Whether you’re debugging a build failure at 2 AM, managing security policies across distributed teams, or leveraging AI capabilities to build your applications, Docker Desktop delivers clear, real-world value that keeps your workflows moving and your infrastructure secure.

Accelerating Daily Development: Productivity and Control for Every Developer

Modern development teams face mounting pressures: complex multi-service applications, frequent context switching between tools, inconsistent local environments, and the constant need to balance productivity with security and governance requirements. For principal engineers managing these challenges, the friction of daily development workflows can significantly impact team velocity and code quality.

Docker Desktop addresses these challenges head-on by delivering seamless experiences that eliminate friction and giving organizations the control necessary to maintain security and compliance without slowing teams down.

Seamless Developer Experiences

Docker Debug is now free for all users, removing barriers to troubleshooting and making it easier for every developer on your team to diagnose issues quickly. The enhanced IDE integration goes deeper than ever before: the Dockerfile debugger in the VSCode Extension enables developers to step through build processes directly within their familiar editing environment, reducing the cognitive overhead of switching between tools. Whether you’re using VSCode, Cursor, or other popular editors, Docker Desktop integrates naturally into your existing workflow. For Windows-based enterprises, Docker Desktop’s ongoing engineering investments are delivering significant stability improvements with WSL2 integration, ensuring consistent performance for development teams at scale.

Getting applications from local development to production environments requires reducing the gap between how developers work locally and how applications run at scale. Compose to Kubernetes capabilities enable teams to translate local multi-service applications into production-ready Kubernetes deployments, while cagent provides a toolkit for running and developing agents that simplifies the development process. Whether you’re orchestrating containerized microservices or developing agentic AI workflows, Docker Desktop accelerates the path from experimentation to production deployment.

Enterprise-Level Control and Governance

For organizations requiring centralized management, Docker Desktop delivers enterprise-grade capabilities that maintain security without sacrificing developer autonomy. Administrators can set proxy settings via macOS configuration profiles, and can specify PAC files and Embedded PAC scripts with installer flags for macOS and Windows Docker, ensuring corporate network policies are automatically enforced during deployment without requiring manual developer configuration, further extending enterprise policy enforcement.

A faster release cadence with continuous updates ensures every developer runs the latest stable version with critical security patches, eliminating the traditional tension between IT requirements and developer productivity. The Kubernetes Dashboard is now part of the left navigation, making it easier to find and use.

Kind (k8s) Enterprise Support brings production-grade Kubernetes tooling to local development, enabling teams to test complex orchestration scenarios before deployment. 

Figure 1: K8 Settings

Together, these capabilities build on Docker Desktop’s position as the foundation for modern development, adding enterprise-grade management that scales with your organization’s needs. You get the visibility and control that enterprise architecture teams require while preserving the speed and flexibility that keeps developers productive.

Securing Container Workloads: Enterprise-Grade Protection Without Sacrificing Speed

As containerized applications move from development to production and AI workloads proliferate across enterprises, security teams face a critical challenge: how do you enforce rigorous security controls without creating bottlenecks that slow development velocity? Traditional approaches often force organizations to choose between security and speed, but that’s a false choice that puts both innovation and infrastructure at risk.

Docker Desktop’s recent releases address this tension directly, delivering enterprise-grade security controls that operate transparently within developer workflows. These aren’t afterthought features; they’re foundational protections designed to give security and platform teams confidence at scale while keeping developers productive.

Granular Control Over Container Behavior

Enforce Local Port Bindings prevents services running in Docker Desktop from being exposed across the local network, ensuring developers maintain network isolation during local development while retaining full functionality. For teams in regulated industries where network segmentation requirements extend to development environments, this capability helps maintain compliance standards without disrupting developer workflows.

Building on Secure Foundations

These runtime protections work in tandem with secure container foundations. Docker’s new Hardened Images, secure, minimal, production-ready container images maintained by Docker with near-zero CVEs and enterprise SLA backing. Recent updates introduced unlimited catalog pricing and the addition of Helm charts to the catalog. We also outlined Docker’s five pillars for Software Supply Chain Security, delivering transparency and eliminating the endless CVE remediation cycle. While Hardened Images are available as a separate add-on, they’re purpose-built to extend the secure-by-default foundation that Docker Desktop provides, giving teams a comprehensive approach to container security from development through production.

Seamless Enterprise Policy Integrations

The Docker CLI now gracefully handles certificates issued by non-conforming certificate authorities (CAs) that use negative serial numbers. While the X.509 standard specifies that certificate serial numbers must be positive, some enterprise PKI systems still produce certificates that violate this rule. Previously, organizations had to choose between adhering to their CA configuration and maintaining Docker compatibility, a frustrating trade-off that often led to insecure workarounds. Now, Docker Desktop works seamlessly with enterprise certificate infrastructure, ensuring developers can authenticate to private registries without security teams compromising their PKI standards.

These improvements reflect Docker’s commitment to being secure by default. Rather than treating security as a feature developers must remember to enable, Docker Desktop builds protection into the platform itself, giving enterprises the confidence to scale container adoption while maintaining the developer experience that drives innovation.

Unlocking AI Development: Making Model Context Protocol (MCP)Accessible for Every Developer

As AI-native development becomes central to modern software engineering, developers face a critical challenge: integrating AI capabilities into their workflows shouldn’t require extensive configuration knowledge or create friction that slows teams down. The Model Context Protocol (MCP) offers powerful capabilities for connecting AI agents to development tools and data sources, but accessing and managing these integrations has historically been complex, creating barriers to adoption, especially for teams with varying technical expertise.

Docker is addressing these challenges directly by making MCP integration seamless and secure within Docker Desktop.

Guided Onboarding Through Learning Center and MCP Toolkit Walkthroughs and Improved MCP Server Discovery

Understanding that accessibility drives adoption, Docker has introduced a redesigned onboarding experience through the Learning Center. The new MCP Toolkit Walkthroughs guide teams through complex setup processes step-by-step, ensuring that engineers of all skill levels can confidently adopt AI-powered workflows. Further, Docker’s MCP Server Discovery feature simplifies discovery by enabling developers to search, filter, and sort available MCP servers efficiently.  By eliminating the knowledge barriers and frictions around discovery, these improvements accelerate time to productivity and help organizations scale AI development practices across their teams.

Expanded Catalog: 270+ MCP Servers and Growing

The Docker MCP Catalog now includes over 270 MCP servers, with support for more than 60 remote servers. We’ve also added one-click connections for popular clients like Claude Code and Codex, making it easier than ever to supercharge your AI coding agents with powerful MCP tools. Getting started takes just a few clicks.

Remote MCP Server Support with Built-In OAuth

Connecting to MCP servers has traditionally meant dealing with manual tokens, fragile config files, and scattered credential management. It’s frustrating, especially for developers new to these workflows, who often don’t know where to find the right credentials in third-party tools. With the latest update to the Docker MCP Toolkit, developers can now securely connect to 60+ remote MCP servers, including Notion and Linear, using built-in OAuth support. This update goes beyond convenience; it lays the foundation for a more connected, intelligent, and automated developer experience, all within Docker Desktop. Read more about connecting to remote MCP servers.

Figure 2: Docker MCP Toolkit now supports remote MCP Servers with OAuth built-in

Smarter, More Efficient, and More Capable Agents with Dynamic MCPs

In this release, we’re introducing dynamic MCPs, a major step forward in enabling AI agents to discover, configure, and compose tools autonomously. Previously, integrating MCP servers required manual setup and static configurations. Now, with new features like Smart Search and Tool Composition, agents can search the MCP Catalog, pull only the tools they need, and even generate code to compose multi-tool workflows, all within a secure, sandboxed environment. These enhancements not only increase agent autonomy but also improve performance by reducing token usage and minimizing context bloat. Ultimately, this leads to less context switching and more focused time for developers. Read more about dynamic MCPs.

Together, these advancements represent Docker’s commitment to making AI-native development accessible and practical for development teams of any size.

Conclusion: Committed to Your Development Success

The innovations across Docker Desktop 4.45 through 4.50 reinforce our commitment to being the development solution teams rely on every day, for every workflow, at any scale.

We’ve made daily development faster and more integrated, with free debugging tools, native IDE support, and enterprise governance that actually works. We’ve strengthened security with controls that protect your infrastructure without creating bottlenecks. And we’ve made AI development accessible, turning complex integrations into guided experiences that accelerate your team’s capabilities. The impact is measurable. Independent research from theCUBE found that Docker Desktop users achieve 50% faster build times and reclaim 10-40+ hours per developer each month, time that goes directly back into innovation

This is Docker Desktop operating as your indispensable foundation: giving developers the tools they need to stay productive, giving security teams the controls they need to stay protected, and giving organizations the confidence they need to innovate at scale.

As we continue our accelerated release cadence, expect Docker to keep delivering the features that matter most to how you build, ship, and run modern applications. We’re committed to being the solution you can count on today and as your needs evolve.

Upgrade to the latest Docker Desktop now →

Learn more

• Subscribe to the Docker Navigator Newsletter
• Read theCUBE research report 
• Explore the MCP Catalog: Discover containerized, security-hardened MCP servers
• Explore cagent and give it a to follow along as it evolves
• New to Docker? Create an account. 
• Have questions? The Docker community is here to help.

Following on from our previous initiative to improve how Docker Desktop delivers updates, we are excited to announce another major improvement to how Docker Desktop keeps your development tools up to date. Starting with Docker Desktop 4.46, we’re introducing automatic component updates and a completely redesigned update experience that puts your productivity first.

Why We’re Making This Change

Your development workflow shouldn’t be interrupted by update notifications and restart requirements. With our new approach, you get:

• Zero workflow interruption – components update automatically in the background when a Docker Desktop restart is not required
• Always-current tools – Scout, Compose, Ask Gordon, and Model Runner stay up-to-date without manual intervention
• Better security posture – automatic updates mean you’re always running the latest, most secure versions
• Enterprise control – admin console cloud setting to control the update behaviour. 

What’s New in Docker Desktop 4.46

Silent Component Updates

Independent tools now update automatically in the background without any user interaction required and without impact on running containers:

• Docker Scout – Latest vulnerability scanning capabilities
• Docker Compose – New features and bug fixes
• Ask Gordon – Enhanced AI assistance improvements
• Model Runner – Updated model support and performance optimizations

Note that the component list above may change in the future as we add or remove features. 

Redesigned Update Experience

We have completely re-imagined how Docker Desktop communicates updates to you:

• Streamlined update flow with clearer messaging
• In-app release highlights showcasing key improvements you actually care about
• Reduced notification fatigue through more thoughtful update communications
• [Coming soon] Smart timing – GUI-only updates happen automatically when you close and reopen Docker 

Full Control When You Need It

Individual User Control

Want to manage updates yourself? You have complete control:

1. Go to Docker Desktop Settings
2. Navigate to Software Updates
3. Toggle “Automatically update components” on or off

Software updates: new setting to control opt in or out of automatic component updates.

Enterprise Management

For Docker Business subscribers, administrators maintain full governance through the admin console:

1. Access Admin Console > Desktop Settings Management
2. Edit your global policy
3. Configure “Automatically update components” to enable, disable, lock, or set defaults for your entire organization

This ensures enterprises can maintain their preferred update policies while giving individual developers the productivity benefits of seamless updates.

Admin console: desktop settings management policy contains a new silent update setting for enterprise control.

We Want Your Feedback

The redesigned update workflow is rolling out to the majority of our users as we gather feedback and refine the experience. We’re committed to getting this right, so please share your thoughts:

• In-app feedback popup – we do read those!
• Docker Slack community – join the conversation with other developers
• GitHub issues – report specific bugs or feature requests

New software update flow and design.

Getting Started

Docker Desktop 4.46 with silent component updates is available now. The new update experience will gradually roll out to all users over the coming weeks.

Already using Docker Desktop? Update in-app to get the latest features. 

New to Docker? Download Docker Desktop here to experience the most seamless development environment we’ve ever built.

Why We’re Making This Change

You’ve told us you want faster access to new features, bug fixes, and security updates. By moving from a monthly to a two-week cadence, you get:

• Earlier access to new features and improvements
• Reduced wait times for critical updates
• Faster bug fixes and security patches

Built on Proven Quality Processes

Our accelerated releases are backed by the same robust quality assurance that enterprise customers depend on:

• Comprehensive automated testing across platforms and configurations
• Docker Captains Community continues as our valued early adopter program, providing crucial feedback through beta channels
• Real-time reliability monitoring to catch issues early
• Feature flags for controlled rollout of major changes
• Canary deployments reaching a small percentage of users first

Coming Soon

Along with faster releases, we’re completely redesigning how updates work. The following changes are going to be rolled out very soon:

Smarter Component Updates

• Independent tools like Scout, Compose, Ask Gordon, and Model Runner update silently in the background
• No workflow interruption – the component updates happen when you’re not actively developing
• GUI updates (Docker Desktop dashboard) happen automatically when you close and reopen Docker Desktop

Clearer Update Information

• Simplified update flow
• In-app release highlights showcasing key improvements

Enterprise Control Maintained

We know enterprises need precise control over updates. The new model maintains the ability to disable in-app updates for local users or set defaults via the cloud admin console.

Getting Started

The new release cadence and update experience are rolling out in phases to all Docker Desktop users starting with version 4.45.0. Enterprise customers can access governance features through existing Docker Business subscriptions.

We’re excited to get improvements into your hands faster while maintaining the enterprise-grade reliability you expect from Docker Desktop.Download Docker Desktop here or update in-app!

Docker Model Runner Enhancements 

Inspectable Model Runner Workflows

Now you can inspect AI inference requests and responses directly from Docker Model Runner (DMR), helping you troubleshoot and debug model behavior quickly. This feature brings transparency and debugging capabilities to AI workflows and provides a major usability upgrade for those users experimenting with AI/LLM-based applications. 

Use the new request and response inspector for deeper visibility into your inference request/response cycle. This inspector captures HTTP request and response payloads, allowing you to examine prompt content, headers, and model outputs within the Model Runner runtime. This level of transparency helps you quickly identify malformed inputs,

Real-time Resource Checks 

Run multiple models concurrently with real-time resource checks. This enhancement prevents lock-ups and system slowdowns, and more importantly, allows running an embedding model together with an inference model, helping developers feel confident using Docker Desktop for advanced AI use cases. 

You’ll see a warning when system constraints may throttle performance, helping you avoid Docker Desktop (and your entire workstation) freezing mid-inference. Docker will detect GPU availability and memory constraints, issue warnings, and allow configuring CORS rules to safeguard the DMR endpoint during local development. These enhancements give developers confidence that even large in-scale model experiments won’t crash their system, ensuring smoother and more predictable local inference workflows.

Goose and Gemini CLI are now supported as MCP clients, with one-click setup via the Docker MCP Toolkit

The Docker MCP Toolkit now includes support for Goose and Gemini CLI as MCP clients, enabling developers to connect seamlessly to over 140 MCP servers available through the Docker MCP Catalog. This expanded client support allows Goose and Gemini users to access containerized MCP servers such as GitHub, Postgres, Neo4j, and many others, all with a single click. 

With one-click integration,  developers can spend less time configuring infrastructure and more time focusing on building intelligent, goal-driven agents. Docker handles the complexity behind the scenes, so teams can iterate faster and deploy with confidence.

Figure 1: Goose and Gemini CLI now supported as MCP clients for easy one-click setup. 

New Kubernetes Command in Docker Desktop CLI

Docker Desktop now includes a new CLI command for managing Kubernetes directly from the Docker Desktop CLI, reducing the need to toggle between tools or UI screens.

docker desktop kubernetes

This new command allows you to enable or disable the Kubernetes cluster included in Docker Desktop, check its status, and view configuration options, all from within the terminal. It integrates tightly with the Docker Desktop CLI, which manages other desktop-specific features like the Model Runner, Dev Environments, and WSL support.

This simplifies workflows because developers often have to move between Docker and Kubernetes environments. By bringing cluster management into the CLI, Docker reduces the cognitive overhead and speeds up workflows, especially for teams prototyping locally before deploying to managed clusters. Whether you’re preparing a microservice for deployment, running integration tests against a local cluster, or just toggling Kubernetes support for a temporary setup, this command helps you stay focused in your terminal and move faster.

Settings Search and Platform Upgrades

Improved search in Settings lets you find configurations faster without digging to locate toggles or preferences.

Figure 2: Improved search settings

Apple Virtualization is now the default virtualization backend

On macOS, Apple Virtualization is now the default virtualization backend, delivering superior performance. QEMU support has been fully removed to streamline startup times and resource usage. With virtualization handled natively via Apple’s hypervisor framework, users benefit from faster cold starts and more efficient memory management for container workloads. These enhancements simplify platform behavior and reduce friction when setting up or troubleshooting environments, saving valuable time during early-stage development. 

WSL2: Performance and Stability Enhancements

Under the hood, Docker has been tuned for smoother performance and improved stability, especially in Windows+WSL environments. Expect fewer freezes, faster startups, and more responsive UI behavior even when running heavy workloads. 

Updates include:

• Reduced background memory consumption
• Smarter CPU throttling for idle containers
• Tighter integration with WSL for graphics-based workloads 

This means you can confidently test graphics-heavy or multi-model pipelines on Windows without sacrificing responsiveness or stability.

Conclusion 

With 4.44, Docker Desktop strengthens both the developer experience and system reliability, whether you’re tuning prompts, orchestrating multiple AI models, or shifting into Kubernetes workflows. The goal is fewer surprises, deeper observability, and faster iteration.

But this release is another step in Docker’s journey to becoming your go-to development toolkit and your go-to platform for building secure AI applications. From new MCP integrations to GPU-powered Model Runner experiences, Docker is doubling down on helping developers build, test, and ship the next generation of intelligent software with simplicity, security, and speed.

We’re committed to evolving alongside the AI ecosystem so that Docker not only meets your current needs, but also becomes the platform you trust to take your ideas from prototype to production, faster and more securely than ever before.

Upgrade to the latest Docker Desktop now →

Learn more

• Authenticate and update today to receive your subscription level’s newest Docker Desktop features.
• Subscribe to the Docker Navigator Newsletter.
• Learn about our sign-in enforcement options.
• New to Docker? Create an account. 
• Have questions? The Docker community is here to help.

Model Runner now includes better model management, expanded OpenAI API compatibility, and fine-grained controls over runtime behavior. The improved MCP Catalog makes it easier to discover and use MCP servers, and now supports submitting your own MCP servers! Meanwhile, the MCP Toolkit streamlines integration with VS Code and GitHub, including built-in OAuth support for secure authentication. Gordon, Docker’s AI agent, now supports multi-threaded conversations with faster, more accurate responses. And with the new Compose Bridge, you can convert local compose.yaml files into Kubernetes configuration in a single command. 

Together, these updates streamline the process of building agentic AI apps and offer a preview of Docker’s ongoing efforts to make it easier to move from local development to production.

New model management commands and expanded OpenAI API support in Model Runner

This release includes improvements to the user interface of the Docker Model Runner, the inference APIs, and the inference engine under the hood.

Starting with the user interface, developers can now inspect models (including those already pulled from Docker Hub and those available remotely in the AI catalog) via model cards available directly in Docker Desktop. Below is a screenshot of what the model cards look like:

Figure 1: View model cards directly in Docker Desktop to get an instant overview of all variants in the model family and their key features.

In addition to the GUI changes, the docker model command adds three new subcommands to  help developers inspect, monitor, and manage models more effectively:

• docker model ps: Show which models are currently loaded into memory
• docker model df: Check disk usage for models and inference engines
• docker model unload: Manually unload a model from memory (before its idle timeout)

For WSL2 users who enable Docker Desktop integration, all of the docker model commands are also now available from their WSL2 distros, making it easier to work with models without changing your Linux-based workflow.

On the API side, Model Runner now offers additional OpenAI API compatibility and configurability. Specifically, tools are now supported with {“stream”: “true”}, making agents built on Docker Model Runner more dynamic and responsive. Model Runner’s API endpoints now support OPTIONS calls for better compatibility with existing tooling. Finally, developers can now configure CORS origins in the Model Runner settings pane, offering better compatibility and control over security. 

Figure 2: CORS Allowed Origins are now configurable in Docker Model Runner settings, giving developers greater flexibility and control.

For developers who need fine-grained control over model behavior, we’re also introducing the ability to set a model’s context size and even the runtime flags for the inference engine via Docker Compose, for example:

models:
  gemma3:
    model: ai/gemma3
    context_size: 8192
    runtime_flags:  ["--no-prefill-assistant"]

In this example, we’re using the (optional) context-size and runtime-flags parameters to control the behavior of the inference engine underneath. In this case, the associated runtime is the default (llama.cpp), and you can find a list of flags here. Certain flags may override the stable default configuration that we ship with Docker Desktop, but we want users to have full control over the inference backend. It’s also worth noting that a particular model architecture may limit the maximum context size. You can find information about maximum context lengths on the associated model cards on Docker Hub.

Under the hood, we’ve focused on improving stability and usability. We now have better error reporting in the event that an inference process crashes, along with more aggressive eviction of crashed engine processes. We’ve also enhanced the Docker CE Model Runner experience with better handling of concurrent usage and more robust support for model providers in Compose on Docker CE.

MCP Catalog & Toolkit: Secure, containerized AI tools at scale

New and redesigned MCP Catalog 

Docker’s MCP Catalog now features an improved experience, making it easier to search, discover, and identify the right MCP servers for your workflows. You can still access the catalog through Docker Hub or directly from the MCP Toolkit in Docker Desktop, and now, it’s also available via a dedicated web link for even faster access. 

Figure 3: Quickly find the right MCP server for your agentic app and use the new Catalog to browse by specific use cases.

The MCP Catalog currently includes over 100 verified, containerized tools, with hundreds more on the way. Unlike traditional npx or uvx workflows that execute code directly on your host, every MCP server in the catalog runs inside an isolated Docker container. Each one includes cryptographic signatures, a Software Bill of Materials (SBOM), and provenance attestations. 

This approach eliminates the risks of running unverified code and ensures consistent, reproducible environments across platforms. Whether you need database connectors, API integrations, or development tools, the MCP Catalog provides a trusted, scalable foundation for AI-powered development workflows that move the entire ecosystem away from risky execution patterns toward production-ready, containerized solutions.

Submit your MCP Server to the Docker MCP Catalog

We’re launching a new submission process, giving developers flexible options to contribute by following the process here.  Developers can choose between two options: Docker-Built and Community-Built servers. 

Docker-Built Servers 

When you see “Built by Docker,” you’re getting our complete security treatment. We control the entire build pipeline, providing cryptographic signatures, SBOMs, provenance attestations, and continuous vulnerability scanning.

Community-Built Servers 

These servers are packaged as Docker images by their developers. While we don’t control their build process, they still benefit from container isolation, which is a massive security improvement over direct execution.

Docker-built servers demonstrate the gold standard for security, while community-built servers ensure we can scale rapidly to meet developer demand. Developers can change their mind after submitting a community-built server and opt to resubmit it as a Docker-built server. 

Get your MCP server featured in the Docker MCP Catalog today and reach over 20 million developers. Learn more about our new MCP Catalog in our announcement blog and get insights on best practices on building, running, and testing MCP servers.  Join us in building the largest library of secure, containerized MCP servers! .

MCP Toolkit adds OAuth support and streamlined Integration with GitHub and VS Code

Many MCP servers’ credentials are passed as plaintext environment variables, exposing sensitive data and increasing the risk of leaks. The MCP Toolkit eliminates that risk with secure credential storage, allowing clients to authenticate with MCP servers and third-party services without hardcoding secrets. We’re taking it a step further with OAuth support, starting with the most widely used developer tool, GitHub. This will make it even easier to integrate secure authentication into your development workflow.

Figure 4: OAuth is now supported for the GitHub MCP server.

To set up your GitHub MCP server, go to the OAuth tab, connect your GitHub account, enable the server, and authorize OAuth for secure authentication.

Figure 5: Go to the configurations tab of the GitHub MCP servers to enable OAuth for secure authentication

The MCP Toolkit allows you to connect MCP servers to any MCP client, with one-click connection to popular ones such as Claude and Cursor. We are also making it easier for developers to connect to VSCode with the docker mcp client connect vscode command. When run in your project’s root folder, it creates an mcp.json configuration file in your .vscode folder. 

Figure 6: Connect to VS Code via MCP commands in the CLI.

Additionally, you can also configure the MCP Toolkit as a global MCP server available to VSCode by adding the following config to your user settings. Check out this doc for more details. Once connected, you can leverage GitHub Copilot in agent mode with full access to your repositories, issues, and pull requests.

"mcp": {
  "servers": {
    "MCP_DOCKER": {
      "command": "docker",
      "args": [
        "mcp",
        "gateway",
        "run"
      ],
      "type": "stdio"
    }
  }
}

Gordon gets smarter: Multi-threaded conversations and 5x faster performance

Docker’s AI Agent Gordon just got a major upgrade: multi-threaded conversation support. You can now run multiple distinct conversations in parallel and switch between topics like debugging a container issue in one thread and refining a Docker Compose setup in another, without losing context. Gordon keeps each thread organized, so you can pick up any conversation exactly where you left off.

Gordon’s new multi-threaded capabilities work hand-in-hand with MCP tools, creating a powerful boost for your development workflow. Use Gordon alongside your favorite MCP tools to get contextual help while keeping conversations organized by task. No more losing focus to context switching!

Figure 7: Gordon’s new multi-threaded support cuts down on context switching and boosts productivity.

We’ve also rolled out major performance upgrades, Gordon now responds 5x faster and delivers more accurate, context-aware answers. With improved understanding of Docker-specific commands, configurations, and troubleshooting scenarios, Gordon is smarter and more helpful than ever!

Compose Bridge: Seamlessly go from local Compose to Kubernetes 

We know that developers love Docker Compose for managing local environments—it’s simple and easy to understand. We’re excited to introduce Compose Bridge to Docker Desktop. This new powerful feature helps you transform your local compose.yaml into Kubernetes configuration with a single command.

Translate Compose to Kubernetes in seconds

Compose Bridge gives you a streamlined, flexible way to bring your Compose application to Kubernetes. With smart defaults and options for customization, it’s designed to support both simple setups and complex microservice architectures.

All it takes is:

docker compose bridge convert

And just like that, Compose Bridge generates the following Kubernetes resources from your Compose file:

• A Namespace to isolate your deployment
• A ConfigMap for every Compose config entry
• Deployments for running and scaling your services
• Services for exposed and published ports—including LoadBalancer services for host access
• Secrets for any secrets in your Compose file (encoded for local use)
• NetworkPolicies that reflect your Compose network topology
• PersistentVolumeClaims using Docker Desktop’s hostpath storage

This approach replicates your local dev environment in Kubernetes quickly and accurately, so you can test in production-like conditions, faster.

Built-in flexibility and upcoming enhancements

Need something more customized? Compose Bridge supports advanced transformation options so you can tweak how services are mapped or tailor the resulting configuration to your infrastructure.

And we’re not stopping here—upcoming releases will allow Compose Bridge to generate Kubernetes config based on your existing cluster setup, helping teams align development with production without rewriting manifests from scratch.

Get started

You can start using Compose Bridge today:

1. Download or update Docker Desktop
2. Open your terminal and run:
3. Review the documentation to explore customization options

docker compose bridge convert

Conclusion 

Docker Desktop 4.43 introduces practical updates for developers building at the intersection of AI and cloud-native apps. Whether you’re running local models, finding and running secure MCP servers, using Gordon for multi-threaded AI assistance, or converting Compose files to Kubernetes, this release cuts down on complexity so you can focus on shipping. From agentic AI projects to scaling workflows from local to production, you’ll get more control, smoother integration, and fewer manual steps throughout.

Learn more

• Learn more about our new MCP Catalog. 
• Submit your MCP servers to the MCP Catalog. 
• Authenticate and update today to receive your subscription level’s newest Docker Desktop features.
• Subscribe to the Docker Navigator Newsletter.
• Learn about our sign-in enforcement options.
• New to Docker? Create an account. 
• Have questions? The Docker community is here to help.

IPv6 support 

Docker Desktop now provides IPv6 networking capabilities with customization options to better support diverse network environments. You can now choose between dual IPv4/IPv6 (default), IPv4-only, or IPv6-only networking modes to align with your organization’s network requirements. The new intelligent DNS resolution behavior automatically detects your host’s network stack and filters unsupported record types, preventing connectivity timeouts in IPv4-only or IPv6-only environments. 

These ipv6 settings are available in Docker Desktop Settings > Resources > Network section and can be enforced across teams using Settings Management, making Docker Desktop more reliable in complex enterprise network configurations including IPv6-only deployments.

Further documentation here.

Figure 1: Docker Desktop IPv6 settings

Docker MCP Toolkit integrated into Docker Desktop

Last month, we launched the Docker MCP Catalog and Toolkit to help developers easily discover MCP servers and securely connect them to their favorite clients and agentic apps. We’re humbled by the incredible support from the community. User growth is up by over 50%, and we’ve crossed 1 million pulls! Now, we’re excited to share that the MCP Toolkit is built right into Docker Desktop, no separate extension required.

You can now access more than 100 MCP servers, including GitHub, MongoDB, Hashicorp, and more, directly from Docker Desktop – just enable the servers you need, configure them, and connect to clients like Claude Desktop, Cursor, Continue.dev, or Docker’s AI agent Gordon.

Unlike typical setups that run MCP servers via npx or uvx processes with broad access to the host system, Docker Desktop runs these servers inside isolated containers with well-defined security boundaries. All container images are cryptographically signed, with proper isolation of secrets and configuration data. 

Figure 2: Docker MCP Toolkit is now integrated natively into Docker Desktop

To meet developers where they are, we’re bringing Docker MCP support to the CLI, using the same command structure you’re already familiar with. With the new docker mcp commands, you can launch, configure, and manage MCP servers directly from the terminal. The CLI plugin offers comprehensive functionality, including catalog management, client connection setup, and secret management.

Figure 3:  Docker MCP CLI commands.

Docker AI Agent Gordon Now Supports MCP Toolkit Integration

In this release, we’ve upgraded Gordon, Docker’s AI agent, with direct integration to the MCP Toolkit in Docker Desktop. To enable it, open Gordon, click the “Tools” button, and toggle on the “MCP” Toolkit option. Once activated, the MCP Toolkit tab will display tools available from any MCP servers you’ve configured.

Figure 4: Docker’s AI Agent Gordon now integrates with Docker’s MCP Toolkit, bringing 100+ MCP servers

This integration gives you immediate access to 100+ MCP servers with no extra setup, letting you experiment with AI capabilities directly in your Docker workflow. Gordon now acts as a bridge between Docker’s native tooling and the broader AI ecosystem, letting you leverage specialized tools for everything from screenshot capture to data analysis and API interactions – all from a consistent, unified interface.

Figure 5: Docker’s AI Agent Gordon uses the GitHub MCP server to pull issues and suggest solutions.

Finally, we’ve also improved the Dockerize feature with expanded support for Java, Kotlin, Gradle, and Maven projects. These improvements make it easier to containerize a wider range of applications with minimal configuration. With expanded containerization capabilities and integrated access to the MCP Toolkit, Gordon is more powerful than ever. It streamlines container workflows, reduces repetitive tasks, and gives you access to specialized tools, so you can stay focused on building, shipping, and running your applications efficiently.

Docker Model Runner adds Qualcomm support, Docker Engine Integration, and UX Upgrades

Staying true to our philosophy of giving developers more flexibility and meeting them where they are, the latest version of Docker Model Runner adds broader OS support, deeper integration with popular Docker tools, and improvements in both performance and usability.

In addition to supporting Apple Silicon and Windows systems with NVIDIA GPUs, Docker Model Runner now works on Windows devices with Qualcomm chipsets. Under the hood, we’ve upgraded our inference engine to use the latest version of llama.cpp, bringing significantly enhanced tool calling capabilities to your AI applications.Docker Model Runner can now be installed directly in Docker Engine Community Edition across multiple Linux distributions supported by Docker Engine. This integration is particularly valuable for developers looking to incorporate AI capabilities into their CI/CD pipelines and automated testing workflows. To get started, check out our documentation for the setup guide.

Get Up and Running with Models Faster

The Docker Model Runner user experience has been upgraded with expanded GUI functionality in Docker Desktop. All of these UI enhancements are designed to help you get started with Model Runner quickly and build applications faster. A dedicated interface now includes three new tabs that simplify model discovery, management, and streamline troubleshooting workflows. Additionally, Docker Desktop’s updated GUI introduces a more intuitive onboarding experience with streamlined “two-click” actions.

After clicking on the Model tab, you’ll see three new sub-tabs. The first, labeled “Local,” displays a set of models in various sizes that you can quickly pull. Once a model is pulled, you can launch a chat interface to test and experiment with it immediately.

Figure 6: Access a set of models of various sizes to get quickly started in Models menu of Docker Desktop

The second tab ”Docker Hub” offers a comprehensive view for browsing and pulling models from Docker Hub’s AI Catalog, making it easy to get started directly within Docker Desktop, without switching contexts.

Figure 7: A shortcut to the Model catalog from Docker Hub in Models menu of Docker Desktop

The third tab “Logs” offers real-time access to the inference engine’s log tail, giving developers immediate visibility into model execution status and debugging information directly within the Docker Desktop interface.

Figure 8: Gain visibility into model execution status and debugging information in Docker Desktop

Model Packaging Made Simple via CLI

As part of the Docker Model CLI, the most significant enhancement is the introduction of the docker model package command. This new command enables developers to package their models from GGUF format into OCI-compliant artifacts, fundamentally transforming how AI models are distributed and shared. It enables seamless publishing to both public and private and OCI-compatible repositories such as Docker Hub and establishes a standardized, secure workflow for model distribution, using the same trusted Docker tools developers already rely on. See our docs for more details. 

Conclusion 

From intelligent networking enhancements to seamless AI integrations, Docker Desktop 4.42 makes it easier than ever to build with confidence. With native support for IPv6, in-app access to 100+ MCP servers, and expanded platform compatibility for Docker Model Runner, this release is all about meeting developers where they are and equipping them with the tools to take their work further. Update to the latest version today and unlock everything Docker Desktop 4.42 has to offer.

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Docker Model Runner now supports Windows, Compose & Testcontainers

This release brings Docker Model Runner to Windows users with NVIDIA GPU support. We’ve also introduced improvements that make it easier to manage, push, and share models on Docker Hub and integrate with familiar tools like Docker Compose and Testcontainers. Docker Model Runner works with Docker Compose projects for orchestrating model pulls and injecting model runner services, and Testcontainers via its libraries. These updates continue our focus on helping developers build AI applications faster using existing tools and workflows. 

In addition to CLI support for managing models, Docker Desktop now includes a dedicated “Models” section in the GUI. This gives developers more flexibility to browse, run, and manage models visually, right alongside their containers, volumes, and images.

Figure 1: Easily browse, run, and manage models from Docker Desktop

Further extending the developer experience, you can now push models directly to Docker Hub, just like you would with container images. This creates a consistent, unified workflow for storing, sharing, and collaborating on models across teams. With models treated as first-class artifacts, developers can version, distribute, and deploy them using the same trusted Docker tooling they already use for containers — no extra infrastructure or custom registries required.

docker model push <model>

The Docker Compose integration makes it easy to define, configure, and run AI applications alongside traditional microservices within a single Compose file. This removes the need for separate tools or custom configurations, so teams can treat models like any other service in their dev environment.

Figure 2: Using Docker Compose to declare services, including running AI models

Similarly, the Testcontainers integration extends testing to AI models, with initial support for Java and Go and more languages on the way. This allows developers to run applications and create automated tests using AI services powered by Docker Model Runner. By enabling full end-to-end testing with Large Language Models, teams can confidently validate application logic, their integration code, and drive high-quality releases.

String modelName = "ai/gemma3";
DockerModelRunnerContainer modelRunnerContainer = new DockerModelRunnerContainer()
       .withModel(modelName);
modelRunnerContainer.start();


OpenAiChatModel model = OpenAiChatModel.builder()
       .baseUrl(modelRunnerContainer.getOpenAIEndpoint())
       .modelName(modelName)
       .logRequests(true)
       .logResponses(true)
       .build();


String answer = model.chat("Give me a fact about Whales.");
System.out.println(answer);

Docker DX Extension in Visual Studio: Catch issues early, code with confidence 

The Docker DX Extension is now live on the Visual Studio Marketplace. This extension streamlines your container development workflow with rich editing, linting features, and built-in vulnerability scanning. You’ll get inline warnings and best-practice recommendations for your Dockerfiles, powered by Build Check — a feature we introduced last year. 

It also flags known vulnerabilities in container image references, helping you catch issues early in the dev cycle. For Bake files, it offers completion, variable navigation, and inline suggestions based on your Dockerfile stages. And for those managing complex Docker Compose setups, an outline view makes it easier to navigate and understand services at a glance.

Figure 3: Docker DX Extension in Visual Studio provides actionable recommendations for fixing vulnerabilities and optimizing Dockerfiles

Read more about this in our announcement blog and GitHub repo. Get started today by installing Docker DX – Visual Studio Marketplace 

MacOS QEMU virtualization option deprecation

The QEMU virtualization option in Docker Desktop for Mac will be deprecated on July 14, 2025. 

With the new Apple Virtualization Framework, you’ll experience improved performance, stability, and compatibility with macOS updates as well as tighter integration with Apple Silicon architecture. 

What this means for you:

• If you’re using QEMU as your virtualization backend on macOS, you’ll need to switch to either Apple Virtualization Framework (default) or Docker VMM (beta) options.
• This does NOT affect QEMU’s role in emulating non-native architectures for multi-platform builds.
• Your multi-architecture builds will continue to work as before.

For complete details, please see our official announcement. 

Introducing Docker Desktop in the Microsoft Store

Docker Desktop is now available for download from the Microsoft Store! We’re rolling out an EXE-based installer for Docker Desktop on Windows. This new distribution channel provides an enhanced installation and update experience for Windows users while simplifying deployment management for IT administrators across enterprise environments.

Key benefits

For developers:

• Automatic Updates: The Microsoft Store handles all update processes automatically, ensuring you’re always running the latest version without manual intervention.
• Streamlined Installation: Experience a more reliable setup process with fewer startup errors.
• Simplified Management: Manage Docker Desktop alongside your other applications in one familiar interface.

For IT admins: 

• Native Intune MDM Integration: Deploy Docker Desktop across your organization with Microsoft’s native management tools.
• Centralized Deployment Control: Roll out Docker Desktop more easily through the Microsoft Store’s enterprise distribution channels.
• Automatic Updates Regardless of Security Settings: Updates are handled automatically by the Microsoft Store infrastructure, even in organizations where users don’t have direct store access.
• Familiar Process: The update mechanism maps to the widget command, providing consistency with other enterprise software management tools.

This new distribution option represents our commitment to improving the Docker experience for Windows users while providing enterprise IT teams with the management capabilities they need.

Unlock greater flexibility: Enable ACH transfer as a payment option for self-serve customers

We’re focused on making it easier for teams to scale, grow, and innovate. All on their own terms. That’s why we’re excited to announce an upgrade to the self-serve purchasing experience: customers can pay via ACH transfer starting on 4/30/25.

Historically, self-serve purchases were limited to credit card payments, forcing many customers who could not use credit cards into manual sales processes, even for small seat expansions. With the introduction of an ACH transfer payment option, customers can choose the payment method that works best for their business. Fewer delays and less unnecessary friction.

This payment option upgrade empowers customers to:

• Purchase more independently without engaging sales
• Choose between credit card or ACH transfer with a verified bank account

By empowering enterprises and developers, we’re freeing up your time, and ours, to focus on what matters most: building, scaling, and succeeding with Docker.

Visit our documentation to explore the new payment options, or log in to your Docker account to get started today!

Wrapping up 

With Docker Desktop 4.41, we’re continuing to meet developers where they are — making it easier to build, test, and ship innovative apps, no matter your stack or setup. Whether you’re pushing AI models to Docker Hub, catching issues early with the Docker DX Extension, or enjoying faster virtualization on macOS, these updates are all about helping you do your best work with the tools you already know and love. We can’t wait to see what you build next!

Learn more

• Authenticate and update today to receive your subscription level’s newest Docker Desktop features.
• Subscribe to the Docker Navigator Newsletter.
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• New to Docker? Create an account. 
• Have questions? The Docker community is here to help.

Keep reading to find updates on new tooling like Model Runner and a more powerful Docker AI Agent with MCP capabilities. Plus, with the AI Tool Catalog, teams can now easily build smarter AI-powered applications and agents with MCPs. And with Docker Desktop Setting Reporting, admins now get greater visibility into compliance and policy enforcement.

Docker Model Runner (Beta): Bringing local AI model execution to developers 

Now in beta with Docker Desktop 4.40, Docker Model Runner makes it easier for developers to run AI models locally. No extra setup, no jumping between tools, and no need to wrangle infrastructure. This first iteration is all about helping developers quickly experiment and iterate on models right from their local machines.

The beta includes three core capabilities:

• Local model execution, right out of the box
  
• GPU acceleration on Apple Silicon for faster performance
  
• Standardized model packaging using OCI Artifacts

Powered by llama.cpp and accessible via the OpenAI API, the built-in inference engine makes running models feel as simple as running a container. On Mac, Model Runner uses host-based execution to tap directly into your hardware — speeding things up with zero extra effort.

Models are also packaged as OCI Artifacts, so you can version, store, and ship them using the same trusted registries and CI/CD workflows you already use. Check out our docs for more detailed info!

Figure 1: Using Docker Model Runner and CLI commands to experiment with models locally

This release lays the groundwork for what’s ahead: support for additional platforms like Windows with GPU, the ability to customize and publish your own models, and deeper integration into the development loop. We’re just getting started with Docker Model Runner and look forward to sharing even more updates and enhancements in the coming weeks.

Docker AI Agent: Smarter and more powerful with MCP integration + AI Tool Catalog

Our vision for the Docker AI Agent is simple: be context-aware, deeply knowledgeable, and available wherever developers build. With this release, we’re one step closer! The Docker AI Agent is now even more capable, making it easier for developers to tap into the Docker ecosystem and streamline their workflows beyond Docker. 

Your trusted AI Agent for all things Docker 

The Docker AI agent now has built-in support for many new popular developer capabilities like:

• Running shell commands
• Performing Git operations
• Downloading resources
• Managing local files

Thanks to a Docker Scout integration, we also now support other tools from the Docker ecosystem, such as performing security analysis on your Dockerfiles or images. 

Expanding the Docker AI Agent beyond Docker 

The Docker AI Agent now fully embraces the Model Context Protocol (MCP). This new standard for connecting AI agents and models to external data and tools makes them more powerful and tailored to specific needs. In addition to acting as an MCP client, many of Docker AI Agent’s capabilities are now exposed as MCP Servers. This means you can interact with the agent in Docker Desktop GUI or CLI or your favorite client, such as Claude Desktop and Cursor.

Figure 2: Extending Docker AI Agent’s capabilities with many tools, including the MCP Catalog. 

AI Tool Catalog: Your launchpad for experimenting with MCP servers

Thanks to the AI Tool Catalog extension in Docker Desktop, you can explore different MCP servers and seamlessly connect the Docker AI agent to other tools or other LLMs to the Docker ecosystem. No more manually configuring multiple MCP servers! We’ve also added secure handling and injection of MPC servers’ secrets, such as API keys, to simplify log-ins and credential management.

The AI Tool Catalog includes containerized servers that have been pushed to Docker Hub, and we’ll continue to expand them. If you’re working in this space or have an MCP server that you’d like to distribute, please reach out in our public GitHub repo. To install the AI Tool Catalog, go to the extensions menu of Docker Desktop or use this for installation.

Figure 3: Explore and discover MCP servers in the AI Tools Catalog extension in Docker Desktop

Bring compliance into focus with Docker Desktop Setting Reporting

Building on the Desktop Settings Management capabilities introduced in Docker Desktop 4.36, Docker Desktop 4.40 brings robust compliance reporting for Docker Business customers. This new powerful feature gives administrators comprehensive visibility into user compliance with assigned settings policies across the organization.

Key benefits

• Real-time compliance tracking: Easily monitor which users are compliant with their assigned settings policies. This allows administrators to quickly identify and address non-compliant systems and users.
• Streamlined troubleshooting: Detailed compliance status information helps administrators diagnose why certain users might be non-compliant, reducing resolution time and IT overhead.

Figure 4: Desktop settings reporting provides an overview of policy assignment and compliance status, helping organizations stay compliant. 

Get started with Docker Desktop Setting Reporting

The Desktop Setting Reporting dashboard is currently being rolled out through Early Access. Administrators can see which settings policies are assigned to each user and whether those policies are being correctly applied.

Soon, administrators will be able to access the reporting dashboard by navigating to the Admin Console > Docker Desktop > Reporting. The dashboard provides a clear view of all users’ compliance status, with options to:

• Search by username or email address
• Filter by assigned policies
• Toggle visibility of compliant users to focus on potential issues
• View detailed compliance information for specific users
• Download comprehensive compliance data as a CSV file

The dashboard also provides targeted resolution steps for non-compliant users to help administrators quickly address issues and ensure organizational compliance.

This new reporting capability underscores Docker’s commitment to providing enterprise-grade management tools that simplify administration while maintaining security and compliance across diverse development environments. Learn more about Desktop settings reporting here.

Wrapping up 

Docker is expanding its AI tooling to simplify application development and improve team workflows. New additions like Model Runner, the Docker AI Agent with MCP server and client support, and the AI Tool Catalog extension in Docker Desktop help streamline how developers build with AI. We continue to make enterprise tools more useful and robust, giving admins better visibility into compliance and policy enforcement through Docker Desktop Settings Reporting. We can’t wait to see what you build next!

Learn more

• Authenticate and update today to receive your subscription level’s newest Docker Desktop features.
• Subscribe to the Docker Navigator Newsletter.
• Learn about our sign-in enforcement options.
• New to Docker? Create an account. 
• Have questions? The Docker community is here to help.

Docker AI Agent: Smarter, more capable, and now with MCP & Kubernetes

In our last release, we introduced the Docker AI Agent in beta as an AI-powered, context-aware assistant built into Docker Desktop and the CLI. It simplifies container management, troubleshooting, and workflows with guidance and automation. And the response has been incredible: a 9x increase in weekly active users. With each Docker Desktop release, we’re making Docker AI Agent smarter, more helpful, and more versatile across developer container workflows. And if you’re using Docker for GitHub Copilot, you’ll get these upgrades automatically — so you’re always working with the latest and greatest.

Docker AI Agent now supports Model Context Protocol (MCP) and Kubernetes, along with usability upgrades like multiline prompts and easy copying. The agent can now also interact with the Docker Engine to list and clean up containers, images, and volumes. Plus, with access to the Kubernetes cluster, Docker AI Agent can list namespaces, deploy and expose, for example, an Nginx service, and analyze pod logs. 

How Docker AI Agent Uses MCP

MCP is a new standard for connecting AI agents and models to external data and tools. It lets AI-powered apps and agents retrieve data and information from external sources, perform operations with third-party services, and interact with local filesystems, unlocking new and expanded capabilities. MCP works by introducing the concept of MCP clients and MCP Servers, this way clients request resources and the servers handle the request and perform the requested action.

The Docker AI Agent acts as an MCP client and can interact with MCP servers running as containers. When running the docker ai command in the terminal or in the Docker Desktop AI Agent window to ask a question, the agent looks for a gordon-mcp.yml file in the working directory for a list of MCP servers that should be used when in that context. For example, as a specialist in all things Docker, Docker AI Agent can:

• Access the internet via the MCP fetch server.
• Create a project on GitHub with the MCP Github server

To make MCP adoption easier and more secure, Docker has collaborated with Anthropic to build container images for the reference implementations of MCP servers, available on Docker Hub under the mcp namespace. Check out our docs for examples of using MCP with Docker AI Agent. 

Containerizing apps in multiple popular languages: More coming soon

Docker AI Agent is also more capable, and can now support the containerization of applications in new programming languages including:

• JavaScript/TypeScript applications using npm, pnpm, yarn and bun;
• Go applications using Go modules;
• Python applications using pip, poetry, and uv;
• C# applications using nuget

Try it out — just ask, “Can you containerize my application?” 

Once the agent runs through steps such as determining the number of services in the project, the language, package manager, and relevant information for containerization, it’ll generate Docker-related assets. You’ll have an optimized Dockerfile, Docker Compose file, dockerignore file, and a README to jumpstart your application with Docker. 

More language and package manager support will be available soon!

Figure 1: Docker AI Agent helps with containerizing your app and shows steps of its work

No need to write scripts, just ask Docker AI Agent

The Docker AI Agent also comes with built-in capabilities such as interfacing with containers, images, and volumes. Instead of writing scripts, you can simply ask in natural language to perform complex operations.  For example, combining various servers, to do complex tasks such as finding and cleaning unused images.

Figure 2: Finding and optimizing unused images storage with a simple ask to Docker AI Agent

Docker Desktop CLI: Now in GA

With the Docker Desktop 4.37 release, we introduced the Docker Desktop CLI controller in Beta, a command-line tool to manage Docker Desktop. In addition to performing tasks like starting, stopping, restarting, and checking the status of Docker Desktop directly from the command line, developers can also print logs and update to the latest version of Docker Desktop. 

Docker meets developers where they work — whether in the CLI or GUI. With the Docker Desktop CLI, developers can seamlessly switch between GUI and command-line workflows, tailoring their workflows to their needs. 

This feature lets you automate Docker Desktop operations in CI/CD pipelines, expedites troubleshooting directly from the terminal, and creates a smoother, distraction-free workflow. IT admins also benefit from this feature; for example, they can use these commands in automation scripts to manage updates. 

Improve multi-platform image management with the new --platform flag 

Containerized applications often need to run across multiple architectures, making efficient platform-specific image management essential. To simplify this, we’ve introduced a --platform flag for docker save, docker load, and docker history. This addition will let developers explicitly select and manage images for specific architectures like linux/amd64, linux/arm64, and more.

The new –platform flag gives you full control over environment variants when saving or loading. For example, exporting only the linux/arm64 version of an image is now as simple as running:

docker save --platform linux/arm64 -o my-image.tar my-app:latest

Similarly, docker load --platform linux/amd64 ensures that only the amd64 variant is imported from a multi-architecture archive, reducing ambiguity and improving cross-platform workflows. For debugging and optimization, docker history --platform provides detailed insights into the build history of a specific architecture.

These enhancements streamline multi-platform development by giving developers full control over how they build, store, and distribute images. 

Head over to our history, load, and save documentation to learn more! 

Wrapping up 

Docker Desktop 4.39 reinforces our commitment to streamlining the developer experience. With Docker AI Agent’s expanded support for MCP, Kubernetes, built-in capabilities of interacting with containers, and more, developers can simplify and customize their workflow. They can also seamlessly switch between the GUI and command-line, while creating automations with the Docker Desktop CLI. Plus, with the new --platform flag, developers now have full control over how they build, store, and distribute images. 

Less friction, more flexibility — we can’t wait to see what you build next!

Authenticate and update today to receive your subscription level’s newest Docker Desktop features.

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• Subscribe to the Docker Navigator Newsletter.
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We’re excited to announce the General Availability of Bake, a powerful feature for optimizing build performance and multi-node Kubernetes testing to help teams “shift left.” We’re also expanding availability for several enterprise features designed to boost operational efficiency. And last but not least, Docker AI Agent (formerly Project: Agent Gordon) is now in Beta, delivering intelligent, real-time Docker-related suggestions across Docker CLI, Desktop, and Hub. It’s here to help developers navigate Docker concepts, fix errors, and boost productivity.

Docker’s AI Agent boosts developer productivity  

We’re thrilled to introduce Docker AI Agent (also known as Project: Agent Gordon) — an embedded, context-aware assistant seamlessly integrated into the Docker suite. Available within Docker Desktop and CLI, this innovative agent delivers real-time, tailored guidance for tasks like container management and Docker-specific troubleshooting — eliminating disruptive context-switching. Docker AI agent can be used for every Docker-related concept and technology, whether you’re getting started, optimizing an existing Dockerfile or Compose file, or understanding Docker technologies in general. By addressing challenges precisely when and where developers encounter them, Docker AI Agent ensures a smoother, more productive workflow. 

The first iteration of Docker’s AI Agent is now available in Beta for all signed-in users. The agent is disabled by default, so user activation is required. Read more about Docker’s New AI Agent and how to use it to accelerate developer velocity here. 

Figure 1: Asking questions to Docker AI Agent in Docker Desktop

Simplify build configurations and boost performance with Docker Bake

Docker Bake is an orchestration tool that simplifies and speeds up Docker builds. After launching as an experimental feature, we’re thrilled to make it generally available with exciting new enhancements.

While Dockerfiles are great for defining build steps, teams often juggle docker build commands with various options and arguments — a tedious and error-prone process. Bake changes the game by introducing a declarative file format that consolidates all options and image dependencies (also known as targets) in one place. No more passing flags to every build command! Plus, Bake’s ability to parallelize and deduplicate work ensures faster and more efficient builds.

Key benefits of Docker Bake

• Simplicity: Abstract complex build configurations into one simple command.
• Flexibility: Write build configurations in a declarative syntax, with support for custom functions, matrices, and more.
• Consistency: Share and maintain build configurations effortlessly across your team.
• Performance: Bake parallelizes multi-image workflows, enabling faster and more efficient builds.

Developers can simplify multi-service builds by integrating Bake directly into their Compose files — Bake supports Compose files natively. It enables easy, efficient building of multiple images from a single repository with shared configurations. Plus, it works seamlessly with Docker Build Cloud locally and in CI. With Bake-optimized builds as the foundation, developers can achieve more efficient Docker Build Cloud performance and faster builds.

Learn more about streamlining build configurations, boosting performance, and improving team workflows with Bake in our announcement blog. 

Shift Left with Multi-Node Kubernetes testing in Docker Desktop

In today’s complex production environments, “shifting left”  is more essential than ever. By addressing concerns earlier in the development cycle, teams reduce costs and simplify fixes, leading to more efficient workflows and better outcomes. That’s why we continue to bring new features and enhancements to integrate feedback directly into the developer’s inner loop

Docker Desktop now includes Multi-Node Kubernetes integration, enabling easier and extensive testing directly on developers’ machines. While single-node clusters allow for quick verification of app deployments, they fall short when it comes to testing resilience and handling the complex, unpredictable issues of distributed systems. To tackle this, we’re updating our Kubernetes distribution with kind — a lightweight, fast, and user-friendly solution for local test and multi-node cluster simulations.

Figure 2: Selecting Kubernetes version and cluster number for testing

Key Benefits:

• Multi-node cluster support: Replicate a more realistic production environment to test critical features like node affinity, failover, and networking configurations.
• Multiple Kubernetes versions: Easily test across different Kubernetes versions, which is a must for validating migration paths.
• Up-to-date maintenance: Since kind is an actively maintained open-source project, developers can update to the latest version on demand without waiting for the next Docker Desktop release.

Head over to our documentation to discover how to use multi-node Kubernetes clusters for local testing and simulation.

General availability of administration features for Docker Business subscription

With the Docker Desktop 4.36 release, we introduced Beta enterprise admin tools to streamline administration, improve security, and enhance operational efficiency. And the feedback from our Early Access Program customers has been overwhelmingly positive. 

For instance, enforcing sign-in with macOS configuration files and across multiple organizations makes deployment easier and more flexible for large enterprises. Also, the PKG installer simplifies managing large-scale Docker Desktop deployments on macOS by eliminating the need to convert DMG files into PKG first.

Today, the features below are now available to all Docker Business customers.  

• Enforce sign-in with macOS configuration profiles 
• Enforce sign-in for more than one organization at a time 
• Deploy Docker Desktop for Mac in bulk with the PKG installer 

Looking ahead, Docker is dedicated to continue expanding enterprise administration capabilities. Stay tuned for more announcements!

Wrapping up 

Docker Desktop 4.38 reinforces our commitment to simplifying the developer experience while equipping enterprises with robust tools. 

With Bake now in GA, developers can streamline complex build configurations into a single command. The new Docker AI Agent offers real-time, on-demand guidance within their preferred Docker tools. Plus, with Multi-node Kubernetes testing in Docker Desktop, they can replicate realistic production environments and address issues earlier in the development cycle. Finally, we made a few new admin tools available to all our Business customers, simplifying deployment, management, and monitoring. 

We look forward to how these innovations accelerate your workflows and supercharge your operations! 

Learn more

• Authenticate and update to receive your subscription level’s newest Docker Desktop features.
• Subscribe to the Docker Navigator Newsletter.
• Learn about our sign-in enforcement options.
• New to Docker? Create an account. 
• Have questions? The Docker community is here to help.