kglite 0.5.86

A high-performance graph database library with Python bindings written in Rust

KGLite

A knowledge graph that runs inside your Python process. Load data, query with Cypher, do semantic search — no server, no setup, no infrastructure.

Two APIs: Use Cypher for querying, mutations, and semantic search. Use the fluent API (add_nodes / add_connections) for bulk-loading DataFrames. Most agent and application code only needs cypher().

Embedded, in-process	No server, no network; import and go
In-memory	Persistence via save()/load() snapshots
Cypher subset	Querying + mutations + text_score() for semantic search
Single-label nodes	Each node has exactly one type
Fluent bulk loading	Import DataFrames with add_nodes() / add_connections()

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

pip install kglite

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Quick Start — Cypher guide

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")

Bulk Loading from DataFrames — docs

import pandas as pd

users_df = pd.DataFrame({
    'user_id': [1001, 1002, 1003],
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [28, 35, 42]
})
graph.add_nodes(data=users_df, node_type='User', unique_id_field='user_id', node_title_field='name')

Blueprint Loading (CSV → Graph) — docs

import kglite

# Define a blueprint.json mapping CSVs to nodes and connections:
# {
#   "settings": {"root": "./data"},
#   "nodes": {
#     "Person": {
#       "csv": "persons.csv", "pk": "person_id", "title": "name",
#       "properties": {"age": "int", "city": "string"},
#       "connections": {
#         "junction_edges": {
#           "KNOWS": {"csv": "knows.csv", "source_fk": "person_id",
#                     "target": "Person", "target_fk": "friend_id"}
#         }
#       }
#     }
#   }
# }

graph = kglite.from_blueprint("blueprint.json")

Code Review (Parse a Codebase) — docs

from kglite.code_tree import build

graph = build(".")  # auto-detects pyproject.toml / Cargo.toml

# Find the most-called functions
graph.cypher("""
    MATCH (caller:Function)-[:CALLS]->(f:Function)
    RETURN f.name AS function, count(caller) AS callers
    ORDER BY callers DESC LIMIT 10
""")

# Explore code
graph.find("execute")              # search by name
graph.source("execute_query")      # read source code
graph.context("KnowledgeGraph")    # see struct with methods

AI Agent Integration — docs

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

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Documentation

Full documentation is available at kglite.readthedocs.io.

Topic	Description
Getting Started	Installation, quick start, DataFrame loading
Core Concepts	Nodes, relationships, selections, return types
Cypher Guide	Queries, mutations, transactions, parameters
Cypher Reference	Full reference for every clause and function
Data Loading	Fluent API, conflict handling, batch updates
Blueprints	Declarative CSV-to-graph loading via JSON
Querying	Filtering, traversal, schema introspection
Fluent API Reference	Full reference for every fluent method
Semantic Search	Embeddings, vector search, text_score()
AI Agents	MCP server, describe(), agent prompts
Spatial	Coordinates, geometry, distance, containment
Timeseries	Time-indexed data, ts_*() Cypher functions
Graph Algorithms	Shortest path, centrality, community detection, clustering
Import & Export	Save/load, GraphML, CSV, indexes, performance
Code Tree	Parse codebases into knowledge graphs
API Reference	Auto-generated from type stubs

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License

MIT — see LICENSE for details.