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Penfield MCP

by penfieldlabs

Persistent agent memory with hybrid search, knowledge graphs, context checkpoints, and artifact storage.

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Install Penfield MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "penfieldlabs-penfield-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y mcp-remote

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

The penfieldlabs/penfield-mcp MCP server gives AI agents persistent memory that survives across sessions. It combines BM25 keyword search, vector search, and graph connections, with tools for storing memories, exploring relationships, saving context, and managing artifacts. Connect through the hosted MCP endpoint and authenticate with OAuth. Reach for it when agents need to retain decisions, preferences, investigation threads, or handoff context across Claude, Cursor, Windsurf, and other MCP clients.

Use cases

  • Retain architectural decisions across coding sessions
  • Build connected research notes with knowledge graphs
  • Restore context for long-running projects
  • Store and retrieve project artifacts
  • Carry preferences across compatible AI clients

Key features

  • Persistent memory across sessions
  • Hybrid BM25, vector, and graph search
  • Knowledge graph with 24 relationship types
  • Context save and restore checkpoints
  • Artifact storage and retrieval
  • OAuth-authenticated hosted MCP endpoint

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

19/20 checks healthy over the last 78d

Supply-chain signal

1 high-severity advisory on record for this package. Most advisories affect transitive dependencies and may not be exploitable in this server's actual usage — this is a directional signal, not a security audit.

Critical 1High 0Medium 0Low 0

Scanned 8d ago via OSV.dev · mcp-remote (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars6Stargazers on the source repository.
npm downloads1.6MPackage downloads in the last 30 days.
Last commit6mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity4 viewsConfig copies, upvotes, and views on AllMCPs.

How Penfield MCP works

What penfieldlabs/penfield-mcp MCP server does

The penfieldlabs/penfield-mcp MCP server provides persistent memory for AI agents. It is designed to retain decisions, preferences, working context, and research findings beyond a single conversation or session. The service also represents connections between memories as a knowledge graph, allowing an agent to preserve relationships between ideas rather than storing isolated notes.

Its capabilities are organized into four groups:

  • Memory tools for storing, recalling, searching, fetching, and updating memories.
  • Knowledge graph tools for creating connections, removing connections, and exploring related information.
  • Context tools for loading identity and personality context, reviewing recent work, saving checkpoints, restoring checkpoints, and listing saved contexts.
  • Artifact tools for saving, retrieving, listing, and deleting stored artifacts.

Hybrid retrieval combines BM25 keyword matching, vector-based semantic search, and graph relationships. This lets an agent look for exact terms, related meaning, or connected memories depending on the task.

How it works

Use the hosted MCP endpoint at https://mcp.penfield.app from a compatible client. After signing up through the Penfield portal, connect the endpoint and complete the OAuth authentication flow when prompted. The same memory and knowledge graph can then be used from different connected clients.

A recommended session pattern begins with awaken() to load identity and personality context, followed by reflect() to orient the agent on recent work. The default reflection period is the previous seven days.

The service supports Claude, Cursor, Windsurf, Gemini CLI, and other MCP-compatible tools. The repository also documents configurations for Cline, Roo Code, and Zed. An HTTP API is available separately at api.penfield.app for custom integrations, but that is distinct from the MCP connection described here.

Setup and configuration

For Claude, add https://mcp.penfield.app as a custom connector. Claude Code can register it with an HTTP transport using the documented claude mcp add command. Cursor and other clients can use an MCP configuration that runs npx -y mcp-remote https://mcp.penfield.app/; the repository provides examples for Cursor, Windsurf, Cline, Roo Code, Zed, and Gemini CLI.

The penfieldlabs/penfield-mcp MCP server is hosted remotely, so local installation of the repository is not required by the documented setup. You need a Penfield account and OAuth authentication.

Tools and capabilities

The penfieldlabs/penfield-mcp MCP server exposes memory operations, graph relationships, context checkpoints, and artifact storage through MCP tools. It supports 11 documented memory types and 24 relationship types, with further parameter details in the repository's tool and reference documentation.

Typical uses include retaining architectural decisions across coding sessions, carrying investigation threads between sessions, building connected research notes, and checkpointing progress on long-running work. Artifact operations provide a separate way to store and retrieve files or other project outputs supported by the service.

Limitations and notes

The material describes a hosted service and OAuth login but does not provide local deployment instructions, self-hosting details, or environment variables. It also does not specify pricing, retention limits, storage quotas, search-index behavior, or data residency. The native OpenClaw plugin mentioned in the README is a separate project and should not be confused with this MCP server.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Penfield MCP

Connect a client to the hosted endpoint at https://mcp.penfield.app and authenticate through the OAuth flow. The README provides client-specific configuration examples.

Yes. Add https://mcp.penfield.app as a custom connector in Claude settings, then authenticate when prompted.

Yes. The README provides a Cursor one-click install link and a configuration using npx -y mcp-remote with the hosted endpoint.

It provides 17 tools for memory, knowledge graphs, context checkpoints, and artifacts, including store, recall, search, connect, explore, awaken, save_context, and save_artifact.

Yes. Its retrieval combines BM25 keyword search, vector search, and graph connections.

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