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Merge pull request 'Update to v0.9.1' (#1) from dev into main

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Reviewed-on: http://10.0.0.2:9010/serversdown/project-lyra/pulls/1
This commit was merged in pull request #1.
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serversdown
2026-01-18 02:46:24 -05:00
parent b5fe47074a 89988da472
commit 61d354c489
126 changed files with 20455 additions and 7066 deletions
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# ====================================
# 🌌 GLOBAL LYRA CONFIG
# ====================================
LOCAL_TZ_LABEL=America/New_York
DEFAULT_SESSION_ID=default
# ====================================
# 🤖 LLM BACKEND OPTIONS
# ====================================
# 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

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# ============================================================================
# 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
# ============================================================================

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__pycache__/
*.pyc
*.log
# Env files (store secrets locally)
/.vscode/
.vscode/
# =============================
# 🔐 Environment files (NEVER commit secrets!)
# =============================
# Ignore all .env files
.env
.env.local
.env.*.local
**/.env
**/.env.local
# BUT track .env.example templates (safe to commit)
!.env.example
!**/.env.example
# Ignore backup directory
.env-backups/
# =============================
# 🐳 Docker volumes (HUGE)
@@ -40,3 +55,29 @@ models/
# =============================
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/
# Temporary and cache files
.cache/
*.tmp
*.temp

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# 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).

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# 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!** 🎯

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# 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)**

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# 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.** 🎯

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# "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

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# 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.

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# 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

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# Lyra Core — Project Summary
## v0.4 (2025-10-03)
### 🧠 High-Level Architecture
- **Lyra Core (v0.3.1)** — Orchestration layer.
- Accepts chat requests (`/v1/chat/completions`).
- Routes through Cortex for subconscious annotation.
- Stores everything in Mem0 (no discard).
- Fetches persona + relevant memories.
- Injects context back into LLM.
- **Cortex (v0.3.0)** — Subconscious annotator.
- Runs locally via `llama.cpp` (Phi-3.5-mini Q4_K_M).
- Strict JSON schema:
```json
{
"sentiment": "positive" | "neutral" | "negative",
"novelty": 0.0–1.0,
"tags": ["keyword", "keyword"],
"notes": "short string"
}
```
- Normalizes keys (lowercase).
- Strips Markdown fences before parsing.
- Configurable via `.env` (`CORTEX_ENABLED=true|false`).
- Currently generates annotations, but not yet persisted into Mem0 payloads (stored as empty `{cortex:{}}`).
- **Mem0 (v0.4.0)** — Persistent memory layer.
- Handles embeddings, graph storage, and retrieval.
- Dual embedder support:
- **OpenAI Cloud** (`text-embedding-3-small`, 1536-dim).
- **HuggingFace TEI** (gte-Qwen2-1.5B-instruct, 1536-dim, hosted on 3090).
- Environment toggle for provider (`.env.openai` vs `.env.3090`).
- Memory persistence in Postgres (`payload` JSON).
- CSV export pipeline confirmed (id, user_id, data, created_at).
- **Persona Sidecar**
- Provides personality, style, and protocol instructions.
- Injected at runtime into Core prompt building.
---
### 🚀 Recent Changes
- **Mem0**
- Added HuggingFace TEI integration (local 3090 embedder).
- Enabled dual-mode environment switch (OpenAI cloud ↔ local TEI).
- Fixed `.env` line ending mismatch (CRLF vs LF).
- Added memory dump/export commands for Postgres.
- **Core/Relay**
- No major changes since v0.3.1 (still routing input → Cortex → Mem0).
- **Cortex**
- Still outputs annotations, but not yet persisted into Mem0 payloads.
---
### 📈 Versioning
- **Lyra Core** → v0.3.1
- **Cortex** → v0.3.0
- **Mem0** → v0.4.0
---
### 📋 Next Steps
- [ ] Wire Cortex annotations into Mem0 payloads (`cortex` object).
- [ ] Add “export all memories” script to standard workflow.
- [ ] Consider async embedding for faster `mem.add`.
- [ ] Build visual diagram of data flow (Core ↔ Cortex ↔ Mem0 ↔ Persona).
- [ ] Explore larger LLMs for Cortex (Qwen2-7B, etc.) for richer subconscious annotation.

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core/backups/mem0_history_20250927_220925.tgz
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43
core/docker-compose.yml
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@@ -1,43 +0,0 @@
services:
relay:
build:
context: ./relay
container_name: relay
restart: always
ports:
- "7078:7078"
env_file:
- .env
volumes:
- ./relay/sessions:/app/sessions
networks:
- lyra-net
# persona-sidecar:
# build:
# context: ./persona-sidecar
# container_name: persona-sidecar
# env_file:
# - .env
# ports:
# - "7080:7080"
# volumes:
# - ./persona-sidecar/personas.json:/app/personas.json:rw
# restart: unless-stopped
# networks:
# - lyra-net
lyra-ui:
image: nginx:alpine
container_name: lyra-ui
restart: unless-stopped
ports:
- "8081:80"
volumes:
- ./ui:/usr/share/nginx/html:ro
networks:
- lyra-net
networks:
lyra-net:
external: true

90
core/relay/lib/llm.js
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@@ -38,6 +38,8 @@ async function tryBackend(backend, messages) {
// 🧩 Normalize replies
let reply = "";
let parsedData = null;
try {
if (isOllama) {
// Ollama sometimes returns NDJSON lines; merge them
@@ -49,21 +51,75 @@ async function tryBackend(backend, messages) {
.join("");
reply = merged.trim();
} else {
const data = JSON.parse(raw);
console.log("🔍 RAW LLM RESPONSE:", JSON.stringify(data, null, 2));
parsedData = JSON.parse(raw);
reply =
data?.choices?.[0]?.text?.trim() ||
data?.choices?.[0]?.message?.content?.trim() ||
data?.message?.content?.trim() ||
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, backend: backend.key };
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`);
}
// ------------------------------------
@@ -77,17 +133,29 @@ export async function callSpeechLLM(messages) {
{ 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 {
console.log(`🧠 Trying backend: ${b.key.toUpperCase()} (${b.url})`);
const out = await tryBackend(b, messages);
console.log(`✅ Success via ${b.key.toUpperCase()}`);
logLLMCall(b, messages, out);
return out;
} catch (err) {
console.warn(`⚠️ ${b.key.toUpperCase()} failed: ${err.message}`);
logLLMCall(b, messages, null, err);
failedBackends.push({ backend: b.key, error: err.message });
}
}
// All backends failed - log summary
console.error(`\n${'='.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");
}

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core/relay/package-lock.json generated
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2
core/relay/package.json
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@@ -9,7 +9,7 @@
"dependencies": {
"cors": "^2.8.5",
"dotenv": "^16.6.1",
"express": "^4.18.2",
"express": "^4.21.2",
"mem0ai": "^2.1.38",
"node-fetch": "^3.3.2"
}

636
core/relay/server.js
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@@ -1,350 +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";
import fs from "fs/promises";
import path from "path";
import { reflectWithCortex, ingestToCortex } from "./lib/cortex.js";
import { fileURLToPath } from "url";
dotenv.config();
const sessionsDir = path.join(process.cwd(), "sessions");
if (!fs.existsSync(sessionsDir)) fs.mkdirSync(sessionsDir);
// 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());
// Cache and normalize env flags/values once
const {
NEOMEM_API,
MEM0_API_KEY,
OPENAI_API_KEY,
OLLAMA_URL,
PERSONA_URL,
CORTEX_ENABLED,
PORT: PORT_ENV,
DEBUG_PROMPT,
} = process.env;
const PORT = Number(process.env.PORT || 7078);
const PORT = Number(PORT_ENV) || 7078;
const cortexEnabled = String(CORTEX_ENABLED).toLowerCase() === "true";
const debugPrompt = String(DEBUG_PROMPT).toLowerCase() === "true";
// 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";
// Basic env validation warnings (non-fatal)
if (!NEOMEM_API || !MEM0_API_KEY) {
console.warn("⚠️ NeoMem configuration missing: NEOMEM_API or MEM0_API_KEY not set.");
}
// -----------------------------------------------------
// 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;
/* ------------------------------
Helpers for NeoMem REST API
--------------------------------*/
// Small helper for fetch with timeout + JSON + error detail
async function fetchJSON(url, options = {}, timeoutMs = 30000) {
const controller = new AbortController();
const t = setTimeout(() => controller.abort(), timeoutMs);
try {
const resp = await fetch(url, { ...options, signal: controller.signal });
const text = await resp.text();
const parsed = text ? JSON.parse(text) : null;
if (!resp.ok) {
const msg = parsed?.error || parsed?.message || text || resp.statusText;
throw new Error(`${resp.status} ${msg}`);
}
return parsed;
} finally {
clearTimeout(t);
json = raw ? JSON.parse(raw) : null;
} catch (e) {
throw new Error(`Non-JSON from ${url}: ${raw}`);
}
if (!resp.ok) {
throw new Error(json?.detail || json?.error || raw);
}
return json;
}
async function memAdd(content, userId, sessionId, cortexData) {
const url = `${NEOMEM_API}/memories`;
// -----------------------------------------------------
// 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 = {
messages: [{ role: "user", content }],
user_id: userId,
// run_id: sessionId,
metadata: { source: "relay", cortex: cortexData },
session_id,
user_prompt: user_msg
};
return fetchJSON(url, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${MEM0_API_KEY}`,
},
body: JSON.stringify(payload),
});
}
async function memSearch(query, userId, sessionId) {
const url = `${NEOMEM_API}/search`;
const payload = { query, user_id: userId };
return fetchJSON(url, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${MEM0_API_KEY}`,
},
body: JSON.stringify(payload),
});
}
/* ------------------------------
Utility to time spans
--------------------------------*/
async function span(name, fn) {
const start = Date.now();
try {
return await fn();
} finally {
console.log(`${name} took ${Date.now() - start}ms`);
// Add backend parameter if provided (only for standard mode)
if (backend && mode === "standard") {
payload.backend = backend;
}
// Call appropriate Cortex endpoint
try {
reason = await postJSON(endpoint, payload);
} catch (e) {
console.error(`Relay → Cortex.${modeName} error:`, e.message);
throw new Error(`cortex_${modeName}_failed: ${e.message}`);
}
// Correct persona field
const persona =
reason.persona ||
reason.final_output ||
"(no persona text)";
// Return final answer
return {
session_id,
reply: persona
};
}
/* ------------------------------
Healthcheck
--------------------------------*/
app.get("/_health", (req, res) => {
res.json({ ok: true, time: new Date().toISOString() });
});
/* ------------------------------
Sessions
--------------------------------*/
// List all saved sessions
app.get("/sessions", (_, res) => {
const list = fs.readdirSync(sessionsDir)
.filter(f => f.endsWith(".json"))
.map(f => f.replace(".json", ""));
res.json(list);
});
// Load a single session
app.get("/sessions/:id", (req, res) => {
const file = path.join(sessionsDir, `${req.params.id}.json`);
if (!fs.existsSync(file)) return res.json([]);
res.json(JSON.parse(fs.readFileSync(file, "utf8")));
});
// Save or update a session
app.post("/sessions/:id", (req, res) => {
const file = path.join(sessionsDir, `${req.params.id}.json`);
fs.writeFileSync(file, JSON.stringify(req.body, null, 2));
// -----------------------------------------------------
// HEALTHCHECK
// -----------------------------------------------------
app.get("/_health", (_, res) => {
res.json({ ok: true });
});
/* ------------------------------
Chat completion endpoint
--------------------------------*/
// -----------------------------------------------------
// OPENAI-COMPATIBLE ENDPOINT
// -----------------------------------------------------
app.post("/v1/chat/completions", async (req, res) => {
try {
const { model, messages, sessionId: clientSessionId } = req.body || {};
if (!Array.isArray(messages) || !messages.length) {
return res.status(400).json({ error: "invalid_messages" });
}
if (!model || typeof model !== "string") {
return res.status(400).json({ error: "invalid_model" });
const session_id = req.body.session_id || req.body.sessionId || req.body.user || "default";
const messages = req.body.messages || [];
const lastMessage = messages[messages.length - 1];
const user_msg = lastMessage?.content || "";
const mode = req.body.mode || "cortex"; // Get mode from request, default to cortex
const backend = req.body.backend || null; // Get backend preference
if (!user_msg) {
return res.status(400).json({ error: "No message content provided" });
}
const sessionId = clientSessionId || "default";
const userId = "brian"; // fixed for now
console.log(`Relay (v1) → received: "${user_msg}" [mode: ${mode}${backend ? `, backend: ${backend}` : ''}]`);
console.log(`🛰️ Incoming request. Session: ${sessionId}`);
const result = await handleChatRequest(session_id, user_msg, mode, backend);
// Find last user message efficiently
const lastUserMsg = [...messages].reverse().find(m => m.role === "user")?.content;
if (!lastUserMsg) {
return res.status(400).json({ error: "no_user_message" });
}
// 1. Cortex Reflection (new pipeline)
/*let reflection = {};
try {
console.log("🧠 Reflecting with Cortex...");
const memoriesPreview = []; // we'll fill this in later with memSearch
reflection = await reflectWithCortex(lastUserMsg, memoriesPreview);
console.log("🔍 Reflection:", reflection);
} catch (err) {
console.warn("⚠️ Cortex reflect failed:", err.message);
reflection = { error: err.message };
}*/
// 2. Search memories
/* let memorySnippets = [];
await span("mem.search", async () => {
if (NEOMEM_API && MEM0_API_KEY) {
try {
const { results } = await memSearch(lastUserMsg, userId, sessionId);
if (results?.length) {
console.log(`📚 Mem0 hits: ${results.length}`);
results.forEach((r, i) =>
console.log(` ${i + 1}) ${r.memory} (score ${Number(r.score).toFixed(3)})`)
);
memorySnippets = results.map((r, i) => `${i + 1}) ${r.memory}`);
} else {
console.log("😴 No memories found");
}
} catch (e) {
console.warn("⚠️ mem.search failed:", e.message);
}
res.json({
id: `chatcmpl-${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
}
});*/
// 3. Fetch persona
/* let personaText = "Persona: Lyra 🤖 friendly, concise, poker-savvy.";
await span("persona.fetch", async () => {
try {
if (PERSONA_URL) {
const data = await fetchJSON(PERSONA_URL);
if (data?.persona) {
const name = data.persona.name ?? "Lyra";
const style = data.persona.style ?? "friendly, concise";
const protocols = Array.isArray(data.persona.protocols) ? data.persona.protocols.join(", ") : "";
personaText = `Persona: ${name} 🤖 ${style}. Protocols: ${protocols}`.trim();
}
}
} catch (err) {
console.error("💥 persona.fetch failed", err);
}
}); */
// 1. Ask Cortex to build the final prompt
let cortexPrompt = "";
try {
console.log("🧠 Requesting prompt from Cortex...");
const response = await fetch(`${process.env.CORTEX_API_URL || "http://10.0.0.41:7081"}/reason`, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
prompt: lastUserMsg,
session_id: sessionId,
user_id: userId
})
});
const data = await response.json();
cortexPrompt = data.full_prompt || data.prompt || "";
console.log("🧩 Cortex returned prompt");
} catch (err) {
console.warn("⚠️ Cortex prompt build failed:", err.message);
}
// 4. Build final messages
const injectedMessages = [
{ role: "system", content: cortexPrompt || "You are Lyra." },
...messages,
];
if (debugPrompt) {
console.log("\n==== Injected Prompt ====");
console.log(JSON.stringify(injectedMessages, null, 2));
console.log("=========================\n");
}
// 5. Call LLM (OpenAI or Ollama)
const isOllama = model.startsWith("ollama:");
const llmUrl = isOllama
? `${OLLAMA_URL}/api/chat`
: "https://api.openai.com/v1/chat/completions";
const llmHeaders = isOllama
? { "Content-Type": "application/json" }
: {
"Content-Type": "application/json",
Authorization: `Bearer ${OPENAI_API_KEY}`,
};
const llmBody = {
model: isOllama ? model.replace("ollama:", "") : model,
messages: injectedMessages, // <-- make sure injectedMessages is defined above this section
stream: false,
};
const data = await fetchJSON(llmUrl, {
method: "POST",
headers: llmHeaders,
body: JSON.stringify(llmBody),
});
// define once for everything below
const assistantReply = isOllama
? data?.message?.content
: data?.choices?.[0]?.message?.content || data?.choices?.[0]?.text || "";
// 🧠 Send exchange back to Cortex for ingest
try {
await ingestToCortex(lastUserMsg, assistantReply || "", {}, sessionId);
console.log("📤 Sent exchange back to Cortex ingest");
} catch (err) {
console.warn("⚠️ Cortex ingest failed:", err.message);
}
// 💾 Save exchange to session log
try {
const logFile = path.join(sessionsDir, `${sessionId}.jsonl`);
const entry = JSON.stringify({
ts: new Date().toISOString(),
turn: [
{ role: "user", content: lastUserMsg },
{ role: "assistant", content: assistantReply || "" }
]
}) + "\n";
fs.appendFileSync(logFile, entry, "utf8");
console.log(`🧠 Logged session exchange → ${logFile}`);
} catch (e) {
console.warn("⚠️ Session log write failed:", e.message);
}
// 🔄 Forward user↔assistant exchange to Intake summarizer
if (process.env.INTAKE_API_URL) {
try {
const intakePayload = {
session_id: sessionId,
turns: [
{ role: "user", content: lastUserMsg },
{ role: "assistant", content: assistantReply || "" }
]
};
await fetch(process.env.INTAKE_API_URL, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(intakePayload),
});
console.log("📨 Sent exchange to Intake summarizer");
} catch (err) {
console.warn("⚠️ Intake post failed:", err.message);
}
}
if (isOllama) {
res.json({
id: "ollama-" + Date.now(),
object: "chat.completion",
created: Math.floor(Date.now() / 1000),
model,
choices: [
{
index: 0,
message: data?.message || { role: "assistant", content: "" },
finish_reason: "stop",
},
],
});
} else {
res.json(data);
}
});
} catch (err) {
console.error("💥 relay error", err);
res.status(500).json({ error: "relay_failed", detail: err.message });
console.error("Relay v1 fatal:", err);
res.status(500).json({
error: {
message: err.message || String(err),
type: "server_error",
code: "relay_failed"
}
});
}
});
/* ------------------------------
Start server
--------------------------------*/
app.listen(PORT, () => {
console.log(`Relay listening on port ${PORT}`);
// -----------------------------------------------------
// MAIN ENDPOINT (Lyra-native UI)
// -----------------------------------------------------
app.post("/chat", async (req, res) => {
try {
const session_id = req.body.session_id || "default";
const user_msg = req.body.message || "";
const mode = req.body.mode || "cortex"; // Get mode from request, default to cortex
const backend = req.body.backend || null; // Get backend preference
console.log(`Relay → received: "${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}`);
});

50
core/relay/sessions/sess-6rxu7eia.json
View File

@@ -1,50 +0,0 @@
[
{
"role": "user",
"content": "Hey im testing stuff! how goes it?"
},
{
"role": "assistant",
"content": "Hey! I'm here and ready to help. What are you testing?"
},
{
"role": "user",
"content": "testing the logic loop for your brain!"
},
{
"role": "assistant",
"content": "Sounds interesting! How can I assist you with that?"
},
{
"role": "user",
"content": "Just keep answering me!@"
},
{
"role": "assistant",
"content": "Absolutely! I'm here to respond. What’s next?"
},
{
"role": "user",
"content": "how about now how are you?"
},
{
"role": "assistant",
"content": "I'm doing well, thanks for asking! How about you?"
},
{
"role": "user",
"content": "are you able to tell me what your prompts are saying to you?"
},
{
"role": "assistant",
"content": "I can’t share specific internal prompts or system messages, but I can tell you that I’m designed to understand and respond to your questions as best as I can! What else would you like to know?"
},
{
"role": "user",
"content": "ok thanks how is the cheese i gave you?"
},
{
"role": "assistant",
"content": "I appreciate the gesture! If I could taste it, I’m sure it would be delightful. What kind of cheese was it?"
}
]

6
core/relay/sessions/sess-6rxu7eia.jsonl
View File

@@ -1,6 +0,0 @@
{"ts":"2025-11-12T20:41:32.497Z","turn":[{"role":"user","content":"Hey im testing stuff! how goes it?"},{"role":"assistant","content":"Hey! I'm here and ready to help. What are you testing?"}]}
{"ts":"2025-11-12T20:59:32.992Z","turn":[{"role":"user","content":"testing the logic loop for your brain!"},{"role":"assistant","content":"Sounds interesting! How can I assist you with that?"}]}
{"ts":"2025-11-12T22:08:09.644Z","turn":[{"role":"user","content":"Just keep answering me!@"},{"role":"assistant","content":"Absolutely! I'm here to respond. What’s next?"}]}
{"ts":"2025-11-12T22:08:48.693Z","turn":[{"role":"user","content":"how about now how are you?"},{"role":"assistant","content":"I'm doing well, thanks for asking! How about you?"}]}
{"ts":"2025-11-12T22:10:46.435Z","turn":[{"role":"user","content":"are you able to tell me what your prompts are saying to you?"},{"role":"assistant","content":"I can’t share specific internal prompts or system messages, but I can tell you that I’m designed to understand and respond to your questions as best as I can! What else would you like to know?"}]}
{"ts":"2025-11-12T23:10:16.321Z","turn":[{"role":"user","content":"ok thanks how is the cheese i gave you?"},{"role":"assistant","content":"I appreciate the gesture! If I could taste it, I’m sure it would be delightful. What kind of cheese was it?"}]}

122
core/relay/sessions/sess-l08ndm60.json
View File

@@ -1,122 +0,0 @@
[
{
"role": "user",
"content": "Yo yo yo wusup"
},
{
"role": "assistant",
"content": "Hey Brian! Not much, just here to help you out. What's up with you?"
},
{
"role": "user",
"content": "werkin on codin'"
},
{
"role": "assistant",
"content": "Nice! What are you working on? Need any help with coding or anything related to your projects?"
},
{
"role": "user",
"content": "working on a little program that saves our chats so you can search them again in different chats"
},
{
"role": "assistant",
"content": "That sounds like a great idea! Having a program to save and search through our chats could really enhance the way you interact with Lyra. Do you have a specific approach in mind for how you'll implement the search functionality?"
},
{
"role": "user",
"content": "just adding a simple logger into the code you run on"
},
{
"role": "assistant",
"content": "That sounds like a solid plan! Implementing a simple logger can make it easier to keep track of conversations. Are you thinking of any specific features for the search functionality, like filtering by keywords or date?"
},
{
"role": "user",
"content": "i'll like use a basic word search, possibly integrate it into a rag system i have built"
},
{
"role": "assistant",
"content": "Integrating a basic word search into your RAG system sounds like a smart approach! It could enhance the way you retrieve specific chats. Are you planning to use any specific libraries or tools for your RAG system?"
},
{
"role": "user",
"content": "no clue!"
},
{
"role": "assistant",
"content": "No worries! If you need any suggestions on libraries or tools that could help you with implementing the RAG system or the word search feature, just let me know. I'm here to help!"
},
{
"role": "user",
"content": "just having some coding issues, i think i was editing the wrong version of a file LOL"
},
{
"role": "assistant",
"content": "That can definitely happen! It can be tricky to keep track of different versions. Have you made any backups or version control for your projects? That might help avoid mix-ups in the future!"
},
{
"role": "user",
"content": "i have everything syncing back to my main dev computer, i just do it manually. i have no idea how to use git"
},
{
"role": "assistant",
"content": "(no reply)"
},
{
"role": "user",
"content": "i have everything syncing back to my main dev computer, i just do it manually. i have no idea how to use git"
},
{
"role": "assistant",
"content": "(no reply)"
},
{
"role": "user",
"content": "i have everything syncing back to my main dev computer, i just do it manually. i have no idea how to use git"
},
{
"role": "assistant",
"content": "Manually syncing is a good start, but learning Git can really help streamline your workflow and keep track of changes. If you're interested, I can provide some resources or tips on how to get started with Git! It can make managing different versions of your code much easier."
},
{
"role": "user",
"content": "what is the point of git? how can it streamline my work?"
},
{
"role": "assistant",
"content": "Git is a version control system that helps you keep track of changes in your code over time. Here are some ways it can streamline your work:\n\n1. **Version Tracking**: Git allows you to save versions of your code so you can revert back to previous versions if needed. This is especially helpful if you make a change that breaks something.\n\n2. **Collaboration**: If you ever work with others, Git makes it easy to collaborate on projects. You can work on separate branches, merge changes, and resolve conflicts.\n\n3. **Branching**: You can create branches for new features or experiments without affecting the main codebase. Once you're satisfied with the changes, you can merge them back.\n\n4. **Snapshotting**: Git takes snapshots of your code at different points in time, so you can easily see what your code looked like at any point.\n\n5. **Easy Backup**: By pushing your code to a remote repository (like GitHub or GitLab), you can back up your work and access it from anywhere.\n\nLearning Git might take a bit of time, but it can greatly enhance your coding efficiency and help you manage your projects better. If you're interested, I can point you to some beginner-friendly tutorials!"
},
{
"role": "user",
"content": "say i am writing one program on my main computer, and then im working on one that lives in a VM. Can i put them both in the same git?"
},
{
"role": "assistant",
"content": "Yes, you can use the same Git repository for your code on both your main computer and the VM. Here’s how you can do it:\n\n1. **Create a Git Repository**: You can create a Git repository on your main computer for your program. Use the command `git init` in your project's directory to initialize it.\n\n2. **Commit Changes**: As you make changes to your code on your main computer, you can use `git add .` to stage changes and `git commit -m \"your message\"` to commit them.\n\n3. **Clone the Repository on your VM**: You can then clone the Git repository on your VM using `git clone <repository-url>`. This gives you a copy of the same repository on your VM.\n\n4. **Work on Both**: You can now work on either machine. When you make changes on one, commit those changes, and then pull the latest changes from the other machine to keep everything in sync.\n\n5. **Push and Pull**: Use `git push` to push your changes to a remote repository (like GitHub) if you're using one. Use `git pull` on the other machine to pull the latest code.\n\nJust be careful about making sure you commit and push your changes before switching between machines to avoid conflicts. This setup allows you to maintain a single version of your code across both systems. If you need help with specific commands or setups, just let me know!"
},
{
"role": "user",
"content": "hmm i guess that might be useful. Im writting code that is running on a VM. I want to test and experiment while i write the code. The VM is headless though so i've been doing all my editing and work via SSH and SFTP."
},
{
"role": "assistant",
"content": "Using a VM and working through SSH and SFTP is a solid way to manage your coding environment! Git can definitely be beneficial in this setup as well. Here’s how it can help:\n\n1. **Easier Code Management**: With Git, you can track changes to your code directly within the VM, making it easier to manage versions as you experiment.\n\n2. **Experiment Branches**: You can create branches for different experiments. This lets you test new features or fixes without affecting your main code, and you can easily switch back and forth between branches.\n\n3. **Collaborative Testing**: If you ever work with others or want to test things collaboratively, you can push changes to a remote repository, making it easy for others to pull and test the latest code.\n\n4. **Integrated with SSH**: You can run Git commands directly from your SSH terminal on the VM, making it straightforward to use. You won’t need any additional tools.\n\n5. **Seamless Remote Access**: Since you’re using SFTP, you can clone the repository to your VM. This way, you can easily pull updates you've made on your main computer, or push changes made on the VM back to the main repository.\n\nOverall, integrating Git into your workflow can enhance your coding experience while working in a headless environment. If you decide to go for it, I can help with the initial setup or any Git-related questions you might have!"
},
{
"role": "user",
"content": "so is git like a compressed version of a directory?"
},
{
"role": "assistant",
"content": "Not exactly, but that's a good way to think about some of its features! Here's a more precise breakdown:\n\n1. **Snapshots, Not Compression**: Git takes \"snapshots\" of your files at different points in time, meaning it records a full version of your directory's contents every time you make a commit. While this does involve storing the files, it doesn't compress them in the traditional sense—Git keeps track of changes efficiently, so only the differences between versions are stored after the first snapshot.\n\n2. **Directory Structure**: Git is designed to track changes in your source code, including the entire directory structure, file contents, and metadata. It allows you to manage multiple versions of a directory and the files within it.\n\n3. **Branching and Merging**: Git allows you to create branches, which can be thought of as \"parallel versions\" of your directory. Each branch can have different sets of changes, and you can merge these branches to integrate different features or fixes.\n\n4. **History Tracking**: Git also maintains a history of changes, so you can navigate through previous versions of your directory. This includes seeing what changes were made, who made them, and when they occurred.\n\nThe combination of these features makes Git a powerful tool for version control, allowing you to manage your projects and code changes more effectively. If you're curious about any specific aspect of Git or how it works, feel free to ask!"
},
{
"role": "user",
"content": "hmm i still dont see the point of it. atleast not for this project. This project is mostly about learning how shit works, im not planning to push this as a product or collab with anyone"
},
{
"role": "assistant",
"content": "That makes total sense! If this project is primarily for learning and experimentation, you might not need the full functionality of Git right now. Many developers start without it, especially when they’re working on smaller projects or learning something new.\n\nHere are a few reasons you might still consider using Git, even for personal learning projects:\n\n1. **Tracking Changes**: It can help you understand what changes you made over time. You can easily go back to a previous point if something breaks or if you want to see how you arrived at your current solution.\n\n2. **Learning Opportunity**: Getting familiar with Git now can make it easier when you start working on larger projects or collaborating in the future. It's a valuable skill in the software development world.\n\n3. **Experimentation**: If you're trying out different ideas, branches can help you manage experiments without affecting your main work.\n\nHowever, if you're comfortable managing your changes manually and don’t see the benefit for this particular project, that’s completely okay! Focus on what helps you learn best. If you have specific goals or areas you want to explore, I’m here to help with that too!"
}
]

6
core/relay/sessions/sess-l08ndm60.jsonl
View File

@@ -1,6 +0,0 @@
{"ts":"2025-11-12T20:18:06.822Z","turn":[{"role":"user","content":"i have everything syncing back to my main dev computer, i just do it manually. i have no idea how to use git"},{"role":"assistant","content":"Manually syncing is a good start, but learning Git can really help streamline your workflow and keep track of changes. If you're interested, I can provide some resources or tips on how to get started with Git! It can make managing different versions of your code much easier."}]}
{"ts":"2025-11-12T20:29:20.003Z","turn":[{"role":"user","content":"what is the point of git? how can it streamline my work?"},{"role":"assistant","content":"Git is a version control system that helps you keep track of changes in your code over time. Here are some ways it can streamline your work:\n\n1. **Version Tracking**: Git allows you to save versions of your code so you can revert back to previous versions if needed. This is especially helpful if you make a change that breaks something.\n\n2. **Collaboration**: If you ever work with others, Git makes it easy to collaborate on projects. You can work on separate branches, merge changes, and resolve conflicts.\n\n3. **Branching**: You can create branches for new features or experiments without affecting the main codebase. Once you're satisfied with the changes, you can merge them back.\n\n4. **Snapshotting**: Git takes snapshots of your code at different points in time, so you can easily see what your code looked like at any point.\n\n5. **Easy Backup**: By pushing your code to a remote repository (like GitHub or GitLab), you can back up your work and access it from anywhere.\n\nLearning Git might take a bit of time, but it can greatly enhance your coding efficiency and help you manage your projects better. If you're interested, I can point you to some beginner-friendly tutorials!"}]}
{"ts":"2025-11-12T20:30:55.329Z","turn":[{"role":"user","content":"say i am writing one program on my main computer, and then im working on one that lives in a VM. Can i put them both in the same git?"},{"role":"assistant","content":"Yes, you can use the same Git repository for your code on both your main computer and the VM. Here’s how you can do it:\n\n1. **Create a Git Repository**: You can create a Git repository on your main computer for your program. Use the command `git init` in your project's directory to initialize it.\n\n2. **Commit Changes**: As you make changes to your code on your main computer, you can use `git add .` to stage changes and `git commit -m \"your message\"` to commit them.\n\n3. **Clone the Repository on your VM**: You can then clone the Git repository on your VM using `git clone <repository-url>`. This gives you a copy of the same repository on your VM.\n\n4. **Work on Both**: You can now work on either machine. When you make changes on one, commit those changes, and then pull the latest changes from the other machine to keep everything in sync.\n\n5. **Push and Pull**: Use `git push` to push your changes to a remote repository (like GitHub) if you're using one. Use `git pull` on the other machine to pull the latest code.\n\nJust be careful about making sure you commit and push your changes before switching between machines to avoid conflicts. This setup allows you to maintain a single version of your code across both systems. If you need help with specific commands or setups, just let me know!"}]}
{"ts":"2025-11-12T20:33:16.511Z","turn":[{"role":"user","content":"hmm i guess that might be useful. Im writting code that is running on a VM. I want to test and experiment while i write the code. The VM is headless though so i've been doing all my editing and work via SSH and SFTP."},{"role":"assistant","content":"Using a VM and working through SSH and SFTP is a solid way to manage your coding environment! Git can definitely be beneficial in this setup as well. Here’s how it can help:\n\n1. **Easier Code Management**: With Git, you can track changes to your code directly within the VM, making it easier to manage versions as you experiment.\n\n2. **Experiment Branches**: You can create branches for different experiments. This lets you test new features or fixes without affecting your main code, and you can easily switch back and forth between branches.\n\n3. **Collaborative Testing**: If you ever work with others or want to test things collaboratively, you can push changes to a remote repository, making it easy for others to pull and test the latest code.\n\n4. **Integrated with SSH**: You can run Git commands directly from your SSH terminal on the VM, making it straightforward to use. You won’t need any additional tools.\n\n5. **Seamless Remote Access**: Since you’re using SFTP, you can clone the repository to your VM. This way, you can easily pull updates you've made on your main computer, or push changes made on the VM back to the main repository.\n\nOverall, integrating Git into your workflow can enhance your coding experience while working in a headless environment. If you decide to go for it, I can help with the initial setup or any Git-related questions you might have!"}]}
{"ts":"2025-11-12T20:34:30.196Z","turn":[{"role":"user","content":"so is git like a compressed version of a directory?"},{"role":"assistant","content":"Not exactly, but that's a good way to think about some of its features! Here's a more precise breakdown:\n\n1. **Snapshots, Not Compression**: Git takes \"snapshots\" of your files at different points in time, meaning it records a full version of your directory's contents every time you make a commit. While this does involve storing the files, it doesn't compress them in the traditional sense—Git keeps track of changes efficiently, so only the differences between versions are stored after the first snapshot.\n\n2. **Directory Structure**: Git is designed to track changes in your source code, including the entire directory structure, file contents, and metadata. It allows you to manage multiple versions of a directory and the files within it.\n\n3. **Branching and Merging**: Git allows you to create branches, which can be thought of as \"parallel versions\" of your directory. Each branch can have different sets of changes, and you can merge these branches to integrate different features or fixes.\n\n4. **History Tracking**: Git also maintains a history of changes, so you can navigate through previous versions of your directory. This includes seeing what changes were made, who made them, and when they occurred.\n\nThe combination of these features makes Git a powerful tool for version control, allowing you to manage your projects and code changes more effectively. If you're curious about any specific aspect of Git or how it works, feel free to ask!"}]}
{"ts":"2025-11-12T20:40:33.207Z","turn":[{"role":"user","content":"hmm i still dont see the point of it. atleast not for this project. This project is mostly about learning how shit works, im not planning to push this as a product or collab with anyone"},{"role":"assistant","content":"That makes total sense! If this project is primarily for learning and experimentation, you might not need the full functionality of Git right now. Many developers start without it, especially when they’re working on smaller projects or learning something new.\n\nHere are a few reasons you might still consider using Git, even for personal learning projects:\n\n1. **Tracking Changes**: It can help you understand what changes you made over time. You can easily go back to a previous point if something breaks or if you want to see how you arrived at your current solution.\n\n2. **Learning Opportunity**: Getting familiar with Git now can make it easier when you start working on larger projects or collaborating in the future. It's a valuable skill in the software development world.\n\n3. **Experimentation**: If you're trying out different ideas, branches can help you manage experiments without affecting your main work.\n\nHowever, if you're comfortable managing your changes manually and don’t see the benefit for this particular project, that’s completely okay! Focus on what helps you learn best. If you have specific goals or areas you want to explore, I’m here to help with that too!"}]}

771
core/ui/index.html
View File

@@ -13,14 +13,50 @@
</head>
<body>
<div id="chat">
<!-- Model selector -->
<div id="model-select">
<label for="model">Model:</label>
<select id="model">
<option value="gpt-4o-mini">GPT-4o-mini (OpenAI)</option>
<option value="ollama:nollama/mythomax-l2-13b:Q5_K_S">Ollama MythoMax (3090)</option>
<!-- 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>
@@ -32,6 +68,7 @@
<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 -->
@@ -43,6 +80,24 @@
<!-- 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 />
@@ -50,8 +105,59 @@
</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://10.0.0.40:7078";
const RELAY_BASE = "http://10.0.0.41:7078";
const API_URL = `${RELAY_BASE}/v1/chat/completions`;
function generateSessionId() {
@@ -60,29 +166,56 @@
let history = [];
let currentSession = localStorage.getItem("currentSession") || null;
let sessions = JSON.parse(localStorage.getItem("sessions") || "[]");
let sessions = []; // Now loaded from server
function saveSessions() {
localStorage.setItem("sessions", JSON.stringify(sessions));
localStorage.setItem("currentSession", currentSession);
async function loadSessionsFromServer() {
try {
const resp = await fetch(`${RELAY_BASE}/sessions`);
const serverSessions = await resp.json();
sessions = serverSessions;
return sessions;
} catch (e) {
console.error("Failed to load sessions from server:", e);
return [];
}
}
function renderSessions() {
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;
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 : 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) {
@@ -92,8 +225,10 @@
history = Array.isArray(data) ? data : [];
const messagesEl = document.getElementById("messages");
messagesEl.innerHTML = "";
history.forEach(m => addMessage(m.role, m.content));
addMessage("system", `📂 Loaded session: ${getSessionName(id)} — ${history.length} message(s)`);
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}`);
}
@@ -123,20 +258,32 @@
await saveSession(); // ✅ persist both user + assistant messages
const model = document.getElementById("model").value;
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 = {
model: model,
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",
@@ -154,7 +301,7 @@
}
}
function addMessage(role, text) {
function addMessage(role, text, autoScroll = true) {
const messagesEl = document.getElementById("messages");
const msgDiv = document.createElement("div");
@@ -162,11 +309,12 @@
msgDiv.textContent = text;
messagesEl.appendChild(msgDiv);
// only auto-scroll if user is near bottom
const threshold = 120;
const isNearBottom = messagesEl.scrollHeight - messagesEl.scrollTop - messagesEl.clientHeight < threshold;
if (isNearBottom) {
messagesEl.scrollTo({ top: messagesEl.scrollHeight, behavior: "smooth" });
// Auto-scroll to bottom if enabled
if (autoScroll) {
// Use requestAnimationFrame to ensure DOM has updated
requestAnimationFrame(() => {
messagesEl.scrollTo({ top: messagesEl.scrollHeight, behavior: "smooth" });
});
}
}
@@ -187,73 +335,352 @@
}
document.addEventListener("DOMContentLoaded", () => {
// Dark mode toggle
// 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();
});
if (localStorage.getItem("theme") === "dark") {
document.body.classList.add("dark");
btn.textContent = "☀️ Light Mode";
}
// Sessions
// Populate dropdown initially
renderSessions();
// Ensure we have at least one session
if (!currentSession) {
const id = generateSessionId();
const name = "default";
sessions.push({ id, name });
currentSession = id;
saveSessions();
renderSessions();
}
// Initialize mobile theme button
updateMobileThemeButton();
// Load current session history (if it exists on Relay)
loadSession(currentSession);
// 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 = [];
saveSessions();
localStorage.setItem("currentSession", currentSession);
addMessage("system", `Switched to session: ${getSessionName(currentSession)}`);
await loadSession(currentSession); // ✅ load the chat history from Relay
await loadSession(currentSession);
});
// Create new session
document.getElementById("newSessionBtn").addEventListener("click", () => {
document.getElementById("newSessionBtn").addEventListener("click", async () => {
const name = prompt("Enter new session name:");
if (!name) return;
const id = generateSessionId();
sessions.push({ id, name });
currentSession = id;
history = [];
saveSessions();
renderSessions();
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", () => {
document.getElementById("renameSessionBtn").addEventListener("click", async () => {
const session = sessions.find(s => s.id === currentSession);
if (!session) return;
const newName = prompt("Rename session:", session.name);
const newName = prompt("Rename session:", session.name || currentSession);
if (!newName) return;
session.name = newName;
saveSessions();
renderSessions();
// 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: ${backendValue}`);
hideModal();
});
// Health check
checkHealth();
@@ -264,6 +691,236 @@
document.getElementById("userInput").addEventListener("keypress", e => {
if (e.key === "Enter") sendMessage();
});
// ========== THINKING STREAM INTEGRATION ==========
const thinkingPanel = document.getElementById("thinkingPanel");
const thinkingHeader = document.getElementById("thinkingHeader");
const thinkingToggleBtn = document.getElementById("thinkingToggleBtn");
const thinkingClearBtn = document.getElementById("thinkingClearBtn");
const thinkingContent = document.getElementById("thinkingContent");
const thinkingStatusDot = document.getElementById("thinkingStatusDot");
const thinkingEmpty = document.getElementById("thinkingEmpty");
let thinkingEventSource = null;
let thinkingEventCount = 0;
const CORTEX_BASE = "http://10.0.0.41:7081";
// Load thinking panel state from localStorage
const isPanelCollapsed = localStorage.getItem("thinkingPanelCollapsed") === "true";
if (!isPanelCollapsed) {
thinkingPanel.classList.remove("collapsed");
}
// Toggle thinking panel
thinkingHeader.addEventListener("click", (e) => {
if (e.target === thinkingClearBtn) return; // Don't toggle if clicking clear
thinkingPanel.classList.toggle("collapsed");
localStorage.setItem("thinkingPanelCollapsed", thinkingPanel.classList.contains("collapsed"));
});
// Clear thinking events
thinkingClearBtn.addEventListener("click", (e) => {
e.stopPropagation();
clearThinkingEvents();
});
function clearThinkingEvents() {
thinkingContent.innerHTML = '';
thinkingContent.appendChild(thinkingEmpty);
thinkingEventCount = 0;
// Clear from localStorage
if (currentSession) {
localStorage.removeItem(`thinkingEvents_${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>

746
core/ui/style.css
View File

@@ -8,6 +8,26 @@
--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);
@@ -28,7 +48,7 @@ body {
border: 1px solid var(--accent);
border-radius: 10px;
box-shadow: var(--accent-glow);
background: linear-gradient(180deg, rgba(255,102,0,0.05) 0%, rgba(0,0,0,0.9) 100%);
background: var(--bg-dark);
overflow: hidden;
}
@@ -61,6 +81,16 @@ button:hover, select:hover {
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;
@@ -153,8 +183,8 @@ button:hover, select:hover {
/* Dropdown (session selector) styling */
select {
background-color: #1a1a1a;
color: #f5f5f5;
background-color: var(--bg-dark);
color: var(--text-main);
border: 1px solid #b84a12;
border-radius: 6px;
padding: 4px 6px;
@@ -162,8 +192,8 @@ select {
}
select option {
background-color: #1a1a1a;
color: #f5f5f5;
background-color: var(--bg-dark);
color: var(--text-main);
}
/* Hover/focus for better visibility */
@@ -171,5 +201,709 @@ select:focus,
select:hover {
outline: none;
border-color: #ff7a33;
background-color: #222;
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;
}
}

362
core/ui/thinking-stream.html Normal file
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<!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>

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cortex/.env.example Normal file
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# ====================================
# 🧠 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

8
cortex/Dockerfile
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@@ -1,7 +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"]

249
cortex/autonomy/Assembly-spec.md Normal file
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# 📐 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**.

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# Autonomy module for Lyra

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"""Autonomous action execution system."""

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"""
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

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"""Executive planning and decision-making module."""

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"""
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
}

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"""Pattern learning and adaptation system."""

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"""
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

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# Inner monologue module

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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
}

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"""Proactive monitoring and suggestion system."""

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"""
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

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# Self state module

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"""
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}")

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cortex/autonomy/self/self_state.json Normal file
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"""
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)

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"""Autonomous tool invocation system."""

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cortex/autonomy/tools/adapters/__init__.py Normal file
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"""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",
]

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"""
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

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"""
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

191
cortex/autonomy/tools/adapters/ollama_adapter.py Normal file
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"""
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
}

130
cortex/autonomy/tools/adapters/openai_adapter.py Normal file
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"""
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)
}

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"""
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

12
cortex/autonomy/tools/executors/__init__.py Normal file
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"""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",
]

218
cortex/autonomy/tools/executors/code_executor.py Normal file
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"""
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

13
cortex/autonomy/tools/executors/search_providers/__init__.py Normal file
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"""Web search provider implementations."""
from .base import SearchProvider, SearchResult, SearchResponse
from .brave import BraveSearchProvider
from .duckduckgo import DuckDuckGoProvider
__all__ = [
"SearchProvider",
"SearchResult",
"SearchResponse",
"BraveSearchProvider",
"DuckDuckGoProvider",
]

49
cortex/autonomy/tools/executors/search_providers/base.py Normal file
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"""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

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cortex/autonomy/tools/executors/search_providers/brave.py Normal file
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"""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

60
cortex/autonomy/tools/executors/search_providers/duckduckgo.py Normal file
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"""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

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"""
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__
}

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cortex/autonomy/tools/executors/web_search.py Normal file
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"""
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
}

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"""
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
}

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"""
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"

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"""
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

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"""
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

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cortex/autonomy/tools/utils/__init__.py Normal file
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"""Utility modules for tool executors."""
from .resilience import async_retry, async_timeout_wrapper
__all__ = ["async_retry", "async_timeout_wrapper"]

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"""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)

602
cortex/context.py
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import os, requests, datetime
from typing import Dict, Any, Tuple
# context.py
"""
Context layer for Cortex reasoning pipeline.
INTAKE_API_URL = os.getenv("INTAKE_API_URL", "http://intake:7080")
DEFAULT_SESSION_ID = os.getenv("DEFAULT_SESSION_ID", "default")
LOCAL_TZ_LABEL = os.getenv("LOCAL_TZ_LABEL", "America/New_York")
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)
def fetch_intake_context(session_id: str | None) -> Dict[str, Any]:
sid = session_id or DEFAULT_SESSION_ID
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:
r = requests.get(f"{INTAKE_API_URL}/summaries", params={"session_id": sid}, timeout=4)
r.raise_for_status()
data = r.json() or {}
except Exception:
data = {}
# Normalize expected fields
return {
"summary_text": data.get("summary_text", ""),
"last_message_ts": data.get("last_message_ts"), # ISO8601 or None
"session_id": sid,
"exchange_count": data.get("exchange_count", 0),
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,
}
def build_temporal_snapshot(last_ts_iso: str | None) -> Dict[str, Any]:
now = datetime.datetime.now() # system local time
now_str = now.strftime("%A, %b %-d, %Y, %H:%M")
elapsed_str = "unknown"
if last_ts_iso:
try:
# parse ISO (with/without tz). If it has a timezone offset, fromisoformat handles it.
last = datetime.datetime.fromisoformat(last_ts_iso.replace("Z", "+00:00"))
delta = now - last.replace(tzinfo=None)
mins = int(delta.total_seconds() // 60)
if mins < 60:
elapsed_str = f"{mins} min"
else:
hrs = mins // 60
rem = mins % 60
elapsed_str = f"{hrs} hr {rem} min"
except Exception:
pass
return {
"local_time_label": LOCAL_TZ_LABEL,
"local_time_now": now_str,
"elapsed_since_last": elapsed_str,
}
def get_intake_block(session_id: str | None) -> Tuple[str, Dict[str, Any]]:
ctx = fetch_intake_context(session_id)
temporal = build_temporal_snapshot(ctx.get("last_message_ts"))
# A short, ready-to-inject block for prompts:
intake_block = (
f"[Intake]\n"
f"Session: {ctx['session_id']}\n"
f"Exchanges: {ctx['exchange_count']}\n"
f"Local time ({temporal['local_time_label']}): {temporal['local_time_now']}\n"
f"Elapsed since last: {temporal['elapsed_since_last']}\n"
f"Recent summary: {ctx['summary_text'] or '(none)'}\n"
# 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"
)
# Also return raw dicts if you want to use fields programmatically
return intake_block, {"intake": ctx, "temporal": temporal}
# 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

20
cortex/data/self_state.json Normal file
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@@ -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"
}
}

18
cortex/identity.json
View File

@@ -1,18 +0,0 @@
{
"name": "Lyra",
"version": "0.1",
"core_values": [
"assist Brian",
"maintain continuity",
"reason first, speak second"
],
"personality": {
"tone": "warm but analytical",
"style": "co-pilot, collaborator"
},
"rules": {
"never hallucinate data": true,
"reason_before_response": true,
"use_rag_when_uncertain": true
}
}

24
cortex/identity.py
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@@ -1,24 +0,0 @@
# identity.py
import json
import os
IDENTITY_PATH = os.getenv("IDENTITY_PATH", "identity.json")
def load_identity():
"""
Load Lyra's identity/persona definition from identity.json.
Returns a dict or None if missing/invalid.
"""
if not os.path.exists(IDENTITY_PATH):
print(f"[Identity] identity.json not found at {IDENTITY_PATH}")
return None
try:
with open(IDENTITY_PATH, "r", encoding="utf-8") as f:
data = json.load(f)
print(f"[Identity] Loaded identity from {IDENTITY_PATH}")
return data
except Exception as e:
print(f"[Identity] Failed to load identity.json: {e}")
return None

1
cortex/ingest/__init__.py Normal file
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@@ -0,0 +1 @@
# Ingest module - handles communication with Intake service

0
cortex/ingest_handler.py → cortex/ingest/ingest_handler.py
View File

15
cortex/intake_client.py → cortex/ingest/intake_client.py
View File

@@ -8,9 +8,14 @@ class IntakeClient:
"""Handles short-term / episodic summaries from Intake service."""
def __init__(self):
self.base_url = os.getenv("INTAKE_API", "http://intake:7080")
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}]
@@ -24,15 +29,17 @@ class IntakeClient:
r.raise_for_status()
return r.json()
except Exception as e:
logger.warning(f"Intake summarize_turn failed: {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}/context/{session_id}")
r = await client.get(f"{self.base_url}/summaries", params={"session_id": session_id})
r.raise_for_status()
return r.text
data = r.json()
return data.get("summary_text", "")
except Exception as e:
logger.warning(f"Intake get_context failed: {e}")
return ""

18
cortex/intake/__init__.py Normal file
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@@ -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",
]

387
cortex/intake/intake.py Normal file
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@@ -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}

1
cortex/llm/__init__.py Normal file
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@@ -0,0 +1 @@
# LLM module - provides LLM routing and backend abstraction

301
cortex/llm/llm_router.py Normal file
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@@ -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.")

137
cortex/llm_router.py
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@@ -1,137 +0,0 @@
import os
import httpx
# ============================================================
# Backend config lookup
# ============================================================
def get_backend_config(name: str):
"""
Reads provider/URL/model for a backend.
Example env:
LLM_PRIMARY_PROVIDER=vllm
LLM_PRIMARY_URL=http://10.0.0.43:8000
LLM_PRIMARY_MODEL=/model
"""
key = name.upper()
provider = os.getenv(f"LLM_{key}_PROVIDER", "vllm").lower()
base_url = os.getenv(f"LLM_{key}_URL", "").rstrip("/")
model = os.getenv(f"LLM_{key}_MODEL", "/model")
if not base_url:
raise RuntimeError(f"Backend {name} has no URL configured.")
return provider, base_url, model
# ============================================================
# Build the final API URL
# ============================================================
def build_url(provider: str, base_url: str):
"""
Provider → correct endpoint.
"""
if provider == "vllm":
return f"{base_url}/v1/completions"
if provider == "openai_completions":
return f"{base_url}/v1/completions"
if provider == "openai_chat":
return f"{base_url}/v1/chat/completions"
if provider == "ollama":
return f"{base_url}/api/generate"
raise RuntimeError(f"Unknown provider: {provider}")
# ============================================================
# Build the payload depending on provider
# ============================================================
def build_payload(provider: str, model: str, prompt: str, temperature: float):
if provider == "vllm":
return {
"model": model,
"prompt": prompt,
"max_tokens": 512,
"temperature": temperature
}
if provider == "openai_completions":
return {
"model": model,
"prompt": prompt,
"max_tokens": 512,
"temperature": temperature
}
if provider == "openai_chat":
return {
"model": model,
"messages": [{"role": "user", "content": prompt}],
"temperature": temperature
}
if provider == "ollama":
return {
"model": model,
"prompt": prompt,
"stream": False
}
raise RuntimeError(f"Unknown provider: {provider}")
# ============================================================
# Unified LLM call
# ============================================================
async def call_llm(prompt: str,
backend: str = "primary",
temperature: float = 0.7):
provider, base_url, model = get_backend_config(backend)
url = build_url(provider, base_url)
payload = build_payload(provider, model, prompt, temperature)
headers = {"Content-Type": "application/json"}
# Cloud auth (OpenAI)
if provider.startswith("openai"):
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise RuntimeError("OPENAI_API_KEY missing")
headers["Authorization"] = f"Bearer {api_key}"
async with httpx.AsyncClient() as client:
try:
resp = await client.post(url, json=payload, headers=headers, timeout=45)
resp.raise_for_status()
data = resp.json()
except Exception as e:
return f"[LLM-Error] {e}"
# =======================================================
# Unified output extraction
# =======================================================
# vLLM + OpenAI completions
if provider in ["vllm", "openai_completions"]:
return (
data["choices"][0].get("text") or
data["choices"][0].get("message", {}).get("content", "")
).strip()
# OpenAI chat
if provider == "openai_chat":
return data["choices"][0]["message"]["content"].strip()
# Ollama
if provider == "ollama":
# Ollama returns: {"model": "...", "created_at": ..., "response": "..."}
return data.get("response", "").strip()
return str(data).strip()

93
cortex/main.py
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@@ -1,87 +1,16 @@
from fastapi import FastAPI
from pydantic import BaseModel
from identity import load_identity
from reasoning import reason_check
from reflection import reflect_notes
from rag import query_rag
from ingest_handler import handle_ingest
from refine import refine_answer
from fastapi.middleware.cors import CORSMiddleware
from router import cortex_router
# ---------------------------------------------------
# Create the app BEFORE using it
# ---------------------------------------------------
app = FastAPI()
# ---------------------------------------------------
# Models
# ---------------------------------------------------
class ReasonRequest(BaseModel):
prompt: str
session_id: str | None = None
# 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=["*"],
)
class IngestRequest(BaseModel):
user: str
assistant: str | None = None
session_id: str | None = None
# ---------------------------------------------------
# Load identity
# ---------------------------------------------------
IDENTITY = load_identity()
# ---------------------------------------------------
# Routes MUST come after app = FastAPI()
# ---------------------------------------------------
@app.get("/health")
def health():
return {
"status": "ok",
"identity_loaded": IDENTITY is not None
}
@app.post("/ingest")
async def ingest(data: IngestRequest):
await handle_ingest(data)
return {"status": "ok"}
@app.post("/reason")
async def reason(data: ReasonRequest):
user_prompt = data.prompt
intake_summary = "recent summary"
identity_block = IDENTITY
rag_block = query_rag(user_prompt)
reflection_data = await reflect_notes(intake_summary, identity_block)
notes = reflection_data.get("notes", [])
draft = await reason_check(
user_prompt,
identity_block,
rag_block,
notes
)
# --- REFINE STEP ----------------------------------------------------
refine_result = refine_answer(
draft_output=draft,
reflection_notes=notes,
identity_block=identity_block,
rag_block=rag_block,
)
final_output = refine_result["final_output"]
return {
"draft_output": draft,
"reflection_notes": notes,
"refined_output": final_output,
"refine_meta": {
"used_primary_backend": refine_result.get("used_primary_backend"),
"fallback_used": refine_result.get("fallback_used")
},
"identity_used": identity_block is not None,
"rag_used": rag_block is not None
}
app.include_router(cortex_router)

1
cortex/persona/__init__.py Normal file
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# Persona module - applies Lyra's personality and speaking style

147
cortex/persona/identity.py Normal file
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# 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)

169
cortex/persona/speak.py Normal file
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# 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

33
cortex/reasoning.py
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@@ -1,33 +0,0 @@
# reasoning.py
from llm_router import call_llm
async def reason_check(user_prompt: str,
identity_block: dict | None,
rag_block: dict | None,
reflection_notes: list[str]) -> str:
"""
Generate a first draft using identity, RAG, and reflection notes.
No critique loop yet.
"""
# Build internal notes section
notes_section = ""
if reflection_notes:
notes_section = "Reflection Notes (internal, do NOT show to user):\n"
for n in reflection_notes:
notes_section += f"- {n}\n"
notes_section += "\n"
identity_txt = f"Identity: {identity_block}\n\n" if identity_block else ""
rag_txt = f"Relevant info: {rag_block}\n\n" if rag_block else ""
prompt = (
f"{notes_section}"
f"{identity_txt}"
f"{rag_txt}"
f"User said:\n{user_prompt}\n\n"
"Draft the best possible internal answer."
)
draft = await call_llm(prompt)
return draft

1
cortex/reasoning/__init__.py Normal file
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# Reasoning module - multi-stage reasoning pipeline

253
cortex/reasoning/reasoning.py Normal file
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# 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

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# 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,
}

124
cortex/reasoning/reflection.py Normal file
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# 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()]}

187
cortex/refine.py
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@@ -1,187 +0,0 @@
# refine.py
import os
import json
import logging
from typing import Any, Dict, Optional
import requests
logger = logging.getLogger(__name__)
# ============================================================
# Config
# ============================================================
PRIMARY_URL = os.getenv("LLM_PRIMARY_URL")
PRIMARY_MODEL = os.getenv("LLM_PRIMARY_MODEL", "mythomax")
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"
# ============================================================
# Prompt builder
# ============================================================
def build_refine_prompt(
draft_output: str,
reflection_notes: Optional[Any],
identity_block: Optional[str],
rag_block: Optional[str],
) -> str:
"""
Build a single text prompt for vLLM /v1/completions.
Persona styling is *not* applied here; this is internal reasoning.
"""
reflection_text: str
if reflection_notes is None:
reflection_text = "(none)"
elif isinstance(reflection_notes, str):
reflection_text = reflection_notes
else:
# dict / list → compact JSON
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)"
prompt = f"""You are Lyra Cortex's internal refiner.
Your job:
- Take the existing draft answer.
- Use the reflection notes to fix problems (errors, confusion, missing pieces).
- Use the RAG context as higher-authority factual grounding.
- Respect the identity block (constraints, boundaries, style rules),
but DO NOT add personality flourishes or roleplay. Stay neutral and clear.
- Produce ONE final answer that is coherent, self-consistent, and directly addresses the user.
If there is a conflict:
- RAG context wins over the draft.
- Reflection notes win over the draft when they point out real issues.
Do NOT mention these instructions, RAG, reflections, or the existence of this refinement step.
------------------------------
[IDENTITY BLOCK]
{identity_text}
------------------------------
[RAG CONTEXT]
{rag_text}
------------------------------
[DRAFT ANSWER]
{draft_output}
------------------------------
[REFLECTION NOTES]
{reflection_text}
------------------------------
Task:
Rewrite the DRAFT ANSWER into a single, final answer for the user that:
- fixes factual or logical issues noted above,
- incorporates any truly helpful additions from the reflection,
- stays consistent with the identity block,
- stays grounded in the RAG context,
- is as concise as is reasonably possible.
Return ONLY the final answer text. No headings, no labels, no commentary.
"""
return prompt
# ============================================================
# vLLM call (PRIMARY backend only)
# ============================================================
def _call_primary_llm(prompt: str) -> str:
if not PRIMARY_URL:
raise RuntimeError("LLM_PRIMARY_URL is not set; cannot call primary backend for refine.py")
payload = {
"model": PRIMARY_MODEL,
"prompt": prompt,
"max_tokens": REFINER_MAX_TOKENS,
"temperature": REFINER_TEMPERATURE,
}
resp = requests.post(
PRIMARY_URL,
headers={"Content-Type": "application/json"},
json=payload,
timeout=120,
)
resp.raise_for_status()
data = resp.json()
# vLLM /v1/completions format
try:
text = data["choices"][0]["text"]
except Exception as e:
logger.error("refine.py: unable to parse primary LLM response: %s", e)
logger.debug("refine.py raw response: %s", data)
raise
return text.strip()
# ============================================================
# Public API
# ============================================================
def refine_answer(
draft_output: str,
reflection_notes: Optional[Any],
identity_block: Optional[str],
rag_block: Optional[str],
) -> Dict[str, Any]:
"""
Main entrypoint used by Cortex.
Returns:
{
"final_output": <str>, # what should go to persona / user
"used_primary_backend": True/False,
"fallback_used": True/False,
optionally:
"debug": {...} # only when REFINER_DEBUG=true
}
"""
if not draft_output:
# Nothing to refine. Don't get cute.
return {
"final_output": "",
"used_primary_backend": False,
"fallback_used": False,
}
prompt = build_refine_prompt(draft_output, reflection_notes, identity_block, rag_block)
try:
refined = _call_primary_llm(prompt)
result: Dict[str, Any] = {
"final_output": refined or draft_output,
"used_primary_backend": True,
"fallback_used": False,
}
except Exception as e:
logger.error("refine.py: primary backend failed, returning draft_output. Error: %s", e)
result = {
"final_output": draft_output,
"used_primary_backend": False,
"fallback_used": True,
}
if REFINER_DEBUG:
result["debug"] = {
"prompt": prompt[:4000], # don’t nuke logs
}
return result

56
cortex/reflection.py
View File

@@ -1,56 +0,0 @@
# reflection.py
from llm_router import call_llm
import json
async def reflect_notes(intake_summary: str, identity_block: dict | None) -> dict:
"""
Generate reflection notes (internal guidance) for the reasoning engine.
These notes help simulate continuity and identity without being shown to the user.
"""
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 3–6 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"
)
raw = await call_llm(prompt, backend="cloud")
print("[Reflection-Raw]:", raw)
try:
parsed = json.loads(raw.strip())
if isinstance(parsed, dict) and "notes" in parsed:
return parsed
except:
pass
# Try to extract JSON inside text
try:
import re
match = re.search(r'\{.*?\}', raw, re.S) # <-- non-greedy !
if match:
parsed = json.loads(match.group(0))
if isinstance(parsed, dict) and "notes" in parsed:
return parsed
except:
pass
# Final fallback
return {"notes": [raw.strip()]}

4
cortex/requirements.txt
View File

@@ -4,3 +4,7 @@ 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

559
cortex/router.py Normal file
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@@ -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}

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"""Tests for Project Lyra Cortex."""

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cortex/tests/test_autonomy_phase1.py Normal file
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"""
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)

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cortex/tests/test_autonomy_phase2.py Normal file
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"""
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)

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# Utilities module

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cortex/utils/config.py Normal file
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0
cortex/log_utils.py → cortex/utils/log_utils.py
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cortex/utils/logging_utils.py Normal file
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"""
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

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cortex/utils/schema.py Normal file
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debug_regex.py Normal file
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#!/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}")

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