Docker Agent

// Overview

Docker Agent (`docker-agent`) is an AI agent builder and runtime from Docker Engineering. You create agents without writing code: each agent is a block in a YAML file with a model, a description, an instruction and a list of toolsets. It ships as a plugin for the `docker` CLI, so an agent runs with `docker agent run`, and Docker Desktop 4.63 and later already include it. Environment variables and some lint rules in its release notes still refer to the project's earlier name, cagent.

A single YAML file can describe a team instead of one agent. The README calls this a multi-agent architecture: specialised agents that delegate tasks to each other automatically. Tools come from built-in toolsets (think, todo and memory tools for reasoning) and from any Model Context Protocol server, whether it runs locally, remotely or as a Docker container — the README's example gives an agent DuckDuckGo search through `docker:duckduckgo`. Retrieval is pluggable, with BM25, embeddings, hybrid search and reranking.

Docker Agent is provider-agnostic. It works with OpenAI, Anthropic, Gemini, AWS Bedrock, Mistral, xAI and others, and with Docker Model Runner for local models. Because an agent is just a config file, it can be versioned in Git, pushed to any OCI registry and pulled to run on another machine. The project is written in Go, licensed Apache-2.0, and releases often: the 1.149.0 release in October 2026 added loading skills straight from public GitHub repositories and a dedicated evaluator backend for agent evals.

// What it does

• Agents and agent teams defined declaratively in YAML — versionable and shareable, no code required
• Multi-agent delegation: a root agent hands sub-tasks to specialised agents
• Any MCP server as a toolset — local, remote or Docker-based — plus built-in think, todo and memory tools
• Pluggable RAG with BM25, embeddings, hybrid search and reranking
• Provider-agnostic: OpenAI, Anthropic, Gemini, AWS Bedrock, Mistral, xAI and local models through Docker Model Runner
• Package agents as OCI artifacts: push to any registry and run them elsewhere with `docker agent run org/agent:tag`
• Skills loaded from public GitHub repositories, hooks, and an evals system with LLM-as-judge or a dedicated evaluator backend

// Getting started

Docker Desktop 4.63 or later already includes the `docker agent` plugin. Otherwise install it with Homebrew or a binary from the GitHub releases page. You need at least one model provider API key, or Docker Model Runner for local models.

brew install docker-agent

export OPENAI_API_KEY=sk-...        # or ANTHROPIC_API_KEY, GOOGLE_API_KEY, etc.

# Run the default agent
docker agent run

# Generate a new agent interactively
docker agent new

agents:
  root:
    model: openai/gpt-5-mini
    description: A helpful AI assistant
    instruction: |
      You are a knowledgeable assistant that helps users with various tasks.
      Be helpful, accurate, and concise in your responses.
    toolsets:
      - type: mcp
        ref: docker:duckduckgo

# Run your own config
docker agent run agent.yaml

# Run from an OCI registry
docker agent run myorg/agent:tag

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Build a team of agents — for example a root agent that delegates research and writing to specialists — from one YAML file
• Give an agent real tools by attaching MCP servers that run as Docker containers
• Run agents on local models through Docker Model Runner instead of a cloud API
• Share a team's agents through a container registry, the same way you share images

// How Docker Agent compares

Docker Agent alongside other open-source agent frameworks & builders tools AI/TLDR tracks, ranked by GitHub stars.