Kairn

Kairn

Watch the 30-second tour on GitHub.

Context-aware knowledge engine for AI assistants.

Status: pre-1.0. In daily use since February 2026, with a test suite that runs on Linux, macOS and Windows (see Development) and a published LongMemEval-S benchmark (measured before 0.3.0). Interfaces may still change between releases until 1.0. Feedback and issues welcome.

Other tools give your AI a memory. Kairn gives it a knowledge graph with intelligent context routing. It knows what to load, when to load it, and how much - so your AI stays focused, not overwhelmed.

pip install kairn-ai
kairn init ~/brain
kairn serve ~/brain

Claude Code and Codex each take one line; --init creates ~/brain and its database on first start, so this works even without kairn init:

claude mcp add kairn -- kairn serve --init ~/brain
codex mcp add kairn -- kairn serve --init ~/brain

Or install it as a one-click bundle, no Python setup required: download the .mcpb file from the latest release and open it with a bundle-aware app such as Claude Desktop.

For other clients, see Quick Start below. New to Kairn? Jump to First 5 Minutes.

Install routes

The bundle carries no Kairn source of its own. It declares kairn-ai as a dependency and the host resolves it with uv, so a bundle install and a pip install run identical code. Where the database lives is configurable when you install the bundle; it defaults to ~/.kairn and never leaves your machine.

Why Kairn?

Every AI conversation starts from scratch. Previous insights, decisions, and patterns - gone. Existing memory tools store flat key-value pairs that can't represent relationships or surface the right context at the right time.

Kairn is different:

• Context Router + Progressive Disclosure - Automatically loads relevant subgraphs based on keywords, starting with summaries and drilling into details only when needed. No other tool does this.
• Knowledge Graph with FTS5 - Not flat storage. Typed relationships (depends-on, resolves, causes) between nodes with provenance tracking and full-text search across everything.
• Experience Decay + Auto-Promotion - Experiences lose relevance over time (biological decay model). Frequently-accessed experiences auto-promote to permanent knowledge. Your AI naturally forgets what doesn't matter.
• 22 MCP Tools - Works with Claude Code, Codex, Claude Desktop, Cursor, VS Code, Windsurf, and any MCP client. Includes kn_judge for 5-verb relationship judgments and kn_doctor for read-only health diagnostics.
• Per-Workspace Isolation - Each workspace is its own isolated SQLite store. JWT auth and role-based access control (owner / maintainer / contributor / reader) ship for team deployments.

Quick Start

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows:

{
  "mcpServers": {
    "kairn": {
      "command": "kairn",
      "args": ["serve", "--init", "~/brain"]
    }
  }
}

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "kairn": {
      "command": "kairn",
      "args": ["serve", "--init", "~/brain"]
    }
  }
}

VS Code

Add to .vscode/mcp.json:

{
  "servers": {
    "kairn": {
      "type": "stdio",
      "command": "kairn",
      "args": ["serve", "--init", "~/brain"]
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "kairn": {
      "command": "kairn",
      "args": ["serve", "--init", "~/brain"]
    }
  }
}

Restart your editor. Kairn's 22 tools appear in the MCP section.

First 5 Minutes

A guided first run, end to end:

pip install kairn-ai
kairn init ~/brain              # creates the workspace + database (optional: serve --init does it on first start)

Add the one-liner from above (or your client's Quick Start snippet), then restart the client. Once connected, ask your assistant to remember something:

"Remember that we chose Postgres over SQLite for the analytics service because we needed concurrent writers."

That calls kn_learn under the hood and returns a JSON envelope like this (captured from a real run, via kairn learn, the CLI mirror of the tool):

{"_v": "1.0", "stored_as": "node", "node_id": "002d9c22", "experience_id": "d0710c2f", "type": "decision", "confidence": "high", "namespace": "knowledge", "candidates": []}

Start a new session and ask it to recall the same thing - that calls kn_recall and surfaces what you just stored, no re-explaining required:

{"_v": "1.0", "count": 2, "results": [
  {"source": "node", "id": "002d9c22", "name": "Decision: we chose Postgres over SQLite for the analytics service beca", "type": "learned_decision", "description": "we chose Postgres over SQLite for the analytics service because we needed concurrent writers", "relevance": 1.0, "relevance_kind": "match"},
  {"source": "experience", "id": "d0710c2f", "type": "decision", "content": "we chose Postgres over SQLite for the analytics service because we needed concurrent writers", "confidence": "high", "relevance": 1.0, "relevance_kind": "recency"}
]}

kn_learn stored both a permanent graph node and a decaying experience (high confidence does both, see Confidence routing); kn_recall found both from a three-word topic.

Read relevance_kind before you read relevance. Both rows above show 1.0 and they do not mean the same thing. match is lexical match strength (bm25); the experience's recency is time-decay - it is 1.0 because the row was created seconds ago, not because it matched well. A third value, similarity, is embedding cosine on the semantic-recall path, and unscored marks a row the surface had no ranking for and filled in with a constant. The numbers are not comparable across kinds, so do not sort a mixed result set on relevance alone. Same caution for min_relevance on kn_recall: it gates nodes on match strength and experiences on recency, one number against two scales. On kn_memories and kn_prune, which see experiences only, it is recency - and on kn_prune it deletes.

Run kairn status ~/brain any time as a smoke test - if it prints a JSON stats block (nodes/edges/experiences counts), the workspace is healthy. Want a scripted tour of every core feature instead of doing it by hand? Run kairn demo ~/brain - it walks through node creation, querying, experience saving, learning, recall, and context in about 30 seconds.

Which tool when

22 tools is a lot to hold in your head on day one. Most sessions only need these:

Everything else (kn_crossref, kn_related, kn_connect, kn_judge, kn_project/kn_projects/kn_log, kn_idea/kn_ideas, kn_promote_pending, kn_prune, kn_remove, kn_status, kn_doctor) is advanced usage - see the full 22 Tools reference below once you're past the basics.

22 Tools (kn_ prefix)

All tools follow MCP protocol with JSON responses.

Graph (6)