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Operonx

Operonx is a workflow engine that runs anything as a workflow — from IO-bound AI tasks (LLMs, agents, RAG) to CPU-bound workloads needing native performance. Define complex pipelines as DAGs with async execution and built-in tracing.

Why Operonx

  • DAG-based workflows — nodes and edges, inspired by Airflow operators.
  • Yield-based streaming — the same engine handles batch jobs and event-driven pipelines (VAD → STT → LLM → TTS).
  • Built-in tracing — Langfuse + OpenTelemetry, plus a local viewer.
  • Provider agnostic — OpenAI, Azure, Gemini, Anthropic, vLLM, ONNX — swap with one line.
  • Type-safe state — O(1) state access with schema validation.

Start a project

You build with operonx through a coding assistant. operonx init makes a project it can work in at once: a layout, a first feature with its tests, the rules in AGENTS.md, and the API guide of the installed operonx in .operonx/guide/.

# with uv
uvx operonx init myapp
cd myapp && uv sync && uv run pytest

# with pip only
pip install operonx
operonx init myapp && cd myapp
python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate
pip install -e ".[test]" && pytest

Then open the folder in your coding assistant and ask for what you want. Installation shows what the project holds and which files the assistant reads.

Where to go next

Repository