Monat: Oktober 2025

Join Us in Rebooting the Docker Model Runner Community!

We’re thrilled to announce that we’re breathing new life into the Docker Model Runner community, and we want you to be a part of it! Our goal is to make it easier than ever for you to contribute, collaborate, and help shape the future of running AI models with Docker.

From a Limited Beta to a Universe of Possibilities

When we first announced Docker Model Runner, it was in its beta phase, exclusively available on Docker Desktop and limited to Apple and Nvidia hardware. We received a ton of valuable feedback, and we’ve been hard at work making it more accessible and powerful.

Today, we’re proud to say that Docker Model Runner is now Generally Available (GA) and can be used in all versions of Docker! But that’s not all. We’ve added Vulkan support, which means you can now run your models on virtually any GPU. This is a huge leap forward, and it’s all thanks to the incredible potential we see in this project and the community that surrounds it.

Making Contributions a Breeze

We’ve listened to your feedback about the contribution process, and we’ve made some significant changes to make it as smooth as possible.

To start, we’ve consolidated all the repositories into a single, unified home. This makes it much easier to find everything you need in one place.

We have also invested a lot of effort in updating our documentation for contributors. Whether you’re a seasoned open-source veteran or a first-time contributor, you’ll find the information you need to get started.

Your Mission, Should You Choose to Accept It

The success of Docker Model Runner depends on you, our amazing community. We’re calling on you to help us make this project the best it can be. Here’s how you can get involved:

Star our repository: Show your support and help us gain visibility by starring the Docker Model Runner repo.

Fork and Contribute: Have an idea for a new feature or a bug fix? Fork the repository, make your changes, and submit a pull request. We’re excited to see what you come up with!

Spread the word: Tell your friends, colleagues, and anyone else who might be interested in running AI models with Docker.

We’re incredibly excited about this new chapter for Docker Model Runner, and we can’t wait to see what we can build together. Let’s get to work!

Learn more

Check out the Docker Model Runner General Availability announcement

Visit our Model Runner GitHub repo! Docker Model Runner is open-source, and we welcome collaboration and contributions from the community!

Get started with Docker Model Runner with a simple hello GenAI application

Quelle: https://blog.docker.com/feed/

Build a Multi-Agent System in 5 Minutes with cagent

Models are advancing quickly. GPT-5, Claude Sonnet, Gemini. Each release gives us more capabilities. But most real work isn’t solved by a single model.

Developers are realizing they need a system of agents: different types of agents working together to accomplish more complex tasks. For example, a researcher to find information, a writer to summarize, a planner to coordinate, and a reviewer to check accuracy.

The challenge is that today, building a multi-agent system is harder than it should be. Context doesn’t flow cleanly between agents. Tools require custom integration. Sharing with a teammate means sending instructions and hoping they can re-create your setup.

That’s the problem cagent solves.

In this blog, we’ll walk you through the basics, how to create a multi-agent AI system in minutes, and how cagent makes this possible. 

What’s a multi-agent system?

A multi-agent system is a coordinated group of AI agents that collaborate to complete complex tasks. Using cagent, you can build and run these systems declaratively, no complex wiring or reconfiguration needed.

Meet cagent: The best (and open source) way to build multi-agent systems

Figure 1: cagent workflow for multi-agent orchestration. 

cagent is an open-source tool for building agents and a part of Docker’s growing ecosystem of AI tools. 

Instead of writing glue code to wire up models, tools, and workflows, describe an agent (or a team of agents) in a single YAML file:

Which model the agent uses (OpenAI, Anthropic, Gemini, or a local one)

What its role or instructions are

Which tools it can use (like GitHub, search, or the filesystem)

And, if needed, which sub-agents it delegates to

This turns agents into portable, reproducible artifacts you can run anywhere and share with anyone. 

Multi-agent challenges that cagent is solving

Create, run, and share multi-agent AI systems more easily with cagent.

Orchestrate agents (and sub-agents) more easily – Define roles and delegation (sub-agents). cagent manages calls and context.

Let agents use tools with guardrails – Grant capabilities with MCP: search, GitHub, files, databases. Each agent gets only the tools you list and is auditable.

Use (and swap) models – OpenAI, Anthropic, Gemini, or local models through Docker Model Runner. Swap providers without rewriting your system.

Treat agents like artifacts – Package, version, and share agents like containers.

How to build a multi-agent system with Docker cagent

Here’s what that looks like in practice.

Step 1: Define your multi-agent system

version: "2"

agents:
root:
model: anthropic/claude-sonnet-4-0
instruction: |
Break down a user request.
Ask the researcher to gather facts, then pass them to the writer.
sub_agents: ["researcher", "writer"]

researcher:
model: openai/gpt-5-mini
description: Agent to research and gather information.
instruction: Collect sources and return bullet points with links.
toolsets:
– type: mcp
ref: docker:duckduckgo

writer:
model: dmr/ai/qwen3
description: Agent to summarize notes.

instruction: Write a concise, clear summary from the researcher’s notes.

Step 2: Run the YAML file

cagent run team.yaml

The coordinator delegates, the researcher gathers, and the writer drafts. You now have a functioning team of agents.

Step 3: Share it on Docker Hub

cagent push ./team.yaml org/research-writer

Now, anyone on your team can run the exact same setup with:

cagent run docker.io/org/research-writer

That’s a full multi-agent workflow, built and shared in under 5 minutes.

First principles: Why cagent works

These principles keep cagent an easy-to-use and customizable multi-agent runtime to orchestrate AI agents.

Declarative > imperative. Multi-agent systems are mostly wiring: roles, tools, and topology. YAML keeps that wiring declarative, making it easy to define, read, and review.

Agents as artifacts. Agents become portable artifacts you can pull, pin, and trust.

Small surface area. A thin runtime that does one job well: coordinate agents.

What developers are building with cagent

Developers are already exploring different multi-agent use cases with cagent. Here are some examples:

1. PR and issue triaging

Collector reads PRs/issues, labels, failing checks

Writer drafts comments or changelogs

Coordinator enforces rules, routes edge cases

2. Research summarizing

Researcher finds and cites sources

Writer produces a clean summary

Reviewer checks for hallucinations and tone

3. Knowledge routing

Router classifies requests

KB agent queries internal docs

Redactor strips PII before escalation

Each one starts the same way: a YAML file and an idea. And they can be pushed to a registry and run by anyone.

Get started

cagent gives you the fastest path forward to build multi-agent systems. It’s open-source, easy to use, and built for the way developers already work. Define your agents, run them locally, and share them, all in a few lines of YAML.

YAML in, agents out.

Run the following to get started:

brew install cagent
cagent new
cagent run agent.yaml

Learn more

Get the technical details from our cagent documentation. 

We’d love to hear what you think. Join us in the Docker Community Slack. 

Dive into more topics about AI and Docker. 

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Quelle: https://blog.docker.com/feed/