Run parallel AI coding agents parallel to container-use from fanatal

In AI-based development, coding agents have become indispensable collaborators. This can write, test autonomous or semi-autonomous equipment, and refector code, dramatically accelerating the development cycle. Nevertheless, as the number of agents working on the same codebase increases, challenges: the contradiction of dependence, state leakage between agents and the difficulty of tracking the actions of each agent. Container-use from the column addresses these challenges by offering a containerized environment designed for project coding agents. By separating each agent in its container, developers can run multiple agents simultaneously without interference, monitor their activities in real-time and intervene directly when needed.

Traditionally, when a coding agent operates tasks, such as installing dependence, running build scripts or launching servers, it does so in the developer’s local environment. This approach quickly leads to contradiction: an agent can upgrade a shared library that breaks the workflow of another agent, or the rugs that blur the afterlike script can leave the artifacts. Containment solves these issues beautifully by containing the atmosphere of each agent. Instead of one after a baby, you can spin in a completely fresh environment, experiment safely, and immediately discard the failure by maintaining the visibility of each agent exactly.

Moreover, because containers can be governed by familiar equipment, docker, git and standard CLI utilities, container-use integrated into existing workflows. Instead of doing King K in ownership solutions, teams can take advantage of their preferred tech stack, even if it means Python virtual environment, node.JJS toolchaens or system-level packages. The result is a flexible architecture that empowers developers to increase the full probability of coding agents, without sacrificing control or transparency.

Installation and setup

Starting with container-use is straightforward. The project offers a Go-based CLI tool, ‘Q’, which you create and install by a simple ‘Mac’ command. Diff default, as a build targets your current platform, but the cross-compilation standard ‘Targetplatform’ is supported by environment variables.

# Build the CLI tool
make

# (Optional) Install into your PATH
make install && hash -r

After running these commands, the ‘Q’ binary becomes available in your shell, ready to start containerized sessions for any MCP-compatible agent. If you need to compile for a different architecture, say, ARM 64 for raspberry pie, only the build with the target platform:

TARGETPLATFORM=linux/arm64 make

This flexibility ensures that you are developing on the Macose, any taste of Windows subsystem or Linux for Linux, you can ease the environment-specific binary.

Integrated with your favorite agents

One of the power of container-use is the consistency with any agent that speaks the model reference protocol (MCP). The project provides an example integration for popular tools such as cloud code, cursor, gitthb copilot and goose. Integration usually includes adding ‘container-use’ as an MCP server in your agent’s configuration and enable it:

Cloud code uses an NPM assistant to register the server. You can merge Dagger’s recommended instructions into your ‘Cloud.MD’ so that the ‘cloud’ automatically distinguishes agents:

  npx @anthropic-ai/claude-code mcp add container-use -- $(which cu) stdio
  curl -o CLAUDE.md https://raw.githubusercontent.com/dagger/container-use/main/rules/agent.md

Goose, browser-based agent framework, reads from ‘~/.config/goose/config.yaml’. Adding the ‘container-use’ section there is to direct the goose to launch each browsing agent inside its own container:

  extensions:
    container-use:
      name: container-use
      type: stdio
      enabled: true
      cmd: cu
      args:
        - stdio
      envs: {}

The cursor, AI Code Assistant, can be hooked by placing a rule file in your project. With ‘Curl’ you bring the recommended rule and it ‘. Put in cursor/rules/container-umdc ‘.

Wiscode and Gittub Copilot users can update their ‘Settings.Jesson’ and ‘.Githb/Copilot-Instruction.MD’ respectively, pointing to the ‘CU’ command as an MCP server. The copilot then runs within the atmosphere containing its code perfection. The kilo code integrates by the JSON-based settings file, allows you to refer to any necessary arguments under the ‘CU’ command and ‘McPSERVERS’. Each of these integration ensures that, regardless of which accessory you choose, your agents work in their sandbox, where there is the risk of facilitating cross-milk and cleanup after each run.

On examples

Container-use includes many prepared examples in the Cutter reserves, to explain how to revolution in your development workflow. This shows the cases of typical use and illuminates the flexibility of the tool:

• Hello World: In this minimum example, an agent scores a simple HTTP server, say, using a flask or node’s ‘HTTP’ module, and launches it in its container. To confirm that you can hit the ‘localhost’ in your browser or the code produced by the agent runs as expected, is completely different from your host system.
• Parallel Development: Here, two agents spin the specific variations of the same application, using one flask and the other using Fastapi, each in its own containers and at different ports. This scenario shows how to evaluate multiple approaches simultaneously without worrying about a port’s collision or dependence conflict.
• Security scanning: In this pipeline, the agent does regular maintenance, updates the sensitive dependence, will rebuild the build to ensure that nothing is broken, and produces a patch file that attracts all changes. The whole process appears in a throwing container, leaving your reserves in its original state until you decide to merge the patches.

Running these examples is as easy as piping an example file in your agent command. For example, with a cloud code:

cat examples/hello_world.md | claude

Or with a swan:

goose run -i examples/hello_world.md -s

After execution, you will see that each agent does his or her work for a dedicated git branch that represents its container. Inspection of these branches by ‘Git Checkout’ allows you to review, test or merge on your terms.

A common anxiety when assigned to agents is to know what they did. The container-use addresses this through the integrated l-ging interface. When you start a session, the tool records every command, output and file change in your repository. As you spin up the container, the agent operates commands and follow as the environment develops.

If an agent faces an error or goes away from the track, you do not need to see LS GS in a separate window. A simple command brings an interactive view:

This live view shows you which container branch is active, the latest output and also gives you the option to put in the agent’s shell. From there, you can manually debug: monitor environment variants, run your own commands or edit files on the fly. This directly ensures the ability of the intervention that the agents remain collaborative instead of the unclear black boxes.

While the default-use of the container-use covers the cases of many nodes, python and system-level use cases, you may have special requirements, custom compilers or owned libraries. Luckily, you can handle the dockfail that underpines each container. By placing a ‘containerfile’ (or ‘dockerfile’) at the root of your project, the ‘CU’ will create a tailor -made image before launching the CLI agent. This approach enables you to pre-install system packages, without affecting your host environment, cloning private stores, or organizing complex toolchens.

A typical custom dockfile can start with an official base, add OS-level packages, set environment variables, and establish language-specific dependence:

FROM ubuntu:22.04
RUN apt-get update && apt-get install -y git build-essential
WORKDIR /workspace
COPY requirements.txt .
RUN pip install -r requirements.txt

Once you have defined your container, the agent you use will work in that regard as a default lt, you will inherit all the pre-formed tools and libraries you need.

In conclusion, such as AI agents perform more and more complex development tasks, the need for strong isolation and transparency in parallel. The container-use of the dagger offers a practical solution: an atmosphere with a container that ensures reliability, fertility and real-time visibility. By building on standard equipment, including docker, git and shell scripts, and offering seamless integration with popular MCP-compatible agents, it reduces the barrier to safe, scalable, multi-agent workflows.