Claudemem

claudemem — Persistent Memory for AI Agents

Captures and retrieves knowledge across conversations. Two behaviors:

1. Automatically save knowledge notes during work (silent, no user action needed)
2. On command (/wrapup) save a structured session report with cross-linked notes

Slash Commands

• /wrapup [title] — End-of-session: extract knowledge notes + save detailed session report + cross-link everything.
• /recall [topic] — Search persistent memory for a topic, or show recent activity.

Natural triggers: "remember this", "what do you remember about...", "wrap up", "what did we do last time"

Setup

If claudemem is not on PATH:

curl -fsSL https://raw.githubusercontent.com/zelinewang/claudemem/main/skills/claudemem/scripts/install.sh | bash

CLI Reference

All commands support --format json for structured output.

# Notes
claudemem note add <category> --title "..." --content "..." --tags "..." [--session-id "..."]
claudemem note search "query" [--in category] [--tag tags]
claudemem note list [category]
claudemem note get <id>                        # Supports 8-char prefix
claudemem note append <id> "additional content"
claudemem note update <id> --content "..." [--title "..."] [--tags "..."]
claudemem note delete <id>
claudemem note categories
claudemem note tags

# Sessions
claudemem session save --title "..." --branch "..." --project "..." --session-id "..." [--related-notes "id:title:cat,..."]
claudemem session list [--last N] [--date today] [--date-range 7d] [--branch X]
claudemem session search "query" [--branch X]
claudemem session get <id>

# Search
claudemem search "query" [--type note|session] [--limit N]
claudemem search "query" --compact              # IDs + titles only
claudemem search "query" --category X --tag Y   # Faceted filters
claudemem search "query" --after 2025-01-01     # Date range
claudemem search "query" --sort date            # Chronological (default: relevance + recency boost)
claudemem search "query" --semantic             # Force semantic even if feature flag off

# Context
claudemem context inject [--limit N] [--project path]  # Recent notes + sessions overview

# Code
claudemem code outline <file>                   # Structural symbols (Go/Python/TS/Rust)

# Knowledge Graph
claudemem graph                                 # DOT format (pipe to Graphviz)
claudemem graph --format json                   # Adjacency list

# Utilities
claudemem stats [--top-accessed]                # Storage stats + access tracking
claudemem reindex [--vectors] [--all]           # Rebuild search/vector indexes
claudemem config set/get/list/delete | export | import | verify | repair

Protocols (Fixed — Always Follow These)

These have predictable inputs and outputs. Follow them exactly every time.

Protocol 1: Note Saving

Every time you save a note, follow this sequence:

1. Search for duplicates: note search "<key phrase>" --format json
2. If topic exists: note append <id> "new info"
3. If new: note add <category> --title "..." --content "..." --tags "..."
4. Check existing categories first: note categories
5. Show indicator: [noted: "title" -> category]

Protocol 2: Session Reports (/wrapup)

Session reports are ONLY created via explicit /wrapup or "wrap up" — never automatically. Every session report uses this exact template:

## Summary
2-3 substantial paragraphs: goal, accomplished, current state, significance.

## What Happened
Numbered paragraphs. Each phase: what, why, file paths, cause-and-effect, decisions. Minimum 3 phases.

## Key Decisions
- **Decision**: Rationale. Alternatives considered and why rejected.

## What Changed
- `path/to/file` — What changed and why

## Problems & Solutions
- **Problem**: Root cause (not just symptoms)
  **Solution**: Fix and why it works

## Learning Insights
- Reusable knowledge for future sessions

## Related Notes
- `note-id` — "Title" (category)

## Next Steps
- [ ] Concrete actionable follow-up

Protocol 3: Cross-Referencing

Notes and sessions MUST be bidirectionally linked during /wrapup:

• Notes saved during wrapup: include --session-id "$SESSION_REF" to link note → session
• Session save: include --related-notes "id:title:category,..." to link session → notes

This enables tracing: from any note → which session produced it; from any session → what knowledge was extracted.

Protocol 4: Retrieval Indicators

When prior knowledge is found and used, always show:

[memory: Found "TikTok Rate Limits" — 100/min per API key]

Capabilities (Flexible — Agent Decides When/How)

These vary by context. Use the trade-offs below to make your own judgment call.

Search Modes

Default sort uses relevance with recency boost (entries <7 days get up to 20% score boost, decaying over 30 days). Use --sort date for chronological ordering.

Hybrid search is automatic when features.semantic_search is enabled. Uses Ollama embeddings (or TF-IDF fallback) combined with FTS5 via Reciprocal Rank Fusion. No --semantic flag needed — every search benefits from semantic matching. Setup: config set features.semantic_search true && reindex --vectors.

Code Intelligence

code outline <file> extracts structural symbols — function signatures, classes, types — without bodies. ~10-20 tokens per symbol vs ~500+ for full file reads.

Supports Go (95% accuracy), Python (90%), TypeScript/JS (80%), Rust (85%). Unsupported languages return empty result.

Context Injection

context inject returns recent notes + sessions + stats (~1-2KB). Can be configured as a SessionStart hook for automatic loading, or run manually when starting significant work.

{
  "hooks": {
    "SessionStart": [{
      "matcher": "",
      "hooks": [{ "type": "command", "command": "claudemem context inject --limit 5", "timeout": 10000 }]
    }]
  }
}

Domain Knowledge (Reference — Informs Decisions)

What's Worth Saving

High-value (things Claude won't know in a future session):

• Project-specific decisions with rationale (why X over Y, rejected alternatives)
• Bug root causes and diagnosis patterns specific to this codebase
• Configuration quirks, environment-specific gotchas, undocumented API behaviors
• User preferences discovered during work (naming conventions, workflow choices)

Low-value (skip — Claude already knows or doesn't need):

• Transient debugging state, temporary output
• General programming knowledge from public documentation
• Information the user explicitly says is ephemeral

Data Model

• Notes: Knowledge fragments with category, tags, content, metadata. Source of truth: ~/.claudemem/notes/<category>/<title>.md
• Sessions: Work reports with structured sections. Source of truth: ~/.claudemem/sessions/<title>.md
• Cross-refs: Note.metadata.session_id → Session; Session.RelatedNotes[] → Notes
• Index: SQLite FTS5 at ~/.claudemem/.index/search.db (regenerable cache, not source of truth)

All data is plain Markdown with YAML frontmatter — human-readable, git-friendly, portable.