AgentField provides backend infrastructure for autonomous AI agents, treating them as scalable, observable, and identity-aware microservices akin to Kubernetes. It targets backend engineers and platform teams building decision-making AI systems, offering production-ready features beyond typical prompt frameworks by focusing on infrastructure rather than just LLM interaction wrappers.

How It Works

AgentField functions as a control plane, enabling agents to discover and invoke each other via standard REST APIs, facilitating a microservices architecture for AI. It supports asynchronous, long-running tasks with built-in durable state management, including vector search capabilities. A key differentiator is its "Trust Infrastructure," which assigns W3C Decentralized Identifiers (DIDs) to each agent and generates Verifiable Credentials for every action, providing tamper-proof audit trails and cryptographic proof for compliance and debugging.

Quick Start & Requirements

• Install: curl -fsSL https://agentfield.ai/install.sh | bash
• Prerequisites: Go 1.21+, Python 3.9+, Docker.
• Setup: Requires running the control plane (af server) and agent nodes (e.g., python main.py). Docker networking configuration is detailed for containerized deployments.
• Links: Docs, Quick Start, Python SDK, Go SDK, TypeScript SDK, REST API.

Highlighted Details

• Kubernetes-like infrastructure for AI agents: routing, discovery, async execution, and observability.
• Built-in durable state with vector search, eliminating reliance on external services like Redis or Pinecone.
• W3C DIDs and Verifiable Credentials ensure agent identity and provide tamper-proof audit trails.
• Multi-language support via native REST APIs and dedicated SDKs (Python, Go, TypeScript).
• Automatic generation of workflow DAGs for visualization and Prometheus metrics for monitoring.
• Support for long-running tasks (hours/days) with webhook callbacks and automatic retries.

Maintenance & Community

• Community: Discord
• Resources: Docs, GitHub Issues. The project is developed by engineers seeking robust agent infrastructure.

Licensing & Compatibility

• License: Apache 2.0.
• Compatibility: Permissive license suitable for commercial use. The REST API enables integration with any language or platform, treating agents as standard backend services.

Limitations & Caveats

AgentField is designed for complex AI backends and multi-agent systems requiring production-grade infrastructure, not for simple chatbots or prompt orchestration tasks where frameworks like LangChain or CrewAI are more appropriate. Careful Docker networking configuration is necessary when the control plane and agent nodes are in different network environments.