Memory MCP
Provides persistent cross-session memory and full-text search for AI coding assistants, storing project context, decisions, and preferences while enabling searchable access to conversation history via local SQLite.
README
Memory MCP
Persistent memory and full-text session search for AI coding assistants, exposed as an MCP server.
The problem
AI coding assistants forget everything between sessions. Architecture decisions, user preferences, project context, what you debugged last Tuesday -- gone. You re-explain the same things constantly.
Memory MCP fixes this with two capabilities:
- Explicit memory -- save notes, decisions, patterns, and preferences that persist across sessions. Your assistant remembers what you told it.
- Session search -- full-text search across your entire conversation history. Find that thing you discussed three weeks ago without scrolling through logs.
No database servers. No background processes. No cloud. One SQLite file on your machine.
Supported session sources
| Source | Location | Format |
|---|---|---|
| Claude Code | ~/.claude/projects/ |
JSONL (streamed content blocks) |
| Claude Code history | ~/.claude/history.jsonl |
JSONL (survives session file pruning) |
| OpenCode | ~/.local/share/opencode/opencode.db |
SQLite (sessions, messages, parts tables) |
| Oh My Pi | ~/.omp/agent/sessions/ |
JSONL (event-per-line) |
Adding a new source requires one parser file and a registry entry. See Adding a new source.
Installation
Requires Python 3.11+ with SQLite FTS5 support (included in standard Python builds).
pip install -e .
Or run directly with uv (no install needed):
uv run --directory /path/to/memory_mcp python -m memory_mcp
MCP configuration
Add to your MCP client config (e.g., ~/.claude/mcp.json or project-level .mcp.json):
With pip install:
{
"mcpServers": {
"memory": {
"command": "memory-mcp"
}
}
}
With uv (no install):
{
"mcpServers": {
"memory": {
"command": "uv",
"args": ["run", "--directory", "/path/to/memory_mcp", "python", "-m", "memory_mcp"]
}
}
}
Tools
Memory (explicit knowledge store)
| Tool | Description |
|---|---|
save_memory |
Persist a note with optional tags and context. Survives across all future sessions. |
search_memory |
Full-text search across saved memories. Keyword-based, ranked by relevance. |
list_memories |
Browse recent memories, optionally filtered by tag. |
delete_memory |
Remove a memory by ID. |
Sessions (historical conversation search)
| Tool | Description |
|---|---|
list_sessions |
Browse past sessions. Filter by source (claude_code, omp) or project path. |
get_session |
Retrieve the full conversation from a specific session. |
search_sessions |
Full-text search across all session messages, thinking blocks, and tool usage. |
refresh_sessions |
Re-scan session directories and index new or changed files. |
How it works
On startup, Memory MCP scans configured session directories and indexes every conversation into a local SQLite database with FTS5 full-text search indexes. Subsequent startups skip files whose mtime hasn't changed.
- Database location:
~/.memory_mcp/memory.db(override withMEMORY_MCP_DBenv var) - Session sources: auto-detected from standard locations (extend with
MEMORY_MCP_SOURCESenv var, format:type:path;type:path) - Indexing: incremental by file mtime, parallelized across 8 threads
- Search: FTS5 with BM25 ranking, prefix matching, phrase support
Adding a new session source
- Create
memory_mcp/parsers/your_source.pyimplementing theSessionParserprotocol:source_type: strattributeparse_file(path: str) -> ParsedSession | Nonemethod
- Register it in
memory_mcp/parsers/__init__.py - Add directory detection in
memory_mcp/config.py
See parsers/claude_code.py or parsers/omp.py for examples.
Testing
python tests/test_e2e.py
The end-to-end test starts the MCP server as a subprocess, exercises all 8 tools over the stdio protocol, and asserts tool responses. Uses a throwaway database so your real data is untouched.
Architecture
memory_mcp/
server.py # FastMCP entry point, lifespan manages DB + startup scan
config.py # Auto-detects session dirs, DB path
db.py # SQLite + FTS5 schema, all queries, sync triggers
scanner.py # Walks session dirs, dispatches to parsers, parallel indexing
parsers/
base.py # ParsedSession / ParsedMessage dataclasses, SessionParser protocol
claude_code.py # Claude Code JSONL parser (merges streamed assistant blocks)
claude_history.py # Claude Code history.jsonl parser (one file, many sessions)
omp.py # OMP JSONL parser
opencode.py # OpenCode SQLite parser (reads DB directly, read-only)
tools/
memory.py # save_memory, search_memory, list_memories, delete_memory
sessions.py # list_sessions, get_session, search_sessions, refresh_sessions
License
MIT
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。