kgn-mcp 0.11.3

CLI tool for parsing .kgn/.kge files, ingesting them into a PostgreSQL knowledge graph DB, and generating AI agent context packages via subgraph queries

kgn

Manage your AI agent's knowledge — parse, store, query, and collaborate.

KGN is a developer-friendly CLI + MCP server for teams building with AI agents. Write knowledge nodes in simple YAML+Markdown files (.kgn), define relationships between them (.kge), and let KGN handle storage, similarity search, conflict detection, and multi-agent task handoffs — all backed by PostgreSQL + pgvector.

Hybrid architecture: PostgreSQL is the local working engine; GitHub is the long-term source of truth. Export, commit, and push in one command.

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Table of Contents

• Architecture
• Why KGN?
• Quick Start
• MCP Server (Claude Integration)
• Multi-Agent Orchestration
• CLI Commands
• File Formats
• Development
• Tech Stack

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Architecture

For a deep dive into KGN's internal design — layer structure, module dependencies, database schema, data flows, and more — see the full Architecture Guide with 16 interactive Mermaid diagrams.

graph LR
    A[".kgn / .kge"] --> B["Parser"]
    B --> C["IngestService"]
    C --> D[("PostgreSQL<br/>+ pgvector")]
    D --> E["CLI · MCP · LSP · Web"]
    E --> F["Git / GitHub Sync"]

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Why KGN?

AI agents are powerful, but they forget everything between sessions — and when multiple agents collaborate, they can conflict, duplicate work, or lose track of decisions.

KGN gives your agents a shared, queryable memory:

Problem	KGN Solution
Agents forget past decisions	Persistent knowledge graph in PostgreSQL
Duplicate work across agents	Conflict detection + similarity search
No task coordination	Built-in task queue with lease management
Hard to audit agent actions	Structured activity log per agent
Context window overflow	Subgraph extraction — only what's relevant
IDE friction for .kgn files	VS Code extension with LSP support

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Quick Start

📖 New to KGN? Follow the step-by-step Getting Started Guide ( 한국어 ) — no prior experience required.

Install

pip install kgn-mcp

Start Database

git clone https://github.com/baobab00/kgn.git && cd kgn
docker compose -f docker/docker-compose.yml up -d postgres

First Run

kgn init --project my-project
kgn ingest examples/ --project my-project --recursive
kgn status --project my-project

Embedding Provider Setup

To use embedding features, set your OpenAI API key in the .env file:

# .env
KGN_OPENAI_API_KEY=sk-your-api-key-here
KGN_OPENAI_EMBED_MODEL=text-embedding-3-small    # default

If the API key is not set, ingest works normally and embedding is silently skipped (graceful degradation).

# Test provider connection
kgn embed provider test

Docker All-in-One

Run PostgreSQL + kgn CLI together with Docker:

docker compose -f docker/docker-compose.yml up -d --build
docker compose -f docker/docker-compose.yml exec kgn kgn init --project my-project
docker compose -f docker/docker-compose.yml exec kgn kgn --help

Place .kgn/.kge files in docker/workspace/ directory.

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MCP Server (Claude Integration)

The MCP (Model Context Protocol) server enables Claude to directly read, write, and manage tasks in the knowledge graph.

# stdio mode (Claude Desktop / Claude Code default)
kgn mcp serve --project my-project

# HTTP SSE mode
KGN_MCP_TRANSPORT=sse KGN_MCP_PORT=8000 kgn mcp serve --project my-project

# streamable-http mode
KGN_MCP_TRANSPORT=streamable-http kgn mcp serve --project my-project

Claude Desktop integration — add to claude_desktop_config.json:

{
  "mcpServers": {
    "kgn": {
      "command": "uv",
      "args": ["run", "kgn", "mcp", "serve", "--project", "my-project"]
    }
  }
}

MCP Tools (12 tools)

Tool	Category	Description
get_node	Read	Get node by ID
query_nodes	Read	Search nodes in project (type/status filter)
get_subgraph	Read	BFS subgraph extraction from node
query_similar	Read	Vector similarity Top-K search
task_checkout	Task	Check out highest-priority task (with auto lease recovery)
task_complete	Task	Mark task as complete (auto-unblocks dependent tasks)
task_fail	Task	Mark task as failed
workflow_list	Workflow	List registered workflow templates
workflow_run	Workflow	Execute a workflow template (creates subtask DAG)
ingest_node	Write	Ingest node from .kgn string
ingest_edge	Write	Ingest edge from .kge string
enqueue_task	Write	Enqueue TASK node

Git/GitHub Sync

# Export DB → filesystem (+ auto-generate Mermaid README)
kgn sync export --project my-project --target ./sync

# Import filesystem → DB
kgn sync import --project my-project --source ./sync

# Push/pull to GitHub
kgn sync push --project my-project --target ./sync
kgn sync pull --project my-project --target ./sync

# Mermaid visualization
kgn graph mermaid --project my-project
kgn graph readme --project my-project --target ./sync

# Branch/PR management
kgn git branch list --target ./sync
kgn git pr create --project my-project --target ./sync --title "PR title"

Web Dashboard

pip install kgn-mcp[web]
kgn web serve --project my-project --port 8080

Open http://localhost:8080 — Graph View, Task Board, Health Dashboard, Search & Filter.

VS Code Extension

code --install-extension baobab00.vscode-kgn
pip install kgn-mcp[lsp]    # for LSP features

Syntax Highlighting, Diagnostics, Auto-completion, Hover, Go to Definition, CodeLens, Subgraph Preview.

Error Code System

All MCP error responses are returned as structured JSON:

{
  "error": "Error message",
  "code": "KGN-300",
  "detail": "Detailed description",
  "recoverable": false
}

Code	Category	Description	Retryable
KGN-100	Infrastructure	Database connection failed	✅
KGN-101	Infrastructure	Embedding provider unavailable	✅
KGN-200	Ingest	YAML front matter parse error	❌
KGN-201	Ingest	Required field missing	❌
KGN-202	Ingest	Invalid field value	❌
KGN-300	Query	Node not found	❌
KGN-301	Query	Invalid UUID format	❌
KGN-302	Query	Subgraph depth limit exceeded	❌
KGN-400	Task	No READY tasks available	❌
KGN-401	Task	Task not in expected state	❌
KGN-402	Task	Lease expired	✅
KGN-999	Internal	Unexpected server error	✅

Multi-Agent Orchestration

KGN supports multi-agent collaborative workflows where multiple AI agents work together on a knowledge graph with role-based access control, task handoff, and conflict resolution.

• 5 Agent Roles — genesis, worker, reviewer, indexer, admin with role-based access control
• 3 Workflow Templates — design-to-impl, issue-resolution, knowledge-indexing
• Task Handoff — Automatic context propagation between workflow steps
• Advisory Locking — Prevents concurrent modifications to the same node
• Conflict Resolution — Detects conflicts and auto-creates review tasks
• Observability — Agent activity timeline, task flow stats, bottleneck detection

Agent Roles & Workflow Details

Role	Create	Checkout	Description
genesis	GOAL, SPEC, ARCH, CONSTRAINT, ASSUMPTION	—	Project bootstrapping
worker	SPEC, ARCH, LOGIC, TASK, SUMMARY	✅ (role-filtered)	Implementation work
reviewer	DECISION, ISSUE, SUMMARY	✅ (role-filtered)	Code review & decisions
indexer	SUMMARY	—	Knowledge indexing
admin	All types	✅ (all tasks)	Full access

Template	Steps	Description
design-to-impl	GOAL → SPEC → ARCH → TASK(impl) → TASK(review)	Full design-to-implementation pipeline
issue-resolution	ISSUE → TASK(fix) → TASK(verify)	Bug fix workflow
knowledge-indexing	GOAL → TASK(index) → TASK(review)	Knowledge capture pipeline

kgn agent list --project my-project
kgn agent role --project my-project --agent-id <uuid> --role worker
kgn agent stats --project my-project --agent-id <uuid>
kgn agent timeline --project my-project --agent-id <uuid>

CLI Commands

Run kgn --help for the full command list.

Key commands summary:

Group	Example	Description
Core	kgn init, kgn ingest, kgn status, kgn health	Initialize, ingest, status, health
Query	kgn query nodes, kgn query subgraph, kgn query similar	Search, subgraph, similarity
Task	kgn task enqueue/checkout/complete/fail/list/log	Task orchestration
Embed	kgn embed, kgn embed provider test	Embedding management
Conflict	kgn conflict scan/approve/dismiss	Conflict detection/management
Sync	kgn sync export/import/status/push/pull	DB ↔ file ↔ GitHub sync
Git	kgn git init/status/diff/log/branch/pr	Git/GitHub management
Graph	kgn graph mermaid/readme	Mermaid visualization
MCP	kgn mcp serve	MCP server (stdio/sse/streamable-http)
Agent	kgn agent list/role/stats/timeline	Multi-agent orchestration
Web	kgn web serve	Web visualization dashboard
LSP	kgn lsp serve	Language Server (VS Code integration)

Expired Task Recovery

When a checked-out task exceeds its lease_expires_at, it is considered expired. requeue_expired resets expired IN_PROGRESS tasks to READY and increments attempts.

• MCP: checkout automatically calls requeue_expired beforehand
• CLI: Manual invocation or cron schedule required
• When max_attempts (default 3) is exceeded, the task transitions to FAILED

File Formats

.kgn — Knowledge Graph Node

---
kgn_version: "0.1"
id: "new:my-node"        # UUID or new:slug
type: SPEC               # GOAL, ARCH, SPEC, LOGIC, DECISION, ISSUE, TASK, CONSTRAINT, ASSUMPTION, SUMMARY
title: "Node title"
status: ACTIVE            # ACTIVE, DEPRECATED, SUPERSEDED, ARCHIVED
project_id: "my-project"
agent_id: "my-agent"
tags: ["tag1", "tag2"]
confidence: 0.9
---

## Context
...
## Content
...

.kge — Edge Definition

---
kgn_version: "0.1"
project_id: "my-project"
agent_id: "my-agent"
edges:
  - from: "new:node-a"
    to:   "new:node-b"
    type: DEPENDS_ON      # DEPENDS_ON, IMPLEMENTS, RESOLVES, SUPERSEDES, DERIVED_FROM, CONTRADICTS, CONSTRAINED_BY
    note: "Edge description"
---

See examples/ directory for practical .kgn and .kge file examples.

Development

# Lint
uv run ruff check .

# Format
uv run ruff format .

# Test
uv run pytest --tb=short -q

# Coverage
uv run pytest --cov=kgn --cov-report=term-missing

Tech Stack

Layer	Technology
Language	Python 3.12+
CLI	Typer + Rich
DB	PostgreSQL 16 + pgvector
ORM/SQL	psycopg3 (native async-ready)
Validation	Pydantic v2
AI Protocol	MCP 1.26.0 via FastMCP
Embeddings	OpenAI text-embedding-3-small (optional)
Git/GitHub	bidirectional sync (DB \u2194 GitHub)
Logging	structlog (JSON / console)
Web	FastAPI + Uvicorn + Jinja2 + Cytoscape.js (optional extra)
IDE	VS Code extension + pygls LSP (optional extra)
Infra	Docker Compose + GitHub Actions CI
Quality	ruff + pytest (2031 tests, 93%+ coverage)

License

MIT