How do I install ClawMem?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does ClawMem need an API key?

No required environment variable is listed on this record.

What transport does ClawMem use?

ClawMem runs locally over stdio.

When was ClawMem last checked?

Last checked 2026-11-08. On-device memory layer for AI agents. Claude Code, OpenClaw and Hermes. Hooks + MCP server + hybrid RAG search.

Servidor MCP ClawMem

ClawMem — On-device memory layer for Claude Code, OpenClaw, and Hermes agents

On-device memory for Claude Code, OpenClaw, Hermes, and AI agents. Retrieval-augmented search, hooks, and an MCP server in a single local system. No API keys, no cloud dependencies.

ClawMem fuses recent research into a retrieval-augmented memory layer that agents actually use. The hybrid architecture combines QMD-derived multi-signal retrieval (BM25 + vector search + reciprocal rank fusion + query expansion + cross-encoder reranking), SAME-inspired composite scoring (recency decay, confidence, content-type half-lives, co-activation reinforcement), MAGMA-style intent classification with multi-graph traversal (semantic, temporal, and causal beam search), and A-MEM self-evolving memory notes that enrich documents with keywords, tags, and causal links between entries. Pattern extraction from Engram adds deduplication windows, frequency-based durability scoring, and temporal navigation.

Integrates via Claude Code hooks, an MCP server (works with any MCP-compatible client), a native OpenClaw plugin, or a Hermes Agent MemoryProvider plugin. All paths write to the same local SQLite vault. A decision captured during a Claude Code session shows up immediately when an OpenClaw or Hermes agent picks up the same project.

TypeScript on Bun. MIT License.

What It Does

ClawMem turns your markdown notes, project docs, and research dumps into persistent memory for AI coding agents. It automatically:

Surfaces relevant context on every prompt (context-surfacing hook)
Bootstraps sessions with your profile, latest handoff, recent decisions, and stale notes
Captures decisions, preferences, milestones, and problems from session transcripts using a local GGUF observer model
Imports conversation exports from Claude Code, ChatGPT, Claude.ai, Slack, and plain text via clawmem mine, with optional post-import LLM fact extraction (--synthesize) that pulls structured decisions / preferences / milestones / problems and cross-fact links out of otherwise full-text conversation dumps (v0.7.2). Imports preserve authorship time (v0.27.0): ranking recency and temporal queries run on when content was actually written, so historical conversations mined today don't rank as fresh — with a metadata-only --backfill-dates lane for vaults mined earlier
Generates handoffs at session end so the next session can pick up where you left off
Learns what matters via a feedback loop that boosts referenced notes and decays unused ones
Guards against prompt injection in surfaced content
Classifies query intent (WHY / WHEN / ENTITY / WHAT) to weight search strategies
Traverses multi-graphs (semantic, temporal, causal) via adaptive beam search
Evolves memory metadata as new documents create or refine connections
Infers causal relationships between facts extracted from session observations
Detects contradictions between new and prior decisions, auto-decaying superseded ones — when a contradiction judge is configured (CLAWMEM_JUDGE_*, v0.29.0; disabled otherwise) (with an additional merge-time contradiction gate in the consolidation worker that blocks cross-observation contradictions before they land, v0.7.1)
Guards against cross-entity merges during consolidation — name-aware dual-threshold merge safety compares entity anchors before merging similar observations, preventing "Alice decided X" from merging into "Bob decided X" (v0.7.1)
Prevents context bleed in derived insights — the Phase 3 deductive synthesis pipeline validates every draft against an anti-contamination wrapper (deterministic entity contamination check + LLM validator + dedupe) before writing cross-session deductive observations (v0.7.1)
Frames surfaced facts as background knowledge — context-surfacing wraps injected content in + + blocks, telling the model to treat facts as already-known and exposing memory-graph edges between surfaced docs directly in-prompt (v0.7.1)

Runs fully local with no API keys and no cloud services. Integrates via Claude Code hooks and MCP tools, as a native OpenClaw plugin, or as a Hermes Agent MemoryProvider plugin. All modes share the same vault for cross-runtime memory. Works with any MCP-compatible client.

Full version history is in RELEASE_NOTES.md. Upgrade instructions for existing vaults are in docs/guides/upgrading.md.

Architecture

Install

Platform Support

Platform	Status	Notes
Linux	Full support	Primary target. systemd services for watcher + embed timer.
macOS	Full support	Homebrew SQLite handled automatically. GPU via Metal (llama.cpp).
Windows (WSL2)	Full support	Recommended for Windows users. Install Bun + ClawMem inside WSL2.
Windows (native)	Not recommended	Bun and sqlite-vec work, but `bin/clawmem` wrapper is bash, hooks expect bash commands, and systemd services have no equivalent. Use WSL2 instead.

Prerequisites

Required:

Bun v1.0+ — runtime for ClawMem. On Linux, install via curl -fsSL https://bun.sh/install | bash (not snap — snap Bun cannot read stdin, which breaks hooks).
SQLite with FTS5 — included with Bun. On macOS, install brew install sqlite for extension loading support (ClawMem detects and uses Homebrew SQLite automatically).

Optional (for better performance):

llama.cpp (llama-server) — for dedicated GPU inference. Without it, node-llama-cpp runs models in-process (auto-downloads on first use). GPU servers give better throughput and prevent silent CPU fallback.
systemd (Linux) or launchd (macOS) — for persistent background services (watcher, embed timer, GPU servers). ClawMem ships systemd unit templates; macOS users can create equivalent launchd plists. See systemd services.

Optional integrations:

Claude Code — for hooks + MCP integration
OpenClaw — for native plugin integration
Hermes Agent — for MemoryProvider plugin integration
bd CLI v0.58.0+ (verified through v1.1.0) — for Beads issue tracker sync (only if using Beads)

Install from npm (recommended)

npm install -g clawmem

If you use Bun as your package manager:

bun add -g clawmem

Install from source

git clone https://github.com/yoloshii/clawmem.git ~/clawmem
cd ~/clawmem && bun install
ln -sf ~/clawmem/bin/clawmem ~/.bun/bin/clawmem

Setup roadmap

After installing, here's the full journey from zero to working memory:

Step	What	How	Details
1. Bootstrap	Create a vault, index your first collection, embed, install hooks and MCP	`clawmem bootstrap ~/notes --name notes`	One command does it all. Or run each step manually (see below).
2. Choose models	Pick embedding + reranker models based on your hardware	16GB+ VRAM → SOTA stack (zembed-1 + zerank-2). Less → QMD native combo. No GPU → cloud embedding or CPU fallback.	Inference services
3. Download models	Get the model files for your chosen stack (GGUFs for all three services; for zerank-2, only a GGUF that carries its score head works)	`wget` from HuggingFace, let `node-llama-cpp` auto-download the QMD native models, or build the zerank-2 bf16 sidecar	Inference services
4. Start services	Run GPU servers (if using dedicated GPU) and background services. Optionally enable the v0.8.2 background maintenance workers in the watcher unit so consolidation + deductive synthesis run automatically.	`llama-server` for each model. systemd units for watcher + embed timer. Drop-in for the watcher to enable workers + tune intervals + set the quiet window.	systemd services, background workers
5. Decide what to index	Add collections for your projects, notes, research, and domain docs	`clawmem collection add ~/project --name project`	The more relevant markdown you index, the better retrieval works. See building a rich context field.
6. Connect your agent	Hook into Claude Code, OpenClaw, Hermes, or any MCP client

Fastest path: Step 1 alone gets you a working system with in-process CPU/GPU inference and default models — no manual model downloads or service configuration needed. Steps 2-4 are optional upgrades for better performance. Steps 5-6 are where you customize what gets indexed and how your agent connects.

Customize what gets indexed: Each collection has a pattern field in ~/.config/clawmem/config.yaml (default: **/*.md). Tailor it per collection — index project docs, research notes, decision records, Obsidian vaults, or anything else your agents should know about. The more relevant content in the vault, the better retrieval works. See the quickstart for config examples.

Quick start commands

# One command: init + index + embed + hooks + MCP
clawmem bootstrap ~/notes --name notes

# Or step by step:
clawmem init
clawmem collection add ~/notes --name notes
clawmem update --embed
clawmem setup hooks
clawmem setup mcp

# Add more collections (the more you index, the richer retrieval gets)
clawmem collection add ~/projects/myapp --name myapp
clawmem collection add ~/research --name research
clawmem update --embed

# Verify
clawmem doctor

Upgrading

bun update -g clawmem   # or: npm update -g clawmem

Database schema migrates automatically on next startup (new tables and columns are added via CREATE IF NOT EXISTS / ALTER TABLE ADD COLUMN).

After major version updates (e.g. 0.1.x → 0.2.0) that add new enrichment pipelines, run a full enrichment pass to backfill existing documents:

clawmem reindex --enrich  # Full enrichment: entity extraction (backfill) + links + evolution for all docs
clawmem embed             # Re-embed if upgrading embedding models (not needed for most updates)

--enrich runs the complete A-MEM pipeline (entity extraction, link generation, memory evolution) on all documents, not just new ones; entity extraction inside it skips a document unchanged since its last extraction (enrichment lifecycle). Without it, reindex only refreshes metadata for existing docs.

Routine patch updates (e.g. 0.2.0 → 0.2.1) do not require reindexing.

For version-specific upgrade notes (opt-in features, optional cleanup steps, verification commands), see docs/guides/upgrading.md.

Integration

Claude Code

ClawMem integrates via hooks (settings.json) and an MCP stdio server. Hooks handle 90% of retrieval automatically - the agent never needs to call tools for routine context.

clawmem setup hooks    # Install lifecycle hooks (SessionStart, UserPromptSubmit, Stop, PreCompact)
clawmem setup mcp      # Register MCP server in ~/.claude.json (33 tools)

Automatic (90%): context-surfacing injects relevant memory on every prompt. postcompact-inject re-injects state after compaction. decision-extractor, handoff-generator, feedback-loop capture session state on stop.

Agent-initiated (10%): MCP tools (query, intent_search, find_causal_links, timeline, etc.) for targeted retrieval when hooks don't surface what's needed.

OpenClaw

ClawMem registers as a native OpenClaw memory plugin (kind: memory, v0.10.0+). Same 90/10 automatic retrieval, delivered through OpenClaw's plugin-hook bus instead of Claude Code hooks.

# v0.10.4+: profile-aware. Delegates to `openclaw plugins install --force` when the OpenClaw CLI
# is on PATH (auto-enables the plugin, honors OPENCLAW_STATE_DIR, OPENCLAW_CONFIG_PATH, --profile).
# Falls back to a recursive copy honoring OPENCLAW_STATE_DIR when the CLI is absent.
clawmem setup openclaw

# Custom profile (e.g. dev profile at ~/.openclaw-dev):
OPENCLAW_STATE_DIR=~/.openclaw-dev clawmem setup openclaw

What the plugin provides:

before_prompt_build hook (load-bearing) - prompt-aware retrieval (context-surfacing + session-bootstrap) AND the pre-emptive precompact-extract run when token usage approaches the compaction threshold. This is the authoritative path for precompact state capture because it runs synchronously before the LLM call that would trigger compaction, so it cannot race the compactor.
agent_end hook - decision extraction, handoff generation, feedback loop (parallel, fire-and-forget at the OpenClaw call site). OpenClaw v2026.4.26+ also enforces a 30s default void-hook timeout on agent_end — slow handlers are logged but the underlying postrun work is not cancelled (fail-open).
before_compaction hook (defense-in-depth fallback) - fires precompact-extract again for the rare case where before_prompt_build's proximity heuristic missed a sudden token-count jump. Fire-and-forget at OpenClaw's call site, so it races the compactor and offers no correctness guarantee on its own — the before_prompt_build path is what actually holds the invariant.
session_start hook - session registration + cached first-turn bootstrap context
5 agent tools - clawmem_search, clawmem_get, clawmem_session_log, clawmem_timeline, clawmem_similar

Disable OpenClaw's native memory search to avoid duplicate injection:

openclaw config set agents.defaults.memorySearch.extraPaths "[]"

ClawMem coexists cleanly with OpenClaw's Active Memory plugin (v2026.4.10+) and, on OpenClaw v2026.4.18+ (#65411), with the memory-core dreaming sidecar — both run alongside ClawMem instead of being mutually exclusive. They search different backends and inject into different prompt regions, so they do not conflict. See the OpenClaw plugin guide — Active Memory coexistence and the memory-core dreaming sidecar section for the two patterns.

Pair ClawMem (memory) with a context-engine plugin (v0.10.0+). OpenClaw and Hermes maintainers have converged on a two-surface plugin model: one slot for memory plugins (cross-session, retrieval-first) and a separate slot for context-engine plugins (in-session, compression/compaction-first). Under that model ClawMem is a memory layer — it has always been one in Hermes via the MemoryProvider ABC, and v0.10.0 moves the OpenClaw integration to the same semantic slot. You can now run ClawMem in the memory slot alongside an LCM-style compression plugin (for example, lossless-claw) in the context-engine slot. The two plugins do not overlap: one persists across sessions, the other reshapes the live window. See the OpenClaw plugin guide — memory vs context engine for the full rationale.

OpenClaw v2026.4.11+ recommended (required for ClawMem v0.10.0+). v2026.4.11 introduced a new plugin discovery contract that requires each plugin directory to ship a package.json with openclaw.extensions declared, and that rejects symlinked plugin directories. ClawMem v0.10.0 includes both fixes. Older ClawMem versions (< v0.10.0) on OpenClaw v2026.4.11+ will fail to discover silently — upgrade ClawMem, then re-run clawmem setup openclaw. See docs/guides/upgrading.md.

Alternative: OpenClaw agents can also use ClawMem's MCP server directly (clawmem setup mcp), with or without hooks. This gives full access to all 33 MCP tools but bypasses OpenClaw's plugin lifecycle, so you lose token budget awareness, native compaction orchestration, and the agent_end message pipeline. The native OpenClaw plugin is recommended for new setups; MCP is available as an additional or standalone integration.

Hermes Agent

ClawMem integrates as a native MemoryProvider plugin — Hermes's pluggable interface for agent memory. Same automatic retrieval and extraction, delivered through Hermes's memory lifecycle instead of Claude Code hooks.

Install:

# Preferred — user-plugin path (Hermes #10529, v2026.4.13+).
# Survives `git pull` of hermes-agent and avoids dual-registration with bundled providers.
# Copies the directory's CONTENTS, so the same command installs and upgrades.
mkdir -p "${HERMES_HOME:-$HOME/.hermes}/plugins/clawmem"
cp -r /path/to/ClawMem/src/hermes/. "${HERMES_HOME:-$HOME/.hermes}/plugins/clawmem/"

# Or, the bundled-style path (always supported, takes precedence on name collisions).
# Recommended only when you actively work in the hermes-agent source tree.
mkdir -p /path/to/hermes-agent/plugins/memory/clawmem
cp -r /path/to/ClawMem/src/hermes/. /path/to/hermes-agent/plugins/memory/clawmem/

# Symlink alternative for in-place development (either path).
ln -s /path/to/ClawMem/src/hermes ${HERMES_HOME:-~/.hermes}/plugins/clawmem

Configure in your Hermes profile's .env or environment:

CLAWMEM_BIN=/path/to/clawmem          # Path to clawmem binary (or ensure it's on PATH)
CLAWMEM_SERVE_PORT=7438                # REST API port (default: 7438)
CLAWMEM_SERVE_MODE=external            # "external" (you run clawmem serve) or "managed" (plugin manages it)
CLAWMEM_PROFILE=balanced               # speed | balanced | deep

Then set memory.provider: clawmem in your Hermes config.yaml, or run hermes memory setup to configure interactively.

What the plugin provides:

prefetch() — prompt-aware retrieval via context-surfacing hook (automatic every turn)
sync_turn() — appends the turn to the plugin's transcript, then runs decision extraction, handoff generation and the feedback loop over it in the background (v0.41.0: after every turn; each turn is processed once)
on_session_end() — a last pass, then the SessionEnd handoff flush
on_pre_compress() — pre-compaction state preservation
session-bootstrap — session registration + first-turn context injection
5 agent tools — clawmem_retrieve, clawmem_get, clawmem_session_log, clawmem_timeline, clawmem_similar
Plugin-managed transcript — maintains its own JSONL transcript for ClawMem hooks

Requirements: clawmem binary on PATH and clawmem serve running (external mode) or the plugin starts it automatically (managed mode). Python 3.10+. No pip dependencies beyond Hermes itself (uses urllib for REST calls, httpx optional for better performance).

Alternative: Hermes also has built-in MCP client support. You can add ClawMem as an MCP server in Hermes's config.yaml under mcp_servers for tool-only access. But this misses the lifecycle hooks (prefetch, session_end, pre_compress), so the native plugin is recommended.

See Hermes plugin guide for architecture details, lifecycle mapping, and troubleshooting.

Multi-Framework Operation

All three integrations share the same SQLite vault by default. Claude Code, OpenClaw, and Hermes can run simultaneously — decisions captured in one runtime are immediately available in the others, giving agents persistent shared memory across sessions and platforms. WAL mode + busy_timeout handles concurrent access.

Multi-Vault (Optional)

By default, ClawMem uses a single vault at ~/.cache/clawmem/index.sqlite. For users who want separate memory domains (e.g., work vs personal, or isolated vaults per project), ClawMem supports named vaults.

Configure in ~/.config/clawmem/config.yaml:

vaults:
  work: ~/.cache/clawmem/work.sqlite
  personal: ~/.cache/clawmem/personal.sqlite

Or via environment variable:

export CLAWMEM_VAULTS='{"work":"~/.cache/clawmem/work.sqlite","personal":"~/.cache/clawmem/personal.sqlite"}'

Using vaults with MCP tools:

All retrieval tools (memory_retrieve, query, search, vsearch, intent_search) accept an optional vault parameter. Omit it to use the default vault.

# Search the default vault (no vault param needed)
query("authentication flow")

# Search a named vault
query("project timeline", vault="work")

# List configured vaults
list_vaults()

# Sync content into a vault
vault_sync(vault="work", content_root="~/work/docs")

Single-vault users: No action needed. Everything works without configuration. The vault parameter is always optional and ignored when no vaults are configured.

Inference services (GPU, in-process, or cloud)

ClawMem uses three inference services — embedding, LLM (query expansion / intent / A-MEM / the Stop hooks' observer), and reranker. In the default stack all three run as llama-server instances, each with an in-process node-llama-cpp fallback that auto-downloads on first use, so ClawMem works without a dedicated GPU (Metal on Apple Silicon, Vulkan where available, CPU as last resort). The bin/clawmem wrapper points at localhost:8088/8089/8090. Always run via bin/clawmem — it sets the endpoints.

Choose a stack:

Stack	Models	VRAM	License	When
QMD native (default)	EmbeddingGemma-300M + qmd-query-expansion-1.7B + qwen3-reranker-0.6B	~4 GB, or in-process	Permissive — commercial OK	Any GPU or none; zero-config start
z / SOTA	zembed-1 + qmd-query-expansion-1.7B + zerank-2 (Q8_0 GGUF that carries its score head)	~13 GB	Apache-2.0 — commercial OK	16 GB+ GPU; best recall
Cloud embedding	Jina / OpenAI / Voyage / Cohere (embedding only)	none	provider ToS	No local GPU for embedding; LLM + reranker stay local

Heads-up before you serve: most zerank-2 GGUFs are inert — llama.cpp's standard converter drops the score head. Serve the Q8_0 GGUF that carries it (launch line) or the bf16 seq-cls sidecar, and verify with clawmem rerank-health. zembed-1 / reranking need -ub = -b (non-causal attention). Changing embedding dimensions requires clawmem embed --force. Set CLAWMEM_NO_LOCAL_MODELS=true to fail fast instead of silent CPU fallback.

→ Full stack decision matrix, model/VRAM tables, and server-setup commands: docs/guides/inference-services.md. Cloud providers, batch + TPM behavior: docs/guides/cloud-embedding.md. All environment variables: docs/reference/configuration.md. Keeping servers running: docs/guides/systemd-services.md.

MCP Server

ClawMem exposes 33 MCP tools via the Model Context Protocol and an optional HTTP REST API. Any MCP-compatible client or HTTP client can use it.

Claude Code (automatic):

./bin/clawmem setup mcp   # Registers in ~/.claude.json

Manual (any MCP client):

Add to your MCP config (e.g. ~/.claude.json, claude_desktop_config.json, or your client's equivalent):

{
  "mcpServers": {
    "clawmem": {
      "command": "/absolute/path/to/clawmem/bin/clawmem",
      "args": ["mcp"]
    }
  }
}

The server runs via stdio — no network port needed. The bin/clawmem wrapper sets the GPU endpoint env vars automatically.

Verify: After registering, your client should see tools including memory_retrieve, search, vsearch, query, query_plan, intent_search, timeline, etc.

HTTP REST API (optional)

For web dashboards, non-MCP agents, cross-machine access, or programmatic use:

./bin/clawmem serve                          # localhost:7438; every request needs the token (below)
./bin/clawmem serve --port 8080              # custom port
./bin/clawmem serve-token                    # print the token serve uses

Endpoints:

Method	Path	Description
GET	`/health`	Liveness probe + version + doc count
GET	`/stats`	Full index statistics
POST	`/search`	Unified search (`mode`: auto/keyword/semantic/hybrid)
POST	`/retrieve`	Smart retrieve with auto-routing (`mode`: auto/keyword/semantic/causal/timeline/hybrid)
GET	`/documents/:docid`	Single document by 6-char hash prefix
GET	`/documents?pattern=...`	Multi-get by glob pattern
GET	`/timeline/:docid`	Temporal neighborhood (before/after)
GET	`/sessions`	Recent session history
GET	`/collections`	List all collections
GET	`/lifecycle/status`	Active/archived/forgotten/pinned/snoozed counts + `deactivation_reasons` (`absent`/`forget`/`archive`/`unknown_legacy`)
POST	`/documents/:docid/pin`	Pin/unpin
POST	`/documents/:docid/snooze`	Snooze until date
POST	`/documents/:docid/forget`	Deactivate
POST	`/lifecycle/sweep`	Archive stale docs (dry_run default)
GET	`/graph/causal/:docid`	Causal chain traversal
GET	`/graph/similar/:docid`	k-NN neighbors
GET	`/export`	Active-document export as JSON (`?full=true` includes legacy `precompact-state.md` snapshot copies)
POST

Auth (v0.42.0): every request needs Authorization: Bearer <token>: CLAWMEM_API_TOKEN when set (32+ characters), otherwise the token file serve generates at ~/.config/clawmem/serve-token; clawmem serve-token prints it. Every POST needs Content-Type: application/json, and requests from web pages (a foreign Origin or Host) are refused. See the REST API reference.

Search example:

curl -X POST http://localhost:7438/search \
  -H "Authorization: Bearer $(clawmem serve-token)" \
  -H 'Content-Type: application/json' \
  -d '{"query": "authentication decisions", "mode": "hybrid", "compact": true}'

Verify Installation

./bin/clawmem doctor   # Full health check
./bin/clawmem status   # Quick index status
bun test               # Run test suite (from the repository root)

Agent Instructions

ClawMem ships agent instruction files and an optional maintenance agent:

File	Loaded	Purpose
`AGENTS.md`	Automatically (most agents, when working in this repo)	Lean root SSOT — agent-facing quick reference (inference at a glance, install, retrieval routing, MCP tools, indexing rules, integrations) + an index into `docs/`
`CLAUDE.md`	Automatically (Claude Code)	`@AGENTS.md` import — Claude Code reads `CLAUDE.md` natively, so it just pulls in the SSOT
`SKILL.md`	On-demand (agent reads when needed)	Portable operating reference — escalation gate, tool routing, the 4 query-optimization levers, pipeline behavior, composite scoring, lifecycle. Shipped with the package for cross-project use
`docs/`	On-demand (linked throughout `AGENTS.md`)	Deep reference — inference setup, configuration, concepts, internals, guides, troubleshooting
`agents/clawmem-curator.md`	On-demand via `clawmem setup curator`	Maintenance agent — lifecycle triage, retrieval health checks, dedup sweeps, graph rebuilds

Working in the ClawMem repo: No action needed — CLAUDE.md (which imports AGENTS.md) loads automatically.

Using ClawMem from other projects: Your agent needs instructions on how to use ClawMem's hooks and MCP tools. Two options:

Option A: Copy instructions into your project

Copy the contents of AGENTS.md (or the relevant sections) into your project's own AGENTS.md or CLAUDE.md. Simple but requires manual updates when ClawMem changes.

Option B: Add a trigger block (recommended)

Add this minimal trigger block to your global ~/.claude/CLAUDE.md. It gives the agent routing rules always loaded, and tells it how to find the full reference (SKILL.md) shipped with your installation when deeper guidance is needed:

## ClawMem

Architecture: hooks (automatic, ~90%) + MCP tools (explicit, ~10%).

Vault: `~/.cache/clawmem/index.sqlite` | Config: `~/.config/clawmem/config.yaml`

### Escalation Gate (3 rules — ONLY escalate to MCP tools when one fires)

1. **Low-specificity injection** — `<vault-context>` is empty or lacks the specific fact needed
2. **Cross-session question** — "why did we decide X", "what changed since last time"
3. **Pre-irreversible check** — before destructive or hard-to-reverse changes

### Tool Routing (once escalated)

**Preferred:** `memory_retrieve(query)` — auto-classifies and routes to the optimal backend.

**Direct routing** (when calling specific tools):

    "why did we decide X"         → intent_search(query)          NOT query()
    "what happened last session"  → session_log()                 NOT query()
    "what else relates to X"      → find_similar(file)            NOT query()
    Complex multi-topic           → query_plan(query)             NOT query()
    General recall                → query(query, compact=true)
    Keyword spot check            → search(query, compact=true)
    Conceptual/fuzzy              → vsearch(query, compact=true)
    Full content                  → multi_get("path1,path2")
    Lifecycle health              → lifecycle_status()
    Stale sweep                   → lifecycle_sweep(dry_run=true)
    Restore archived              → lifecycle_restore(query)

ALWAYS `compact=true` first → review → `multi_get` for full content.

### Proactive Use (no escalation gate needed)

- User says "remember this" / critical decision made → `memory_pin(query)` immediately
- User corrects a misconception → `memory_pin(query)` the correction
- `<vault-context>` surfaces irrelevant/noisy content → `memory_snooze(query, until)` for 30 days
- Need to correct a memory → `memory_forget(query)`
- After bulk ingestion → `build_graphs`

### Anti-Patterns

- Do NOT use `query()` for everything — match query type to tool, or use `memory_retrieve`
- Do NOT call query/intent_search every turn — 3 rules above are the only gates
- Do NOT re-search what's already in `<vault-context>`
- Do NOT pin everything — pin is for persistent high-priority items, not routine decisions
- Do NOT forget memories to "clean up" — let confidence decay handle it
- Do NOT wait for curator to pin decisions — pin immediately when critical

For detailed operating guidance (escalation gate, tool routing, query-optimization levers, pipeline behavior, composite scoring, lifecycle), find and read the shipped SKILL.md (setup, inference, and internals live in AGENTS.md + docs/):
  Bash: CLAWMEM_ROOT=$(cd "$(dirname "$(which clawmem)")/.." && pwd) && echo "$CLAWMEM_ROOT/SKILL.md"
  Then: Read the file at that path.

This gives your agent the 3-rule gate, tool routing, and proactive behaviors always loaded. When it needs deeper guidance, it locates and reads the full SKILL.md reference shipped with your installation — no symlinks or skill registration required.

CLI Reference

clawmem init                                    Create DB + config
clawmem bootstrap <vault> [--name N] [--skip-embed]  One-command setup
clawmem collection add <path> --name <name>     Add a collection
clawmem collection list                         List collections
clawmem collection remove <name>                Remove a collection

clawmem update [--pull] [--embed]               Incremental re-scan
clawmem mine <dir> [-c name] [--embed] [--synthesize]  Import conversation exports (--synthesize runs post-import LLM fact extraction, v0.7.2; preserves per-exchange authored_at, v0.27.0)
clawmem mine <dir> -c name --backfill-dates [--apply]  Derive authored_at for already-mined docs (metadata-only; dry-run without --apply)
clawmem embed [-f]                              Generate fragment embeddings
clawmem reindex [--force]                       Full re-index
clawmem watch                                   File watcher daemon

clawmem search <query> [-n N] [--json]          BM25 keyword search
clawmem vsearch <query> [-n N] [--json]         Vector semantic search
clawmem query <query> [-n N] [--json]           Full hybrid pipeline

clawmem profile                                 Show user profile
clawmem profile rebuild                         Force profile rebuild
clawmem update-context                          Regenerate per-folder CLAUDE.md

clawmem list [-n/--limit N] [-c col] [--json]   Browse recent documents
clawmem budget [--session ID]                   Token utilization
clawmem log [--last N]                          Session history
clawmem hook <name>                             Manual hook trigger
clawmem surface --context --stdin               IO6: pre-prompt context injection
clawmem surface --bootstrap --stdin             IO6: per-session bootstrap injection

clawmem reflect [N]                             Cross-session reflection (last N days, default 14)
clawmem consolidate [--dry-run] [N]             Find and archive duplicate low-confidence docs
clawmem eval run --gold <file.jsonl>            Offline eval: replay gold-labeled queries, score J_doc/recall/MRR

clawmem install-service [--enable] [--remove]   Systemd watcher service
clawmem setup hooks [--remove]                  Install/remove Claude Code hooks
clawmem setup mcp [--remove]                    Register/remove MCP server
clawmem setup curator [--remove]                Install/remove curator maintenance agent
clawmem mcp                                     Start stdio MCP server
clawmem serve [--port 7438] [--host 127.0.0.1] [--no-token]  Start HTTP REST API server (token required)
clawmem serve-token                             Print the token serve uses
clawmem path                                    Print database path
clawmem doctor                                  Full health check
clawmem status                                  Quick index status

MCP Tools (31)

Registered by clawmem setup mcp. Available to any MCP-compatible client.

Internal-collection visibility (v0.21.0): the retrieval tools search, vsearch, query, query_plan, memory_retrieve, and find_similar (plus memory_rank since v0.36.0) exclude the system-internal _clawmem collection by default — pass includeInternal: true (or name _clawmem in an explicit collection filter where the tool has one) to include it. find_similar auto-includes internal neighbors when the reference document is itself internal; intent_search, find_causal_links, kg_query, session_log, and timeline are unfiltered by design. Full contract (including the degraded under-fill markers): docs/reference/mcp-tools.md.

Tool	Description
`__IMPORTANT`	Workflow guide: prefer `memory_retrieve` → match query type to tool → `multi_get` for full content

Core Search & Retrieval

Tool	Description
`memory_retrieve`	Preferred entry point. Auto-classifies query and routes to optimal backend (query, intent_search, session_log, find_similar, or query_plan). Use instead of manually choosing a search tool.
`search`	BM25 keyword search — for exact terms, config names, error codes, filenames. v0.24.0: non-recency queries rank by the RAW BM25 transform (`scoreBasis: "fts-bm25"`; judged keyword eval MRR 0.848 vs composite 0.415; metadata breaks exact ties only; `minScore` filters raw, no default); recency-intent queries keep composite. Collection filter supports comma-separated values. Prefer `memory_retrieve` for auto-routing.
`vsearch`	Vector semantic search — for conceptual/fuzzy matching when exact keywords are unknown. v0.22.0: non-recency queries rank by RAW cosine (`scoreBasis: "vector-cosine"`; metadata breaks exact ties only; `minScore` filters raw with no default); recency-intent queries keep composite. Collection filter supports comma-separated values. Prefer `memory_retrieve` for auto-routing.
`query`	Full hybrid pipeline (BM25 + vector + rerank) — general-purpose when query type is unclear. WRONG for "why" questions (use `intent_search`) or cross-session queries (use `session_log`). Prefer `memory_retrieve` for auto-routing. Intent hint, strong-signal bypass, chunk dedup, candidateLimit, MMR diversity, compact mode.
`get`	Retrieve single document by path or docid
`multi_get`	Retrieve multiple docs by glob or comma-separated list
`find_similar`	USE THIS for "what else relates to X", "show me similar docs". Finds k-NN vector neighbors — discovers connections beyond keyword overlap that search/query cannot find.

Intent-Aware Search

Tool	Description
`intent_search`	USE THIS for "why did we decide X", "what caused Y", "who worked on Z". Classifies intent (WHY/WHEN/ENTITY/WHAT), traverses causal + semantic graph edges. Returns decision chains that `query()` cannot find.
`query_plan`	USE THIS for complex multi-topic queries ("tell me about X and also Y", "compare A with B"). Decomposes into parallel typed clauses (bm25/vector/graph), executes each, merges via RRF. `query()` searches as one blob — this tool splits topics and routes each optimally.

intent_search pipeline: Query → Intent Classification → BM25 + Vector → Intent-Weighted RRF → Graph Expansion (WHY/ENTITY intents) → Cross-Encoder Reranking → Composite Scoring

query_plan pipeline: Query → LLM decomposition into 2-4 typed clauses → Parallel execution (BM25/vector/graph per clause) → RRF merge across clauses → Composite scoring. Falls back to single-query for simple inputs.

Multi-Graph & Causal

Tool	Description
`build_graphs`	Build temporal and/or semantic graphs from document corpus
`find_causal_links`	Trace decision chains: "what led to X", "how we got from A to B". Follow up `intent_search` with this tool on a top result to walk the full causal chain. Traverses causes / caused_by / both up to N hops with depth-annotated reasoning.
`kg_query`	Query the SPO knowledge graph: "what does X relate to?", "what was true about X when?". Returns temporal entity-relationship triples with validity windows. Accepts an entity name or a canonical ID in `vault:type:slug` form; an exact name wins over a partial match, and entities that share the name are listed separately (resolution). Triples are populated by the decision-extractor hook from observer-emitted `<triples>` blocks.
`memory_evolution_status`	Show how a document's A-MEM metadata evolved over time
`timeline`	Show the temporal neighborhood around a document — what was created/modified before and after it. Progressive disclosure: search → timeline (context) → get (full content). Supports same-collection scoping and session correlation.

Beads Integration

Tool	Description
`beads_sync`	Sync Beads issues from Dolt backend (`bd` CLI) into memory: syncs the full backlog (no 50-issue cap), creates docs, bridges all dep types to `memory_relations`, surfaces bd ≥1.1.0 claim leases, runs A-MEM enrichment

Vault Management

Tool	Description
`list_vaults`	Show configured vault names and paths. Empty in single-vault mode.
`vault_sync`	Index markdown from a directory into a named vault. Restricted-path validation rejects sensitive directories.

Agent Diary

Tool	Description
`diary_write`	Write a diary entry. Use for recording important events, decisions, or observations in environments without hook support. Stored as searchable memories.
`diary_read`	Read recent diary entries. Filter by agent name.

Memory Management & Lifecycle

Tool	Description
`memory_forget`	Search → deactivate closest match (with audit trail). Weak matches return a disambiguation list instead of acting (v0.23.0)
`memory_pin`	Pin a memory: lifecycle retention + priority among relevance-equivalent results (+0.3 composite boost on hook/`query` surfaces; exact-tie precedence on the raw routes — vector + `search` non-recency). USE PROACTIVELY when: user states a persistent constraint, makes an architecture decision, or corrects a misconception. Don't wait for curator — pin critical decisions immediately.
`memory_snooze`	Temporarily hide a memory from context surfacing until a date. USE PROACTIVELY when `<vault-context>` repeatedly surfaces irrelevant content — snooze for 30 days instead of ignoring it.
`status`	Index health with content type distribution
`reindex`	Trigger vault re-scan
`index_stats`	Detailed stats: types, staleness, access counts, sessions
`memory_stats`	Lifecycle + ranking-metadata aggregates per collection (v0.36.0): origin×active cross-tabs, pinned, accrual, deactivation reasons, and access/confidence/quality/effective-age distributions (mean/median/min/max over active rows). Deeper than `index_stats`; includes system collections.
`memory_rank`	Ranking diagnostic (v0.36.0): real-pipeline composite breakdown per result — weights, recency, confidence blend, quality/length/frequency/canonical multipliers, signed pinΔ, co-activation — plus raw-vs-composite rank shifts; demoted raw winners stay visible. FTS-only candidates; read-only.
`session_log`	USE THIS for "last time", "yesterday", "what happened", "what did we do". Returns session history with handoffs and file changes. DO NOT use `query()` for cross-session questions — this tool has session-specific data that search cannot find.
`profile`	Current static + dynamic user profile
`lifecycle_status`	Document lifecycle statistics: active, archived, forgotten, pinned, snoozed counts, the deactivation-reason breakdown, and policy summary

ClawMem never physically deletes a document row (v0.30.0). Forget and archive deactivate; lifecycle_restore reverses archival; purge_after_days is inert. Through v0.29.0 a configured retention window permanently deleted archived rows from a non-dry-run sweep and from the SessionStart hook, without reporting it — see upgrading.

Compact Mode

search, vsearch, and query accept compact: true to return { id, path, title, score, snippet, content_type, fragment } instead of full content. Saves ~5x tokens for initial filtering.

Hooks (Claude Code Integration)

Hooks installed by clawmem setup hooks:

Hook	Event	What It Does
`context-surfacing`	UserPromptSubmit	Retrieval gate → lane-based hybrid search (BM25 + vector + file-aware (E13) + gated prior-turn + deep expansion variants) → weighted RRF fusion onto one channel-aware ordering key → snooze/noise filters → relevance admission with query-level abstention (v0.38.0) → tiered injection (HOT/WARM/COLD) → `<vault-context>` + `<vault-routing>` hint. Profile-driven budget/results/timeout; `CLAWMEM_HOOK_BUDGET_MS` is the authoritative internal deadline.
`postcompact-inject`	SessionStart (compact)	Re-injects context after compaction, framed as reference data: this session's pre-compaction state + recent decisions + antipatterns + vault context (1200 token budget)
`curator-nudge`	SessionStart	Surfaces curator report actions, nudges when report is stale (>7 days)
`precompact-extract`	PreCompact	Extracts the last typed request, decisions, file paths and open questions before compaction → the vault's session-keyed `compaction_state` row, taken once by the same session's `postcompact-inject`
`decision-extractor`	Stop	GGUF observer extracts structured observations (decisions, preferences, milestones, problems, bugfixes, features, refactors, discoveries) from the turns after its per-transcript cursor, each turn once (v0.41.0), into the session's own decision and antipattern docs; detects contradictions with prior decisions (judge-gated — requires `CLAWMEM_JUDGE_*`, v0.29.0)
`handoff-generator`	Stop	Digests each new turn without a model, then folds the digests into an incremental, throttled observer summary → the session's handoff
`handoff-generator`	SessionEnd	Renders the handoff's latest turns at session end (no transcript read, no model; v0.41.0)
`feedback-loop`	Stop	Silently credits each surfaced note once per turn when that turn verifiably names it (path, file name or rendered title): access count, co-activation + usage relations among the notes that turn referenced, utility signals (surfaced vs referenced ratio for lifecycle automation)

Additional hooks available but not installed by default:

Hook	Event	Why Not Default
`session-bootstrap`	SessionStart	Injects ~2000 tokens before user types anything. `context-surfacing` on first prompt is more precise.
`staleness-check`	SessionStart	Redundant without `session-bootstrap` (stale notes are part of its output).
`pretool-inject`	PreToolUse	Disabled in HOOK_EVENT_MAP (cannot inject additionalContext via PreToolUse).

Hooks handle ~90% of retrieval automatically. For agent escalation logic (when to use MCP tools vs rely on hooks), see CLAUDE.md.

Search Pipeline

User Query + optional intent hint
  → BM25 Probe → Strong Signal Check (skip expansion if top hit ≥ 0.85 with gap ≥ 0.15; disabled when intent provided)
  → Query Expansion (intent steers LLM prompt when provided)
  → BM25 + Vector Search (parallel, original query 2× weight)
  → Reciprocal Rank Fusion → slice to candidateLimit (default 30)
  → Intent-Aware Chunk Selection (intent terms at 0.5× weight alongside query terms at 1.0×)
  → Cross-Encoder Reranking (4000 char context; intent prepended; chunk dedup; batch cap=4)
  → Rerank/RRF Blend (blendRerank: 0.9·reranker + 0.1·normalized-RRF tiebreaker; reranker can promote over RRF #1; falls back to RRF order if reranker unavailable)
  → Composite Scoring (query-tuned: (search × 0.70 + recency × 0.15 + confidence × 0.15) × qualityMultiplier × lengthNorm × coActivationBoost + pinBoost; recency-intent queries → 0.10/0.70/0.20; other tools keep the 0.50/0.25/0.25 default)
  → MMR Diversity Filter (Jaccard bigram similarity > 0.6 → demoted)
  → Ranked Results

For agent-facing query optimization (tool selection, query string quality, intent parameter, candidateLimit), see CLAUDE.md.

Multi-Graph Traversal

For WHY and ENTITY queries, the search pipeline expands results through the memory graph:

Start from top-10 baseline results as anchor nodes
For each frontier node: get neighbors via any relation type
Score transitions: λ1·structure + λ2·semantic_affinity
Apply decay: new_score = parent_score * γ + transition_score
Keep top-k (beam search), repeat until max depth or budget

Graph types:

Semantic — vector similarity edges (threshold > 0.7)
Temporal — chronological document ordering
Causal — LLM-inferred cause→effect from Observer facts + Beads blocks/waits-for deps
Supporting — LLM-analyzed document relationships + Beads discovered-from deps
Contradicts — LLM-analyzed document relationships. Additional contradicts edges are inserted by Phase 3 deductive synthesis when a new draft contradicts a prior deductive observation. (Phase 2's merge-time gate does not insert edges — a blocked merge under link sets the old consolidated row's invalidated_by backlink instead.)

Content Type Scoring

Type	Half-life	Baseline	Notes
`decision`	180d	0.85	Very slow ranking decay (§36.11) — silently-abandoned decisions stop winning ranking; attention-decay-exempt; lifecycle policy separate
`deductive`	∞	0.85	Never decays — cross-session derived insights with source provenance
`preference`	∞	0.80	Never decays — user preferences are durable facts
`hub`	∞	0.80	Never decays
`antipattern`	∞	0.75	Never decays — accumulated negative patterns persist
`problem`	60 days	0.75	High priority but resolves over time
`research`	90 days	0.70
`milestone`	60 days	0.70	Important at the time, fades as project moves forward
`project`	120 days	0.65
`handoff`	30 days	0.60	Fast decay — most recent matters
`conversation`	45 days	0.55	Imported chat exchanges
`progress`	45 days	0.50
`note`	60 days	0.50	Default

Content types are inferred from frontmatter or file path patterns. Half-lives extend up to 3× for frequently-accessed memories (access reinforcement, decays over 90 days). Non-durable types (handoff, progress, conversation, note, project) lose 5% confidence per week without access (attention decay). Decision/deductive/preference/hub/research/antipattern are exempt.

Quality scoring: Each document gets a quality_score (0.0–1.0) computed during indexing based on length, structure (headings, lists), decision/correction keywords, and frontmatter richness. Applied as qualityMultiplier = 0.7 + 0.6 × qualityScore (range: 0.7× penalty to 1.3× boost).

Length normalization: 1/(1 + 0.5 × log2(max(bodyLength/500, 1))) — penalizes verbose entries that dominate via keyword density. Floor at 30% of original score.

Frequency boost: Documents with higher revision counts or duplicate counts get a durability signal: freqSignal = (revisions - 1) × 2 + (duplicates - 1), freqBoost = min(0.10, log1p(freqSignal) × 0.03). Revision count (content evolution) is weighted 2× vs duplicate count (ingest repetition). Capped at 10%.

Pin boost: Pin = lifecycle retention + priority among relevance-equivalent results. On composite surfaces pinned documents get a +0.3 additive boost (capped at 1.0); on the raw routes — the vector tools (v0.22.0) and non-recency search (v0.24.0) — pin breaks exact raw-score ties only. Use memory_pin to pin critical memories.

Snooze: Snoozed documents are filtered out of context surfacing until their snooze date. Use memory_snooze for temporary suppression.

Contradiction detection (judge-gated, v0.29.0): Contradiction analysis runs only when a judge is configured via CLAWMEM_JUDGE_* — without one it is disabled as an audited no-op, because the stock expansion model cannot meet the judge contract (details). With a judge: when decision-extractor identifies a new decision that contradicts a prior one, the old decision's confidence is automatically lowered (−0.25 for contradictions, −0.15 for updates). Superseded decisions naturally fade from context surfacing without manual intervention. When erosion reaches the 0.2 floor the hook can additionally set invalidated_at, which removes the document from FTS and vector retrieval entirely. That step applies only to content_type='observation' rows — a superseded decision is eroded but never retired — and is off by default behind CLAWMEM_CONTRADICTION_INVALIDATE and logs WOULD invalidate instead of writing, because its exposure depends on the vault's confidence distribution and content-type mix (calibration guide). Two contract defects fixed in v0.28.0 had previously prevented this path from applying any classification at all — earlier versions classified but never mutated a document. v0.7.1 adds a second layer at the consolidation boundary: before the background worker merges a new pattern into an existing consolidated observation, a deterministic heuristic (negation asymmetry, number/date mismatch) plus the configured judge detect contradictions (heuristic-only, and constrained to link, when no judge is configured). Blocked merges route to either link policy (both rows remain active; the old row gains an invalidated_by backlink, default) or supersede policy (old row marked status='inactive' — requires a configured judge, otherwise loudly constrained to link). Configurable via CLAWMEM_CONTRADICTION_POLICY and CLAWMEM_CONTRADICTION_MIN_CONFIDENCE. Mutation-authorizing evaluations commit their durable judge_runs/judge_events audit rows in the same transaction as the mutation (Phase-3 deductive checks commit a precondition audit first; non-mutating outcomes write standalone rows) — that audit is what calibration reads. Data-egress note: a cloud or subscription judge receives the new decisions and the retrieved candidate snippets.

Features

A-MEM (Adaptive Memory Evolution)

Documents are automatically enriched with structured metadata when indexed:

Keywords (3-7 specific terms)
Tags (3-5 broad categories)
Context (1-2 sentence description)

When new documents create links, neighboring documents' metadata evolves — keywords merge, context updates, and the evolution history is tracked with version numbers and reasoning.

Causal Inference

The decision-extractor hook analyzes Observer facts for causal relationships. When multiple facts exist in an observation, an LLM identifies cause→effect pairs (confidence ≥ 0.6). Causal chains can be queried via find_causal_links with multi-hop traversal using recursive CTEs.

Beads Integration

Projects using Beads (v0.58.0+, Dolt backend) issue tracking are fully integrated into the MAGMA memory graph:

Auto-sync: Watcher detects .beads/ directory changes → syncBeadsIssues() queries bd CLI for live Dolt data → creates markdown docs in beads collection
Dependency bridging: All Beads dependency types map to memory_relations edges — blocks/conditional-blocks/waits-for/caused-by→causal, discovered-from/supersedes/duplicates→supporting, relates-to/related/parent-child→semantic. Tagged {origin: "beads"} for traceability.
A-MEM enrichment: New beads docs get full postIndexEnrich() — memory note construction, semantic/entity link generation, memory evolution
Graph traversal: intent_search and find_causal_links traverse beads dependency edges alongside observation-inferred causal chains
Requirement: bd binary on PATH or at ~/go/bin/bd

beads_sync MCP tool for manual sync; watcher handles routine operations automatically.

Fragment-Level Embedding

Documents are split into semantic fragments (sections, lists, code blocks, frontmatter, facts) and each fragment gets its own vector embedding. Full-doc embedding is preserved for broad-match queries.

Local Observer Agent

Uses the LLM server (shared with query expansion and intent classification) to extract structured observations from session transcripts. Observation types: decision, bugfix, feature, refactor, discovery, change, preference, milestone, problem. Each observation includes title, facts, narrative, concepts, and files read/modified. Preferences, milestones, and problems get first-class content_type treatment with dedicated confidence baselines and half-lives instead of being flattened to generic "observation". Regex patterns find decisions and antipatterns beside the model. A batch the model cannot answer (unavailable, or refusing the request) is quarantined and replayed later, never committed as empty; its regex finds are committed when the replay succeeds (v0.41.0). Each prompt is fitted in tokens to the LLM server's own context, with room kept for the reply, and a turn too long for one prompt runs as checkpointed windows that resume after a timeout (v0.41.2). Only the reply <none/> means "nothing to record"; a reply that does not parse gets up to two retries whose feedback names the failing field (a batch whose replies never parse is quarantined like an unanswered one), and on llama-server the observer asks for grammar-constrained replies — a GBNF grammar that admits only well-formed ones, sent unless the server refused one in the last 24 hours; enforcement is not verified in advance, and clawmem doctor counts completed replies that break it (v0.41.4). Serve the observer with -c 8192; inference services covers the server's context size.

Recall Tracking

Empirical tracking of which documents are surfaced by retrieval, which queries surfaced them, and whether the assistant actually cited them. Provides signals beyond raw search relevance for lifecycle decisions:

Per-query diversity: docs surfaced by multiple distinct queries have proven cross-domain relevance
Multi-day spacing: docs surfaced across separate calendar days (spaced frequency) are more valuable than binge recalls in one session
Negative signals: docs surfaced frequently but rarely referenced are noise candidates for snooze
Per-turn attribution: feedback-loop pairs each transcript turn with the context-surfacing row that served it (prompt hash + the host's order of events, never position) and tests only that turn's answer against that turn's injected documents (v0.41.0)

Data feeds lifecycle_status (pin/snooze candidate reports) and lifecycle_sweep (recall-based recommendations). Adapted from OpenClaw dreaming promotion patterns.

User Profile

Two-tier auto-curated profile extracted from your decisions and hub documents:

Static: persistent facts (Levenshtein-deduplicated)
Dynamic: recent session context

Injected at session start for instant personalization.

Prompt Injection Filtering

Five detection layers protect injected content: legacy string patterns, role injection regex, instruction override patterns, delimiter injection, and unicode obfuscation detection. Filtered results are skipped entirely (no placeholder tokens wasted).

Consolidation Worker

Optional background process that enriches documents missing A-MEM metadata. Runs on a configurable interval, processing 3 documents per tick. Non-blocking (Timer.unref).

Per-Folder CLAUDE.md Generation

Automatically generates context sections in per-folder CLAUDE.md files from recent decisions and session activity related to that directory.

Feedback Loop

A surfaced note gets access_count + 1 when a turn it was injected into names it — once per turn, when the turn is over (v0.41.0; earlier versions counted the whole session again at every Stop). Unreferenced notes decay via recency. Over time, useful notes rise and noise fades.

Offline Eval Harness

clawmem eval run --gold <file.jsonl> replays hand-labeled queries through the real query tool handler and scores the retrieved documents against gold evidence: doc-level Jaccard (|C∩E|/|C∪E|), precision/recall@k, hit@k, MRR. Touches no retrieval, lifecycle, or telemetry state (normal inference caches may populate, as in any live query); writes run.json + report.md for A/B comparison between checkouts or DB snapshots. Gold schema, trust gates, and labeling discipline: docs/guides/eval-harness.md.

Feature Flags

Variable	Default	Effect
`CLAWMEM_ENABLE_AMEM`	enabled	A-MEM note construction + link generation during indexing
`CLAWMEM_ENABLE_CONSOLIDATION`	disabled	Background worker for backlog A-MEM enrichment
`CLAWMEM_CONSOLIDATION_INTERVAL`	300000	Worker interval in ms (min 15000)
`CLAWMEM_EMBED_URL`	`http://localhost:8088`	Embedding server URL. Uses llama-server (GPU or CPU) or cloud API. Falls back to in-process `node-llama-cpp` if unset.
`CLAWMEM_EMBED_API_KEY`	(none)	API key for cloud embedding. Enables cloud mode: batch embedding, provider-specific params, TPM-aware pacing.
`CLAWMEM_EMBED_MODEL`	`embedding`	Model name for embedding requests. Override for cloud providers (e.g. `jina-embeddings-v5-text-small`).
`CLAWMEM_EMBED_TPM_LIMIT`	`100000`	Tokens-per-minute limit for cloud embedding pacing. Match to your provider tier.
`CLAWMEM_EMBED_WAIT_SEC`	`120`	v0.43.5: how long `clawmem embed` waits for a self-hosted embedding server at start (URL set, no API key, `CLAWMEM_NO_LOCAL_MODELS=true`). Not ready in time → the embedding is skipped, exit 1. `embed --wait` overrides it.
`CLAWMEM_EMBED_WAIT_INTERVAL_MS`	`5000`	v0.43.5: time between that wait's probes.
`CLAWMEM_EMBED_PROBE_TIMEOUT_MS`	`15000`	v0.43.5: how long one probe of that wait may take.
`CLAWMEM_EMBED_DIMENSIONS`	(none)	Output dimensions for OpenAI `text-embedding-3-*` Matryoshka models (e.g. `512`, `1024`).
`CLAWMEM_LLM_URL`	`http://localhost:8089`	LLM server URL for intent/query/A-MEM and the Stop hooks' observer. Without it, falls to (if allowed).

Configuration

Collection Config

~/.config/clawmem/config.yaml:

collections:
  notes:
    path: /home/user/notes
    pattern: "**/*.md"
    autoEmbed: true
  docs:
    path: /home/user/docs
    pattern: "**/*.md"
    update: "git pull"
directoryContext: false  # opt-in per-folder CLAUDE.md generation

Database

~/.cache/clawmem/index.sqlite — single SQLite file with FTS5 + sqlite-vec extensions.

Frontmatter

Parsed via gray-matter. Supported fields:

---
title: "Document Title"
tags: [tag1, tag2]
domain: "infrastructure"
workstream: "project-name"
content_type: "decision"   # decision|preference|hub|research|project|handoff|conversation|progress|note
review_by: "2026-03-01"
---

Suggested Memory Filesystem

This structure separates human-curated content from auto-generated memories, and within auto-generated content, separates user memories (persist across agents, owned by the human) from agent memories (operational, generated from sessions). Static knowledge lives in resources/ with no recency decay, distinct from ephemeral session logs. The layout works with any MCP-compatible client (Claude Code, OpenClaw, custom agents).

Workspace Collection

The primary workspace where the agent operates. Path varies by client (e.g., ~/workspace/, ~/.openclaw/workspace/, or any directory you choose).

<workspace>/                         ← Collection: "workspace"
├── MEMORY.md                        # Human-curated long-term memory
├── memory/                          # Session logs (daily entries)
│   ├── 2026-02-05.md
│   └── 2026-02-06.md
├── resources/                       # Static knowledge — use content_type: hub (∞ half-life)
│   ├── runbooks/
│   │   └── deploy-checklist.md
│   └── onboarding.md
├── _clawmem/                        # Auto-generated — DO NOT EDIT
│   ├── user/                        #   User memories (persist across agents/sessions)
│   │   ├── profile.md               #     Static facts + dynamic context
│   │   ├── preferences/             #     Extracted preferences
│   │   └── entities/                #     Named entities (people, services, repos)
│   └── agent/                       #   Agent memories (operational, session-derived)
│       ├── observations/            #     Decisions + observations from transcripts
│       ├── handoffs/                #     Session summaries with next steps
│       └── antipatterns/            #     Accumulated negative patterns (∞ half-life)
└── ...

Project Collections

Each project gets its own collection. Same structure, with optional Beads integration.

~/Projects/<project>/               ← Collection: "<project>"
├── .beads/                          # Beads issue tracker (Dolt backend, auto-synced)
│   └── dolt/                        #   Dolt SQL database (source of truth)
├── MEMORY.md                        # Human-curated project memory
├── memory/                          # Project session logs
│   └── 2026-02-06.md
├── resources/                       # Static project knowledge (∞ half-life)
│   ├── architecture.md
│   └── api-reference.md
├── research/                        # Research dumps (fragment-embedded, 90-day decay)
│   └── 2026-02-06-topic-slug.md
├── _clawmem/                        # Auto-generated per-project
│   ├── user/
│   │   └── preferences/
│   └── agent/
│       ├── observations/
│       ├── handoffs/
│       ├── antipatterns/
│       └── beads/                   #   Beads issues as searchable markdown
├── CLAUDE.md
├── src/
└── README.md

Design Principles

Principle	Rationale	ClawMem Mechanism
User/agent separation	User memories (preferences, entities, profile) are owned by the human and persist indefinitely. Agent memories (observations, handoffs) are operational artifacts with lifecycle management.	`_clawmem/user/` vs `_clawmem/agent/` — different decay rules per content type
Resources are first-class	Static knowledge (runbooks, architecture docs, API refs) should never lose relevance due to recency decay.	`resources/` indexed with `content_type: hub` → ∞ half-life in composite scoring
Progressive disclosure	Hook injection (2000 token budget) benefits from tiered loading: compact snippets first, full content on demand.	`compact=true` (L1) → `multi_get` (L2) at query time. Pre-computed abstracts not yet implemented — candidate for future L0 tier.
Beads as memory edges	Issue tracker data bridges into the knowledge graph via typed relations, not just as flat documents.	`syncBeadsIssues()` maps deps → `memory_relations`: blocks→causal, discovered-from→supporting, relates-to→semantic
Each turn captured once, per session	Session-derived memory must neither repeat itself at every response nor overwrite another session's.	Transcript cursors for extraction and handoffs, a once-per-turn feedback verdict, items rendered into per-session decision/antipattern/handoff docs (v0.41.0; the cross-session merge policies and hash-window dedup they replaced are gone)

Layer Mapping

Layer	Path	Owner	Decay	ClawMem Role
Long-Term Memory	`MEMORY.md`	Human	∞	Indexed, profile supplements, human-curated anchor
Session Logs	`memory/*.md`	Human	60 days	Indexed, daily entries, handoff auto-generated
Static Resources	`resources/*/.md`	Human	∞ (hub)	Fragment-embedded, no recency penalty
Research	`research/*.md`	Human	90 days	Fragment-embedded for granular retrieval
User Profile	`_clawmem/user/profile.md`	Auto	∞	Static facts + dynamic context
User Preferences	`_clawmem/user/preferences/*.md`	Auto	∞	Extracted preferences
User Entities	`_clawmem/user/entities/*.md`	Auto	∞	Named entities across sessions
Observations	`_clawmem/agent/observations/*.md`	Auto	180d (decision)	Decisions + observations from transcripts
Handoffs	`_clawmem/agent/handoffs/*.md`	Auto	30 days	Session summaries with next steps
Antipatterns	`_clawmem/agent/antipatterns/*.md`	Auto	∞	Accumulated negative patterns
Beads	`_clawmem/agent/beads/*.md`	Auto	∞	Beads issues synced from Dolt, relations in memory graph

Manual layers benefit from periodic re-indexing — a cron job running clawmem update --embed keeps the index fresh for content edited outside of watched directories.

Setup

# Bootstrap a content directory (creates vault + indexes + embeds + installs hooks + MCP)
clawmem bootstrap ~/notes --name notes

# Bootstrap each project you want indexed
clawmem bootstrap ~/Projects/my-project --name my-project

# Install watcher + embed timer as systemd services
clawmem install-service --enable

OpenClaw-specific

# Install the OpenClaw plugin
# v0.10.4+: delegates to `openclaw plugins install --force` when the CLI is on PATH (auto-enables;
#   honors OPENCLAW_STATE_DIR / OPENCLAW_CONFIG_PATH / --profile). Falls back to recursive copy
#   honoring OPENCLAW_STATE_DIR when the CLI is absent.
# v0.10.0–v0.10.3: hardcoded recursive copy into ~/.openclaw/extensions/clawmem (issue #11).
clawmem setup openclaw
# Then follow the printed next steps. The exact list depends on which path ran:
#   Path 1 (delegated): plugin already auto-enabled — just `openclaw gateway restart` + GPU config
#   Path 3 (CLI absent): manually `openclaw plugins en

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Server facts

Tools

Questions

How do I install ClawMem?

Does ClawMem need an API key?

What transport does ClawMem use?

When was ClawMem last checked?

Documentation

ClawMem — On-device memory layer for Claude Code, OpenClaw, and Hermes agents

What It Does

Architecture

Install

Platform Support

Prerequisites

Install from npm (recommended)

Install from source

Setup roadmap

Quick start commands

Upgrading

Integration

Claude Code

OpenClaw

Hermes Agent

Multi-Framework Operation

Multi-Vault (Optional)

Inference services (GPU, in-process, or cloud)

MCP Server

HTTP REST API (optional)

Verify Installation

Agent Instructions

Option A: Copy instructions into your project

Option B: Add a trigger block (recommended)

CLI Reference

MCP Tools (31)

Core Search & Retrieval

Intent-Aware Search

Multi-Graph & Causal

Beads Integration

Vault Management

Agent Diary

Memory Management & Lifecycle

Compact Mode

Hooks (Claude Code Integration)

Search Pipeline

Multi-Graph Traversal

Content Type Scoring

Features

A-MEM (Adaptive Memory Evolution)

Causal Inference

Beads Integration

Fragment-Level Embedding

Local Observer Agent

Recall Tracking

User Profile

Prompt Injection Filtering

Consolidation Worker

Per-Folder CLAUDE.md Generation

Feedback Loop

Offline Eval Harness

Feature Flags

Configuration

Collection Config

Database

Frontmatter

Suggested Memory Filesystem

Workspace Collection

Project Collections

Design Principles

Layer Mapping

Setup

OpenClaw-specific