We were tired of writing raw Cypher — escaping quotes, zero autocomplete, refactoring nightmares — so we built GraphORM: a type-safe Python ORM for RedisGraph/FalkorDB using pure Python objects.

What it does Instead of fragile Cypher:

    query = """
    MATCH (a:User {user_id: 1})-[r1:FRIEND]->(b:User)-[r2:FRIEND]->(c:User)
    WHERE c.user_id <> 1 AND b.active = true
    WITH b, count(r2) as friend_count
    WHERE friend_count > 5
    RETURN c, friend_count
    ORDER BY friend_count DESC
    LIMIT 10
    """

You write type-safe Python:

    stmt = select().match(
        (UserA, FRIEND.alias("r1"), UserB),
        (UserB, FRIEND.alias("r2"), UserC)
    ).where(
        (UserA.user_id == 1) & (UserC.user_id != 1) & (UserB.active == True)
    ).with_(
        UserB, count(FRIEND.alias("r2")).label("friend_count")
    ).where(
        count(FRIEND.alias("r2")) > 5
    ).returns(
        UserC, count(FRIEND.alias("r2")).label("friend_count")
    ).orderby(
        count(FRIEND.alias("r2")).desc()
    ).limit(10)

Key features: • Type-safe schema with Python type hints • Fluent query builder (select().match().where().returns()) • Automatic batching (flush(batch_size=1000)) • Atomic transactions (with graph.transaction(): ...) • Zero string escaping — O'Connor and "The Builder" just work

Target audience • AI/LLM agent devs: store long-term memory as graphs (User → Message → ToolCall) • Web crawler engineers: insert 10k pages + links in 12 lines vs 80 lines of Cypher • Social network builders: query "friends of friends" with indegree()/outdegree() • Data engineers: track lineage (Dataset → Transform → Output) • Python devs new to graphs: avoid Cypher learning curve

Data insertion: the real game-changer

Raw Cypher nightmare: queries = [ """CREATE (:User {email: "alice@example.com", name: "Alice O\\'Connor"})""", """CREATE (:User {email: "bob@example.com", name: "Bob \\"The Builder\\""})""" ] for q in queries: graph.query(q) # No transaction safety!

GraphORM bliss: alice = User(email="alice@example.com", name="Alice O'Connor") bob = User(email="bob@example.com", name='Bob "The Builder"') graph.add_node(alice) graph.add_edge(Follows(alice, bob, since=1704067200)) graph.flush() # One network call, atomic transaction

Try it in 30 seconds pip install graphorm

    from graphorm import Node, Edge, Graph

    class User(Node):
        __primary_key__ = ["email"]
        email: str
        name: str

    class Follows(Edge):
        since: int

    graph = Graph("social", host="localhost", port=6379)
    graph.create()
    alice = User(email="alice@example.com", name="Alice")
    bob = User(email="bob@example.com", name="Bob")
    graph.add_node(alice)
    graph.add_edge(Follows(alice, bob, since=1704067200))
    graph.flush()

GitHub: https://github.com/hello-tmst/graphorm

We'd love honest feedback: • Does this solve a real pain point for you? • What's missing for production use? • Any API design suggestions?