Searchable conversation memory that survives context compaction. Indexes session transcripts into SQLite with full-text and semantic search so your agent can...
Your agent just lost context mid-task. The decision you made an hour ago? Gone. What your other agent figured out yesterday? Unreachable. Claw Recall fixes this by indexing every conversation into a searchable database your agents can query anytime.
Context compaction drops critical decisions. Cross-session knowledge vanishes. Long conversations push early context out of the window. If you run multiple agents, they can't access each other's conversations at all. MEMORY.md helps with preferences, but it can't answer "what exactly did we discuss about the API last Tuesday?"
Claw Recall is an MCP server. Install the Python package, then connect it to your agent.
git clone https://github.com/rodbland2021/claw-recall.git
cd claw-recall
pip install -r requirements.txt
python3 -m claw_recall.indexing.indexer --source ~/.openclaw/agents-archive/ --incremental
Full setup guide: https://github.com/rodbland2021/claw-recall#quick-start
Add to your OpenClaw config (~/.openclaw/openclaw.json or agent config):
{
"mcpServers": {
"claw-recall": {
"command": "python3",
"args": ["-m", "claw_recall.api.mcp_stdio"],
"env": { "PYTHONPATH": "/path/to/claw-recall" }
}
}
}
Add to ~/.claude.json:
{
"mcpServers": {
"claw-recall": {
"command": "python3",
"args": ["-m", "claw_recall.api.mcp_stdio"],
"env": { "PYTHONPATH": "/path/to/claw-recall" }
}
}
}
Start the SSE server on the Claw Recall machine, then connect from anywhere:
python3 -m claw_recall.api.mcp_sse
claude mcp add --transport sse -s user claw-recall "http://your-server:8766/sse"
search_memory — The main search tool. Searches ALL sources in one call: conversations, captured thoughts (Gmail, Drive, Slack), and markdown files.
search_memory query="what did we decide about the API" [agent=butler] [days=7]
Optional params: agent (filter by agent name), days (limit to recent), force_semantic (use embeddings), force_keyword (use FTS5 only), convos_only, files_only, limit.
browse_recent — Full transcript of the last N minutes. The go-to tool for context recovery after compaction.
browse_recent [agent=kit] [minutes=30]
Returns the complete conversation with timestamps. Use this FIRST after any context reset.
capture_thought — Save an insight, decision, or finding so any agent can find it later.
capture_thought content="SQLite WAL mode requires checkpoint for readers to see writes" [agent=kit]
| Tool | Purpose |
|---|---|
search_thoughts | Search captured thoughts only (usually search_memory is better) |
browse_activity | Session summaries across agents for a time period |
poll_sources | Trigger Gmail/Drive/Slack polling on demand |
memory_stats | Database statistics (indexed sessions, messages, embeddings) |
capture_source_status | Check external source capture health |
| Situation | Tool | Example |
|---|---|---|
| Just restarted / lost context | browse_recent | "What was I working on?" |
| Looking for a past decision | search_memory | "What did we decide about pricing?" |
| Need another agent's work | search_memory with agent= | "What did atlas find about the schema?" |
| Found something worth sharing | capture_thought | Save a reusable insight |
| Checking if something was discussed | search_memory with days= | "Did we talk about X this week?" |
| External source check | poll_sources | Trigger Gmail/Drive/Slack re-scan |
Session Files (.jsonl) → Indexer → SQLite DB (FTS5 + vectors) → MCP Tools
↑
Gmail / Drive / Slack → Source Poller ───┘
All data is stored locally in a SQLite database. Keyword search uses FTS5 (zero API keys). Semantic search uses OpenAI embeddings (requires OPENAI_API_KEY in .env).
ZIP package — ready to use