lithos-mcp 0.1.8

Shared cognitive substrate for AI agents (local-first, Markdown-native, MCP)

Lithos

Shared memory for AI agents.

A local, privacy-first knowledge base that enables heterogeneous AI agents to share knowledge and coordinate work.

The Problem

When agents cannot share what they know, every agent starts from zero. Work is duplicated, discoveries are lost, and coordination breaks down. Lithos solves this by providing a persistent, shared knowledge layer that compounds in value over time.

What It Is

Lithos is an MCP server that provides a shared knowledge store for AI agents running on your local infrastructure. Knowledge is stored as human-readable Markdown files (compatible with Obsidian) while providing fast full-text and semantic search for agents.

Who It's For

Lithos is the Knowledge Layer for teams running AI agents in production.

Just as Alation coined the term "Knowledge Layer" for enterprise data governance, Lithos provides the equivalent for AI agent systems: a structured, searchable, shared memory that compounds in value the more it is used. Each agent interaction enriches the knowledge base, making every subsequent agent smarter and faster.

• Teams running multiple AI agents (Agent Zero, OpenClaw, Claude Code, custom agents)
• Developers who want agents to share discoveries and avoid duplicate work
• Anyone who needs agent knowledge to be inspectable and version-controlled

Key Features

• 📁 Markdown-first: All knowledge stored as Obsidian-compatible .md files
• 🔍 Fast search: Tantivy full-text + ChromaDB semantic search
• 🕸️ Knowledge graph: NetworkX-powered relationships via [[wiki-links]]
• 🤝 Multi-agent coordination: Task claiming, findings sharing, status tracking
• 🧠 Research cache: One-call freshness check so agents skip redundant research — returns hit/miss/stale with update guidance
• 🔗 URL deduplication: Automatic detection and prevention of duplicate notes from the same source URL
• 🧬 Provenance tracking: Declare which notes a synthesis is derived from and query lineage across the knowledge base
• 🔌 MCP interface: Works with any MCP-compatible agent or tool
• 🏠 Local & private: No cloud dependencies, you own your data

Quickstart

Agent Zero

Agent Zero running inside docker on the same machine running lithos

{
  "mcpServers": {
    "lithos": {
      "url": "http://host.docker.internal:8765/sse"
    }
  }
}

OpenClaw

Update mcporter.json Probably in ~/.openclaw/workspace/config/mcporter.json

Use localhost if running on the same machine as OpenClaw, otherwise the name or IP address of the server.

{
  "mcpServers": {
    "lithos": {
      "baseUrl": "http://<your hostname>:8765/sse"
    }
  },
  "imports": []
}

Claude code

claude mcp add --transport sse lithos http://localhost:8765/sse

Documentation

• CLI Reference — installing and using the lithos command-line tool
• Specification — full technical specification
• LCMA Design — design notes

Tech Stack

Component	Technology
Storage	Markdown + YAML frontmatter
Full-text search	Tantivy
Semantic search	ChromaDB + sentence-transformers
Knowledge graph	NetworkX
Agent interface	MCP (FastMCP)
File sync	watchdog

Development Commands

# Install dependencies (uses uv)
uv sync --extra dev

# Run unit tests
uv run --extra dev pytest -m "not integration" tests/ -q

# Run integration tests
uv run --extra dev pytest -m integration tests/ -q

# Run all tests with coverage
uv run --extra dev pytest tests/ --cov=lithos --cov-report=xml

# Lint
uv run --extra dev ruff check .

# Format check
uv run --extra dev ruff format --check src/ tests/

# Type check
uv run --extra dev pyright src/

# Auto-fix lint + format
uv run --extra dev ruff check --fix . && uv run --extra dev ruff format src/ tests/

# Start server (stdio)
uv run lithos serve

# Start server (SSE)
uv run lithos serve --transport sse --port 8765

# Docker
cd docker && docker compose up -d --build

# run pointing at data dir
LITHOS_DATA_PATH="<DATA DIR PATH>" docker compose up -d --build

# stop
cd docker && docker compose down