Observability slim layer. Every full run appends one JSON line to
~/.claude/logs/orchestrator/YYYY-MM-DD.jsonl with timestamp, sha256-prefix
task hash (raw task is never logged), retrieved memory names, router
choice, runtime, tokens in/out, success/error. risk_class and confidence
fields are reserved nulls for Phase 5.
- telemetry.py: log_run(), build_record(), task_hash(), token-usage
extraction from AIMessage.usage_metadata. log_run swallows all
exceptions — telemetry never kills a run.
- run.py: wraps app.invoke in try/except with a monotonic-clock window;
logs on both success and failure. --route-only path is left unlogged
(no agent work, doesn't represent a "run").
- scripts/weekly_summary.py: scans the last 7 days of JSONL and prints
a markdown digest (routes, unknown rate, cross-review rate, success
rate, total spend, mean tokens/route). Schedule via /schedule and
pipe stdout to Slack from the scheduler.
Cost rates per route are rough Sonnet/Haiku/GPT/Gemini/DeepSeek defaults
suitable for spotting runaway prompts, not finance. Router tokens for
structured-output calls aren't captured (they don't surface through the
message trail); agent tokens are the dominant component anyway.
Validated: golden-set 21/21 still passing; full-run smoke writes
expected fields; weekly_summary.py prints clean markdown from a 2-run
log.
Plugs the orchestrator into Adam's existing memory store at
~/.claude/projects/-Users-adammoussa-Documents-repositories/memory/. Every
run starts with a top-3 retrieval pass that is then surfaced in the CLI
output and injected as system context into the router and downstream agent.
- retriever.py: load *.md memories (skipping the MEMORY.md index), embed
with text-embedding-3-small, cache to .cache/embeddings.json keyed on
file mtime. Cosine similarity, top-k=3 default. Reads only — never
writes back to the memory store.
- state.py: add `retrieved: list[dict]` to OrchestratorState; relax to
total=False to match LangGraph's partial-update semantics.
- graph.py: new retriever_node wired as START -> retriever -> router.
router_node and connector_node now inject retrieved memories into their
SystemMessage. Retrieval failures are caught and the run continues with
empty memory context (logged).
- agents.py: make_agent_node injects retrieved memories into each agent's
system prompt.
- run.py: prints `[retrieved: name1, name2, name3]` (or `[retrieved: none]`)
before route/result for both --route-only and full-run modes, so bad
retrieval is visible at a glance.
- .gitignore: add .cache/, .pytest_cache/, .ruff_cache/.
Validated: golden-set still 21/21 passing; smoke tests retrieve plausible
memories ("Send a Slack message to ops about the new exec-aide deploy" ->
project_exec_aide, feedback_exec_aide_vip_management, project_seahaven_slack_bot).
Phase 1 stabilization. Removes the four-copy prompt/agent-description drift
surface and the silent router fallback.
- models.py: hoist model IDs to module-level constants; add with_retries()
helper (2 retries on Anthropic+OpenAI transient errors via with_retry).
- agents.py: single AGENTS dict (model_fn, prompt, description) and a
make_agent_node() factory that collapses six near-identical node functions.
- graph.py: router prompt is generated from AGENTS; router_node uses
with_structured_output(RouteDecision) and returns an explicit "unknown"
route instead of the silent "researcher" fallback. New unknown_node wires
to END. All LLM invocations go through with_retries.
- state.py: add "unknown" to the route Literal.
- run.py: --route-only now imports router_node from graph.py, killing the
fourth prompt copy.
- tests/: pytest golden-set (20 labelled tasks + size guard). Skips cleanly
without ANTHROPIC_API_KEY or COMPOSIO_API_KEY. Validated 21/21 passing.
