An open-source tool for building and running AI agents, developed by Docker, Inc. Instead of writing application code, you declare an agent’s role, available tools, and model in a YAML (or HCL) configuration file and run it from the CLI. The official documentation positions it as “to AI agents what docker run is to containers.” It supports teams of multiple specialized agents working together, tool connections through MCP servers, RAG, and the Agent-to-Agent (A2A) protocol. Finished agents can be pushed to an OCI-compatible registry and shared just like Docker images. It is licensed under Apache-2.0, and the GitHub repository has over 3,000 stars as of August 2026.
Key Features
- Declarative agent definitions in YAML/HCL: Write the agent’s instructions, scope, available tools, and model in a configuration file and it runs. No application code required to get started
- Multi-agent teams: Define several specialized agents ─ a researcher, an implementer, a reviewer ─ and let them delegate to each other to split up complex tasks
- Model-provider agnostic: Works with OpenAI, Anthropic, Google Gemini, AWS Bedrock, Mistral, and xAI, plus Docker Model Runner for local execution. Switching models is a one-line change in the config file
- Built-in tools and MCP integration: Ships with standard tools for the filesystem, shell, memory, and web fetching. Adding MCP (Model Context Protocol) servers extends it to external services and local environments
- Four runtime modes: An interactive TUI, a headless CLI for scripting, an HTTP API server, and an MCP mode that exposes the agent itself as an MCP server ─ the same definition carries from development to embedding
- Distribution through OCI registries: Push and pull completed agents the way you would container images, so sharing and version control fit into the Docker workflow a team already has
Pricing
| Item | Price | Details |
|---|---|---|
| Docker Agent itself | Free (Apache-2.0) | CLI and runtime are both open source. Install via Homebrew or binaries from GitHub Releases |
| Docker Personal | $0 | Docker Desktop (the CLI plugin ships with 4.63 and later), 1 user |
| Docker Pro | $11/month ($9/month billed annually) | For individual professionals. Includes Build Cloud, Testcontainers Cloud, and more |
| Docker Team | $16/user/month ($15 billed annually) | Up to 100 users, bulk user management, audit logs |
| Docker Business | $24/user/month | Unlimited users, SSO, SCIM, Hardened Docker Desktop |
Pricing is as of August 2026. No subscription is required to use Docker Agent itself, but API usage fees for the LLMs it calls are paid separately to each provider. Check the official site for current pricing.
Pros & Cons
✅ Pros
- Agents run from a configuration file alone, so you can try one without learning a framework or writing implementation code
- The provider-agnostic design lets you move between commercial APIs and local models with the same definition
- Agent distribution goes through OCI registries, fitting naturally into the existing workflow of teams that use Docker daily
- MCP support means the set of reachable tools grows as the ecosystem grows
- Apache-2.0 open source, so you can inspect the internals or extend it yourself
⚠️ Cons
- Operation is CLI-centric, which is a higher barrier for non-engineers than a GUI no-code flow builder
- You supply your own LLM API keys and pay the usage fees, so running costs can grow depending on how you use it
- Releases come frequently (v1.127.x as of August 2026), so configuration options may change between updates
- Granting shell and filesystem tools affects the local environment directly, and the permission design is left to the user
Comparison with Similar Services
| Criteria | Docker Agent | LangGraph | CrewAI | Dify |
|---|---|---|---|---|
| How agents are defined | YAML/HCL config file | Python code | Python code | Visual GUI editor |
| Main audience | Developers and operators already using Docker | Developers who want fine-grained control flow | Developers building multi-agent setups | Non-engineers to developers |
| Runtime form | CLI / TUI / API server / MCP | Embedded in your own app | Embedded in your own app | Web app and API |
| Distribution | OCI registry | Packages, self-deployed | Packages, self-deployed | Hosted or self-hosted |
| License | Apache-2.0 | Open source | Open source | Open source (paid cloud available) |
Who Is It For
- Developers who already use the Docker CLI daily and want to handle AI agents the same way
- People who want to try agent behavior in a configuration file before learning a Python agent framework
- Teams experimenting with multiple agents split by role ─ research, implementation, review
- Anyone who wants to switch between commercial APIs and local models as circumstances change
- Organizations that want to share agents across a team, version control included
Summary
Docker Agent brings the definition, execution, and distribution of AI agents into the Docker way of working, as an open-source tool. Writing YAML to get something running and shipping it through an OCI registry keeps the learning curve small for teams already using containers. On the other hand, CLI-centric operation and paying your own LLM bills are the premises, so a good starting point is a free Docker Personal setup with a local model or an API key you already have, running one small agent end to end.