Agentfield
VerifiedOpen-source platform for turning AI agent code into production APIs.
What is Agentfield?
AgentField acts as backend infrastructure for AI agents that need to integrate with existing services, cron jobs, or other agents. Developers define logic using familiar languages and receive automatic handling of async execution, identity management, and traceable decisions without custom glue code.
The platform works by wrapping agent functions into production endpoints while adding orchestration features such as multi-turn coordination and human approval workflows. It generates Docker Compose setups from natural language specs entered in supported coding tools.
It targets engineering teams that require reliable, observable agent deployments rather than simple chat interfaces. Users gain SDK access across languages plus REST compatibility for broad stack integration.
What you can build with Agentfield
Claims Processing Backend
Create an agent that scores risk, detects patterns, and routes low-confidence cases for human review through a secure API endpoint.
Multi-Agent Orchestration
Deploy coordinated agents that handle complex tasks across coding assistants like Claude Code or Gemini CLI with built-in containerization.
Audit-Ready Automation
Run agents that produce cryptographic records of every decision for compliance-heavy environments while exposing standard REST interfaces.
Install Agentfield
curl -fsSL https://agentfield.ai/install.sh | bashcurl -fsSL https://agentfield.ai/install.sh | bash- 1Run the install script via curl to set up the CLI on your machine.
- 2Open a supported coding agent and enter a system description prefixed with /agentfield.
- 3Review the generated Docker Compose files and agent code produced from the prompt.
- 4Start the stack to expose the agent as a live REST endpoint.
- 5Test the endpoint directly with curl or integrate it into other services.
Agentfield: pros & cons
Pros
- +Supports Python, Go, and TypeScript with minimal abstraction layers.
- +Generates complete containerized deployments from single-line prompts.
- +Includes cryptographic identities and decision audit trails by default.
- +Offers SDKs plus direct REST access for flexible integration.
Cons
- –Requires familiarity with coding agents or CLI workflows to start.
- –Newer project may have limited community examples and extensions.
- –Focuses on backend use cases rather than simple chatbot interfaces.
Frequently asked questions
Agent logic is written in Python, Go, or TypeScript using provided SDKs.
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