focus-area now recomputes at most once per hour, and only if the task list
actually changed since the last compute.
- focus-area TTL: 43200s → 3600s; version bumped to 2.1.0
- computeAndStore hashes sorted task contents (MD5) and checks the stored
_task_hash in the existing snapshot; skips the ml-serving call when the
hash matches and the output isn't expired
- ml-serving injects _task_hash into the snapshot so the next cycle can compare
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each unique task title is now enriched by LiteLLM once and cached in the DB.
Subsequent agent compute cycles (every 12h) fetch the cache before calling
ml-serving; only new titles hit the tip-generator.
- DB: task_enrichments(content_hash PK, description, model, created_at)
- TS: fetchEnrichmentCache / persistEnrichments helpers in agent-outputs.ts;
enrichment_cache passed in compute request, new_enrichments persisted from response
- Python: AgentComputeRequest.enrichment_cache / AgentComputeResponse.new_enrichments;
AgentInput.enrichment_cache; _enrich_batch returns (descriptions, new_entries);
cluster_tasks returns (clusters, new_enrichments)
- FocusAreaAgent stashes new_enrichments in signals_snapshot under _new_enrichments;
compute_agent endpoint pops it before storing the snapshot
Closes part of #129
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Small models (qwen2.5:1.5b) mirror the language of task title content
in the prompt. Adding an explicit English note to snippets that embed
raw task titles (focus-area, overdue-task) prevents language bleed.
Also added the instruction to the orchestrator system prompt and user
message as belt-and-suspenders.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
All four agents bumped to v1.1.0.
momentum (#114): infers engagement_trend ('up'|'stable'|'down') by comparing
done-rate in the last 7 days vs the prior 7 days. Agent surfaces the trend
in its snippet ("trending up — build on the momentum").
overdue-task (#115): infers lateness_tolerance_days (0/1/2) from snooze rate.
Agent now filters tasks against the tolerance so low-urgency users aren't
nagged about tasks that are only hours overdue.
recent-patterns (#116): infers window_days (7/14/30) from feedback event
density — sparse users get a wider window so the snippet isn't always empty.
focus-area (#113): no inferred params (project-level feedback linkage needed,
tracked under #78). preferred_areas pref was declared but ignored; agent now
honours it as a tiebreaker and mentions it in the snippet.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Each agent now exports a module-level MANIFEST declaring id, version,
pref_schema, required_consents, ttl_sec, and silenced_in_contexts. The
registry surfaces both the agent and its manifest, and rejects on
mismatch so the two cannot drift.
ml/serving exposes GET /agents/registry; services/api proxies it as
GET /api/agents/registry with a 60s in-process cache so admin pageviews
don't hammer upstream. Failures aren't cached.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds ml/agents/ — five specialised sub-agents (overdue_task, momentum,
time_of_day, recent_patterns, focus_area) each producing a prompt snippet
from user signals. A registry wires them up; the orchestrator prompt in
ml/serving/prompts.py synthesises their outputs into one tip via LiteLLM.
Also wires /api/agents route in the API and updates the Dockerfile to copy
the full ml/ tree with PYTHONPATH=/app so agent imports resolve correctly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
