KGLite

The embedded openCypher engine for LLM-agent workloads. A knowledge graph that runs inside your Python process — load data, query with Cypher, hand the graph to an agent via the bundled MCP server. No server to run, no infrastructure to manage, one .kgl file to ship.

The engine is a pure-Rust crate (kglite); the wheel (pip install kglite) is a PyO3 wrapper around it. Bolt and MCP protocol servers are standalone Rust binaries that wrap the same engine. The .kgl file format is portable across all bindings.

Cypher first

Cypher is the primary query surface — agents already know it, and the engine targets honest openCypher semantics (three-valued NULL logic, conformance-tested against Neo4j on demand). DataFrame loaders add_nodes() / add_connections() exist to get bulk data in; once it’s in, you query with Cypher.

Embedded, in-process

No server, no network; import and go

LLM-agent surface

Bundled MCP server + describe() schema for system prompts

Cypher subset, honest semantics

Querying + mutations + text_score() for semantic search

In-memory by default

Mapped + disk modes for Wikidata-scale; in-memory is the design centre

Single-label nodes

Each node has exactly one type — see design decisions

One-file persistence

.kgl snapshots — copy, share, reload elsewhere

Rust-embeddable

Pure-Rust core; embed without PyO3 — see Rust track

See ROADMAP.md for what’s next.

Pick your track

  • Python guide — pip install kglite, then import kglite. The headline track; covers data loading, Cypher, the MCP server, datasets, agents.

  • Rust guide — embed the engine in a Rust binary (cargo add kglite). For graph-as-a-library use cases without the Python wheel.

  • Operators — running the Bolt server (Neo4j wire compat for cluster-aware drivers).

  • Reference — Cypher subset reference + fluent API reference + auto-generated Python API.

  • Concepts — architecture + design decisions + contributor docs.