Practical overview
When it fits
Use Agentfield when an AI-agent system needs backend infrastructure for routing, coordination, asynchronous execution, fan-out, queues, retries, or observability.
Who it is for
- Developers building AI-agent backends
- Teams coordinating multiple agents or execution branches
- Engineers needing API-style access to agent functions
Documented uses
- Expose agent logic through API endpoints
- Coordinate fan-out across multiple agents or branches
- Queue and retry agent execution
- Trace execution branches
- Run agent workflows as backend microservices
Requirements & limits
The repository is publicly accessible and identifies the project as open source under the Apache-2.0 license. The documented project supports cloud, self-hosted, and server environments.
- Whether the project needs a control plane rather than only an individual agent implementation
- Need for fan-out, queuing, retries, and tracing across agent execution branches
- Preferred implementation language: Python, Go, or TypeScript
- Whether a REST API and SDK-based integration model fits the backend
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Create a Docker Compose agent backend from a specification
Generate a containerized agent, control plane, and REST API stack from a natural-language system specification.
Before you start
- The Agentfield installer command documented in the README.
- A supported coding environment using the documented /agentfield workflow.
- A system specification to provide to the coding agent.
Steps
Run the documented Agentfield installer.
In the coding agent, submit a specification with /agentfield.
Use the generated Docker Compose stack containing the agent, control plane, and REST API endpoint.
Send a request to the endpoint with curl as described in the README.
Expected outcome
A generated Docker Compose stack with an agent, control plane, and REST API endpoint for testing.
Guide limitations
- The workflow depends on the coding environments and installer behavior described by the project.
Compare options for Agent orchestration
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Agentfield | Expose agent logic through API endpoints | Cloud, Self-hosted, Server | The repository is publicly accessible and identifies the project as open source under the Apache-2.0 license. The documented project supports cloud, self-hosted, and server environments. |
| Agentfm Core | Run a containerized agent in a Podman sandbox on a worker machine | Desktop, Docker, Self-hosted | The README lists desktop installers for macOS and Linux; it says the Windows app is in progress. Its desktop quick start says the bundled node connects to the public mesh. The README notes that the v1 installers are unsigned and macOS may block the first launch. |
| Plano | Route incoming requests to agents based on their declared roles. | Self-hosted, Server | Plano is public source code under the Apache-2.0 license. The README describes hosted Plano models free of charge in the US-central region for a first-run developer experience; for production scale it points to running models locally or contacting the project for API keys. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- Agent-Field/agentfield · Official homepage title · retained in its source language