graphon 0.5.0

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
• DSL import support with Slim-backed LLM nodes
• 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. make test is the progressive local validation entrypoint: it formats, applies lint fixes, runs ty check, and then runs pytest.

Run the Example Workflows

The repository includes minimal runnable Slim LLM examples at examples/slim_llm.

Both versions execute this workflow:

start -> llm -> answer

To run it:

make dev
source .venv/bin/activate
cd examples/slim_llm
cp credentials.example.json credentials.json
python3 dsl.py "Reply with only the word Graphon."
python3 code.py "Reply with only the word Graphon."

Before running the example, fill in the required values in credentials.json.

The example currently expects:

• OpenAI-compatible model credentials in model_credentials
• slim.mode set to either local or remote
• dify-plugin-daemon-slim in PATH, SLIM_BINARY_PATH, or a local slim binary in the example directory
• for remote mode, daemon_addr and daemon_key

For the exact credential shape and runtime notes, see examples/slim_llm/README.md.

How Graphon Fits Together

At a high level, direct 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.

For Dify DSL documents, use graphon.dsl.loads() to build the engine from the workflow YAML and credentials. The resulting engine uses the DSL Slim adapter for LLM nodes:

engine = loads(
    dsl,
    credentials=credentials,
    workflow_id="example-dsl-openai-slim",
    start_inputs={"query": query},
)

events = list(engine.run())

See examples/slim_llm/dsl.py for the DSL import version and examples/slim_llm/code.py for the Python graph construction version.

Default DSL import currently supports start, end, answer, if-else, template-transform, code, llm, tool, http-request, variable-aggregator, assigner, list-operator, question-classifier, and parameter-extractor. HTTP request import covers text request bodies and text responses; file request bodies still require application-level file adapters.

For direct Python graph construction, use graphon.dsl.slim.SlimLLM as the standard Slim-backed LLM runtime. Integrations that need to replace model execution, routing, credential injection, or token counting can implement graphon.protocols.LLMProtocol. A higher-level model factory/resolver layer is planned as a separate follow-up.

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 shared model entities
• src/graphon/dsl: DSL import support, including Slim-backed runtime adapters
• 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/slim_llm/README.md: runnable Slim LLM 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 pull request titles, runs make check including uv.lock freshness validation, and runs uv run pytest 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.