Operonx

Operonx is a workflow engine where ops can yield — so the same async DAG handles batch jobs (Airflow-style) and event-driven streaming pipelines (pipecat-style callbot / voice / STT → LLM → TTS).

The Rust execution backend now lives in its own repo: batman1m2001-cyber/operonx-rs (crates.io). It shares the shared JSON spec fixtures with this repo but ships independently.

Why Operonx

• Yield-based streaming. Generator ops emit per-item; downstream dispatches per-frame, not per-batch. The for_loop / map_op / VAD → STT → LLM → TTS shapes work without bolt-on map/reduce ops.
• Operator reference syntax. op["key"], PARENT["key"], op["src"] >> PARENT["dst"], outputs={"*": PARENT} — explicit and local. No xcom_pull per node, no JSON serialisation per hop.
• Multi-provider LLM / embedding / rerank. OpenAI, Azure, Gemini, Anthropic, vLLM, TEI, HuggingFace, ONNX, Pinecone — swap with one line in resources.yaml. Built-in weighted load balancing + fallback chains.
• Tracing built-in. Langfuse, OpenTelemetry, and a local file consumer. All async-flushed; never blocks the run.
• Lean tier-1. pip install operonx is just pydantic / pyyaml / rich / orjson. Provider SDKs are extras.

Quick Start

pip install operonx

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

@op
def greet(who: str):
    return {"message": f"Hello, {who}!"}

async def main():
    with GraphOp(name="hello") as graph:
        step = greet(who=PARENT["who"])
        START >> step >> END

    result = await Operon(graph).run(inputs={"who": "World"})
    print(result["message"])  # Hello, World!

asyncio.run(main())

Streaming with yield

The differentiator. A generator op yields per item; downstream ops dispatch on each frame. The same engine that runs a batch DAG runs a callbot pipeline.

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

@op
def chunk_text(text: str, chunk_size: int):
    for i, words in enumerate(words_in(text, chunk_size)):
        yield {"chunk": " ".join(words), "index": i}

@op
def analyze(chunk: str, index: int):
    return {"result": f"[{index}] {len(chunk.split())} words"}

with GraphOp(name="pipeline") as g:
    src = chunk_text(text=PARENT["text"], chunk_size=PARENT["chunk_size"])
    step = analyze(chunk=src["chunk"], index=src["index"])
    START >> src >> step >> END

Each yield triggers a dispatch on a fresh (parent_ctx, "yield_N") sub-context. Empty yield = zero downstream dispatches (matches Python's skipped yield). N-to-M flows (one VAD chunk → multiple speech segments) work because each yield is independent.

See examples/python/ex14 for the streaming + tracing demo, examples/python/ex15 for the callbot pipeline (audio → VAD → STT → intent → handler → TTS).

LLMs in one line

pip install "operonx[standard]"

import asyncio
import operonx
from operonx.core import Operon, GraphOp, START, END, PARENT
from operonx.providers import LLMOp

async def main():
    operonx.bootstrap()  # loads ./.env + ./resources.yaml

    with GraphOp(name="qa") as graph:
        c = LLMOp(
            name="llm",
            resource="gpt-4o-mini",
            inputs={
                "prompt": {"system": "You are a helpful assistant.", "user": "{question}"},
                "*": PARENT,
            },
            outputs={"*": PARENT},
        )
        START >> c >> END

    result = await Operon(graph).run(inputs={"question": "What is Python?"})
    print(result["content"])

asyncio.run(main())

LLMOp.prompt accepts a string, {"system": ..., "user": ...} dict, or a full messages list — every non-reserved kwarg becomes a {var} substitution.

Multi-model load balancing + fallback

from operonx.providers import LLMOp

llm = LLMOp.of(
    resource=["gpt-4o", "gpt-4o-mini"],
    ratios=[0.7, 0.3],          # 70 / 30 split
    fallback=["claude-haiku"],  # tried in order on failure
    messages=PARENT["messages"],
)

Branching

from operonx.core import START, END, GraphOp, PARENT
from operonx.core.ops.flow.branch_op import if_

router = (if_(PARENT["score"] >= 90, "excellent")
          .if_(PARENT["score"] >= 70, "good")
          .else_("fail"))
START >> router >> excellent >> merge >> END
router >> good >> merge
router >> fail >> merge

if_() evaluates conditions in order; the first match routes through a soft edge (>>~ semantically — branch outputs use soft edges so non-matching branches don't block downstream).

Loops

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

with GraphOp.loop(until="count >= 5", count=0) as loop:
    inc = increment(counter=PARENT["count"])
    inc["counter"] >> PARENT["count"]
    START >> inc >> END

until accepts a string expression evaluated against graph outputs.

Installation

Single Python package, optional extras for each integration:

pip install operonx                  # Tier 1 — engine only, ~10 MB
pip install "operonx[openai]"        # OpenAI / Azure
pip install "operonx[anthropic]"     # Anthropic via httpx
pip install "operonx[gemini]"        # Vertex AI
pip install "operonx[onnx]"          # Local ONNX inference
pip install "operonx[langfuse]"      # Langfuse tracing
pip install "operonx[otel]"          # OpenTelemetry tracing
pip install "operonx[standard]"      # Recommended — providers + Langfuse + OTEL
pip install "operonx[all]"           # Everything except torch / HuggingFace

Tracing

import operonx
from operonx.core import Operon

operonx.bootstrap()  # registers consumer configs from resources.yaml

engine = Operon(graph, trace=["trace_langfuse:default"])

Consumers are configured in resources.yaml (trace_local:, trace_langfuse:) and referenced by key. See docs/api/telemetry.md for the full V3 tracing API.

Documentation

Contributing

git clone https://github.com/batman1m2001-cyber/Operonx.git
cd Operonx
uv sync --all-extras
pre-commit install
uv run pytest tests/ -m "not integration"

See CONTRIBUTING.md for the full contributor guide.

License

Apache 2.0