← 返回
数据分析 中文

Claw Recall

Searchable conversation memory that survives context compaction. Indexes session transcripts into SQLite with full-text and semantic search so your agent can...
可搜索的对话记忆,在上下文压缩后仍可保留。将会话记录索引到SQLite数据库中,支持全文搜索和语义搜索,使您的智能体能够...
rodbland2021
数据分析 clawhub v2.1.2 1 版本 99870.5 Key: 无需
★ 1
Stars
📥 751
下载
💾 9
安装
1
版本
#compaction#context#conversation#latest#memory#multi-agent#recall#search

概述

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.

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):

{
  "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:

{
  "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:

python3 -m claw_recall.api.mcp_sse
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.

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]

Secondary Tools

ToolPurpose
---------------
search_thoughtsSearch captured thoughts only (usually search_memory is better)
browse_activitySession summaries across agents for a time period
poll_sourcesTrigger Gmail/Drive/Slack polling on demand
memory_statsDatabase statistics (indexed sessions, messages, embeddings)
capture_source_statusCheck external source capture health

When to Use Each Tool

SituationToolExample
--------------------------
Just restarted / lost contextbrowse_recent"What was I working on?"
Looking for a past decisionsearch_memory"What did we decide about pricing?"
Need another agent's worksearch_memory with agent="What did atlas find about the schema?"
Found something worth sharingcapture_thoughtSave a reusable insight
Checking if something was discussedsearch_memory with days="Did we talk about X this week?"
External source checkpoll_sourcesTrigger Gmail/Drive/Slack re-scan

How It Works

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

版本历史

共 1 个版本

  • v2.1.2 当前
    2026-03-30 12:01 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

data-analysis

Data Analysis

ivangdavila
{"answer":"数据分析与可视化。查询数据库、生成报告、自动化电子表格,将原始数据转化为清晰可行的见解。适用于:(1) 您……"}
★ 199 📥 65,193
data-analysis

Stock Analysis

udiedrichsen
{"answer":"基于雅虎财经数据,分析股票与加密货币。支持投资组合管理、自选股预警、股息分析、8维评分、热门趋势扫描及传闻/早期信号探测。适用于股票分析、持仓追踪、财报异动、加密监控、热门股追踪或提前发掘非主流传闻。"}
★ 270 📥 57,006
data-analysis

Excel / XLSX

ivangdavila
创建、检查和编辑 Microsoft Excel 工作簿及 XLSX 文件,支持可靠的公式、日期、类型、格式、重算及模板保留功能。
★ 368 📥 140,661