Agentfield

Agentfield provides a control plane for building, deploying, and operating autonomous AI agents as production-grade microservices. It gives every agent a cryptographic identity (DID) and issues verifiable credentials for tamper-proof audit trails, while exposing auto-generated APIs and runtime observability. The platform supports long-running asynchronous workflows, secure inter-agent communication, zero-config shared memory, and built-in deployment primitives to run agent fleets at scale.

• Agents as microservices — Write reasoners and skills as typed functions; each becomes an auto-generated REST API endpoint and OpenAPI spec.
• Cryptographic identity & verifiable credentials — Each agent receives a DID and every execution can produce a signed verifiable credential for audit and compliance.
• Async execution & webhooks — Support for long-running tasks (hours/days) with async execution models and webhook callbacks for completion events.
• Secure inter-agent calls — Built-in, identity-verified ctx.call-style RPC with automatic discovery and load balancing between agent instances.
• Shared memory & vector search — Zero-config distributed memory fabric with semantic/vector search capabilities to share context across agents.
• Observability & production operations — Auto-generated health probes, metrics, retry policies, webhooks, and workflow DAG tracing out of the box.
• Multi-language SDKs and real-time APIs — Python, TypeScript, and Go SDKs plus REST, WebSocket, and Server-Sent Events for streaming and real-time progress updates.

Get started by cloning or installing the source, using a provided SDK to define an agent function, and deploying the agent node (container/Kubernetes-ready). Call agents via the generated REST endpoints or SDKs, monitor workflows and audit trails, and extend with custom webhooks and integrations.