Give Lyra a model of *herself* (vs the profile/narrative which model Brian):
- persona: a real origin/identity — she's an AI and knows it (Bender/C-3PO
style), with the Cortex/NeoMem lineage as her actual past, so "how were you
made" stops falling through to generic-assistant deflection.
- memory: self_state table (JSON blob) + get/set_self_state.
- lyra/self_state.py: evolving first-person inner state (mood, valence, energy,
confidence, curiosity, self_narrative, relationship, reflections). render_for_
context injects it; reflect() updates it from recent activity. `lyra-reflect`.
- chat.build_messages injects her interiority right after the persona — she
speaks from a continuous self, not a reset.
The state -> behavior -> reflection -> updated state loop is the substrate for
the emergence experiment. Verified: reflection shifted mood curious->reflective
and produced genuine first-person self-observations.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Complete the consolidation pipeline: summaries -> profile + eras -> narrative.
- memory: eras table (per-month digests) + Era, summaries_by_month, store_era,
list_eras, recall_eras; narrative table + set/get_narrative
- lyra/era.py (lyra-era): groups session gists by the month the session occurred
(real timestamps) and map-reduces each month into a "what was happening" digest
- lyra/narrative.py (lyra-narrative): distills profile + recent eras into the
current arc/trends/callbacks ("remember when…", "you're trending toward…")
- chat.build_messages injects the narrative alongside the profile
Verified on the real corpus: 17 monthly eras (Dec 2024-Jun 2026) + a narrative
that surfaces specific callbacks (the $573 Hollywood session, 4 years sober).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Derive a standing profile of the user from session gists and inject it into
every prompt, so identity/abstract questions ("what kind of player am I",
"what are my leaks") are answered from distilled knowledge instead of noisy
single-vector recall (which finds passages, not patterns).
- memory: profile table + get/set_profile, list_summaries
- lyra/profile.py: rebuild_profile map-reduces all gists (batch -> extract
durable facts -> fold-merge) into one profile doc; `lyra-profile` CLI
- chat.build_messages injects "What you know about Brian" after the persona
Run after lyra-summarize (needs gists). Verified (stubbed): map-reduce, storage,
and prompt injection.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Import the parser's {title, messages} JSON into Lyra's memory so past
conversations seed recall (and, later, the era-rollup tier).
- lyra/ingest.py: one conversation -> one session, text messages -> exchanges;
skips non-text (image asset) messages and non user/assistant roles; embeddings
batched; idempotent by filename-derived session id; `lyra-import <dir>` CLI
- memory.add_exchanges_bulk: batched insert of pre-embedded rows
Format has no timestamps yet, so imports are stamped at import time; a future
dated export will let era memory group by real calendar time.
Verified on the 68-file lyra dev set: 7519 exchanges, idempotent re-run, recall
returns relevant history.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Older sessions fade to a general idea; details stay retrievable.
- memory: summaries table (one compacted gist per session, embedded), plus
store_summary/get_summary/recall_summaries and unsummarized_count (tracks
exchanges newer than the current summary)
- lyra/summary.py: summarize_session compacts a session's raw turns into a
third-person gist (default SUMMARY_BACKEND=local, so compaction is free);
maybe_summarize re-summarizes once SUMMARIZE_AFTER new turns accumulate
- chat.build_messages now layers context in tiers: persona -> gists of other
sessions -> a few sharp raw cross-session details -> current session raw
turns -> new message; respond() compacts the session after each turn
- web: POST /sessions/{id}/summarize to compact on demand
- summarization activity surfaces in the live log
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Phase 1 — persona + persistent memory chat loop:
- lyra/persona.py + personas/lyra.md: editable identity/voice (friend-first,
honest, never invents poker math)
- lyra/chat.py: turn loop assembling persona + cross-session recall + recent
context, persisting both sides to SQLite
- lyra/session.py, lyra/__main__.py: session lifecycle + `lyra` REPL
Phase 1.25 — reuse the old web UI:
- vendored the prior single-page UI into lyra/web/static, repointed to
same-origin
- lyra/web/server.py (FastAPI): serves the UI and backs its endpoint contract
(/v1/chat/completions, session CRUD, health, inert thinking-stream) with the
new chat loop + memory; SQLite stays the single source of truth
- `lyra-web` console script
Local backends — test for free, no OpenAI key:
- llm.embed routes via EMBED_BACKEND (cloud=OpenAI, local=Ollama /api/embed)
- simplified UI backend selector to Local (Ollama) / Cloud (OpenAI), default local
- memory connection opened check_same_thread=False for the threaded server
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- lyra.memory.remember(session_id, role, content) embeds and stores
- lyra.memory.recent(session_id, n) returns the last N from a session
- lyra.memory.recall(query, k, session_id=None) returns top-k by cosine
similarity across the chosen scope (all sessions by default)
- Embeddings live in the exchanges.embedding BLOB column as float32 bytes
- Connection reopens automatically if LYRA_DB_PATH changes (test-friendly)
serversdown
Claude