OpenClaw Memory
Automatically records AI conversation turns and code changes to local Markdown files to provide persistent context across chat sessions. It enables AI agents to search history through MCP tools and provides a web viewer for browsing past discussions.
README
OpenClaw Memory
Your AI conversations disappear after every session. OpenClaw Memory fixes that.
Every time you chat with an AI coding assistant, valuable context — decisions, solutions, debugging steps — vanishes when the session ends. The next session starts from zero.
OpenClaw Memory automatically records every conversation turn to local Markdown files, making your entire AI chat history searchable and browsable. No cloud, no database — just plain text files in your project.
How It Works
You chat with AI → Every turn auto-saved to .openclaw_memory/journal/2026-02-24.md
→ Search past conversations via MCP tool or web viewer
Each journal entry captures the complete conversation: timestamps, model used, your input, the AI's full response, and any code changes made.
Quick Start
1. Install
pip install claw-memory
2. Initialize in your project
cd your-project
claw-memory init
This creates:
.openclaw_memory/journal/— where chat history lives.cursor/mcp.json— connects the MCP server to Cursor.cursor/rules/memory.mdc— tells the AI agent to auto-record
3. Restart Cursor — that's it. Every conversation is now being recorded.
Searching Past Conversations
The AI agent can search your history automatically. Just ask naturally:
"We discussed this before, what was the solution?"
"Last time we fixed a similar bug, how did we do it?"
The agent will call memory_search() behind the scenes and find matching conversations.
Search via Web Viewer
# Single project (current directory)
claw-memory web
# Multiple projects — scan a parent directory
claw-memory web --scan-dir ~/projects
Opens a browser-based viewer where you can:
- Browse journal files by date
- Full-text search across all conversations
- Dark/light mode
- Multi-project view: use
--scan-dirto scan a parent directory and browse all projects in one place, with sidebar grouped by project
What Gets Recorded
Each conversation turn is saved as Markdown:
## 14:32 | claude-4-opus
### User
How do I fix the N+1 query problem in the user list endpoint?
### Agent
The issue is in `api/users.py` where each user triggers a separate query for their roles...
### Code Changes
- `api/users.py` (modified)
- `tests/test_users.py` (modified)
MCP Tools
| Tool | Purpose |
|---|---|
memory_log_conversation |
Record a complete conversation turn |
memory_log_conversation_append |
Append to the last turn (for long responses) |
memory_search |
Search chat history by keyword |
Storage
All data is stored locally in .openclaw_memory/journal/ as plain Markdown files — one file per day. No database, no cloud sync. You own your data.
The .openclaw_memory/ directory is auto-gitignored to prevent accidental commits of chat history.
Project Isolation
Each project gets its own .openclaw_memory/ directory. MCP tools always operate on the current project only.
To view multiple projects together, use the web viewer with --scan-dir.
License
Apache 2.0
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。