This release is a pre-release and may not be stable for production use.
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 planeToolCallConfig— Filter by tags, agent IDs, health status- Lazy hydration — Send only tool names/descriptions first, hydrate schemas on demand
- Guardrails —
max_turnsandmax_tool_callsprevent runaway loops - Observability —
result.tracetracks 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.
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