A new open-source tool called Container-Use has been released to address a growing challenge in AI-assisted development: managing multiple coding agents working simultaneously without conflicts. The tool was unveiled live at the AI Engineer World Fair, offering developers a way to move beyond supervising one agent at a time to enabling multiple agents to work independently and safely.
Isolation Through Containers and Git Integration
Container-Use creates separate containerized environments for each coding agent, combining Docker containers with Git worktrees to provide both file and execution isolation. Each agent operates in its own fresh container within a dedicated Git branch, preventing conflicts when multiple agents work on the same project. This dual-layer approach ensures that while agents can modify files independently through Git worktrees, their execution environments remain completely separate through containerization.
The community has shown particular interest in this isolation approach, with some developers already using simpler manual methods involving multiple Git clones and Docker Compose. The new tool aims to make this workflow smoother and more accessible, especially for junior team members who might struggle with complex multi-agent setups.
Key Features:
- Isolated Environments: Each agent gets a fresh container in its own Git branch
- Real-time Visibility: Complete command history and logs of agent activities
- Direct Intervention: Drop into any agent's terminal to take control
- Environment Control: Standard Git workflow with branch-based agent separation
- Universal Compatibility: Works with any agent, model, or infrastructure
Real-Time Monitoring and Control Features
One of the standout features generating discussion is the tool's real-time visibility capabilities. Developers can see complete command histories and logs of what agents actually execute, rather than relying solely on what the agents report they're doing. The system also allows direct intervention, letting developers drop into any agent's terminal to assess its state and take control when agents become stuck.
Your agents will automatically commit to a container-use remote on your local filesystem. You can watch the progress of your agents in real time.
This monitoring approach addresses a common pain point in AI-assisted development where developers often lose track of what multiple agents are actually accomplishing.
Technical Implementation and Compatibility
Container-Use operates as a Model Control Protocol (MCP) server, making it compatible with various AI coding tools including Claude Code, Cursor, and other MCP-compatible agents. The tool integrates with standard development workflows through familiar Git commands, allowing developers to review any agent's work simply by checking out the appropriate branch.
Some community members have questioned whether specialized protocols like MCP are necessary, suggesting that large language models should be capable of generating code to interact with any API directly. However, others argue that such protocols provide essential resilience against AI hallucinations and help maintain more reliable agent behavior within defined boundaries.
Supported Platforms:
- Claude Code (via MCP configuration)
- Goose (via config.yaml setup)
- Cursor (via .cursor/rules/)
- VSCode/GitHub Copilot (via .github/copilot-instructions.md)
Future Considerations and Remote Development
While the tool addresses current multi-agent challenges, some developers are questioning its long-term relevance. There's growing sentiment that the industry may be moving toward remote development environments, where agents would work directly on cloud-based platforms rather than local containers. This shift could potentially make local containerization tools less critical as development workflows become more cloud-native.
The project remains in early development with active evolution expected, including potential rough edges and breaking changes as the team responds to community feedback and usage patterns.
Reference: container-use