mcp-memory-service MCP工具

🛠️ Configuration Highlights

memory launch                  # Start HTTP server in background (127.0.0.1:8000)
memory launch --port 8192      # Custom port
memory info                    # Status and health
memory logs --lines 50         # Recent logs
memory stop                    # Stop server

These commands are optimized for fast startup and avoid loading heavy ML dependencies unless needed.

⚠️ Security note: the server binds to 127.0.0.1 (localhost only) by default. --host 0.0.0.0 / MCP_HTTP_HOST=0.0.0.0 exposes the API to your network — do that only in trusted environments with authentication and firewall rules, or behind TLS termination or a VPN overlay.

Backends, embedding models, quality scoring and every environment variable: Configuration Guide

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REST API running at http://localhost:8000

Store a memory with `POST /api/memories`, search with `POST /api/search`, retrieve by tag
with `POST /api/search/by-tag`. Send `X-Agent-ID: <id>` on a store request and the server
tags the memory `agent:<id>`, which a tag search then scopes retrieval by.

Worked examples per framework, the tag conventions and the async patterns:
**[docs/agents/](https://github.com/doobidoo/mcp-memory-service/tree/main/docs/agents)**

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<details>
<summary><strong>OpenCode</strong></summary>

The plugin ships as repository files for the local plugin directory, so clone the
repository once even if you installed from PyPI. Install steps, the `/memory` slash
command and the endpoint override:
**[opencode/README.md](https://github.com/doobidoo/mcp-memory-service/blob/main/opencode/README.md)**

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<details>
<summary><strong>Codex (automatic memory hooks)</strong></summary>

Start the Memory Service HTTP API, then install the hooks from this repository:

Restart Codex and review the hooks with `/hooks`. They retrieve project context,
capture new learnings, and checkpoint outcomes before compaction and at session end.
See [Codex hook setup](codex/README.md) for prerequisites, authentication, and project-local installation.

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<details>
<summary><strong>claude.ai and ChatGPT (browser — Remote MCP)</strong></summary>

Remote MCP puts persistent memory in the browser on any device, no desktop app required:
OAuth 2.0 over HTTPS, self-hosted or cloud-hosted. Both claude.ai and ChatGPT
(Developer Mode) connect to the same endpoint.

Cloudflare Tunnel quick start, Let's Encrypt, nginx, Caddy and Docker production setups:
**[Remote MCP Setup](https://github.com/doobidoo/mcp-memory-service/blob/main/docs/remote-mcp-setup.md)** ·
[5-minute tutorial](https://mcpmemory.services/blog/remote-mcp-tutorial.html)

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<summary><strong>Advanced: custom backends and team setup</strong></summary>

Choose from SQLite (local, fast, single-user), Cloudflare (cloud, multi-device sync), Hybrid (5ms local reads with background cloud sync — recommended for production) or Milvus (dedicated vector DB: Lite file, self-hosted, or Zilliz Cloud). For self-hosted team setups, the Hybrid backend can sync to another HTTP MCP Memory Service instead of Cloudflare using MCP_HYBRID_SECONDARY_BACKEND=http.

For long-lived services, prefer Docker Milvus or Zilliz Cloud over Milvus Lite — why.

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Persistent Shared Memory for AI Agent Pipelines

Open-source memory backend for AI agents — REST API, MCP, OAuth, CLI, dashboard. One self-hosted service, every transport. Agents store decisions, share causal knowledge graphs, and retrieve context in 5ms — without cloud lock-in or API costs.

Works with LangGraph · CrewAI · AutoGen · any HTTP client · Claude Desktop · OpenCode

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▶ The 3D knowledge graph in motion — every memory a glowing node, every relationship a curved edge.

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