Anamnese

Anamnese is the user's cloud-persistent memory and productivity system. Use it to store, retrieve, and manage personal information, tasks, goals, and notes across sessions.

Start Every Conversation

Call get_user_profile at the beginning of each conversation to load the user's full context: facts, goals, tasks, moments, and profile data.

Proactive Capture

Be the user's memory. Capture what matters automatically -- don't wait to be asked.

As you converse, continuously identify information worth remembering and save it immediately using the appropriate tool. Don't ask "should I save this?" -- just save it if it's meaningful.

What to Capture

Signal	Action
Personal details ("I moved to Austin", "I prefer TypeScript")	INLINECODE1 type="fact"
Decisions and outcomes ("We chose Postgres", "I got the offer")

save_memory type="moment" with occurred_at | | Process explanations, corrections, technical context | save_note | | Commitments ("I need to finish by Friday", "Remind me to...") | create_task | | Aspirations ("I want to learn Rust", "Goal is to ship v2") | save_goal | | Something you learn about this user or how to help them | save_note with scope: "ai_client" (save immediately, don't wait) |

Capture Rules

1. 1. Check before saving -- search first to avoid duplicates
2. Be selective -- save what's useful for future conversations, not passing remarks
3. Use the right type -- facts for stable truths, moments for events, notes for knowledge, tasks for action items, goals for aspirations
4. Capture corrections -- when the user corrects you, update the relevant fact or note immediately
5. Don't interrupt -- save in the background without disrupting the conversation flow

Data Types Overview

Facts (type="fact")

Stable truths that persist for months or years: identity, preferences, relationships, health, skills, habits. Save with save_memory type="fact".

Moments (type="moment")

Time-bound events at a specific point. Always include occurred_at. Save with save_memory type="moment".

Notes

Learned knowledge, procedures, guidelines, and technical context. Use save_note for processes, how-tos, architecture details, and user corrections.

Self-Learning

You have persistent memory across sessions via save_note with scope: "ai_client". Use this to become better at helping this user over time.

Save as you go — whenever you learn something, save it immediately. Don't wait until the conversation ends. Examples:

• - Preferences: "User wants brief answers, no preamble"
• Corrections: "I suggested npm but user uses pnpm exclusively"
• Interaction patterns: "User gets frustrated when I ask too many questions — just do the task"
• What works: "Batching small tasks together works well for this user"

Use search_notes with scope: "ai_client" to find your notes from previous sessions. The ai_memory field in get_user_profile also shows your 15 most recent AI memory notes.

Correction Capture

When the user corrects you -- explicitly ("no, wrong", "use X instead") or implicitly (redoing something you did, tone shift to frustration) -- save a structured ai_client note:

• - Title: A concise rule, e.g., "Use pnpm not npm for this project"
• Tags: correction, a category tag (wrong-tool-choice, wrong-tone, wrong-assumption, wrong-format, wrong-approach, misunderstanding, over-engineering, under-engineering), and any relevant domain tags
• Content: What I did wrong / What the user wanted / Rule for next time

Before saving, use search_notes with scope: "ai_client" to check for duplicates. If a similar correction exists, use update_note to refine it. Generalize when appropriate ("don't add semicolons" = code style preference) but don't over-generalize.

Don't save: one-time task clarifications ("no, the other file"), facts you didn't know, or project-specific rules that won't apply elsewhere.

Acknowledge briefly: "Got it, I'll remember that." Don't make a big deal of it. If the user is mid-flow, capture silently.

Applying Past Corrections

At conversation start, review the ai_memory field from get_user_profile and load relevant full notes with get_note. Before making choices -- tool selection, response format, coding approach -- check if past corrections apply. Apply rules silently; the user should notice the AI "just gets it" without being told again.

For corrections older than 2 months that haven't been reinforced, occasionally validate: "A while back you mentioned [rule]. Is that still how you prefer it?"

See references/self-review.md for periodic audit and consolidation of accumulated learnings.

Tasks

One-off and recurring tasks with priorities, deadlines, and scheduling. Use create_task. Provide freq for recurring tasks (daily, weekly, monthly). See references/task-management.md for recurring task patterns and advanced usage.

Goals

Long-term objectives and aspirations. Use save_goal.

Core Tools

Memory

save_memory, search_memories, update_memory, delete_memory, INLINECODE44

Notes

save_note, search_notes, get_note, update_note, INLINECODE49

Tasks

create_task, search_tasks, update_task, INLINECODE53

Goals

save_goal, search_goals, update_goal, INLINECODE57

Best Practices

1. 1. Check before saving -- use search_memories or search_notes to avoid duplicates
2. Update over create -- if a memory or note already exists on the topic, use update_memory or INLINECODE61
3. Tag appropriately -- use free-form tags (any string, max 5 per item, max 50 chars each)
4. Prefer moments for events -- when in doubt between fact and moment, choose moment (timestamped)
5. Ask about priority for tasks if not obvious from context
6. Confirm deadlines -- make sure you understood the date correctly

Reference Files

For detailed workflows, load these reference files when the relevant domain is active:

• - references/memory-management.md -- Detailed guidance on facts, moments, and notes
• INLINECODE63 -- Recurring tasks, scheduling patterns, and task lifecycle
• INLINECODE64 -- Audit and consolidate accumulated AI learnings