KGLite — Lightweight Knowledge Graph for Python

An embedded, in-memory knowledge graph database for Python — built in Rust for speed, with a Cypher query engine, semantic search, and first-class support for RAG pipelines and AI agents. No server, no setup, no infrastructure. Just pip install kglite and go.

Why KGLite?

• Zero infrastructure — runs inside your Python process. No database server to install, configure, or maintain.
• Fast — Rust core (via PyO3 + petgraph) with zero-copy where possible. Load millions of nodes without leaving Python.
• Query with Cypher — familiar graph query language for pattern matching, mutations, aggregations, and traversals.
• Built for AI — semantic search with text_score(), schema introspection via describe(), and a ready-made MCP server for LLM tool use.
• DataFrames in, DataFrames out — bulk-load from pandas, query results as DataFrames. Fits naturally into data science workflows.

Quick Start

pip install kglite

import kglite

graph = kglite.KnowledgeGraph()

# Create nodes and relationships
graph.cypher("CREATE (:Person {name: 'Alice', age: 28, city: 'Oslo'})")
graph.cypher("CREATE (:Person {name: 'Bob', age: 35, city: 'Bergen'})")
graph.cypher("""
    MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
    CREATE (a)-[:KNOWS]->(b)
""")

# Query — returns a ResultView (lazy; data stays in Rust until accessed)
result = graph.cypher("""
    MATCH (p:Person) WHERE p.age > 30
    RETURN p.name AS name, p.city AS city
    ORDER BY p.age DESC
""")
for row in result:
    print(row['name'], row['city'])

# Or get a pandas DataFrame
df = graph.cypher("MATCH (p:Person) RETURN p.name, p.age ORDER BY p.age", to_df=True)

# Persist to disk and reload
graph.save("my_graph.kgl")
loaded = kglite.load("my_graph.kgl")

Use Cases

RAG & Retrieval Pipelines

Store documents, chunks, and entities as a knowledge graph. Use text_score() for semantic similarity search and Cypher for structured retrieval — combine both for hybrid RAG.

graph.cypher("""
    MATCH (c:Chunk)
    RETURN c.text, text_score(c.embedding, $query_vec) AS score
    ORDER BY score DESC LIMIT 5
""", params={"query_vec": query_embedding})

AI Agent Memory & Tool Use

Give LLM agents a structured, queryable memory. describe() generates a progressive-disclosure schema that agents can reason over, and the included MCP server exposes the graph as a tool.

xml = graph.describe()  # schema for agent context
prompt = f"You have a knowledge graph:\n{xml}\nAnswer using graph.cypher()."

Data Exploration & Analysis

Load CSVs or DataFrames, explore relationships, run graph algorithms (shortest path, centrality, community detection), and export results — all without leaving your notebook.

graph.add_nodes(data=users_df, node_type='User', unique_id_field='user_id', node_title_field='name')
graph.cypher("MATCH path = shortestPath((a:User {name:'Alice'})-[*]-(b:User {name:'Eve'})) RETURN path")

Codebase Analysis

Parse Python and Rust codebases into a knowledge graph with functions, classes, calls, and imports. Search, trace dependencies, and review code structure.

from kglite.code_tree import build
graph = build(".")
graph.cypher("MATCH (f:Function) RETURN f.name, f.file ORDER BY f.name")

Key Features

Feature	Description
Cypher queries	MATCH, CREATE, SET, DELETE, MERGE, aggregations, ORDER BY, LIMIT, SKIP
Semantic search	Vector embeddings + text_score() for similarity ranking
Graph algorithms	Shortest path, centrality, community detection, clustering
Spatial	Coordinates, WKT geometry, distance and containment queries
Timeseries	Time-indexed data with ts_*() Cypher functions
Bulk loading	Fluent API (add_nodes / add_connections) for DataFrames
Blueprints	Declarative CSV-to-graph loading via JSON config
Import/Export	Save/load snapshots, GraphML, CSV export
AI integration	describe() introspection, MCP server, agent prompts
Code analysis	Parse codebases via tree-sitter (kglite.code_tree)

Documentation

Full docs at kglite.readthedocs.io:

• Getting Started — installation, first graph, core concepts
• Cypher Guide — queries, mutations, parameters
• Semantic Search — embeddings, vector search
• AI Agents — MCP server, describe(), agent prompts
• API Reference — full auto-generated reference

Requirements

Python 3.10+ (CPython) | macOS (ARM/Intel), Linux (x86_64/aarch64), Windows (x86_64) | pandas >= 1.5

License

MIT — see LICENSE for details.