Agent-Field/agentfield
Build, run and scale AI agents like API and microservices
What it solves
AgentField provides a production-grade control plane for AI agents, solving the problem of moving from a local prototype to a scalable, observable, and durable infrastructure. It eliminates the need for developers to manually build queues, retry logic, service discovery, and tracing when deploying multi-agent systems that need to be callable as APIs.
How it works
Developers write agent logic as plain functions in Python, Go, or TypeScript. AgentField transforms these functions into REST endpoints. A stateless Go-based control plane manages the routing, coordination, and execution of these agents. It uses a durable PostgreSQL queue to ensure jobs survive crashes and restarts, and supports distributed fan-out where one request can trigger thousands of parallel agent invocations.
Who it’s for
It is designed for developers and engineers building AI agent backends that require production-level reliability, horizontal scaling, and enterprise governance (such as audit trails and human-in-the-loop approvals).
Highlights
- Prompt-to-Production: Ability to generate a full Docker Compose stack for a multi-agent backend from a single-line description.
- Distributed Fan-out: Supports massive parallel execution of agents with built-in queuing and retries.
- Enterprise Governance: Includes cryptographic identities (W3C DID) for agents, signed audit trails, and tag-based policy enforcement.
- Human-in-the-Loop: Built-in
app.pause()functionality to suspend execution for human approval. - Observability: Automatic generation of workflow DAGs, execution traces, and Prometheus metrics.
- Multi-language Support: SDKs for Python, Go, and TypeScript.