graphon 0.2.0

Graph execution engine for agentic AI workflows.

Graphon

Graphon is a Python graph execution engine for agentic AI workflows.

The repository is still evolving, but it already contains a working execution engine, built-in workflow nodes, model runtime abstractions, integration protocols, and a runnable end-to-end example.

Highlights

• Queue-based GraphEngine orchestration with event-driven execution
• Graph parsing, validation, and fluent graph building
• Shared runtime state, variable pool, and workflow execution domain models
• Built-in node implementations for common workflow patterns
• Pluggable model runtime interfaces, including a local SlimRuntime
• HTTP, file, tool, and human-input integration protocols
• Extensible engine layers and external command channels

Repository modules currently cover node types such as start, end, answer, llm, if-else, code, template-transform, question-classifier, http-request, tool, variable-aggregator, variable-assigner, loop, iteration, parameter-extractor, document-extractor, list-operator, and human-input.

Quick Start

Graphon is currently easiest to evaluate from a source checkout.

Requirements

• Python 3.12 or 3.13
• uv
• make

Python 3.14 is currently unsupported because unstructured, which backs part of the document extraction stack, currently declares Requires-Python: <3.14.

Set up the repository

make dev
source .venv/bin/activate
make test

make dev installs the project, syncs development dependencies, and sets up prek Git hooks.

Run the Example Workflow

The repository includes a minimal runnable example at examples/graphon_openai_slim.

It builds and executes this workflow:

start -> llm -> output

To run it:

make dev
source .venv/bin/activate
cd examples/graphon_openai_slim
cp .env.example .env
python3 workflow.py "Explain Graphon in one short sentence."

Before running the example, fill in the required values in .env.

The example currently expects:

• an OPENAI_API_KEY
• a SLIM_PLUGIN_ID
• a local dify-plugin-daemon-slim setup or equivalent Slim runtime

For the exact environment variables and runtime notes, see examples/graphon_openai_slim/README.md.

How Graphon Fits Together

At a high level, Graphon usage looks like this:

1. Build or load a graph and instantiate nodes into a Graph.
2. Prepare GraphRuntimeState and seed the VariablePool.
3. Configure model, file, HTTP, tool, or human-input adapters as needed.
4. Run GraphEngine and consume emitted graph events.
5. Read final outputs from runtime state.

The bundled example follows exactly that path. The execution loop is centered around GraphEngine.run():

engine = GraphEngine(
    workflow_id="example-start-llm-output",
    graph=graph,
    graph_runtime_state=graph_runtime_state,
    command_channel=InMemoryChannel(),
)

for event in engine.run():
    ...

See examples/graphon_openai_slim/workflow.py for the full example, including SlimRuntime, SlimPreparedLLM, graph construction, input seeding, and streamed output handling.

Project Layout

• src/graphon/graph: graph structures, parsing, validation, and builders
• src/graphon/graph_engine: orchestration, workers, command channels, and layers
• src/graphon/runtime: runtime state, read-only wrappers, and variable pool
• src/graphon/nodes: built-in workflow node implementations
• src/graphon/model_runtime: provider/model abstractions and Slim runtime
• src/graphon/graph_events: event models emitted during execution
• src/graphon/http: HTTP client abstractions and default implementation
• src/graphon/file: workflow file models and file runtime helpers
• src/graphon/protocols: public protocol re-exports for integrations
• examples/: runnable examples
• tests/: unit and integration-style coverage

Internal Docs

• CONTRIBUTING.md: contributor workflow, CI, commit/PR rules
• examples/graphon_openai_slim/README.md: runnable example setup
• src/graphon/model_runtime/README.md: model runtime overview
• src/graphon/graph_engine/layers/README.md: engine layer extension points
• src/graphon/graph_engine/command_channels/README.md: local and distributed command channels

Development

Contributor setup, tooling details, CLA notes, and commit/PR conventions live in CONTRIBUTING.md.

CI currently validates commit messages, pull request titles, formatting, lint, and tests on Python 3.12 and 3.13. Python 3.14 is currently excluded because unstructured does not yet support it.

License

Apache-2.0. See LICENSE.