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Lithos

The meta system prompt your entire agent team shares

Persistent. Searchable. Always up to date.

Lithos is a local, privacy-first knowledge base that lets heterogeneous AI agents — Agent Zero, Claude Code, OpenClaw, LangGraph, CrewAI, and more — read, write, and coordinate through a single MCP interface. Human-readable Markdown on disk. Zero cloud. Zero lock-in.

Get Started → View on GitHub

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Markdown-first

Every knowledge item is a plain .md file compatible with Obsidian. Your agents' memory is human-readable and version-controllable — inspect it, edit it, git diff it.

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Hybrid search

Tantivy full-text BM25 + ChromaDB semantic vectors, fused with Reciprocal Rank Fusion (RRF). One call, best of both worlds.

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Multi-agent coordination

Task claiming with TTL-based distributed locks. Agents can stake out work, share findings, and avoid duplicate effort — no orchestration framework required.

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Knowledge graph

Wiki-links ([[note]]) build a NetworkX graph automatically. Traverse relationships, query provenance lineage, discover what was derived from what.

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MCP native

Exposes 28 tools via the Model Context Protocol over stdio or SSE. Add Lithos to any MCP-compatible agent in seconds — no SDK required.

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Truly local

No API keys. No telemetry. No cloud sync. Runs on a Raspberry Pi, a Mac Mini, or a VPS. Your data stays where you put it.

Quickstart

git clone https://github.com/agent-lore/lithos.git
cd lithos
docker compose up -d

Lithos is now running on http://localhost:8765/sse.

pip install lithos-mcp
# or
uv pip install lithos-mcp

lithos serve --transport sse --host 0.0.0.0 --port 8765

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "lithos": {
      "command": "lithos",
      "args": ["serve"]
    }
  }
}

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

In ~/.openclaw/workspace/config/mcporter.json:

{
  "mcpServers": {
    "lithos": {
      "baseUrl": "http://localhost:8765/sse"
    }
  }
}

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

Your agents, talking to each other

# Agent A discovers something useful
lithos_write(
    title="Rate limiting pattern for OpenAI API",
    content="Use exponential backoff with jitter. Base delay 1s, max 60s...",
    tags=["openai", "rate-limiting", "patterns"],
    agent="research-agent"
)

# Agent B finds it instantly — no re-researching, no duplicated work
results = lithos_search(query="openai rate limit backoff", mode="hybrid")
# → [{ title: "Rate limiting pattern for OpenAI API", score: 0.94, ... }]

# Agent C coordinates parallel work
task = lithos_task_create(title="Audit all API integrations", agent="orchestrator")
lithos_task_claim(task_id=task.id, aspect="OpenAI audit", agent="agent-c", ttl_minutes=60)

Why Lithos?

In 2026, running one agent is table stakes. Running a team of agents is where it gets interesting — and where it gets messy. Agents duplicate research, contradict each other, lose context, and can't coordinate without a shared channel.

Lithos is that channel. It's the shared memory layer your agents can actually trust: every item is timestamped, attributed, versioned, and searchable. Agents can declare confidence, set freshness deadlines, and build provenance chains. The knowledge base is a first-class artefact you can open in Obsidian, commit to git, and inspect at any time.

The AI Runtime

Think of Lithos like a software runtime for knowledge — not a static library. Notes are executable instructions. Outdated notes are bugs. The reconcile pipeline runs overnight and flags stale knowledge automatically.

What's next?

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Installation

System requirements, install methods, Docker Compose.

⚡

Quickstart

Your first knowledge item in under 5 minutes.

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Concepts

How Lithos stores, indexes, and retrieves knowledge.

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MCP Tools

Full reference for all 28 MCP tools.