openclaw

 agentmemory for OpenClaw

Your OpenClaw agents remember everything. No more re-explaining.
_{Persistent cross-session memory via agentmemory — 95.2% retrieval accuracy on LongMemEval-S.}

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Install it in 30 seconds

Paste this prompt into OpenClaw and it does the whole setup for you:

Install agentmemory for OpenClaw. Run `npx @agentmemory/agentmemory` in a
separate terminal to start the memory server on localhost:3111. Then add
this to my OpenClaw MCP config so agentmemory is available as an MCP
server with all 43 memory tools (memory_recall, memory_save,
memory_smart_search, memory_timeline, memory_profile, etc.):

{
  "mcpServers": {
    "agentmemory": {
      "command": "npx",
      "args": ["-y", "@agentmemory/mcp"]
    }
  }
}

Restart OpenClaw. Verify it's working with
`curl http://localhost:3111/agentmemory/health` — it should return
{"status":"healthy"}. Open the real-time viewer at
http://localhost:3113 to watch memories being captured live.

If I want deeper integration with pre-LLM context injection and
automatic tool-use capture, copy `integrations/openclaw` from the
agentmemory repo to `~/.openclaw/plugins/memory/agentmemory` — that
gives me the 4-hook gateway plugin instead of just the MCP server.

That's it. OpenClaw handles the rest.

Why you want this

OpenClaw agents restart fresh every session. You waste tokens re-explaining architecture, re-discovering bugs, re-teaching preferences. agentmemory captures every tool use automatically and injects relevant context when the next session starts.

• 92% fewer tokens per session vs full-context pasting
• 12 auto-capture hooks — zero manual memory.add() calls
• MCP-native — same server works for Claude Code, Cursor, Gemini CLI, Hermes, and OpenClaw at the same time
• Self-hosted — no external database, no cloud, no API key needed for embeddings

Quick setup

Option 1: MCP server (zero code)

Start the agentmemory server in a separate terminal:

npx @agentmemory/agentmemory

Then add to your OpenClaw MCP config:

{
  "mcpServers": {
    "agentmemory": {
      "command": "npx",
      "args": ["-y", "@agentmemory/mcp"]
    }
  }
}

OpenClaw now has access to all 43 MCP tools including memory_recall, memory_save, memory_smart_search, memory_timeline, memory_profile, and more.

Option 2: Gateway plugin (deeper integration)

If you're running an OpenClaw gateway, drop this folder into your gateway's plugins directory:

cp -r integrations/openclaw ~/.openclaw/plugins/memory/agentmemory

Start the agentmemory server:

npx @agentmemory/agentmemory

The plugin auto-detects the running server and hooks into the OpenClaw agent loop:

• onSessionStart starts a new session on the agentmemory server and injects any returned context
• onPreLlmCall injects token-budgeted memories before each LLM call (BM25 + vector + graph fusion)
• onPostToolUse records every tool use, error, and decision after execution
• onSessionEnd marks the session complete so raw observations can be compressed into structured memory

Configure via ~/.openclaw/plugins/memory/agentmemory/config.yaml:

enabled: true
base_url: http://localhost:3111
token_budget: 2000
min_confidence: 0.5

What your agent gets

Automatic context injection

When a session starts, agentmemory injects ~1,900 tokens of the most relevant past context:

Project profile:
  - Auth uses JWT middleware in src/middleware/auth.ts (jose, not jsonwebtoken)
  - Tests in test/auth.test.ts cover token validation
  - Database uses Prisma with include{} to avoid N+1 queries
  - Rate limiting: 100 req/min default, Redis for prod

Recent decisions:
  - Chose jose over jsonwebtoken for Edge compatibility (2026-03-15)
  - N+1 fix dropped query time 450ms → 28ms (2026-03-20)

Semantic search across sessions

Ask "what was that fix for slow user queries?" and the agent finds the Prisma include{} decision from three weeks ago. BM25 + vector + knowledge graph fusion.

Privacy filtering

Every captured observation is scanned for API keys, secrets, bearer tokens, and <private> tags. These are stripped before storage. Modern token formats supported: sk-, sk-proj-, ghp_/ghs_/ghu_, AWS keys, and more.

Multi-agent coordination

If you're running multiple OpenClaw agents on the same codebase:

• Leases give one agent exclusive claim on an action so they don't stomp each other
• Signals let agents send threaded messages to each other with read receipts
• Mesh sync shares memory between agentmemory instances (requires AGENTMEMORY_SECRET)

Troubleshooting

"Connection refused on port 3111" — The agentmemory server isn't running. Start it with npx @agentmemory/agentmemory in a separate terminal.

"No memories returned" — Check http://localhost:3113 (the real-time viewer). If there are no observations, the hooks aren't firing. Make sure your OpenClaw plugin is loaded and enabled.

"Search returns irrelevant results" — Install local embeddings: npm install @xenova/transformers. This enables vector search for +8pp recall over BM25-only.

"I want to see what agentmemory is learning" — Open http://localhost:3113 in a browser. Live observation stream, session explorer, memory graph, and health dashboard.

See also

• agentmemory main README
• Benchmark results — 95.2% R@5 on LongMemEval-S
• Competitor comparison — vs mem0, Letta, Khoj, claude-mem, Hippo
• Hermes integration — same server also works with Hermes Agent

License

Apache-2.0 (same as agentmemory)