Agents The core container that hosts reasoners, skills, and connects to the AgentField control plane
Agent container — one file, multiple endpoints The top-level container that turns your code into a discoverable, governed, production microservice.
Without Agent, you would wire an HTTP server, registration, routing, identity, tracing, memory access, and cross-agent calls separately. With Agent, that infrastructure boundary is the object you instantiate.
Python TypeScript Go
from agentfield import Agent, AIConfig from pydantic import BaseModel app = Agent( node_id="support-triage", # unique ID in the network ai_config=AIConfig(model="anthropic/claude-sonnet-4-20250514"), ) class TicketClassification(BaseModel): priority: str # "critical" | "high" | "normal" | "low" department: str # route to the right team summary: str # one-line summary for the queue @app.reasoner() # AI-powered — gets an LLM client automatically async def classify_ticket(subject: str, body: str, customer_id: str) -> TicketClassification: result = await app.ai( system="You triage customer support tickets.", user=f"Subject: {subject}\n\n{body}", schema=TicketClassification, # validated, typed output ) await app.memory.set(f"ticket:{customer_id}:last_priority", result.priority) return result @app.skill() # deterministic — no AI, just business logic def escalation_policy(priority: str) -> dict: sla = {"critical": 15, "high": 60, "normal": 240, "low": 1440} return {"sla_minutes": sla.get(priority, 240)} app.run() # starts HTTP server + registers with control plane
What just happened
One Agent instance exposed both AI and deterministic operations The reasoner got model access, validation, and workflow context automatically The deterministic function became a separate callable endpoint without extra server code In all three SDKs, deterministic endpoints can be registered separately from AI-powered reasoners The memory write used the same execution context as the reasoner Example generated surface:
Python/TypeScript: POST /reasoners/classify_ticket POST /skills/escalation_policy target: support-triage.classify_ticket Go equivalent: POST /reasoners/classify_ticket POST /skills/escalation_policy What You Get HTTP server with auto-generated REST endpoints for every reasoner and skill Control plane registration with heartbeat, lease renewal, and graceful shutdown Cryptographic identity via automatic DID registration and verifiable credentials Cross-agent communication through the AgentField execution gateway Built-in AI client for structured LLM output with any provider Memory system for distributed state across workflows and sessions CLI mode for local testing and interactive debugging Constructor Parameters SDK Reference Patterns Environment-based configuration Python TypeScript Go
import os from agentfield import Agent, AIConfig app = Agent( node_id=os.getenv("AGENT_ID", "my-agent"), agentfield_server=os.getenv("AGENTFIELD_SERVER"), ai_config=AIConfig( model=os.getenv("LLM_MODEL", "openai/gpt-4o"), ), dev_mode=os.getenv("DEV_MODE", "false").lower() == "true", ) Serverless deployment Python TypeScript Go
from agentfield import Agent app = Agent( node_id="serverless-agent", callback_url="https://my-function.vercel.app", ) @app.reasoner() async def process(data: dict) -> dict: return {"processed": True, **data}
Cross-agent communication Python TypeScript Go
@app.reasoner() async def orchestrate(task: str) -> dict: # Call another agent's reasoner through the control plane analysis = await app.call("analyzer-agent.analyze", text=task) summary = await app.call("summarizer-agent.summarize", data=analysis) return {"task": task, "result": summary}