knowledge-graph-rdbms

An embedded knowledge graph you own completely — in one SQLite file.

SQLite-native · zero-dependency core · MCP-ready · event-sourced · reversible.

Model anything as entities and relationships — agent memory, app ontologies, declarative datasets, "SQLite but my data is a graph" — without running Neo4j, RDF, Docker, or a separate graph service. No Cypher, no JVM, no server: five tables and one file you can copy, inspect, and version.

pip install "knowledge-graph-rdbms[mcp]"

# write a fact — auto-creates the graph in one SQLite file
kg node add person:ada --kind Person --name "Ada Lovelace"

# every write is logged; tail the log and roll any event back by id
kg events -n 5
kg revert <event_id>

# rebuild the graph as of any past moment — literal time travel
kg replay --upto 2026-01-01T00:00:00

# expose the whole graph to an AI agent over MCP
kg serve

   Agent / CLI / Python
            │
     gated + logged writes
            │
   SQLite label property graph
            │
    replay · revert · time-travel

Python 3.10+ · MIT · zero-dependency core · library + CLI + MCP

The world isn't rows in a table — it's things, the kinds of things they are, and how they relate. A label property graph captures exactly that, and not much more: nodes (entities), typed edges (relationships), labels (sets), and JSON properties (everything else). There's no schema to design up front; meaning accretes as facts, and the shape stays as flexible as the domain it describes.

That flexibility is what makes it a natural substrate for AI agents. Hand an agent an MCP connection to this graph and it can do what agents are uniquely good at: read a domain, model what it learns, connect ideas, and reason over structure instead of prose. Every write is gated, attributed, and appended to an event log — so an agent can reshape the graph freely while you keep the receipts: audit it, replay it to any point in time, or roll a change back with one command. A memory that records why, not just what — in one embeddable file that travels wherever the agent runs: a laptop, a CI job, a serverless function, a Pi.

Small enough to hold in your head. Flexible enough to model anything.

Contents

• Where it fits
• Why not X?
• The idea in 30 seconds
• Design philosophy
• The data model
• Architecture: three front doors, one engine
• Many ontologies: one control plane
• Discovery: read the schema before you query
• Cross-ontology: federation and the backbone
• Event sourcing: the graph is a projection
• The safety gate: invariants vs. policy
• Install
• Quickstart
• Performance
• RDF interop: export, SPARQL, RDF-star
• Command reference
• Project layout
• [Development](

... View full README on GitHub