matrx-graph 0.1.7

Channel-based, super-step workflow orchestration engine for the Matrx ecosystem

matrx-graph

Channel-based, super-step workflow orchestration engine for the Matrx ecosystem. Think of it as a typed DAG + Pregel scheduler designed for mixing deterministic nodes with agent-style nodes in the same graph, with durable checkpoints and optional human-in-the-loop interrupts.

Install

pip install matrx-graph                # core engine, in-memory checkpointer
pip install "matrx-graph[postgres]"    # + durable Postgres checkpointer (via matrx-orm)
pip install "matrx-graph[otel]"        # + OpenTelemetry instrumentation

Python 3.13+ required. Depends on matrx-connect (for AppContext and Emitter), matrx-utils, pydantic.

Design pillars

• Channels + reducers, not mutable state. Nodes return partial updates; a reducer merges them into typed channels. Safe under parallel writes.
• Pregel super-steps. Each step: active nodes run in parallel via asyncio.TaskGroup → writes reduced into state → next wave scheduled. Predictable and debuggable.
• Durable checkpoints. Every super-step produces a checkpoint. Resume, fork, or time-travel from any point. MemoryCheckpointer for tests, PostgresCheckpointer (via matrx-graph[postgres]) for prod.
• JSON-only channel values. Pydantic models are dumped at write. Checkpoints round-trip losslessly.
• Typed nodes. Every NodeSpec declares input_schema, output_schema, config_schema (Pydantic). JSON Schema is auto-exported for UI form generation.
• Explicit edge semantics. Data / Control / Conditional / Error / Stream edges — no guessing from topology.
• Interrupts + Send. First-class primitives for human-in-the-loop pauses and dynamic fanout.

Design principle

Choose where to be deterministic and where to delegate to an agent.

A workflow can do everything an agent can (branch, loop, dispatch tools) but is more predictable, debuggable, and cheaper. An agent can do everything a workflow can but adapts to unexpected input. matrx-graph exists so authors draw that line consciously per node.

Usage sketch

from matrx_graph import (
    Definition, NodeDef, EdgeDef, EdgeKind,
    compile_graph, Scheduler, register, register_builtin_nodes,
)

register_builtin_nodes()

# A custom action — registered once, referenced by name in node definitions
@register("greet")
async def greet(ctx, inputs, config):
    return {"message": f"Hello, {inputs['name']}!"}

definition = Definition(
    nodes=[
        NodeDef(id="start", action="noop"),
        NodeDef(id="greet", action="greet"),
    ],
    edges=[
        EdgeDef(source="start", target="greet", kind=EdgeKind.Data),
    ],
)

graph = compile_graph(definition)
scheduler = Scheduler(graph)
result = await scheduler.run(inputs={"name": "world"})
print(result.channels["greet"])   # {"message": "Hello, world!"}

For a full example including conditional edges, dynamic ctx.send(...) fanout, checkpoints, and resume, see examples/self_validating_news.py.

Dependency posture

matrx-graph is a generic engine. It depends only on matrx-connect, matrx-utils, and pydantic. Optional postgres extra adds matrx-orm for durable checkpointing.

Domain-specific node packs (LLM, agent, scraper) live in their sibling packages (matrx-ai, matrx-scraper) and register executors with the engine at runtime via matrx_graph.registry. No hard import cycles.

Status

Phase 1 in progress — foundation + in-process executor. Not yet production-ready. The legacy root-level workflows/ and workflows_v2/ directories in the monorepo are being consolidated into this package.

Contributing

See CLAUDE.md for package-specific rules. This package lives in the aidream monorepo at github.com/AI-Matrix-Engine/aidream-current.

License

MIT.