Installation¶

Operonx is a single Python package with optional extras for each provider or integration.

Pick an extra¶

pip install operonx                    # Core engine, no providers
pip install "operonx[standard]"        # Recommended — OpenAI + Langfuse + OTEL + serve
pip install "operonx[anthropic]"       # Anthropic SDK
pip install "operonx[gemini]"          # Google Vertex AI
pip install "operonx[bedrock]"         # AWS Bedrock
pip install "operonx[onnx]"            # Local ONNX inference
pip install "operonx[huggingface]"     # transformers + torch (heavy, ~2.5 GB)
pip install "operonx[langfuse]"        # Langfuse tracer
pip install "operonx[otel]"            # OpenTelemetry tracer
pip install "operonx[serve]"           # FastAPI + uvicorn HTTP server
pip install "operonx[all]"             # All providers + tracers (excludes huggingface)

Extra	Contents
`standard`	OpenAI + Langfuse + OpenTelemetry + FastAPI/uvicorn
`all`	Everything in `standard` plus Anthropic, Gemini, Bedrock, ONNX (excludes `huggingface` for size)
`dev`	pytest, ruff, pre-commit
`docs`	mkdocs, mkdocs-material, mkdocstrings

Extras compose: pip install "operonx[anthropic,langfuse]".

Rust users¶

cargo add operonx

Or add to your Cargo.toml:

[dependencies]
operonx = "0.6"

Python version support¶

Python 3.10, 3.11, and 3.12 are tested in CI. Older versions are not supported.

Configure environment¶

Provider ops resolve credentials and model configs through a singleton ResourceHub. Two files set this up:

.env — secret values (API keys). Copy env.example to .env and fill in the keys you use.
resources.yaml — model and tracer configurations. Reference env vars with ${VAR_NAME}.

Then call operonx.bootstrap() once at process startup:

import operonx
operonx.bootstrap()

bootstrap() is explicit — Operon(graph) does not auto-load anything. See Resource hub for the full setup model and failure surface.

Pure-compute graphs¶

If your graph doesn't reference any resource by name (no LLMOp, EmbeddingOp, etc.), you can skip bootstrap() entirely:

from operonx.core import Operon, GraphOp, op, START, END, PARENT

@op
def double(x: int):
    return {"result": x * 2}

with GraphOp(name="pure") as graph:
    step = double(x=PARENT["x"])
    START >> step >> END

result = await Operon(graph).run(inputs={"x": 5})

Verify¶

python -c "import operonx; print(operonx.__version__)"

If you installed an extra, also import a provider symbol:

python -c "from operonx.providers import LLMOp; print(LLMOp)"