KGLite is an embedded, Cypher-queryable knowledge graph for Python and Rust, built so the same graph can serve an application, an analyst, or an LLM agent. The Python wheel has no required Python runtime dependencies; the graph engine runs in-process without an external database service. Every crate ships under MIT. If you are embedding a graph engine in something you distribute, see Licensing and embedded distribution.

pip install kglite             # the DataFrame walk-through below assumes pandas
pip install fastembed          # (or sentence-transformers) bring-your-own embedder for text_score()

import pandas as pd
import kglite

# Three storage modes, picked by graph size: default (in-memory) is fastest,
# storage="mapped" mmaps columns as you grow, storage="disk", path=… goes to
# 100M+ nodes, Wikidata-scale, loaded lazily.
graph = kglite.KnowledgeGraph()

# Bulk-load nodes from a DataFrame.
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.
knows = pd.DataFrame({"src": ["alice", "bob"], "tgt": ["bob", "eve"]})
graph.add_relationships(knows, connection_type="KNOWS",
                      source_type="Person", source_id_field="src",
                      target_type="Person", target_id_field="tgt")

# Query: returns a ResultView; eligible projections stay lazy until accessed.
for row in graph.cypher("""
    MATCH (p:Person) WHERE p.age > 30
    RETURN p.name AS name, p.city AS city
    ORDER BY p.age DESC
"""):
    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 and reload. save() is atomic + fsync by default (crash-safe, no torn
# file); load() raises a typed kglite.FileFormatError on a corrupt file.
graph.save("my_graph.kgl")
loaded = kglite.load("my_graph.kgl")
blob = graph.to_bytes(); loaded = kglite.from_bytes(blob)   # or without a path

# Immutable, lock-free snapshot: concurrent snapshot.cypher(...) from many threads.
snapshot = graph.freeze()

# No data yet? A realistic demo graph in one line (bundled, no extra deps):
demo = kglite.graphgen("medium")               # ~25k nodes, ready to query

Then hand the same file to an agent. The MCP server is bundled in the wheel:

kglite-mcp-server --graph my_graph.kgl

→ MCP servers guide · CLI guide. Prefer a runnable file? examples/csv_to_graph.py loads real CSVs end to end.

Large agent results are bounded with targeted expansion. MCP offers session-local controls; CLI --format agent retrieves retained values later without rerunning the query. JSON and CSV remain complete. See Bounded agent responses.

Two guides cover most first sessions:

• Getting Started: install, first graph, storage choices
• AI agents: hand a graph to an LLM agent: describe() prompts, semantic search, MCP

Everything else is linked where it comes up, and the Documentation section at the bottom indexes all five tracks.

Three things the graph does that you would otherwise build yourself.

describe(): progressive-disclosure schema for LLM context windows. One call returns a schema sized for a prompt, not for a DBA: the inventory switches between four detail tiers as the graph's type count grows (full inline detail under 16 core types, a compact listing, a top-50 listing, then a statistical summary with a search hint), each type carrying size, complexity, and capability flags (ts, geo, loc, vec for timeseries, geometry, location, and embeddings). The declared ontology's is_a class forest comes with it, and on graphs small enough to sample, so do join-candidate hints: unconnected types sharing an identically-named, type-compatible property with overlapping values. Serve it over MCP with skills: true and the tool arrives with methodology attached, gated by applies_when predicates to what the graph actually contains (a non-code graph never sees code-tool guidance), so the agent comes pre-loaded with how to use your graph rather than discovering it through trial-and-error. → AI Agents guide.

As-of queries over history. Declare the two bound properties of each dated type once, and one prefix answers "how did the world look on ⟨date⟩?" for every node and relationship a statement touches:

FOR VALID_TIME AS OF date('1999-06-30')
MATCH (l:Licence)-[:HAS_OPERATOR]->(c:Company)
RETURN c.title

Move the date and the answer moves with it: the operator of record in 1999. No hop can be left undated (paths, graph algorithms and vector and BM25 ranking see only what was valid then); cypher(valid_at=…), the MCP tools and Java's ValidAt write the same prefix. → Valid-time guide.

A declared ontology that gates the build. define_ontology() records what must hold: domain and range over an is_a class forest, required edge properties, property types, cardinality. Each check carries its own enforcement level, so a document referenced from a blueprint fails the build on an error-level breach, with every violated rule counted and no output graph written, while CALL ontology_audit() scores the same declarations against a live graph. Observe → fix → enforce is configuration, not code review. → Ontology guide.

kglite-mcp-server --graph path/to/graph.kgl

Reach for it when you want a graph kept warm across many calls. The server exposes cypher_query, graph_overview, schema introspection, and structural validators over MCP stdio, plus source-file read/search tools when a valid source_root is configured. Drop it into Claude Desktop, Cursor, or another MCP-capable client and any KGLite graph is queryable. Code-graph construction, repository cloning, and code-watch workflows belong to codingest-mcp, which embeds this same graph-serving surface.

A second MCP surface is visual: while kglite-visual serves a .kgl in a browser window, the same port speaks MCP, so an agent can put a Cypher result on screen, expand it, and re-lay it out: the agent drives the window you are watching. → Agents and MCP.

When you register it, point command at the absolute path to the binary (/abs/path/to/venv/bin/kglite-mcp-server), not a bare name: a bare command can silently launch an older PATH-shadowing install. Then confirm it with kglite-mcp-server --selftest --graph path/to/graph.kgl, which drives a real handshake and prints green/red per capability.

Two ready-made code-intelligence recipes ship in examples/; run both under codingest-mcp: open_source_workspace_mcp.yaml (repo_management('org/repo') clones and builds a code graph on demand) and local_code_review_mcp.yaml (set_root_dir(path) swaps roots, watch-mode auto-rebuilds).

→ MCP server operations.

Drop <basename>_mcp.yaml next to the graph (e.g. wikidata_mcp.yaml beside wikidata.kgl) and the server auto-loads it at boot.

name: Wikidata Explorer
source_root: /path/to/related/source        # exposes read/grep/list
skills: true                                # load bundled + project tool guidance
trust:
  allow_embedder: true
extensions:
  embedder: { library: fastembed, model: BAAI/bge-small-en-v1.5 }  # enables text_score()
  csv_http_server: true                              # bulk CSV exports
tools:                                               # inline parameterised Cypher
  - name: who_invented
    cypher: |
      MATCH (i:Q5)-[:P61]->(t {label:$thing})
      RETURN i.label LIMIT 5

skills: true composes four layers of per-tool methodology (kglite-bundled defaults, skills the served .kgl carries about itself, operator-declared domain packs, and your project's <basename>.skills/*.md overrides), so a graph can ship its own guidance. → MCP server guide.

The same agent-facing surface works whether the graph holds legal precedents, a Wikidata slice, a SQL warehouse, a RAG corpus, or a parsed codebase.

• 🏛️ Domain knowledge for agents. Legal precedents + citations, regulatory rules, medical ontologies, manufacturing BOMs, scientific catalogues: anything with structure becomes a queryable graph an MCP-capable agent can reason over. See the legal-graph example for a Norwegian-Supreme-Court walk-through.
• 📊 Business data → queryable graph. Any tabular source (SQL, CSV, Parquet, REST API responses, pandas DataFrames) goes straight in via add_nodes(df, ...) and add_relationships(df, ...). Layer a graph on your warehouse and the agent reasons over the relationships without you writing a server. → Data Loading guide.
• 🌐 Public datasets. Loaders for SEC EDGAR filings, Wikidata (the full latest-truthy RDF dump), and Sodir petroleum data live in kglite-datasets, each handling the fetch + build + cache cycle; kglite's mapped and disk storage then query graphs that don't fit in RAM, up to the 124M-node / 861M-edge Wikidata graph on a 16 GB laptop. The core engine itself needs no network access.
• 📚 RAG with structure. Documents, chunks, entities, and the edges between them in one graph. Combine text_score() vector similarity with Cypher traversal ("find court cases semantically similar to my fact pattern, then walk one hop to related precedents"): hybrid retrieval in one query, no second vector DB, scaling with an opt-in HNSW index (build_vector_index()). → Semantic Search guide.
• 🔎 Keyword and meaning in one ranking. An opt-in BM25 lexical index (build_text_index() + text_bm25()) finds the exact term an embedding blurs away, and score_fuse() blends it with the vector lane in a single Cypher query, with no second search service and no merge step in your code. → Text Search guide.
• 📂 Codebase analysis. The codingest builder parses 14 languages into Function / Class / Module / Route nodes with web-framework route detection (Flask, FastAPI, Django), from any git revision or several merged into one multi-revision graph for structural diffs. kglite serves and queries those graphs; the builder lives in the codingest project.
• 🤝 A shared graph as an agent contract. One .kgl as the two-way contract between collaborating agents: ownership layers (define_schema(layer=…) + add_nodes(managed_reload=True)) separate batch-rebuilt types from live agent-mutated ones, role-scoped writes (cypher(..., write_scope=[...])) fence what each agent may touch, a verbatim instructions slot (set_instructions) leads describe(), and CALL ready_set(...) hands out the next actionable work. These are opt-in guards, not an enforced perimeter; the exact boundaries, and what each does not cover, are in the MCP servers guide.
• 🧠 Markdown knowledge bases & agent memory. kglite.okf.build(dir) ingests an Open Knowledge Format bundle (or a Claude memory dir, skills folder, or Obsidian vault) into a graph: frontmatter → node properties, markdown links → typed edges. Then cluster it (CALL leiden), find stale notes, surface dangling references: the query engine OKF itself doesn't ship. Vault spec: VAULT.md. → OKF guide.

Why Cypher? Questions over connected data (which insiders sold this stock, who sits on two boards, what cites this case) are pattern matches. In SQL they become multi-table joins; in Cypher the pattern is the query, and it pays off most when the data has real structure and your questions traverse it:

-- Insider sells, most recent first
MATCH (t:InsiderTransaction {direction: 'sale'})-[:BY_INSIDER]->(p:Person)
MATCH (t)-[:IN_COMPANY]->(c:Company)
RETURN p.title, c.title, t.shares, t.price_per_share
ORDER BY t.transaction_date DESC LIMIT 10

→ Cypher guide · Cypher reference.

Every wrapper drives the same engine over the same .kgl files with the same Cypher. Pick the doorway that matches your stack; a graph built through any of them is readable through all of them.

The engine itself is a pure-Rust crate (crates/kglite) packaged for Python via pip install kglite; the shell, Bolt-server, and MCP-server binaries are sibling crates wrapping it. See Use from Rust to build against it without the wheel. The wheel also installs the kglite command, a sqlite3-style REPL: kglite app.kgl opens a Cypher prompt with .import, .dump, .schema, multi-line input, and tab-completion. The operators index has a decision table for the server-shaped doorways.

kglite is the engine. Four companion projects surround it, each released and versioned on its own cadence. Three build graphs it serves; one looks at them:

• codingest parses codebases into code graphs (14 languages, web-framework route detection). Build with it, query the .kgl here.
• kglite-datasets carries fetch-build-cache loaders for public registries (SEC EDGAR, Wikidata, Sodir).
• sonagram turns a local music library into a kglite knowledge graph via sonara audio analysis (tempo, energy, mood, key); AI agents curate playlists over it through a bundled skill and CLI (pip install sonagram).
• kglite-visual opens a .kgl in a browser (pip install kglite-visual, then kglite-visual graph.kgl), landing on the type-level meta-graph so a 100M-node file still has an entry screen; render draws the same views headlessly and export writes GraphML, GEXF, CSV, or JSON. No required runtime dependencies.

("manual" = expressible in application code or a WHERE clause, but no engine primitive. The KGLite row refers specifically to its class-forest ontology, audit scorecard, and build gate; the other engines have their own schema and constraint capabilities.)

Pick KGLite when you want one embedded package combining Python and pure-Rust Cypher APIs with a bundled MCP binary, prompt-shaped describe(), agent-contract primitives (role-scoped writes, ownership layers, set_instructions, CALL ready_set(...)), a declared ontology with build-time data-quality gates and audit scorecards, and as-of temporal filtering (valid_at) over dated edges and lifecycle windows, plus companion projects that build code and public-registry graphs it serves. Pick LadybugDB when columnar analytical scans and its broader language ecosystem are the priority; it also provides Rust bindings and a separately installed MCP server. Pick NetworkX when you need its enormous graph-algorithm library and your data fits in RAM. Pick rustworkx when you want a Rust-backed Python graph API with no query language. Pick Neo4j Embedded when you need a Java-embedded DBMS with the broader Neo4j platform.

📊 Benchmarks →: wall-to-wall time per topic (load, filter/aggregate, traversal, pathfinding, algorithms, mutations) against other embedded graph engines, NetworkX, rustworkx, igraph, and DuckDB on one shared synthetic graph. Reproduce with python benchmarks/benchmark.py; maintainer-only storage and release-regression probes live under tests/benchmarks/.

Two shapes, both supported, with different guarantees. Knowing which one you are building saves a lot of argument later.

• Derived index: the authoritative copy lives elsewhere (a warehouse, an API, a repo) and the graph is a rebuildable projection you query. Most kglite deployments are this, and it is the cheapest correct answer. → Derived index guide.
• Primary store: the graph is the authoritative copy, with crash-safe open() for the in-memory and mapped backends (disk checkpoints on save()), atomic statements, snapshot isolation for readers, and UNIQUE / NOT NULL / node-key constraints enforced on every write path including the bulk loaders. One process owns the writes; the scope statement lists the limits rather than softening them. → Primary store: scope and limits.

Valid time is a declared property of the graph, not a filter you remember to write, so one instant governs every hop, path, algorithm and ranking in every binding, and recording time is modelled beside it in the same embedded engine. Org charts, licence tables and price lists keep history: each fact is valid over a period, and the usual question is "as of when?". Declare a type's interval properties once (closed or half-open); after that one prefix, or valid_at= on any binding, answers as if the graph held only what was valid then, on every hop, path, algorithm and ranking.

Loading is the declaration: name the two bound columns and the convention, and the type is temporal from then on (set_temporal(), CALL db.temporal.declare and a blueprint's temporal key do the same for data already loaded):

memberships = pd.DataFrame({
    "employee":   ["ada", "ada", "ben"],
    "team":       ["data", "platform", "platform"],
    "valid_from": ["2020-03-01", "2024-06-01", "2022-01-01"],
    "valid_to":   ["2024-06-01", None, None],         # None = still valid
})
graph.add_relationships(memberships, "MEMBER_OF", "Employee", "employee", "Team", "team",
                        column_types={"valid_from": "validFrom", "valid_to": "validTo"},
                        convention="half_open")       # a transfer ends where the next begins

Rows on a declared relationship type are versions, so a later load of the same pair with a new interval adds a version rather than overwriting. Then ask as of a date:

// Every hop sees only what was valid on that day; no hop can be left undated
FOR VALID_TIME AS OF date('2023-06-30')
MATCH (e:Employee)-[:MEMBER_OF]->(t:Team)-[:PART_OF]->(d:Department) RETURN d.title, count(e)
// Per-element tests, when only one side of a pattern is dated
MATCH (e:Employee)-[m:MEMBER_OF]->(t:Team) WHERE valid_at(m, date('2024-06-30')) RETURN e.title, t.title
MATCH (e:Employee)-[m:MEMBER_OF]->(t:Team) WHERE valid_during(m, date('2024-01-01'), date('2024-12-31')) RETURN e.title, t.title

graph.cypher(q, valid_at="2023-06-30"), the fluent graph.date("2023-06-30") context, the MCP tools and Java's ValidAt write the same prefix, and every write onto a declared type is checked against its declaration. Recording time ("as known when?") is a second pair of bounds, such as recorded_from / recorded_to, that you load beside the first and test in the query; the bitemporal guide shows the full pattern on an HR change feed with a runnable example.

• Valid-time guide: declarations, as-of queries, the fluent context, modelling history, scale.
• Bitemporal data: late-recorded changes and daily deliveries, as-known-at, lineage, the feature checklist.
• Cypher reference: FOR VALID_TIME, valid_at() / valid_during(), db.temporal.*.

kglite is MIT-licensed throughout: every crate in the workspace ships under MIT. No separate commercial tier, no development/production distinction, and no copyleft obligation attached to shipping it: if you can use kglite, you can distribute it inside your own product.

One honest qualification about the default build: the optional fastembed backend is off by default everywhere, so neither the published wheel nor the default MCP-server binary contains it, and a --features fastembed build pulls one transitive MPL-2.0 crate (option-ext, four dependencies down). The reviewed policy is in dependency licences.

Short patterns for the most-common shapes. Each is self-contained.

Vector similarity (text_score()) and Cypher pattern matching in one query, with a bring-your-own embedder passed to g.set_embedder(...):

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

→ Semantic Search guide.

Fifteen built-in CALL procedures find the gaps normal queries don't show: orphan nodes, missing required edges, two-step cycles, duplicate titles, parallel edges, cardinality violations, more.

# Wellbores in our sodir graph that lack a production licence
graph.cypher("""
    CALL missing_required_edge({type: 'Wellbore', edge: 'IN_LICENCE'}) YIELD node
    RETURN node.id, node.title
""")

missing_required_edge and missing_inbound_edge validate the (type, edge) direction against the graph's actual schema and refuse to execute when misused. → Procedure examples and discovery.

Shortest path (BFS or Dijkstra), centrality, community detection, and clustering are Cypher-callable: shortestPath((a)-[*]-(b)), CALL leiden, CALL pagerank. → Graph algorithms guide · Traversal patterns · Recipes index.

The same engine is available as a pure-Rust crate. Embed it in a Rust binary without the Python wheel in your build:

# Cargo.toml
[dependencies]
kglite = "0.19"

use kglite::api::{io::load_file, session, Value};
use std::collections::HashMap;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let graph = load_file("my_graph.kgl")?;     // same .kgl as Python writes
    let params = HashMap::new();
    let opts = session::ExecuteOptions::eager(&params);
    let query = "MATCH (p:Person) RETURN p.name LIMIT 5";
    let outcome = session::execute_read(&graph, query, &opts)?;
    for row in &outcome.result.rows {
        if let Some(Value::String(name)) = row.first() {
            println!("{name}");
        }
    }
    Ok(())
}

Zero PyO3 in the dependency tree: cargo tree -p your-crate | rg pyo3 → empty. The Bolt server (crates/kglite-bolt-server) and the Rust MCP server (crates/kglite-mcp-server) are standalone binaries on the same engine. → Rust quickstart · embedding guide · session abstraction · docs.rs · Operators guide.

For Java, an official binding is on Maven Central: io.github.kkollsga:kglite (Panama/FFM over the C ABI, natives bundled; see kglite-java/README.md). For other non-Rust bindings (Go via cgo, JavaScript via napi, .NET via P/Invoke), crates/kglite-c exposes the engine through a stable C ABI covering lifecycle, sessions, Cypher, results, persistence, and embedders, plus a cbindgen-generated kglite.h. → C ABI design · implementing a binding (cgo / napi / JNI worked examples).

The examples/ directory has runnable, self-contained artifacts:

• csv_to_graph.py: pd.read_csv → add_nodes / add_relationships on a tiny org chart. The fastest way in.
• legal_graph.py: end-to-end pandas → graph with laws, regulations, court decisions, citation edges.
• incremental_update.py: merge a second snapshot with add_nodes(conflict_handling='update').
• bitemporal_org_chart.py: valid time and a modelled recording time on an HR change feed, applied in one transaction.
• spatial_graph.py: declarative CSV→graph loading via a JSON blueprint; lat/lon coordinates and pipeline-path traversal.
• crates/kglite-mcp-server/: a Rust-native single-binary MCP server (rmcp + the mcp-methods framework), the reference for layering domain-specific tools when a manifest isn't enough.

→ Recipes index.

Full docs at kglite.readthedocs.io, in five tracks by audience, each with its own index:

• Python (pip install kglite): getting started, then one guide per subject. Load: data loading · inline records · blueprints · import/export · structured data · schema migrations. Query: Cypher · fluent API · traversal and hierarchy · graph algorithms · semantic search · text search · spatial · timeseries · valid time · ontology · recipes. Ship: durable apps · derived index · primary store · OKF ingestion · help-vault lifecycle · AI agents · MCP servers · MCP skills.
• Rust (cargo add kglite): quickstart, embedding, sessions, C ABI · docs.rs.
• Operators: running the Bolt, MCP, and CLI front-ends.
• Reference: the supported Cypher subset, the fluent API, the auto-generated Python API.
• Concepts: architecture, design decisions, Cypher conformance, concurrency.

Looking at a graph rather than querying it is documented next door, at kglite-visual.readthedocs.io: getting started · agents and MCP · Python API · render.

Quick reference to the feature set; each row links into the appropriate guide.

CPython 3.10+ | macOS (arm64/x86_64), Linux (glibc/musl; x86_64 and best-effort aarch64), Windows (x86_64). The base wheel has no Python runtime dependencies; integrations install their named extras. See the artifact support policy for tested/build-only tiers, libc floors, PyPy status, and source-build fallback.

KGLite is beta software and remains pre-1.0. Any release, including a patch, may make an intentional breaking source-API change, documented in the changelog with migration guidance. Review the changelog before upgrading. Saved graph files have a separate format lifecycle: a release either reads an older format or refuses it with an explicit rebuild/migration error; see CHANGELOG.md.

Every change runs a cross-storage parity matrix and a differential Cypher corpus: the same query must return the same rows on the in-memory, mmap, and disk backends, and again with every optimiser pass disabled. → Cypher conformance.

MIT. See LICENSE, and Licensing and embedded distribution for what that means when kglite ships inside a product you distribute.