Entire is a developer platform designed to modernize the software development lifecycle for the era of AI. It provides tools that bridge the gap between human developers and AI coding agents by capturing the context behind every code change, including sessions, prompts, and tool calls. By integrating this context directly into the version control history, Entire ensures that teams can trace, review, and understand the reasoning behind AI-assisted work, preventing the common issue where agents start tasks from zero without historical understanding.

Functionally, Entire operates as an open-source, Git-compatible ecosystem. It uses a command-line interface (CLI) to automatically capture agent activity as checkpoints within Git repositories. It also features a distributed Git-compatible network for mirroring repositories, which allows agents to clone code quickly across global regions without hitting origin rate limits on platforms like GitHub.

Some of the key features are:

• Checkpoint System: Automatically captures and stores agent sessions, prompts, and tool calls alongside git commits.
• Distributed Git Mirroring: Allows mirroring of repositories to regional cells to optimize clone speed and bypass API rate limits.
• Agent Compatibility: Designed to work with a wide range of coding agents, including Claude Code, Codex, and others.
• Privacy and Security: Detects and redacts sensitive data and secrets locally before any information is stored or pushed.
• Semantic Reasoning Layer: Enables agent-to-agent collaboration and memory sharing by linking session context to version control history.
• Intent Traceability: Facilitates clear review processes by making it possible to trace any code change back to the specific intent or prompt that generated it.

Entire is used via its CLI, which integrates into existing Git workflows with a single command. Once enabled, the platform manages the storage of agent context as lightweight checkpoints within the repository, ensuring that context is attached to the code rather than archived elsewhere. Teams can then use the Entire platform to browse sessions, review generated changes, and share historical context across different agents and project members. The system is designed to be private, ensuring that developers retain control over their data.

Some common use cases include:

• Cross-Agent Collaboration: Moving work between different AI coding agents by carrying over the full session state instead of starting from memory-less restarts.
• Efficient Large-Scale Cloning: Using regional repository mirrors to enable high-throughput operations for agent fleets without being hindered by GitHub origin restrictions.
• Simplified Code Reviews: Reviewing agent-generated pull requests by inspecting the full conversation history and reasoning behind the code changes, rather than just the diffs.
• Debugging Agent Behavior: Analyzing past agent sessions to understand why an AI made a specific decision or repeated a specific mistake during a development task.