AgentField is an open-source control plane that lets you build AI agents callable by any service in your stack - frontends, backends, other agents, cron jobs - just like any other API. You write agent logic in Python, Go, or TypeScript. AgentField turns it into production infrastructure: routing, coordination, memory, async execution, and cryptographic audit trails. Every function becomes a REST endpoint. Every agent gets a cryptographic identity. Every decision is traceable.

Build production agents with a prompt.

Describe the system in one line. Get a production-ready multi-agent backend. Works in Claude Code, Codex, Gemini CLI, OpenCode, Aider, Windsurf, and Cursor.

curl -fsSL https://agentfield.ai/install.sh | bash

Then in your coding agent, paste any spec with /agentfield :

/agentfield Build a claims processor with risk scoring, pattern detection,
and human approval for low-confidence decisions.

You get a Docker Compose stack wired up end-to-end — the agent, the control plane, and a production ready REST API endpoint you can paste and curl into a terminal to try it. See it in action →

The DX you get

Best in class Python (or Go / TypeScript) DX. With least intrusive abstraction. No DSL, no YAML, no graph wiring.

from agentfield import Agent, AIConfig
from pydantic import BaseModel

app = Agent(
    node_id="claims-processor",
    version="2.1.0",# Canary deploys, A/B testing, blue-green rollouts
    ai_config=AIConfig(model="anthropic/claude-sonnet-4-20250514"),
)

class Decision(BaseModel):
    action: str# "approve", "deny", "escalate"
    confidence: float
    reasoning: str

@app.reasoner(tags=["insurance", "critical"])
async def evaluate_claim(claim: dict) -> dict:

    # Structured AI judgment - returns typed Pydantic output
    decision = await app.ai(
        system="Insurance claims adjuster. Evaluate and decide.",
        user=f"Claim #{claim['id']}: {claim['description']}",
        schema=Decision,
    )

    if decision.confidence < 0.85:
        # Human approval - suspends execution, notifies via webhook, resumes when approved
        await app.pause(
            approval_request_id=f"claim-{claim['id']}",
            approval_request_url=f"https://internal.acme.com/approvals/claim-{claim['id']}",
            expires_in_hours=48,
        )

    # Route to the next agent - traced through the control plane
    await app.call("notifier.send_decision", input={
        "claim_id": claim["id"],
        "decision": decision.model_dump(),
    })

    return decision.model_dump()

app.run()
# This single line exposes: POST /api/v1/execute/claims-processor.evaluate_claim
# The agent auto-registers with the control plane, gets a cryptographic identity, and every
# execution produces a verifiable, tamper-proof audit trail.

What you just saw: app.ai() calls an LLM and returns structured output. app.pause() suspends for human approval. app.call() routes to other agents through the control plane. app.run() auto-exposes everything as REST. Read the full docs →

af init my-agent --defaults                            # Scaffold agent
cd my-agent && pip install -r requirements.txt
af server          # Terminal 1 → Dashboard at http://localhost:8080
python main.py     # Terminal 2 → Agent auto-registers

# Call your agent
curl -X POST http://localhost:8080/api/v1/execute/my-agent.demo_echo \
  -H "Content-Type: application/json" \
  -d '{"input": {"message": "Hello!"}}'

# Go
af init my-agent --defaults --language go && cd my-agent && go run .

# TypeScript
af init my-agent --defaults --language typescript && cd my-agent && npm install && npm run dev

# Docker (control plane only)
docker run -p 8080:8080 agentfield/control-plane:latest

How AgentField fits in your stack

Most agent tools help you write agent logic. AgentField is what runs it in production — the operating layer that makes agents callable by software, durable across failures, governed by policy, and provable by audit.

_{● full · ◐ partial · — not the focus}

Use a framework when you're proving behavior. Use AgentField when agents need to be production systems — callable by software, coordinating across services, surviving failures, and governed under audit.

Full comparison & decision guide →

What You Get

Build - Python, Go, or TypeScript. Every function becomes a REST endpoint.

• Reasoners & Skills - @app.reasoner() for AI judgment, @app.skill() for deterministic code
• Structured AI - app.ai(schema=MyModel) → typed Pydantic/Zod output from any LLM
• Harness - app.harness("Fix the bug") dispatches multi-turn tasks to Claude Code, Codex, Gemini CLI, or OpenCode
• Cross-Agent Calls - app.call("other-agent.func") routes through the control plane with full tracing
• Discovery - app.discover(tags=["ml*"]) finds agents and capabilities across the mesh. tools="discover" lets LLMs auto-invoke them.
• Memory - app.memory.set() / .get() / .search() - KV + vector search, four scopes, no Redis needed

Run - Production infrastructure for non-deterministic AI.

• Async Execution - Fire-and-forget with webhooks, SSE streaming, retries. No timeout limits - agents run for hours or days.
• Human-in-the-Loop - app.pause() suspends execution for human approval. Crash-safe, durable, audited.
• Canary Deployments - Traffic weight routing, A/B testing, blue-green deploys. Roll out agent versions at 5% → 50% → 100%.
• Observability - Automatic workflow DAGs, Prometheus /metrics, structured logs, execution timeline.

Govern - IAM for AI agents. Identity, access control, and audit trails - built in.

• Cryptographic Identity - Every agent gets a W3C DID (decentralized identifier) - not a shared API key. Agents authenticate to each other the way services authenticate with mTLS, but with cryptographic signatures that travel with the agent.
• Verifiable Credentials - Tamper-proof receipt for every execution. Offline-verifiable: af vc verify audit.json.
• Policy Enforcement - Tag-based policy gates with cryptographic verification. "Only agents tagged 'finance' can call this" - enforced by infrastructure, not prompts.

See the full production-ready feature set →

Built With AgentField

Autonomous Engineering Team One API call spins up PM, architect, coders, QA, reviewers - hundreds of coordinated agents that plan, build, test, and ship. View project →	Deep Research Engine Recursive research backend. Spawns parallel agents, evaluates quality, generates deeper agents, and recurses -10,000+ agents per query. View project →
Reactive MongoDB Intelligence Atlas Triggers + agent reasoning. Documents arrive raw and leave enriched - risk scores, pattern detection, evidence chains. View project →	Autonomous Security Audit 250 coordinated agents trace every vulnerability source-to-sink and adversarially verify each finding. Confirmed exploits, not pattern flags. View project →
CloudSecurity AF AI-native cloud infrastructure security scanner that performs shift-left attack path analysis directly from IaC, prioritizing the most dangerous risk chains before deployment. View project →	Agentic PR Reviewer Builds a custom review strategy for every PR - spawns parallel reviewer agents with runtime-crafted prompts, adversarially challenges its own findings, and posts evidence-grounded inline comments. View project →

See all examples →

Built something with AgentField? Submit your project to be featured on the examples page.

See It In Action

_{Real-time workflow DAGs · Execution traces · Agent fleet management · Audit trails}

Architecture

The control plane is a stateless Go service. Agents connect from anywhere - your laptop, Docker, Kubernetes. They register capabilities, the control plane routes calls between them, tracks execution as DAGs, and enforces policies. Full architecture docs →

Learn More

The thinking behind AgentField - essays on AI backends, harness orchestration, and the infrastructure production agents actually need.

What is harness orchestration? The atomic unit of intelligence is climbing from the model call to the autonomous harness - and what changes when it does. Read post →	Part 1: The Black Box Treating harnesses like Claude Code and Codex as autonomous, embodied, persistent computational entities. Read post →
Part 2: Engineering the Membrane Shaping the boundary surface of a harness across four engineerable dimensions: workspace, drift, verifier placement, and recovery budget. Read post →	The AI Backend Our thesis: in five years every serious software company will run an AI backend - a reasoning layer that makes the decisions that used to be hardcoded. Read post →
IAM for AI Backends Agents need identity, not API keys - how decentralized identifiers and verifiable credentials make agent-to-agent delegation auditable and accountable. Read post →

Documentation

• vs Agent Frameworks - How AgentField compares to LangChain, CrewAI, and workflow engines
• Full Documentation

Community

License

Apache 2.0