kglite 0.8.15

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

KGLite — Lightweight Knowledge Graph for Python

KGLite is an embedded knowledge graph for Python: pip install, no server, no setup. It speaks Cypher, loads pandas DataFrames, and ships with the connective tissue for AI agents — an MCP server so Claude / Cursor / any MCP-capable LLM can query your graph as a tool, a describe() method that emits a compact XML schema for system prompts, and a code_tree parser that turns any source directory into a graph of functions, classes, calls, and imports across 9 languages.

Three storage modes scale from in-memory (millisecond queries on small graphs) to mmap-backed on disk (1 B+ edges, Wikidata-scale).

Why KGLite?

• Built for LLM agents — describe() XML schema, bundled MCP server, and an agent-oriented query surface (cypher(), graph.select(...).traverse(...)).
• Codebase → graph in one line — kglite.code_tree.build(".") parses Python, Rust, TypeScript, Go, Java, C#, C++, and more into Function / Class / Module nodes with CALLS / DEFINES / IMPORTS edges.
• Scales without leaving Python — in-memory for prototyping, mmap-backed for notebook-scale, disk-mode CSR for graphs too large for RAM. Same API across modes.
• Query with Cypher — MATCH, MERGE, OPTIONAL MATCH, aggregations, parameters, semantic search via text_score().
• DataFrames in, DataFrames out — bulk-load from pandas, query results as DataFrames.

Quick Start

pip install kglite

import pandas as pd
import kglite

# Three storage modes — pick by graph size:
#   default (in-memory)   — small/medium graphs, fastest queries
#   storage="mapped"      — mmap columns, RAM-friendly as you grow
#   storage="disk", path=…  — 100M+ nodes, Wikidata-scale, loaded lazily
graph = kglite.KnowledgeGraph()

# Bulk-load nodes from a DataFrame (also: add_nodes_bulk, from_blueprint,
# load_ntriples, or Cypher CREATE for ad-hoc inserts).
people = pd.DataFrame({
    "id":   ["alice", "bob", "eve"],
    "name": ["Alice", "Bob", "Eve"],
    "age":  [28, 35, 41],
    "city": ["Oslo", "Bergen", "Trondheim"],
})
graph.add_nodes(people, node_type="Person", unique_id_field="id", node_title_field="name")

# Bulk-load relationships the same way (also: add_connections_bulk,
# add_connections_from_source for auto-filter by loaded types).
knows = pd.DataFrame({"src": ["alice", "bob"], "tgt": ["bob", "eve"]})
graph.add_connections(knows, connection_type="KNOWS",
                      source_type="Person", source_id_field="src",
                      target_type="Person", target_id_field="tgt")

# 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 directly.
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

Codebase analysis

Parse Python, Rust, TypeScript, Go, Java, C#, and C++ into a graph of functions, classes, calls, and imports. Trace who-calls-what, find dead code, and review structure without leaving your editor. Pairs naturally with the MCP server so an agent can reason over your repo.

from kglite.code_tree import build

graph = build(".")                                # parse current directory
graph.cypher("""
    MATCH (f:Function)-[:CALLS]->(g:Function)
    RETURN g.name, count(f) AS callers
    ORDER BY callers DESC LIMIT 10
""")

Agentic AI — memory and tool use

Give an LLM a structured memory it can query. describe() emits a compact XML schema that fits in a system prompt, and the bundled MCP server exposes the whole graph as a Cypher tool — drop-in for Claude, Cursor, or any MCP-capable agent.

xml = graph.describe()                            # schema for the agent's context
prompt = f"You have a knowledge graph:\n{xml}\nAnswer via graph.cypher()."
# Or: python examples/mcp_server.py path/to/graph.kgl

RAG retrieval

Store documents, chunks, and entities together as one graph. Combine text_score() semantic similarity with Cypher structure — hybrid retrieval in one query, no second vector DB.

graph.cypher("""
    MATCH (c:Chunk)-[:IN_DOC]->(d:Document)
    RETURN c.text, d.title,
           text_score(c.embedding, $query_vec) AS score
    ORDER BY score DESC LIMIT 5
""", params={"query_vec": query_embedding})

Data exploration and analysis

Load CSVs or DataFrames, walk relationships, run graph algorithms (shortest path, centrality, community detection), and export — all from a notebook.

graph.add_nodes(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
""")

Examples

The examples/ directory has runnable, self-contained scripts covering each of the use cases above:

• code_graph.py — build a code knowledge graph from a source directory via code_tree.build. Produces Function, Class, Module, File nodes with CALLS, DEFINES, IMPORTS edges.
• legal_graph.py — end-to-end add_nodes / add_connections from pandas DataFrames, covering laws, regulations, and court decisions with citation relationships. Good template for adapting to your own domain.
• mcp_server.py — drop-in MCP server that exposes any .kgl file to an LLM (Claude, Cursor, …) as a Cypher query tool, with schema disclosure and code-graph–aware helpers.
• spatial_graph.py — declarative CSV→graph loading via a JSON blueprint; regions, facilities, and sensors with lat/lon coordinates and pipeline-path traversal queries.
• wikidata_disk.py — Wikidata-scale build + disk-mode storage; loads hundreds of millions of triples via load_ntriples into a mmap-backed graph.

Benchmarks

KGLite builds and queries Wikidata-scale graphs on a laptop. Measured with bench/wiki_benchmark.py on an M-series MacBook.

Ingest — full pipeline from compressed N-Triples to a queryable graph:

dataset	triples	nodes	edges	ingest	throughput	peak RAM
wiki100m	100 M	938 K	748 K	29 s	3.4 M triples/s	1.3 GB
wiki500m	500 M	5.6 M	6.7 M	157 s	3.2 M triples/s	5.2 GB
wiki1000m	1 B	14.7 M	15.4 M	395 s	2.5 M triples/s	7.0 GB

Reloading a saved 1 B-triple graph from disk (7 GB on-disk): 3.5 s.

Query latency on the 1 B-triple graph (mapped storage):

Cypher	wall
MATCH (n)-[:P31]->(:Q5) RETURN count(n) — typed aggregation	0.5 ms
MATCH (a)-[:P31]->(b)-[:P279]->(c) LIMIT 10 — 2-hop typed	0.9 ms
MATCH (a)-[:P31]->(b {nid:'Q64'}) RETURN a LIMIT 20 — pivot	1 ms
MATCH (a)-[:P31]->(:Q5) MATCH (a)-[:P27]->(c) LIMIT 10 — join	44 ms

Disk and mapped storage track within 1 % on build; mapped wins on query shapes backed by its in-memory inverted index, disk wins on unbounded typed traversals by staying on sorted-CSR mmap I/O.

No server, no tuning, same Python process as your code.

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.