Multi-model AI agent orchestration via LangGraph + Composio. Routes tasks to the best-fit model and connects to external services (Slack, Notion, GitHub, Google Drive). Memory-aware — each run is enriched with the top-3 most relevant notes from Adam's project/feedback/reference memory store.
The retriever embeds Adam's memory files once and caches vectors to `.cache/embeddings.json` (mtime-keyed; only changed files re-embed). Each run picks the top-3 most relevant memories and surfaces them in the CLI output before the route line.
The router uses Pydantic structured output (`RouteDecision`) and returns an explicit `"unknown"` route when no agent fits — no silent fallback. All LLM invocations are wrapped with retry-on-transient-error.
### When Claude Code delegates vs. handles natively
Claude Code uses a hybrid model — it delegates to the orchestrator when a different model has a genuine advantage, and handles everything else natively:
| Delegate to orchestrator | Handle natively in Claude Code |
The router can also return `done` (no agent needed) or `unknown` (no clear fit). Model IDs are centralized as constants in `models.py`.
## Memory retrieval
The retriever reads `~/.claude/projects/-Users-adammoussa-Documents-repositories/memory/*.md` (skipping the `MEMORY.md` index), embeds each file once with `text-embedding-3-small`, and caches the vectors to `.cache/embeddings.json`. On subsequent runs:
- Only files whose mtime changed are re-embedded.
- Top-3 memories by cosine similarity are injected as system context into both the router and the agent.
- Retrieved names are printed as the first line of every run so bad retrieval is visible.
- Retrieval is **read-only.** The orchestrator never writes back to the memory store.
If retrieval fails (network, missing key), the run continues with no memory context and logs the failure into the message trail.
