cagent
Agent Builder and Runtime by Docker Engineering
Stars: 1197
cagent is a powerful and easy-to-use multi-agent runtime that orchestrates AI agents with specialized capabilities and tools, allowing users to quickly build, share, and run a team of virtual experts to solve complex problems. It supports creating agents with YAML configuration, improving agents with MCP servers, and delegating tasks to specialists. Key features include multi-agent architecture, rich tool ecosystem, smart delegation, YAML configuration, advanced reasoning tools, and support for multiple AI providers like OpenAI, Anthropic, Gemini, and Docker Model Runner.
README:
A powerful, easy to use, customizable multi-agent runtime that orchestrates AI agents with specialized capabilities and tools, and the interactions between agents.
cagent lets you create and run intelligent AI agents, where each agent has
specialized knowledge, tools, and capabilities.
Think of it as allowing you to quickly build, share and run a team of virtual experts that collaborate to solve complex problems for you.
And it's dead easy to use!
cagent is in active development, breaking changes are to be expected
Example basic_agent.yaml:
Creating agents with cagent is very simple. They are described in a short yaml file, like this one:
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.Run it in a terminal with cagent run basic_agent.yaml.
Many more examples can be found here!
cagent supports MCP servers, enabling agents to use a wide variety of external tools and services.
It supports three transport types: stdio, http and sse.
Giving an agent access to tools via MCP is a quick way to greatly improve its capabilities, the quality of its results and its general useful-ness.
Get started quickly with the Docker MCP Toolkit and catalog
Here, we're giving the same basic agent from the example above access to a containerized duckduckgo mcp server and it's tools by using Docker's MCP Gateway:
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.
toolset:
- type: mcp
ref: docker:duckduckgo # stdio transportWhen using a containerized server via the Docker MCP gateway, you can configure any required settings/secrets/authentication using the Docker MCP Toolkit in Docker Desktop.
Aside from the containerized MCP severs the Docker MCP Gateway provides, any standard MCP server can be used with cagent!
Here's an example similar to the above but adding read_file and write_file tools from the rust-mcp-filesystem MCP server:
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. Write your search results to disk.
toolset:
- type: mcp
ref: docker:duckduckgo
- type: mcp
command: rust-mcp-filesystem # installed with `cargo install rust-mcp-filesystem`
args: ["--allow-write", "."]
tools: ["read_file", "write_file"] # Optional: specific tools only
env:
- "RUST_LOG=debug"See the USAGE docs for more detailed information and examples
- 🏗️ Multi-agent architecture - Create specialized agents for different domains.
- 🔧 Rich tool ecosystem - Agents can use external tools and APIs via the MCP protocol.
- 🔄 Smart delegation - Agents can automatically route tasks to the most suitable specialist.
- 📝 YAML configuration - Declarative model and agent configuration.
- 💭 Advanced reasoning - Built-in "think", "todo" and "memory" tools for complex problem-solving.
- 🌐 Multiple AI providers - Support for OpenAI, Anthropic, Gemini and Docker Model Runner.
Prebuilt binaries for Windows, macOS and Linux can be found on the releases page of the project's GitHub repository
Once you've downloaded the appropriate binary for your platform, you may need to give it executable permissions. On macOS and Linux, this is done with the following command:
# linux amd64 build example
chmod +x /path/to/downloads/cagent-linux-amd64You can then rename the binary to cagent and configure your PATH to be able to find it (configuration varies by platform).
Based on the models you configure your agents to use, you will need to set the corresponding provider API key accordingly, all theses keys are optional, you will likely need at least one of these, though:
# For OpenAI models
export OPENAI_API_KEY=your_api_key_here
# For Anthropic models
export ANTHROPIC_API_KEY=your_api_key_here
# For Gemini models
export GOOGLE_API_KEY=your_api_key_here# Run an agent!
cagent run ./examples/pirate.yaml
# or specify a different starting agent from the config, useful for agent teams
cagent run ./examples/pirate.yaml -a root
# or run directly from an image reference here I'm pulling the pirate agent from the creek repository
cagent run creek/pirateagents:
root:
model: claude
description: "Main coordinator agent that delegates tasks and manages workflow"
instruction: |
You are the root coordinator agent. Your job is to:
1. Understand user requests and break them down into manageable tasks
2. Delegate appropriate tasks to your helper agent
3. Coordinate responses and ensure tasks are completed properly
4. Provide final responses to the user
When you receive a request, analyze what needs to be done and decide whether to:
- Handle it yourself if it's simple
- Delegate to the helper agent if it requires specific assistance
- Break complex requests into multiple sub-tasks
sub_agents: ["helper"]
helper:
model: claude
description: "Assistant agent that helps with various tasks as directed by the root agent"
instruction: |
You are a helpful assistant agent. Your role is to:
1. Complete specific tasks assigned by the root agent
2. Provide detailed and accurate responses
3. Ask for clarification if tasks are unclear
4. Report back to the root agent with your results
Focus on being thorough and helpful in whatever task you're given.
models:
claude:
provider: anthropic
model: claude-sonnet-4-0
max_tokens: 64000You'll find a curated list of agents examples, spread into 3 categories, Basic, Advanced and multi-agents in the /examples/ directory.
When using the dmr provider, you can use the provider_opts key for DMR runtime-specific (e.g. llama.cpp) options:
models:
local-qwen:
provider: dmr
model: ai/qwen3
max_tokens: 8192
provider_opts:
runtime_flags: ["--ngl=33", "--repeat-penalty=1.2", ...] # or comma/space-separated stringThe default base_url cagent will use for dmr providers is http://localhost:12434/engines/llama.cpp/v1. DMR itself might need to be enabled via Docker Desktop's settings on MacOS and Windows, and via command line on Docker CE on Linux.
Using the command cagent new you can quickly generate agents or multi-agent teams using a single prompt!
cagent has a built-in agent dedicated to this task.
To use the feature, you must have an Anthropic, OpenAI or Google API key available in your environment, or specify a local model to run with DMR (Docker Model Runner).
You can choose what provider and model gets used by passing the --model provider/modelname flag to cagent new
If --model is unspecified, cagent new will automatically choose between these 3 providers in order based on the first api key it finds in your environment.
export ANTHROPIC_API_KEY=your_api_key_here # first choice. default model claude-sonnet-4-0
export OPENAI_API_KEY=your_api_key_here # if anthropic key not set. default model gpt-5-mini
export GOOGLE_API_KEY=your_api_key_here # if anthropic and openai keys are not set. default model gemini-2.5-flash--max-tokens can be specified to override the context limit used.
When using DMR, the default is 16k to limit memory usage. With all other providers the default is 64k
--max-iterations can be specified to override how many times the agent is allowed to loop when doing tool calling etc.
When using DMR, the default is set to 20 (small local models have the highest chance of getting confused and looping endlessly). For all other providers, the default is 0 (unlimited).
Example of provider, model, context size and max iterations overriding:
# Use GPT-5 via OpenAI
cagent new --model openai/gpt-5
# Use a local model (ai/gemma3-qat:12B) via DMR
cagent new --model dmr/ai/gemma3-qat:12B
# Override the max_tokens used during generation, default is 64k, 16k when using the dmr provider
cagent new --model openai/gpt-5-mini --max-tokens 32000
# Override max_iterations to limit how much the model can loop autonomously when tool calling
cagent new --model dmr/ai/gemma3n:2B-F16 --max-iterations 15$ cagent new
------- Welcome to cagent! -------
(Ctrl+C to stop the agent or exit)
What should your agent/agent team do? (describe its purpose):
> I need an agent team that connects to <some-service> and does...
Agent configurations can be packaged and shared to Docker Hub using the cagent push command
cagent push ./<agent-file>.yaml namespace/reponamecagent will automatically build an OCI image and push it to the desired repository using your Docker credentials
Pulling agents from Docker Hub is also just one cagent pull command away.
cagent pull creek/piratecagent will pull the image, extract the yaml file and place it in your working directory for ease of use.
cagent run creek.yaml will run your newly pulled agent
More details on the usage and configuration of cagent can be found in USAGE.md
We track anonymous usage data to improve the tool. See TELEMETRY.md for details.
Want to hack on cagent, or help us fix bugs and build out some features? 🔧
Read the information on how to build from source and contribute to the project in CONTRIBUTING.md
A smart way to improve cagent's codebase and feature set is to do it with the help of a cagent agent!
We have one that we use and that you should use too:
cd cagent
cagent run ./golang_developer.yamlThis agent is an expert Golang developer specializing in the cagent multi-agent AI system architecture.
Ask it anything about cagent. It can be questions about the current code or about
improvements to the code. It can also fix issues and implement new features!
We’d love to hear your thoughts on this project. You can find us on Slack
For Tasks:
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