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

---

Getting started: English · Deutsch · Español · 日本語 · 简体中文 · 繁體中文 · Français · Português (Brasil) · 한국어

---

---

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.

Without mcp-memory-service	With mcp-memory-service
Each agent run starts from zero	Agents retrieve prior decisions in 5ms
Memory is local to one graph/run	Memory is shared across all agents and runs
You manage Redis + Pinecone + glue code	One self-hosted service, zero cloud cost
No causal relationships between facts	Knowledge graph with typed edges (causes, fixes, contradicts)
Context window limits create amnesia	Autonomous consolidation compresses old memories

Key capabilities for agent pipelines:

• Framework-agnostic REST API — no MCP client library needed; the live endpoint list is at /api/docs
• 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? The Setup Guide walks you to the right path in under a minute.

1. Install:

pip install "mcp-memory-service[sqlite]"

The [sqlite] extra brings the local ONNX embedding model. Without it, semantic search falls back to hash embeddings.

Install the memory command into its own environment:

uv tool install "mcp-memory-service[sqlite]"

Or skip the install and let your client start the server through uvx:

{
  "mcpServers": {
    "memory": {
      "command": "uvx",
      "args": ["--from", "mcp-memory-service[sqlite]", "memory", "server"]
    }
  }
}

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

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/

MCP_ALLOW_ANONYMOUS_ACCESS=true memory server --http

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

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

python3 codex/install_hooks.py

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 for prerequisites, authentication, and project-local installation.

This is the beginning of the README. Read the rest at the original. Read the full README ↗