kairn-ai 0.2.1

Context-aware knowledge engine for AI — memory, graph, intelligent routing

Kairn

Context-aware knowledge engine for AI assistants.

Status: Alpha. The API and CLI are functional and tested (see Development), but interfaces may still change between releases. 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

Add it to Claude Code in one line:

claude mcp add kairn -- kairn serve ~/brain

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

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 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:

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

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "kairn": {
      "command": "kairn",
      "args": ["serve", "~/brain"],
      "env": {
        "KAIRN_LOG_LEVEL": "WARNING"
      }
    }
  }
}

VS Code

Add to .vscode/mcp.json:

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

Windsurf

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

{
  "mcpServers": {
    "kairn": {
      "command": "kairn",
      "args": ["serve", "~/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

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},
  {"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}
]}

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.

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:

You want to...	Use	Why
Remember something new (a decision, gotcha, pattern, solution)	kn_learn	Default entry point - auto-routes to a permanent node (high confidence) or a decaying experience (medium/low), no need to decide yourself
Capture a stated user preference the moment it is expressed	kn_preference	Dedicated preference write path - you (the calling model) state the preference as one explicit sentence; stored with the longest half-life of any type
Add a permanent named concept you already know is durable	kn_add	Skips decay entirely - for structural knowledge, not day-to-day experience
Log a one-off experience with explicit confidence/decay control	kn_save	Lower-level primitive kn_learn wraps - reach for it when you want to set confidence/decay yourself
Search the permanent knowledge graph by text, type, tags, or namespace	kn_query	You're looking for nodes, not decaying experiences
Search saved experiences, ranked by relevance and decay	kn_memories	You're looking for experience content (solutions, gotchas, workarounds), not graph nodes
Surface everything relevant to a topic in one call	kn_recall (flat list) or kn_context (subgraph, progressive disclosure: summary first, full detail on demand)	You don't know yet whether the answer is a node or an experience - let Kairn search both

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)

Tool	Description
kn_add	Add node to knowledge graph
kn_connect	Create typed edge between nodes (lax-mode vocabulary)
kn_judge	Record 5-verb judgment edge (strict mode: conflicts_with / supersedes / compatible / scoped / related)
kn_query	Search by text, type, tags, namespace
kn_remove	Soft-delete node or edge (undo-safe)
kn_status	Graph stats, health, system overview

Project Memory (3)

Tool	Description
kn_project	Create or update project
kn_projects	List projects, switch active
kn_log	Log progress or failure entry

Experience Memory (5)

Tool	Description
kn_save	Save experience with decay
kn_preference	Capture a stated user preference at utterance time (longest half-life)
kn_memories	Decay-aware experience search
kn_prune	Remove expired experiences
kn_promote_pending	Promote high-access experiences to permanent nodes

Ideas (2)

Tool	Description
kn_idea	Create or update idea
kn_ideas	List/filter ideas by status, category

Intelligence (5)

Tool	Description
kn_learn	Store knowledge with confidence routing
kn_recall	Surface relevant past knowledge
kn_crossref	Find similar past solutions in the current workspace
kn_context	Keywords → relevant subgraph with progressive disclosure
kn_related	Graph traversal (BFS) to find connected nodes

Diagnostic (1)

Tool	Description
kn_doctor	Read-only health checks (lock mode, FTS5 parity, promotion backlog, namespace sprawl, orphan edges) - returns structured envelope with per-check verdicts and roll-up summary

Resources & Prompts

Resources (read-only context for MCP clients):

• kn://status - Graph overview, active project
• kn://projects - All projects with recent progress
• kn://memories - Recent high-relevance experiences

Prompts (session management):

• kn_bootup - Load active project, recent progress, and top memories (session start)
• kn_review - Summarize session and suggest next steps (session end)

How It Works

Architecture

Any MCP Client (Claude, Cursor, VS Code)
        │
        ▼ MCP Protocol (stdio)
FastMCP Server (22 tools)
        │
   ┌────┼────┐
   ▼    ▼    ▼
Graph  Memory  Intelligence
Engine Engine  Layer
   │    │      │
   └────┼──────┘
        ▼
   SQLite + FTS5
   (per-workspace)

Decay Model

Experiences decrease in relevance exponentially:

relevance(t) = initial_score × e^(-decay_rate × days)

Type	Half-life	Notes
solution	120 days	Stable, durable
pattern	90 days	Architectural knowledge
decision	100 days	Context-dependent
workaround	40 days	Temporary fixes fade fast
gotcha	70 days	Tricky pitfalls stay relevant
preference	180 days	Durable user preferences - initial estimate, not yet tail-calibrated

Half-lives are calibrated against the real access tail of a production experience store, not guessed (one exception: preference is a new type with no access history yet, so its value is a documented initial estimate until real data accumulates).

Confidence routing via kn_learn:

• high → Permanent node + experience (no decay)
• medium → Experience with 2× decay
• low → Experience with 4× decay
• Auto-promotion: 5+ accesses → permanent node
• Node access tracking: kn_recall, kn_context, and kn_crossref log which nodes were accessed, feeding the decay and promotion pipeline

Benchmarks

Kairn scores 56.2% overall on LongMemEval-S (500/500 questions scored, GPT-4o reader + judge, single run, 0 errors). These are the real per-category numbers, including the bad ones - each red cell links to its diagnosis:

Category	n	Accuracy	Diagnosis
single-session-user	70	91.4%	-
single-session-assistant	56	83.9%	-
knowledge-update	78	70.5%	-
temporal-reasoning	133	42.9%	why
multi-session	133	41.4%	why
single-session-preference	30	10.0%	why

The 500 questions include 30 abstention variants (the right answer is to decline); they are counted inside their categories above and scored separately: Kairn declines correctly on 96.7% of them.

Recall latency is ~1.4 ms per query (FTS5, in-process, no network). Protocol, honesty notes, and reproduction steps: BENCHMARKS.md.

This scorecard stays current: every release that touches recall re-publishes these numbers, and a weak cell stays on the board until the number actually moves. No cherry-picked runs, no hidden categories.

CLI

kairn init <path>              # Initialize workspace
kairn serve <path>             # Start MCP server (stdio)
kairn status <path>            # Graph stats
kairn demo <path>              # Interactive tutorial
kairn benchmark <path>         # Local performance benchmarks (latency, not LongMemEval)
kairn token-audit <path>       # Audit tool token usage

Configuration

KAIRN_LOG_LEVEL=INFO|DEBUG|WARNING    # Default: WARNING
KAIRN_DB_PATH=~/brain/.kairn         # Default: {workspace}/.kairn
KAIRN_CACHE_SIZE=100                  # LRU cache entries
KAIRN_JWT_SECRET=<your-secret>        # Required for team features

Development

git clone https://github.com/primeline-ai/kairn
cd kairn
pip install -e ".[dev,team]"
pytest tests/ -v --cov
ruff check src/ && ruff format src/

Project Structure

src/kairn/
├── server.py              # FastMCP server + 22 tools
├── cli.py                 # CLI commands
├── config.py              # Configuration
├── core/
│   ├── graph.py           # GraphEngine (6 tools)
│   ├── memory.py          # ProjectMemory (3 tools)
│   ├── experience.py      # ExperienceEngine (4 tools)
│   ├── ideas.py           # IdeaEngine (2 tools)
│   ├── intelligence.py    # IntelligenceLayer (5 tools)
│   └── router.py          # ContextRouter
├── storage/
│   ├── base.py            # Storage interface
│   └── sqlite_store.py    # SQLite + FTS5 implementation
├── models/                # Data models
├── events/                # Event bus
└── auth/                  # JWT + RBAC (team feature)

Performance

Typical operation times on modern hardware:

Operation	Time
kn_add	2-5ms
kn_query (100 nodes)	5-15ms
kn_connect	1-3ms
kn_recall (graph traversal)	10-50ms
kn_crossref (similarity search)	20-100ms

Used By

Project	What It Uses Kairn For
Quantum Lens	Persistent insight storage, cross-analysis pattern tracking, lens effectiveness metrics
Claude Code Starter System	Session memory, project state, learning persistence

License

MIT

---

Part of the PrimeLine Ecosystem

Tool	What It Does	Deep Dive
Evolving Lite	Self-improving Claude Code plugin - memory, delegation, self-correction	Blog
Kairn	Persistent knowledge graph with context routing for AI	Blog
tmux Orchestration	Parallel Claude Code sessions with heartbeat monitoring	Blog
UPF	3-stage planning with adversarial hardening	Blog
Quantum Lens	7 cognitive lenses for multi-perspective analysis	Blog
PrimeLine Skills	5 production-grade workflow skills for Claude Code	Blog
Starter System	Lightweight session memory and handoffs	Blog

@PrimeLineAI · primeline.cc · Free Guide