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. 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.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.163)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for armanisadeghi from gravatar.com armanisadeghi

Credits

Author/Maintainer: Matrx

License

MIT License (MIT)

Requires

Python >=3.13

Provides Extra

otel postgres

Tags

agentic checkpoint dag matrx orchestration workflow

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

matrx_graph-0.2.32.tar.gz (550.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

matrx_graph-0.2.32-py3-none-any.whl (415.7 kB view details)

Uploaded Python 3

File details

Details for the file matrx_graph-0.2.32.tar.gz.

File metadata

  • Download URL: matrx_graph-0.2.32.tar.gz
  • Upload date:
  • Size: 550.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for matrx_graph-0.2.32.tar.gz
Algorithm Hash digest
SHA256 074199ba42cc57272f965599ec62208d434ec76a3d13b09108bfd3bb18b47bb7
MD5 1a0afa05d37f31568f6de639606f9712
BLAKE2b-256 40801868f4c49001fa233870bdbce36adefc4b94dbfb8fb1fd4dc47fe191c3be

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_graph-0.2.32.tar.gz:

Publisher: publish-package.yml on AI-Matrix-Engine/aidream

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file matrx_graph-0.2.32-py3-none-any.whl.

File metadata

  • Download URL: matrx_graph-0.2.32-py3-none-any.whl
  • Upload date:
  • Size: 415.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for matrx_graph-0.2.32-py3-none-any.whl
Algorithm Hash digest
SHA256 0a8771dcd23ee879ef98d504bdf19a8f6e58a47662fd9304b7c1662967c5c645
MD5 edd131a111d578ef764db5bcdf4ead8a
BLAKE2b-256 6f125f283cd8171edacb81dd5692bfb53659135a72e17f959103cba9d30bb4b0

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_graph-0.2.32-py3-none-any.whl:

Publisher: publish-package.yml on AI-Matrix-Engine/aidream

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.2.163

2 files

0.2.162

2 files

0.2.161

2 files

0.2.160

2 files

0.2.159

2 files

0.2.158

2 files

0.2.157

2 files

0.2.156

2 files

0.2.155

2 files

0.2.154

2 files

0.2.153

2 files

0.2.152

2 files

0.2.151

2 files

0.2.150

2 files

0.2.149

2 files

0.2.148

2 files

0.2.147

2 files

0.2.146

2 files

0.2.145

2 files

0.2.144

2 files

0.2.143

2 files

0.2.142

2 files

0.2.141

2 files

0.2.140

2 files

0.2.139

2 files

0.2.138

2 files

0.2.137

2 files

0.2.136

2 files

0.2.135

2 files

0.2.134

2 files

0.2.133

2 files

0.2.132

2 files

0.2.131

2 files

0.2.130

2 files

0.2.129

2 files

0.2.128

2 files

0.2.127

2 files

0.2.126

2 files

0.2.125

2 files

0.2.124

2 files

0.2.123

2 files

0.2.122

2 files

0.2.121

2 files

0.2.120

2 files

0.2.119

2 files

0.2.118

2 files

0.2.117

2 files

0.2.116

2 files

0.2.115

2 files

0.2.114

2 files

0.2.113

2 files

0.2.112

2 files

0.2.111

2 files

0.2.110

2 files

0.2.109

2 files

0.2.108

2 files

0.2.107

2 files

0.2.106

2 files

0.2.105

2 files

0.2.104

2 files

0.2.103

2 files

0.2.102

2 files

0.2.101

2 files

0.2.100

2 files

0.2.99

2 files

0.2.98

2 files

0.2.97

2 files

0.2.96

2 files

0.2.95

2 files

0.2.94

2 files

0.2.93

2 files

0.2.92

2 files

0.2.91

2 files

0.2.90

2 files

0.2.89

2 files

0.2.88

2 files

0.2.87

2 files

0.2.86

2 files

0.2.85

2 files

0.2.84

2 files

0.2.83

2 files

0.2.82

2 files

0.2.81

2 files

0.2.80

2 files

0.2.79

2 files

0.2.78

2 files

0.2.77

2 files

0.2.76

2 files

0.2.75

2 files

0.2.74

2 files

0.2.73

2 files

0.2.72

2 files

0.2.71

2 files

0.2.70

2 files

0.2.69

2 files

0.2.68

2 files

0.2.67

2 files

0.2.66

2 files

0.2.65

2 files

0.2.64

2 files

0.2.63

2 files

0.2.62

2 files

0.2.61

2 files

0.2.60

2 files

0.2.59

2 files

0.2.58

2 files

0.2.57

2 files

0.2.56

2 files

0.2.55

2 files

0.2.54

2 files

0.2.53

2 files

0.2.52

2 files

0.2.51

2 files

0.2.50

2 files

0.2.49

2 files

0.2.48

2 files

0.2.47

2 files

0.2.46

2 files

0.2.45

2 files

0.2.44

2 files

0.2.43

2 files

0.2.42

2 files

0.2.41

2 files

0.2.40

2 files

0.2.39

2 files

0.2.38

2 files

0.2.37

2 files

0.2.36

2 files

0.2.35

2 files

0.2.34

2 files

0.2.33

2 files

This release

0.2.32 This release

2 files

0.2.31

2 files

0.2.30

2 files

0.2.29

2 files

0.2.28

2 files

0.2.27

2 files

0.2.26

2 files

0.2.25

2 files

0.2.24

2 files

0.2.23

2 files

0.2.22

2 files

0.2.21

2 files

0.2.20

2 files

0.2.19

2 files

0.2.18

2 files

0.2.17

2 files

0.2.16

2 files

0.2.15

2 files

0.2.14

2 files

0.2.13

2 files

0.2.12

2 files

0.2.11

2 files

0.2.10

2 files

0.2.9

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.27

2 files

0.1.26

2 files

0.1.25

2 files

0.1.24

2 files

0.1.23

2 files

0.1.22

2 files

0.1.21

2 files

0.1.20

2 files

0.1.19

2 files

0.1.18

2 files

0.1.17

2 files

0.1.16

2 files

0.1.15

2 files

0.1.14

2 files

0.1.13

2 files

0.1.12

2 files

0.1.11

2 files

0.1.10

2 files

0.1.9

2 files

0.1.8

2 files

0.1.7

2 files

0.1.6

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

0.1.2

2 files

0.1.1

2 files

0.1.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page