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Exocortex MCP Server

@fuwasegu

Repository

Basic information

Category

Other

Last updated

Transports

stdio

Author

fuwasegu

Submitted by

Fuwasegu

About Exocortex MCP Server

xocortex — an MCP server that gives your AI persistent memory across ALL your projects.

#mcp#ai-memory#knowledge-graph

Exocortex MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "exocortex": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/fuwasegu/exocortex",
        "exocortex",
        "--mode",
        "proxy",
        "--ensure-server"
      ]
    }
  }
}

Exocortex MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

What is Exocortex?

Exocortex is a local MCP (Model Context Protocol) server that acts as a developer’s “second brain,” persisting development insights, technical decisions, and troubleshooting records in a centralized knowledge store. It allows AI assistants like Cursor to retrieve contextually relevant memories across all projects, enabling cross-project learning and pattern recognition.

How to use Exocortex?

Install by cloning the repository and running uv sync, then start the server with uv run exocortex. Configure Cursor by adding the appropriate entry to ~/.cursor/mcp.json; options include direct from GitHub (recommended), local installation, proxy mode for multiple Cursor instances, or manual SSE server management on a custom port.

Key features of Exocortex

  • Fully local: all data and AI processing stays on your machine
  • Semantic search: find memories by meaning, not just keywords
  • Knowledge graph: maintains relationships between projects, tags, and memories
  • Memory links: connect related memories to build a traversable knowledge network
  • Lightweight and fast: uses embedded KùzuDB and fastembed models
  • Memory dynamics: smart recall based on recency and frequency
  • Frustration indexing: prioritize “painful memories” (debugging nightmares)
  • Web dashboard: cyberpunk-style UI for browsing and health monitoring

Use cases of Exocortex

  • Cross-project knowledge sharing: lessons from one project immediately available in others
  • Architecture archaeology: trace the evolution of technical decisions over time via temporal reasoning
  • Root cause analysis: understand what led to a bug or change by following memory lineage
  • Pattern recognition: discover common problems and solutions across your entire development history
  • Automatic memory consolidation: deduplication, auto-linking, and health checks after storing memories

Frequently asked questions

1

Where is data stored?

Your data is stored in `~/.exocortex/` and is preserved regardless of which installation or startup method you choose.

2

How does proxy mode help with multiple Cursor windows?

Proxy mode automatically starts a single SSE server in the background, allowing multiple Cursor instances to connect to the same server and avoid KùzuDB lock conflicts from concurrent writes.

3

What is frustration indexing?

Frustration indexing prioritizes “painful memories”—debugging nightmares get boosted in search results, making frequently accessed or emotionally charged insights surface higher.

4

Are next_actions guaranteed to be executed?

No, execution of `next_actions` is at the AI agent’s discretion. While the server strongly instructs consolidation, it is not 100% guaranteed due to inherent limitations of the MCP protocol

Comments

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