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.
For standalone Postgres, apply
matrx_graph/db/migrations/0100_baseline_post_reorg.sql, register the database
with matrx-orm, then call
matrx_graph.db.configure_standalone_db("<registered-name>"). Aidream hosts use
their generated package wiring instead.
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.
MemoryCheckpointerfor tests,PostgresCheckpointer(viamatrx-graph[postgres]) for prod. - JSON-only channel values. Pydantic models are dumped at write. Checkpoints round-trip losslessly.
- Typed nodes. Every
NodeSpecdeclaresinput_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.
1 maintainer
Release files for matrx-graph 0.2.172
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