Maven/docs/plans/03-memory-evaluation.md

Plan: Background Memory Evaluation & Idea Generation

Goal: Maven periodically reviews her own memory stores (facts, notes, events, nudges), evaluates coherence and gaps, and generates proactive proposals — new routines, configuration tweaks, observations she can share with the user.

Done when:

• internal/memory/eval.go — periodic evaluation loop runs on a slow cadence (1h)
• Evaluation reads RecentFacts, RecentNotes, RecentNudges, RecentEvents via store.Store or ipc.CoreAPI
• LLM summarizes state, detects anomalies (e.g. "you haven't recorded a meal in 3 days — is your routine broken?"), proposes new care rules
• Generated proposals are written as notes (kind note, source infer:memory-eval) and/or trigger nudges through the dispatcher
• Evaluation trace visible on /history page in mavweb

Scope:

• New internal/memory/eval.go — evaluator struct calling internal/llm.Client with a summarization prompt
• Reuses internal/delivery.Dispatcher for surfacing insights as care nudges (sev1)
• Reuses internal/store for reading memory state and writing evaluation notes
• Daemon wiring: new evaluation goroutine in cmd/mavend/main.go
• Config: memory_eval_interval in config.Config (default 1h, 0 to disable)

Steps:

1. Create internal/memory/eval.go — Evaluator struct holding *store.Store, *llm.Client, *delivery.Dispatcher
2. Implement Evaluate(ctx) — reads last N facts, notes, nudges, events, builds a prompt summarizing patterns, anomalies, gaps
3. LLM call returns structured observations: {"observation":"...","confidence":0.8,"suggested_action":"remind|propose|notify"}
4. High-confidence observations written as notes (source:infer:memory-eval) or dispatched as care nudges (sev1) through dispatcher.DispatchNudge
5. Wire evaluator goroutine in cmd/mavend/main.go — separate ticker, not on the main tick loop
6. Add /eval API method to ipc.CoreAPI (or reuse Chat with system context) so mavweb can show evaluation history
7. Add memory_eval block to deploy/mavend.json
8. Test with synthetic store state — verify observations match expected patterns

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Status 2026-08-01 — foundation shipped (Vikunja #248)

Shipped: internal/memeval (not internal/memory/eval.go — internal/store imports internal/memory for the vector backend, so an evaluator that reads store.Fact there would close an import cycle). Evaluator.Evaluate reads RecentFacts / RecentNotes / RecentNudges, prompts the resident model under a GBNF grammar for at most three {observation, confidence, suggested_action} objects, drops anything under min_confidence, deduplicates against what earlier evaluations wrote, and records the rest as notes with source infer:memory-eval. Driver: cmd/mavend/memoryeval.go, its own goroutine on its own ticker. Config: the memory_eval block — absent ⇒ the loop does not run. Visibility: /dash already renders notes with their source, so evaluation output is visible with no UI change.

Deliberately not shipped — this is policy, not an unfinished edge:

• Dispatching observations as care nudges (plan step 4). An hourly LLM loop with permission to speak is a machine for generating interruptions, and the content is model-generated text about his own life. The evaluator has no dispatcher reference at all, so it cannot reach a channel by accident. Wiring it to delivery.Dispatcher is a separate decision with its own opt-in.
• Acting on suggested_action. It is recorded inside the note text and interpreted by nobody. No reminder, routine or fact is created.
• Writing observation embeddings. Notes are written with a nil embedding, so they stay out of the RAG recall pool. Feeding generated text back into the pool it came from is how a small model starts citing its own guesses as evidence.

Deferred, wants a decision or another capability:

• Plan step 6, the /eval IPC method and an evaluation-history view. /dash covers reading the output; a dedicated trace surface is worth building once there is real output to look at, and it should probably show the prompt too.
• RecentEvents. The plan lists it; the evaluator reads facts, notes and nudges. Detected action/object events already drive pattern proposals (#43), and duplicating them here would mostly re-derive that.
• Output quality is unmeasured. There is no fixture for "did she notice something true". The tests cover the machinery — empty store, confidence floor, dedupe, own-notes exclusion, error handling — not the observations. Until someone reads a week of real output on /dash, treat the wording and the min_confidence default as unvalidated.