dagger/container-use

Features

• Coding Agent Orchestrators - Orchestrates isolated container sessions for coding agents with deep integration into git branches and terminal management.
• Container-Based Sandboxes - Isolates AI agent code execution within ephemeral container environments to prevent unauthorized access to host system resources.
• Model Context Protocol - Implements the Model Context Protocol to link AI models with local tool execution and containerized data sources.
• AI Execution Sandboxes - Provides secure, isolated execution sandboxes specifically designed for running AI coding agents.
• Coding Agent Integrations - Integrates containerized execution spaces with external AI coding agents to support automated programming workflows.
• MCP Server Integrations - Provides MCP-based server integrations that expose tool definitions and environment access to AI agents.
• MCP Protocol Integrations - Implements the Model Context Protocol to connect AI agents to containerized tools using standard input and output streams.
• Model Context Protocol Servers - Acts as a Model Context Protocol server to bridge AI agents to containerized environments for standardized tool access.
• Git Workspace Isolations - Provisions isolated workspaces using git branches to ensure parallel AI agent operations do not conflict.
• Git Integration Management - Orchestrates the mapping of git branches to isolated container environments to manage agent state.
• Sandboxing Environments - Creates secure containerized sandboxing environments specifically for validating and executing AI agent-generated code.
• Agentic Workspace Provisioning - Automates the creation of isolated coding environments mapped to specific git branches for parallel agent execution.
• Command History Trackers - Maintains detailed searchable logs of shell activity and process output within the sandbox for agent auditing.
• AI Agent Execution Monitors - Captures execution events and console output to audit and verify AI agent actions in real time.
• Session Activity Monitors - Tracks, searches, and analyzes the complete history of commands and logs within AI agent development sessions.
• AI Agent Activity Monitors - Implements tracking of command histories and logs to audit the actions performed by AI agents within sessions.
• Bidirectional File Synchronization - Provides two-way synchronization of files between the isolated container environment and the local host repository.
• Agent Change Merging - Enables applying code changes from isolated agent containers back to the local repository while preserving commit history.
• Change Review Interfaces - Provides interfaces for visualizing and reviewing diffs between agent worktrees and parent branches before merging.
• Agent - Manages the workflow of transferring AI agent contributions from isolated containers to the local git repository.
• Remote Session Bridges - Provides a bridge between a local command line interface and remote containerized execution environments for real-time intervention.
• Remote Session Terminals - Allows developers to attach a local terminal directly to a running agent container for real-time state inspection.
• Agent Contribution Workflows - Enables a workflow for reviewing and merging changes made by agents within isolated environments back into a local repository.
• Real-time Session Interventions - Provides a direct terminal to running AI agent containers for real-time debugging and manual resolution of blockers.
• Interactive Session Interventions - Allows users to manually intervene in active agent sessions via a terminal to resolve execution blockers.
• Code Execution Sandboxes - Isolated containerized environments for independent agent tasks.

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