Maven/docs/plans/09-behavioral-memory.md

Plan: Behavioral Memory — How I Do Stuff

Goal: Maven builds a rich behavioral model of the user over time: habits, routines, preferences, recurring tasks, deadlines, and commitments. She uses this model to proactively propose plans, surface reminders, and adjust her behavior — all grounded in the existing fact/event/note stores.

Done when:

• internal/memory/behavior.go — behavioral model builder reads facts, events, notes, nudges, reminders, tools, calendar events
• Model is exposed as a structured profile: {"routines": [...], "preferences": {...}, "recurring_tasks": [...], "typical_schedule": {...}}
• LLM generates this profile periodically (daily) and stores it as a note/fact
• Proactive loop uses the profile to propose daily plans: "сегодня ты обычно делаешь X, Y, Z. напомнить?"
• Profile is queryable via voice: "что я обычно делаю по вторникам?"
• Profile updates on fact write — not just periodic — so a new "walk" fact immediately adjusts the walking schedule

Scope:

• internal/memory/behavior.go — behavior builder
• internal/memory/profile.go — profile data structures (routines, preferences, schedule, commitments)
• Reuses internal/llm.Client for profile generation
• Reuses internal/pattern/detector.go for interval detection on behavioral data
• Extends internal/router/intent.go — IntentQuery extended with behavioral sub-queries
• Reuses internal/delivery.Dispatcher for surfacing proposals as nudges

Steps:

1. Design profile schema: BehaviorProfile struct with Routines []Routine, Preferences map[string]string, RecurringTasks []Task, WeeklySchedule map[string][]Activity
2. Create internal/memory/behavior.go — Builder that reads store.RecentFacts(1000), store.EventsFor (all action+object combos), store.RecentNotes(500), store.ListReminders
3. Implement profile generation — prompt for llm.Client that takes raw facts and outputs a structured JSON profile; stores result as a fact (kind=config, key=behavior_profile)
4. Create internal/memory/planner.go — reads the profile each morning (via routine cron "0 8 * * *") and proposes a daily plan through dispatcher.DispatchNudge
5. Add real-time updates — when a fact is written via WriteFact, the behavior builder incrementally updates the relevant profile section (append-only, no full rebuild)
6. Wire voice query — "что я обычно делаю?" routes to IntentQuery → behavior profile lookup → LLM-phrased answer
7. Add IPC read method MethodGetBehaviorProfile so mavweb can display it on /dash
8. Test with synthetic fact history — verify weekly schedule is correctly inferred

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Status (2026-08-01) — partially shipped, deliberately narrowed

Shipped on overnight/behavior-profile:

• internal/memory/behavior.go — BuildProfile counts habits per weekday out of self-facts: distinct-day counts (MinHabitDays = 2), a median time-of-day, and FormatWeekdayRU / FormatOverallRU for the spoken answer.
• internal/router/habit.go — ParseHabitQuery, which requires a habit marker ("обычно", "каждую", "привычки", …) and parses the weekday deterministically.
• cmd/mavend/actions_query.go — a habits query source, so "что я обычно делаю по вторникам?" is answered.

Not shipped, and not to be shipped as written:

• Step 3, LLM-generated profile stored as a fact. The profile is COUNTED, not generated. A 1.7B asked to summarise a year of habits produces fluent claims about the owner's life that no row supports, and a wrong claim about him is the most expensive kind of wrong maven can be. Counting is verifiable and cheap.
• Step 5, incremental updates on fact write. There is no cache to keep fresh — the profile is recomputed on the question, so a new fact is already in the next answer. A cached profile that can disagree with its own rows is two truths.
• Step 4, proactive daily plan proposals via the dispatcher. Maven is not a nag, and a nudge at 08:00 every day proposing the day is the definition of one. The sanctioned path from "she noticed a pattern" to "she acts on it" already exists: internal/pattern/detector.go proposes a routine, and the owner accepts it on /routines. It goes through him.

Still open, if wanted later: MethodGetBehaviorProfile + a /dash panel (step 7). The counted profile needs no new IPC method to be asked about — the query source reads RecentFacts over the existing surface — so this is a display concern only.