AgentField Documentation

The AI backend. Build, deploy, and govern AI agents like APIs.

Code to API — write a function, get a production endpoint

curl -sSf https://agentfield.ai/get | sh

Fastest way in: one prompt, full backend

After install, type /agentfield in Claude Code — or describe your system in plain English. Stack is live under docker compose in one shot.

Try it →

from agentfield import Agent, AIConfig
from pydantic import BaseModel

app = Agent("demo", ai_config=AIConfig(model="anthropic/claude-sonnet-4-20250514"))

class Decision(BaseModel):
    action: str

@app.reasoner()
async def route(text: str) -> dict:
    out = await app.ai(
        system="Pick one action: summarize | escalate | done.",
        user=text,
        schema=Decision,
    )
    return out.model_dump()

app.run()

// Same idea in TS: Agent + reasoner + app.ai with a schema.
// See Quickstart for the full snippet.

// Same idea in Go: agent package + reasoner + structured AI call.
// See Quickstart for the full snippet.

APIs — decorated functions become HTTP endpoints with discovery and tracing.
Models — 100+ LLMs, structured output (Pydantic / Zod / structs), tool calling.
Multi-agent — app.call, shared memory, async, webhooks, governance (DIDs, policy, audit).

Quickstart

Build with your coding agent, or write the first agent yourself.

AgentField vs frameworks

When an agent becomes an AI backend, not just an app feature.

How AgentField works

Primitives, control plane, sync, async, and cross-agent calls.

Production capabilities

Agent APIs, triggers, IAM, rollouts, audit, and SDKs.

Build

Agents, intelligence, coordination, execution, governance

Reference

SDKs, REST, CLI, deploy, testing