AgentField is an open-source backend platform for building, running and scaling AI agents the way you would APIs and microservices. It gives agents the production plumbing they usually lack — identity, authentication, orchestration and cloud-native scaling — so teams can move from demos to dependable multi-agent systems. The project has 2,615 stars on GitHub and is under very active development in 2026.
Most agent frameworks stop at the prompt loop; AgentField focuses on what happens when agents have to run in production. Written primarily in Go with SDKs for Python and TypeScript, it treats each agent as a service with its own identity, and handles the surrounding concerns: authenticating agents, scaling them on Kubernetes, and composing them into multi-agent systems. It is Apache-2.0 licensed, cloud-native by design, and model-agnostic, working with the major LLM providers.
The project is evolving quickly, with releases landing through 2026 as the agent-infrastructure space matures. Its bet is that agents will be operated like any other distributed system — versioned, authenticated, observable and horizontally scalable — and that an open platform, rather than a single vendor’s cloud, should provide that substrate. Teams evaluating agent platforms will find its microservices framing immediately familiar.
AgentField is free and open source under the Apache-2.0 license. You run it on your own infrastructure and pay only for your cloud resources and the LLM API usage your agents consume.
AgentField is aimed at platform engineers and AI teams who have outgrown notebook prototypes and need agents that behave like real production services. If identity, scaling and reliability matter as much as the model itself, this is the layer worth evaluating.