Agent-Field/agentfield

What it solves

AgentField provides a production-ready control plane for AI agents, transforming simple agent logic into scalable, durable, and observable infrastructure. It solves the "glue code" problem where developers must manually implement queues, retries, service discovery, and tracing when moving from a prototype to a distributed system of agents.

How it works

Developers write agent logic as plain functions in Python, Go, or TypeScript. AgentField's control plane (a stateless Go service) then manages the execution of these functions, automatically exposing them as REST endpoints. It handles the fan-out of requests across thousands of agents, manages async execution via webhooks and SSE, and provides a shared memory system (KV and vector search) without requiring external databases like Redis.

Who it’s for

It is designed for developers building multi-agent systems that need to scale beyond a single laptop, specifically those requiring high reliability, audit trails, and the ability to integrate agents into existing software stacks via APIs.

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: Support for massive parallel execution where one request can trigger thousands of agent branches with built-in queuing and retries.
• Harness Orchestration: Integration with coding harnesses like AForge, Claude Code, and Gemini CLI for multi-turn coding tasks.
• Governance & Identity: Every agent is assigned a cryptographic identity (W3C DID) for secure, tamper-proof audit trails and policy enforcement.
• Observability: Real-time workflow DAGs, execution traces, and Prometheus metrics for fleet management.