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-0.2.5.tar.gz (511.2 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-0.2.5-py3-none-any.whl (395.9 kB view details)

Uploaded Python 3

File details

Details for the file matrx_graph-0.2.5.tar.gz.

File metadata

  • Download URL: matrx_graph-0.2.5.tar.gz
  • Upload date:
  • Size: 511.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for matrx_graph-0.2.5.tar.gz
Algorithm Hash digest
SHA256 95ff76c95d8cc5634bcde2bd34f1ca13e4757d1b2761fc8a80cfb19b146d8c9f
MD5 9d0f7b35db3b41c8b57d5673743e413c
BLAKE2b-256 1cb49ddd00e05a75ef6cf2d2aa34fe9391de713d1f55185ca30d12c2b3b9461c

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_graph-0.2.5.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-0.2.5-py3-none-any.whl.

File metadata

  • Download URL: matrx_graph-0.2.5-py3-none-any.whl
  • Upload date:
  • Size: 395.9 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-0.2.5-py3-none-any.whl
Algorithm Hash digest
SHA256 1991e2afc7502284a9e73fafd2b4e51e57e1305a38b14f6d6ac5fb503e4c1a46
MD5 8d77a755440586471a4ef4307372eb9e
BLAKE2b-256 1ffc22000c9d6771da97891e2065d02395393d367e3ecb16b75efe3aee64f356

See more details on using hashes here.

Provenance

The following attestation bundles were made for matrx_graph-0.2.5-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

0.2.32

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

This release

0.2.5 This release

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