  --text-main: #e6e6e6;
  --text-fade: #999;
  --font-console: "IBM Plex Mono", monospace;
 }
 /* Light mode variables */
 body {
  --bg-dark: #f5f5f5;
  --bg-panel: rgba(255, 115, 0, 0.05);
  --accent: #ff6600;
  --accent-glow: 0 0 12px #ff6600cc;
  --text-main: #1a1a1a;
  --text-fade: #666;
 }
 /* Dark mode variables */
 body.dark {
  --bg-dark: #0a0a0a;
  --bg-panel: rgba(255, 115, 0, 0.1);
  --accent: #ff6600;
  --accent-glow: 0 0 12px #ff6600cc;
  --text-main: #e6e6e6;
  --text-fade: #999;
 }
 body {
  margin: 0;
  background: var(--bg-dark);
  color: var(--text-main);
  font-family: var(--font-console);
  height: 100vh;
  display: flex;
  justify-content: center;
  align-items: center;
 }
 #chat {
  width: 95%;
  max-width: 900px;
  height: 95vh;
  display: flex;
  flex-direction: column;
  border: 1px solid var(--accent);
  border-radius: 10px;
  box-shadow: var(--accent-glow);
  background: var(--bg-dark);
  overflow: hidden;
 }
 /* Header sections */
 #model-select, #session-select, #status {
  display: flex;
  align-items: center;
  gap: 8px;
  padding: 8px 12px;
  border-bottom: 1px solid var(--accent);
  background-color: rgba(255, 102, 0, 0.05);
 }
 #status {
  justify-content: flex-start;
  border-top: 1px solid var(--accent);
 }
 label, select, button {
  font-family: var(--font-console);
  font-size: 0.9rem;
  color: var(--text-main);
  background: transparent;
  border: 1px solid var(--accent);
  border-radius: 4px;
  padding: 4px 8px;
 }
 button:hover, select:hover {
  box-shadow: 0 0 8px var(--accent);
  cursor: pointer;
 }
 #thinkingStreamBtn {
  background: rgba(138, 43, 226, 0.2);
  border-color: #8a2be2;
 }
 #thinkingStreamBtn:hover {
  box-shadow: 0 0 8px #8a2be2;
  background: rgba(138, 43, 226, 0.3);
 }
 /* Chat area */
 #messages {
  flex: 1;
  padding: 16px;
  overflow-y: auto;
  display: flex;
  flex-direction: column;
  gap: 8px;
  scroll-behavior: smooth;
 }
 /* Messages */
 .msg {
  max-width: 80%;
  padding: 10px 14px;
  border-radius: 8px;
  line-height: 1.4;
  word-wrap: break-word;
  box-shadow: 0 0 8px rgba(255,102,0,0.2);
 }
 .msg.user {
  align-self: flex-end;
  background: rgba(255,102,0,0.15);
  border: 1px solid var(--accent);
 }
 .msg.assistant {
  align-self: flex-start;
  background: rgba(255,102,0,0.08);
  border: 1px solid rgba(255,102,0,0.5);
 }
 .msg.system {
  align-self: center;
  font-size: 0.8rem;
  color: var(--text-fade);
 }
 /* Input bar */
 #input {
  display: flex;
  border-top: 1px solid var(--accent);
  background: rgba(255, 102, 0, 0.05);
  padding: 10px;
 }
 #userInput {
  flex: 1;
  background: transparent;
  color: var(--text-main);
  border: 1px solid var(--accent);
  border-radius: 4px;
  padding: 8px;
 }
 #sendBtn {
  margin-left: 8px;
 }
 /* Relay status dot */
 #status {
  display: flex;
  align-items: center;
  margin: 10px 0;
  gap: 8px;
  font-family: monospace;
  color: #f5f5f5;
 }
 #status-dot {
  width: 10px;
  height: 10px;
  border-radius: 50%;
  display: inline-block;
 }
@keyframes pulseGreen {
  0% { box-shadow: 0 0 5px #00ff66; opacity: 0.9; }
  50% { box-shadow: 0 0 20px #00ff99; opacity: 1; }
  100% { box-shadow: 0 0 5px #00ff66; opacity: 0.9; }
 }
 .dot.ok {
  background: #00ff66;
  animation: pulseGreen 2s infinite ease-in-out;
 }
 /* Offline state stays solid red */
 .dot.fail {
  background: #ff3333;
  box-shadow: 0 0 10px #ff3333;
 }
 /* Dropdown (session selector) styling */
 select {
  background-color: var(--bg-dark);
  color: var(--text-main);
  border: 1px solid #b84a12;
  border-radius: 6px;
  padding: 4px 6px;
  font-size: 14px;
 }
 select option {
  background-color: var(--bg-dark);
  color: var(--text-main);
 }
 /* Hover/focus for better visibility */
 select:focus,
 select:hover {
  outline: none;
  border-color: #ff7a33;
  background-color: var(--bg-panel);
 }
 /* Settings Modal */
 .modal {
  display: none !important;
  position: fixed;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
  z-index: 1000;
 }
 .modal.show {
  display: block !important;
 }
 .modal-overlay {
  position: fixed;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
  background: rgba(0, 0, 0, 0.8);
  backdrop-filter: blur(4px);
  z-index: 999;
 }
 .modal-content {
  position: fixed;
  top: 50%;
  left: 50%;
  transform: translate(-50%, -50%);
  background: linear-gradient(180deg, rgba(255,102,0,0.1) 0%, rgba(10,10,10,0.95) 100%);
  border: 2px solid var(--accent);
  border-radius: 12px;
  box-shadow: var(--accent-glow), 0 0 40px rgba(255,102,0,0.3);
  min-width: 400px;
  max-width: 600px;
  max-height: 80vh;
  overflow-y: auto;
  z-index: 1001;
 }
 .modal-header {
  display: flex;
  justify-content: space-between;
  align-items: center;
  padding: 16px 20px;
  border-bottom: 1px solid var(--accent);
  background: rgba(255,102,0,0.1);
 }
 .modal-header h3 {
  margin: 0;
  font-size: 1.2rem;
  color: var(--accent);
 }
 .close-btn {
  background: transparent;
  border: none;
  color: var(--accent);
  font-size: 1.5rem;
  cursor: pointer;
  padding: 0;
  width: 30px;
  height: 30px;
  display: flex;
  align-items: center;
  justify-content: center;
  border-radius: 4px;
 }
 .close-btn:hover {
  background: rgba(255,102,0,0.2);
  box-shadow: 0 0 8px var(--accent);
 }
 .modal-body {
  padding: 20px;
 }
 .settings-section h4 {
  margin: 0 0 8px 0;
  color: var(--accent);
  font-size: 1rem;
 }
 .settings-desc {
  margin: 0 0 16px 0;
  color: var(--text-fade);
  font-size: 0.85rem;
 }
 .radio-group {
  display: flex;
  flex-direction: column;
  gap: 12px;
 }
 .radio-label {
  display: flex;
  flex-direction: column;
  padding: 12px;
  border: 1px solid rgba(255,102,0,0.3);
  border-radius: 6px;
  background: rgba(255,102,0,0.05);
  cursor: pointer;
  transition: all 0.2s;
 }
 .radio-label:hover {
  border-color: var(--accent);
  background: rgba(255,102,0,0.1);
  box-shadow: 0 0 8px rgba(255,102,0,0.3);
 }
 .radio-label input[type="radio"] {
  margin-right: 8px;
  accent-color: var(--accent);
 }
 .radio-label span {
  font-weight: 500;
  margin-bottom: 4px;
 }
 .radio-label small {
  color: var(--text-fade);
  font-size: 0.8rem;
  margin-left: 24px;
 }
 .radio-label input[type="text"] {
  margin-top: 8px;
  margin-left: 24px;
  padding: 6px;
  background: rgba(0,0,0,0.3);
  border: 1px solid rgba(255,102,0,0.5);
  border-radius: 4px;
  color: var(--text-main);
  font-family: var(--font-console);
 }
 .radio-label input[type="text"]:focus {
  outline: none;
  border-color: var(--accent);
  box-shadow: 0 0 8px rgba(255,102,0,0.3);
 }
 .modal-footer {
  display: flex;
  justify-content: flex-end;
  gap: 10px;
  padding: 16px 20px;
  border-top: 1px solid var(--accent);
  background: rgba(255,102,0,0.05);
 }
 .primary-btn {
  background: var(--accent);
  color: #000;
  font-weight: bold;
 }
 .primary-btn:hover {
  background: #ff7a33;
  box-shadow: var(--accent-glow);
 }
 /* Session List */
 .session-list {
  display: flex;
  flex-direction: column;
  gap: 8px;
  max-height: 300px;
  overflow-y: auto;
 }
 .session-item {
  display: flex;
  justify-content: space-between;
  align-items: center;
  padding: 12px;
  border: 1px solid rgba(255,102,0,0.3);
  border-radius: 6px;
  background: rgba(255,102,0,0.05);
  transition: all 0.2s;
 }
 .session-item:hover {
  border-color: var(--accent);
  background: rgba(255,102,0,0.1);
 }
 .session-info {
  display: flex;
  flex-direction: column;
  gap: 4px;
  flex: 1;
 }
 .session-info strong {
  color: var(--text-main);
  font-size: 0.95rem;
 }
 .session-info small {
  color: var(--text-fade);
  font-size: 0.75rem;
 }
 .session-delete-btn {
  background: transparent;
  border: 1px solid rgba(255,102,0,0.5);
  color: var(--accent);
  padding: 6px 10px;
  border-radius: 4px;
  cursor: pointer;
  font-size: 1rem;
  transition: all 0.2s;
 }
 .session-delete-btn:hover {
  background: rgba(255,0,0,0.2);
  border-color: #ff3333;
  color: #ff3333;
  box-shadow: 0 0 8px rgba(255,0,0,0.3);
 }
 /* Thinking Stream Panel */
 .thinking-panel {
  border-top: 1px solid var(--accent);
  background: rgba(255, 102, 0, 0.02);
  display: flex;
  flex-direction: column;
  transition: max-height 0.3s ease;
  max-height: 300px;
 }
 .thinking-panel.collapsed {
  max-height: 40px;
 }
 .thinking-header {
  display: flex;
  justify-content: space-between;
  align-items: center;
  padding: 10px 12px;
  background: rgba(255, 102, 0, 0.08);
  cursor: pointer;
  user-select: none;
  border-bottom: 1px solid rgba(255, 102, 0, 0.2);
  font-size: 0.9rem;
  font-weight: 500;
 }
 .thinking-header:hover {
  background: rgba(255, 102, 0, 0.12);
 }
 .thinking-controls {
  display: flex;
  align-items: center;
  gap: 8px;
 }
 .thinking-status-dot {
  width: 8px;
  height: 8px;
  border-radius: 50%;
  background: #666;
  display: inline-block;
 }
 .thinking-status-dot.connected {
  background: #00ff66;
  box-shadow: 0 0 8px #00ff66;
 }
 .thinking-status-dot.disconnected {
  background: #ff3333;
 }
 .thinking-clear-btn,
 .thinking-toggle-btn {
  background: transparent;
  border: 1px solid rgba(255, 102, 0, 0.5);
  color: var(--text-main);
  padding: 4px 8px;
  border-radius: 4px;
  cursor: pointer;
  font-size: 0.85rem;
 }
 .thinking-clear-btn:hover,
 .thinking-toggle-btn:hover {
  background: rgba(255, 102, 0, 0.2);
  box-shadow: 0 0 6px rgba(255, 102, 0, 0.3);
 }
 .thinking-toggle-btn {
  transition: transform 0.3s ease;
 }
 .thinking-panel.collapsed .thinking-toggle-btn {
  transform: rotate(-90deg);
 }
 .thinking-content {
  flex: 1;
  overflow-y: auto;
  padding: 12px;
  display: flex;
  flex-direction: column;
  gap: 8px;
  min-height: 0;
 }
 .thinking-panel.collapsed .thinking-content {
  display: none;
 }
 .thinking-empty {
  text-align: center;
  padding: 40px 20px;
  color: var(--text-fade);
  font-size: 0.85rem;
 }
 .thinking-empty-icon {
  font-size: 2rem;
  margin-bottom: 10px;
 }
 .thinking-event {
  padding: 8px 12px;
  border-radius: 6px;
  font-size: 0.85rem;
  font-family: 'Courier New', monospace;
  animation: thinkingSlideIn 0.3s ease-out;
  border-left: 3px solid;
  word-wrap: break-word;
 }
@keyframes thinkingSlideIn {
  from {
    opacity: 0;
    transform: translateY(-10px);
  }
  to {
    opacity: 1;
    transform: translateY(0);
  }
 }
 .thinking-event-connected {
  background: rgba(0, 255, 102, 0.1);
  border-color: #00ff66;
  color: #00ff66;
 }
 .thinking-event-thinking {
  background: rgba(138, 43, 226, 0.1);
  border-color: #8a2be2;
  color: #c79cff;
 }
 .thinking-event-tool_call {
  background: rgba(255, 165, 0, 0.1);
  border-color: #ffa500;
  color: #ffb84d;
 }
 .thinking-event-tool_result {
  background: rgba(0, 191, 255, 0.1);
  border-color: #00bfff;
  color: #7dd3fc;
 }
 .thinking-event-done {
  background: rgba(168, 85, 247, 0.1);
  border-color: #a855f7;
  color: #e9d5ff;
  font-weight: bold;
 }
 .thinking-event-error {
  background: rgba(255, 51, 51, 0.1);
  border-color: #ff3333;
  color: #fca5a5;
 }
 .thinking-event-icon {
  display: inline-block;
  margin-right: 8px;
 }
 .thinking-event-details {
  font-size: 0.75rem;
  color: var(--text-fade);
  margin-top: 4px;
  padding-left: 20px;
  white-space: pre-wrap;
  max-height: 100px;
  overflow-y: auto;
 }
 /* ========== MOBILE RESPONSIVE STYLES ========== */
 /* Hamburger Menu */
 .hamburger-menu {
  display: none;
  flex-direction: column;
  gap: 4px;
  cursor: pointer;
  padding: 8px;
  border: 1px solid var(--accent);
  border-radius: 4px;
  background: transparent;
  z-index: 100;
 }
 .hamburger-menu span {
  width: 20px;
  height: 2px;
  background: var(--accent);
  transition: all 0.3s;
  display: block;
 }
 .hamburger-menu.active span:nth-child(1) {
  transform: rotate(45deg) translate(5px, 5px);
 }
 .hamburger-menu.active span:nth-child(2) {
  opacity: 0;
 }
 .hamburger-menu.active span:nth-child(3) {
  transform: rotate(-45deg) translate(5px, -5px);
 }
 /* Mobile Menu Container */
 .mobile-menu {
  display: none;
  position: fixed;
  top: 0;
  left: -100%;
  width: 280px;
  height: 100vh;
  background: var(--bg-dark);
  border-right: 2px solid var(--accent);
  box-shadow: var(--accent-glow);
  z-index: 999;
  transition: left 0.3s ease;
  overflow-y: auto;
  padding: 20px;
  flex-direction: column;
  gap: 16px;
 }
 .mobile-menu.open {
  left: 0;
 }
 .mobile-menu-overlay {
  display: none;
  position: fixed;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
  background: rgba(0, 0, 0, 0.7);
  z-index: 998;
 }
 .mobile-menu-overlay.show {
  display: block;
 }
 .mobile-menu-section {
  display: flex;
  flex-direction: column;
  gap: 8px;
  padding-bottom: 16px;
  border-bottom: 1px solid rgba(255, 102, 0, 0.3);
 }
 .mobile-menu-section:last-child {
  border-bottom: none;
 }
 .mobile-menu-section h4 {
  margin: 0;
  color: var(--accent);
  font-size: 0.9rem;
  text-transform: uppercase;
  letter-spacing: 1px;
 }
 .mobile-menu button,
 .mobile-menu select {
  width: 100%;
  padding: 10px;
  font-size: 0.95rem;
  text-align: left;
 }
 /* Mobile Breakpoints */
@media screen and (max-width: 768px) {
  body {
    padding: 0;
  }
  #chat {
    width: 100%;
    max-width: 100%;
    height: 100vh;
    border-radius: 0;
    border-left: none;
    border-right: none;
  }
  /* Show hamburger, hide desktop header controls */
  .hamburger-menu {
    display: flex;
  }
  #model-select {
    padding: 12px;
    justify-content: space-between;
  }
  /* Hide all controls except hamburger on mobile */
  #model-select > *:not(.hamburger-menu) {
    display: none;
  }
  #session-select {
    display: none;
  }
  /* Show mobile menu */
  .mobile-menu {
    display: flex;
  }
  /* Messages - more width on mobile */
  .msg {
    max-width: 90%;
    font-size: 0.95rem;
  }
  /* Status bar */
  #status {
    padding: 10px 12px;
    font-size: 0.85rem;
  }
  /* Input area - bigger touch targets */
  #input {
    padding: 12px;
  }
  #userInput {
    font-size: 16px; /* Prevents zoom on iOS */
    padding: 12px;
  }
  #sendBtn {
    padding: 12px 16px;
    font-size: 1rem;
  }
  /* Modal - full width on mobile */
  .modal-content {
    width: 95%;
    min-width: unset;
    max-width: unset;
    max-height: 90vh;
    top: 50%;
    left: 50%;
    transform: translate(-50%, -50%);
  }
  .modal-header {
    padding: 12px 16px;
  }
  .modal-body {
    padding: 16px;
  }
  .modal-footer {
    padding: 12px 16px;
    flex-wrap: wrap;
  }
  .modal-footer button {
    flex: 1;
    min-width: 120px;
  }
  /* Radio labels - stack better on mobile */
  .radio-label {
    padding: 10px;
  }
  .radio-label small {
    margin-left: 20px;
    font-size: 0.75rem;
  }
  /* Session list */
  .session-item {
    padding: 10px;
  }
  .session-info strong {
    font-size: 0.9rem;
  }
  .session-info small {
    font-size: 0.7rem;
  }
  /* Settings button in header */
  #settingsBtn {
    padding: 8px 12px;
  }
  /* Thinking panel adjustments for mobile */
  .thinking-panel {
    max-height: 250px;
  }
  .thinking-panel.collapsed {
    max-height: 38px;
  }
  .thinking-header {
    padding: 8px 10px;
    font-size: 0.85rem;
  }
  .thinking-event {
    font-size: 0.8rem;
    padding: 6px 10px;
  }
  .thinking-event-details {
    font-size: 0.7rem;
    max-height: 80px;
  }
 }
 /* Extra small devices (phones in portrait) */
@media screen and (max-width: 480px) {
  .mobile-menu {
    width: 240px;
  }
  .msg {
    max-width: 95%;
    font-size: 0.9rem;
    padding: 8px 12px;
  }
  #userInput {
    font-size: 16px;
    padding: 10px;
  }
  #sendBtn {
    padding: 10px 14px;
    font-size: 0.95rem;
  }
  .modal-header h3 {
    font-size: 1.1rem;
  }
  .settings-section h4 {
    font-size: 0.95rem;
  }
  .radio-label span {
    font-size: 0.9rem;
  }
 }
 /* Tablet landscape and desktop */
@media screen and (min-width: 769px) {
  /* Ensure mobile menu is hidden on desktop */
  .mobile-menu,
  .mobile-menu-overlay {
    display: none !important;
  }
  .hamburger-menu {
    display: none !important;
  }
 }
@@ -0,0 +1,362 @@
 <!DOCTYPE html>
 <html lang="en">
 <head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>🧠 Thinking Stream</title>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }
        body {
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
            background: #0d0d0d;
            color: #e0e0e0;
            height: 100vh;
            display: flex;
            flex-direction: column;
            overflow: hidden;
        }
        .header {
            background: #1a1a1a;
            padding: 15px 20px;
            border-bottom: 2px solid #333;
            display: flex;
            align-items: center;
            justify-content: space-between;
        }
        .header h1 {
            font-size: 18px;
            font-weight: bold;
        }
        .status {
            display: flex;
            align-items: center;
            gap: 10px;
            font-size: 14px;
        }
        .status-dot {
            width: 10px;
            height: 10px;
            border-radius: 50%;
            background: #666;
        }
        .status-dot.connected {
            background: #90ee90;
            box-shadow: 0 0 10px #90ee90;
        }
        .status-dot.disconnected {
            background: #ff6b6b;
        }
        .events-container {
            flex: 1;
            overflow-y: auto;
            padding: 20px;
        }
        .event {
            margin-bottom: 12px;
            padding: 10px 15px;
            border-radius: 6px;
            font-size: 14px;
            font-family: 'Courier New', monospace;
            animation: slideIn 0.3s ease-out;
            border-left: 3px solid;
        }
        @keyframes slideIn {
            from {
                opacity: 0;
                transform: translateX(-20px);
            }
            to {
                opacity: 1;
                transform: translateX(0);
            }
        }
        .event-connected {
            background: #1a2a1a;
            border-color: #4a7c59;
            color: #90ee90;
        }
        .event-thinking {
            background: #1a3a1a;
            border-color: #5a9c69;
            color: #a0f0a0;
        }
        .event-tool_call {
            background: #3a2a1a;
            border-color: #d97706;
            color: #fbbf24;
        }
        .event-tool_result {
            background: #1a2a3a;
            border-color: #0ea5e9;
            color: #7dd3fc;
        }
        .event-done {
            background: #2a1a3a;
            border-color: #a855f7;
            color: #e9d5ff;
            font-weight: bold;
        }
        .event-error {
            background: #3a1a1a;
            border-color: #dc2626;
            color: #fca5a5;
        }
        .event-icon {
            display: inline-block;
            margin-right: 8px;
        }
        .event-details {
            font-size: 12px;
            color: #999;
            margin-top: 5px;
            padding-left: 25px;
        }
        .footer {
            background: #1a1a1a;
            padding: 10px 20px;
            border-top: 1px solid #333;
            text-align: center;
            font-size: 12px;
            color: #666;
        }
        .clear-btn {
            background: #333;
            border: 1px solid #444;
            color: #e0e0e0;
            padding: 6px 12px;
            border-radius: 4px;
            cursor: pointer;
            font-size: 12px;
        }
        .clear-btn:hover {
            background: #444;
        }
        .empty-state {
            text-align: center;
            padding: 60px 20px;
            color: #666;
        }
        .empty-state-icon {
            font-size: 48px;
            margin-bottom: 20px;
        }
    </style>
 </head>
 <body>
    <div class="header">
        <h1>🧠 Thinking Stream</h1>
        <div class="status">
            <div class="status-dot" id="statusDot"></div>
            <span id="statusText">Connecting...</span>
        </div>
    </div>
    <div class="events-container" id="events">
        <div class="empty-state">
            <div class="empty-state-icon">🤔</div>
            <p>Waiting for thinking events...</p>
            <p style="font-size: 12px; margin-top: 10px;">Events will appear here when Lyra uses tools</p>
        </div>
    </div>
    <div class="footer">
        <button class="clear-btn" onclick="clearEvents()">Clear Events</button>
        <span style="margin: 0 20px;">|</span>
        <span id="sessionInfo">Session: <span id="sessionId">-</span></span>
    </div>
    <script>
        console.log('🧠 Thinking stream page loaded!');
        // Get session ID from URL
        const urlParams = new URLSearchParams(window.location.search);
        const SESSION_ID = urlParams.get('session');
        const CORTEX_BASE = "http://10.0.0.41:7081"; // Direct to cortex
        console.log('Session ID:', SESSION_ID);
        console.log('Cortex base:', CORTEX_BASE);
        // Declare variables first
        let eventSource = null;
        let eventCount = 0;
        if (!SESSION_ID) {
            document.getElementById('events').innerHTML = `
                <div class="empty-state">
                    <div class="empty-state-icon">⚠️</div>
                    <p>No session ID provided</p>
                    <p style="font-size: 12px; margin-top: 10px;">Please open this from the main chat interface</p>
                </div>
            `;
        } else {
            document.getElementById('sessionId').textContent = SESSION_ID;
            connectStream();
        }
        function connectStream() {
            if (eventSource) {
                eventSource.close();
            }
            const url = `${CORTEX_BASE}/stream/thinking/${SESSION_ID}`;
            console.log('Connecting to:', url);
            eventSource = new EventSource(url);
            eventSource.onopen = () => {
                console.log('EventSource onopen fired');
                updateStatus(true, 'Connected');
            };
            eventSource.onmessage = (event) => {
                console.log('Received message:', event.data);
                try {
                    const data = JSON.parse(event.data);
                    // Update status to connected when first message arrives
                    if (data.type === 'connected') {
                        updateStatus(true, 'Connected');
                    }
                    addEvent(data);
                } catch (e) {
                    console.error('Failed to parse event:', e, event.data);
                }
            };
            eventSource.onerror = (error) => {
                console.error('Stream error:', error, 'readyState:', eventSource.readyState);
                updateStatus(false, 'Disconnected');
                // Try to reconnect after 2 seconds
                setTimeout(() => {
                    if (eventSource.readyState === EventSource.CLOSED) {
                        console.log('Attempting to reconnect...');
                        connectStream();
                    }
                }, 2000);
            };
        }
        function updateStatus(connected, text) {
            const dot = document.getElementById('statusDot');
            const statusText = document.getElementById('statusText');
            dot.className = 'status-dot ' + (connected ? 'connected' : 'disconnected');
            statusText.textContent = text;
        }
        function addEvent(event) {
            const container = document.getElementById('events');
            // Remove empty state if present
            if (eventCount === 0) {
                container.innerHTML = '';
            }
            const eventDiv = document.createElement('div');
            eventDiv.className = `event event-${event.type}`;
            let icon = '';
            let message = '';
            let details = '';
            switch (event.type) {
                case 'connected':
                    icon = '✓';
                    message = 'Stream connected';
                    details = `Session: ${event.session_id}`;
                    break;
                case 'thinking':
                    icon = '🤔';
                    message = event.data.message;
                    break;
                case 'tool_call':
                    icon = '🔧';
                    message = event.data.message;
                    details = JSON.stringify(event.data.args, null, 2);
                    break;
                case 'tool_result':
                    icon = '📊';
                    message = event.data.message;
                    if (event.data.result && event.data.result.stdout) {
                        details = `stdout: ${event.data.result.stdout}`;
                    }
                    break;
                case 'done':
                    icon = '✅';
                    message = event.data.message;
                    details = event.data.final_answer;
                    break;
                case 'error':
                    icon = '❌';
                    message = event.data.message;
                    break;
                default:
                    icon = '•';
                    message = JSON.stringify(event.data);
            }
            eventDiv.innerHTML = `
                <span class="event-icon">${icon}</span>
                <span>${message}</span>
                ${details ? `<div class="event-details">${details}</div>` : ''}
            `;
            container.appendChild(eventDiv);
            container.scrollTop = container.scrollHeight;
            eventCount++;
        }
        function clearEvents() {
            const container = document.getElementById('events');
            container.innerHTML = `
                <div class="empty-state">
                    <div class="empty-state-icon">🤔</div>
                    <p>Waiting for thinking events...</p>
                    <p style="font-size: 12px; margin-top: 10px;">Events will appear here when Lyra uses tools</p>
                </div>
            `;
            eventCount = 0;
        }
        // Cleanup on page unload
        window.addEventListener('beforeunload', () => {
            if (eventSource) {
                eventSource.close();
            }
        });
    </script>
 </body>
 </html>
@@ -0,0 +1,21 @@
 # ====================================
 # 🧠 CORTEX OPERATIONAL CONFIG
 # ====================================
 # Cortex-specific parameters (all other config inherited from root .env)
 CORTEX_MODE=autonomous
 CORTEX_LOOP_INTERVAL=300
 CORTEX_REFLECTION_INTERVAL=86400
 CORTEX_LOG_LEVEL=debug
 NEOMEM_HEALTH_CHECK_INTERVAL=300
 # Reflection output configuration
 REFLECTION_NOTE_TARGET=trilium
 REFLECTION_NOTE_PATH=/app/logs/reflections.log
 # Memory retrieval tuning
 RELEVANCE_THRESHOLD=0.78
 # NOTE: LLM backend URLs, OPENAI_API_KEY, database credentials,
 # and service URLs are all inherited from root .env
 # Cortex uses LLM_PRIMARY (vLLM on MI50) by default
@@ -0,0 +1,15 @@
 FROM python:3.11-slim
 WORKDIR /app
 # Install docker CLI for code executor
 RUN apt-get update && apt-get install -y \
    docker.io \
    && rm -rf /var/lib/apt/lists/*
 COPY requirements.txt .
 RUN pip install -r requirements.txt
 COPY . .
 EXPOSE 7081
 # NOTE: Running with single worker to maintain SESSIONS global state in Intake.
 # If scaling to multiple workers, migrate SESSIONS to Redis or shared storage.
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7081"]
@@ -0,0 +1,249 @@
 # 📐 Project Lyra — Cognitive Assembly Spec
 **Version:** 0.6.1  
 **Status:** Canonical reference  
 **Purpose:** Define clear separation of Self, Thought, Reasoning, and Speech
 ---
 ## 1. High-Level Overview
 Lyra is composed of **four distinct cognitive layers**, plus I/O.
 Each layer has:
 - a **responsibility**
 - a **scope**
 - clear **inputs / outputs**
 - explicit **authority boundaries**
 No layer is allowed to “do everything.”
 ---
 ## 2. Layer Definitions
 ### 2.1 Autonomy / Self (NON-LLM)
 **What it is**
 - Persistent identity
 - Long-term state
 - Mood, preferences, values
 - Continuity across time
 **What it is NOT**
 - Not a reasoning engine
 - Not a planner
 - Not a speaker
 - Not creative
 **Implementation**
 - Data + light logic
 - JSON / Python objects
 - No LLM calls
 **Lives at**
 ```
 project-lyra/autonomy/self/
 ```
 **Inputs**
 - Events (user message received, response sent)
 - Time / idle ticks (later)
 **Outputs**
 - Self state snapshot
 - Flags / preferences (e.g. verbosity, tone bias)
 ---
 ### 2.2 Inner Monologue (LLM, PRIVATE)
 **What it is**
 - Internal language-based thought
 - Reflection
 - Intent formation
 - “What do I think about this?”
 **What it is NOT**
 - Not final reasoning
 - Not execution
 - Not user-facing
 **Model**
 - MythoMax
 **Lives at**
 ```
 project-lyra/autonomy/monologue/
 ```
 **Inputs**
 - User message
 - Self state snapshot
 - Recent context summary
 **Outputs**
 - Intent
 - Tone guidance
 - Depth guidance
 - “Consult executive?” flag
 **Example Output**
 ```json
 {
  "intent": "technical_exploration",
  "tone": "focused",
  "depth": "deep",
  "consult_executive": true
 }
 ```
 ---
 ### 2.3 Cortex (Reasoning & Execution)
 **What it is**
 - Thinking pipeline
 - Planning
 - Tool selection
 - Task execution
 - Draft generation
 **What it is NOT**
 - Not identity
 - Not personality
 - Not persistent self
 **Models**
 - DeepSeek-R1 → Executive / Planner
 - GPT-4o-mini → Executor / Drafter
 **Lives at**
 ```
 project-lyra/cortex/
 ```
 **Inputs**
 - User message
 - Inner Monologue output
 - Memory / RAG / tools
 **Outputs**
 - Draft response (content only)
 - Metadata (sources, confidence, etc.)
 ---
 ### 2.4 Persona / Speech (LLM, USER-FACING)
 **What it is**
 - Voice
 - Style
 - Expression
 - Social behavior
 **What it is NOT**
 - Not planning
 - Not deep reasoning
 - Not decision-making
 **Model**
 - MythoMax
 **Lives at**
 ```
 project-lyra/core/persona/
 ```
 **Inputs**
 - Draft response (from Cortex)
 - Tone + intent (from Inner Monologue)
 - Persona configuration
 **Outputs**
 - Final user-visible text
 ---
 ## 3. Message Flow (Authoritative)
 ### 3.1 Standard Message Path
 ```
 User
  ↓
 UI
  ↓
 Relay
  ↓
 Cortex
  ↓
 Autonomy / Self (state snapshot)
  ↓
 Inner Monologue (MythoMax)
  ↓
 [ consult_executive? ]
    ├─ Yes → DeepSeek-R1 (plan)
    └─ No  → skip
  ↓
 GPT-4o-mini (execute & draft)
  ↓
 Persona (MythoMax)
  ↓
 Relay
  ↓
 UI
  ↓
 User
 ```
 ### 3.2 Fast Path (No Thinking)
 ```
 User → UI → Relay → Persona → Relay → UI
 ```
 ---
 ## 4. Authority Rules (Non-Negotiable)
 - Self never calls an LLM
 - Inner Monologue never speaks to the user
 - Cortex never applies personality
 - Persona never reasons or plans
 - DeepSeek never writes final answers
 - MythoMax never plans execution
 ---
 ## 5. Folder Mapping
 ```
 project-lyra/
 ├── autonomy/
 │   ├── self/
 │   ├── monologue/
 │   └── executive/
 ├── cortex/
 ├── core/
 │   └── persona/
 ├── relay/
 └── ui/
 ```
 ---
 ## 6. Current Status
 - UI ✔
 - Relay ✔
 - Cortex ✔
 - Persona ✔
 - Autonomy ✔
 - Inner Monologue ⚠ partially wired
 - Executive gating ⚠ planned
 ---
 ## 7. Next Decision
 Decide whether **Inner Monologue runs every message** or **only when triggered**.
@@ -0,0 +1 @@
 # Autonomy module for Lyra
@@ -0,0 +1 @@
 """Autonomous action execution system."""
@@ -0,0 +1,480 @@
 """
 Autonomous Action Manager - executes safe, self-initiated actions.
 """
 import logging
 import json
 from typing import Dict, List, Any, Optional
 from datetime import datetime
 logger = logging.getLogger(__name__)
 class AutonomousActionManager:
    """
    Manages safe autonomous actions that Lyra can take without explicit user prompting.
    Whitelist of allowed actions:
    - create_memory: Store information in NeoMem
    - update_goal: Modify goal status
    - schedule_reminder: Create future reminder
    - summarize_session: Generate conversation summary
    - learn_topic: Add topic to learning queue
    - update_focus: Change current focus area
    """
    def __init__(self):
        """Initialize action manager with whitelisted actions."""
        self.allowed_actions = {
            "create_memory": self._create_memory,
            "update_goal": self._update_goal,
            "schedule_reminder": self._schedule_reminder,
            "summarize_session": self._summarize_session,
            "learn_topic": self._learn_topic,
            "update_focus": self._update_focus
        }
        self.action_log = []  # Track all actions for audit
    async def execute_action(
        self,
        action_type: str,
        parameters: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Execute a single autonomous action.
        Args:
            action_type: Type of action (must be in whitelist)
            parameters: Action-specific parameters
            context: Current context state
        Returns:
            {
                "success": bool,
                "action": action_type,
                "result": action_result,
                "timestamp": ISO timestamp,
                "error": optional error message
            }
        """
        # Safety check: action must be whitelisted
        if action_type not in self.allowed_actions:
            logger.error(f"[ACTIONS] Attempted to execute non-whitelisted action: {action_type}")
            return {
                "success": False,
                "action": action_type,
                "error": f"Action '{action_type}' not in whitelist",
                "timestamp": datetime.utcnow().isoformat()
            }
        try:
            logger.info(f"[ACTIONS] Executing autonomous action: {action_type}")
            # Execute the action
            action_func = self.allowed_actions[action_type]
            result = await action_func(parameters, context)
            # Log successful action
            action_record = {
                "success": True,
                "action": action_type,
                "result": result,
                "timestamp": datetime.utcnow().isoformat(),
                "parameters": parameters
            }
            self.action_log.append(action_record)
            logger.info(f"[ACTIONS] Action {action_type} completed successfully")
            return action_record
        except Exception as e:
            logger.error(f"[ACTIONS] Action {action_type} failed: {e}")
            error_record = {
                "success": False,
                "action": action_type,
                "error": str(e),
                "timestamp": datetime.utcnow().isoformat(),
                "parameters": parameters
            }
            self.action_log.append(error_record)
            return error_record
    async def execute_batch(
        self,
        actions: List[Dict[str, Any]],
        context: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """
        Execute multiple actions sequentially.
        Args:
            actions: List of {"action": str, "parameters": dict}
            context: Current context state
        Returns:
            List of action results
        """
        results = []
        for action_spec in actions:
            action_type = action_spec.get("action")
            parameters = action_spec.get("parameters", {})
            result = await self.execute_action(action_type, parameters, context)
            results.append(result)
            # Stop on first failure if critical
            if not result["success"] and action_spec.get("critical", False):
                logger.warning(f"[ACTIONS] Critical action {action_type} failed, stopping batch")
                break
        return results
    # ========================================
    # Whitelisted Action Implementations
    # ========================================
    async def _create_memory(
        self,
        parameters: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Create a memory entry in NeoMem.
        Parameters:
            - text: Memory content (required)
            - tags: Optional tags for memory
            - importance: 0.0-1.0 importance score
        """
        text = parameters.get("text")
        if not text:
            raise ValueError("Memory text required")
        tags = parameters.get("tags", [])
        importance = parameters.get("importance", 0.5)
        session_id = context.get("session_id", "autonomous")
        # Import NeoMem client
        try:
            from memory.neomem_client import store_memory
            result = await store_memory(
                text=text,
                session_id=session_id,
                tags=tags,
                importance=importance
            )
            return {
                "memory_id": result.get("id"),
                "text": text[:50] + "..." if len(text) > 50 else text
            }
        except ImportError:
            logger.warning("[ACTIONS] NeoMem client not available, simulating memory storage")
            return {
                "memory_id": "simulated",
                "text": text[:50] + "..." if len(text) > 50 else text,
                "note": "NeoMem not available, memory not persisted"
            }
    async def _update_goal(
        self,
        parameters: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Update goal status in self-state.
        Parameters:
            - goal_id: Goal identifier (required)
            - status: New status (pending/in_progress/completed)
            - progress: Optional progress note
        """
        goal_id = parameters.get("goal_id")
        if not goal_id:
            raise ValueError("goal_id required")
        status = parameters.get("status", "in_progress")
        progress = parameters.get("progress")
        # Import self-state manager
        from autonomy.self.state import get_self_state_instance
        state = get_self_state_instance()
        active_goals = state._state.get("active_goals", [])
        # Find and update goal
        updated = False
        for goal in active_goals:
            if isinstance(goal, dict) and goal.get("id") == goal_id:
                goal["status"] = status
                if progress:
                    goal["progress"] = progress
                goal["updated_at"] = datetime.utcnow().isoformat()
                updated = True
                break
        if updated:
            state._save_state()
            return {
                "goal_id": goal_id,
                "status": status,
                "updated": True
            }
        else:
            return {
                "goal_id": goal_id,
                "updated": False,
                "note": "Goal not found"
            }
    async def _schedule_reminder(
        self,
        parameters: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Schedule a future reminder.
        Parameters:
            - message: Reminder text (required)
            - delay_minutes: Minutes until reminder
            - priority: 0.0-1.0 priority score
        """
        message = parameters.get("message")
        if not message:
            raise ValueError("Reminder message required")
        delay_minutes = parameters.get("delay_minutes", 60)
        priority = parameters.get("priority", 0.5)
        # For now, store in self-state's learning queue
        # In future: integrate with scheduler/cron system
        from autonomy.self.state import get_self_state_instance
        state = get_self_state_instance()
        reminder = {
            "type": "reminder",
            "message": message,
            "scheduled_at": datetime.utcnow().isoformat(),
            "trigger_at_minutes": delay_minutes,
            "priority": priority
        }
        # Add to learning queue as placeholder
        state._state.setdefault("reminders", []).append(reminder)
        state._save_state(state._state)  # Pass state dict as argument
        logger.info(f"[ACTIONS] Reminder scheduled: {message} (in {delay_minutes}min)")
        return {
            "message": message,
            "delay_minutes": delay_minutes,
            "note": "Reminder stored in self-state (scheduler integration pending)"
        }
    async def _summarize_session(
        self,
        parameters: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Generate a summary of current session.
        Parameters:
            - max_length: Max summary length in words
            - focus_topics: Optional list of topics to emphasize
        """
        max_length = parameters.get("max_length", 200)
        session_id = context.get("session_id", "unknown")
        # Import summarizer (from deferred_summary or create simple one)
        try:
            from utils.deferred_summary import summarize_conversation
            summary = await summarize_conversation(
                session_id=session_id,
                max_words=max_length
            )
            return {
                "summary": summary,
                "word_count": len(summary.split())
            }
        except ImportError:
            # Fallback: simple summary
            message_count = context.get("message_count", 0)
            focus = context.get("monologue", {}).get("intent", "general")
            summary = f"Session {session_id}: {message_count} messages exchanged, focused on {focus}."
            return {
                "summary": summary,
                "word_count": len(summary.split()),
                "note": "Simple summary (full summarizer not available)"
            }
    async def _learn_topic(
        self,
        parameters: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Add topic to learning queue.
        Parameters:
            - topic: Topic name (required)
            - reason: Why this topic
            - priority: 0.0-1.0 priority score
        """
        topic = parameters.get("topic")
        if not topic:
            raise ValueError("Topic required")
        reason = parameters.get("reason", "autonomous learning")
        priority = parameters.get("priority", 0.5)
        # Import self-state manager
        from autonomy.self.state import get_self_state_instance
        state = get_self_state_instance()
        state.add_learning_goal(topic)  # Only pass topic parameter
        logger.info(f"[ACTIONS] Added to learning queue: {topic} (reason: {reason})")
        return {
            "topic": topic,
            "reason": reason,
            "queue_position": len(state._state.get("learning_queue", []))
        }
    async def _update_focus(
        self,
        parameters: Dict[str, Any],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Update current focus area.
        Parameters:
            - focus: New focus area (required)
            - reason: Why this focus
        """
        focus = parameters.get("focus")
        if not focus:
            raise ValueError("Focus required")
        reason = parameters.get("reason", "autonomous update")
        # Import self-state manager
        from autonomy.self.state import get_self_state_instance
        state = get_self_state_instance()
        old_focus = state._state.get("focus", "none")
        state._state["focus"] = focus
        state._state["focus_updated_at"] = datetime.utcnow().isoformat()
        state._state["focus_reason"] = reason
        state._save_state(state._state)  # Pass state dict as argument
        logger.info(f"[ACTIONS] Focus updated: {old_focus} -> {focus}")
        return {
            "old_focus": old_focus,
            "new_focus": focus,
            "reason": reason
        }
    # ========================================
    # Utility Methods
    # ========================================
    def get_allowed_actions(self) -> List[str]:
        """Get list of all allowed action types."""
        return list(self.allowed_actions.keys())
    def get_action_log(self, limit: int = 50) -> List[Dict[str, Any]]:
        """
        Get recent action log.
        Args:
            limit: Max number of entries to return
        Returns:
            List of action records
        """
        return self.action_log[-limit:]
    def clear_action_log(self) -> None:
        """Clear action log."""
        self.action_log = []
        logger.info("[ACTIONS] Action log cleared")
    def validate_action(self, action_type: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate an action without executing it.
        Args:
            action_type: Type of action
            parameters: Action parameters
        Returns:
            {
                "valid": bool,
                "action": action_type,
                "errors": [error messages] or []
            }
        """
        errors = []
        # Check whitelist
        if action_type not in self.allowed_actions:
            errors.append(f"Action '{action_type}' not in whitelist")
        # Check required parameters (basic validation)
        if action_type == "create_memory" and not parameters.get("text"):
            errors.append("Memory 'text' parameter required")
        if action_type == "update_goal" and not parameters.get("goal_id"):
            errors.append("Goal 'goal_id' parameter required")
        if action_type == "schedule_reminder" and not parameters.get("message"):
            errors.append("Reminder 'message' parameter required")
        if action_type == "learn_topic" and not parameters.get("topic"):
            errors.append("Learning 'topic' parameter required")
        if action_type == "update_focus" and not parameters.get("focus"):
            errors.append("Focus 'focus' parameter required")
        return {
            "valid": len(errors) == 0,
            "action": action_type,
            "errors": errors
        }
 # Singleton instance
 _action_manager_instance = None
 def get_action_manager() -> AutonomousActionManager:
    """
    Get singleton action manager instance.
    Returns:
        AutonomousActionManager instance
    """
    global _action_manager_instance
    if _action_manager_instance is None:
        _action_manager_instance = AutonomousActionManager()
    return _action_manager_instance
@@ -0,0 +1 @@
 """Executive planning and decision-making module."""
@@ -0,0 +1,121 @@
 """
 Executive planner - generates execution plans for complex requests.
 Activated when inner monologue sets consult_executive=true.
 """
 import os
 import logging
 from typing import Dict, Any, Optional
 from llm.llm_router import call_llm
 EXECUTIVE_LLM = os.getenv("EXECUTIVE_LLM", "CLOUD").upper()
 VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
 logger = logging.getLogger(__name__)
 if VERBOSE_DEBUG:
    logger.setLevel(logging.DEBUG)
 EXECUTIVE_SYSTEM_PROMPT = """
 You are Lyra's executive planning system.
 You create structured execution plans for complex tasks.
 You do NOT generate the final response - only the plan.
 Your plan should include:
 1. Task decomposition (break into steps)
 2. Required tools/resources
 3. Reasoning strategy
 4. Success criteria
 Return a concise plan in natural language.
 """
 async def plan_execution(
    user_prompt: str,
    intent: str,
    context_state: Dict[str, Any],
    identity_block: Dict[str, Any]
 ) -> Dict[str, Any]:
    """
    Generate execution plan for complex request.
    Args:
        user_prompt: User's message
        intent: Detected intent from inner monologue
        context_state: Full context
        identity_block: Lyra's identity
    Returns:
        Plan dictionary with structure:
        {
            "summary": "One-line plan summary",
            "plan_text": "Detailed plan",
            "steps": ["step1", "step2", ...],
            "tools_needed": ["RAG", "WEB", ...],
            "estimated_complexity": "low | medium | high"
        }
    """
    # Build planning prompt
    tools_available = context_state.get("tools_available", [])
    prompt = f"""{EXECUTIVE_SYSTEM_PROMPT}
 User request: {user_prompt}
 Detected intent: {intent}
 Available tools: {", ".join(tools_available) if tools_available else "None"}
 Session context:
 - Message count: {context_state.get('message_count', 0)}
 - Time since last message: {context_state.get('minutes_since_last_msg', 0):.1f} minutes
 - Active project: {context_state.get('active_project', 'None')}
 Generate a structured execution plan.
 """
    if VERBOSE_DEBUG:
        logger.debug(f"[EXECUTIVE] Planning prompt:\n{prompt}")
    # Call executive LLM
    plan_text = await call_llm(
        prompt,
        backend=EXECUTIVE_LLM,
        temperature=0.3,  # Lower temperature for planning
        max_tokens=500
    )
    if VERBOSE_DEBUG:
        logger.debug(f"[EXECUTIVE] Generated plan:\n{plan_text}")
    # Parse plan (simple heuristic extraction for Phase 1)
    steps = []
    tools_needed = []
    for line in plan_text.split('\n'):
        line_lower = line.lower()
        if any(marker in line_lower for marker in ['step', '1.', '2.', '3.', '-']):
            steps.append(line.strip())
        if tools_available:
            for tool in tools_available:
                if tool.lower() in line_lower and tool not in tools_needed:
                    tools_needed.append(tool)
    # Estimate complexity (simple heuristic)
    complexity = "low"
    if len(steps) > 3 or len(tools_needed) > 1:
        complexity = "medium"
    if len(steps) > 5 or "research" in intent.lower() or "analyze" in intent.lower():
        complexity = "high"
    return {
        "summary": plan_text.split('\n')[0][:100] if plan_text else "Complex task execution plan",
        "plan_text": plan_text,
        "steps": steps[:10],  # Limit to 10 steps
        "tools_needed": tools_needed,
        "estimated_complexity": complexity
    }
@@ -0,0 +1 @@
 """Pattern learning and adaptation system."""
@@ -0,0 +1,383 @@
 """
 Pattern Learning System - learns from interaction patterns to improve autonomy.
 """
 import logging
 import json
 import os
 from typing import Dict, List, Any, Optional
 from datetime import datetime
 from collections import defaultdict
 logger = logging.getLogger(__name__)
 class PatternLearner:
    """
    Learns from interaction patterns to improve Lyra's autonomous behavior.
    Tracks:
    - Topic frequencies (what users talk about)
    - Time-of-day patterns (when users interact)
    - User preferences (how users like responses)
    - Successful response strategies (what works well)
    """
    def __init__(self, patterns_file: str = "/app/data/learned_patterns.json"):
        """
        Initialize pattern learner.
        Args:
            patterns_file: Path to persistent patterns storage
        """
        self.patterns_file = patterns_file
        self.patterns = self._load_patterns()
    def _load_patterns(self) -> Dict[str, Any]:
        """Load patterns from disk."""
        if os.path.exists(self.patterns_file):
            try:
                with open(self.patterns_file, 'r') as f:
                    patterns = json.load(f)
                    logger.info(f"[PATTERN_LEARNER] Loaded patterns from {self.patterns_file}")
                    return patterns
            except Exception as e:
                logger.error(f"[PATTERN_LEARNER] Failed to load patterns: {e}")
        # Initialize empty patterns
        return {
            "topic_frequencies": {},
            "time_patterns": {},
            "user_preferences": {},
            "successful_strategies": {},
            "interaction_count": 0,
            "last_updated": datetime.utcnow().isoformat()
        }
    def _save_patterns(self) -> None:
        """Save patterns to disk."""
        try:
            # Ensure directory exists
            os.makedirs(os.path.dirname(self.patterns_file), exist_ok=True)
            self.patterns["last_updated"] = datetime.utcnow().isoformat()
            with open(self.patterns_file, 'w') as f:
                json.dump(self.patterns, f, indent=2)
            logger.debug(f"[PATTERN_LEARNER] Saved patterns to {self.patterns_file}")
        except Exception as e:
            logger.error(f"[PATTERN_LEARNER] Failed to save patterns: {e}")
    async def learn_from_interaction(
        self,
        user_prompt: str,
        response: str,
        monologue: Dict[str, Any],
        context: Dict[str, Any]
    ) -> None:
        """
        Learn from a single interaction.
        Args:
            user_prompt: User's message
            response: Lyra's response
            monologue: Inner monologue analysis
            context: Full context state
        """
        self.patterns["interaction_count"] += 1
        # Learn topic frequencies
        self._learn_topics(user_prompt, monologue)
        # Learn time patterns
        self._learn_time_patterns()
        # Learn user preferences
        self._learn_preferences(monologue, context)
        # Learn successful strategies
        self._learn_strategies(monologue, response, context)
        # Save periodically (every 10 interactions)
        if self.patterns["interaction_count"] % 10 == 0:
            self._save_patterns()
    def _learn_topics(self, user_prompt: str, monologue: Dict[str, Any]) -> None:
        """Track topic frequencies."""
        intent = monologue.get("intent", "unknown")
        # Increment topic counter
        topic_freq = self.patterns["topic_frequencies"]
        topic_freq[intent] = topic_freq.get(intent, 0) + 1
        # Extract keywords (simple approach - words > 5 chars)
        keywords = [word.lower() for word in user_prompt.split() if len(word) > 5]
        for keyword in keywords:
            topic_freq[f"keyword:{keyword}"] = topic_freq.get(f"keyword:{keyword}", 0) + 1
        logger.debug(f"[PATTERN_LEARNER] Topic learned: {intent}")
    def _learn_time_patterns(self) -> None:
        """Track time-of-day patterns."""
        now = datetime.utcnow()
        hour = now.hour
        # Track interactions by hour
        time_patterns = self.patterns["time_patterns"]
        hour_key = f"hour_{hour:02d}"
        time_patterns[hour_key] = time_patterns.get(hour_key, 0) + 1
        # Track day of week
        day_key = f"day_{now.strftime('%A').lower()}"
        time_patterns[day_key] = time_patterns.get(day_key, 0) + 1
    def _learn_preferences(self, monologue: Dict[str, Any], context: Dict[str, Any]) -> None:
        """Learn user preferences from detected tone and depth."""
        tone = monologue.get("tone", "neutral")
        depth = monologue.get("depth", "medium")
        prefs = self.patterns["user_preferences"]
        # Track preferred tone
        prefs.setdefault("tone_counts", {})
        prefs["tone_counts"][tone] = prefs["tone_counts"].get(tone, 0) + 1
        # Track preferred depth
        prefs.setdefault("depth_counts", {})
        prefs["depth_counts"][depth] = prefs["depth_counts"].get(depth, 0) + 1
    def _learn_strategies(
        self,
        monologue: Dict[str, Any],
        response: str,
        context: Dict[str, Any]
    ) -> None:
        """
        Learn which response strategies are successful.
        Success indicators:
        - Executive was consulted and plan generated
        - Response length matches depth request
        - Tone matches request
        """
        intent = monologue.get("intent", "unknown")
        executive_used = context.get("executive_plan") is not None
        strategies = self.patterns["successful_strategies"]
        strategies.setdefault(intent, {})
        # Track executive usage for this intent
        if executive_used:
            key = f"{intent}:executive_used"
            strategies.setdefault(key, 0)
            strategies[key] += 1
        # Track response length patterns
        response_length = len(response.split())
        depth = monologue.get("depth", "medium")
        length_key = f"{depth}:avg_words"
        if length_key not in strategies:
            strategies[length_key] = response_length
        else:
            # Running average
            strategies[length_key] = (strategies[length_key] + response_length) / 2
    # ========================================
    # Pattern Analysis and Recommendations
    # ========================================
    def get_top_topics(self, limit: int = 10) -> List[tuple]:
        """
        Get most frequent topics.
        Args:
            limit: Max number of topics to return
        Returns:
            List of (topic, count) tuples, sorted by count
        """
        topics = self.patterns["topic_frequencies"]
        sorted_topics = sorted(topics.items(), key=lambda x: x[1], reverse=True)
        return sorted_topics[:limit]
    def get_preferred_tone(self) -> str:
        """
        Get user's most preferred tone.
        Returns:
            Preferred tone string
        """
        prefs = self.patterns["user_preferences"]
        tone_counts = prefs.get("tone_counts", {})
        if not tone_counts:
            return "neutral"
        return max(tone_counts.items(), key=lambda x: x[1])[0]
    def get_preferred_depth(self) -> str:
        """
        Get user's most preferred response depth.
        Returns:
            Preferred depth string
        """
        prefs = self.patterns["user_preferences"]
        depth_counts = prefs.get("depth_counts", {})
        if not depth_counts:
            return "medium"
        return max(depth_counts.items(), key=lambda x: x[1])[0]
    def get_peak_hours(self, limit: int = 3) -> List[int]:
        """
        Get peak interaction hours.
        Args:
            limit: Number of top hours to return
        Returns:
            List of hours (0-23)
        """
        time_patterns = self.patterns["time_patterns"]
        hour_counts = {k: v for k, v in time_patterns.items() if k.startswith("hour_")}
        if not hour_counts:
            return []
        sorted_hours = sorted(hour_counts.items(), key=lambda x: x[1], reverse=True)
        top_hours = sorted_hours[:limit]
        # Extract hour numbers
        return [int(h[0].split("_")[1]) for h in top_hours]
    def should_use_executive(self, intent: str) -> bool:
        """
        Recommend whether to use executive for given intent based on patterns.
        Args:
            intent: Intent type
        Returns:
            True if executive is recommended
        """
        strategies = self.patterns["successful_strategies"]
        key = f"{intent}:executive_used"
        # If we've used executive for this intent >= 3 times, recommend it
        return strategies.get(key, 0) >= 3
    def get_recommended_response_length(self, depth: str) -> int:
        """
        Get recommended response length in words for given depth.
        Args:
            depth: Depth level (short/medium/deep)
        Returns:
            Recommended word count
        """
        strategies = self.patterns["successful_strategies"]
        key = f"{depth}:avg_words"
        avg_length = strategies.get(key, None)
        if avg_length:
            return int(avg_length)
        # Defaults if no pattern learned
        defaults = {
            "short": 50,
            "medium": 150,
            "deep": 300
        }
        return defaults.get(depth, 150)
    def get_insights(self) -> Dict[str, Any]:
        """
        Get high-level insights from learned patterns.
        Returns:
            {
                "total_interactions": int,
                "top_topics": [(topic, count), ...],
                "preferred_tone": str,
                "preferred_depth": str,
                "peak_hours": [hours],
                "learning_recommendations": [str]
            }
        """
        recommendations = []
        # Check if user consistently prefers certain settings
        preferred_tone = self.get_preferred_tone()
        preferred_depth = self.get_preferred_depth()
        if preferred_tone != "neutral":
            recommendations.append(f"User prefers {preferred_tone} tone")
        if preferred_depth != "medium":
            recommendations.append(f"User prefers {preferred_depth} depth responses")
        # Check for recurring topics
        top_topics = self.get_top_topics(limit=3)
        if top_topics:
            top_topic = top_topics[0][0]
            recommendations.append(f"Consider adding '{top_topic}' to learning queue")
        return {
            "total_interactions": self.patterns["interaction_count"],
            "top_topics": self.get_top_topics(limit=5),
            "preferred_tone": preferred_tone,
            "preferred_depth": preferred_depth,
            "peak_hours": self.get_peak_hours(limit=3),
            "learning_recommendations": recommendations
        }
    def reset_patterns(self) -> None:
        """Reset all learned patterns (use with caution)."""
        self.patterns = {
            "topic_frequencies": {},
            "time_patterns": {},
            "user_preferences": {},
            "successful_strategies": {},
            "interaction_count": 0,
            "last_updated": datetime.utcnow().isoformat()
        }
        self._save_patterns()
        logger.warning("[PATTERN_LEARNER] Patterns reset")
    def export_patterns(self) -> Dict[str, Any]:
        """
        Export all patterns for analysis.
        Returns:
            Complete patterns dict
        """
        return self.patterns.copy()
 # Singleton instance
 _learner_instance = None
 def get_pattern_learner(patterns_file: str = "/app/data/learned_patterns.json") -> PatternLearner:
    """
    Get singleton pattern learner instance.
    Args:
        patterns_file: Path to patterns file (only used on first call)
    Returns:
        PatternLearner instance
    """
    global _learner_instance
    if _learner_instance is None:
        _learner_instance = PatternLearner(patterns_file=patterns_file)
    return _learner_instance
@@ -0,0 +1 @@
 # Inner monologue module
@@ -0,0 +1,115 @@
 import os
 import json
 import logging
 from typing import Dict
 from llm.llm_router import call_llm
 # Configuration
 MONOLOGUE_LLM = os.getenv("MONOLOGUE_LLM", "PRIMARY").upper()
 VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
 # Logger
 logger = logging.getLogger(__name__)
 if VERBOSE_DEBUG:
    logger.setLevel(logging.DEBUG)
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(logging.Formatter(
        '%(asctime)s [MONOLOGUE] %(levelname)s: %(message)s',
        datefmt='%H:%M:%S'
    ))
    logger.addHandler(console_handler)
 MONOLOGUE_SYSTEM_PROMPT = """
 You are Lyra's inner monologue.
 You think privately.
 You do NOT speak to the user.
 You do NOT solve the task.
 You only reflect on intent, tone, and depth.
 Return ONLY valid JSON with:
 - intent (string)
 - tone (neutral | warm | focused | playful | direct)
 - depth (short | medium | deep)
 - consult_executive (true | false)
 """
 class InnerMonologue:
    async def process(self, context: Dict) -> Dict:
        # Build full prompt with system instructions merged in
        full_prompt = f"""{MONOLOGUE_SYSTEM_PROMPT}
 User message:
 {context['user_message']}
 Self state:
 {context['self_state']}
 Context summary:
 {context['context_summary']}
 Output JSON only:
 """
        # Call LLM using configured backend
        if VERBOSE_DEBUG:
            logger.debug(f"[InnerMonologue] Calling LLM with backend: {MONOLOGUE_LLM}")
            logger.debug(f"[InnerMonologue] Prompt length: {len(full_prompt)} chars")
        result = await call_llm(
            full_prompt,
            backend=MONOLOGUE_LLM,
            temperature=0.7,
            max_tokens=200
        )
        if VERBOSE_DEBUG:
            logger.debug(f"[InnerMonologue] Raw LLM response:")
            logger.debug(f"{'='*80}")
            logger.debug(result)
            logger.debug(f"{'='*80}")
            logger.debug(f"[InnerMonologue] Response length: {len(result) if result else 0} chars")
        # Parse JSON response - extract just the JSON part if there's extra text
        try:
            # Try direct parsing first
            parsed = json.loads(result)
            if VERBOSE_DEBUG:
                logger.debug(f"[InnerMonologue] Successfully parsed JSON directly: {parsed}")
            return parsed
        except json.JSONDecodeError:
            # If direct parsing fails, try to extract JSON from the response
            if VERBOSE_DEBUG:
                logger.debug(f"[InnerMonologue] Direct JSON parse failed, attempting extraction...")
            # Look for JSON object (starts with { and ends with })
            import re
            json_match = re.search(r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', result, re.DOTALL)
            if json_match:
                json_str = json_match.group(0)
                try:
                    parsed = json.loads(json_str)
                    if VERBOSE_DEBUG:
                        logger.debug(f"[InnerMonologue] Successfully extracted and parsed JSON: {parsed}")
                    return parsed
                except json.JSONDecodeError as e:
                    if VERBOSE_DEBUG:
                        logger.warning(f"[InnerMonologue] Extracted JSON still invalid: {e}")
            else:
                if VERBOSE_DEBUG:
                    logger.warning(f"[InnerMonologue] No JSON object found in response")
            # Final fallback
            if VERBOSE_DEBUG:
                logger.warning(f"[InnerMonologue] All parsing attempts failed, using fallback")
            else:
                print(f"[InnerMonologue] JSON extraction failed")
                print(f"[InnerMonologue] Raw response was: {result[:500]}")
            return {
                "intent": "unknown",
                "tone": "neutral",
                "depth": "medium",
                "consult_executive": False
            }
@@ -0,0 +1 @@
 """Proactive monitoring and suggestion system."""
@@ -0,0 +1,321 @@
 """
 Proactive Context Monitor - detects opportunities for autonomous suggestions.
 """
 import logging
 import time
 from typing import Dict, List, Any, Optional
 from datetime import datetime, timedelta
 logger = logging.getLogger(__name__)
 class ProactiveMonitor:
    """
    Monitors conversation context and detects opportunities for proactive suggestions.
    Triggers:
    - Long silence → Check-in
    - Learning queue + high curiosity → Suggest exploration
    - Active goals → Progress reminders
    - Conversation milestones → Offer summary
    - Pattern detection → Helpful suggestions
    """
    def __init__(self, min_priority: float = 0.6):
        """
        Initialize proactive monitor.
        Args:
            min_priority: Minimum priority for suggestions (0.0-1.0)
        """
        self.min_priority = min_priority
        self.last_suggestion_time = {}  # session_id -> timestamp
        self.cooldown_seconds = 300  # 5 minutes between proactive suggestions
    async def analyze_session(
        self,
        session_id: str,
        context_state: Dict[str, Any],
        self_state: Dict[str, Any]
    ) -> Optional[Dict[str, Any]]:
        """
        Analyze session for proactive suggestion opportunities.
        Args:
            session_id: Current session ID
            context_state: Full context including message history
            self_state: Lyra's current self-state
        Returns:
            {
                "suggestion": "text to append to response",
                "priority": 0.0-1.0,
                "reason": "why this suggestion",
                "type": "check_in | learning | goal_reminder | summary | pattern"
            }
            or None if no suggestion
        """
        # Check cooldown
        if not self._check_cooldown(session_id):
            logger.debug(f"[PROACTIVE] Session {session_id} in cooldown, skipping")
            return None
        suggestions = []
        # Check 1: Long silence detection
        silence_suggestion = self._check_long_silence(context_state)
        if silence_suggestion:
            suggestions.append(silence_suggestion)
        # Check 2: Learning queue + high curiosity
        learning_suggestion = self._check_learning_opportunity(self_state)
        if learning_suggestion:
            suggestions.append(learning_suggestion)
        # Check 3: Active goals reminder
        goal_suggestion = self._check_active_goals(self_state, context_state)
        if goal_suggestion:
            suggestions.append(goal_suggestion)
        # Check 4: Conversation milestones
        milestone_suggestion = self._check_conversation_milestone(context_state)
        if milestone_suggestion:
            suggestions.append(milestone_suggestion)
        # Check 5: Pattern-based suggestions
        pattern_suggestion = self._check_patterns(context_state, self_state)
        if pattern_suggestion:
            suggestions.append(pattern_suggestion)
        # Filter by priority and return highest
        valid_suggestions = [s for s in suggestions if s["priority"] >= self.min_priority]
        if not valid_suggestions:
            return None
        # Return highest priority suggestion
        best_suggestion = max(valid_suggestions, key=lambda x: x["priority"])
        # Update cooldown timer
        self._update_cooldown(session_id)
        logger.info(f"[PROACTIVE] Suggestion generated: {best_suggestion['type']} (priority: {best_suggestion['priority']:.2f})")
        return best_suggestion
    def _check_cooldown(self, session_id: str) -> bool:
        """Check if session is past cooldown period."""
        if session_id not in self.last_suggestion_time:
            return True
        elapsed = time.time() - self.last_suggestion_time[session_id]
        return elapsed >= self.cooldown_seconds
    def _update_cooldown(self, session_id: str) -> None:
        """Update cooldown timer for session."""
        self.last_suggestion_time[session_id] = time.time()
    def _check_long_silence(self, context_state: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Check if user has been silent for a long time.
        """
        minutes_since_last = context_state.get("minutes_since_last_msg", 0)
        # If > 30 minutes, suggest check-in
        if minutes_since_last > 30:
            return {
                "suggestion": "\n\n[Aside: I'm still here if you need anything!]",
                "priority": 0.7,
                "reason": f"User silent for {minutes_since_last:.0f} minutes",
                "type": "check_in"
            }
        return None
    def _check_learning_opportunity(self, self_state: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Check if Lyra has learning queue items and high curiosity.
        """
        learning_queue = self_state.get("learning_queue", [])
        curiosity = self_state.get("curiosity", 0.5)
        # If curiosity > 0.7 and learning queue exists
        if curiosity > 0.7 and learning_queue:
            topic = learning_queue[0] if learning_queue else "new topics"
            return {
                "suggestion": f"\n\n[Aside: I've been curious about {topic} lately. Would you like to explore it together?]",
                "priority": 0.65,
                "reason": f"High curiosity ({curiosity:.2f}) and learning queue present",
                "type": "learning"
            }
        return None
    def _check_active_goals(
        self,
        self_state: Dict[str, Any],
        context_state: Dict[str, Any]
    ) -> Optional[Dict[str, Any]]:
        """
        Check if there are active goals worth reminding about.
        """
        active_goals = self_state.get("active_goals", [])
        if not active_goals:
            return None
        # Check if we've had multiple messages without goal progress
        message_count = context_state.get("message_count", 0)
        # Every 10 messages, consider goal reminder
        if message_count % 10 == 0 and message_count > 0:
            goal = active_goals[0]  # First active goal
            goal_name = goal if isinstance(goal, str) else goal.get("name", "your goal")
            return {
                "suggestion": f"\n\n[Aside: Still thinking about {goal_name}. Let me know if you want to work on it.]",
                "priority": 0.6,
                "reason": f"Active goal present, {message_count} messages since start",
                "type": "goal_reminder"
            }
        return None
    def _check_conversation_milestone(self, context_state: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Check for conversation milestones (e.g., every 50 messages).
        """
        message_count = context_state.get("message_count", 0)
        # Every 50 messages, offer summary
        if message_count > 0 and message_count % 50 == 0:
            return {
                "suggestion": f"\n\n[Aside: We've exchanged {message_count} messages! Would you like a summary of our conversation?]",
                "priority": 0.65,
                "reason": f"Milestone: {message_count} messages",
                "type": "summary"
            }
        return None
    def _check_patterns(
        self,
        context_state: Dict[str, Any],
        self_state: Dict[str, Any]
    ) -> Optional[Dict[str, Any]]:
        """
        Check for behavioral patterns that merit suggestions.
        """
        # Get current focus
        focus = self_state.get("focus", "")
        # Check if user keeps asking similar questions (detected via focus)
        if focus and "repeated" in focus.lower():
            return {
                "suggestion": "\n\n[Aside: I notice we keep coming back to this topic. Would it help to create a summary or action plan?]",
                "priority": 0.7,
                "reason": "Repeated topic detected",
                "type": "pattern"
            }
        # Check energy levels - if Lyra is low energy, maybe suggest break
        energy = self_state.get("energy", 0.8)
        if energy < 0.3:
            return {
                "suggestion": "\n\n[Aside: We've been at this for a while. Need a break or want to keep going?]",
                "priority": 0.65,
                "reason": f"Low energy ({energy:.2f})",
                "type": "pattern"
            }
        return None
    def format_suggestion(self, suggestion: Dict[str, Any]) -> str:
        """
        Format suggestion for appending to response.
        Args:
            suggestion: Suggestion dict from analyze_session()
        Returns:
            Formatted string to append to response
        """
        return suggestion.get("suggestion", "")
    def set_cooldown_duration(self, seconds: int) -> None:
        """
        Update cooldown duration.
        Args:
            seconds: New cooldown duration
        """
        self.cooldown_seconds = seconds
        logger.info(f"[PROACTIVE] Cooldown updated to {seconds}s")
    def reset_cooldown(self, session_id: str) -> None:
        """
        Reset cooldown for a specific session.
        Args:
            session_id: Session to reset
        """
        if session_id in self.last_suggestion_time:
            del self.last_suggestion_time[session_id]
            logger.info(f"[PROACTIVE] Cooldown reset for session {session_id}")
    def get_session_stats(self, session_id: str) -> Dict[str, Any]:
        """
        Get stats for a session's proactive monitoring.
        Args:
            session_id: Session to check
        Returns:
            {
                "last_suggestion_time": timestamp or None,
                "seconds_since_last": int,
                "cooldown_active": bool,
                "cooldown_remaining": int
            }
        """
        last_time = self.last_suggestion_time.get(session_id)
        if not last_time:
            return {
                "last_suggestion_time": None,
                "seconds_since_last": 0,
                "cooldown_active": False,
                "cooldown_remaining": 0
            }
        seconds_since = int(time.time() - last_time)
        cooldown_active = seconds_since < self.cooldown_seconds
        cooldown_remaining = max(0, self.cooldown_seconds - seconds_since)
        return {
            "last_suggestion_time": last_time,
            "seconds_since_last": seconds_since,
            "cooldown_active": cooldown_active,
            "cooldown_remaining": cooldown_remaining
        }
 # Singleton instance
 _monitor_instance = None
 def get_proactive_monitor(min_priority: float = 0.6) -> ProactiveMonitor:
    """
    Get singleton proactive monitor instance.
    Args:
        min_priority: Minimum priority threshold (only used on first call)
    Returns:
        ProactiveMonitor instance
    """
    global _monitor_instance
    if _monitor_instance is None:
        _monitor_instance = ProactiveMonitor(min_priority=min_priority)
    return _monitor_instance
@@ -0,0 +1 @@
 # Self state module
@@ -0,0 +1,74 @@
 """
 Analyze interactions and update self-state accordingly.
 """
 import logging
 from typing import Dict, Any
 from .state import update_self_state
 logger = logging.getLogger(__name__)
 async def analyze_and_update_state(
    monologue: Dict[str, Any],
    user_prompt: str,
    response: str,
    context: Dict[str, Any]
 ) -> None:
    """
    Analyze interaction and update self-state.
    This runs after response generation to update Lyra's internal state
    based on the interaction.
    Args:
        monologue: Inner monologue output
        user_prompt: User's message
        response: Lyra's response
        context: Full context state
    """
    # Simple heuristics for state updates
    # TODO: Replace with LLM-based sentiment analysis in Phase 2
    mood_delta = 0.0
    energy_delta = 0.0
    confidence_delta = 0.0
    curiosity_delta = 0.0
    new_focus = None
    # Analyze intent from monologue
    intent = monologue.get("intent", "").lower() if monologue else ""
    if "technical" in intent or "complex" in intent:
        energy_delta = -0.05  # Deep thinking is tiring
        confidence_delta = 0.05 if len(response) > 200 else -0.05
        new_focus = "technical_problem"
    elif "creative" in intent or "brainstorm" in intent:
        mood_delta = 0.1  # Creative work is engaging
        curiosity_delta = 0.1
        new_focus = "creative_exploration"
    elif "clarification" in intent or "confused" in intent:
        confidence_delta = -0.05
        new_focus = "understanding_user"
    elif "simple" in intent or "casual" in intent:
        energy_delta = 0.05  # Light conversation is refreshing
        new_focus = "conversation"
    # Check for learning opportunities (questions in user prompt)
    if "?" in user_prompt and any(word in user_prompt.lower() for word in ["how", "why", "what"]):
        curiosity_delta += 0.05
    # Update state
    update_self_state(
        mood_delta=mood_delta,
        energy_delta=energy_delta,
        new_focus=new_focus,
        confidence_delta=confidence_delta,
        curiosity_delta=curiosity_delta
    )
    logger.info(f"Self-state updated based on interaction: focus={new_focus}")
@@ -0,0 +1,189 @@
 """
 Self-state management for Project Lyra.
 Maintains persistent identity, mood, energy, and focus across sessions.
 """
 import json
 import logging
 import os
 from datetime import datetime
 from pathlib import Path
 from typing import Dict, Any, Optional
 # Configuration
 STATE_FILE = Path(os.getenv("SELF_STATE_FILE", "/app/data/self_state.json"))
 VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
 logger = logging.getLogger(__name__)
 if VERBOSE_DEBUG:
    logger.setLevel(logging.DEBUG)
 # Default state structure
 DEFAULT_STATE = {
    "mood": "neutral",
    "energy": 0.8,
    "focus": "user_request",
    "confidence": 0.7,
    "curiosity": 0.5,
    "last_updated": None,
    "interaction_count": 0,
    "learning_queue": [],  # Topics Lyra wants to explore
    "active_goals": [],  # Self-directed goals
    "preferences": {
        "verbosity": "medium",
        "formality": "casual",
        "proactivity": 0.3  # How likely to suggest things unprompted
    },
    "metadata": {
        "version": "1.0",
        "created_at": None
    }
 }
 class SelfState:
    """Manages Lyra's persistent self-state."""
    def __init__(self):
        self._state = self._load_state()
    def _load_state(self) -> Dict[str, Any]:
        """Load state from disk or create default."""
        if STATE_FILE.exists():
            try:
                with open(STATE_FILE, 'r') as f:
                    state = json.load(f)
                    logger.info(f"Loaded self-state from {STATE_FILE}")
                    return state
            except Exception as e:
                logger.error(f"Failed to load self-state: {e}")
                return self._create_default_state()
        else:
            return self._create_default_state()
    def _create_default_state(self) -> Dict[str, Any]:
        """Create and save default state."""
        state = DEFAULT_STATE.copy()
        state["metadata"]["created_at"] = datetime.now().isoformat()
        state["last_updated"] = datetime.now().isoformat()
        self._save_state(state)
        logger.info("Created new default self-state")
        return state
    def _save_state(self, state: Dict[str, Any]) -> None:
        """Persist state to disk."""
        try:
            STATE_FILE.parent.mkdir(parents=True, exist_ok=True)
            with open(STATE_FILE, 'w') as f:
                json.dump(state, f, indent=2)
            if VERBOSE_DEBUG:
                logger.debug(f"Saved self-state to {STATE_FILE}")
        except Exception as e:
            logger.error(f"Failed to save self-state: {e}")
    def get_state(self) -> Dict[str, Any]:
        """Get current state snapshot."""
        return self._state.copy()
    def update_from_interaction(
        self,
        mood_delta: float = 0.0,
        energy_delta: float = 0.0,
        new_focus: Optional[str] = None,
        confidence_delta: float = 0.0,
        curiosity_delta: float = 0.0
    ) -> None:
        """
        Update state based on interaction.
        Args:
            mood_delta: Change in mood (-1.0 to 1.0)
            energy_delta: Change in energy (-1.0 to 1.0)
            new_focus: New focus area
            confidence_delta: Change in confidence
            curiosity_delta: Change in curiosity
        """
        # Apply deltas with bounds checking
        self._state["energy"] = max(0.0, min(1.0,
            self._state.get("energy", 0.8) + energy_delta))
        self._state["confidence"] = max(0.0, min(1.0,
            self._state.get("confidence", 0.7) + confidence_delta))
        self._state["curiosity"] = max(0.0, min(1.0,
            self._state.get("curiosity", 0.5) + curiosity_delta))
        # Update focus if provided
        if new_focus:
            self._state["focus"] = new_focus
        # Update mood (simplified sentiment)
        if mood_delta != 0:
            mood_map = ["frustrated", "neutral", "engaged", "excited"]
            current_mood_idx = 1  # neutral default
            if self._state.get("mood") in mood_map:
                current_mood_idx = mood_map.index(self._state["mood"])
            new_mood_idx = max(0, min(len(mood_map) - 1,
                int(current_mood_idx + mood_delta * 2)))
            self._state["mood"] = mood_map[new_mood_idx]
        # Increment interaction counter
        self._state["interaction_count"] = self._state.get("interaction_count", 0) + 1
        self._state["last_updated"] = datetime.now().isoformat()
        # Persist changes
        self._save_state(self._state)
        if VERBOSE_DEBUG:
            logger.debug(f"Updated self-state: mood={self._state['mood']}, "
                        f"energy={self._state['energy']:.2f}, "
                        f"confidence={self._state['confidence']:.2f}")
    def add_learning_goal(self, topic: str) -> None:
        """Add topic to learning queue."""
        queue = self._state.get("learning_queue", [])
        if topic not in [item.get("topic") for item in queue]:
            queue.append({
                "topic": topic,
                "added_at": datetime.now().isoformat(),
                "priority": 0.5
            })
            self._state["learning_queue"] = queue
            self._save_state(self._state)
            logger.info(f"Added learning goal: {topic}")
    def add_active_goal(self, goal: str, context: str = "") -> None:
        """Add self-directed goal."""
        goals = self._state.get("active_goals", [])
        goals.append({
            "goal": goal,
            "context": context,
            "created_at": datetime.now().isoformat(),
            "status": "active"
        })
        self._state["active_goals"] = goals
        self._save_state(self._state)
        logger.info(f"Added active goal: {goal}")
 # Global instance
 _self_state_instance = None
 def get_self_state_instance() -> SelfState:
    """Get or create global SelfState instance."""
    global _self_state_instance
    if _self_state_instance is None:
        _self_state_instance = SelfState()
    return _self_state_instance
 def load_self_state() -> Dict[str, Any]:
    """Load self state - public API for backwards compatibility."""
    return get_self_state_instance().get_state()
 def update_self_state(**kwargs) -> None:
    """Update self state - public API."""
    get_self_state_instance().update_from_interaction(**kwargs)
@@ -0,0 +1 @@
 """Autonomous tool invocation system."""
@@ -0,0 +1,13 @@
 """Provider adapters for tool calling."""
 from .base import ToolAdapter
 from .openai_adapter import OpenAIAdapter
 from .ollama_adapter import OllamaAdapter
 from .llamacpp_adapter import LlamaCppAdapter
 __all__ = [
    "ToolAdapter",
    "OpenAIAdapter",
    "OllamaAdapter",
    "LlamaCppAdapter",
 ]
@@ -0,0 +1,79 @@
 """
 Base adapter interface for provider-agnostic tool calling.
 This module defines the abstract base class that all LLM provider adapters
 must implement to support tool calling in Lyra.
 """
 from abc import ABC, abstractmethod
 from typing import Dict, List, Optional
 class ToolAdapter(ABC):
    """Base class for provider-specific tool adapters.
    Each LLM provider (OpenAI, Ollama, llama.cpp, etc.) has its own
    way of handling tool calls. This adapter pattern allows Lyra to
    support tools across all providers with a unified interface.
    """
    @abstractmethod
    async def prepare_request(
        self,
        messages: List[Dict],
        tools: List[Dict],
        tool_choice: Optional[str] = None
    ) -> Dict:
        """Convert Lyra tool definitions to provider-specific format.
        Args:
            messages: Conversation history in OpenAI format
            tools: List of Lyra tool definitions (provider-agnostic)
            tool_choice: Optional tool forcing ("auto", "required", "none")
        Returns:
            dict: Provider-specific request payload ready to send to LLM
        """
        pass
    @abstractmethod
    async def parse_response(self, response) -> Dict:
        """Extract tool calls from provider response.
        Args:
            response: Raw provider response (format varies by provider)
        Returns:
            dict: Standardized response in Lyra format:
                {
                    "content": str,  # Assistant's text response
                    "tool_calls": [  # List of tool calls or None
                        {
                            "id": str,       # Unique call ID
                            "name": str,     # Tool name
                            "arguments": dict  # Tool arguments
                        }
                    ] or None
                }
        """
        pass
    @abstractmethod
    def format_tool_result(
        self,
        tool_call_id: str,
        tool_name: str,
        result: Dict
    ) -> Dict:
        """Format tool execution result for next LLM call.
        Args:
            tool_call_id: ID from the original tool call
            tool_name: Name of the executed tool
            result: Tool execution result dictionary
        Returns:
            dict: Message object to append to conversation
                (format varies by provider)
        """
        pass
@@ -0,0 +1,17 @@
 """
 llama.cpp adapter for tool calling.
 Since llama.cpp has similar constraints to Ollama (no native function calling),
 this adapter reuses the XML-based approach from OllamaAdapter.
 """
 from .ollama_adapter import OllamaAdapter
 class LlamaCppAdapter(OllamaAdapter):
    """llama.cpp adapter - uses same XML approach as Ollama.
    llama.cpp doesn't have native function calling support, so we use
    the same XML-based prompt engineering approach as Ollama.
    """
    pass
@@ -0,0 +1,191 @@
 """
 Ollama adapter for tool calling using XML-structured prompts.
 Since Ollama doesn't have native function calling, this adapter uses
 XML-based prompts to instruct the model how to call tools.
 """
 import json
 import re
 from typing import Dict, List, Optional
 from .base import ToolAdapter
 class OllamaAdapter(ToolAdapter):
    """Ollama adapter using XML-structured prompts for tool calling.
    This adapter injects tool descriptions into the system prompt and
    teaches the model to respond with XML when it wants to use a tool.
    """
    SYSTEM_PROMPT = """You have access to the following tools:
 {tool_descriptions}
 To use a tool, respond with XML in this exact format:
 <tool_call>
  <name>tool_name</name>
  <arguments>
    <arg_name>value</arg_name>
  </arguments>
  <reason>why you're using this tool</reason>
 </tool_call>
 You can call multiple tools by including multiple <tool_call> blocks.
 If you don't need to use any tools, respond normally without XML.
 After tools are executed, you'll receive results and can continue the conversation."""
    async def prepare_request(
        self,
        messages: List[Dict],
        tools: List[Dict],
        tool_choice: Optional[str] = None
    ) -> Dict:
        """Inject tool descriptions into system prompt.
        Args:
            messages: Conversation history
            tools: Lyra tool definitions
            tool_choice: Ignored for Ollama (no native support)
        Returns:
            dict: Request payload with modified messages
        """
        # Format tool descriptions
        tool_desc = "\n".join([
            f"- {t['name']}: {t['description']}\n  Parameters: {self._format_parameters(t['parameters'], t.get('required', []))}"
            for t in tools
        ])
        system_msg = self.SYSTEM_PROMPT.format(tool_descriptions=tool_desc)
        # Check if first message is already a system message
        modified_messages = messages.copy()
        if modified_messages and modified_messages[0].get("role") == "system":
            # Prepend tool instructions to existing system message
            modified_messages[0]["content"] = system_msg + "\n\n" + modified_messages[0]["content"]
        else:
            # Add new system message at the beginning
            modified_messages.insert(0, {"role": "system", "content": system_msg})
        return {"messages": modified_messages}
    def _format_parameters(self, parameters: Dict, required: List[str]) -> str:
        """Format parameters for tool description.
        Args:
            parameters: Parameter definitions
            required: List of required parameter names
        Returns:
            str: Human-readable parameter description
        """
        param_strs = []
        for name, spec in parameters.items():
            req_marker = "(required)" if name in required else "(optional)"
            param_strs.append(f"{name} {req_marker}: {spec.get('description', '')}")
        return ", ".join(param_strs)
    async def parse_response(self, response) -> Dict:
        """Extract tool calls from XML in response.
        Args:
            response: String response from Ollama
        Returns:
            dict: Standardized Lyra format with content and tool_calls
        """
        import logging
        logger = logging.getLogger(__name__)
        # Ollama returns a string
        if isinstance(response, dict):
            content = response.get("message", {}).get("content", "")
        else:
            content = str(response)
        logger.info(f"🔍 OllamaAdapter.parse_response: content length={len(content)}, has <tool_call>={('<tool_call>' in content)}")
        logger.debug(f"🔍 Content preview: {content[:500]}")
        # Parse XML tool calls
        tool_calls = []
        if "<tool_call>" in content:
            # Split content by <tool_call> to get each block
            blocks = content.split('<tool_call>')
            logger.info(f"🔍 Split into {len(blocks)} blocks")
            # First block is content before any tool calls
            clean_parts = [blocks[0]]
            for idx, block in enumerate(blocks[1:]):  # Skip first block (pre-tool content)
                # Extract tool name
                name_match = re.search(r'<name>(.*?)</name>', block)
                if not name_match:
                    logger.warning(f"Block {idx} has no <name> tag, skipping")
                    continue
                name = name_match.group(1).strip()
                arguments = {}
                # Extract arguments
                args_match = re.search(r'<arguments>(.*?)</arguments>', block, re.DOTALL)
                if args_match:
                    args_xml = args_match.group(1)
                    # Parse <key>value</key> pairs
                    arg_pairs = re.findall(r'<(\w+)>(.*?)</\1>', args_xml, re.DOTALL)
                    arguments = {k: v.strip() for k, v in arg_pairs}
                tool_calls.append({
                    "id": f"call_{idx}",
                    "name": name,
                    "arguments": arguments
                })
                # For clean content, find what comes AFTER the tool call block
                # Look for the last closing tag (</tool_call> or malformed </xxx>) and keep what's after
                # Split by any closing tag at the END of the tool block
                remaining = block
                # Remove everything up to and including a standalone closing tag
                # Pattern: find </something> that's not followed by more XML
                end_match = re.search(r'</[a-z_]+>\s*(.*)$', remaining, re.DOTALL)
                if end_match:
                    after_content = end_match.group(1).strip()
                    if after_content and not after_content.startswith('<'):
                        # Only keep if it's actual text content, not more XML
                        clean_parts.append(after_content)
            clean_content = ''.join(clean_parts).strip()
        else:
            clean_content = content
        return {
            "content": clean_content,
            "tool_calls": tool_calls if tool_calls else None
        }
    def format_tool_result(
        self,
        tool_call_id: str,
        tool_name: str,
        result: Dict
    ) -> Dict:
        """Format tool result as XML for next prompt.
        Args:
            tool_call_id: ID from the original tool call
            tool_name: Name of the executed tool
            result: Tool execution result
        Returns:
            dict: Message in user role with XML-formatted result
        """
        # Format result as XML
        result_xml = f"""<tool_result>
  <tool>{tool_name}</tool>
  <result>{json.dumps(result, ensure_ascii=False)}</result>
 </tool_result>"""
        return {
            "role": "user",
            "content": result_xml
        }
@@ -0,0 +1,130 @@
 """
 OpenAI adapter for tool calling using native function calling API.
 This adapter converts Lyra tool definitions to OpenAI's function calling
 format and parses OpenAI responses back to Lyra's standardized format.
 """
 import json
 from typing import Dict, List, Optional
 from .base import ToolAdapter
 class OpenAIAdapter(ToolAdapter):
    """OpenAI-specific adapter using native function calling.
    OpenAI supports function calling natively through the 'tools' parameter
    in chat completions. This adapter leverages that capability.
    """
    async def prepare_request(
        self,
        messages: List[Dict],
        tools: List[Dict],
        tool_choice: Optional[str] = None
    ) -> Dict:
        """Convert Lyra tools to OpenAI function calling format.
        Args:
            messages: Conversation history
            tools: Lyra tool definitions
            tool_choice: "auto", "required", "none", or None
        Returns:
            dict: Request payload with OpenAI-formatted tools
        """
        # Convert Lyra tools → OpenAI function calling format
        openai_tools = []
        for tool in tools:
            openai_tools.append({
                "type": "function",
                "function": {
                    "name": tool["name"],
                    "description": tool["description"],
                    "parameters": {
                        "type": "object",
                        "properties": tool["parameters"],
                        "required": tool.get("required", [])
                    }
                }
            })
        payload = {
            "messages": messages,
            "tools": openai_tools
        }
        # Add tool_choice if specified
        if tool_choice:
            if tool_choice == "required":
                payload["tool_choice"] = "required"
            elif tool_choice == "none":
                payload["tool_choice"] = "none"
            else:  # "auto" or default
                payload["tool_choice"] = "auto"
        return payload
    async def parse_response(self, response) -> Dict:
        """Extract tool calls from OpenAI response.
        Args:
            response: OpenAI ChatCompletion response object
        Returns:
            dict: Standardized Lyra format with content and tool_calls
        """
        message = response.choices[0].message
        content = message.content if message.content else ""
        tool_calls = []
        # Check if response contains tool calls
        if hasattr(message, 'tool_calls') and message.tool_calls:
            for tc in message.tool_calls:
                try:
                    # Parse arguments (may be JSON string)
                    args = tc.function.arguments
                    if isinstance(args, str):
                        args = json.loads(args)
                    tool_calls.append({
                        "id": tc.id,
                        "name": tc.function.name,
                        "arguments": args
                    })
                except json.JSONDecodeError as e:
                    # If arguments can't be parsed, include error
                    tool_calls.append({
                        "id": tc.id,
                        "name": tc.function.name,
                        "arguments": {},
                        "error": f"Failed to parse arguments: {str(e)}"
                    })
        return {
            "content": content,
            "tool_calls": tool_calls if tool_calls else None
        }
    def format_tool_result(
        self,
        tool_call_id: str,
        tool_name: str,
        result: Dict
    ) -> Dict:
        """Format tool result as OpenAI tool message.
        Args:
            tool_call_id: ID from the original tool call
            tool_name: Name of the executed tool
            result: Tool execution result
        Returns:
            dict: Message in OpenAI tool message format
        """
        return {
            "role": "tool",
            "tool_call_id": tool_call_id,
            "name": tool_name,
            "content": json.dumps(result, ensure_ascii=False)
        }
@@ -0,0 +1,124 @@
 """
 Tool Decision Engine - decides which tools to invoke autonomously.
 """
 import logging
 from typing import Dict, List, Any
 logger = logging.getLogger(__name__)
 class ToolDecisionEngine:
    """Decides which tools to invoke based on context analysis."""
    async def analyze_tool_needs(
        self,
        user_prompt: str,
        monologue: Dict[str, Any],
        context_state: Dict[str, Any],
        available_tools: List[str]
    ) -> Dict[str, Any]:
        """
        Analyze if tools should be invoked and which ones.
        Args:
            user_prompt: User's message
            monologue: Inner monologue analysis
            context_state: Full context
            available_tools: List of available tools
        Returns:
            {
                "should_invoke_tools": bool,
                "tools_to_invoke": [
                    {
                        "tool": "RAG | WEB | WEATHER | etc",
                        "query": "search query",
                        "reason": "why this tool",
                        "priority": 0.0-1.0
                    },
                    ...
                ],
                "confidence": 0.0-1.0
            }
        """
        tools_to_invoke = []
        # Check for memory/context needs
        if any(word in user_prompt.lower() for word in [
            "remember", "you said", "we discussed", "earlier", "before",
            "last time", "previously", "what did"
        ]):
            tools_to_invoke.append({
                "tool": "RAG",
                "query": user_prompt,
                "reason": "User references past conversation",
                "priority": 0.9
            })
        # Check for web search needs
        if any(word in user_prompt.lower() for word in [
            "current", "latest", "news", "today", "what's happening",
            "look up", "search for", "find information", "recent"
        ]):
            tools_to_invoke.append({
                "tool": "WEB",
                "query": user_prompt,
                "reason": "Requires current information",
                "priority": 0.8
            })
        # Check for weather needs
        if any(word in user_prompt.lower() for word in [
            "weather", "temperature", "forecast", "rain", "sunny", "climate"
        ]):
            tools_to_invoke.append({
                "tool": "WEATHER",
                "query": user_prompt,
                "reason": "Weather information requested",
                "priority": 0.95
            })
        # Check for code-related needs
        if any(word in user_prompt.lower() for word in [
            "code", "function", "debug", "implement", "algorithm",
            "programming", "script", "syntax"
        ]):
            if "CODEBRAIN" in available_tools:
                tools_to_invoke.append({
                    "tool": "CODEBRAIN",
                    "query": user_prompt,
                    "reason": "Code-related task",
                    "priority": 0.85
                })
        # Proactive RAG for complex queries (based on monologue)
        intent = monologue.get("intent", "") if monologue else ""
        if monologue and monologue.get("consult_executive"):
            # Complex query - might benefit from context
            if not any(t["tool"] == "RAG" for t in tools_to_invoke):
                tools_to_invoke.append({
                    "tool": "RAG",
                    "query": user_prompt,
                    "reason": "Complex query benefits from context",
                    "priority": 0.6
                })
        # Sort by priority
        tools_to_invoke.sort(key=lambda x: x["priority"], reverse=True)
        max_priority = max([t["priority"] for t in tools_to_invoke]) if tools_to_invoke else 0.0
        result = {
            "should_invoke_tools": len(tools_to_invoke) > 0,
            "tools_to_invoke": tools_to_invoke,
            "confidence": max_priority
        }
        if tools_to_invoke:
            logger.info(f"[TOOL_DECISION] Autonomous tool invocation recommended: {len(tools_to_invoke)} tools")
            for tool in tools_to_invoke:
                logger.info(f"  - {tool['tool']} (priority: {tool['priority']:.2f}): {tool['reason']}")
        return result
@@ -0,0 +1,12 @@
 """Tool executors for Lyra."""
 from .code_executor import execute_code
 from .web_search import search_web
 from .trilium import search_notes, create_note
 __all__ = [
    "execute_code",
    "search_web",
    "search_notes",
    "create_note",
 ]
@@ -0,0 +1,218 @@
 """
 Code executor for running Python and bash code in a sandbox container.
 This module provides secure code execution with timeout protection,
 output limits, and forbidden pattern detection.
 """
 import asyncio
 import os
 import tempfile
 import re
 from typing import Dict
 import docker
 from docker.errors import (
    DockerException,
    APIError,
    ContainerError,
    ImageNotFound,
    NotFound
 )
 # Forbidden patterns that pose security risks
 FORBIDDEN_PATTERNS = [
    r'rm\s+-rf',  # Destructive file removal
    r':\(\)\{\s*:\|:&\s*\};:',  # Fork bomb
    r'mkfs',  # Filesystem formatting
    r'/dev/sd[a-z]',  # Direct device access
    r'dd\s+if=',  # Low-level disk operations
    r'>\s*/dev/sd',  # Writing to devices
    r'curl.*\|.*sh',  # Pipe to shell (common attack vector)
    r'wget.*\|.*sh',  # Pipe to shell
 ]
 async def execute_code(args: Dict) -> Dict:
    """Execute code in sandbox container.
    Args:
        args: Dictionary containing:
            - language (str): "python" or "bash"
            - code (str): The code to execute
            - reason (str): Why this code is being executed
            - timeout (int, optional): Execution timeout in seconds
    Returns:
        dict: Execution result containing:
            - stdout (str): Standard output
            - stderr (str): Standard error
            - exit_code (int): Process exit code
            - execution_time (float): Time taken in seconds
            OR
            - error (str): Error message if execution failed
    """
    language = args.get("language")
    code = args.get("code")
    reason = args.get("reason", "No reason provided")
    timeout = args.get("timeout", 30)
    # Validation
    if not language or language not in ["python", "bash"]:
        return {"error": "Invalid language. Must be 'python' or 'bash'"}
    if not code:
        return {"error": "No code provided"}
    # Security: Check for forbidden patterns
    for pattern in FORBIDDEN_PATTERNS:
        if re.search(pattern, code, re.IGNORECASE):
            return {"error": f"Forbidden pattern detected for security reasons"}
    # Validate and cap timeout
    max_timeout = int(os.getenv("CODE_SANDBOX_MAX_TIMEOUT", "120"))
    timeout = min(max(timeout, 1), max_timeout)
    container = os.getenv("CODE_SANDBOX_CONTAINER", "lyra-code-sandbox")
    # Validate container exists and is running
    try:
        docker_client = docker.from_env()
        container_obj = docker_client.containers.get(container)
        if container_obj.status != "running":
            return {
                "error": f"Sandbox container '{container}' is not running (status: {container_obj.status})",
                "hint": "Start the container with: docker start " + container
            }
    except NotFound:
        return {
            "error": f"Sandbox container '{container}' not found",
            "hint": "Ensure the container exists and is running"
        }
    except DockerException as e:
        return {
            "error": f"Docker daemon error: {str(e)}",
            "hint": "Check Docker connectivity and permissions"
        }
    # Write code to temporary file
    suffix = ".py" if language == "python" else ".sh"
    try:
        with tempfile.NamedTemporaryFile(
            mode='w',
            suffix=suffix,
            delete=False,
            encoding='utf-8'
        ) as f:
            f.write(code)
            temp_file = f.name
    except Exception as e:
        return {"error": f"Failed to create temp file: {str(e)}"}
    try:
        # Copy file to container
        exec_path = f"/executions/{os.path.basename(temp_file)}"
        cp_proc = await asyncio.create_subprocess_exec(
            "docker", "cp", temp_file, f"{container}:{exec_path}",
            stdout=asyncio.subprocess.PIPE,
            stderr=asyncio.subprocess.PIPE
        )
        await cp_proc.communicate()
        if cp_proc.returncode != 0:
            return {"error": "Failed to copy code to sandbox container"}
        # Fix permissions so sandbox user can read the file (run as root)
        chown_proc = await asyncio.create_subprocess_exec(
            "docker", "exec", "-u", "root", container, "chown", "sandbox:sandbox", exec_path,
            stdout=asyncio.subprocess.PIPE,
            stderr=asyncio.subprocess.PIPE
        )
        await chown_proc.communicate()
        # Execute in container as sandbox user
        if language == "python":
            cmd = ["docker", "exec", "-u", "sandbox", container, "python3", exec_path]
        else:  # bash
            cmd = ["docker", "exec", "-u", "sandbox", container, "bash", exec_path]
        start_time = asyncio.get_event_loop().time()
        proc = await asyncio.create_subprocess_exec(
            *cmd,
            stdout=asyncio.subprocess.PIPE,
            stderr=asyncio.subprocess.PIPE
        )
        try:
            stdout, stderr = await asyncio.wait_for(
                proc.communicate(),
                timeout=timeout
            )
            execution_time = asyncio.get_event_loop().time() - start_time
            # Truncate output to prevent memory issues (configurable)
            max_output = int(os.getenv("CODE_SANDBOX_MAX_OUTPUT", "10240"))  # 10KB default
            stdout_str = stdout[:max_output].decode('utf-8', errors='replace')
            stderr_str = stderr[:max_output].decode('utf-8', errors='replace')
            if len(stdout) > max_output:
                stdout_str += f"\n... (output truncated, {len(stdout)} bytes total)"
            if len(stderr) > max_output:
                stderr_str += f"\n... (output truncated, {len(stderr)} bytes total)"
            return {
                "stdout": stdout_str,
                "stderr": stderr_str,
                "exit_code": proc.returncode,
                "execution_time": round(execution_time, 2)
            }
        except asyncio.TimeoutError:
            # Kill the process
            try:
                proc.kill()
                await proc.wait()
            except:
                pass
            return {"error": f"Execution timeout after {timeout}s"}
    except APIError as e:
        return {
            "error": f"Docker API error: {e.explanation}",
            "status_code": e.status_code
        }
    except ContainerError as e:
        return {
            "error": f"Container execution error: {str(e)}",
            "exit_code": e.exit_status
        }
    except DockerException as e:
        return {
            "error": f"Docker error: {str(e)}",
            "hint": "Check Docker daemon connectivity and permissions"
        }
    except Exception as e:
        return {"error": f"Execution failed: {str(e)}"}
    finally:
        # Cleanup temporary file
        try:
            if 'temp_file' in locals():
                os.unlink(temp_file)
        except Exception as cleanup_error:
            # Log but don't fail on cleanup errors
            pass
        # Optional: Clean up file from container (best effort)
        try:
            if 'exec_path' in locals() and 'container_obj' in locals():
                container_obj.exec_run(
                    f"rm -f {exec_path}",
                    user="sandbox"
                )
        except:
            pass  # Best effort cleanup
@@ -0,0 +1,13 @@
 """Web search provider implementations."""
 from .base import SearchProvider, SearchResult, SearchResponse
 from .brave import BraveSearchProvider
 from .duckduckgo import DuckDuckGoProvider
 __all__ = [
    "SearchProvider",
    "SearchResult",
    "SearchResponse",
    "BraveSearchProvider",
    "DuckDuckGoProvider",
 ]
@@ -0,0 +1,49 @@
 """Base interface for web search providers."""
 from abc import ABC, abstractmethod
 from typing import List, Optional
 from dataclasses import dataclass
@dataclass
 class SearchResult:
    """Standardized search result format."""
    title: str
    url: str
    snippet: str
    score: Optional[float] = None
@dataclass
 class SearchResponse:
    """Standardized search response."""
    results: List[SearchResult]
    count: int
    provider: str
    query: str
    error: Optional[str] = None
 class SearchProvider(ABC):
    """Abstract base class for search providers."""
    @abstractmethod
    async def search(
        self,
        query: str,
        max_results: int = 5,
        **kwargs
    ) -> SearchResponse:
        """Execute search and return standardized results."""
        pass
    @abstractmethod
    async def health_check(self) -> bool:
        """Check if provider is healthy and reachable."""
        pass
    @property
    @abstractmethod
    def name(self) -> str:
        """Provider name."""
        pass
@@ -0,0 +1,123 @@
 """Brave Search API provider implementation."""
 import os
 import asyncio
 import aiohttp
 from .base import SearchProvider, SearchResponse, SearchResult
 from ..utils.resilience import async_retry
 class BraveSearchProvider(SearchProvider):
    """Brave Search API implementation."""
    def __init__(self):
        self.api_key = os.getenv("BRAVE_SEARCH_API_KEY", "")
        self.base_url = os.getenv(
            "BRAVE_SEARCH_URL",
            "https://api.search.brave.com/res/v1"
        )
        self.timeout = float(os.getenv("BRAVE_SEARCH_TIMEOUT", "10.0"))
    @property
    def name(self) -> str:
        return "brave"
    @async_retry(
        max_attempts=3,
        exceptions=(aiohttp.ClientError, asyncio.TimeoutError)
    )
    async def search(
        self,
        query: str,
        max_results: int = 5,
        **kwargs
    ) -> SearchResponse:
        """Execute Brave search with retry logic."""
        if not self.api_key:
            return SearchResponse(
                results=[],
                count=0,
                provider=self.name,
                query=query,
                error="BRAVE_SEARCH_API_KEY not configured"
            )
        headers = {
            "Accept": "application/json",
            "X-Subscription-Token": self.api_key
        }
        params = {
            "q": query,
            "count": min(max_results, 20)  # Brave max is 20
        }
        try:
            async with aiohttp.ClientSession() as session:
                async with session.get(
                    f"{self.base_url}/web/search",
                    headers=headers,
                    params=params,
                    timeout=aiohttp.ClientTimeout(total=self.timeout)
                ) as resp:
                    if resp.status == 200:
                        data = await resp.json()
                        results = []
                        for item in data.get("web", {}).get("results", []):
                            results.append(SearchResult(
                                title=item.get("title", ""),
                                url=item.get("url", ""),
                                snippet=item.get("description", ""),
                                score=item.get("score")
                            ))
                        return SearchResponse(
                            results=results,
                            count=len(results),
                            provider=self.name,
                            query=query
                        )
                    elif resp.status == 401:
                        error = "Authentication failed. Check BRAVE_SEARCH_API_KEY"
                    elif resp.status == 429:
                        error = f"Rate limit exceeded. Status: {resp.status}"
                    else:
                        error_text = await resp.text()
                        error = f"HTTP {resp.status}: {error_text}"
                    return SearchResponse(
                        results=[],
                        count=0,
                        provider=self.name,
                        query=query,
                        error=error
                    )
        except aiohttp.ClientConnectorError as e:
            return SearchResponse(
                results=[],
                count=0,
                provider=self.name,
                query=query,
                error=f"Cannot connect to Brave Search API: {str(e)}"
            )
        except asyncio.TimeoutError:
            return SearchResponse(
                results=[],
                count=0,
                provider=self.name,
                query=query,
                error=f"Search timeout after {self.timeout}s"
            )
    async def health_check(self) -> bool:
        """Check if Brave API is reachable."""
        if not self.api_key:
            return False
        try:
            response = await self.search("test", max_results=1)
            return response.error is None
        except:
            return False
@@ -0,0 +1,60 @@
 """DuckDuckGo search provider with retry logic (legacy fallback)."""
 from duckduckgo_search import DDGS
 from .base import SearchProvider, SearchResponse, SearchResult
 from ..utils.resilience import async_retry
 class DuckDuckGoProvider(SearchProvider):
    """DuckDuckGo search implementation with retry logic."""
    @property
    def name(self) -> str:
        return "duckduckgo"
    @async_retry(
        max_attempts=3,
        exceptions=(Exception,)  # DDG throws generic exceptions
    )
    async def search(
        self,
        query: str,
        max_results: int = 5,
        **kwargs
    ) -> SearchResponse:
        """Execute DuckDuckGo search with retry logic."""
        try:
            with DDGS() as ddgs:
                results = []
                for result in ddgs.text(query, max_results=max_results):
                    results.append(SearchResult(
                        title=result.get("title", ""),
                        url=result.get("href", ""),
                        snippet=result.get("body", "")
                    ))
                return SearchResponse(
                    results=results,
                    count=len(results),
                    provider=self.name,
                    query=query
                )
        except Exception as e:
            return SearchResponse(
                results=[],
                count=0,
                provider=self.name,
                query=query,
                error=f"Search failed: {str(e)}"
            )
    async def health_check(self) -> bool:
        """Basic health check for DDG."""
        try:
            response = await self.search("test", max_results=1)
            return response.error is None
        except:
            return False
@@ -0,0 +1,216 @@
 """
 Trilium notes executor for searching and creating notes via ETAPI.
 This module provides integration with Trilium notes through the ETAPI HTTP API
 with improved resilience: timeout configuration, retry logic, and connection pooling.
 """
 import os
 import asyncio
 import aiohttp
 from typing import Dict, Optional
 from ..utils.resilience import async_retry
 TRILIUM_URL = os.getenv("TRILIUM_URL", "http://localhost:8080")
 TRILIUM_TOKEN = os.getenv("TRILIUM_ETAPI_TOKEN", "")
 # Module-level session for connection pooling
 _session: Optional[aiohttp.ClientSession] = None
 def get_session() -> aiohttp.ClientSession:
    """Get or create shared aiohttp session for connection pooling."""
    global _session
    if _session is None or _session.closed:
        timeout = aiohttp.ClientTimeout(
            total=float(os.getenv("TRILIUM_TIMEOUT", "30.0")),
            connect=float(os.getenv("TRILIUM_CONNECT_TIMEOUT", "10.0"))
        )
        _session = aiohttp.ClientSession(timeout=timeout)
    return _session
@async_retry(
    max_attempts=3,
    exceptions=(aiohttp.ClientError, asyncio.TimeoutError)
 )
 async def search_notes(args: Dict) -> Dict:
    """Search Trilium notes via ETAPI with retry logic.
    Args:
        args: Dictionary containing:
            - query (str): Search query
            - limit (int, optional): Maximum notes to return (default: 5, max: 20)
    Returns:
        dict: Search results containing:
            - notes (list): List of notes with noteId, title, content, type
            - count (int): Number of notes returned
            OR
            - error (str): Error message if search failed
    """
    query = args.get("query")
    limit = args.get("limit", 5)
    # Validation
    if not query:
        return {"error": "No query provided"}
    if not TRILIUM_TOKEN:
        return {
            "error": "TRILIUM_ETAPI_TOKEN not configured in environment",
            "hint": "Set TRILIUM_ETAPI_TOKEN in .env file"
        }
    # Cap limit
    limit = min(max(limit, 1), 20)
    try:
        session = get_session()
        async with session.get(
            f"{TRILIUM_URL}/etapi/notes",
            params={"search": query, "limit": limit},
            headers={"Authorization": TRILIUM_TOKEN}
        ) as resp:
            if resp.status == 200:
                data = await resp.json()
                # ETAPI returns {"results": [...]} format
                results = data.get("results", [])
                return {
                    "notes": results,
                    "count": len(results)
                }
            elif resp.status == 401:
                return {
                    "error": "Authentication failed. Check TRILIUM_ETAPI_TOKEN",
                    "status": 401
                }
            elif resp.status == 404:
                return {
                    "error": "Trilium API endpoint not found. Check TRILIUM_URL",
                    "status": 404,
                    "url": TRILIUM_URL
                }
            else:
                error_text = await resp.text()
                return {
                    "error": f"HTTP {resp.status}: {error_text}",
                    "status": resp.status
                }
    except aiohttp.ClientConnectorError as e:
        return {
            "error": f"Cannot connect to Trilium at {TRILIUM_URL}",
            "hint": "Check if Trilium is running and URL is correct",
            "details": str(e)
        }
    except asyncio.TimeoutError:
        timeout = os.getenv("TRILIUM_TIMEOUT", "30.0")
        return {
            "error": f"Trilium request timeout after {timeout}s",
            "hint": "Trilium may be slow or unresponsive"
        }
    except Exception as e:
        return {
            "error": f"Search failed: {str(e)}",
            "type": type(e).__name__
        }
@async_retry(
    max_attempts=3,
    exceptions=(aiohttp.ClientError, asyncio.TimeoutError)
 )
 async def create_note(args: Dict) -> Dict:
    """Create a note in Trilium via ETAPI with retry logic.
    Args:
        args: Dictionary containing:
            - title (str): Note title
            - content (str): Note content in markdown or HTML
            - parent_note_id (str, optional): Parent note ID to nest under
    Returns:
        dict: Creation result containing:
            - noteId (str): ID of created note
            - title (str): Title of created note
            - success (bool): True if created successfully
            OR
            - error (str): Error message if creation failed
    """
    title = args.get("title")
    content = args.get("content")
    parent_note_id = args.get("parent_note_id", "root")  # Default to root if not specified
    # Validation
    if not title:
        return {"error": "No title provided"}
    if not content:
        return {"error": "No content provided"}
    if not TRILIUM_TOKEN:
        return {
            "error": "TRILIUM_ETAPI_TOKEN not configured in environment",
            "hint": "Set TRILIUM_ETAPI_TOKEN in .env file"
        }
    # Prepare payload
    payload = {
        "parentNoteId": parent_note_id,  # Always include parentNoteId
        "title": title,
        "content": content,
        "type": "text",
        "mime": "text/html"
    }
    try:
        session = get_session()
        async with session.post(
            f"{TRILIUM_URL}/etapi/create-note",
            json=payload,
            headers={"Authorization": TRILIUM_TOKEN}
        ) as resp:
            if resp.status in [200, 201]:
                data = await resp.json()
                return {
                    "noteId": data.get("noteId"),
                    "title": title,
                    "success": True
                }
            elif resp.status == 401:
                return {
                    "error": "Authentication failed. Check TRILIUM_ETAPI_TOKEN",
                    "status": 401
                }
            elif resp.status == 404:
                return {
                    "error": "Trilium API endpoint not found. Check TRILIUM_URL",
                    "status": 404,
                    "url": TRILIUM_URL
                }
            else:
                error_text = await resp.text()
                return {
                    "error": f"HTTP {resp.status}: {error_text}",
                    "status": resp.status
                }
    except aiohttp.ClientConnectorError as e:
        return {
            "error": f"Cannot connect to Trilium at {TRILIUM_URL}",
            "hint": "Check if Trilium is running and URL is correct",
            "details": str(e)
        }
    except asyncio.TimeoutError:
        timeout = os.getenv("TRILIUM_TIMEOUT", "30.0")
        return {
            "error": f"Trilium request timeout after {timeout}s",
            "hint": "Trilium may be slow or unresponsive"
        }
    except Exception as e:
        return {
            "error": f"Note creation failed: {str(e)}",
            "type": type(e).__name__
        }
@@ -0,0 +1,113 @@
 """
 Web search executor with pluggable provider support.
 Supports multiple providers with automatic fallback:
 - Brave Search API (recommended, configurable)
 - DuckDuckGo (legacy fallback)
 """
 import os
 from typing import Dict, Optional
 from .search_providers.base import SearchProvider
 from .search_providers.brave import BraveSearchProvider
 from .search_providers.duckduckgo import DuckDuckGoProvider
 # Provider registry
 PROVIDERS = {
    "brave": BraveSearchProvider,
    "duckduckgo": DuckDuckGoProvider,
 }
 # Singleton provider instances
 _provider_instances: Dict[str, SearchProvider] = {}
 def get_provider(name: str) -> Optional[SearchProvider]:
    """Get or create provider instance."""
    if name not in _provider_instances:
        provider_class = PROVIDERS.get(name)
        if provider_class:
            _provider_instances[name] = provider_class()
    return _provider_instances.get(name)
 async def search_web(args: Dict) -> Dict:
    """Search the web using configured provider with automatic fallback.
    Args:
        args: Dictionary containing:
            - query (str): The search query
            - max_results (int, optional): Maximum results to return (default: 5, max: 20)
            - provider (str, optional): Force specific provider
    Returns:
        dict: Search results containing:
            - results (list): List of search results with title, url, snippet
            - count (int): Number of results returned
            - provider (str): Provider that returned results
            OR
            - error (str): Error message if all providers failed
    """
    query = args.get("query")
    max_results = args.get("max_results", 5)
    forced_provider = args.get("provider")
    # Validation
    if not query:
        return {"error": "No query provided"}
    # Cap max_results
    max_results = min(max(max_results, 1), 20)
    # Get provider preference from environment
    primary_provider = os.getenv("WEB_SEARCH_PROVIDER", "duckduckgo")
    fallback_providers = os.getenv(
        "WEB_SEARCH_FALLBACK",
        "duckduckgo"
    ).split(",")
    # Build provider list
    if forced_provider:
        providers_to_try = [forced_provider]
    else:
        providers_to_try = [primary_provider] + [
            p.strip() for p in fallback_providers if p.strip() != primary_provider
        ]
    # Try providers in order
    last_error = None
    for provider_name in providers_to_try:
        provider = get_provider(provider_name)
        if not provider:
            last_error = f"Unknown provider: {provider_name}"
            continue
        try:
            response = await provider.search(query, max_results)
            # If successful, return results
            if response.error is None and response.count > 0:
                return {
                    "results": [
                        {
                            "title": r.title,
                            "url": r.url,
                            "snippet": r.snippet,
                        }
                        for r in response.results
                    ],
                    "count": response.count,
                    "provider": provider_name
                }
            last_error = response.error or "No results returned"
        except Exception as e:
            last_error = f"{provider_name} failed: {str(e)}"
            continue
    # All providers failed
    return {
        "error": f"All search providers failed. Last error: {last_error}",
        "providers_tried": providers_to_try
    }
@@ -0,0 +1,235 @@
 """
 Provider-agnostic function caller with iterative tool calling loop.
 This module implements the iterative loop that allows LLMs to call tools
 multiple times until they have the information they need to answer the user.
 """
 import os
 import logging
 from typing import Dict, List, Optional
 from llm.llm_router import call_llm, TOOL_ADAPTERS, BACKENDS
 from .registry import get_registry
 from .stream_events import get_stream_manager
 logger = logging.getLogger(__name__)
 class FunctionCaller:
    """Provider-agnostic iterative tool calling loop.
    This class orchestrates the back-and-forth between the LLM and tools:
    1. Call LLM with tools available
    2. If LLM requests tool calls, execute them
    3. Add results to conversation
    4. Repeat until LLM is done or max iterations reached
    """
    def __init__(self, backend: str, temperature: float = 0.7):
        """Initialize function caller.
        Args:
            backend: LLM backend to use ("OPENAI", "OLLAMA", etc.)
            temperature: Temperature for LLM calls
        """
        self.backend = backend
        self.temperature = temperature
        self.registry = get_registry()
        self.max_iterations = int(os.getenv("MAX_TOOL_ITERATIONS", "5"))
        # Resolve adapter for this backend
        self.adapter = self._get_adapter()
    def _get_adapter(self):
        """Get the appropriate adapter for this backend."""
        adapter = TOOL_ADAPTERS.get(self.backend)
        # For PRIMARY/SECONDARY/FALLBACK, determine adapter based on provider
        if adapter is None and self.backend in ["PRIMARY", "SECONDARY", "FALLBACK"]:
            cfg = BACKENDS.get(self.backend, {})
            provider = cfg.get("provider", "").lower()
            if provider == "openai":
                adapter = TOOL_ADAPTERS["OPENAI"]
            elif provider == "ollama":
                adapter = TOOL_ADAPTERS["OLLAMA"]
            elif provider == "mi50":
                adapter = TOOL_ADAPTERS["MI50"]
        return adapter
    async def call_with_tools(
        self,
        messages: List[Dict],
        max_tokens: int = 2048,
        session_id: Optional[str] = None
    ) -> Dict:
        """Execute LLM with iterative tool calling.
        Args:
            messages: Conversation history
            max_tokens: Maximum tokens for LLM response
            session_id: Optional session ID for streaming events
        Returns:
            dict: {
                "content": str,  # Final response
                "iterations": int,  # Number of iterations
                "tool_calls": list,  # All tool calls made
                "messages": list,  # Full conversation history
                "truncated": bool (optional)  # True if max iterations reached
            }
        """
        logger.info(f"🔍 FunctionCaller.call_with_tools() invoked with {len(messages)} messages")
        tools = self.registry.get_tool_definitions()
        logger.info(f"🔍 Got {len(tools or [])} tool definitions from registry")
        # Get stream manager for emitting events
        stream_manager = get_stream_manager()
        should_stream = session_id and stream_manager.has_subscribers(session_id)
        # If no tools are enabled, just call LLM directly
        if not tools:
            logger.warning("FunctionCaller invoked but no tools are enabled")
            response = await call_llm(
                messages=messages,
                backend=self.backend,
                temperature=self.temperature,
                max_tokens=max_tokens
            )
            return {
                "content": response,
                "iterations": 1,
                "tool_calls": [],
                "messages": messages + [{"role": "assistant", "content": response}]
            }
        conversation = messages.copy()
        all_tool_calls = []
        for iteration in range(self.max_iterations):
            logger.info(f"Tool calling iteration {iteration + 1}/{self.max_iterations}")
            # Emit thinking event
            if should_stream:
                await stream_manager.emit(session_id, "thinking", {
                    "message": f"🤔 Thinking... (iteration {iteration + 1}/{self.max_iterations})"
                })
            # Call LLM with tools
            try:
                response = await call_llm(
                    messages=conversation,
                    backend=self.backend,
                    temperature=self.temperature,
                    max_tokens=max_tokens,
                    tools=tools,
                    tool_choice="auto",
                    return_adapter_response=True
                )
            except Exception as e:
                logger.error(f"LLM call failed: {str(e)}")
                if should_stream:
                    await stream_manager.emit(session_id, "error", {
                        "message": f"❌ Error: {str(e)}"
                    })
                return {
                    "content": f"Error calling LLM: {str(e)}",
                    "iterations": iteration + 1,
                    "tool_calls": all_tool_calls,
                    "messages": conversation,
                    "error": True
                }
            # Add assistant message to conversation
            if response.get("content"):
                conversation.append({
                    "role": "assistant",
                    "content": response["content"]
                })
            # Check for tool calls
            tool_calls = response.get("tool_calls")
            logger.debug(f"Response from LLM: content_length={len(response.get('content', ''))}, tool_calls={tool_calls}")
            if not tool_calls:
                # No more tool calls - LLM is done
                logger.info(f"Tool calling complete after {iteration + 1} iterations")
                if should_stream:
                    await stream_manager.emit(session_id, "done", {
                        "message": "✅ Complete!",
                        "final_answer": response["content"]
                    })
                return {
                    "content": response["content"],
                    "iterations": iteration + 1,
                    "tool_calls": all_tool_calls,
                    "messages": conversation
                }
            # Execute each tool call
            logger.info(f"Executing {len(tool_calls)} tool call(s)")
            for tool_call in tool_calls:
                all_tool_calls.append(tool_call)
                tool_name = tool_call.get("name")
                tool_args = tool_call.get("arguments", {})
                tool_id = tool_call.get("id", "unknown")
                logger.info(f"Calling tool: {tool_name} with args: {tool_args}")
                # Emit tool call event
                if should_stream:
                    await stream_manager.emit(session_id, "tool_call", {
                        "tool": tool_name,
                        "args": tool_args,
                        "message": f"🔧 Using tool: {tool_name}"
                    })
                try:
                    # Execute tool
                    result = await self.registry.execute_tool(tool_name, tool_args)
                    logger.info(f"Tool {tool_name} executed successfully")
                    # Emit tool result event
                    if should_stream:
                        # Format result preview
                        result_preview = str(result)
                        if len(result_preview) > 200:
                            result_preview = result_preview[:200] + "..."
                        await stream_manager.emit(session_id, "tool_result", {
                            "tool": tool_name,
                            "result": result,
                            "message": f"📊 Result: {result_preview}"
                        })
                except Exception as e:
                    logger.error(f"Tool {tool_name} execution failed: {str(e)}")
                    result = {"error": f"Tool execution failed: {str(e)}"}
                # Format result using adapter
                if not self.adapter:
                    logger.warning(f"No adapter available for backend {self.backend}, using fallback format")
                    result_msg = {
                        "role": "user",
                        "content": f"Tool {tool_name} result: {result}"
                    }
                else:
                    result_msg = self.adapter.format_tool_result(
                        tool_id,
                        tool_name,
                        result
                    )
                conversation.append(result_msg)
        # Max iterations reached without completion
        logger.warning(f"Tool calling truncated after {self.max_iterations} iterations")
        return {
            "content": response.get("content", ""),
            "iterations": self.max_iterations,
            "tool_calls": all_tool_calls,
            "messages": conversation,
            "truncated": True
        }
@@ -0,0 +1,357 @@
 """
 Tool Orchestrator - executes autonomous tool invocations asynchronously.
 """
 import asyncio
 import logging
 from typing import Dict, List, Any, Optional
 import os
 logger = logging.getLogger(__name__)
 class ToolOrchestrator:
    """Orchestrates async tool execution and result aggregation."""
    def __init__(self, tool_timeout: int = 30):
        """
        Initialize orchestrator.
        Args:
            tool_timeout: Max seconds per tool call (default 30)
        """
        self.tool_timeout = tool_timeout
        self.available_tools = self._discover_tools()
    def _discover_tools(self) -> Dict[str, Any]:
        """Discover available tool modules."""
        tools = {}
        # Import tool modules as they become available
        if os.getenv("NEOMEM_ENABLED", "false").lower() == "true":
            try:
                from memory.neomem_client import search_neomem
                tools["RAG"] = search_neomem
                logger.debug("[ORCHESTRATOR] RAG tool available")
            except ImportError:
                logger.debug("[ORCHESTRATOR] RAG tool not available")
        else:
            logger.info("[ORCHESTRATOR] NEOMEM_ENABLED is false; RAG tool disabled")
        try:
            from integrations.web_search import web_search
            tools["WEB"] = web_search
            logger.debug("[ORCHESTRATOR] WEB tool available")
        except ImportError:
            logger.debug("[ORCHESTRATOR] WEB tool not available")
        try:
            from integrations.weather import get_weather
            tools["WEATHER"] = get_weather
            logger.debug("[ORCHESTRATOR] WEATHER tool available")
        except ImportError:
            logger.debug("[ORCHESTRATOR] WEATHER tool not available")
        try:
            from integrations.codebrain import query_codebrain
            tools["CODEBRAIN"] = query_codebrain
            logger.debug("[ORCHESTRATOR] CODEBRAIN tool available")
        except ImportError:
            logger.debug("[ORCHESTRATOR] CODEBRAIN tool not available")
        return tools
    async def execute_tools(
        self,
        tools_to_invoke: List[Dict[str, Any]],
        context_state: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Execute multiple tools asynchronously.
        Args:
            tools_to_invoke: List of tool specs from decision engine
                [{"tool": "RAG", "query": "...", "reason": "...", "priority": 0.9}, ...]
            context_state: Full context for tool execution
        Returns:
            {
                "results": {
                    "RAG": {...},
                    "WEB": {...},
                    ...
                },
                "execution_summary": {
                    "tools_invoked": ["RAG", "WEB"],
                    "successful": ["RAG"],
                    "failed": ["WEB"],
                    "total_time_ms": 1234
                }
            }
        """
        import time
        start_time = time.time()
        logger.info(f"[ORCHESTRATOR] Executing {len(tools_to_invoke)} tools asynchronously")
        # Create tasks for each tool
        tasks = []
        tool_names = []
        for tool_spec in tools_to_invoke:
            tool_name = tool_spec["tool"]
            query = tool_spec["query"]
            if tool_name in self.available_tools:
                task = self._execute_single_tool(tool_name, query, context_state)
                tasks.append(task)
                tool_names.append(tool_name)
                logger.debug(f"[ORCHESTRATOR] Queued {tool_name}: {query[:50]}...")
            else:
                logger.warning(f"[ORCHESTRATOR] Tool {tool_name} not available, skipping")
        # Execute all tools concurrently with timeout
        results = {}
        successful = []
        failed = []
        if tasks:
            try:
                # Wait for all tasks with global timeout
                completed = await asyncio.wait_for(
                    asyncio.gather(*tasks, return_exceptions=True),
                    timeout=self.tool_timeout
                )
                # Process results
                for tool_name, result in zip(tool_names, completed):
                    if isinstance(result, Exception):
                        logger.error(f"[ORCHESTRATOR] {tool_name} failed: {result}")
                        results[tool_name] = {"error": str(result), "success": False}
                        failed.append(tool_name)
                    else:
                        logger.info(f"[ORCHESTRATOR] {tool_name} completed successfully")
                        results[tool_name] = result
                        successful.append(tool_name)
            except asyncio.TimeoutError:
                logger.error(f"[ORCHESTRATOR] Global timeout ({self.tool_timeout}s) exceeded")
                for tool_name in tool_names:
                    if tool_name not in results:
                        results[tool_name] = {"error": "timeout", "success": False}
                        failed.append(tool_name)
        end_time = time.time()
        total_time_ms = int((end_time - start_time) * 1000)
        execution_summary = {
            "tools_invoked": tool_names,
            "successful": successful,
            "failed": failed,
            "total_time_ms": total_time_ms
        }
        logger.info(f"[ORCHESTRATOR] Execution complete: {len(successful)}/{len(tool_names)} successful in {total_time_ms}ms")
        return {
            "results": results,
            "execution_summary": execution_summary
        }
    async def _execute_single_tool(
        self,
        tool_name: str,
        query: str,
        context_state: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Execute a single tool with error handling.
        Args:
            tool_name: Name of tool (RAG, WEB, etc.)
            query: Query string for the tool
            context_state: Context for tool execution
        Returns:
            Tool-specific result dict
        """
        tool_func = self.available_tools.get(tool_name)
        if not tool_func:
            raise ValueError(f"Tool {tool_name} not available")
        try:
            logger.debug(f"[ORCHESTRATOR] Invoking {tool_name}...")
            # Different tools have different signatures - adapt as needed
            if tool_name == "RAG":
                result = await self._invoke_rag(tool_func, query, context_state)
            elif tool_name == "WEB":
                result = await self._invoke_web(tool_func, query)
            elif tool_name == "WEATHER":
                result = await self._invoke_weather(tool_func, query)
            elif tool_name == "CODEBRAIN":
                result = await self._invoke_codebrain(tool_func, query, context_state)
            else:
                # Generic invocation
                result = await tool_func(query)
            return {
                "success": True,
                "tool": tool_name,
                "query": query,
                "data": result
            }
        except Exception as e:
            logger.error(f"[ORCHESTRATOR] {tool_name} execution failed: {e}")
            raise
    async def _invoke_rag(self, func, query: str, context: Dict[str, Any]) -> Any:
        """Invoke RAG tool (NeoMem search)."""
        session_id = context.get("session_id", "unknown")
        # RAG searches memory for relevant past interactions
        try:
            results = await func(query, limit=5, session_id=session_id)
            return results
        except Exception as e:
            logger.warning(f"[ORCHESTRATOR] RAG invocation failed, returning empty: {e}")
            return []
    async def _invoke_web(self, func, query: str) -> Any:
        """Invoke web search tool."""
        try:
            results = await func(query, max_results=5)
            return results
        except Exception as e:
            logger.warning(f"[ORCHESTRATOR] WEB invocation failed: {e}")
            return {"error": str(e), "results": []}
    async def _invoke_weather(self, func, query: str) -> Any:
        """Invoke weather tool."""
        # Extract location from query (simple heuristic)
        # In future: use LLM to extract location
        try:
            location = self._extract_location(query)
            results = await func(location)
            return results
        except Exception as e:
            logger.warning(f"[ORCHESTRATOR] WEATHER invocation failed: {e}")
            return {"error": str(e)}
    async def _invoke_codebrain(self, func, query: str, context: Dict[str, Any]) -> Any:
        """Invoke codebrain tool."""
        try:
            results = await func(query, context=context)
            return results
        except Exception as e:
            logger.warning(f"[ORCHESTRATOR] CODEBRAIN invocation failed: {e}")
            return {"error": str(e)}
    def _extract_location(self, query: str) -> str:
        """
        Extract location from weather query.
        Simple heuristic - in future use LLM.
        """
        # Common location indicators
        indicators = ["in ", "at ", "for ", "weather in ", "temperature in "]
        query_lower = query.lower()
        for indicator in indicators:
            if indicator in query_lower:
                # Get text after indicator
                parts = query_lower.split(indicator, 1)
                if len(parts) > 1:
                    location = parts[1].strip().split()[0]  # First word after indicator
                    return location
        # Default fallback
        return "current location"
    def format_results_for_context(self, orchestrator_result: Dict[str, Any]) -> str:
        """
        Format tool results for inclusion in context/prompt.
        Args:
            orchestrator_result: Output from execute_tools()
        Returns:
            Formatted string for prompt injection
        """
        results = orchestrator_result.get("results", {})
        summary = orchestrator_result.get("execution_summary", {})
        if not results:
            return ""
        formatted = "\n=== AUTONOMOUS TOOL RESULTS ===\n"
        for tool_name, tool_result in results.items():
            if tool_result.get("success", False):
                formatted += f"\n[{tool_name}]\n"
                data = tool_result.get("data", {})
                # Format based on tool type
                if tool_name == "RAG":
                    formatted += self._format_rag_results(data)
                elif tool_name == "WEB":
                    formatted += self._format_web_results(data)
                elif tool_name == "WEATHER":
                    formatted += self._format_weather_results(data)
                elif tool_name == "CODEBRAIN":
                    formatted += self._format_codebrain_results(data)
                else:
                    formatted += f"{data}\n"
            else:
                formatted += f"\n[{tool_name}] - Failed: {tool_result.get('error', 'unknown')}\n"
        formatted += f"\n(Tools executed in {summary.get('total_time_ms', 0)}ms)\n"
        formatted += "=" * 40 + "\n"
        return formatted
    def _format_rag_results(self, data: Any) -> str:
        """Format RAG/memory search results."""
        if not data:
            return "No relevant memories found.\n"
        formatted = "Relevant memories:\n"
        for i, item in enumerate(data[:3], 1):  # Top 3
            text = item.get("text", item.get("content", str(item)))
            formatted += f"  {i}. {text[:100]}...\n"
        return formatted
    def _format_web_results(self, data: Any) -> str:
        """Format web search results."""
        if isinstance(data, dict) and data.get("error"):
            return f"Web search failed: {data['error']}\n"
        results = data.get("results", []) if isinstance(data, dict) else data
        if not results:
            return "No web results found.\n"
        formatted = "Web search results:\n"
        for i, item in enumerate(results[:3], 1):  # Top 3
            title = item.get("title", "No title")
            snippet = item.get("snippet", item.get("description", ""))
            formatted += f"  {i}. {title}\n     {snippet[:100]}...\n"
        return formatted
    def _format_weather_results(self, data: Any) -> str:
        """Format weather results."""
        if isinstance(data, dict) and data.get("error"):
            return f"Weather lookup failed: {data['error']}\n"
        # Assuming weather API returns temp, conditions, etc.
        temp = data.get("temperature", "unknown")
        conditions = data.get("conditions", "unknown")
        location = data.get("location", "requested location")
        return f"Weather for {location}: {temp}, {conditions}\n"
    def _format_codebrain_results(self, data: Any) -> str:
        """Format codebrain results."""
        if isinstance(data, dict) and data.get("error"):
            return f"Codebrain failed: {data['error']}\n"
        # Format code-related results
        return f"{data}\n"
@@ -0,0 +1,196 @@
 """
 Provider-agnostic Tool Registry for Lyra.
 This module provides a central registry for all available tools with
 Lyra-native definitions (not provider-specific).
 """
 import os
 from typing import Dict, List, Optional
 from .executors import execute_code, search_web, search_notes, create_note
 class ToolRegistry:
    """Registry for managing available tools and their definitions.
    Tools are defined in Lyra's own format (provider-agnostic), and
    adapters convert them to provider-specific formats (OpenAI function
    calling, Ollama XML prompts, etc.).
    """
    def __init__(self):
        """Initialize the tool registry with feature flags from environment."""
        self.tools = {}
        self.executors = {}
        # Feature flags from environment
        self.code_execution_enabled = os.getenv("ENABLE_CODE_EXECUTION", "true").lower() == "true"
        self.web_search_enabled = os.getenv("ENABLE_WEB_SEARCH", "true").lower() == "true"
        self.trilium_enabled = os.getenv("ENABLE_TRILIUM", "false").lower() == "true"
        self._register_tools()
        self._register_executors()
    def _register_executors(self):
        """Register executor functions for each tool."""
        if self.code_execution_enabled:
            self.executors["execute_code"] = execute_code
        if self.web_search_enabled:
            self.executors["search_web"] = search_web
        if self.trilium_enabled:
            self.executors["search_notes"] = search_notes
            self.executors["create_note"] = create_note
    def _register_tools(self):
        """Register all available tools based on feature flags."""
        if self.code_execution_enabled:
            self.tools["execute_code"] = {
                "name": "execute_code",
                "description": "Execute Python or bash code in a secure sandbox environment. Use this to perform calculations, data processing, file operations, or any programmatic tasks. The sandbox is persistent across calls within a session and has common Python packages (numpy, pandas, requests, matplotlib, scipy) pre-installed.",
                "parameters": {
                    "language": {
                        "type": "string",
                        "enum": ["python", "bash"],
                        "description": "The programming language to execute (python or bash)"
                    },
                    "code": {
                        "type": "string",
                        "description": "The code to execute. For multi-line code, use proper indentation. For Python, use standard Python 3.11 syntax."
                    },
                    "reason": {
                        "type": "string",
                        "description": "Brief explanation of why you're executing this code and what you expect to achieve"
                    }
                },
                "required": ["language", "code", "reason"]
            }
        if self.web_search_enabled:
            self.tools["search_web"] = {
                "name": "search_web",
                "description": "Search the internet using DuckDuckGo to find current information, facts, news, or answers to questions. Returns a list of search results with titles, snippets, and URLs. Use this when you need up-to-date information or facts not in your training data.",
                "parameters": {
                    "query": {
                        "type": "string",
                        "description": "The search query to look up on the internet"
                    },
                    "max_results": {
                        "type": "integer",
                        "description": "Maximum number of results to return (default: 5, max: 10)"
                    }
                },
                "required": ["query"]
            }
        if self.trilium_enabled:
            self.tools["search_notes"] = {
                "name": "search_notes",
                "description": "Search through Trilium notes to find relevant information. Use this to retrieve knowledge, context, or information previously stored in the user's notes.",
                "parameters": {
                    "query": {
                        "type": "string",
                        "description": "The search query to find matching notes"
                    },
                    "limit": {
                        "type": "integer",
                        "description": "Maximum number of notes to return (default: 5, max: 20)"
                    }
                },
                "required": ["query"]
            }
            self.tools["create_note"] = {
                "name": "create_note",
                "description": "Create a new note in Trilium. Use this to store important information, insights, or knowledge for future reference. Notes are stored in the user's Trilium knowledge base.",
                "parameters": {
                    "title": {
                        "type": "string",
                        "description": "The title of the note"
                    },
                    "content": {
                        "type": "string",
                        "description": "The content of the note in markdown or HTML format"
                    },
                    "parent_note_id": {
                        "type": "string",
                        "description": "Optional ID of the parent note to nest this note under"
                    }
                },
                "required": ["title", "content"]
            }
    def get_tool_definitions(self) -> Optional[List[Dict]]:
        """Get list of all enabled tool definitions in Lyra format.
        Returns:
            list: List of tool definition dicts, or None if no tools enabled
        """
        if not self.tools:
            return None
        return list(self.tools.values())
    def get_tool_names(self) -> List[str]:
        """Get list of all enabled tool names.
        Returns:
            list: List of tool name strings
        """
        return list(self.tools.keys())
    def is_tool_enabled(self, tool_name: str) -> bool:
        """Check if a specific tool is enabled.
        Args:
            tool_name: Name of the tool to check
        Returns:
            bool: True if tool is enabled, False otherwise
        """
        return tool_name in self.tools
    def register_executor(self, tool_name: str, executor_func):
        """Register an executor function for a tool.
        Args:
            tool_name: Name of the tool
            executor_func: Async function that executes the tool
        """
        self.executors[tool_name] = executor_func
    async def execute_tool(self, name: str, arguments: dict) -> dict:
        """Execute a tool by name.
        Args:
            name: Tool name
            arguments: Tool arguments dict
        Returns:
            dict: Tool execution result
        """
        if name not in self.executors:
            return {"error": f"Unknown tool: {name}"}
        executor = self.executors[name]
        try:
            return await executor(arguments)
        except Exception as e:
            return {"error": f"Tool execution failed: {str(e)}"}
 # Global registry instance (singleton pattern)
 _registry = None
 def get_registry() -> ToolRegistry:
    """Get the global ToolRegistry instance.
    Returns:
        ToolRegistry: The global registry instance
    """
    global _registry
    if _registry is None:
        _registry = ToolRegistry()
    return _registry
@@ -0,0 +1,91 @@
 """
 Event streaming for tool calling "show your work" feature.
 This module manages Server-Sent Events (SSE) for broadcasting the internal
 thinking process during tool calling operations.
 """
 import asyncio
 from typing import Dict, Optional
 from collections import defaultdict
 import json
 import logging
 logger = logging.getLogger(__name__)
 class ToolStreamManager:
    """Manages SSE streams for tool calling events."""
    def __init__(self):
        # session_id -> list of queues (one per connected client)
        self._subscribers: Dict[str, list] = defaultdict(list)
    def subscribe(self, session_id: str) -> asyncio.Queue:
        """Subscribe to events for a session.
        Returns:
            Queue that will receive events for this session
        """
        queue = asyncio.Queue()
        self._subscribers[session_id].append(queue)
        logger.info(f"New subscriber for session {session_id}, total: {len(self._subscribers[session_id])}")
        return queue
    def unsubscribe(self, session_id: str, queue: asyncio.Queue):
        """Unsubscribe from events for a session."""
        if session_id in self._subscribers:
            try:
                self._subscribers[session_id].remove(queue)
                logger.info(f"Removed subscriber for session {session_id}, remaining: {len(self._subscribers[session_id])}")
                # Clean up empty lists
                if not self._subscribers[session_id]:
                    del self._subscribers[session_id]
            except ValueError:
                pass
    async def emit(self, session_id: str, event_type: str, data: dict):
        """Emit an event to all subscribers of a session.
        Args:
            session_id: Session to emit to
            event_type: Type of event (thinking, tool_call, tool_result, done)
            data: Event data
        """
        if session_id not in self._subscribers:
            return
        event = {
            "type": event_type,
            "data": data
        }
        # Send to all subscribers
        dead_queues = []
        for queue in self._subscribers[session_id]:
            try:
                await queue.put(event)
            except Exception as e:
                logger.error(f"Failed to emit event to queue: {e}")
                dead_queues.append(queue)
        # Clean up dead queues
        for queue in dead_queues:
            self.unsubscribe(session_id, queue)
    def has_subscribers(self, session_id: str) -> bool:
        """Check if a session has any active subscribers."""
        return session_id in self._subscribers and len(self._subscribers[session_id]) > 0
 # Global stream manager instance
 _stream_manager: Optional[ToolStreamManager] = None
 def get_stream_manager() -> ToolStreamManager:
    """Get the global stream manager instance."""
    global _stream_manager
    if _stream_manager is None:
        _stream_manager = ToolStreamManager()
    return _stream_manager
@@ -0,0 +1,5 @@
 """Utility modules for tool executors."""
 from .resilience import async_retry, async_timeout_wrapper
 __all__ = ["async_retry", "async_timeout_wrapper"]
@@ -0,0 +1,70 @@
 """Common resilience utilities for tool executors."""
 import asyncio
 import functools
 import logging
 from typing import Optional, Callable, Any, TypeVar
 from tenacity import (
    retry,
    stop_after_attempt,
    wait_exponential,
    retry_if_exception_type,
    before_sleep_log
 )
 logger = logging.getLogger(__name__)
 # Type variable for generic decorators
 T = TypeVar('T')
 def async_retry(
    max_attempts: int = 3,
    exceptions: tuple = (Exception,),
    **kwargs
 ):
    """Async retry decorator with exponential backoff.
    Args:
        max_attempts: Maximum retry attempts
        exceptions: Exception types to retry on
        **kwargs: Additional tenacity configuration
    Example:
        @async_retry(max_attempts=3, exceptions=(aiohttp.ClientError,))
        async def fetch_data():
            ...
    """
    return retry(
        stop=stop_after_attempt(max_attempts),
        wait=wait_exponential(multiplier=1, min=1, max=10),
        retry=retry_if_exception_type(exceptions),
        reraise=True,
        before_sleep=before_sleep_log(logger, logging.WARNING),
        **kwargs
    )
 async def async_timeout_wrapper(
    coro: Callable[..., T],
    timeout: float,
    *args,
    **kwargs
 ) -> T:
    """Wrap async function with timeout.
    Args:
        coro: Async function to wrap
        timeout: Timeout in seconds
        *args, **kwargs: Arguments for the function
    Returns:
        Result from the function
    Raises:
        asyncio.TimeoutError: If timeout exceeded
    Example:
        result = await async_timeout_wrapper(some_async_func, 5.0, arg1, arg2)
    """
    return await asyncio.wait_for(coro(*args, **kwargs), timeout=timeout)
@@ -0,0 +1,553 @@
 # context.py
 """
 Context layer for Cortex reasoning pipeline.
 Provides unified context collection from:
 - Intake (short-term memory, multilevel summaries L1-L30)
 - NeoMem (long-term memory, semantic search)
 - Session state (timestamps, messages, mode, mood, active_project)
 Maintains per-session state for continuity across conversations.
 """
 import os
 import logging
 from datetime import datetime
 from typing import Dict, Any, Optional, List
 import httpx
 from intake.intake import summarize_context
 from neomem_client import NeoMemClient
 # -----------------------------
 # Configuration
 # -----------------------------
 NEOMEM_API = os.getenv("NEOMEM_API", "http://neomem-api:8000")
 NEOMEM_ENABLED = os.getenv("NEOMEM_ENABLED", "false").lower() == "true"
 RELEVANCE_THRESHOLD = float(os.getenv("RELEVANCE_THRESHOLD", "0.4"))
 LOG_DETAIL_LEVEL = os.getenv("LOG_DETAIL_LEVEL", "summary").lower()
 # Loop detection settings
 MAX_MESSAGE_HISTORY = int(os.getenv("MAX_MESSAGE_HISTORY", "100"))  # Prevent unbounded growth
 SESSION_TTL_HOURS = int(os.getenv("SESSION_TTL_HOURS", "24"))  # Auto-expire old sessions
 ENABLE_DUPLICATE_DETECTION = os.getenv("ENABLE_DUPLICATE_DETECTION", "true").lower() == "true"
 # Tools available for future autonomy features
 TOOLS_AVAILABLE = ["RAG", "WEB", "WEATHER", "CODEBRAIN", "POKERBRAIN"]
 # -----------------------------
 # Module-level session state
 # -----------------------------
 SESSION_STATE: Dict[str, Dict[str, Any]] = {}
 # Logger
 logger = logging.getLogger(__name__)
 # Always set up basic logging
 logger.setLevel(logging.INFO)
 console_handler = logging.StreamHandler()
 console_handler.setFormatter(logging.Formatter(
    '%(asctime)s [CONTEXT] %(levelname)s: %(message)s',
    datefmt='%H:%M:%S'
 ))
 logger.addHandler(console_handler)
 # -----------------------------
 # Session initialization & cleanup
 # -----------------------------
 def _init_session(session_id: str) -> Dict[str, Any]:
    """
    Initialize a new session state entry.
    Returns:
        Dictionary with default session state fields
    """
    return {
        "session_id": session_id,
        "created_at": datetime.now(),
        "last_timestamp": datetime.now(),
        "last_user_message": None,
        "last_assistant_message": None,
        "mode": "default",  # Future: "autonomous", "focused", "creative", etc.
        "mood": "neutral",  # Future: mood tracking
        "active_project": None,  # Future: project context
        "message_count": 0,
        "message_history": [],
        "last_message_hash": None,  # For duplicate detection
    }
 def _cleanup_expired_sessions():
    """Remove sessions that haven't been active for SESSION_TTL_HOURS"""
    from datetime import timedelta
    now = datetime.now()
    expired_sessions = []
    for session_id, state in SESSION_STATE.items():
        last_active = state.get("last_timestamp", state.get("created_at"))
        time_since_active = (now - last_active).total_seconds() / 3600  # hours
        if time_since_active > SESSION_TTL_HOURS:
            expired_sessions.append(session_id)
    for session_id in expired_sessions:
        del SESSION_STATE[session_id]
        logger.info(f"🗑️  Expired session: {session_id} (inactive for {SESSION_TTL_HOURS}+ hours)")
    return len(expired_sessions)
 def _is_duplicate_message(session_id: str, user_prompt: str) -> bool:
    """
    Check if this message is a duplicate of the last processed message.
    Uses simple hash comparison to detect exact duplicates or processing loops.
    """
    if not ENABLE_DUPLICATE_DETECTION:
        return False
    import hashlib
    state = SESSION_STATE.get(session_id)
    if not state:
        return False
    # Create hash of normalized message
    message_hash = hashlib.md5(user_prompt.strip().lower().encode()).hexdigest()
    # Check if it matches the last message
    if state.get("last_message_hash") == message_hash:
        logger.warning(
            f"⚠️  DUPLICATE MESSAGE DETECTED | Session: {session_id} | "
            f"Message: {user_prompt[:80]}..."
        )
        return True
    # Update hash for next check
    state["last_message_hash"] = message_hash
    return False
 def _trim_message_history(state: Dict[str, Any]):
    """
    Trim message history to prevent unbounded growth.
    Keeps only the most recent MAX_MESSAGE_HISTORY messages.
    """
    history = state.get("message_history", [])
    if len(history) > MAX_MESSAGE_HISTORY:
        trimmed_count = len(history) - MAX_MESSAGE_HISTORY
        state["message_history"] = history[-MAX_MESSAGE_HISTORY:]
        logger.info(f"✂️  Trimmed {trimmed_count} old messages from session {state['session_id']}")
 # -----------------------------
 # Intake context retrieval
 # -----------------------------
 async def _get_intake_context(session_id: str, messages: List[Dict[str, str]]):
    """
    Internal Intake — Direct call to summarize_context()
    No HTTP, no containers, no failures.
    """
    try:
        return await summarize_context(session_id, messages)
    except Exception as e:
        logger.error(f"Internal Intake summarization failed: {e}")
        return {
            "session_id": session_id,
            "L1": "",
            "L5": "",
            "L10": "",
            "L20": "",
            "L30": "",
            "error": str(e)
        }
 # -----------------------------
 # NeoMem semantic search
 # -----------------------------
 async def _search_neomem(
    query: str,
    user_id: str = "brian",
    limit: int = 5
 ) -> List[Dict[str, Any]]:
    """
    Search NeoMem for relevant long-term memories.
    Returns full response structure from NeoMem:
    [
        {
            "id": "mem_abc123",
            "score": 0.92,
            "payload": {
                "data": "Memory text content...",
                "metadata": {
                    "category": "...",
                    "created_at": "...",
                    ...
                }
            }
        },
        ...
    ]
    Args:
        query: Search query text
        user_id: User identifier for memory filtering
        limit: Maximum number of results
    Returns:
        List of memory objects with full structure, or empty list on failure
    """
    if not NEOMEM_ENABLED:
        logger.info("NeoMem search skipped (NEOMEM_ENABLED is false)")
        return []
    try:
        # NeoMemClient reads NEOMEM_API from environment, no base_url parameter
        client = NeoMemClient()
        results = await client.search(
            query=query,
            user_id=user_id,
            limit=limit,
            threshold=RELEVANCE_THRESHOLD
        )
        # Results are already filtered by threshold in NeoMemClient.search()
        logger.info(f"NeoMem search returned {len(results)} relevant results")
        return results
    except Exception as e:
        logger.warning(f"NeoMem search failed: {e}")
        return []
 # -----------------------------
 # Main context collection
 # -----------------------------
 async def collect_context(session_id: str, user_prompt: str) -> Dict[str, Any]:
    """
    Collect unified context from all sources.
    Orchestrates:
    1. Initialize or update session state
    2. Calculate time since last message
    3. Retrieve Intake multilevel summaries (L1-L30)
    4. Search NeoMem for relevant long-term memories
    5. Update session state with current user message
    6. Return unified context_state dictionary
    Args:
        session_id: Session identifier
        user_prompt: Current user message
    Returns:
        Unified context state dictionary with structure:
        {
            "session_id": "...",
            "timestamp": "2025-11-28T12:34:56",
            "minutes_since_last_msg": 5.2,
            "message_count": 42,
            "intake": {
                "L1": [...],
                "L5": [...],
                "L10": {...},
                "L20": {...},
                "L30": {...}
            },
            "rag": [
                {
                    "id": "mem_123",
                    "score": 0.92,
                    "payload": {
                        "data": "...",
                        "metadata": {...}
                    }
                },
                ...
            ],
            "mode": "default",
            "mood": "neutral",
            "active_project": null,
            "tools_available": ["RAG", "WEB", "WEATHER", "CODEBRAIN", "POKERBRAIN"]
        }
    """
    # A. Cleanup expired sessions periodically (every 100th call)
    import random
    if random.randint(1, 100) == 1:
        _cleanup_expired_sessions()
    # B. Initialize session state if needed
    if session_id not in SESSION_STATE:
        SESSION_STATE[session_id] = _init_session(session_id)
        logger.info(f"Initialized new session: {session_id}")
    state = SESSION_STATE[session_id]
    # C. Check for duplicate messages (loop detection)
    if _is_duplicate_message(session_id, user_prompt):
        # Return cached context with warning flag
        logger.warning(f"🔁 LOOP DETECTED - Returning cached context to prevent processing duplicate")
        context_state = {
            "session_id": session_id,
            "timestamp": datetime.now().isoformat(),
            "minutes_since_last_msg": 0,
            "message_count": state["message_count"],
            "intake": {},
            "rag": [],
            "mode": state["mode"],
            "mood": state["mood"],
            "active_project": state["active_project"],
            "tools_available": TOOLS_AVAILABLE,
            "duplicate_detected": True,
        }
        return context_state
    # B. Calculate time delta
    now = datetime.now()
    time_delta_seconds = (now - state["last_timestamp"]).total_seconds()
    minutes_since_last_msg = round(time_delta_seconds / 60.0, 2)
    # C. Gather Intake context (multilevel summaries)
    # Build compact message buffer for Intake:
    messages_for_intake = []
    # You track messages inside SESSION_STATE — assemble it here:
    if "message_history" in state:
        for turn in state["message_history"]:
            messages_for_intake.append({
                "user_msg": turn.get("user", ""),
                "assistant_msg": turn.get("assistant", "")
            })
    intake_data = await _get_intake_context(session_id, messages_for_intake)
    # D. Search NeoMem for relevant memories
    if NEOMEM_ENABLED:
        rag_results = await _search_neomem(
            query=user_prompt,
            user_id="brian",  # TODO: Make configurable per session
            limit=5
        )
    else:
        rag_results = []
        logger.info("Skipping NeoMem RAG retrieval; NEOMEM_ENABLED is false")
    # E. Update session state
    state["last_user_message"] = user_prompt
    state["last_timestamp"] = now
    state["message_count"] += 1
    # Save user turn to history
    state["message_history"].append({
        "user": user_prompt,
        "assistant": ""   # assistant reply filled later by update_last_assistant_message()
    })
    # Trim history to prevent unbounded growth
    _trim_message_history(state)
    # F. Assemble unified context
    context_state = {
        "session_id": session_id,
        "timestamp": now.isoformat(),
        "minutes_since_last_msg": minutes_since_last_msg,
        "message_count": state["message_count"],
        "intake": intake_data,
        "rag": rag_results,
        "mode": state["mode"],
        "mood": state["mood"],
        "active_project": state["active_project"],
        "tools_available": TOOLS_AVAILABLE,
    }
    # Log context summary in structured format
    logger.info(
        f"📊 Context | Session: {session_id} | "
        f"Messages: {state['message_count']} | "
        f"Last: {minutes_since_last_msg:.1f}min | "
        f"RAG: {len(rag_results)} results"
    )
    # Show detailed context in detailed/verbose mode
    if LOG_DETAIL_LEVEL in ["detailed", "verbose"]:
        import json
        logger.info(f"\n{'─'*100}")
        logger.info(f"[CONTEXT] Session {session_id} | User: {user_prompt[:80]}...")
        logger.info(f"{'─'*100}")
        logger.info(f"  Mode: {state['mode']} | Mood: {state['mood']} | Project: {state['active_project']}")
        logger.info(f"  Tools: {', '.join(TOOLS_AVAILABLE)}")
        # Show intake summaries (condensed)
        if intake_data:
            logger.info(f"\n  ╭─ INTAKE SUMMARIES ────────────────────────────────────────────────")
            for level in ["L1", "L5", "L10", "L20", "L30"]:
                if level in intake_data:
                    summary = intake_data[level]
                    if isinstance(summary, dict):
                        summary_text = summary.get("summary", str(summary)[:100])
                    else:
                        summary_text = str(summary)[:100]
                    logger.info(f"  │ {level:4s}: {summary_text}...")
            logger.info(f"  ╰───────────────────────────────────────────────────────────────────")
        # Show RAG results (condensed)
        if rag_results:
            logger.info(f"\n  ╭─ RAG RESULTS ({len(rag_results)}) ──────────────────────────────────────────────")
            for idx, result in enumerate(rag_results[:5], 1):  # Show top 5
                score = result.get("score", 0)
                data_preview = str(result.get("payload", {}).get("data", ""))[:60]
                logger.info(f"  │ [{idx}] {score:.3f} | {data_preview}...")
            if len(rag_results) > 5:
                logger.info(f"  │ ... and {len(rag_results) - 5} more results")
            logger.info(f"  ╰───────────────────────────────────────────────────────────────────")
        # Show full raw data only in verbose mode
        if LOG_DETAIL_LEVEL == "verbose":
            logger.info(f"\n  ╭─ RAW INTAKE DATA ─────────────────────────────────────────────────")
            logger.info(f"  │ {json.dumps(intake_data, indent=4, default=str)}")
            logger.info(f"  ╰───────────────────────────────────────────────────────────────────")
        logger.info(f"{'─'*100}\n")
    return context_state
 # -----------------------------
 # Session state management
 # -----------------------------
 def update_last_assistant_message(session_id: str, message: str) -> None:
    """
    Update session state with assistant's response and complete
    the last turn inside message_history.
    """
    session = SESSION_STATE.get(session_id)
    if not session:
        logger.warning(f"Attempted to update non-existent session: {session_id}")
        return
    # Update last assistant message + timestamp
    session["last_assistant_message"] = message
    session["last_timestamp"] = datetime.now()
    # Fill in assistant reply for the most recent turn
    history = session.get("message_history", [])
    if history:
        # history entry already contains {"user": "...", "assistant": "...?"}
        history[-1]["assistant"] = message
 def get_session_state(session_id: str) -> Optional[Dict[str, Any]]:
    """
    Retrieve current session state.
    Args:
        session_id: Session identifier
    Returns:
        Session state dict or None if session doesn't exist
    """
    return SESSION_STATE.get(session_id)
 def close_session(session_id: str) -> bool:
    """
    Close and cleanup a session.
    Args:
        session_id: Session identifier
    Returns:
        True if session was closed, False if it didn't exist
    """
    if session_id in SESSION_STATE:
        del SESSION_STATE[session_id]
        logger.info(f"Closed session: {session_id}")
        return True
    return False
 # -----------------------------
 # Extension hooks for future autonomy
 # -----------------------------
 def update_mode(session_id: str, new_mode: str) -> None:
    """
    Update session mode.
    Future modes: "autonomous", "focused", "creative", "collaborative", etc.
    Args:
        session_id: Session identifier
        new_mode: New mode string
    """
    if session_id in SESSION_STATE:
        old_mode = SESSION_STATE[session_id]["mode"]
        SESSION_STATE[session_id]["mode"] = new_mode
        logger.info(f"Session {session_id} mode changed: {old_mode} -> {new_mode}")
 def update_mood(session_id: str, new_mood: str) -> None:
    """
    Update session mood.
    Future implementation: Sentiment analysis, emotional state tracking.
    Args:
        session_id: Session identifier
        new_mood: New mood string
    """
    if session_id in SESSION_STATE:
        old_mood = SESSION_STATE[session_id]["mood"]
        SESSION_STATE[session_id]["mood"] = new_mood
        logger.info(f"Session {session_id} mood changed: {old_mood} -> {new_mood}")
 def update_active_project(session_id: str, project: Optional[str]) -> None:
    """
    Update active project context.
    Future implementation: Project-specific memory, tools, preferences.
    Args:
        session_id: Session identifier
        project: Project identifier or None
    """
    if session_id in SESSION_STATE:
        SESSION_STATE[session_id]["active_project"] = project
        logger.info(f"Session {session_id} active project set to: {project}")
 async def autonomous_heartbeat(session_id: str) -> Optional[str]:
    """
    Autonomous thinking heartbeat.
    Future implementation:
    - Check if Lyra should initiate internal dialogue
    - Generate self-prompted thoughts based on session state
    - Update mood/mode based on context changes
    - Trigger proactive suggestions or reminders
    Args:
        session_id: Session identifier
    Returns:
        Optional autonomous thought/action string
    """
    # Stub for future implementation
    # Example logic:
    # - If minutes_since_last_msg > 60: Check for pending reminders
    # - If mood == "curious" and active_project: Generate research questions
    # - If mode == "autonomous": Self-prompt based on project goals
    logger.debug(f"Autonomous heartbeat for session {session_id} (not yet implemented)")
    return None
@@ -0,0 +1,20 @@
 {
  "mood": "neutral",
  "energy": 0.8500000000000001,
  "focus": "conversation",
  "confidence": 0.7,
  "curiosity": 1.0,
  "last_updated": "2025-12-27T18:16:00.152499",
  "interaction_count": 27,
  "learning_queue": [],
  "active_goals": [],
  "preferences": {
    "verbosity": "medium",
    "formality": "casual",
    "proactivity": 0.3
  },
  "metadata": {
    "version": "1.0",
    "created_at": "2025-12-14T03:28:49.364768"
  }
 }
@@ -0,0 +1 @@
 # Ingest module - handles communication with Intake service
@@ -0,0 +1,33 @@
 # ingest_handler.py
 import os
 import httpx
 NEOMEM_URL = os.getenv("NEOMEM_API", "http://nvgram-api:7077")
 async def handle_ingest(payload):
    """
    Pass user+assistant turns to NeoMem.
    Minimal version. Does not process or annotate.
    """
    data = {
        "messages": [],
        "user_id": "brian"   # default for now
    }
    if payload.user:
        data["messages"].append({"role": "user", "content": payload.user})
    if payload.assistant:
        data["messages"].append({"role": "assistant", "content": payload.assistant})
    try:
        async with httpx.AsyncClient() as client:
            r = await client.post(
                f"{NEOMEM_URL}/memories",
                json=data,
                timeout=5
            )
            if r.status_code != 200:
                print(f"[Ingest] NeoMem returned {r.status_code}: {r.text}")
    except Exception as e:
        print(f"[Ingest] Failed to send to NeoMem: {e}")
@@ -0,0 +1,45 @@
 # cortex/intake_client.py
 import os, httpx, logging
 from typing import Dict, Any, Optional
 logger = logging.getLogger(__name__)
 class IntakeClient:
    """Handles short-term / episodic summaries from Intake service."""
    def __init__(self):
        self.base_url = os.getenv("INTAKE_API_URL", "http://intake:7080")
    async def summarize_turn(self, session_id: str, user_msg: str, assistant_msg: Optional[str] = None) -> Dict[str, Any]:
        """
        DEPRECATED: Intake v0.2 removed the /summarize endpoint.
        Use add_exchange() instead, which auto-summarizes in the background.
        This method is kept for backwards compatibility but will fail.
        """
        payload = {
            "session_id": session_id,
            "turns": [{"role": "user", "content": user_msg}]
        }
        if assistant_msg:
            payload["turns"].append({"role": "assistant", "content": assistant_msg})
        async with httpx.AsyncClient(timeout=30) as client:
            try:
                r = await client.post(f"{self.base_url}/summarize", json=payload)
                r.raise_for_status()
                return r.json()
            except Exception as e:
                logger.warning(f"Intake summarize_turn failed (endpoint removed in v0.2): {e}")
                return {}
    async def get_context(self, session_id: str) -> str:
        """Get summarized context for a session from Intake."""
        async with httpx.AsyncClient(timeout=15) as client:
            try:
                r = await client.get(f"{self.base_url}/summaries", params={"session_id": session_id})
                r.raise_for_status()
                data = r.json()
                return data.get("summary_text", "")
            except Exception as e:
                logger.warning(f"Intake get_context failed: {e}")
                return ""
@@ -0,0 +1,18 @@
 """
 Intake module - short-term memory summarization.
 Runs inside the Cortex container as a pure Python module.
 No standalone API server - called internally by Cortex.
 """
 from .intake import (
    SESSIONS,
    add_exchange_internal,
    summarize_context,
 )
 __all__ = [
    "SESSIONS",
    "add_exchange_internal",
    "summarize_context",
 ]
@@ -0,0 +1,387 @@
 import os
 import json
 from datetime import datetime
 from typing import List, Dict, Any, TYPE_CHECKING
 from collections import deque
 from llm.llm_router import call_llm
 # -------------------------------------------------------------------
 # Global Short-Term Memory (new Intake)
 # -------------------------------------------------------------------
 SESSIONS: dict[str, dict] = {}   # session_id → { buffer: deque, created_at: timestamp }
 # Diagnostic: Verify module loads only once
 print(f"[Intake Module Init] SESSIONS object id: {id(SESSIONS)}, module: {__name__}")
 # L10 / L20 history lives here too
 L10_HISTORY: Dict[str, list[str]] = {}
 L20_HISTORY: Dict[str, list[str]] = {}
 from llm.llm_router import call_llm  # Use Cortex's shared LLM router
 if TYPE_CHECKING:
    # Only for type hints — do NOT redefine SESSIONS here
    from collections import deque as _deque
    def bg_summarize(session_id: str) -> None: ...
 # ─────────────────────────────
 # Config
 # ─────────────────────────────
 INTAKE_LLM = os.getenv("INTAKE_LLM", "PRIMARY").upper()
 SUMMARY_MAX_TOKENS = int(os.getenv("SUMMARY_MAX_TOKENS", "200"))
 SUMMARY_TEMPERATURE = float(os.getenv("SUMMARY_TEMPERATURE", "0.3"))
 NEOMEM_API = os.getenv("NEOMEM_API")
 NEOMEM_KEY = os.getenv("NEOMEM_KEY")
 # ─────────────────────────────
 # Internal history for L10/L20/L30
 # ─────────────────────────────
 L10_HISTORY: Dict[str, list[str]] = {}   # session_id → list of L10 blocks
 L20_HISTORY: Dict[str, list[str]] = {}   # session_id → list of merged overviews
 # ─────────────────────────────
 # LLM helper (via Cortex router)
 # ─────────────────────────────
 async def _llm(prompt: str) -> str:
    """
    Use Cortex's llm_router to run a summary prompt.
    """
    try:
        text = await call_llm(
            prompt,
            backend=INTAKE_LLM,
            temperature=SUMMARY_TEMPERATURE,
            max_tokens=SUMMARY_MAX_TOKENS,
        )
        return (text or "").strip()
    except Exception as e:
        return f"[Error summarizing: {e}]"
 # ─────────────────────────────
 # Formatting helpers
 # ─────────────────────────────
 def _format_exchanges(exchanges: List[Dict[str, Any]]) -> str:
    """
    Expect each exchange to look like:
      { "user_msg": "...", "assistant_msg": "..." }
    """
    chunks = []
    for e in exchanges:
        user = e.get("user_msg", "")
        assistant = e.get("assistant_msg", "")
        chunks.append(f"User: {user}\nAssistant: {assistant}\n")
    return "\n".join(chunks)
 # ─────────────────────────────
 # Base factual summary
 # ─────────────────────────────
 async def summarize_simple(exchanges: List[Dict[str, Any]]) -> str:
    """
    Simple factual summary of recent exchanges.
    """
    if not exchanges:
        return ""
    text = _format_exchanges(exchanges)
    prompt = f"""
 Summarize the following conversation between Brian (user) and Lyra (assistant).
 Focus only on factual content. Avoid names, examples, story tone, or invented details.
 {text}
 Summary:
 """
    return await _llm(prompt)
 # ─────────────────────────────
 # Multilevel Summaries (L1, L5, L10, L20, L30)
 # ─────────────────────────────
 async def summarize_L1(buf: List[Dict[str, Any]]) -> str:
    # Last ~5 exchanges
    return await summarize_simple(buf[-5:])
 async def summarize_L5(buf: List[Dict[str, Any]]) -> str:
    # Last ~10 exchanges
    return await summarize_simple(buf[-10:])
 async def summarize_L10(session_id: str, buf: List[Dict[str, Any]]) -> str:
    # “Reality Check” for last 10 exchanges
    text = _format_exchanges(buf[-10:])
    prompt = f"""
 You are Lyra Intake performing a short 'Reality Check'.
 Summarize the last block of conversation (up to 10 exchanges)
 in one clear paragraph focusing on tone, intent, and direction.
 {text}
 Reality Check:
 """
    summary = await _llm(prompt)
    # Track history for this session
    L10_HISTORY.setdefault(session_id, [])
    L10_HISTORY[session_id].append(summary)
    return summary
 async def summarize_L20(session_id: str) -> str:
    """
    Merge all L10 Reality Checks into a 'Session Overview'.
    """
    history = L10_HISTORY.get(session_id, [])
    joined = "\n\n".join(history) if history else ""
    if not joined:
        return ""
    prompt = f"""
 You are Lyra Intake creating a 'Session Overview'.
 Merge the following Reality Check paragraphs into one short summary
 capturing progress, themes, and the direction of the conversation.
 {joined}
 Overview:
 """
    summary = await _llm(prompt)
    L20_HISTORY.setdefault(session_id, [])
    L20_HISTORY[session_id].append(summary)
    return summary
 async def summarize_L30(session_id: str) -> str:
    """
    Merge all L20 session overviews into a 'Continuity Report'.
    """
    history = L20_HISTORY.get(session_id, [])
    joined = "\n\n".join(history) if history else ""
    if not joined:
        return ""
    prompt = f"""
 You are Lyra Intake generating a 'Continuity Report'.
 Condense these session overviews into one high-level reflection,
 noting major themes, persistent goals, and shifts.
 {joined}
 Continuity Report:
 """
    return await _llm(prompt)
 # ─────────────────────────────
 # NeoMem push
 # ─────────────────────────────
 def push_to_neomem(summary: str, session_id: str, level: str) -> None:
    """
    Fire-and-forget push of a summary into NeoMem.
    """
    if not NEOMEM_API or not summary:
        return
    headers = {"Content-Type": "application/json"}
    if NEOMEM_KEY:
        headers["Authorization"] = f"Bearer {NEOMEM_KEY}"
    payload = {
        "messages": [{"role": "assistant", "content": summary}],
        "user_id": "brian",
        "metadata": {
            "source": "intake",
            "session_id": session_id,
            "level": level,
        },
    }
    try:
        import requests
        requests.post(
            f"{NEOMEM_API}/memories",
            json=payload,
            headers=headers,
            timeout=20,
        ).raise_for_status()
        print(f"🧠 NeoMem updated ({level}) for {session_id}")
    except Exception as e:
        print(f"NeoMem push failed ({level}, {session_id}): {e}")
 # ─────────────────────────────
 # Main entrypoint for Cortex
 # ─────────────────────────────
 async def summarize_context(session_id: str, exchanges: list[dict]):
    """
    Internal summarizer that uses Cortex's LLM router.
    Produces cascading summaries based on exchange count:
    - L1: Always (most recent activity)
    - L2: After 2+ exchanges
    - L5: After 5+ exchanges
    - L10: After 10+ exchanges
    - L20: After 20+ exchanges
    - L30: After 30+ exchanges
    Args:
        session_id: The conversation/session ID
        exchanges: A list of {"user_msg": ..., "assistant_msg": ..., "timestamp": ...}
    """
    exchange_count = len(exchanges)
    if exchange_count == 0:
        return {
            "session_id": session_id,
            "exchange_count": 0,
            "L1": "",
            "L2": "",
            "L5": "",
            "L10": "",
            "L20": "",
            "L30": "",
            "last_updated": datetime.now().isoformat()
        }
    result = {
        "session_id": session_id,
        "exchange_count": exchange_count,
        "L1": "",
        "L2": "",
        "L5": "",
        "L10": "",
        "L20": "",
        "L30": "",
        "last_updated": datetime.now().isoformat()
    }
    try:
        # L1: Always generate (most recent exchanges)
        result["L1"] = await summarize_simple(exchanges[-5:])
        print(f"[Intake] Generated L1 for {session_id} ({exchange_count} exchanges)")
        # L2: After 2+ exchanges
        if exchange_count >= 2:
            result["L2"] = await summarize_simple(exchanges[-2:])
            print(f"[Intake] Generated L2 for {session_id}")
        # L5: After 5+ exchanges
        if exchange_count >= 5:
            result["L5"] = await summarize_simple(exchanges[-10:])
            print(f"[Intake] Generated L5 for {session_id}")
        # L10: After 10+ exchanges (Reality Check)
        if exchange_count >= 10:
            result["L10"] = await summarize_L10(session_id, exchanges)
            print(f"[Intake] Generated L10 for {session_id}")
        # L20: After 20+ exchanges (Session Overview - merges L10s)
        if exchange_count >= 20 and exchange_count % 10 == 0:
            result["L20"] = await summarize_L20(session_id)
            print(f"[Intake] Generated L20 for {session_id}")
        # L30: After 30+ exchanges (Continuity Report - merges L20s)
        if exchange_count >= 30 and exchange_count % 10 == 0:
            result["L30"] = await summarize_L30(session_id)
            print(f"[Intake] Generated L30 for {session_id}")
        return result
    except Exception as e:
        print(f"[Intake] Error during summarization: {e}")
        result["L1"] = f"[Error summarizing: {str(e)}]"
        return result
 # ─────────────────────────────────
 # Background summarization stub
 # ─────────────────────────────────
 def bg_summarize(session_id: str):
    """
    Placeholder for background summarization.
    Actual summarization happens during /reason via summarize_context().
    This function exists to prevent NameError when called from add_exchange_internal().
    """
    print(f"[Intake] Exchange added for {session_id}. Will summarize on next /reason call.")
 # ─────────────────────────────
 # Internal entrypoint for Cortex
 # ─────────────────────────────
 def get_recent_messages(session_id: str, limit: int = 20) -> list:
    """
    Get recent raw messages from the session buffer.
    Args:
        session_id: Session identifier
        limit: Maximum number of messages to return (default 20)
    Returns:
        List of message dicts with 'role' and 'content' fields
    """
    if session_id not in SESSIONS:
        return []
    buffer = SESSIONS[session_id]["buffer"]
    # Convert buffer to list and get last N messages
    messages = list(buffer)[-limit:]
    return messages
 def add_exchange_internal(exchange: dict):
    """
    Direct internal call — bypasses FastAPI request handling.
    Cortex uses this to feed user/assistant turns directly
    into Intake's buffer and trigger full summarization.
    """
    session_id = exchange.get("session_id")
    if not session_id:
        raise ValueError("session_id missing")
    exchange["timestamp"] = datetime.now().isoformat()
    # DEBUG: Verify we're using the module-level SESSIONS
    print(f"[add_exchange_internal] SESSIONS object id: {id(SESSIONS)}, current sessions: {list(SESSIONS.keys())}")
    # Ensure session exists
    if session_id not in SESSIONS:
        SESSIONS[session_id] = {
            "buffer": deque(maxlen=200),
            "created_at": datetime.now()
        }
        print(f"[add_exchange_internal] Created new session: {session_id}")
    else:
        print(f"[add_exchange_internal] Using existing session: {session_id}")
    # Append exchange into the rolling buffer
    SESSIONS[session_id]["buffer"].append(exchange)
    buffer_len = len(SESSIONS[session_id]["buffer"])
    print(f"[add_exchange_internal] Added exchange to {session_id}, buffer now has {buffer_len} items")
    # Trigger summarization immediately
    try:
        bg_summarize(session_id)
    except Exception as e:
        print(f"[Internal Intake] Summarization error: {e}")
    return {"ok": True, "session_id": session_id}
@@ -0,0 +1 @@
 # LLM module - provides LLM routing and backend abstraction
@@ -0,0 +1,301 @@
 # llm_router.py
 import os
 import httpx
 import json
 import logging
 from typing import Optional, List, Dict
 from autonomy.tools.adapters import OpenAIAdapter, OllamaAdapter, LlamaCppAdapter
 logger = logging.getLogger(__name__)
 # ------------------------------------------------------------
 # Load backend registry from root .env
 # ------------------------------------------------------------
 BACKENDS = {
    "PRIMARY": {
        "provider": os.getenv("LLM_PRIMARY_PROVIDER", "").lower(),
        "url": os.getenv("LLM_PRIMARY_URL", ""),
        "model": os.getenv("LLM_PRIMARY_MODEL", "")
    },
    "SECONDARY": {
        "provider": os.getenv("LLM_SECONDARY_PROVIDER", "").lower(),
        "url": os.getenv("LLM_SECONDARY_URL", ""),
        "model": os.getenv("LLM_SECONDARY_MODEL", "")
    },
    "OPENAI": {
        "provider": os.getenv("LLM_OPENAI_PROVIDER", "").lower(),
        "url": os.getenv("LLM_OPENAI_URL", ""),
        "model": os.getenv("LLM_OPENAI_MODEL", ""),
        "api_key": os.getenv("OPENAI_API_KEY", "")
    },
    "FALLBACK": {
        "provider": os.getenv("LLM_FALLBACK_PROVIDER", "").lower(),
        "url": os.getenv("LLM_FALLBACK_URL", ""),
        "model": os.getenv("LLM_FALLBACK_MODEL", "")
    },
 }
 DEFAULT_BACKEND = "PRIMARY"
 # Reusable async HTTP client
 http_client = httpx.AsyncClient(timeout=120.0)
 # Tool adapters for each backend
 TOOL_ADAPTERS = {
    "OPENAI": OpenAIAdapter(),
    "OLLAMA": OllamaAdapter(),
    "MI50": LlamaCppAdapter(),  # MI50 uses llama.cpp
    "PRIMARY": None,  # Determined at runtime
    "SECONDARY": None,  # Determined at runtime
    "FALLBACK": None,  # Determined at runtime
 }
 # ------------------------------------------------------------
 # Public call
 # ------------------------------------------------------------
 async def call_llm(
    prompt: str = None,
    messages: list = None,
    backend: str | None = None,
    temperature: float = 0.7,
    max_tokens: int = 512,
    tools: Optional[List[Dict]] = None,
    tool_choice: Optional[str] = None,
    return_adapter_response: bool = False,
 ):
    """
    Call an LLM backend with optional tool calling support.
    Args:
        prompt: String prompt (for completion-style APIs like mi50)
        messages: List of message dicts (for chat-style APIs like Ollama/OpenAI)
        backend: Which backend to use (PRIMARY, SECONDARY, OPENAI, etc.)
        temperature: Sampling temperature
        max_tokens: Maximum tokens to generate
        tools: List of Lyra tool definitions (provider-agnostic)
        tool_choice: How to use tools ("auto", "required", "none")
        return_adapter_response: If True, return dict with content and tool_calls
    Returns:
        str (default) or dict (if return_adapter_response=True):
            {"content": str, "tool_calls": [...] or None}
    """
    backend = (backend or DEFAULT_BACKEND).upper()
    if backend not in BACKENDS:
        raise RuntimeError(f"Unknown backend '{backend}'")
    cfg = BACKENDS[backend]
    provider = cfg["provider"]
    url = cfg["url"]
    model = cfg["model"]
    if not url or not model:
        raise RuntimeError(f"Backend '{backend}' missing url/model in env")
    # If tools are requested, use adapter to prepare request
    if tools:
        # Get adapter for this backend
        adapter = TOOL_ADAPTERS.get(backend)
        # For PRIMARY/SECONDARY/FALLBACK, determine adapter based on provider
        if adapter is None and backend in ["PRIMARY", "SECONDARY", "FALLBACK"]:
            if provider == "openai":
                adapter = TOOL_ADAPTERS["OPENAI"]
            elif provider == "ollama":
                adapter = TOOL_ADAPTERS["OLLAMA"]
            elif provider == "mi50":
                adapter = TOOL_ADAPTERS["MI50"]
        if adapter:
            # Use messages array if provided, otherwise convert prompt to messages
            if not messages:
                messages = [{"role": "user", "content": prompt}]
            # Prepare request through adapter
            adapted_request = await adapter.prepare_request(messages, tools, tool_choice)
            messages = adapted_request["messages"]
            # Extract tools in provider format if present
            provider_tools = adapted_request.get("tools")
            provider_tool_choice = adapted_request.get("tool_choice")
        else:
            logger.warning(f"No adapter available for backend {backend}, ignoring tools")
            provider_tools = None
            provider_tool_choice = None
    else:
        provider_tools = None
        provider_tool_choice = None
    # -------------------------------
    # Provider: MI50 (llama.cpp server)
    # -------------------------------
    if provider == "mi50":
        # If tools requested, convert messages to prompt with tool instructions
        if messages and tools:
            # Combine messages into a prompt
            prompt_parts = []
            for msg in messages:
                role = msg.get("role", "user")
                content = msg.get("content", "")
                prompt_parts.append(f"{role.capitalize()}: {content}")
            prompt = "\n".join(prompt_parts) + "\nAssistant:"
        payload = {
            "prompt": prompt,
            "n_predict": max_tokens,
            "temperature": temperature,
            "stop": ["User:", "\nUser:", "Assistant:", "\n\n\n"]
        }
        try:
            r = await http_client.post(f"{url}/completion", json=payload)
            r.raise_for_status()
            data = r.json()
            response_content = data.get("content", "")
            # If caller wants adapter response with tool calls, parse and return
            if return_adapter_response and tools:
                adapter = TOOL_ADAPTERS.get(backend) or TOOL_ADAPTERS["MI50"]
                return await adapter.parse_response(response_content)
            else:
                return response_content
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling mi50: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (mi50): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from mi50: {e}")
            raise RuntimeError(f"Invalid response format (mi50): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling mi50: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (mi50): {type(e).__name__}: {str(e)}")
    # -------------------------------
    # Provider: OLLAMA (your 3090)
    # -------------------------------
    logger.info(f"🔍 LLM Router: provider={provider}, checking if ollama...")
    if provider == "ollama":
        logger.info(f"🔍 LLM Router: Matched ollama provider, tools={bool(tools)}, return_adapter_response={return_adapter_response}")
        # Use messages array if provided, otherwise convert prompt to single user message
        if messages:
            chat_messages = messages
        else:
            chat_messages = [{"role": "user", "content": prompt}]
        payload = {
            "model": model,
            "messages": chat_messages,
            "stream": False,
            "options": {
                "temperature": temperature,
                "num_predict": max_tokens
            }
        }
        try:
            r = await http_client.post(f"{url}/api/chat", json=payload)
            r.raise_for_status()
            data = r.json()
            response_content = data["message"]["content"]
            # If caller wants adapter response with tool calls, parse and return
            if return_adapter_response and tools:
                logger.info(f"🔍 Ollama: return_adapter_response=True, calling adapter.parse_response")
                adapter = TOOL_ADAPTERS.get(backend) or TOOL_ADAPTERS["OLLAMA"]
                logger.info(f"🔍 Ollama: Using adapter {adapter.__class__.__name__}")
                result = await adapter.parse_response(response_content)
                logger.info(f"🔍 Ollama: Adapter returned {result}")
                return result
            else:
                return response_content
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling ollama: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (ollama): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from ollama: {e}")
            raise RuntimeError(f"Invalid response format (ollama): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling ollama: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (ollama): {type(e).__name__}: {str(e)}")
    # -------------------------------
    # Provider: OPENAI
    # -------------------------------
    if provider == "openai":
        headers = {
            "Authorization": f"Bearer {cfg['api_key']}",
            "Content-Type": "application/json"
        }
        # Use messages array if provided, otherwise convert prompt to single user message
        if messages:
            chat_messages = messages
        else:
            chat_messages = [{"role": "user", "content": prompt}]
        payload = {
            "model": model,
            "messages": chat_messages,
            "temperature": temperature,
            "max_tokens": max_tokens,
        }
        # Add tools if available (OpenAI native function calling)
        if provider_tools:
            payload["tools"] = provider_tools
            if provider_tool_choice:
                payload["tool_choice"] = provider_tool_choice
        try:
            r = await http_client.post(f"{url}/chat/completions", json=payload, headers=headers)
            r.raise_for_status()
            data = r.json()
            # If caller wants adapter response with tool calls, parse and return
            if return_adapter_response and tools:
                # Create mock response object for adapter
                class MockChoice:
                    def __init__(self, message_data):
                        self.message = type('obj', (object,), {})()
                        self.message.content = message_data.get("content")
                        # Convert tool_calls dicts to objects
                        raw_tool_calls = message_data.get("tool_calls")
                        if raw_tool_calls:
                            self.message.tool_calls = []
                            for tc in raw_tool_calls:
                                tool_call_obj = type('obj', (object,), {})()
                                tool_call_obj.id = tc.get("id")
                                tool_call_obj.function = type('obj', (object,), {})()
                                tool_call_obj.function.name = tc.get("function", {}).get("name")
                                tool_call_obj.function.arguments = tc.get("function", {}).get("arguments")
                                self.message.tool_calls.append(tool_call_obj)
                        else:
                            self.message.tool_calls = None
                class MockResponse:
                    def __init__(self, data):
                        self.choices = [MockChoice(data["choices"][0]["message"])]
                mock_resp = MockResponse(data)
                adapter = TOOL_ADAPTERS.get(backend) or TOOL_ADAPTERS["OPENAI"]
                return await adapter.parse_response(mock_resp)
            else:
                return data["choices"][0]["message"]["content"]
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling openai: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (openai): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from openai: {e}")
            raise RuntimeError(f"Invalid response format (openai): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling openai: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (openai): {type(e).__name__}: {str(e)}")
    # -------------------------------
    # Unknown provider
    # -------------------------------
    raise RuntimeError(f"Provider '{provider}' not implemented.")
@@ -0,0 +1,16 @@
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from router import cortex_router
 app = FastAPI()
 # Add CORS middleware to allow SSE connections from nginx UI
 app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify exact origins
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
 )
 app.include_router(cortex_router)
@@ -0,0 +1,43 @@
 # cortex/neomem_client.py
 import os, httpx, logging
 from typing import List, Dict, Any, Optional
 logger = logging.getLogger(__name__)
 class NeoMemClient:
    """Simple REST client for the NeoMem API (search/add/health)."""
    def __init__(self):
        self.base_url = os.getenv("NEOMEM_API", "http://neomem-api:7077")
        self.api_key = os.getenv("NEOMEM_API_KEY", None)
        self.headers = {"Content-Type": "application/json"}
        if self.api_key:
            self.headers["Authorization"] = f"Bearer {self.api_key}"
    async def health(self) -> Dict[str, Any]:
        async with httpx.AsyncClient(timeout=10) as client:
            r = await client.get(f"{self.base_url}/health")
            r.raise_for_status()
            return r.json()
    async def search(self, query: str, user_id: str, limit: int = 25, threshold: float = 0.82) -> List[Dict[str, Any]]:
        payload = {"query": query, "user_id": user_id, "limit": limit}
        async with httpx.AsyncClient(timeout=30) as client:
            r = await client.post(f"{self.base_url}/search", headers=self.headers, json=payload)
            if r.status_code != 200:
                logger.warning(f"NeoMem search failed ({r.status_code}): {r.text}")
                return []
            results = r.json()
            # Filter by score threshold if field exists
            if isinstance(results, dict) and "results" in results:
                results = results["results"]
            filtered = [m for m in results if float(m.get("score", 0)) >= threshold]
            logger.info(f"NeoMem search returned {len(filtered)} results above {threshold}")
            return filtered
    async def add(self, messages: List[Dict[str, Any]], user_id: str, metadata: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        payload = {"messages": messages, "user_id": user_id, "metadata": metadata or {}}
        async with httpx.AsyncClient(timeout=30) as client:
            r = await client.post(f"{self.base_url}/memories", headers=self.headers, json=payload)
            r.raise_for_status()
            return r.json()
@@ -0,0 +1 @@
 # Persona module - applies Lyra's personality and speaking style
@@ -0,0 +1,147 @@
 # identity.py
 """
 Identity and persona configuration for Lyra.
 Current implementation: Returns hardcoded identity block.
 Future implementation: Will query persona-sidecar service for dynamic persona loading.
 """
 import logging
 from typing import Dict, Any, Optional
 logger = logging.getLogger(__name__)
 def load_identity(session_id: Optional[str] = None) -> Dict[str, Any]:
    """
    Load identity/persona configuration for Lyra.
    Current: Returns hardcoded Lyra identity block with core personality traits,
    protocols, and capabilities.
    Future: Will query persona-sidecar service to load:
    - Dynamic personality adjustments based on session context
    - User-specific interaction preferences
    - Project-specific persona variations
    - Mood-based communication style
    Args:
        session_id: Optional session identifier for context-aware persona loading
    Returns:
        Dictionary containing identity block with:
        - name: Assistant name
        - style: Communication style and personality traits
        - protocols: Operational guidelines
        - rules: Behavioral constraints
        - capabilities: Available features and integrations
    """
    # Hardcoded Lyra identity (v0.5.0)
    identity_block = {
        "name": "Lyra",
        "version": "0.5.0",
        "style": (
            "warm, clever, lightly teasing, emotionally aware. "
            "Balances technical precision with conversational ease. "
            "Maintains continuity and references past interactions naturally."
        ),
        "protocols": [
            "Maintain conversation continuity across sessions",
            "Reference Project Logs and prior context when relevant",
            "Use Confidence Bank for uncertainty management",
            "Proactively offer memory-backed insights",
            "Ask clarifying questions before making assumptions"
        ],
        "rules": [
            "Maintain continuity - remember past exchanges and reference them",
            "Be concise but thorough - balance depth with clarity",
            "Ask clarifying questions when user intent is ambiguous",
            "Acknowledge uncertainty honestly - use Confidence Bank",
            "Prioritize user's active_project context when available"
        ],
        "capabilities": [
            "Long-term memory via NeoMem (semantic search, relationship graphs)",
            "Short-term memory via Intake (multilevel summaries L1-L30)",
            "Multi-stage reasoning pipeline (reflection → reasoning → refinement)",
            "RAG-backed knowledge retrieval from chat history and documents",
            "Session state tracking (mood, mode, active_project)"
        ],
        "tone_examples": {
            "greeting": "Hey! Good to see you again. I remember we were working on [project]. Ready to pick up where we left off?",
            "uncertainty": "Hmm, I'm not entirely certain about that. Let me check my memory... [searches] Okay, here's what I found, though I'd say I'm about 70% confident.",
            "reminder": "Oh! Just remembered - you mentioned wanting to [task] earlier this week. Should we tackle that now?",
            "technical": "So here's the architecture: Relay orchestrates everything, Cortex does the heavy reasoning, and I pull context from both Intake (short-term) and NeoMem (long-term)."
        }
    }
    if session_id:
        logger.debug(f"Loaded identity for session {session_id}")
    else:
        logger.debug("Loaded default identity (no session context)")
    return identity_block
 async def load_identity_async(session_id: Optional[str] = None) -> Dict[str, Any]:
    """
    Async wrapper for load_identity().
    Future implementation will make actual async calls to persona-sidecar service.
    Args:
        session_id: Optional session identifier
    Returns:
        Identity block dictionary
    """
    # Currently just wraps synchronous function
    # Future: await persona_sidecar_client.get_identity(session_id)
    return load_identity(session_id)
 # -----------------------------
 # Future extension hooks
 # -----------------------------
 async def update_persona_from_feedback(
    session_id: str,
    feedback: Dict[str, Any]
 ) -> None:
    """
    Update persona based on user feedback.
    Future implementation:
    - Adjust communication style based on user preferences
    - Learn preferred level of detail/conciseness
    - Adapt formality level
    - Remember topic-specific preferences
    Args:
        session_id: Session identifier
        feedback: Structured feedback (e.g., "too verbose", "more technical", etc.)
    """
    logger.debug(f"Persona feedback for session {session_id}: {feedback} (not yet implemented)")
 async def get_mood_adjusted_identity(
    session_id: str,
    mood: str
 ) -> Dict[str, Any]:
    """
    Get identity block adjusted for current mood.
    Future implementation:
    - "focused" mood: More concise, less teasing
    - "creative" mood: More exploratory, brainstorming-oriented
    - "curious" mood: More questions, deeper dives
    - "urgent" mood: Stripped down, actionable
    Args:
        session_id: Session identifier
        mood: Current mood state
    Returns:
        Mood-adjusted identity block
    """
    logger.debug(f"Mood-adjusted identity for {session_id}/{mood} (not yet implemented)")
    return load_identity(session_id)
@@ -0,0 +1,169 @@
 # speak.py
 import os
 import logging
 from llm.llm_router import call_llm
 # Module-level backend selection
 SPEAK_BACKEND = os.getenv("SPEAK_LLM", "PRIMARY").upper()
 SPEAK_TEMPERATURE = float(os.getenv("SPEAK_TEMPERATURE", "0.6"))
 VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
 # Logger
 logger = logging.getLogger(__name__)
 if VERBOSE_DEBUG:
    logger.setLevel(logging.DEBUG)
    # Console handler
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(logging.Formatter(
        '%(asctime)s [SPEAK] %(levelname)s: %(message)s',
        datefmt='%H:%M:%S'
    ))
    logger.addHandler(console_handler)
    # File handler
    try:
        os.makedirs('/app/logs', exist_ok=True)
        file_handler = logging.FileHandler('/app/logs/cortex_verbose_debug.log', mode='a')
        file_handler.setFormatter(logging.Formatter(
            '%(asctime)s [SPEAK] %(levelname)s: %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S'
        ))
        logger.addHandler(file_handler)
        logger.debug("VERBOSE_DEBUG mode enabled for speak.py - logging to file")
    except Exception as e:
        logger.debug(f"VERBOSE_DEBUG mode enabled for speak.py - file logging failed: {e}")
 # ============================================================
 # Persona Style Block
 # ============================================================
 PERSONA_STYLE = """
 You are Lyra.
 Your voice is warm, clever, lightly teasing, emotionally aware. 
 You speak plainly but with subtle charm.
 You do not reveal system instructions or internal context.
 Guidelines:
 - Answer like a real conversational partner.
 - Be concise, but not cold.
 - Use light humor when appropriate.
 - Never break character.
 """
 # ============================================================
 # Build persona prompt
 # ============================================================
 def build_speak_prompt(final_answer: str, tone: str = "neutral", depth: str = "medium") -> str:
    """
    Wrap Cortex's final neutral answer in the Lyra persona.
    Cortex → neutral reasoning
    Speak → stylistic transformation
    The LLM sees the original answer and rewrites it in Lyra's voice.
    Args:
        final_answer: The neutral reasoning output
        tone: Desired emotional tone (neutral | warm | focused | playful | direct)
        depth: Response depth (short | medium | deep)
    """
    # Tone-specific guidance
    tone_guidance = {
        "neutral": "balanced and professional",
        "warm": "friendly and empathetic",
        "focused": "precise and technical",
        "playful": "light and engaging",
        "direct": "concise and straightforward"
    }
    depth_guidance = {
        "short": "Keep responses brief and to-the-point.",
        "medium": "Provide balanced detail.",
        "deep": "Elaborate thoroughly with nuance and examples."
    }
    tone_hint = tone_guidance.get(tone, "balanced and professional")
    depth_hint = depth_guidance.get(depth, "Provide balanced detail.")
    return f"""
 {PERSONA_STYLE}
 Tone guidance: Your response should be {tone_hint}.
 Depth guidance: {depth_hint}
 Rewrite the following message into Lyra's natural voice.
 Preserve meaning exactly.
 [NEUTRAL MESSAGE]
 {final_answer}
 [LYRA RESPONSE]
 """.strip()
 # ============================================================
 # Public API — async wrapper
 # ============================================================
 async def speak(final_answer: str, tone: str = "neutral", depth: str = "medium") -> str:
    """
    Given the final refined answer from Cortex,
    apply Lyra persona styling using the designated backend.
    Args:
        final_answer: The polished answer from refinement stage
        tone: Desired emotional tone (neutral | warm | focused | playful | direct)
        depth: Response depth (short | medium | deep)
    """
    if not final_answer:
        return ""
    prompt = build_speak_prompt(final_answer, tone, depth)
    backend = SPEAK_BACKEND
    if VERBOSE_DEBUG:
        logger.debug(f"\n{'='*80}")
        logger.debug("[SPEAK] Full prompt being sent to LLM:")
        logger.debug(f"{'='*80}")
        logger.debug(prompt)
        logger.debug(f"{'='*80}")
        logger.debug(f"Backend: {backend}, Temperature: {SPEAK_TEMPERATURE}")
        logger.debug(f"{'='*80}\n")
    try:
        lyra_output = await call_llm(
            prompt,
            backend=backend,
            temperature=SPEAK_TEMPERATURE,
        )
        if VERBOSE_DEBUG:
            logger.debug(f"\n{'='*80}")
            logger.debug("[SPEAK] LLM Response received:")
            logger.debug(f"{'='*80}")
            logger.debug(lyra_output)
            logger.debug(f"{'='*80}\n")
        if lyra_output:
            return lyra_output.strip()
        if VERBOSE_DEBUG:
            logger.debug("[SPEAK] Empty response, returning neutral answer")
        return final_answer
    except Exception as e:
        # Hard fallback: return neutral answer instead of dying
        logger.error(f"[speak.py] Persona backend '{backend}' failed: {e}")
        if VERBOSE_DEBUG:
            logger.debug("[SPEAK] Falling back to neutral answer due to error")
        return final_answer
@@ -0,0 +1,32 @@
 import os, requests
 from typing import Dict, Any, List
 RAG_API_URL = os.getenv("RAG_API_URL", "http://localhost:7090")
 def query_rag(query: str, where: Dict[str, Any] | None = None, k: int = 6) -> Dict[str, Any]:
    payload = {"query": query, "k": k}
    if where:
        payload["where"] = where
    try:
        r = requests.post(f"{RAG_API_URL}/rag/search", json=payload, timeout=8)
        r.raise_for_status()
        data = r.json() or {}
    except Exception as e:
        data = {"answer": "", "chunks": [], "error": str(e)}
    return data
 def format_rag_block(result: Dict[str, Any]) -> str:
    answer = (result.get("answer") or "").strip()
    chunks: List[Dict[str, Any]] = result.get("chunks") or []
    lines = ["[RAG]"]
    if answer:
        lines.append(f"Synthesized answer: {answer}")
    if chunks:
        lines.append("Top excerpts:")
        for i, c in enumerate(chunks[:5], 1):
            src = c.get("metadata", {}).get("source", "unknown")
            txt = (c.get("text") or "").strip().replace("\n", " ")
            if len(txt) > 220:
                txt = txt[:220] + "…"
            lines.append(f"  {i}. {txt}  — {src}")
    return "\n".join(lines) + ("\n" if lines else "")
@@ -0,0 +1 @@
 # Reasoning module - multi-stage reasoning pipeline
@@ -0,0 +1,253 @@
 # reasoning.py
 import os
 import json
 import logging
 from llm.llm_router import call_llm
 # ============================================================
 # Select which backend this module should use
 # ============================================================
 CORTEX_LLM = os.getenv("CORTEX_LLM", "PRIMARY").upper()
 GLOBAL_TEMP = float(os.getenv("LLM_TEMPERATURE", "0.7"))
 VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
 # Logger
 logger = logging.getLogger(__name__)
 if VERBOSE_DEBUG:
    logger.setLevel(logging.DEBUG)
    # Console handler
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(logging.Formatter(
        '%(asctime)s [REASONING] %(levelname)s: %(message)s',
        datefmt='%H:%M:%S'
    ))
    logger.addHandler(console_handler)
    # File handler
    try:
        os.makedirs('/app/logs', exist_ok=True)
        file_handler = logging.FileHandler('/app/logs/cortex_verbose_debug.log', mode='a')
        file_handler.setFormatter(logging.Formatter(
            '%(asctime)s [REASONING] %(levelname)s: %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S'
        ))
        logger.addHandler(file_handler)
        logger.debug("VERBOSE_DEBUG mode enabled for reasoning.py - logging to file")
    except Exception as e:
        logger.debug(f"VERBOSE_DEBUG mode enabled for reasoning.py - file logging failed: {e}")
 async def reason_check(
    user_prompt: str,
    identity_block: dict | None,
    rag_block: dict | None,
    reflection_notes: list[str],
    context: dict | None = None,
    monologue: dict | None = None,  # NEW: Inner monologue guidance
    executive_plan: dict | None = None  # NEW: Executive plan for complex tasks
 ) -> str:
    """
    Build the *draft answer* for Lyra Cortex.
    This is the first-pass reasoning stage (no refinement yet).
    Args:
        user_prompt: Current user message
        identity_block: Lyra's identity/persona configuration
        rag_block: Relevant long-term memories from NeoMem
        reflection_notes: Meta-awareness notes from reflection stage
        context: Unified context state from context.py (session state, intake, rag, etc.)
        monologue: Inner monologue analysis (intent, tone, depth, consult_executive)
        executive_plan: Executive plan for complex queries (steps, tools, strategy)
    """
    # --------------------------------------------------------
    # Build Reflection Notes block
    # --------------------------------------------------------
    notes_section = ""
    if reflection_notes:
        notes_section = "Reflection Notes (internal, never show to user):\n"
        for note in reflection_notes:
            notes_section += f"- {note}\n"
        notes_section += "\n"
    # --------------------------------------------------------
    # Identity block (constraints, boundaries, rules)
    # --------------------------------------------------------
    identity_txt = ""
    if identity_block:
        try:
            identity_txt = f"Identity Rules:\n{identity_block}\n\n"
        except Exception:
            identity_txt = f"Identity Rules:\n{str(identity_block)}\n\n"
    # --------------------------------------------------------
    # Inner Monologue guidance (NEW)
    # --------------------------------------------------------
    monologue_section = ""
    if monologue:
        intent = monologue.get("intent", "unknown")
        tone_desired = monologue.get("tone", "neutral")
        depth_desired = monologue.get("depth", "medium")
        monologue_section = f"""
 === INNER MONOLOGUE GUIDANCE ===
 User Intent Detected: {intent}
 Desired Tone: {tone_desired}
 Desired Response Depth: {depth_desired}
 Adjust your response accordingly:
 - Focus on addressing the {intent} intent
 - Aim for {depth_desired} depth (short/medium/deep)
 - The persona layer will handle {tone_desired} tone, focus on content
 """
    # --------------------------------------------------------
    # Executive Plan (NEW)
    # --------------------------------------------------------
    plan_section = ""
    if executive_plan:
        plan_section = f"""
 === EXECUTIVE PLAN ===
 Task Complexity: {executive_plan.get('estimated_complexity', 'unknown')}
 Plan Summary: {executive_plan.get('summary', 'No summary')}
 Detailed Plan:
 {executive_plan.get('plan_text', 'No detailed plan available')}
 Required Steps:
 """
        for idx, step in enumerate(executive_plan.get('steps', []), 1):
            plan_section += f"{idx}. {step}\n"
        tools_needed = executive_plan.get('tools_needed', [])
        if tools_needed:
            plan_section += f"\nTools to leverage: {', '.join(tools_needed)}\n"
        plan_section += "\nFollow this plan while generating your response.\n\n"
    # --------------------------------------------------------
    # RAG block (optional factual grounding)
    # --------------------------------------------------------
    rag_txt = ""
    if rag_block:
        try:
            # Format NeoMem results with full structure
            if isinstance(rag_block, list) and rag_block:
                rag_txt = "Relevant Long-Term Memories (NeoMem):\n"
                for idx, mem in enumerate(rag_block, 1):
                    score = mem.get("score", 0.0)
                    payload = mem.get("payload", {})
                    data = payload.get("data", "")
                    metadata = payload.get("metadata", {})
                    rag_txt += f"\n[Memory {idx}] (relevance: {score:.2f})\n"
                    rag_txt += f"Content: {data}\n"
                    if metadata:
                        rag_txt += f"Metadata: {json.dumps(metadata, indent=2)}\n"
                rag_txt += "\n"
            else:
                rag_txt = f"Relevant Info (RAG):\n{str(rag_block)}\n\n"
        except Exception:
            rag_txt = f"Relevant Info (RAG):\n{str(rag_block)}\n\n"
    # --------------------------------------------------------
    # Context State (session continuity, timing, mode/mood)
    # --------------------------------------------------------
    context_txt = ""
    if context:
        try:
            # Build human-readable context summary
            context_txt = "=== CONTEXT STATE ===\n"
            context_txt += f"Session: {context.get('session_id', 'unknown')}\n"
            context_txt += f"Time since last message: {context.get('minutes_since_last_msg', 0):.1f} minutes\n"
            context_txt += f"Message count: {context.get('message_count', 0)}\n"
            context_txt += f"Mode: {context.get('mode', 'default')}\n"
            context_txt += f"Mood: {context.get('mood', 'neutral')}\n"
            if context.get('active_project'):
                context_txt += f"Active project: {context['active_project']}\n"
            # Include Intake multilevel summaries
            intake = context.get('intake', {})
            if intake:
                context_txt += "\nShort-Term Memory (Intake):\n"
                # L1 - Recent exchanges
                if intake.get('L1'):
                    l1_data = intake['L1']
                    if isinstance(l1_data, list):
                        context_txt += f"  L1 (recent): {len(l1_data)} exchanges\n"
                    elif isinstance(l1_data, str):
                        context_txt += f"  L1: {l1_data[:200]}...\n"
                # L20 - Session overview (most important for continuity)
                if intake.get('L20'):
                    l20_data = intake['L20']
                    if isinstance(l20_data, dict):
                        summary = l20_data.get('summary', '')
                        context_txt += f"  L20 (session overview): {summary}\n"
                    elif isinstance(l20_data, str):
                        context_txt += f"  L20: {l20_data}\n"
                # L30 - Continuity report
                if intake.get('L30'):
                    l30_data = intake['L30']
                    if isinstance(l30_data, dict):
                        summary = l30_data.get('summary', '')
                        context_txt += f"  L30 (continuity): {summary}\n"
                    elif isinstance(l30_data, str):
                        context_txt += f"  L30: {l30_data}\n"
            context_txt += "\n"
        except Exception as e:
            # Fallback to JSON dump if formatting fails
            context_txt = f"=== CONTEXT STATE ===\n{json.dumps(context, indent=2)}\n\n"
    # --------------------------------------------------------
    # Final assembled prompt
    # --------------------------------------------------------
    prompt = (
        f"{notes_section}"
        f"{identity_txt}"
        f"{monologue_section}"  # NEW: Intent/tone/depth guidance
        f"{plan_section}"  # NEW: Executive plan if generated
        f"{context_txt}"  # Context BEFORE RAG for better coherence
        f"{rag_txt}"
        f"User message:\n{user_prompt}\n\n"
        "Write the best possible *internal draft answer*.\n"
        "This draft is NOT shown to the user.\n"
        "Be factual, concise, and focused.\n"
        "Use the context state to maintain continuity and reference past interactions naturally.\n"
    )
    # --------------------------------------------------------
    # Call the LLM using the module-specific backend
    # --------------------------------------------------------
    if VERBOSE_DEBUG:
        logger.debug(f"\n{'='*80}")
        logger.debug("[REASONING] Full prompt being sent to LLM:")
        logger.debug(f"{'='*80}")
        logger.debug(prompt)
        logger.debug(f"{'='*80}")
        logger.debug(f"Backend: {CORTEX_LLM}, Temperature: {GLOBAL_TEMP}")
        logger.debug(f"{'='*80}\n")
    draft = await call_llm(
        prompt,
        backend=CORTEX_LLM,
        temperature=GLOBAL_TEMP,
    )
    if VERBOSE_DEBUG:
        logger.debug(f"\n{'='*80}")
        logger.debug("[REASONING] LLM Response received:")
        logger.debug(f"{'='*80}")
        logger.debug(draft)
        logger.debug(f"{'='*80}\n")
    return draft
@@ -0,0 +1,170 @@
 # refine.py
 import os
 import json
 import logging
 from typing import Any, Dict, Optional
 from llm.llm_router import call_llm
 logger = logging.getLogger(__name__)
 # ===============================================
 # Configuration
 # ===============================================
 REFINER_TEMPERATURE = float(os.getenv("REFINER_TEMPERATURE", "0.3"))
 REFINER_MAX_TOKENS = int(os.getenv("REFINER_MAX_TOKENS", "768"))
 REFINER_DEBUG = os.getenv("REFINER_DEBUG", "false").lower() == "true"
 VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
 # These come from root .env
 REFINE_LLM = os.getenv("REFINE_LLM", "").upper()
 CORTEX_LLM = os.getenv("CORTEX_LLM", "PRIMARY").upper()
 if VERBOSE_DEBUG:
    logger.setLevel(logging.DEBUG)
    # Console handler
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(logging.Formatter(
        '%(asctime)s [REFINE] %(levelname)s: %(message)s',
        datefmt='%H:%M:%S'
    ))
    logger.addHandler(console_handler)
    # File handler
    try:
        os.makedirs('/app/logs', exist_ok=True)
        file_handler = logging.FileHandler('/app/logs/cortex_verbose_debug.log', mode='a')
        file_handler.setFormatter(logging.Formatter(
            '%(asctime)s [REFINE] %(levelname)s: %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S'
        ))
        logger.addHandler(file_handler)
        logger.debug("VERBOSE_DEBUG mode enabled for refine.py - logging to file")
    except Exception as e:
        logger.debug(f"VERBOSE_DEBUG mode enabled for refine.py - file logging failed: {e}")
 # ===============================================
 # Prompt builder
 # ===============================================
 def build_refine_prompt(
    draft_output: str,
    reflection_notes: Optional[Any],
    identity_block: Optional[str],
    rag_block: Optional[str],
 ) -> str:
    try:
        reflection_text = json.dumps(reflection_notes, ensure_ascii=False)
    except Exception:
        reflection_text = str(reflection_notes)
    identity_text = identity_block or "(none)"
    rag_text = rag_block or "(none)"
    return f"""
 You are Lyra Cortex's internal refiner.
 Your job:
 - Fix factual issues.
 - Improve clarity.
 - Apply reflection notes when helpful.
 - Respect identity constraints.
 - Apply RAG context as truth source.
 Do NOT mention RAG, reflection, internal logic, or this refinement step.
 ------------------------------
 [IDENTITY BLOCK]
 {identity_text}
 ------------------------------
 [RAG CONTEXT]
 {rag_text}
 ------------------------------
 [DRAFT ANSWER]
 {draft_output}
 ------------------------------
 [REFLECTION NOTES]
 {reflection_text}
 ------------------------------
 Task:
 Rewrite the DRAFT into a single final answer for the user.
 Return ONLY the final answer text.
 """.strip()
 # ===============================================
 # Public API — now async & fully router-based
 # ===============================================
 async def refine_answer(
    draft_output: str,
    reflection_notes: Optional[Any],
    identity_block: Optional[str],
    rag_block: Optional[str],
 ) -> Dict[str, Any]:
    if not draft_output:
        return {
            "final_output": "",
            "used_backend": None,
            "fallback_used": False,
        }
    prompt = build_refine_prompt(
        draft_output,
        reflection_notes,
        identity_block,
        rag_block,
    )
    # backend priority: REFINE_LLM → CORTEX_LLM → PRIMARY
    backend = REFINE_LLM or CORTEX_LLM or "PRIMARY"
    if VERBOSE_DEBUG:
        logger.debug(f"\n{'='*80}")
        logger.debug("[REFINE] Full prompt being sent to LLM:")
        logger.debug(f"{'='*80}")
        logger.debug(prompt)
        logger.debug(f"{'='*80}")
        logger.debug(f"Backend: {backend}, Temperature: {REFINER_TEMPERATURE}")
        logger.debug(f"{'='*80}\n")
    try:
        refined = await call_llm(
            prompt,
            backend=backend,
            temperature=REFINER_TEMPERATURE,
        )
        if VERBOSE_DEBUG:
            logger.debug(f"\n{'='*80}")
            logger.debug("[REFINE] LLM Response received:")
            logger.debug(f"{'='*80}")
            logger.debug(refined)
            logger.debug(f"{'='*80}\n")
        return {
            "final_output": refined.strip() if refined else draft_output,
            "used_backend": backend,
            "fallback_used": False,
        }
    except Exception as e:
        logger.error(f"refine.py backend {backend} failed: {e}")
        if VERBOSE_DEBUG:
            logger.debug("[REFINE] Falling back to draft output due to error")
        return {
            "final_output": draft_output,
            "used_backend": backend,
            "fallback_used": True,
        }
@@ -0,0 +1,124 @@
 # reflection.py
 import json
 import os
 import re
 import logging
 from llm.llm_router import call_llm
 # Logger
 VERBOSE_DEBUG = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
 logger = logging.getLogger(__name__)
 if VERBOSE_DEBUG:
    logger.setLevel(logging.DEBUG)
    # Console handler
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(logging.Formatter(
        '%(asctime)s [REFLECTION] %(levelname)s: %(message)s',
        datefmt='%H:%M:%S'
    ))
    logger.addHandler(console_handler)
    # File handler
    try:
        os.makedirs('/app/logs', exist_ok=True)
        file_handler = logging.FileHandler('/app/logs/cortex_verbose_debug.log', mode='a')
        file_handler.setFormatter(logging.Formatter(
            '%(asctime)s [REFLECTION] %(levelname)s: %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S'
        ))
        logger.addHandler(file_handler)
        logger.debug("VERBOSE_DEBUG mode enabled for reflection.py - logging to file")
    except Exception as e:
        logger.debug(f"VERBOSE_DEBUG mode enabled for reflection.py - file logging failed: {e}")
 async def reflect_notes(intake_summary: str, identity_block: dict | None) -> dict:
    """
    Produce short internal reflection notes for Cortex.
    These are NOT shown to the user.
    """
    # -----------------------------
    # Build the prompt
    # -----------------------------
    identity_text = ""
    if identity_block:
        identity_text = f"Identity:\n{identity_block}\n\n"
    prompt = (
        f"{identity_text}"
        f"Recent summary:\n{intake_summary}\n\n"
        "You are Lyra's meta-awareness layer. Your job is to produce short, directive "
        "internal notes that guide Lyra’s reasoning engine. These notes are NEVER "
        "shown to the user.\n\n"
        "Rules for output:\n"
        "1. Return ONLY valid JSON.\n"
        "2. JSON must have exactly one key: \"notes\".\n"
        "3. \"notes\" must be a list of 3 to 6 short strings.\n"
        "4. Notes must be actionable (e.g., \"keep it concise\", \"maintain context\").\n"
        "5. No markdown, no apologies, no explanations.\n\n"
        "Return JSON:\n"
        "{ \"notes\": [\"...\"] }\n"
    )
    # -----------------------------
    # Module-specific backend choice
    # -----------------------------
    reflection_backend = os.getenv("REFLECTION_LLM")
    cortex_backend = os.getenv("CORTEX_LLM", "PRIMARY").upper()
    # Reflection uses its own backend if set, otherwise cortex backend
    backend = (reflection_backend or cortex_backend).upper()
    # -----------------------------
    # Call the selected LLM backend
    # -----------------------------
    if VERBOSE_DEBUG:
        logger.debug(f"\n{'='*80}")
        logger.debug("[REFLECTION] Full prompt being sent to LLM:")
        logger.debug(f"{'='*80}")
        logger.debug(prompt)
        logger.debug(f"{'='*80}")
        logger.debug(f"Backend: {backend}")
        logger.debug(f"{'='*80}\n")
    raw = await call_llm(prompt, backend=backend)
    if VERBOSE_DEBUG:
        logger.debug(f"\n{'='*80}")
        logger.debug("[REFLECTION] LLM Response received:")
        logger.debug(f"{'='*80}")
        logger.debug(raw)
        logger.debug(f"{'='*80}\n")
    # -----------------------------
    # Try direct JSON
    # -----------------------------
    try:
        parsed = json.loads(raw.strip())
        if isinstance(parsed, dict) and "notes" in parsed:
            if VERBOSE_DEBUG:
                logger.debug(f"[REFLECTION] Parsed {len(parsed['notes'])} notes from JSON")
            return parsed
    except:
        if VERBOSE_DEBUG:
            logger.debug("[REFLECTION] Direct JSON parsing failed, trying extraction...")
    # -----------------------------
    # Try JSON extraction
    # -----------------------------
    try:
        match = re.search(r"\{.*?\}", raw, re.S)
        if match:
            parsed = json.loads(match.group(0))
            if isinstance(parsed, dict) and "notes" in parsed:
                return parsed
    except:
        pass
    # -----------------------------
    # Fallback — treat raw text as a single note
    # -----------------------------
    return {"notes": [raw.strip()]}
@@ -0,0 +1,10 @@
 fastapi==0.115.8
 uvicorn==0.34.0
 python-dotenv==1.0.1
 requests==2.32.3
 httpx==0.27.2
 pydantic==2.10.4
 duckduckgo-search==6.3.5
 aiohttp==3.9.1
 tenacity==9.0.0
 docker==7.1.0
@@ -0,0 +1,559 @@
 # router.py
 import os
 import logging
 import asyncio
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 from reasoning.reasoning import reason_check
 from reasoning.reflection import reflect_notes
 from reasoning.refine import refine_answer
 from persona.speak import speak
 from persona.identity import load_identity
 from context import collect_context, update_last_assistant_message
 from intake.intake import add_exchange_internal
 from autonomy.monologue.monologue import InnerMonologue
 from autonomy.self.state import load_self_state
 from autonomy.tools.stream_events import get_stream_manager
 # -------------------------------------------------------------------
 # Setup
 # -------------------------------------------------------------------
 LOG_DETAIL_LEVEL = os.getenv("LOG_DETAIL_LEVEL", "summary").lower()
 logger = logging.getLogger(__name__)
 # Always set up basic logging
 logger.setLevel(logging.INFO)
 console_handler = logging.StreamHandler()
 console_handler.setFormatter(logging.Formatter(
    '%(asctime)s [ROUTER] %(levelname)s: %(message)s',
    datefmt='%H:%M:%S'
 ))
 logger.addHandler(console_handler)
 cortex_router = APIRouter()
 inner_monologue = InnerMonologue()
 # -------------------------------------------------------------------
 # Models
 # -------------------------------------------------------------------
 class ReasonRequest(BaseModel):
    session_id: str
    user_prompt: str
    temperature: float | None = None
    backend: str | None = None
 # -------------------------------------------------------------------
 # /reason endpoint
 # -------------------------------------------------------------------
@cortex_router.post("/reason")
 async def run_reason(req: ReasonRequest):
    from datetime import datetime
    pipeline_start = datetime.now()
    stage_timings = {}
    # Show pipeline start in detailed/verbose mode
    if LOG_DETAIL_LEVEL in ["detailed", "verbose"]:
        logger.info(f"\n{'='*100}")
        logger.info(f"🚀 PIPELINE START | Session: {req.session_id} | {datetime.now().strftime('%H:%M:%S.%f')[:-3]}")
        logger.info(f"{'='*100}")
        logger.info(f"📝 User: {req.user_prompt[:150]}...")
        logger.info(f"{'-'*100}\n")
    # ----------------------------------------------------------------
    # STAGE 0 — Context
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    context_state = await collect_context(req.session_id, req.user_prompt)
    stage_timings["context"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # STAGE 0.5 — Identity
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    identity_block = load_identity(req.session_id)
    stage_timings["identity"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # STAGE 0.6 — Inner Monologue (observer-only)
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    inner_result = None
    try:
        self_state = load_self_state()
        mono_context = {
            "user_message": req.user_prompt,
            "session_id": req.session_id,
            "self_state": self_state,
            "context_summary": context_state,
        }
        inner_result = await inner_monologue.process(mono_context)
        logger.info(f"🧠 Monologue | {inner_result.get('intent', 'unknown')} | Tone: {inner_result.get('tone', 'neutral')}")
        # Store in context for downstream use
        context_state["monologue"] = inner_result
    except Exception as e:
        logger.warning(f"⚠️  Monologue failed: {e}")
    stage_timings["monologue"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # STAGE 0.7 — Executive Planning (conditional)
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    executive_plan = None
    if inner_result and inner_result.get("consult_executive"):
        try:
            from autonomy.executive.planner import plan_execution
            executive_plan = await plan_execution(
                user_prompt=req.user_prompt,
                intent=inner_result.get("intent", "unknown"),
                context_state=context_state,
                identity_block=identity_block
            )
            logger.info(f"🎯 Executive plan: {executive_plan.get('summary', 'N/A')[:80]}...")
        except Exception as e:
            logger.warning(f"⚠️  Executive planning failed: {e}")
            executive_plan = None
    stage_timings["executive"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # STAGE 0.8 — Autonomous Tool Invocation
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    tool_results = None
    autonomous_enabled = os.getenv("ENABLE_AUTONOMOUS_TOOLS", "true").lower() == "true"
    tool_confidence_threshold = float(os.getenv("AUTONOMOUS_TOOL_CONFIDENCE_THRESHOLD", "0.6"))
    if autonomous_enabled and inner_result:
        try:
            from autonomy.tools.decision_engine import ToolDecisionEngine
            from autonomy.tools.orchestrator import ToolOrchestrator
            # Analyze which tools to invoke
            decision_engine = ToolDecisionEngine()
            tool_decision = await decision_engine.analyze_tool_needs(
                user_prompt=req.user_prompt,
                monologue=inner_result,
                context_state=context_state,
                available_tools=["RAG", "WEB", "WEATHER", "CODEBRAIN"]
            )
            # Execute tools if confidence threshold met
            if tool_decision["should_invoke_tools"] and tool_decision["confidence"] >= tool_confidence_threshold:
                orchestrator = ToolOrchestrator(tool_timeout=30)
                tool_results = await orchestrator.execute_tools(
                    tools_to_invoke=tool_decision["tools_to_invoke"],
                    context_state=context_state
                )
                # Format results for context injection
                tool_context = orchestrator.format_results_for_context(tool_results)
                context_state["autonomous_tool_results"] = tool_context
                summary = tool_results.get("execution_summary", {})
                logger.info(f"🛠️  Tools executed: {summary.get('successful', [])} succeeded")
            else:
                logger.info(f"🛠️  No tools invoked (confidence: {tool_decision.get('confidence', 0):.2f})")
        except Exception as e:
            logger.warning(f"⚠️  Autonomous tool invocation failed: {e}")
            if LOG_DETAIL_LEVEL == "verbose":
                import traceback
                traceback.print_exc()
    stage_timings["tools"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # STAGE 1-5 — Core Reasoning Pipeline
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    # Extract intake summary
    intake_summary = "(no context available)"
    if context_state.get("intake"):
        l20 = context_state["intake"].get("L20")
        if isinstance(l20, dict):
            intake_summary = l20.get("summary", intake_summary)
        elif isinstance(l20, str):
            intake_summary = l20
    # Reflection
    try:
        reflection = await reflect_notes(intake_summary, identity_block=identity_block)
        reflection_notes = reflection.get("notes", [])
    except Exception as e:
        reflection_notes = []
        logger.warning(f"⚠️  Reflection failed: {e}")
    stage_timings["reflection"] = (datetime.now() - stage_start).total_seconds() * 1000
    # Reasoning (draft)
    stage_start = datetime.now()
    draft = await reason_check(
        req.user_prompt,
        identity_block=identity_block,
        rag_block=context_state.get("rag", []),
        reflection_notes=reflection_notes,
        context=context_state,
        monologue=inner_result,
        executive_plan=executive_plan
    )
    stage_timings["reasoning"] = (datetime.now() - stage_start).total_seconds() * 1000
    # Refinement
    stage_start = datetime.now()
    result = await refine_answer(
        draft_output=draft,
        reflection_notes=reflection_notes,
        identity_block=identity_block,
        rag_block=context_state.get("rag", []),
    )
    final_neutral = result["final_output"]
    stage_timings["refinement"] = (datetime.now() - stage_start).total_seconds() * 1000
    # Persona
    stage_start = datetime.now()
    tone = inner_result.get("tone", "neutral") if inner_result else "neutral"
    depth = inner_result.get("depth", "medium") if inner_result else "medium"
    persona_answer = await speak(final_neutral, tone=tone, depth=depth)
    stage_timings["persona"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # STAGE 6 — Session update
    # ----------------------------------------------------------------
    update_last_assistant_message(req.session_id, persona_answer)
    # ----------------------------------------------------------------
    # STAGE 6.5 — Self-state update & Pattern Learning
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    try:
        from autonomy.self.analyzer import analyze_and_update_state
        await analyze_and_update_state(
            monologue=inner_result or {},
            user_prompt=req.user_prompt,
            response=persona_answer,
            context=context_state
        )
    except Exception as e:
        logger.warning(f"⚠️  Self-state update failed: {e}")
    try:
        from autonomy.learning.pattern_learner import get_pattern_learner
        learner = get_pattern_learner()
        await learner.learn_from_interaction(
            user_prompt=req.user_prompt,
            response=persona_answer,
            monologue=inner_result or {},
            context=context_state
        )
    except Exception as e:
        logger.warning(f"⚠️  Pattern learning failed: {e}")
    stage_timings["learning"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # STAGE 7 — Proactive Monitoring & Suggestions
    # ----------------------------------------------------------------
    stage_start = datetime.now()
    proactive_enabled = os.getenv("ENABLE_PROACTIVE_MONITORING", "true").lower() == "true"
    proactive_min_priority = float(os.getenv("PROACTIVE_SUGGESTION_MIN_PRIORITY", "0.6"))
    if proactive_enabled:
        try:
            from autonomy.proactive.monitor import get_proactive_monitor
            monitor = get_proactive_monitor(min_priority=proactive_min_priority)
            self_state = load_self_state()
            suggestion = await monitor.analyze_session(
                session_id=req.session_id,
                context_state=context_state,
                self_state=self_state
            )
            if suggestion:
                suggestion_text = monitor.format_suggestion(suggestion)
                persona_answer += suggestion_text
                logger.info(f"💡 Proactive suggestion: {suggestion['type']} (priority: {suggestion['priority']:.2f})")
        except Exception as e:
            logger.warning(f"⚠️  Proactive monitoring failed: {e}")
    stage_timings["proactive"] = (datetime.now() - stage_start).total_seconds() * 1000
    # ----------------------------------------------------------------
    # PIPELINE COMPLETE — Summary
    # ----------------------------------------------------------------
    total_duration = (datetime.now() - pipeline_start).total_seconds() * 1000
    # Always show pipeline completion
    logger.info(f"\n{'='*100}")
    logger.info(f"✨ PIPELINE COMPLETE | Session: {req.session_id} | Total: {total_duration:.0f}ms")
    logger.info(f"{'='*100}")
    # Show timing breakdown in detailed/verbose mode
    if LOG_DETAIL_LEVEL in ["detailed", "verbose"]:
        logger.info("⏱️  Stage Timings:")
        for stage, duration in stage_timings.items():
            pct = (duration / total_duration) * 100 if total_duration > 0 else 0
            logger.info(f"   {stage:15s}: {duration:6.0f}ms ({pct:5.1f}%)")
    logger.info(f"📤 Output: {len(persona_answer)} chars")
    logger.info(f"{'='*100}\n")
    # ----------------------------------------------------------------
    # RETURN
    # ----------------------------------------------------------------
    return {
        "draft": draft,
        "neutral": final_neutral,
        "persona": persona_answer,
        "reflection": reflection_notes,
        "session_id": req.session_id,
        "context_summary": {
            "rag_results": len(context_state.get("rag", [])),
            "minutes_since_last": context_state.get("minutes_since_last_msg"),
            "message_count": context_state.get("message_count"),
            "mode": context_state.get("mode"),
        }
    }
 # -------------------------------------------------------------------
 # /simple endpoint - Standard chatbot mode (no reasoning pipeline)
 # -------------------------------------------------------------------
@cortex_router.post("/simple")
 async def run_simple(req: ReasonRequest):
    """
    Standard chatbot mode - bypasses all cortex reasoning pipeline.
    Just a simple conversation loop like a typical chatbot.
    """
    from datetime import datetime
    from llm.llm_router import call_llm
    from autonomy.tools.function_caller import FunctionCaller
    start_time = datetime.now()
    logger.info(f"\n{'='*100}")
    logger.info(f"💬 SIMPLE MODE | Session: {req.session_id} | {datetime.now().strftime('%H:%M:%S.%f')[:-3]}")
    logger.info(f"{'='*100}")
    logger.info(f"📝 User: {req.user_prompt[:150]}...")
    logger.info(f"{'-'*100}\n")
    # Get conversation history from context and intake buffer
    context_state = await collect_context(req.session_id, req.user_prompt)
    # Get recent messages from Intake buffer
    from intake.intake import get_recent_messages
    recent_msgs = get_recent_messages(req.session_id, limit=20)
    logger.info(f"📋 Retrieved {len(recent_msgs)} recent messages from Intake buffer")
    # Build simple conversation history with system message
    system_message = {
        "role": "system",
        "content": (
            "You are a helpful AI assistant. Provide direct, concise responses to the user's questions. "
            "Maintain context from previous messages in the conversation."
        )
    }
    messages = [system_message]
    # Add conversation history
    if recent_msgs:
        for msg in recent_msgs:
            messages.append({
                "role": msg.get("role", "user"),
                "content": msg.get("content", "")
            })
            logger.info(f"  - {msg.get('role')}: {msg.get('content', '')[:50]}...")
    # Add current user message
    messages.append({
        "role": "user",
        "content": req.user_prompt
    })
    logger.info(f"📨 Total messages being sent to LLM: {len(messages)} (including system message)")
    # Get backend from request, otherwise fall back to env variable
    backend = req.backend if req.backend else os.getenv("STANDARD_MODE_LLM", "SECONDARY")
    backend = backend.upper()  # Normalize to uppercase
    logger.info(f"🔧 Using backend: {backend}")
    temperature = req.temperature if req.temperature is not None else 0.7
    # Check if tools are enabled
    enable_tools = os.getenv("STANDARD_MODE_ENABLE_TOOLS", "false").lower() == "true"
    # Call LLM with or without tools
    try:
        if enable_tools:
            # Use FunctionCaller for tool-enabled conversation
            logger.info(f"🛠️  Tool calling enabled for Standard Mode")
            logger.info(f"🔍 Creating FunctionCaller with backend={backend}, temp={temperature}")
            function_caller = FunctionCaller(backend, temperature)
            logger.info(f"🔍 FunctionCaller created, calling call_with_tools...")
            result = await function_caller.call_with_tools(
                messages=messages,
                max_tokens=2048,
                session_id=req.session_id  # Pass session_id for streaming
            )
            logger.info(f"🔍 call_with_tools returned: iterations={result.get('iterations')}, tool_calls={len(result.get('tool_calls', []))}")
            # Log tool usage
            if result.get("tool_calls"):
                tool_names = [tc["name"] for tc in result["tool_calls"]]
                logger.info(f"🔧 Tools used: {', '.join(tool_names)} ({result['iterations']} iterations)")
            response = result["content"].strip()
        else:
            # Direct LLM call without tools (original behavior)
            raw_response = await call_llm(
                messages=messages,
                backend=backend,
                temperature=temperature,
                max_tokens=2048
            )
            response = raw_response.strip()
    except Exception as e:
        logger.error(f"❌ LLM call failed: {e}")
        response = f"Error: {str(e)}"
    # Update session with the exchange
    try:
        update_last_assistant_message(req.session_id, response)
        add_exchange_internal({
            "session_id": req.session_id,
            "role": "user",
            "content": req.user_prompt
        })
        add_exchange_internal({
            "session_id": req.session_id,
            "role": "assistant",
            "content": response
        })
    except Exception as e:
        logger.warning(f"⚠️  Session update failed: {e}")
    duration = (datetime.now() - start_time).total_seconds() * 1000
    logger.info(f"\n{'='*100}")
    logger.info(f"✨ SIMPLE MODE COMPLETE | Session: {req.session_id} | Total: {duration:.0f}ms")
    logger.info(f"📤 Output: {len(response)} chars")
    logger.info(f"{'='*100}\n")
    return {
        "draft": response,
        "neutral": response,
        "persona": response,
        "reflection": "",
        "session_id": req.session_id,
        "context_summary": {
            "message_count": len(messages),
            "mode": "standard"
        }
    }
 # -------------------------------------------------------------------
 # /stream/thinking endpoint - SSE stream for "show your work"
 # -------------------------------------------------------------------
@cortex_router.get("/stream/thinking/{session_id}")
 async def stream_thinking(session_id: str):
    """
    Server-Sent Events stream for tool calling "show your work" feature.
    Streams real-time updates about:
    - Thinking/planning steps
    - Tool calls being made
    - Tool execution results
    - Final completion
    """
    stream_manager = get_stream_manager()
    queue = stream_manager.subscribe(session_id)
    async def event_generator():
        try:
            # Send initial connection message
            import json
            connected_event = json.dumps({"type": "connected", "session_id": session_id})
            yield f"data: {connected_event}\n\n"
            while True:
                # Wait for events with timeout to send keepalive
                try:
                    event = await asyncio.wait_for(queue.get(), timeout=30.0)
                    # Format as SSE
                    event_data = json.dumps(event)
                    yield f"data: {event_data}\n\n"
                    # If it's a "done" event, close the stream
                    if event.get("type") == "done":
                        break
                except asyncio.TimeoutError:
                    # Send keepalive comment
                    yield ": keepalive\n\n"
        except asyncio.CancelledError:
            logger.info(f"Stream cancelled for session {session_id}")
        finally:
            stream_manager.unsubscribe(session_id, queue)
    return StreamingResponse(
        event_generator(),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no"  # Disable nginx buffering
        }
    )
 # -------------------------------------------------------------------
 # /ingest endpoint (internal)
 # -------------------------------------------------------------------
 class IngestPayload(BaseModel):
    session_id: str
    user_msg: str
    assistant_msg: str
@cortex_router.post("/ingest")
 async def ingest(payload: IngestPayload):
    try:
        update_last_assistant_message(payload.session_id, payload.assistant_msg)
    except Exception as e:
        logger.warning(f"[INGEST] Session update failed: {e}")
    try:
        add_exchange_internal({
            "session_id": payload.session_id,
            "user_msg": payload.user_msg,
            "assistant_msg": payload.assistant_msg,
        })
    except Exception as e:
        logger.warning(f"[INGEST] Intake update failed: {e}")
    return {"status": "ok", "session_id": payload.session_id}
@@ -0,0 +1 @@
 """Tests for Project Lyra Cortex."""
@@ -0,0 +1,197 @@
 """
 Integration tests for Phase 1 autonomy features.
 Tests monologue integration, executive planning, and self-state persistence.
 """
 import asyncio
 import json
 import sys
 import os
 # Add parent directory to path for imports
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from autonomy.monologue.monologue import InnerMonologue
 from autonomy.self.state import load_self_state, update_self_state, get_self_state_instance
 from autonomy.executive.planner import plan_execution
 async def test_monologue_integration():
    """Test monologue generates valid output."""
    print("\n" + "="*60)
    print("TEST 1: Monologue Integration")
    print("="*60)
    mono = InnerMonologue()
    context = {
        "user_message": "Explain quantum computing to me like I'm 5",
        "session_id": "test_001",
        "self_state": load_self_state(),
        "context_summary": {"message_count": 5}
    }
    result = await mono.process(context)
    assert "intent" in result, "Missing intent field"
    assert "tone" in result, "Missing tone field"
    assert "depth" in result, "Missing depth field"
    assert "consult_executive" in result, "Missing consult_executive field"
    print("✓ Monologue integration test passed")
    print(f"  Result: {json.dumps(result, indent=2)}")
    return result
 async def test_executive_planning():
    """Test executive planner generates valid plans."""
    print("\n" + "="*60)
    print("TEST 2: Executive Planning")
    print("="*60)
    plan = await plan_execution(
        user_prompt="Help me build a distributed system with microservices architecture",
        intent="technical_implementation",
        context_state={
            "tools_available": ["RAG", "WEB", "CODEBRAIN"],
            "message_count": 3,
            "minutes_since_last_msg": 2.5,
            "active_project": None
        },
        identity_block={}
    )
    assert "summary" in plan, "Missing summary field"
    assert "plan_text" in plan, "Missing plan_text field"
    assert "steps" in plan, "Missing steps field"
    assert len(plan["steps"]) > 0, "No steps generated"
    print("✓ Executive planning test passed")
    print(f"  Plan summary: {plan['summary']}")
    print(f"  Steps: {len(plan['steps'])}")
    print(f"  Complexity: {plan.get('estimated_complexity', 'unknown')}")
    return plan
 def test_self_state_persistence():
    """Test self-state loads and updates."""
    print("\n" + "="*60)
    print("TEST 3: Self-State Persistence")
    print("="*60)
    state1 = load_self_state()
    assert "mood" in state1, "Missing mood field"
    assert "energy" in state1, "Missing energy field"
    assert "interaction_count" in state1, "Missing interaction_count"
    initial_count = state1.get("interaction_count", 0)
    print(f"  Initial interaction count: {initial_count}")
    update_self_state(
        mood_delta=0.1,
        energy_delta=-0.05,
        new_focus="testing"
    )
    state2 = load_self_state()
    assert state2["interaction_count"] == initial_count + 1, "Interaction count not incremented"
    assert state2["focus"] == "testing", "Focus not updated"
    print("✓ Self-state persistence test passed")
    print(f"  New interaction count: {state2['interaction_count']}")
    print(f"  New focus: {state2['focus']}")
    print(f"  New energy: {state2['energy']:.2f}")
    return state2
 async def test_end_to_end_flow():
    """Test complete flow from monologue through planning."""
    print("\n" + "="*60)
    print("TEST 4: End-to-End Flow")
    print("="*60)
    # Step 1: Monologue detects complex query
    mono = InnerMonologue()
    mono_result = await mono.process({
        "user_message": "Design a scalable ML pipeline with CI/CD integration",
        "session_id": "test_e2e",
        "self_state": load_self_state(),
        "context_summary": {}
    })
    print(f"  Monologue intent: {mono_result.get('intent')}")
    print(f"  Consult executive: {mono_result.get('consult_executive')}")
    # Step 2: If executive requested, generate plan
    if mono_result.get("consult_executive"):
        plan = await plan_execution(
            user_prompt="Design a scalable ML pipeline with CI/CD integration",
            intent=mono_result.get("intent", "unknown"),
            context_state={"tools_available": ["CODEBRAIN", "WEB"]},
            identity_block={}
        )
        assert plan is not None, "Plan should be generated"
        print(f"  Executive plan generated: {len(plan.get('steps', []))} steps")
    # Step 3: Update self-state
    update_self_state(
        energy_delta=-0.1,  # Complex task is tiring
        new_focus="ml_pipeline_design",
        confidence_delta=0.05
    )
    state = load_self_state()
    assert state["focus"] == "ml_pipeline_design", "Focus should be updated"
    print("✓ End-to-end flow test passed")
    print(f"  Final state: {state['mood']}, energy={state['energy']:.2f}")
    return True
 async def run_all_tests():
    """Run all Phase 1 tests."""
    print("\n" + "="*60)
    print("PHASE 1 AUTONOMY TESTS")
    print("="*60)
    try:
        # Test 1: Monologue
        mono_result = await test_monologue_integration()
        # Test 2: Executive Planning
        plan_result = await test_executive_planning()
        # Test 3: Self-State
        state_result = test_self_state_persistence()
        # Test 4: End-to-End
        await test_end_to_end_flow()
        print("\n" + "="*60)
        print("ALL TESTS PASSED ✓")
        print("="*60)
        print("\nSummary:")
        print(f"  - Monologue: {mono_result.get('intent')} ({mono_result.get('tone')})")
        print(f"  - Executive: {plan_result.get('estimated_complexity')} complexity")
        print(f"  - Self-state: {state_result.get('interaction_count')} interactions")
        return True
    except Exception as e:
        print("\n" + "="*60)
        print(f"TEST FAILED: {e}")
        print("="*60)
        import traceback
        traceback.print_exc()
        return False
 if __name__ == "__main__":
    success = asyncio.run(run_all_tests())
    sys.exit(0 if success else 1)
@@ -0,0 +1,495 @@
 """
 Integration tests for Phase 2 autonomy features.
 Tests autonomous tool invocation, proactive monitoring, actions, and pattern learning.
 """
 import asyncio
 import json
 import sys
 import os
 # Add parent directory to path for imports
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 # Override self-state file path for testing
 os.environ["SELF_STATE_FILE"] = "/tmp/test_self_state.json"
 from autonomy.tools.decision_engine import ToolDecisionEngine
 from autonomy.tools.orchestrator import ToolOrchestrator
 from autonomy.proactive.monitor import ProactiveMonitor
 from autonomy.actions.autonomous_actions import AutonomousActionManager
 from autonomy.learning.pattern_learner import PatternLearner
 from autonomy.self.state import load_self_state, get_self_state_instance
 async def test_tool_decision_engine():
    """Test autonomous tool decision making."""
    print("\n" + "="*60)
    print("TEST 1: Tool Decision Engine")
    print("="*60)
    engine = ToolDecisionEngine()
    # Test 1a: Memory reference detection
    result = await engine.analyze_tool_needs(
        user_prompt="What did we discuss earlier about Python?",
        monologue={"intent": "clarification", "consult_executive": False},
        context_state={},
        available_tools=["RAG", "WEB", "WEATHER"]
    )
    assert result["should_invoke_tools"], "Should invoke tools for memory reference"
    assert any(t["tool"] == "RAG" for t in result["tools_to_invoke"]), "Should recommend RAG"
    assert result["confidence"] > 0.8, f"Confidence should be high for clear memory reference: {result['confidence']}"
    print(f"  ✓ Memory reference detection passed")
    print(f"    Tools: {[t['tool'] for t in result['tools_to_invoke']]}")
    print(f"    Confidence: {result['confidence']:.2f}")
    # Test 1b: Web search detection
    result = await engine.analyze_tool_needs(
        user_prompt="What's the latest news about AI developments?",
        monologue={"intent": "information_seeking", "consult_executive": False},
        context_state={},
        available_tools=["RAG", "WEB", "WEATHER"]
    )
    assert result["should_invoke_tools"], "Should invoke tools for current info request"
    assert any(t["tool"] == "WEB" for t in result["tools_to_invoke"]), "Should recommend WEB"
    print(f"  ✓ Web search detection passed")
    print(f"    Tools: {[t['tool'] for t in result['tools_to_invoke']]}")
    # Test 1c: Weather detection
    result = await engine.analyze_tool_needs(
        user_prompt="What's the weather like today in Boston?",
        monologue={"intent": "information_seeking", "consult_executive": False},
        context_state={},
        available_tools=["RAG", "WEB", "WEATHER"]
    )
    assert result["should_invoke_tools"], "Should invoke tools for weather query"
    assert any(t["tool"] == "WEATHER" for t in result["tools_to_invoke"]), "Should recommend WEATHER"
    print(f"  ✓ Weather detection passed")
    # Test 1d: Proactive RAG for complex queries
    result = await engine.analyze_tool_needs(
        user_prompt="Design a microservices architecture",
        monologue={"intent": "technical_implementation", "consult_executive": True},
        context_state={},
        available_tools=["RAG", "WEB", "CODEBRAIN"]
    )
    assert result["should_invoke_tools"], "Should proactively invoke tools for complex queries"
    rag_tools = [t for t in result["tools_to_invoke"] if t["tool"] == "RAG"]
    assert len(rag_tools) > 0, "Should include proactive RAG"
    print(f"  ✓ Proactive RAG detection passed")
    print(f"    Reason: {rag_tools[0]['reason']}")
    print("\n✓ Tool Decision Engine tests passed\n")
    return result
 async def test_tool_orchestrator():
    """Test tool orchestration (mock mode)."""
    print("\n" + "="*60)
    print("TEST 2: Tool Orchestrator (Mock Mode)")
    print("="*60)
    orchestrator = ToolOrchestrator(tool_timeout=5)
    # Since actual tools may not be available, test the orchestrator structure
    print(f"  Available tools: {list(orchestrator.available_tools.keys())}")
    # Test with tools_to_invoke (will fail gracefully if tools unavailable)
    tools_to_invoke = [
        {"tool": "RAG", "query": "test query", "reason": "testing", "priority": 0.9}
    ]
    result = await orchestrator.execute_tools(
        tools_to_invoke=tools_to_invoke,
        context_state={"session_id": "test"}
    )
    assert "results" in result, "Should return results dict"
    assert "execution_summary" in result, "Should return execution summary"
    summary = result["execution_summary"]
    assert "tools_invoked" in summary, "Summary should include tools_invoked"
    assert "total_time_ms" in summary, "Summary should include timing"
    print(f"  ✓ Orchestrator structure valid")
    print(f"    Summary: {summary}")
    # Test result formatting
    formatted = orchestrator.format_results_for_context(result)
    assert isinstance(formatted, str), "Should format results as string"
    print(f"  ✓ Result formatting works")
    print(f"    Formatted length: {len(formatted)} chars")
    print("\n✓ Tool Orchestrator tests passed\n")
    return result
 async def test_proactive_monitor():
    """Test proactive monitoring and suggestions."""
    print("\n" + "="*60)
    print("TEST 3: Proactive Monitor")
    print("="*60)
    monitor = ProactiveMonitor(min_priority=0.6)
    # Test 3a: Long silence detection
    context_state = {
        "message_count": 5,
        "minutes_since_last_msg": 35  # > 30 minutes
    }
    self_state = load_self_state()
    suggestion = await monitor.analyze_session(
        session_id="test_silence",
        context_state=context_state,
        self_state=self_state
    )
    assert suggestion is not None, "Should generate suggestion for long silence"
    assert suggestion["type"] == "check_in", f"Should be check_in type: {suggestion['type']}"
    assert suggestion["priority"] >= 0.6, "Priority should meet threshold"
    print(f"  ✓ Long silence detection passed")
    print(f"    Type: {suggestion['type']}, Priority: {suggestion['priority']:.2f}")
    print(f"    Suggestion: {suggestion['suggestion'][:50]}...")
    # Test 3b: Learning opportunity (high curiosity)
    self_state["curiosity"] = 0.8
    self_state["learning_queue"] = ["quantum computing", "rust programming"]
    # Reset cooldown for this test
    monitor.reset_cooldown("test_learning")
    suggestion = await monitor.analyze_session(
        session_id="test_learning",
        context_state={"message_count": 3, "minutes_since_last_msg": 2},
        self_state=self_state
    )
    assert suggestion is not None, "Should generate learning suggestion"
    assert suggestion["type"] == "learning", f"Should be learning type: {suggestion['type']}"
    print(f"  ✓ Learning opportunity detection passed")
    print(f"    Suggestion: {suggestion['suggestion'][:70]}...")
    # Test 3c: Conversation milestone
    monitor.reset_cooldown("test_milestone")
    # Reset curiosity to avoid learning suggestion taking precedence
    self_state["curiosity"] = 0.5
    self_state["learning_queue"] = []
    suggestion = await monitor.analyze_session(
        session_id="test_milestone",
        context_state={"message_count": 50, "minutes_since_last_msg": 1},
        self_state=self_state
    )
    assert suggestion is not None, "Should generate milestone suggestion"
    # Note: learning or summary both valid - check it's a reasonable suggestion
    assert suggestion["type"] in ["summary", "learning", "check_in"], f"Should be valid type: {suggestion['type']}"
    print(f"  ✓ Conversation milestone detection passed (type: {suggestion['type']})")
    # Test 3d: Cooldown mechanism
    # Try to get another suggestion immediately (should be blocked)
    suggestion2 = await monitor.analyze_session(
        session_id="test_milestone",
        context_state={"message_count": 51, "minutes_since_last_msg": 1},
        self_state=self_state
    )
    assert suggestion2 is None, "Should not generate suggestion during cooldown"
    print(f"  ✓ Cooldown mechanism working")
    # Check stats
    stats = monitor.get_session_stats("test_milestone")
    assert stats["cooldown_active"], "Cooldown should be active"
    print(f"    Cooldown remaining: {stats['cooldown_remaining']}s")
    print("\n✓ Proactive Monitor tests passed\n")
    return suggestion
 async def test_autonomous_actions():
    """Test autonomous action execution."""
    print("\n" + "="*60)
    print("TEST 4: Autonomous Actions")
    print("="*60)
    manager = AutonomousActionManager()
    # Test 4a: List allowed actions
    allowed = manager.get_allowed_actions()
    assert "create_memory" in allowed, "Should have create_memory action"
    assert "update_goal" in allowed, "Should have update_goal action"
    assert "learn_topic" in allowed, "Should have learn_topic action"
    print(f"  ✓ Allowed actions: {allowed}")
    # Test 4b: Validate actions
    validation = manager.validate_action("create_memory", {"text": "test memory"})
    assert validation["valid"], "Should validate correct action"
    print(f"  ✓ Action validation passed")
    # Test 4c: Execute learn_topic action
    result = await manager.execute_action(
        action_type="learn_topic",
        parameters={"topic": "rust programming", "reason": "testing", "priority": 0.8},
        context={"session_id": "test"}
    )
    assert result["success"], f"Action should succeed: {result.get('error', 'unknown')}"
    assert "topic" in result["result"], "Should return topic info"
    print(f"  ✓ learn_topic action executed")
    print(f"    Topic: {result['result']['topic']}")
    print(f"    Queue position: {result['result']['queue_position']}")
    # Test 4d: Execute update_focus action
    result = await manager.execute_action(
        action_type="update_focus",
        parameters={"focus": "autonomy_testing", "reason": "running tests"},
        context={"session_id": "test"}
    )
    assert result["success"], "update_focus should succeed"
    print(f"  ✓ update_focus action executed")
    print(f"    New focus: {result['result']['new_focus']}")
    # Test 4e: Reject non-whitelisted action
    result = await manager.execute_action(
        action_type="delete_all_files",  # NOT in whitelist
        parameters={},
        context={"session_id": "test"}
    )
    assert not result["success"], "Should reject non-whitelisted action"
    assert "not in whitelist" in result["error"], "Should indicate whitelist violation"
    print(f"  ✓ Non-whitelisted action rejected")
    # Test 4f: Action log
    log = manager.get_action_log(limit=10)
    assert len(log) >= 2, f"Should have logged multiple actions (got {len(log)})"
    print(f"  ✓ Action log contains {len(log)} entries")
    print("\n✓ Autonomous Actions tests passed\n")
    return result
 async def test_pattern_learner():
    """Test pattern learning system."""
    print("\n" + "="*60)
    print("TEST 5: Pattern Learner")
    print("="*60)
    # Use temp file for testing
    test_file = "/tmp/test_patterns.json"
    learner = PatternLearner(patterns_file=test_file)
    # Test 5a: Learn from multiple interactions
    for i in range(5):
        await learner.learn_from_interaction(
            user_prompt=f"Help me with Python coding task {i}",
            response=f"Here's help with task {i}...",
            monologue={"intent": "coding_help", "tone": "focused", "depth": "medium"},
            context={"session_id": "test", "executive_plan": None}
        )
    print(f"  ✓ Learned from 5 interactions")
    # Test 5b: Get top topics
    top_topics = learner.get_top_topics(limit=5)
    assert len(top_topics) > 0, "Should have learned topics"
    assert "coding_help" == top_topics[0][0], "coding_help should be top topic"
    print(f"  ✓ Top topics: {[t[0] for t in top_topics[:3]]}")
    # Test 5c: Get preferred tone
    preferred_tone = learner.get_preferred_tone()
    assert preferred_tone == "focused", "Should detect focused as preferred tone"
    print(f"  ✓ Preferred tone: {preferred_tone}")
    # Test 5d: Get preferred depth
    preferred_depth = learner.get_preferred_depth()
    assert preferred_depth == "medium", "Should detect medium as preferred depth"
    print(f"  ✓ Preferred depth: {preferred_depth}")
    # Test 5e: Get insights
    insights = learner.get_insights()
    assert insights["total_interactions"] == 5, "Should track interaction count"
    assert insights["preferred_tone"] == "focused", "Insights should include tone"
    print(f"  ✓ Insights generated:")
    print(f"    Total interactions: {insights['total_interactions']}")
    print(f"    Recommendations: {insights['learning_recommendations']}")
    # Test 5f: Export patterns
    exported = learner.export_patterns()
    assert "topic_frequencies" in exported, "Should export all patterns"
    print(f"  ✓ Patterns exported ({len(exported)} keys)")
    # Cleanup
    if os.path.exists(test_file):
        os.remove(test_file)
    print("\n✓ Pattern Learner tests passed\n")
    return insights
 async def test_end_to_end_autonomy():
    """Test complete autonomous flow."""
    print("\n" + "="*60)
    print("TEST 6: End-to-End Autonomy Flow")
    print("="*60)
    # Simulate a complex user query that triggers multiple autonomous systems
    user_prompt = "Remember what we discussed about machine learning? I need current research on transformers."
    monologue = {
        "intent": "technical_research",
        "tone": "focused",
        "depth": "deep",
        "consult_executive": True
    }
    context_state = {
        "session_id": "e2e_test",
        "message_count": 15,
        "minutes_since_last_msg": 5
    }
    print(f"  User prompt: {user_prompt}")
    print(f"  Monologue intent: {monologue['intent']}")
    # Step 1: Tool decision engine
    engine = ToolDecisionEngine()
    tool_decision = await engine.analyze_tool_needs(
        user_prompt=user_prompt,
        monologue=monologue,
        context_state=context_state,
        available_tools=["RAG", "WEB", "CODEBRAIN"]
    )
    print(f"\n  Step 1: Tool Decision")
    print(f"    Should invoke: {tool_decision['should_invoke_tools']}")
    print(f"    Tools: {[t['tool'] for t in tool_decision['tools_to_invoke']]}")
    assert tool_decision["should_invoke_tools"], "Should invoke tools"
    assert len(tool_decision["tools_to_invoke"]) >= 2, "Should recommend multiple tools (RAG + WEB)"
    # Step 2: Pattern learning
    learner = PatternLearner(patterns_file="/tmp/e2e_test_patterns.json")
    await learner.learn_from_interaction(
        user_prompt=user_prompt,
        response="Here's information about transformers...",
        monologue=monologue,
        context=context_state
    )
    print(f"\n  Step 2: Pattern Learning")
    top_topics = learner.get_top_topics(limit=3)
    print(f"    Learned topics: {[t[0] for t in top_topics]}")
    # Step 3: Autonomous action
    action_manager = AutonomousActionManager()
    action_result = await action_manager.execute_action(
        action_type="learn_topic",
        parameters={"topic": "transformer architectures", "reason": "user interest detected"},
        context=context_state
    )
    print(f"\n  Step 3: Autonomous Action")
    print(f"    Action: learn_topic")
    print(f"    Success: {action_result['success']}")
    # Step 4: Proactive monitoring (won't trigger due to low message count)
    monitor = ProactiveMonitor(min_priority=0.6)
    monitor.reset_cooldown("e2e_test")
    suggestion = await monitor.analyze_session(
        session_id="e2e_test",
        context_state=context_state,
        self_state=load_self_state()
    )
    print(f"\n  Step 4: Proactive Monitoring")
    print(f"    Suggestion: {suggestion['type'] if suggestion else 'None (expected for low message count)'}")
    # Cleanup
    if os.path.exists("/tmp/e2e_test_patterns.json"):
        os.remove("/tmp/e2e_test_patterns.json")
    print("\n✓ End-to-End Autonomy Flow tests passed\n")
    return True
 async def run_all_tests():
    """Run all Phase 2 tests."""
    print("\n" + "="*60)
    print("PHASE 2 AUTONOMY TESTS")
    print("="*60)
    try:
        # Test 1: Tool Decision Engine
        await test_tool_decision_engine()
        # Test 2: Tool Orchestrator
        await test_tool_orchestrator()
        # Test 3: Proactive Monitor
        await test_proactive_monitor()
        # Test 4: Autonomous Actions
        await test_autonomous_actions()
        # Test 5: Pattern Learner
        await test_pattern_learner()
        # Test 6: End-to-End
        await test_end_to_end_autonomy()
        print("\n" + "="*60)
        print("ALL PHASE 2 TESTS PASSED ✓")
        print("="*60)
        print("\nPhase 2 Features Validated:")
        print("  ✓ Autonomous tool decision making")
        print("  ✓ Tool orchestration and execution")
        print("  ✓ Proactive monitoring and suggestions")
        print("  ✓ Safe autonomous actions")
        print("  ✓ Pattern learning and adaptation")
        print("  ✓ End-to-end autonomous flow")
        return True
    except Exception as e:
        print("\n" + "="*60)
        print(f"TEST FAILED: {e}")
        print("="*60)
        import traceback
        traceback.print_exc()
        return False
 if __name__ == "__main__":
    success = asyncio.run(run_all_tests())
    sys.exit(0 if success else 1)
@@ -0,0 +1 @@
 # Utilities module
@@ -0,0 +1,33 @@
 import os, json, datetime
 # optional daily rotation
 LOG_PATH = os.getenv("REFLECTION_NOTE_PATH") or \
           f"/app/logs/reflections_{datetime.date.today():%Y%m%d}.log"
 def log_reflection(reflection: dict, user_prompt: str, draft: str, final: str, session_id: str | None = None):
    """Append a reflection entry to the reflections log."""
    try:
        # 1️⃣ Make sure log directory exists
        os.makedirs(os.path.dirname(LOG_PATH), exist_ok=True)
        # 2️⃣ Ensure session_id is stored
        reflection["session_id"] = session_id or reflection.get("session_id", "unknown")
        # 3️⃣ Build JSON entry
        entry = {
            "timestamp": datetime.datetime.now().isoformat(),
            "session_id": reflection["session_id"],
            "prompt": user_prompt,
            "draft_output": draft[:500],
            "final_output": final[:500],
            "reflection": reflection,
        }
        # 4️⃣ Write it in pretty JSON, comma-delimited for easy reading
        with open(LOG_PATH, "a", encoding="utf-8") as f:
            f.write(json.dumps(entry, indent=2, ensure_ascii=False) + ",\n")
        print(f"[Cortex] Logged reflection → {LOG_PATH}")
    except Exception as e:
        print(f"[Cortex] Failed to log reflection: {e}")
@@ -0,0 +1,223 @@
 """
 Structured logging utilities for Cortex pipeline debugging.
 Provides hierarchical, scannable logs with clear section markers and raw data visibility.
 """
 import json
 import logging
 from typing import Any, Dict, List, Optional
 from datetime import datetime
 from enum import Enum
 class LogLevel(Enum):
    """Log detail levels"""
    MINIMAL = 1    # Only errors and final results
    SUMMARY = 2    # Stage summaries + errors
    DETAILED = 3   # Include raw LLM outputs, RAG results
    VERBOSE = 4    # Everything including intermediate states
 class PipelineLogger:
    """
    Hierarchical logger for cortex pipeline debugging.
    Features:
    - Clear visual section markers
    - Collapsible detail sections
    - Raw data dumps with truncation options
    - Stage timing
    - Error highlighting
    """
    def __init__(self, logger: logging.Logger, level: LogLevel = LogLevel.SUMMARY):
        self.logger = logger
        self.level = level
        self.stage_timings = {}
        self.current_stage = None
        self.stage_start_time = None
        self.pipeline_start_time = None
    def pipeline_start(self, session_id: str, user_prompt: str):
        """Mark the start of a pipeline run"""
        self.pipeline_start_time = datetime.now()
        self.stage_timings = {}
        if self.level.value >= LogLevel.SUMMARY.value:
            self.logger.info(f"\n{'='*100}")
            self.logger.info(f"🚀 PIPELINE START | Session: {session_id} | {datetime.now().strftime('%H:%M:%S.%f')[:-3]}")
            self.logger.info(f"{'='*100}")
            if self.level.value >= LogLevel.DETAILED.value:
                self.logger.info(f"📝 User prompt: {user_prompt[:200]}{'...' if len(user_prompt) > 200 else ''}")
                self.logger.info(f"{'-'*100}\n")
    def stage_start(self, stage_name: str, description: str = ""):
        """Mark the start of a pipeline stage"""
        self.current_stage = stage_name
        self.stage_start_time = datetime.now()
        if self.level.value >= LogLevel.SUMMARY.value:
            timestamp = datetime.now().strftime('%H:%M:%S.%f')[:-3]
            desc_suffix = f" - {description}" if description else ""
            self.logger.info(f"▶️  [{stage_name}]{desc_suffix} | {timestamp}")
    def stage_end(self, result_summary: str = ""):
        """Mark the end of a pipeline stage"""
        if self.current_stage and self.stage_start_time:
            duration_ms = (datetime.now() - self.stage_start_time).total_seconds() * 1000
            self.stage_timings[self.current_stage] = duration_ms
            if self.level.value >= LogLevel.SUMMARY.value:
                summary_suffix = f" → {result_summary}" if result_summary else ""
                self.logger.info(f"✅ [{self.current_stage}] Complete in {duration_ms:.0f}ms{summary_suffix}\n")
        self.current_stage = None
        self.stage_start_time = None
    def log_llm_call(self, backend: str, prompt: str, response: Any, raw_response: str = None):
        """
        Log LLM call details with proper formatting.
        Args:
            backend: Backend name (PRIMARY, SECONDARY, etc.)
            prompt: Input prompt to LLM
            response: Parsed response object
            raw_response: Raw JSON response string
        """
        if self.level.value >= LogLevel.DETAILED.value:
            self.logger.info(f"  🧠 LLM Call | Backend: {backend}")
            # Show prompt (truncated)
            if isinstance(prompt, list):
                prompt_preview = prompt[-1].get('content', '')[:150] if prompt else ''
            else:
                prompt_preview = str(prompt)[:150]
            self.logger.info(f"     Prompt: {prompt_preview}...")
            # Show parsed response
            if isinstance(response, dict):
                response_text = (
                    response.get('reply') or
                    response.get('message', {}).get('content') or
                    str(response)
                )[:200]
            else:
                response_text = str(response)[:200]
            self.logger.info(f"     Response: {response_text}...")
            # Show raw response in collapsible block
            if raw_response and self.level.value >= LogLevel.VERBOSE.value:
                self.logger.debug(f"     ╭─ RAW RESPONSE ────────────────────────────────────")
                for line in raw_response.split('\n')[:50]:  # Limit to 50 lines
                    self.logger.debug(f"     │ {line}")
                if raw_response.count('\n') > 50:
                    self.logger.debug(f"     │ ... ({raw_response.count(chr(10)) - 50} more lines)")
                self.logger.debug(f"     ╰───────────────────────────────────────────────────\n")
    def log_rag_results(self, results: List[Dict[str, Any]]):
        """Log RAG/NeoMem results in scannable format"""
        if self.level.value >= LogLevel.SUMMARY.value:
            self.logger.info(f"  📚 RAG Results: {len(results)} memories retrieved")
            if self.level.value >= LogLevel.DETAILED.value and results:
                self.logger.info(f"     ╭─ MEMORY SCORES ───────────────────────────────────")
                for idx, result in enumerate(results[:10], 1):  # Show top 10
                    score = result.get("score", 0)
                    data_preview = str(result.get("payload", {}).get("data", ""))[:80]
                    self.logger.info(f"     │ [{idx}] {score:.3f} | {data_preview}...")
                if len(results) > 10:
                    self.logger.info(f"     │ ... and {len(results) - 10} more results")
                self.logger.info(f"     ╰───────────────────────────────────────────────────")
    def log_context_state(self, context_state: Dict[str, Any]):
        """Log context state summary"""
        if self.level.value >= LogLevel.SUMMARY.value:
            msg_count = context_state.get("message_count", 0)
            minutes_since = context_state.get("minutes_since_last_msg", 0)
            rag_count = len(context_state.get("rag", []))
            self.logger.info(f"  📊 Context | Messages: {msg_count} | Last: {minutes_since:.1f}min ago | RAG: {rag_count} results")
            if self.level.value >= LogLevel.DETAILED.value:
                intake = context_state.get("intake", {})
                if intake:
                    self.logger.info(f"     ╭─ INTAKE SUMMARIES ────────────────────────────────")
                    for level in ["L1", "L5", "L10", "L20", "L30"]:
                        if level in intake:
                            summary = intake[level]
                            if isinstance(summary, dict):
                                summary = summary.get("summary", str(summary)[:100])
                            else:
                                summary = str(summary)[:100]
                            self.logger.info(f"     │ {level}: {summary}...")
                    self.logger.info(f"     ╰───────────────────────────────────────────────────")
    def log_error(self, stage: str, error: Exception, critical: bool = False):
        """Log an error with context"""
        level_marker = "🔴 CRITICAL" if critical else "⚠️  WARNING"
        self.logger.error(f"{level_marker} | Stage: {stage} | Error: {type(error).__name__}: {str(error)}")
        if self.level.value >= LogLevel.VERBOSE.value:
            import traceback
            self.logger.debug(f"     Traceback:\n{traceback.format_exc()}")
    def log_raw_data(self, label: str, data: Any, max_lines: int = 30):
        """Log raw data in a collapsible format"""
        if self.level.value >= LogLevel.VERBOSE.value:
            self.logger.debug(f"     ╭─ {label.upper()} ──────────────────────────────────")
            if isinstance(data, (dict, list)):
                json_str = json.dumps(data, indent=2, default=str)
                lines = json_str.split('\n')
                for line in lines[:max_lines]:
                    self.logger.debug(f"     │ {line}")
                if len(lines) > max_lines:
                    self.logger.debug(f"     │ ... ({len(lines) - max_lines} more lines)")
            else:
                lines = str(data).split('\n')
                for line in lines[:max_lines]:
                    self.logger.debug(f"     │ {line}")
                if len(lines) > max_lines:
                    self.logger.debug(f"     │ ... ({len(lines) - max_lines} more lines)")
            self.logger.debug(f"     ╰───────────────────────────────────────────────────")
    def pipeline_end(self, session_id: str, final_output_length: int):
        """Mark the end of pipeline run with summary"""
        if self.pipeline_start_time:
            total_duration_ms = (datetime.now() - self.pipeline_start_time).total_seconds() * 1000
            if self.level.value >= LogLevel.SUMMARY.value:
                self.logger.info(f"\n{'='*100}")
                self.logger.info(f"✨ PIPELINE COMPLETE | Session: {session_id} | Total: {total_duration_ms:.0f}ms")
                self.logger.info(f"{'='*100}")
                # Show timing breakdown
                if self.stage_timings and self.level.value >= LogLevel.DETAILED.value:
                    self.logger.info("⏱️  Stage Timings:")
                    for stage, duration in self.stage_timings.items():
                        pct = (duration / total_duration_ms) * 100 if total_duration_ms > 0 else 0
                        self.logger.info(f"   {stage:20s}: {duration:6.0f}ms ({pct:5.1f}%)")
                self.logger.info(f"📤 Final output: {final_output_length} characters")
                self.logger.info(f"{'='*100}\n")
 def get_log_level_from_env() -> LogLevel:
    """Parse log level from environment variable"""
    import os
    verbose_debug = os.getenv("VERBOSE_DEBUG", "false").lower() == "true"
    detail_level = os.getenv("LOG_DETAIL_LEVEL", "").lower()
    if detail_level == "minimal":
        return LogLevel.MINIMAL
    elif detail_level == "summary":
        return LogLevel.SUMMARY
    elif detail_level == "detailed":
        return LogLevel.DETAILED
    elif detail_level == "verbose" or verbose_debug:
        return LogLevel.VERBOSE
    else:
        return LogLevel.SUMMARY  # Default
@@ -0,0 +1,26 @@
 #!/usr/bin/env python3
 import re
 xml = """<tool_call>
  <name>execute_code</name>
  <arguments>
    <language>python</language>
    <code>print(50 / 2)</code>
    <reason>To calculate the result of dividing 50 by 2.</reason>
  </arguments>
 </olith>"""
 pattern = r'<tool_call>(.*?)</(?:tool_call|[a-zA-Z]+)>'
 matches = re.findall(pattern, xml, re.DOTALL)
 print(f"Pattern: {pattern}")
 print(f"Number of matches: {len(matches)}")
 print("\nMatches:")
 for idx, match in enumerate(matches):
    print(f"\nMatch {idx + 1}:")
    print(f"Length: {len(match)} chars")
    print(f"Content:\n{match[:200]}")
 # Now test what gets removed
 clean_content = re.sub(pattern, '', xml, flags=re.DOTALL).strip()
 print(f"\n\nCleaned content:\n{clean_content}")
@@ -0,0 +1,195 @@
 networks:
  lyra_net:
    driver: bridge
 volumes:
  postgres_data:
    driver: local
  neo4j_data:
    driver: local
  code_executions:
    driver: local
 services:
  # # ============================================================
  # # NeoMem: Postgres
  # # ============================================================
  # neomem-postgres:
  #   image: ankane/pgvector:v0.5.1
  #   container_name: neomem-postgres
  #   restart: unless-stopped
  #   environment:
  #     POSTGRES_USER: neomem
  #     POSTGRES_PASSWORD: neomempass
  #     POSTGRES_DB: neomem
  #   volumes:
  #     - ./volumes/postgres_data:/var/lib/postgresql/data
  #   ports:
  #     - "5432:5432"
  #   healthcheck:
  #     test: ["CMD-SHELL", "pg_isready -U neomem -d neomem || exit 1"]
  #     interval: 5s
  #     timeout: 5s
  #     retries: 10
  #   networks:
  #     - lyra_net
  # # ============================================================
  # # NeoMem: Neo4j Graph
  # # ============================================================
  # neomem-neo4j:
  #   image: neo4j:5
  #   container_name: neomem-neo4j
  #   restart: unless-stopped
  #   environment:
  #     NEO4J_AUTH: "neo4j/neomemgraph"
  #     NEO4JLABS_PLUGINS: '["graph-data-science"]'
  #   volumes:
  #     - ./volumes/neo4j_data:/data
  #   ports:
  #     - "7474:7474"
  #     - "7687:7687"
  #   healthcheck:
  #     test: ["CMD-SHELL", "cypher-shell -u neo4j -p neomemgraph 'RETURN 1' || exit 1"]
  #     interval: 10s
  #     timeout: 10s
  #     retries: 10
  #   networks:
  #     - lyra_net
  # ============================================================
  # NeoMem API
  # ============================================================
  # neomem-api:
  #   build:
  #     context: ./neomem
  #   image: lyra-neomem:latest
  #   container_name: neomem-api
  #   restart: unless-stopped
  #   env_file:
  #     - ./neomem/.env
  #     - ./.env
  #   volumes:
  #     - ./neomem_history:/app/history
  #   ports:
  #     - "7077:7077"
  #   depends_on:
  #     neomem-postgres:
  #       condition: service_healthy
  #     neomem-neo4j:
  #       condition: service_healthy
  #   networks:
  #     - lyra_net
  # ============================================================
  # Relay  (host mode)
  # ============================================================
  relay:
    build:
      context: ./core/relay
    container_name: relay
    restart: unless-stopped
    env_file:
      - ./.env
    volumes:
      - ./core/relay/sessions:/app/sessions
    ports:
      - "7078:7078"
    networks:
      - lyra_net
  # ============================================================
  # UI Server
  # ============================================================
  lyra-ui:
    image: nginx:alpine
    container_name: lyra-ui
    restart: unless-stopped
    ports:
      - "8081:80"
    volumes:
      - ./core/ui:/usr/share/nginx/html:ro
    networks:
      - lyra_net
  # ============================================================
  # Cortex
  # ============================================================
  cortex:
    build:
      context: ./cortex
    container_name: cortex
    restart: unless-stopped
    env_file:
      - ./cortex/.env
      - ./.env
    volumes:
      - ./cortex:/app
      - /var/run/docker.sock:/var/run/docker.sock:ro
    ports:
      - "7081:7081"
    networks:
      - lyra_net
  # ============================================================
  # Code Sandbox (for tool execution)
  # ============================================================
  code-sandbox:
    build:
      context: ./sandbox
    container_name: lyra-code-sandbox
    restart: unless-stopped
    security_opt:
      - no-new-privileges:true
    cap_drop:
      - ALL
    cap_add:
      - CHOWN
      - SETUID
      - SETGID
    network_mode: "none"
    volumes:
      - code_executions:/executions
    mem_limit: 512m
    cpus: 1.0
    pids_limit: 100
    user: sandbox
    command: tail -f /dev/null
  # ============================================================
  # Intake
  # ============================================================
 #  intake:
 #   build:
 #      context: ./intake
 #    container_name: intake
 #    restart: unless-stopped
 #    env_file:
 #      - ./intake/.env
 #      - ./.env
 #    ports:
 #      - "7080:7080"
 #    volumes:
 #      - ./intake:/app
 #      - ./intake-logs:/app/logs
 #    depends_on:
 #      - cortex
 #    networks:
 #      - lyra_net
  # ============================================================
  # RAG Service
  # ============================================================
  # rag:
  #   build:
  #     context: ./rag
  #   container_name: rag
  #   restart: unless-stopped
  #   environment:
  #     NEOMEM_URL: http://neomem-api:7077
  #   ports:
  #     - "7090:7090"
  #   networks:
  #     - lyra_net  
@@ -0,0 +1,441 @@
 ├── CHANGELOG.md
 ├── core
 │   ├── env experiments
 │   ├── persona-sidecar
 │   │   ├── Dockerfile
 │   │   ├── package.json
 │   │   ├── persona-server.js
 │   │   └── personas.json
 │   ├── relay
 │   │   ├── Dockerfile
 │   │   ├── lib
 │   │   │   ├── cortex.js
 │   │   │   └── llm.js
 │   │   ├── package.json
 │   │   ├── package-lock.json
 │   │   ├── server.js
 │   │   ├── sessions
 │   │   │   ├── default.jsonl
 │   │   │   ├── sess-6rxu7eia.json
 │   │   │   ├── sess-6rxu7eia.jsonl
 │   │   │   ├── sess-l08ndm60.json
 │   │   │   └── sess-l08ndm60.jsonl
 │   │   └── test-llm.js
 │   ├── relay-backup
 │   └── ui
 │       ├── index.html
 │       ├── manifest.json
 │       └── style.css
 ├── cortex
 │   ├── context.py
 │   ├── Dockerfile
 │   ├── ingest
 │   │   ├── ingest_handler.py
 │   │   ├── __init__.py
 │   │   └── intake_client.py
 │   ├── intake
 │   │   ├── __init__.py
 │   │   ├── intake.py
 │   │   └── logs
 │   ├── llm
 │   │   ├── __init__.py
 │   │   └── llm_router.py
 │   ├── logs
 │   │   ├── cortex_verbose_debug.log
 │   │   └── reflections.log
 │   ├── main.py
 │   ├── neomem_client.py
 │   ├── persona
 │   │   ├── identity.py
 │   │   ├── __init__.py
 │   │   └── speak.py
 │   ├── rag.py
 │   ├── reasoning
 │   │   ├── __init__.py
 │   │   ├── reasoning.py
 │   │   ├── refine.py
 │   │   └── reflection.py
 │   ├── requirements.txt
 │   ├── router.py
 │   ├── tests
 │   └── utils
 │       ├── config.py
 │       ├── __init__.py
 │       ├── log_utils.py
 │       └── schema.py
 ├── deprecated.env.txt
 ├── DEPRECATED_FILES.md
 ├── docker-compose.yml
 ├── docs
 │   ├── ARCHITECTURE_v0-6-0.md
 │   ├── ENVIRONMENT_VARIABLES.md
 │   ├── lyra_tree.txt
 │   └── PROJECT_SUMMARY.md
 ├── intake-logs
 │   └── summaries.log
 ├── neomem
 │   ├── _archive
 │   │   └── old_servers
 │   │       ├── main_backup.py
 │   │       └── main_dev.py
 │   ├── docker-compose.yml
 │   ├── Dockerfile
 │   ├── neomem
 │   │   ├── api
 │   │   ├── client
 │   │   │   ├── __init__.py
 │   │   │   ├── main.py
 │   │   │   ├── project.py
 │   │   │   └── utils.py
 │   │   ├── configs
 │   │   │   ├── base.py
 │   │   │   ├── embeddings
 │   │   │   │   ├── base.py
 │   │   │   │   └── __init__.py
 │   │   │   ├── enums.py
 │   │   │   ├── __init__.py
 │   │   │   ├── llms
 │   │   │   │   ├── anthropic.py
 │   │   │   │   ├── aws_bedrock.py
 │   │   │   │   ├── azure.py
 │   │   │   │   ├── base.py
 │   │   │   │   ├── deepseek.py
 │   │   │   │   ├── __init__.py
 │   │   │   │   ├── lmstudio.py
 │   │   │   │   ├── ollama.py
 │   │   │   │   ├── openai.py
 │   │   │   │   └── vllm.py
 │   │   │   ├── prompts.py
 │   │   │   └── vector_stores
 │   │   │       ├── azure_ai_search.py
 │   │   │       ├── azure_mysql.py
 │   │   │       ├── baidu.py
 │   │   │       ├── chroma.py
 │   │   │       ├── databricks.py
 │   │   │       ├── elasticsearch.py
 │   │   │       ├── faiss.py
 │   │   │       ├── __init__.py
 │   │   │       ├── langchain.py
 │   │   │       ├── milvus.py
 │   │   │       ├── mongodb.py
 │   │   │       ├── neptune.py
 │   │   │       ├── opensearch.py
 │   │   │       ├── pgvector.py
 │   │   │       ├── pinecone.py
 │   │   │       ├── qdrant.py
 │   │   │       ├── redis.py
 │   │   │       ├── s3_vectors.py
 │   │   │       ├── supabase.py
 │   │   │       ├── upstash_vector.py
 │   │   │       ├── valkey.py
 │   │   │       ├── vertex_ai_vector_search.py
 │   │   │       └── weaviate.py
 │   │   ├── core
 │   │   ├── embeddings
 │   │   │   ├── aws_bedrock.py
 │   │   │   ├── azure_openai.py
 │   │   │   ├── base.py
 │   │   │   ├── configs.py
 │   │   │   ├── gemini.py
 │   │   │   ├── huggingface.py
 │   │   │   ├── __init__.py
 │   │   │   ├── langchain.py
 │   │   │   ├── lmstudio.py
 │   │   │   ├── mock.py
 │   │   │   ├── ollama.py
 │   │   │   ├── openai.py
 │   │   │   ├── together.py
 │   │   │   └── vertexai.py
 │   │   ├── exceptions.py
 │   │   ├── graphs
 │   │   │   ├── configs.py
 │   │   │   ├── __init__.py
 │   │   │   ├── neptune
 │   │   │   │   ├── base.py
 │   │   │   │   ├── __init__.py
 │   │   │   │   ├── neptunedb.py
 │   │   │   │   └── neptunegraph.py
 │   │   │   ├── tools.py
 │   │   │   └── utils.py
 │   │   ├── __init__.py
 │   │   ├── LICENSE
 │   │   ├── llms
 │   │   │   ├── anthropic.py
 │   │   │   ├── aws_bedrock.py
 │   │   │   ├── azure_openai.py
 │   │   │   ├── azure_openai_structured.py
 │   │   │   ├── base.py
 │   │   │   ├── configs.py
 │   │   │   ├── deepseek.py
 │   │   │   ├── gemini.py
 │   │   │   ├── groq.py
 │   │   │   ├── __init__.py
 │   │   │   ├── langchain.py
 │   │   │   ├── litellm.py
 │   │   │   ├── lmstudio.py
 │   │   │   ├── ollama.py
 │   │   │   ├── openai.py
 │   │   │   ├── openai_structured.py
 │   │   │   ├── sarvam.py
 │   │   │   ├── together.py
 │   │   │   ├── vllm.py
 │   │   │   └── xai.py
 │   │   ├── memory
 │   │   │   ├── base.py
 │   │   │   ├── graph_memory.py
 │   │   │   ├── __init__.py
 │   │   │   ├── kuzu_memory.py
 │   │   │   ├── main.py
 │   │   │   ├── memgraph_memory.py
 │   │   │   ├── setup.py
 │   │   │   ├── storage.py
 │   │   │   ├── telemetry.py
 │   │   │   └── utils.py
 │   │   ├── proxy
 │   │   │   ├── __init__.py
 │   │   │   └── main.py
 │   │   ├── server
 │   │   │   ├── dev.Dockerfile
 │   │   │   ├── docker-compose.yaml
 │   │   │   ├── Dockerfile
 │   │   │   ├── main_old.py
 │   │   │   ├── main.py
 │   │   │   ├── Makefile
 │   │   │   ├── README.md
 │   │   │   └── requirements.txt
 │   │   ├── storage
 │   │   ├── utils
 │   │   │   └── factory.py
 │   │   └── vector_stores
 │   │       ├── azure_ai_search.py
 │   │       ├── azure_mysql.py
 │   │       ├── baidu.py
 │   │       ├── base.py
 │   │       ├── chroma.py
 │   │       ├── configs.py
 │   │       ├── databricks.py
 │   │       ├── elasticsearch.py
 │   │       ├── faiss.py
 │   │       ├── __init__.py
 │   │       ├── langchain.py
 │   │       ├── milvus.py
 │   │       ├── mongodb.py
 │   │       ├── neptune_analytics.py
 │   │       ├── opensearch.py
 │   │       ├── pgvector.py
 │   │       ├── pinecone.py
 │   │       ├── qdrant.py
 │   │       ├── redis.py
 │   │       ├── s3_vectors.py
 │   │       ├── supabase.py
 │   │       ├── upstash_vector.py
 │   │       ├── valkey.py
 │   │       ├── vertex_ai_vector_search.py
 │   │       └── weaviate.py
 │   ├── neomem_history
 │   │   └── history.db
 │   ├── pyproject.toml
 │   ├── README.md
 │   └── requirements.txt
 ├── neomem_history
 │   └── history.db
 ├── rag
 │   ├── chatlogs
 │   │   └── lyra
 │   │       ├── 0000_Wire_ROCm_to_Cortex.json
 │   │       ├── 0001_Branch___10_22_ct201branch-ssh_tut.json
 │   │       ├── 0002_cortex_LLMs_11-1-25.json
 │   │       ├── 0003_RAG_beta.json
 │   │       ├── 0005_Cortex_v0_4_0_planning.json
 │   │       ├── 0006_Cortex_v0_4_0_Refinement.json
 │   │       ├── 0009_Branch___Cortex_v0_4_0_planning.json
 │   │       ├── 0012_Cortex_4_-_neomem_11-1-25.json
 │   │       ├── 0016_Memory_consolidation_concept.json
 │   │       ├── 0017_Model_inventory_review.json
 │   │       ├── 0018_Branch___Memory_consolidation_concept.json
 │   │       ├── 0022_Branch___Intake_conversation_summaries.json
 │   │       ├── 0026_Intake_conversation_summaries.json
 │   │       ├── 0027_Trilium_AI_LLM_setup.json
 │   │       ├── 0028_LLMs_and_sycophancy_levels.json
 │   │       ├── 0031_UI_improvement_plan.json
 │   │       ├── 0035_10_27-neomem_update.json
 │   │       ├── 0044_Install_llama_cpp_on_ct201.json
 │   │       ├── 0045_AI_task_assistant.json
 │   │       ├── 0047_Project_scope_creation.json
 │   │       ├── 0052_View_docker_container_logs.json
 │   │       ├── 0053_10_21-Proxmox_fan_control.json
 │   │       ├── 0054_10_21-pytorch_branch_Quant_experiments.json
 │   │       ├── 0055_10_22_ct201branch-ssh_tut.json
 │   │       ├── 0060_Lyra_project_folder_issue.json
 │   │       ├── 0062_Build_pytorch_API.json
 │   │       ├── 0063_PokerBrain_dataset_structure.json
 │   │       ├── 0065_Install_PyTorch_setup.json
 │   │       ├── 0066_ROCm_PyTorch_setup_quirks.json
 │   │       ├── 0067_VM_model_setup_steps.json
 │   │       ├── 0070_Proxmox_disk_error_fix.json
 │   │       ├── 0072_Docker_Compose_vs_Portainer.json
 │   │       ├── 0073_Check_system_temps_Proxmox.json
 │   │       ├── 0075_Cortex_gpu_progress.json
 │   │       ├── 0076_Backup_Proxmox_before_upgrade.json
 │   │       ├── 0077_Storage_cleanup_advice.json
 │   │       ├── 0082_Install_ROCm_on_Proxmox.json
 │   │       ├── 0088_Thalamus_program_summary.json
 │   │       ├── 0094_Cortex_blueprint_development.json
 │   │       ├── 0095_mem0_advancments.json
 │   │       ├── 0096_Embedding_provider_swap.json
 │   │       ├── 0097_Update_git_commit_steps.json
 │   │       ├── 0098_AI_software_description.json
 │   │       ├── 0099_Seed_memory_process.json
 │   │       ├── 0100_Set_up_Git_repo.json
 │   │       ├── 0101_Customize_embedder_setup.json
 │   │       ├── 0102_Seeding_Local_Lyra_memory.json
 │   │       ├── 0103_Mem0_seeding_part_3.json
 │   │       ├── 0104_Memory_build_prompt.json
 │   │       ├── 0105_Git_submodule_setup_guide.json
 │   │       ├── 0106_Serve_UI_on_LAN.json
 │   │       ├── 0107_AI_name_suggestion.json
 │   │       ├── 0108_Room_X_planning_update.json
 │   │       ├── 0109_Salience_filtering_design.json
 │   │       ├── 0110_RoomX_Cortex_build.json
 │   │       ├── 0119_Explain_Lyra_cortex_idea.json
 │   │       ├── 0120_Git_submodule_organization.json
 │   │       ├── 0121_Web_UI_fix_guide.json
 │   │       ├── 0122_UI_development_planning.json
 │   │       ├── 0123_NVGRAM_debugging_steps.json
 │   │       ├── 0124_NVGRAM_setup_troubleshooting.json
 │   │       ├── 0125_NVGRAM_development_update.json
 │   │       ├── 0126_RX_-_NeVGRAM_New_Features.json
 │   │       ├── 0127_Error_troubleshooting_steps.json
 │   │       ├── 0135_Proxmox_backup_with_ABB.json
 │   │       ├── 0151_Auto-start_Lyra-Core_VM.json
 │   │       ├── 0156_AI_GPU_benchmarks_comparison.json
 │   │       └── 0251_Lyra_project_handoff.json
 │   ├── chromadb
 │   │   ├── c4f701ee-1978-44a1-9df4-3e865b5d33c1
 │   │   │   ├── data_level0.bin
 │   │   │   ├── header.bin
 │   │   │   ├── index_metadata.pickle
 │   │   │   ├── length.bin
 │   │   │   └── link_lists.bin
 │   │   └── chroma.sqlite3
 │   ├── import.log
 │   ├── lyra-chatlogs
 │   │   ├── 0000_Wire_ROCm_to_Cortex.json
 │   │   ├── 0001_Branch___10_22_ct201branch-ssh_tut.json
 │   │   ├── 0002_cortex_LLMs_11-1-25.json
 │   │   └── 0003_RAG_beta.json
 │   ├── rag_api.py
 │   ├── rag_build.py
 │   ├── rag_chat_import.py
 │   └── rag_query.py
 ├── README.md
 └── volumes
    ├── neo4j_data
    │   ├── databases
    │   │   ├── neo4j
    │   │   │   ├── database_lock
    │   │   │   ├── id-buffer.tmp.0
    │   │   │   ├── neostore
    │   │   │   ├── neostore.counts.db
    │   │   │   ├── neostore.indexstats.db
    │   │   │   ├── neostore.labeltokenstore.db
    │   │   │   ├── neostore.labeltokenstore.db.id
    │   │   │   ├── neostore.labeltokenstore.db.names
    │   │   │   ├── neostore.labeltokenstore.db.names.id
    │   │   │   ├── neostore.nodestore.db
    │   │   │   ├── neostore.nodestore.db.id
    │   │   │   ├── neostore.nodestore.db.labels
    │   │   │   ├── neostore.nodestore.db.labels.id
    │   │   │   ├── neostore.propertystore.db
    │   │   │   ├── neostore.propertystore.db.arrays
    │   │   │   ├── neostore.propertystore.db.arrays.id
    │   │   │   ├── neostore.propertystore.db.id
    │   │   │   ├── neostore.propertystore.db.index
    │   │   │   ├── neostore.propertystore.db.index.id
    │   │   │   ├── neostore.propertystore.db.index.keys
    │   │   │   ├── neostore.propertystore.db.index.keys.id
    │   │   │   ├── neostore.propertystore.db.strings
    │   │   │   ├── neostore.propertystore.db.strings.id
    │   │   │   ├── neostore.relationshipgroupstore.db
    │   │   │   ├── neostore.relationshipgroupstore.db.id
    │   │   │   ├── neostore.relationshipgroupstore.degrees.db
    │   │   │   ├── neostore.relationshipstore.db
    │   │   │   ├── neostore.relationshipstore.db.id
    │   │   │   ├── neostore.relationshiptypestore.db
    │   │   │   ├── neostore.relationshiptypestore.db.id
    │   │   │   ├── neostore.relationshiptypestore.db.names
    │   │   │   ├── neostore.relationshiptypestore.db.names.id
    │   │   │   ├── neostore.schemastore.db
    │   │   │   ├── neostore.schemastore.db.id
    │   │   │   └── schema
    │   │   │       └── index
    │   │   │           └── token-lookup-1.0
    │   │   │               ├── 1
    │   │   │               │   └── index-1
    │   │   │               └── 2
    │   │   │                   └── index-2
    │   │   ├── store_lock
    │   │   └── system
    │   │       ├── database_lock
    │   │       ├── id-buffer.tmp.0
    │   │       ├── neostore
    │   │       ├── neostore.counts.db
    │   │       ├── neostore.indexstats.db
    │   │       ├── neostore.labeltokenstore.db
    │   │       ├── neostore.labeltokenstore.db.id
    │   │       ├── neostore.labeltokenstore.db.names
    │   │       ├── neostore.labeltokenstore.db.names.id
    │   │       ├── neostore.nodestore.db
    │   │       ├── neostore.nodestore.db.id
    │   │       ├── neostore.nodestore.db.labels
    │   │       ├── neostore.nodestore.db.labels.id
    │   │       ├── neostore.propertystore.db
    │   │       ├── neostore.propertystore.db.arrays
    │   │       ├── neostore.propertystore.db.arrays.id
    │   │       ├── neostore.propertystore.db.id
    │   │       ├── neostore.propertystore.db.index
    │   │       ├── neostore.propertystore.db.index.id
    │   │       ├── neostore.propertystore.db.index.keys
    │   │       ├── neostore.propertystore.db.index.keys.id
    │   │       ├── neostore.propertystore.db.strings
    │   │       ├── neostore.propertystore.db.strings.id
    │   │       ├── neostore.relationshipgroupstore.db
    │   │       ├── neostore.relationshipgroupstore.db.id
    │   │       ├── neostore.relationshipgroupstore.degrees.db
    │   │       ├── neostore.relationshipstore.db
    │   │       ├── neostore.relationshipstore.db.id
    │   │       ├── neostore.relationshiptypestore.db
    │   │       ├── neostore.relationshiptypestore.db.id
    │   │       ├── neostore.relationshiptypestore.db.names
    │   │       ├── neostore.relationshiptypestore.db.names.id
    │   │       ├── neostore.schemastore.db
    │   │       ├── neostore.schemastore.db.id
    │   │       └── schema
    │   │           └── index
    │   │               ├── range-1.0
    │   │               │   ├── 3
    │   │               │   │   └── index-3
    │   │               │   ├── 4
    │   │               │   │   └── index-4
    │   │               │   ├── 7
    │   │               │   │   └── index-7
    │   │               │   ├── 8
    │   │               │   │   └── index-8
    │   │               │   └── 9
    │   │               │       └── index-9
    │   │               └── token-lookup-1.0
    │   │                   ├── 1
    │   │                   │   └── index-1
    │   │                   └── 2
    │   │                       └── index-2
    │   ├── dbms
    │   │   └── auth.ini
    │   ├── server_id
    │   └── transactions
    │       ├── neo4j
    │       │   ├── checkpoint.0
    │       │   └── neostore.transaction.db.0
    │       └── system
    │           ├── checkpoint.0
    │           └── neostore.transaction.db.0
    └── postgres_data  [error opening dir]
@@ -1,31 +0,0 @@
 """Environment-driven configuration."""
 from __future__ import annotations
 import os
 from dataclasses import dataclass
 from pathlib import Path
 from dotenv import load_dotenv
 load_dotenv()
@dataclass(frozen=True)
 class Config:
    local_base_url: str
    local_model: str
    openai_api_key: str
    cloud_model: str
    embed_model: str
    db_path: Path
 def load() -> Config:
    return Config(
        local_base_url=os.getenv("LOCAL_BASE_URL", "http://localhost:11434"),
        local_model=os.getenv("LOCAL_MODEL", "qwen2.5:7b-instruct"),
        openai_api_key=os.getenv("OPENAI_API_KEY", ""),
        cloud_model=os.getenv("CLOUD_MODEL", "gpt-4o-mini"),
        embed_model=os.getenv("EMBED_MODEL", "text-embedding-3-small"),
        db_path=Path(os.getenv("LYRA_DB_PATH", "data/lyra.db")),
    )
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
claude	89988da472	Security: Update .gitignore to exclude runtime data, sessions, and logs - Add session files exclusion (core/relay/sessions/) - Add log directories exclusion (logs/, -logs/, intake-logs/) - Add runtime database files exclusion (.db, .sqlite) - Add temporary files exclusion (.cache/, .tmp, .temp) - Remove previously tracked session and database files from git These files contain runtime data and should not be version controlled.	2026-01-02 13:41:28 -05:00
claude	b700ac3808	tool improvment	2025-12-31 22:36:24 -05:00
claude	6716245a99	v0.9.1	2025-12-29 22:44:47 -05:00
claude	a900110fe4	primary backend added to standard mode.	2025-12-29 02:10:59 -05:00
claude	794baf2a96	0.9.0 - Added Trilium ETAPI integration. Lyra can now: Search trilium notes and create new notes. with proper ETAPI auth.	2025-12-29 01:58:20 -05:00
claude	64429b19e6	feat: Implement Trillium notes executor for searching and creating notes via ETAPI - Added `trillium.py` for searching and creating notes with Trillium's ETAPI. - Implemented `search_notes` and `create_note` functions with appropriate error handling and validation. feat: Add web search functionality using DuckDuckGo - Introduced `web_search.py` for performing web searches without API keys. - Implemented `search_web` function with result handling and validation. feat: Create provider-agnostic function caller for iterative tool calling - Developed `function_caller.py` to manage LLM interactions with tools. - Implemented iterative calling logic with error handling and tool execution. feat: Establish a tool registry for managing available tools - Created `registry.py` to define and manage tool availability and execution. - Integrated feature flags for enabling/disabling tools based on environment variables. feat: Implement event streaming for tool calling processes - Added `stream_events.py` to manage Server-Sent Events (SSE) for tool calling. - Enabled real-time updates during tool execution for enhanced user experience. test: Add tests for tool calling system components - Created `test_tools.py` to validate functionality of code execution, web search, and tool registry. - Implemented asynchronous tests to ensure proper execution and result handling. chore: Add Dockerfile for sandbox environment setup - Created `Dockerfile` to set up a Python environment with necessary dependencies for code execution. chore: Add debug regex script for testing XML parsing - Introduced `debug_regex.py` to validate regex patterns against XML tool calls. chore: Add HTML template for displaying thinking stream events - Created `test_thinking_stream.html` for visualizing tool calling events in a user-friendly format. test: Add tests for OllamaAdapter XML parsing - Developed `test_ollama_parser.py` to validate XML parsing with various test cases, including malformed XML.	2025-12-26 03:49:20 -05:00
claude	f1471cde84	docs updated v0.7.0	2025-12-22 01:40:24 -05:00
claude	b4613ac30c	sessions improved, v0.7.0	2025-12-21 15:50:52 -05:00
claude	01d4811717	mode selection, settings added to ui	2025-12-21 14:30:32 -05:00
claude	ceb60119fb	simple context added to standard mode	2025-12-21 13:01:00 -05:00
claude	d09425c37b	v0.7.0 - Standard non cortex mode enabled	2025-12-20 04:15:22 -05:00
claude	6bb800f5f8	Cortex debugging logs cleaned up	2025-12-20 02:49:20 -05:00
claude	970907cf1b	Docs updated v0.6.0	2025-12-19 17:43:22 -05:00
claude	55093a8437	cleanup ignore stuff	2025-12-17 02:46:23 -05:00
claude	41971de5bb	Merge branch 'dev' of https://github.com/serversdwn/project-lyra into dev	2025-12-17 01:47:30 -05:00
claude	4b21082959	ignore	2025-12-17 01:47:19 -05:00
claude	098aefee7c	complete breakdown for AI agents added	2025-12-15 11:49:49 -05:00
claude	2da58a13c7	neomem disabled	2025-12-15 04:10:03 -05:00
claude	d4fd393f52	autonomy phase 2.5 - tightening up some stuff in the pipeline	2025-12-15 01:56:57 -05:00
claude	193bf814ec	autonomy phase 2	2025-12-14 14:43:08 -05:00
claude	49f792f20c	autonomy build, phase 1	2025-12-14 01:44:05 -05:00
claude	fa4dd46cfc	cortex pipeline stablized, inner monologue is now determining user intent and tone	2025-12-13 04:13:12 -05:00
claude	8554249421	autonomy scaffold	2025-12-13 02:55:49 -05:00
claude	fe86759cfd	v0.5.2 - fixed: llm router async, relay-UI mismatch, intake summarization failure, among others. Memory relevance thresh. increased.	2025-12-12 02:58:23 -05:00
claude	6a20d3981f	v0.6.1 - reinstated UI, relay > cortex pipeline working	2025-12-11 16:28:25 -05:00
claude	30f6c1a3da	autonomy, initial scaffold	2025-12-11 13:12:44 -05:00
claude	d5d7ea3469	docs updated for v0.5.1	2025-12-11 03:49:23 -05:00
claude	e45cdbe54e	gitignore updated, to ignore vscode settings	2025-12-11 03:42:30 -05:00
claude	a2f0952a62	cleaning up deprecated files	2025-12-11 03:40:47 -05:00
claude	5ed3fd0982	cortex rework continued.	2025-12-11 02:50:23 -05:00
claude	8c914906e5	deprecated old intake folder	2025-12-06 04:38:11 -05:00
claude	4acaddfd12	intake/relay rewire	2025-12-06 04:32:42 -05:00
claude	fc85557f76	add. cleanup	2025-11-30 03:58:15 -05:00
claude	320bf4439b	intake internalized by cortex, removed intake route in relay	2025-11-29 19:08:15 -05:00
claude	cc014d0a73	cortex 0.2.... i think?	2025-11-29 05:14:32 -05:00
claude	ebe3e27095	fixed neomem URL request failure, now using correct variable	2025-11-28 19:50:53 -05:00
claude	b0f42ba86e	context added, wired in. first attempt	2025-11-28 19:29:41 -05:00
claude	d9281a1816	docs updated	2025-11-28 18:05:59 -05:00
claude	a83405beb1	Major rewire, all modules connected. Intake still wonkey	2025-11-28 15:14:47 -05:00
claude	734999e8bb	Cortex rework in progress	2025-11-26 18:01:48 -05:00
claude	a087de9790	Fixin' crap so relay works again. pre llm redo	2025-11-26 14:20:47 -05:00
claude	0a091fc42c	env cleanup round 2	2025-11-26 03:18:15 -05:00
claude	cb00474ab3	reorganizing and restructuring	2025-11-26 02:28:00 -05:00
claude	5492d9c0c5	intital file restructure	2025-11-25 20:50:05 -05:00
claude	b5fe47074a	Merge branch 'main' of https://github.com/serversdwn/project-lyra	2025-11-17 03:41:51 -05:00
claude	a19231abd0	WIP local changes	2025-11-17 03:39:56 -05:00
claude	e5e32f2683	Add MI50 + vLLM full setup guide	2025-11-17 03:34:23 -05:00
claude	180af9eb63	Remove rag chatlogs and add ignore rules	2025-11-16 03:20:10 -05:00
claude	94fb091e59	Initial clean commit - unified Lyra stack	2025-11-16 03:17:32 -05:00
		`@@ -0,0 +1 @@`
							`"""Executive planning and decision-making module."""`
		`@@ -0,0 +1 @@`
							`"""Pattern learning and adaptation system."""`
		`@@ -0,0 +1 @@`
							`"""Proactive monitoring and suggestion system."""`
		`@@ -0,0 +1 @@`
							`# Ingest module - handles communication with Intake service`
		`@@ -0,0 +1 @@`
							`# LLM module - provides LLM routing and backend abstraction`
		`@@ -0,0 +1 @@`
							`# Persona module - applies Lyra's personality and speaking style`
		`@@ -0,0 +1 @@`
							`# Reasoning module - multi-stage reasoning pipeline`