Memory Service

mcp-memory-service

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

Why Agents Need This

Your AI assistant forgets everything when you start a new chat. You spend 10 minutes re-explaining your architecture. Again. MCP Memory Service captures project context, architecture decisions, and code patterns automatically — new sessions start with everything already known.

Key capabilities for agent pipelines:

• Framework-agnostic REST API — 76 endpoints, no MCP client library needed
• Knowledge graph — agents share causal chains, not just facts
• X-Agent-ID header — auto-tag memories by agent identity for scoped retrieval
• conversation_id — bypass deduplication for incremental conversation storage
• SSE events — real-time notifications when any agent stores or deletes a memory
• Embeddings run locally via ONNX — memory never leaves your infrastructure

🚀 Get Started in 60 Seconds

Not sure which setup fits your needs? See the Setup Guide — a decision tree walks you to the right path in under a minute.

1. Install:

pip install mcp-memory-service

2. Configure your AI client:

Add to your config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "memory": {
      "command": "memory",
      "args": ["server"]
    }
  }
}

Restart Claude Desktop. Your AI now remembers everything across sessions.

claude mcp add memory -- memory server

Restart Claude Code. Memory tools will appear automatically.

MCP_ALLOW_ANONYMOUS_ACCESS=true memory server --http
# REST API running at http://localhost:8000

import asyncio
import httpx

BASE_URL = "http://localhost:8000"


async def main():
    async with httpx.AsyncClient() as client:
        # Store — auto-tag with X-Agent-ID header
        await client.post(f"{BASE_URL}/api/memories", json={
            "content": "API rate limit is 100 req/min",
            "tags": ["api", "limits"],
        }, headers={"X-Agent-ID": "researcher"})
        # Stored with tags: ["api", "limits", "agent:researcher"]

        # Search — scope to a specific agent
        results = await client.post(f"{BASE_URL}/api/memories/search", json={
            "query": "API rate limits",
            "tags": ["agent:researcher"],
        })
        print(results.json()["memories"])


asyncio.run(main())

Framework-specific guides: docs/agents/

Start the HTTP API:

MCP_ALLOW_ANONYMOUS_ACCESS=true memory server --http

Install the local plugin:

git clone https://codeberg.org/doobidoo/mcp-memory-service.git
cd mcp-memory-service
mkdir -p ~/.config/opencode/plugins
cp opencode/memory-plugin.js ~/.config/opencode/plugins/
cp opencode/memory-plugin.config.example.json ~/.config/opencode/memory-plugin.json

OpenCode automatically loads local plugins from ~/.config/opencode/plugins/ and .opencode/plugins/.

Optional: register the /memory slash command in ~/.config/opencode/opencode.json to query status, search, and health from inside the TUI:

{
  "command": {
    "memory": {
      "description": "Show MCP Memory Service status. Usage: /memory, /memory search <query>, /memory health",
      "template": ""
    }
  }
}

See OpenCode integration guide for configuration, project-local installs, slash command details, TUI toasts, and current limitations.

The current OpenCode integration ships as repository files for the local plugin directory. If you installed only the PyPI package, clone the repository once to copy the plugin files.

The plugin defaults to http://127.0.0.1:8000, but memoryService.endpoint and OPENCODE_MEMORY_ENDPOINT let you target any reachable HTTP deployment.

Unlike desktop-only MCP servers, mcp-memory-service supports Remote MCP: persistent memory directly in your browser, on any device — no Claude Desktop required. Enterprise-ready (OAuth 2.0 + HTTPS + CORS), self-hosted or cloud-hosted.

# 1. Start server with Remote MCP
MCP_STREAMABLE_HTTP_MODE=1 \
MCP_SSE_HOST=0.0.0.0 \
MCP_OAUTH_ENABLED=true \
python -m mcp_memory_service.server

# 2. Expose publicly (Cloudflare Tunnel)
cloudflared tunnel --url http://localhost:8765

# 3. Add connector in claude.ai Settings → Connectors with the tunnel URL
#    OAuth flow will handle authentication automatically

Production Setup: Remote MCP Setup Guide (Let's Encrypt, nginx, Docker, firewall). Step-by-Step Tutorial: Blog: 5-Minute claude.ai Setup | Wiki Guide

For production deployments, team collaboration, or cloud sync:

git clone https://codeberg.org/doobidoo/mcp-memory-service.git
cd mcp-memory-service
python scripts/installation/install.py

Choose from:

• SQLite (local, fast, single-user)
• Cloudflare (cloud, multi-device sync)
• Hybrid (best of both: 5ms local + background cloud sync)
• Milvus (dedicated vector DB — Milvus Lite file, self-hosted, or Zilliz Cloud)

ℹ️ For long-lived services (MCP servers, web backends, notebook sessions), prefer Docker Milvus or Zilliz Cloud over Milvus Lite. See docs/milvus-backend.md for why.

⚡ Works With Your Favorite AI Tools

🤖 Agent Frameworks (REST API)

LangGraph · CrewAI · AutoGen · Any HTTP Client · OpenClaw/Nanobot · Custom Pipelines

🖥️ CLI & Terminal AI (MCP)

Claude Code · Gemini CLI · Gemini Code Assist · OpenCode · Codex CLI · Goose · Aider · GitHub Copilot CLI · Amp · Continue · Zed · Cody

🎨 Desktop & IDE (MCP)

Claude Desktop · VS Code · Cursor · Windsurf · Kilo Code · Raycast · JetBrains · Replit · Sourcegraph · Qodo

💬 Chat Interfaces (MCP)

ChatGPT (Developer Mode) · claude.ai (Remote MCP via HTTPS)

Works seamlessly with any MCP-compatible client or HTTP client - whether you're building agent pipelines, coding in the terminal, IDE, or browser.

💡 NEW: ChatGPT now supports MCP! Enable Developer Mode to connect your memory service directly. See setup guide →

✨ Features

🧠 Persistent Memory – Context survives across sessions with semantic search 🔍 Smart Retrieval – Finds relevant context automatically using AI embeddings ⚡ 5ms Speed – Instant context injection, no latency 🔄 Multi-Client – Works across 25+ AI applications ☁️ Cloud Sync – Optional Cloudflare backend for team collaboration 🔒 Privacy-First – Local-first, you control your data 📊 Web Dashboard – Visualize and manage memories at http://localhost:8000 🧬 Knowledge Graph – Interactive D3.js visualization of memory relationships 🏠 Homelab Quality Scoring – Point scoring at any OpenAI-compatible endpoint (Ollama, LiteLLM, vLLM) 🔗 Entity Extraction – Auto-links @mentions, #tags, URLs, and file paths from memory content to a queryable entity graph 💡 Insight Cards – Consolidation detects patterns, trends, and knowledge gaps across your memory corpus and surfaces them as structured insights 🏷️ Tag Match Filtering – tag_match=AND/OR on memory_search for precise multi-tag queries

🖥️ Dashboard Preview

8 Dashboard Tabs: Dashboard • Search • Browse • Documents • Manage • Analytics • Quality • API Docs

🎬 Watch the Web Dashboard Walkthrough on YouTube — semantic search, tag browser, document ingestion, analytics, quality scoring, and API docs in under 2 minutes. 📖 See [Web Dashboard Guide](https://codeberg.org/doobidoo/mcp-me

…