Boost AI Coding Efficiency: Run Multiple Agents in Isolated Containers with Dagger's Container-Use

Managing Multiple AI Coding Agents with Containers

AI coding agents have become vital collaborators in software development, capable of writing, testing, and refactoring code autonomously. However, running multiple agents concurrently often leads to challenges such as dependency conflicts, state leakage, and difficulty tracking individual agent actions. The container-use project by Dagger tackles these issues by providing containerized environments for each coding agent. This isolation allows developers to run multiple agents in parallel without interference and enables real-time inspection and direct intervention.

Problems with Traditional Environments

Typically, coding agents operate within the developer's local environment, which can cause conflicts when agents update shared dependencies or leave residual artifacts. Containerization offers a clean solution by encapsulating each agent's environment, allowing fresh setups for experimentation and instant disposal of failures while keeping full visibility into each agent's operations.

Seamless Integration with Existing Toolchains

Container-use integrates smoothly with popular tools like Docker, git, and standard CLI utilities, supporting diverse tech stacks including Python virtual environments, Node.js toolchains, and system packages. This flexibility empowers teams to use their preferred environments without sacrificing control or transparency.

Installation and Setup

Getting started involves building and installing the Go-based CLI tool 'cu' using simple make commands:

# Build the CLI tool
make
 
# (Optional) Install into your PATH
make install && hash -r

Cross-compilation is supported by setting the TARGETPLATFORM environment variable, facilitating builds for platforms like ARM64:

TARGETPLATFORM=linux/arm64 make

Integrating With Popular AI Coding Agents

Container-use supports any agent compatible with the Model Context Protocol (MCP). Examples include Claude Code, Cursor, GitHub Copilot, and Goose. Integration typically involves configuring the agent to use 'container-use' as its MCP server:

• Claude Code uses an NPM helper to register the server:

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 configuration in ~/.config/goose/config.yaml:

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

• Cursor requires placing a rule file in .cursor/rules/container-use.mdc.
• VSCode and GitHub Copilot users update respective configuration files to point to the 'cu' command.

Practical Examples

The Dagger repository offers examples demonstrating container-use capabilities:

• Hello World: An agent scaffolds and runs a simple HTTP server inside a container.
• Parallel Development: Two agents run Flask and FastAPI versions of an app in separate containers.
• Security Scanning: An agent updates dependencies and generates patches within a throwaway container.

Running an example with Claude Code:

cat examples/hello_world.md | claude

Or with Goose:

goose run -i examples/hello_world.md -s

Each agent commits its work to a dedicated git branch, enabling review and merge at your convenience.

Monitoring and Debugging Agents

Container-use features a unified logging interface that records all commands, outputs, and file changes into the repository's git history under a special remote named 'container-use'. To watch live activity:

cu watch

This command shows active container branches, recent outputs, and allows dropping into the agent's shell for manual debugging.

Customizing Container Environments

Users can define custom container images by adding a Containerfile or Dockerfile at the project root. For example:

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

Agents launched with 'cu' will run inside these customized containers, inheriting all tools and dependencies pre-installed.

Summary

As AI coding agents take on more complex tasks, container-use from Dagger offers a robust, transparent, and scalable solution for running multiple agents in parallel without conflicts. Leveraging well-known tools and protocols, it integrates effortlessly into existing workflows and empowers developers with full control over their AI collaborators.