SQLite Graph Ext – Graph database with Cypher queries (alpha)
I've been working on adding graph database capabilities to SQLite with support for the Cypher query language. As of this week, both CREATE and MATCH operations work with full relationship support. Here's what it looks like: import sqlite3 conn = sqlite3.connect(":memory:") conn.load_extension("./libgraph.so") conn.execute("CREATE VIRTUAL TABLE graph USING graph()") # Create a social network conn.execute("""SELECT cypher_execute(' CREATE (alice:Person {name: "Alice", age: 30}), (bob:Person {name: "Bob", age: 25}), (alice)-[:KNOWS {since: 2020}]->(bob) ')""") # Query the graph with…
In plain words
SQLite Graph Ext is an extension that adds graph database capabilities to SQLite using the Cypher query language. It allows users to create nodes and relationships, then query them with Cypher's pattern-matching syntax. The extension includes a complete execution pipeline with lexer, parser, logical planner, physical planner, and iterator-based executor. It is designed for developers who want graph database features within SQLite and currently supports CREATE and MATCH operations with full relationship support.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I've been working on adding graph database capabilities to SQLite with support for the Cypher query language. As of this week, both CREATE and MATCH operations work with full relationship support. Here's what it looks like: import sqlite3 conn = sqlite3.connect(":memory:") conn.load_extension("./libgraph.so") conn.execute("CREATE VIRTUAL TABLE graph USING graph()") # Create a social network conn.execute("""SELECT cypher_execute(' CREATE (alice:Person {name: "Alice", age: 30}), (bob:Person {name: "Bob", age: 25}), (alice)-[:KNOWS {since: 2020}]->(bob) ')""") # Query the graph with relationship patterns conn.execute("""SELECT cypher_execute(' MATCH (a:Person)-[r:KNOWS]->(b:Person) WHERE a.age > 25 RETURN a, r, b ')""") The interesting part was building the complete execution pipeline - lexer, parser, logical planner, physical planner, and an iterator-based executor using the Volcano model. All in C99 with no dependencies beyond SQLite. What works now: - Full CREATE: nodes, relationships, properties, chained patterns (70/70 openCypher TCK tests) - MATCH with relationship patterns: (a)-[r:TYPE]->(b) with label and type filtering - WHERE clause: property comparisons on nodes (=, >, <, >=, <=, <>) - RETURN: basic projection with JSON serialization - Virtual table integration for mixing SQL and Cypher Performance: - 340K nodes/sec inserts (consistent to 1M nodes) - 390K edges/sec for relationships - 180K nodes/sec scans with WHERE filtering Current limitations (alpha): - Only forward relationships (no `<-[r]-` or bidirectional `-[r]-`) - No relationship property filtering in WHERE (e.g., `WHERE r.weight > 5`) - No variable-length paths yet (e.g., `[r*1..3]`) - No aggregations, ORDER BY, property projection in RETURN - Must use double quotes for strings: {name: "Alice"} not {name: 'Alice'} This is alpha - API may change. But core graph query patterns work! The execution pipeline handles CREATE/MATCH/WHERE/RETURN end-to-end. Next up: bidirectional relationships, property projection, aggregations. Roadmap targets full Cypher support by Q1 2026. Built as part of Agentflare AI, but it's standalone and MIT licensed. Would love feedback on what to prioritize. GitHub: https://github.com/agentflare-ai/sqlite-graph Happy to answer questions about the implementation!
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Launched alongside, October 2025
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- SA
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AI · Oct 2025 · blog.shashanktomar.com
- ASAutism Simulator▲779
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Life & fun · Oct 2025 · autism-simulator.vercel.app
