agentfield

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 observability. Every function becomes a REST endpoint, and the same code scales from one agent on your laptop to ten thousand in a single workflow: the control plane handles the fan-out, the queues, and the retries.

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

The installer also drops the aforge coding harness beside af in ~/.agentfield/bin, so harness-backed agents work out of the box; skip it with --no-aforge.

On macOS the installer also registers the control plane to start at login (under launchd) and adds a menu-bar icon. Stop it with af service stop or the menu-bar icon — a plain kill looks like a crash and it restarts. af service status shows health and in-flight work; install with --no-tray to skip this entirely.

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

Plain Python (or Go / TypeScript) functions. No DSL, no YAML, no graph wiring.

…

What you just saw: app.ai() calls an LLM and returns structured output. app.call() routes to other agents (or back to itself) through the control plane, so recursion becomes distributed fan-out. asyncio.gather() runs every branch in parallel. app.run() auto-exposes everything as REST. Read the full docs →

…

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

Deployment guide → for Docker Compose and production setups, or use the repository's Kubernetes deployment guide.

See it in action

How AgentField fits in your stack

Most agent tools help you write agent logic. AgentField is what runs it in production: the layer that makes agents callable by other software, durable across failures, and observable when one request fans out to a thousand branches. Keep the framework you already use for authoring; a reasoner is a plain function, so existing LangGraph or CrewAI code can run inside one.

● full · ◐ partial · — not the focus

Prototype in whatever you like. The moment a second service needs to call your agent, put it on AgentField. That is the point where you would otherwise start writing queues, retries, discovery, and tracing by hand.

Full comparison & decision guide →

How it scales

The control plane is a stateless Go service. You put more of them behind a load balancer and the fleet grows horizontally. Work is admitted into a bounded in-process queue with backpressure (429/503 plus Retry-After). On graceful shutdown, in-flight executions are terminated with status_reason control_plane_shutdown rather than silently dropped.

Two examples already run at this load. The deep-research engine fanned out 10,000+ agent invocations in one workflow. The security auditor runs 250 coordinated agents per audit.

Deployment guide → for Docker Compose, Kubernetes, and production setups.

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 AForge, AgentField's own coding harness — no setup. Add provider="claude-code" (or codex, gemini, opencode, pi, omp) to orchestrate someone else's.
• 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() / .similarity_search() - KV + vector search, four scopes, no Redis needed

Scale - 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.
• Canary Deployments - Traffic weight routing, A/B testing, blue-green deploys. Roll out agent versions at 5% → 50% → 100%.
• Human-in-the-Loop - app.pause() suspends execution for human approval. Crash-safe, durable, audited.
• Observability - Automatic workflow DAGs, Prometheus /metrics, structured logs, execution timeline.

Govern - IAM for AI agents. Every agent gets a cryptographic identity. 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 →