Claw Recall

Searchable conversation memory that survives context compaction. Indexes session transcripts into SQLite with full-text and semantic search so your agent can recover context after compaction, search past conversations, and find what other agents discussed. Works across multiple agents (OpenClaw + Claude Code). Also indexes Gmail, Google Drive, and Slack. Self-hosted, open source, no cloud dependency. Use when: recovering lost context, searching conversation history, cross-agent knowledge sharing. NOT for: replacing MEMORY.md or storing secrets.

Claw Recall — Searchable Conversation Memory for AI Agents

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.

The Problem

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?"

What Claw Recall Does

• Post-compaction recovery: Get the full transcript from before compaction wiped your context
• Cross-agent search: Any agent can search any other agent's conversations
• Unified search: Conversations, captured thoughts, Gmail, Google Drive, and Slack in one query
• Hybrid search: Keyword (FTS5) + semantic (OpenAI embeddings) with automatic detection
• Self-hosted: Your data stays on your machine. No cloud, no subscription, no vendor lock-in.

Installation

Claw Recall is an MCP server. Install the Python package, then connect it to your agent.

bash

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

Connect via MCP (OpenClaw)

Add to your OpenClaw config (~/.openclaw/openclaw.json or agent config):

json

{
  "mcpServers": {
    "claw-recall": {
      "command": "python3",
      "args": ["-m", "claw_recall.api.mcp_stdio"],
      "env": { "PYTHONPATH": "/path/to/claw-recall" }
    }
  }
}

Connect via MCP (Claude Code)

Add to ~/.claude.json:

json

{
  "mcpServers": {
    "claw-recall": {
      "command": "python3",
      "args": ["-m", "claw_recall.api.mcp_stdio"],
      "env": { "PYTHONPATH": "/path/to/claw-recall" }
    }
  }
}

Remote agents (SSE)

Start the SSE server on the Claw Recall machine, then connect from anywhere:

bash

python3 -m claw_recall.api.mcp_sse

bash

claude mcp add --transport sse -s user claw-recall "http://your-server:8766/sse"

MCP Tools Reference

Primary Tools (use these most)

search_memory — The main search tool. Searches ALL sources in one call: conversations, captured thoughts (Gmail, Drive, Slack), and markdown files.

text

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.

text

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.

text

capture_thought content="SQLite WAL mode requires checkpoint for readers to see writes" [agent=kit]

Secondary Tools

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

When to Use Each Tool

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

How It Works

text

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).

Requirements

• Python 3.10+
• SQLite 3.35+ (bundled with Python)
• OpenAI API key (optional, only for semantic search)

Links

• GitHub: https://github.com/rodbland2021/claw-recall
• Discord: https://discord.gg/4wGTVa9Bt6
• Full Guide: https://github.com/rodbland2021/claw-recall/blob/master/docs/guide.md
• Changelog: https://github.com/rodbland2021/claw-recall/blob/master/CHANGELOG.md