Exocortex

by fuwasegu

267 downloads
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GitHub

About

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

Details

Author
fuwasegu
Downloads
267
Categories
Other, Knowledge Base

- 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

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Exocortex
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. Enable "Start Automatically" if you want the plugin to start when Highlight launches

From the repository

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.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

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

Exocortex 🧠

> "Extend your mind." - Your External Brain

日本語版はこちら (Japanese)

---

Exocortex is a local MCP (Model Context Protocol) server that acts as a developer's "second brain."

It persists development insights, technical decisions, and troubleshooting records, allowing AI assistants (like Cursor) to retrieve contextually relevant memories when needed.

Why Exocortex?

🌐 Cross-Project Knowledge Sharing

Unlike tools that store data per-repository (e.g., .serena/ in each project), Exocortex uses a single, centralized knowledge store.

Traditional approach (per-repository):
project-A/.serena/    ← isolated knowledge
project-B/.serena/    ← isolated knowledge
project-C/.serena/    ← isolated knowledge

Exocortex approach (centralized):
~/.exocortex/data/ ← shared knowledge across ALL projects
├── Insights from project-A
├── Insights from project-B
└── Insights from project-C

Cross-project learning!

Benefits:
- 🔄 Knowledge Transfer: Lessons learned in one project are immediately available in others
- 🏷️ Tag-based Discovery: Find related memories across projects via shared tags
- 📈 Cumulative Learning: Your external brain grows smarter over time, not per project
- 🔍 Pattern Recognition: Discover common problems and solutions across your entire development history

Features

- 🔒 Fully Local: All data and AI processing stays on your machine. Privacy guaranteed.
- 🔍 Semantic Search: Find memories by meaning, not just keywords.
- 🕸️ Knowledge Graph: Maintains relationships between projects, tags, and memories with explicit links.
- 🔗 Memory Links: Connect related memories to build a traversable knowledge network.
- ⚡ Lightweight & Fast: Uses embedded KùzuDB and lightweight fastembed models.
- 🧠 Memory Dynamics: Smart recall based on recency and frequency—frequently accessed memories surface higher.
- 🔥 Frustration Indexing: Prioritize "painful memories"—debugging nightmares get boosted in search results.
- 🖥️ Web Dashboard: Beautiful cyberpunk-style UI for browsing memories, monitoring health, and visualizing the knowledge graph.

📚 Usage Guide

See the full usage guide

- Tool reference with use cases
- Practical workflows
- Prompting tips
- Tips & Tricks

Installation

```bash

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