memory-manager-mcp
A local-first persistent memory server for AI coding agents, enabling project context to be shared across different MCP clients. It stores curated memories, task states, decisions, and handoffs as plain files under ~/.agent-memory.
README
memory-manage-mcp
Your project remembers, no matter which AI agent you use.
memory-manage-mcp is a local-first, database-free persistent memory server for AI coding agents, exposed over the Model Context Protocol (MCP).
Start a task in VS Code with GitHub Copilot, continue it in Cursor, finish it with Claude Code or Gemini CLI — the next agent automatically recognizes that it is working on the same project and continues from where the previous agent stopped.
- 🗂️ Local-first — everything is stored as plain files under
~/.agent-memory/. No cloud, no API keys, no external database, no network calls. - 🤝 Agent & IDE independent — any MCP client works: VS Code, Cursor, Claude Desktop, Claude Code, Gemini CLI, Windsurf, …
- 🔍 Project-aware — projects are identified by git remote URL (or
.agent-memory.json, or path), so the same repo cloned to different machines/paths shares one memory. - 🧠 Curated memory, not chat logs — decisions, requirements, architecture, tasks, problems, solutions and progress are stored as distilled, ranked entries.
- 🤜🤛 Structured handoffs — before an agent stops, it writes what was done, what remains, known problems and the recommended next action.
- 🛡️ Crash-safe & concurrency-safe — atomic writes (temp → fsync → rename), append-only logs, file locks.
- 🩺 CLI + doctor — inspect projects, search memory, and diagnose your setup.
Requirements
- Node.js >= 18
- (Optional)
giton your PATH — used read-only for project detection and unfinished-work signals.
Install
# from the repository
git clone <this-repo> memory-manager-mcp
cd memory-manager-mcp
pnpm install
pnpm run build
# or install globally
pnpm add -g memory-manage-mcp # once published
Verify the installation:
node dist/cli/index.js doctor
# Memory MCP is ready.
Auto-configure your IDEs (recommended)
One command detects every supported AI client installed on your machine and registers the memory server in each client's MCP config:
memory-manage-mcp setup # or: node dist/cli/index.js setup
Supported clients (one dedicated registry per client):
| Client | Config file(s) written |
|---|---|
| VS Code (Copilot) | %APPDATA%/Code/User/mcp.json — plus Code - Insiders and VSCodium variants (servers key, type: "stdio") |
| Cursor | ~/.cursor/mcp.json |
| Claude Desktop | %APPDATA%/Claude/claude_desktop_config.json — plus Windows Store/MSIX installs (%LOCALAPPDATA%/Packages/Claude_*/LocalCache/...) |
| Claude Code | ~/.claude.json |
| Antigravity | ~/.gemini/config/mcp_config.json and ~/.gemini/antigravity-ide/mcp.json |
| Gemini CLI | ~/.gemini/settings.json |
| Windsurf | ~/.codeium/windsurf/mcp_config.json |
| Codex CLI | ~/.codex/config.toml (TOML [mcp_servers.manager-mcp] table) |
- Only clients that are actually installed are touched; others are skipped.
- Existing config files are preserved (a
.bakbackup is created first) and written atomically — other MCP servers you configured stay intact. - The server is registered under the name
manager-mcp— that is the prefix you will see on its tools in your IDE (e.g.manager-mcp_save_memory). Entries left under the oldmemorykey by earlier versions are migrated automatically on the nextsetup. - Self-registering: the MCP server also registers itself silently the first time it starts, so even a bare
node dist/index.jslaunch ends up configured everywhere. Disable withAGENT_MEMORY_NO_AUTO_SETUP=1.
Useful flags:
memory-manage-mcp setup --dry-run # show what would change, write nothing
memory-manage-mcp setup --client cursor # configure a single client
memory-manage-mcp setup --force # configure even if not detected as installed
memory-manage-mcp setup --json # machine-readable report
memory-manage-mcp uninstall # remove the memory entry from all client configs
memory-manage-mcp uninstall --client vscode
After setup, restart your IDE/client and the 15 memory tools are available. Prefer manual configuration? See the next section.
How do I know it is working?
-
memory-manage-mcp doctor— theClient registrationcheck lists every client where the server is registered asmanager-mcp:✓ Client registration registered as "manager-mcp" in: vscode, cursor -
In your IDE — after restarting, the MCP tool list should show the 15 tools prefixed with
manager-mcp_(e.g.manager-mcp_initialize_project_context,manager-mcp_save_memory). -
Ask your agent — tell it to call
initialize_project_context; a successful briefing response means the server is live and the project is registered.
Connect your AI client manually
The server speaks MCP over stdio. Point any MCP client at node <path-to>/dist/index.js (or memory-manage-mcp if installed globally).
VS Code (GitHub Copilot)
Add to .vscode/mcp.json (workspace) or your user MCP settings:
{
"servers": {
"manager-mcp": {
"type": "stdio",
"command": "node",
"args": ["C:/path/to/memory-manager-mcp/dist/index.js"]
}
}
}
Cursor
Add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project):
{
"mcpServers": {
"manager-mcp": {
"command": "node",
"args": ["C:/path/to/memory-manager-mcp/dist/index.js"]
}
}
}
Claude Desktop / Claude Code
claude_desktop_config.json (or claude mcp add):
{
"mcpServers": {
"manager-mcp": {
"command": "node",
"args": ["C:/path/to/memory-manager-mcp/dist/index.js"]
}
}
}
claude mcp add manager-mcp -- node C:/path/to/memory-manager-mcp/dist/index.js
Gemini CLI
gemini mcp add manager-mcp -- node C:/path/to/memory-manager-mcp/dist/index.js
Any other MCP client
{
"manager-mcp": {
"command": "node",
"args": ["/absolute/path/to/memory-manager-mcp/dist/index.js"]
}
}
Tip: run
pnpm run devduring development — it starts the server from TypeScript sources viatsx.
How project detection works
When a tool receives a workspacePath (or falls back to the current directory), the project identity is derived with this priority:
- Git remote URL —
https://github.com/company/pms.git,git@github.com:company/pms.gitandssh://…all normalize togithub.com/company/pms, then hash to a stableproj_…id. Same repo, any machine, any clone path → same memory. .agent-memory.json— drop this file in a project root to force an identity (for non-git projects or monorepos):{ "projectId": "my-project", "name": "My Project" }- Absolute path — last resort; memory is tied to that exact path.
Projects are auto-registered on first use — no setup step required.
Storage layout
Everything lives under ~/.agent-memory/ (override with the AGENT_MEMORY_HOME environment variable):
~/.agent-memory/
├── config.json # server configuration
├── projects.json # project registry
└── projects/
└── proj_<hash>/
├── project.json # project metadata
├── context.json # compact project context
├── memories.jsonl # append-only memory log (versioned + tombstones)
├── tasks.json # task list
├── decisions.json # decision log
├── sessions.jsonl # agent working sessions
└── handoffs/
├── latest.json # most recent handoff
└── history/ # all previous handoffs
All writes are atomic (temp file → fsync → rename) or append-only with fsync; list mutations happen under a per-project lock file. Corrupt or partially-written lines are skipped gracefully on read.
Configuration
~/.agent-memory/config.json is created with defaults on first run:
{
"maxContextItems": 20,
"enableRawSessions": true,
"search": { "maxResults": 20 }
}
| Key | Meaning |
|---|---|
maxContextItems |
Max items per section in the generated briefing |
enableRawSessions |
Keep raw session records (summaries are always kept) |
search.maxResults |
Default result limit for search_memory |
The MCP tools (16)
| Tool | Purpose |
|---|---|
initialize_project_context |
Call first. Detects/registers the project and returns a compact briefing: current task, latest handoff, previous conversation digest, completed/remaining work, problems, decisions, recommended next action. |
get_project_context |
Lightweight fetch of the stored project context. |
save_memory |
Save a curated memory (decision, requirement, architecture, task, problem, solution, progress, fact, preference, constraint, discovery). Pass id to update. |
get_memory |
Retrieve one memory by id. |
search_memory |
Ranked keyword search across memories, tasks, decisions, handoffs, session summaries, conversation digests and context. |
get_current_task |
Most relevant open task + other open tasks. |
update_task |
Create or update a task (active, in_progress, completed, blocked, abandoned). |
record_decision |
Record an important decision (long-lived in ranking). |
get_decisions |
List decisions, newest first. |
create_handoff |
Call before stopping. Structured handoff: completed, remaining, problems, changed files, next action. |
get_latest_handoff |
Fetch the most recent handoff (optionally with history). |
start_session |
Begin tracking an agent working session. |
save_session_digest |
Call before stopping. Compress the ENTIRE conversation into one detailed digest (max 4000 chars); injected into the next chat's briefing. |
finish_session |
End a session with status + summary. |
delete_project_memory |
Permanently delete one project's memory (confirm: true). |
clear_memory |
Permanently delete all memory (confirm: true + phrase "delete everything"). |
Recommended agent workflow (zero-touch for the user)
The user never types memory commands — everything happens automatically behind the scenes:
- On start → the agent calls
initialize_project_contextby itself. The briefing includes the previous conversation's digest, so the agent understands the last chat from first message to last. If unfinished work is detected, it asks the user once: "Would you like to continue where you left off? (yes/no)" — yes resumes from the recommended next action, no starts fresh. - While working → the agent silently saves decisions, requirements, problems and progress with
save_memory, and tracks work withupdate_task. - Before stopping → the agent silently calls
save_session_digest(compresses the whole conversation into a compact digest), thencreate_handoff+finish_session, so the next chat (even in another IDE) can pick up seamlessly.
A machine-readable version of this guidance lives in docs/AGENT_GUIDE.md — you can reference it from your client's rules/instructions file.
CLI
memory-manage-mcp <command> [--workspace <path>] [--json]
projects List known projects
project current Detect the project for the current directory
project inspect [id] Inspect a project's stored memory
memory search <query> Search memory across a project
handoff latest Show the most recent handoff
sessions List agent sessions
doctor Diagnose the installation
setup [--client <id>] [--force] [--dry-run]
Auto-configure installed AI clients
uninstall [--client <id>] Remove the memory entry from client configs
clear --all --yes Permanently delete ALL memory
Every command has built-in help — use -h / --help after the command, or help <command>:
memory-manage-mcp --help # overview of all commands
memory-manage-mcp help setup # detailed help for one command
memory-manage-mcp setup --help # same thing
memory-manage-mcp doctor -h # short flag works too
Examples:
memory-manage-mcp doctor
memory-manage-mcp project current --workspace ./my-app
memory-manage-mcp memory search "employee permission"
memory-manage-mcp handoff latest --json
When developing from source, prefix commands with
node dist/cli/index.jsinstead ofmemory-manage-mcp.
Privacy
- All data stays on your machine in
~/.agent-memory/. Nothing is ever sent anywhere. - Raw conversation transcripts are never stored by default; only distilled memories you explicitly save.
- Delete a single project with
delete_project_memory, or everything withmemory-manage-mcp clear --all --yes.
Troubleshooting
| Symptom | Fix |
|---|---|
| Client can't see the tools | Make sure command is an absolute path to node and args[0] is the absolute path to dist/index.js. Run pnpm run build first. |
| Wrong project detected | Check memory-manage-mcp project current. Add a .agent-memory.json to pin an identity, or add a git remote. |
| Same repo, different memory per machine | Ensure the git remote URL is set (git remote -v) — it is the primary identity. |
| Anything else | Run memory-manage-mcp doctor (or pnpm run doctor) and read the check list. |
Development
pnpm install
pnpm run build # compile TypeScript → dist/
pnpm run dev # run the MCP server from sources (tsx)
pnpm run typecheck # strict type check
pnpm test # vitest suite (61 tests: unit + CLI + MCP stdio integration)
pnpm run test:watch
Architecture
types ─► storage (MemoryStore interface ─► FileSystemMemoryStore)
│
git service ─►│
▼
project (identity / detector / registry)
▼
memory manager + ranker ─► search ─► context (compressor / unfinished / builder)
▼
service facade ─► MCP tools ─► stdio server
└──────────────► CLI + doctor
The MemoryStore interface (src/storage/interface.ts) is the only place that touches persistence — swap in SQLite, Postgres or a cloud backend later without changing any business logic.
License
MIT — see LICENSE.
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。