agentfield 0.1.92rc19

Python SDK for the AgentField control plane

AgentField Python SDK

The AgentField SDK provides a production-ready Python interface for registering agents, executing workflows, and integrating with the AgentField control plane.

Installation

pip install agentfield

To work on the SDK locally:

git clone https://github.com/Agent-Field/agentfield.git
cd agentfield/sdk/python
python -m pip install -e .[dev]

Quick Start

from agentfield import Agent

agent = Agent(
    node_id="example-agent",
    agentfield_server="http://localhost:8080",
    dev_mode=True,
)

@agent.reasoner()
async def summarize(text: str) -> dict:
    result = await agent.ai(
        prompt=f"Summarize: {text}",
        response_model={"summary": "string", "tone": "string"},
    )
    return result

if __name__ == "__main__":
    agent.serve(port=8001)

AI Tool Calling

Let LLMs automatically discover and invoke agent capabilities across your system:

from agentfield import Agent, AIConfig, ToolCallConfig

app = Agent(
    node_id="orchestrator",
    agentfield_server="http://localhost:8080",
    ai_config=AIConfig(model="openai/gpt-4o-mini"),
)

@app.reasoner()
async def ask_with_tools(question: str) -> dict:
    # Auto-discover all tools and let the LLM use them
    result = await app.ai(
        system="You are a helpful assistant.",
        user=question,
        tools="discover",
    )
    return {"answer": str(result), "trace": result.trace}

# Filter by tags, limit turns, use lazy hydration
result = await app.ai(
    user="Get weather for Tokyo",
    tools=ToolCallConfig(
        tags=["weather"],
        schema_hydration="lazy",  # Reduces token usage for large catalogs
        max_turns=5,
        max_tool_calls=10,
    ),
)

Key features:

• tools="discover" — Auto-discover all capabilities from the control plane
• ToolCallConfig — Filter by tags, agent IDs, health status
• Lazy hydration — Send only tool names/descriptions first, hydrate schemas on demand
• Guardrails — max_turns and max_tool_calls prevent runaway loops
• Observability — result.trace tracks every tool call with latency

See examples/python_agent_nodes/tool_calling/ for a complete orchestrator + worker example.

Human-in-the-Loop Approvals

The Python SDK provides a first-class waiting state for pausing agent execution mid-reasoner and waiting for human approval:

from agentfield import Agent, ApprovalResult

app = Agent(node_id="reviewer", agentfield_server="http://localhost:8080")

@app.reasoner()
async def deploy(environment: str) -> dict:
    plan = await app.ai(f"Create deployment plan for {environment}")

    # Pause execution and wait for human approval
    result: ApprovalResult = await app.pause(
        approval_request_id="req-abc123",
        expires_in_hours=24,
        timeout=3600,
    )

    if result.approved:
        return {"status": "deploying", "plan": str(plan)}
    elif result.changes_requested:
        return {"status": "revising", "feedback": result.feedback}
    else:
        return {"status": result.decision}

Two API levels:

• High-level: app.pause() blocks the reasoner until approval resolves, with automatic webhook registration
• Low-level: client.request_approval(), client.get_approval_status(), client.wait_for_approval() for fine-grained control

See examples/python_agent_nodes/waiting_state/ for a complete working example.

See docs/DEVELOPMENT.md for instructions on wiring agents to the control plane.

Testing

./scripts/run_pytest.sh

To run coverage locally:

./scripts/run_pytest.sh --cov=agentfield --cov-report=term-missing

The wrapper sets a private PYTEST_DEBUG_TEMPROOT automatically so local runs and CI do not rely on pytest's predictable default temp directory layout.

License

Distributed under the Apache 2.0 License. See the project root LICENSE for details.