agentic-workflow-orchestrator 1.1.1

Orchestrate multi-agent workflows with autonomous reasoning, parallelism, rate limiting, and LLM integration

orchestrator (Python reference SDK)

In-process workflow orchestration engine + authoring API. Zero required dependencies (stdlib asyncio); pyyaml is optional (for .yaml specs), anthropic optional (for the real LLM adapter).

Concepts

• Workflow — a DAG of tasks loaded from a template (Workflow.from_file/from_dict/from_json).
• Skill — a registered handler (ctx, inputs) -> output, sync or async.
• Tool — a registered callable a skill invokes via ctx.call_tool(...).
• Orchestrator — runs a workflow, enforcing concurrency + rate limits, and returns a Report.

Minimal usage

from orchestrator import Workflow, Orchestrator, Registry, MockLLM

reg = Registry()

@reg.tool("http.get")
def http_get(url):
    return {"url": url, "ok": True}

@reg.skill("market.fetch")
def fetch(ctx, inputs):
    ctx.call_tool("http.get", url=f"/prices/{inputs['ticker']}")
    ctx.record_decision("selected primary feed", rationale="lowest latency")
    return {"ticker": inputs["ticker"], "last": 199.4}

@reg.skill("llm.classify")
def classify(ctx, inputs):
    r = ctx.llm(messages=[{"role": "user", "content": inputs["text"]}], model="claude-opus-4-8")
    return r.text

wf = Workflow.from_file("../spec/examples/market-analysis.json")
report = Orchestrator(reg, llm=MockLLM()).run_sync(wf, inputs={"ticker": "AAPL"})
print(report.to_json())

The ctx (SkillContext) API

Member	Purpose
ctx.inputs	resolved task inputs
ctx.call_tool(name, **kwargs)	invoke a registered tool
ctx.llm(messages, tools=, model=, **opts)	call the configured LLM provider; usage is recorded
ctx.record_decision(summary, rationale=, data=)	append a critical decision to the trace
ctx.log(msg), ctx.attempt, ctx.cancelled	logging / retry attempt / cooperative cancel flag

Report

report.to_dict() / report.to_json() give: status, duration_ms, per-task {status, duration_ms, attempts, llm, decisions, error}, critical_path (bottleneck chain), totals (llm tokens, tool calls, retries), errors, and the resolved output.

Run

python3 examples/run_market.py
python3 tests/test_engine.py

Using a real LLM

from orchestrator import AnthropicProvider, Orchestrator
orch = Orchestrator(reg, llm=AnthropicProvider(model="claude-opus-4-8"))  # needs ANTHROPIC_API_KEY + `pip install anthropic`

Confirm current Claude model IDs/limits from the Claude API reference before pinning them.