kgmd 0.1.0

A CLI that builds a knowledge graph from markdown files and exposes it via MCP

kgmd

A CLI that builds a knowledge graph from a directory of markdown files and exposes it via MCP.

• Extracts entities and relations using any LLM (via litellm)
• Resolves duplicate entities using local embeddings + LLM verification
• Induces a typed schema from the extracted data
• Stores everything in a single SQLite file (powered by sqlite-vec)
• Exposes the graph via CLI queries and an MCP server

Install

pip install kgmd

Or with uv:

uv tool install kgmd

Requirements

• Python 3.10+
• An API key for any LLM provider supported by litellm (OpenRouter, OpenAI, Anthropic, etc.)
• Embeddings run locally by default via fastembed (no API key needed)

Quickstart

# Initialize a corpus
cd my-notes/
kgmd init

# Set your LLM API key
export OPENROUTER_API_KEY="sk-..."

# Build the knowledge graph (extract -> resolve -> induce)
kgmd build

# Query
kgmd entities
kgmd relations
kgmd find "machine learning"
kgmd entity "Brian Anderson"
kgmd neighbors "Brian Anderson" --depth 2
kgmd path "Brian Anderson" "Acme Corp"

# Export
kgmd export --format graphml --output graph.graphml

# View induced schema
kgmd schema

# Corpus statistics
kgmd stats

How it works

kgmd build runs three stages:

1. Extract -- Each markdown file is chunked and sent to an LLM, which returns structured JSON with entities (people, organizations, projects, etc.) and relations between them.
2. Resolve -- Entity mentions are embedded locally, clustered by cosine similarity, and duplicate clusters are verified by the LLM before merging.
3. Induce -- Aggregate statistics about entity types and relation predicates are sent to the LLM, which produces a typed YAML schema with hierarchies.

All state lives in .kgmd/graph.db, a single SQLite file. Re-running kgmd build is incremental -- unchanged files are skipped.

MCP Server

kgmd mcp launches an MCP server over stdio, exposing 7 tools:

Tool	Description
search	Semantic search over chunks
get_entity	Full entity record with mentions and relations
list_entities	List entities, optionally filtered by type
get_neighbors	Subgraph traversal around an entity
find_path	Shortest path between two entities
list_relations	List relations with optional filters
get_schema	The current induced schema

Claude Desktop setup

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "kgmd": {
      "command": "kgmd",
      "args": ["mcp"],
      "cwd": "/path/to/your/corpus"
    }
  }
}

Configuration

Per-corpus config lives in .kgmd/config.yaml. Global defaults in ~/.config/kgmd/config.yaml (or the platform equivalent). Corpus config overrides global.

embedding:
  backend: fastembed                    # or "litellm" for API embeddings
  model: BAAI/bge-small-en-v1.5

llm:
  model: openrouter/anthropic/claude-sonnet-4-5
  temperature: 0.0
  max_tokens: 4096
  timeout_seconds: 120

chunking:
  max_chars: 4000
  overlap_chars: 200
  split_on: paragraph                   # or "heading", "fixed"

extraction:
  max_entities_per_chunk: 30
  max_relations_per_chunk: 30
  retry_on_parse_failure: 2

resolution:
  similarity_threshold: 0.85
  llm_verify_clusters: true
  max_cluster_size: 10

induction:
  include_attribute_summary: true
  hierarchy_depth: 3

Export formats

kgmd export --format jsonld   # JSON-LD with schema.org context
kgmd export --format cypher   # Cypher CREATE statements (Neo4j)
kgmd export --format graphml  # GraphML (Gephi, yEd)

Development

git clone https://github.com/2lines/kgmd.git
cd kgmd
pip install -e .
make test    # run tests
make lint    # ruff check
make format  # ruff format

Note: Your Python must be built with SQLite extension loading enabled. If using pyenv:

LDFLAGS="-L$(brew --prefix sqlite)/lib" \
CPPFLAGS="-I$(brew --prefix sqlite)/include -DSQLITE_ENABLE_LOAD_EXTENSION" \
PYTHON_CONFIGURE_OPTS="--enable-loadable-sqlite-extensions" \
pyenv install 3.12

License

MIT