MARM: Local-First Persistent Multi-Agent Memory Layer for MCP Clients v2.18.0

Contributions welcome! Browse open issues to contribute, or join the MARM Discord to share workflows, get setup help, and connect with other builders.

Table of Contents

• Why MARM MCP
• Quick Start
• Code Graph
• Complete MCP Tool Suite
• MARM Dashboard
• Performance & Scaling Benchmarks
• Contributing
• Project Documentation

Why MARM MCP: The Problem & Solution

Your AI forgets everything. MARM MCP doesn't.

MARM MCP is a local memory infrastructure layer for AI agents. It gives Claude, Codex, Gemini, Qwen, IDE agents, and other MCP clients one persistent place to store decisions, retrieve context, reuse notebooks, and keep long-running work from drifting.

MARM is built around two focused surfaces: 7 core memory tools for daily agent context and 5 code-graph tools for repo intelligence (bundled over both HTTP and STDIO). The server handles the heavy work behind those tools: protocol delivery, hybrid recall, serialized writes, rate-limit presets, write-time consolidation, agent-assisted compaction, and lazy graph startup. Agents get a compact memory workflow plus codebase lookup when they need it, without rereading the whole project or flooding the model with duplicate context.

How It Works

See Performance & Scaling Benchmarks for retrieval latency, concurrency, and write-cost numbers.

MARM Demo

https://github.com/user-attachments/assets/dabfe44f-689d-404f-a2c7-dcf8fa4ef0c1

MARM gives AI agents persistent local memory, shared context, write-queue safety, swarm presets, and hybrid recall so commands, config keys, and project meaning all stay reachable.

Start Now

Recommended: guided setup with marm-init

The easiest way to install MARM is to let your agent do the setup with you. marm-init turns the usual MCP setup mess into one guided conversation: Python or Docker, HTTP or STDIO, local or remote server, API keys, config paths, dashboard startup, and multi-agent linking for Claude, Codex, Gemini, Qwen, Cursor, VS Code, and other MCP clients. No hunting through install docs, no guessing which config file your client uses, and no rewriting the same connection by hand for every agent.

npx degit Lyellr88/MARM-Systems/skills

Then tell your agent: "Use the marm-init skill to set up MARM."

Manual pip install

pip install marm-mcp-server

Code Graph: repo indexing and code lookup

marm-graph is bundled into the HTTP server. It indexes a repository once, then lets agents ask code-structure questions without repeatedly scanning files. The graph backend starts lazily on first graph-tool use, so normal memory, logging, notebook, and summary tools still start fast.

Use HTTP mode, then ask your agent to index the repo:

Use marm_graph_index to index this repository.
Then use marm_code_lookup when you need symbols, files, or source snippets.
Use marm_graph_trace for call paths, marm_graph_architecture for an overview, and marm_graph_impact for change-risk checks.

Graph tools are bundled on both HTTP and STDIO. STDIO stays a single local process with no port and no API key; the graph engine still starts lazily on first use.

🚀 Quick Start for MCP (HTTP & STDIO)

Use this quick rule of thumb to choose your setup

• Local HTTP/STDIO = fastest single-machine setup.
• Docker HTTP = shared/always-on server (key required).
• Docker STDIO = private containerized local use (no HTTP key).

Swarm / multi-agent note: The write queue is enabled by default to serialize memory writes through one worker. For shared HTTP deployments, use --swarm (200 RPM) or --swarm-max (600 RPM) when starting the server. --trusted disables rate limiting entirely for private deployments. STDIO is still best for private single-agent/local use. See MCP-HANDBOOK.md for more info.

"agent" refers to claude, gemini, grok, qwen, or any MCP client. Codex uses --url instead of --transport to add MCP tools.

pip install marm-mcp-server
python -m marm_mcp_server
# Stuck on client setup? Open a Q&A thread: https://github.com/Lyellr88/MARM-Systems/discussions
# most agents use this --transport command
"agent" mcp add --transport http marm-memory http://localhost:8001/mcp
codex mcp add marm-memory --url http://localhost:8001/mcp

</details>

<details>
<summary><strong>Local pip STDIO</strong></summary>

#### Local pip STDIO

```bash
pip install marm-mcp-server
python -m marm_mcp_server.server_stdio
# most agents use this --transport command
"agent" mcp add --transport stdio marm-memory-stdio marm-mcp-stdio
codex mcp add marm-memory-stdio -- marm-mcp-stdio

Use HTTP when multiple agents need to share one live MARM server. STDIO is still best for private single-agent use because each client owns its own local process.

# HTTP shared server, normal multi-agent use
python -m marm_mcp_server --swarm

# HTTP shared server, heavier private swarm
python -m marm_mcp_server --swarm-max

# HTTP trusted private lab/server, rate limiting disabled
python -m marm_mcp_server --trusted

# STDIO remains keyless/private and does not use swarm flags
marm-mcp-stdio

Docker HTTP requires an API key because it exposes MARM as a network server; STDIO stays local to the client process and does not need one.

# Step 1: generate key (do not add < > around the key)
docker run --rm lyellr88/marm-mcp-server:latest --generate-key

# Step 2: run server
docker pull lyellr88/marm-mcp-server:latest
docker run -d --name marm-mcp-server \
  -p 127.0.0.1:8001:8001 \
  -e SERVER_HOST=0.0.0.0 \
  -e MARM_API_KEY=your-generated-key \
  -v ~/.marm:/home/marm/.marm \
  lyellr88/marm-mcp-server:latest

# Step 3: connect client
"agent" mcp add --transport http marm-memory http://localhost:8001/mcp --header "Authorization: Bearer your-generated-key"

# PowerShell: set this before starting/restarting Codex
$env:MARM_API_KEY="your-generated-key"
codex mcp add marm-memory --url http://localhost:8001/mcp --bearer-token-env-var MARM_API_KEY

# Quick auth smoke test
curl -i -H "Authorization: Bearer $env:MARM_API_KEY" http://127.0.0.1:8001/mcp

--bearer-token-env-var takes the environment variable name, not the raw key. Start or restart Codex from the same shell after setting $env:MARM_API_KEY. For local Docker smoke tests, MARM_API_KEY=test is fine and avoids shell escaping problems; use a generated key for real deployments. A 406 Not Acceptable from the smoke-test GET /mcp means auth reached the MCP endpoint; 401 Unauthorized means the key is missing or mismatched.

# --swarm: write queue on, 200 RPM - recommended for multi-agent shared servers
docker run -d --name marm-mcp-server \
  -p 127.0.0.1:8001:8001 \
  -e SERVER_HOST=0.0.0.0 \
  -e MARM_API_KEY=your-generated-key \
  -v ~/.marm:/home/marm/.marm \
  lyellr88/marm-mcp-server:latest --swarm

Docker graph tools run inside the container, so they cannot see host paths unless you mount them at docker run.

$env:MARM_API_KEY="test"

docker run -d --name marm-mcp-server `
  -p 127.0.0.1:8001:8001 `
  -e SERVER_HOST=0.0.0.0 `
  -e MARM_API_KEY=$env:MARM_API_KEY `
  -v ~/.marm:/home/marm/.marm `
  -v C:\Users\lyell\Desktop\MARM-Systems:/workspace/MARM-Systems `
  lyellr88/marm-mcp-server:latest

Then index the container path, not the Windows host path:

marm_graph_index(repo_path="/workspace/MARM-Systems")

Graph tools must use the container path. Mounts cannot be added to an already-running container; stop and restart the container with the repo mount when you want Docker graph indexing.

Docker STDIO includes the same built-in marm-graph tools; no extra image or install step is required.

docker run --rm -i \
  -v ~/.marm:/home/marm/.marm \
  --entrypoint python \
  lyellr88/marm-mcp-server:latest \
  -m marm_mcp_server.server_stdio

• Docker HTTP requires a key; Docker STDIO does not.
• If you get 401, verify key match and client restart after env var changes.
• For full key setup, rotation, and troubleshooting: INSTALL-DOCKER.md

Claude Code remains the recommended first setup path, but MARM also works with other MCP clients and IDE agents.

CLI clients - Claude Code · Codex · Gemini CLI · Qwen CLI · Linux variants · Docker/key

IDE agents - VS Code / Copilot Agent · Cursor · Docker/key IDE setup

Remote/API platforms - xAI / Grok Remote MCP · Platform integration

Using a client that isn't listed? Open an issue and let us know; client adapters are a first-class feature request.

MARM Dashboard

A local web UI for browsing and managing your MARM memory. It is bundled with marm-mcp-server and mounts at /dashboard when the HTTP server starts.

Start MARM HTTP, then open the dashboard:

python -m marm_mcp_server
# browser: http://localhost:8001/dashboard

Docker uses the same unified image and key:

docker run -d --name marm-mcp-server \
  -p 127.0.0.1:8001:8001 \
  -e MARM_API_KEY=your-key \
  -v ~/.marm:/home/marm/.marm \
  lyellr88/marm-mcp-server:latest
# browser: http://localhost:8001/dashboard

Complete MCP Tool Suite (12 Tools)

💡 Pro Tip: You don't need to manually call these tools! Just tell your AI agent what you want in natural language:

• "Claude, log this session as 'Project Alpha' and add this conversation as 'database design discussion'"
• "Remember this code snippet in your notebook for later"
• "Search for what we discussed about authentication yesterday"

The AI agent will automatically use the appropriate tools. Manual tool access is available for power users who want direct control.

A Deeper Look

MARM keeps the core MCP surface lean with 7 tools by grouping domain operations behind explicit parameters like marm_notebook(action=...), marm_delete(type=...), and marm_compaction(action=...). Behind those tools, the server handles lifecycle setup, protocol refresh, docs indexing, date context, summary-cache maintenance, write queue handling, project/platform attribution, and health checks. marm-graph's 5 code-structure tools are bundled by default on both HTTP and STDIO, bringing the discoverable surface to 12; the code-graph engine starts lazily on first use and never blocks the 7 core tools if it fails to start (GRAPH_ENABLED=false disables it outright).

Under the hood, MARM uses SQLite WAL mode, connection pooling, serialized writes, HTTP swarm presets, safe local defaults, exact-query routing for syntax-heavy lookups, FTS→semantic reranking, bounded fallback search, chunk-aware long-memory recall, and summary/context/full recall depths to keep memory fast, stable, and token-efficient as projects grow.

For a deeper look into the MCP behavior, tool parameters, automation, and workflows, see MCP-HANDBOOK.md and FAQ.md.

Performance & Scaling Benchmarks

MARM is tuned for fast recall first, even as memory grows and long memories are chunked behind the scenes.

1. Retrieval Latency Scaling

2. Encoder + Concurrency

• Cold model load: 972ms
• Warm encode: median 10.3ms, p95 11.2ms
• Concurrent recall: 10 gathered recalls completed in 394.7ms vs 436.6ms serial. The current path is intentionally serialized around shared encoder/SQLite work, so this is stable under load rather than true parallel speedup.

3. Write-Time Ingestion Cost

• Consolidation off: median 10.3ms, p95 11.6ms
• Consolidation on: median 42.0ms, p95 46.3ms
• Tradeoff: write-time dedupe/clustering adds 4.1x median cost so recall stays fast and cleaner over time.

4. Hybrid Search Scaling

Benchmarks used a throwaway real SQLite database and the live fastembed-backed all-MiniLM-L6-v2 encoder on local hardware. Reproduce them: marm-mcp-server/scripts/bench_hotpath.py

⭐ Star the Project

If MARM helps with your AI memory needs, please star the repository to support development!

Contributing

MARM welcomes contributors at every level. Code helps, but so do docs, setup notes, client testing, bug reports, benchmarks, and real workflow feedback from people using AI tools every day.

Good places to help:

• Test MARM with more MCP clients, IDE agents, and operating systems
• Improve docs, screenshots, examples, and platform-specific setup notes
• Report bugs or confusing install steps with clear reproduction details
• Share memory workflows, agent habits, and tool ideas from real use
• Check out open issues

💡 Want to get your name on this list? Check out our CONTRIBUTING.md guide to get started!

Join the MARM Community

Help build the future of AI memory - no coding required!

Connect: MARM Discord | GitHub Discussions

License & Usage Notice

MARM is released under the Apache 2.0 License, and forks, experiments, and integrations are welcome. MARM also wraps third-party open-source components such as codebase-memory-mcp under MIT; see THIRD_PARTY_NOTICES.md for attribution. If you build on it, please make unofficial versions easy to distinguish from releases published by the official MARM repository so users know what they are installing.

Project Documentation

Usage Guides

• MCP-HANDBOOK.md - Complete MCP server usage guide with commands, workflows, and examples
• PROTOCOL.md - MCP operating protocol
• FAQ.md - Answers to common questions about using MARM

MCP Server Installation

• INSTALL-DOCKER.md - Docker deployment (recommended)
• INSTALL-WINDOWS.md - Windows installation guide
• INSTALL-LINUX.md - Linux installation guide
• INSTALL-PLATFORMS.md - Platform installation guide

Project Information

• README.md - This file - ecosystem overview and MCP server guide
• CONTRIBUTING.md - How to contribute to MARM
• CHANGELOG.md - Version history and updates
• ACKNOWLEDGMENTS.md - Contributors and acknowledgments
• ROADMAP.md - Planned features and development roadmap
• LICENSE - Apache 2.0 license terms