What problem it solves for AI coding agents

Entire addresses what Dohmke calls the next bottleneck in software development: the reviewability, auditability, and transparency of AI-generated code . When AI coding agents write code, their session context — the prompts, reasoning, tool calls, and decisions — is ephemeral and disappears when the terminal closes . Traditional Git captures the "what" (code diffs) but not the "why" . Entire means to make agent session context a first-class, versioned, and searchable part of the repository .

Key features and product

The first product is Checkpoints, an open-source CLI (MIT licensed) that hooks into Git workflows :

• Captures agent context automatically — when git commit runs, Entire attaches the full agent session (prompts, responses, tool calls, files touched) to the commit as a permanent checkpoint .
• Searchable provenance — developers can later query "why code changed" and see the full transcript that led to any diff .
• Supports multiple AI coding agents — Claude Code, Codex, Gemini CLI, OpenCode, and others .
• Checkpoint branch — session metadata is stored on a dedicated branch, keeping the user's active branch clean .
• Upcoming context graph — a broader platform layer (beyond the CLI) to navigate agent decision trees across the repo .

The Entire.io website also mentions a repo mirroring feature for "maximum agent throughput," and the GitHub organization (entireio) hosts the open-source CLI code .

Benchmarks announced

The reporting around benchmarks is limited. Available sources describe performance claims in qualitative terms — for example, that Checkpoints makes it "instantly" searchable to recover the context behind any line of code . No standardized quantitative benchmark numbers (e.g., latency, throughput, code review time reduction) were found in the credible sources reviewed. This appears to be a gap in public disclosure as of the launch period.

Funding background

How it fits into the broader trend of decentralized Git hosting

Entire is one of several high-profile moves signaling that the developer tooling stack is being rethought for an AI-agent-heavy world:

• Open-source and independent. Entire is positioning itself as an open, independent platform — not tied to any single AI model vendor. Dohmke has emphasized that openness should sit at the heart of the next developer platform .
• Git-native, not Git-replacement. Entire extends Git rather than replacing it. It treats agent context as first-class Git metadata, which is a different approach from fully decentralized alternatives (like Radicle or GitButler's reimagined Git client) .
• Parallel moves by other GitHub founders. Scott Chacon (co-founder of GitHub) raised $17M from a16z for GitButler, a reimagined Git client for the AI era. Both Dohmke (Entire) and Chacon (GitButler) are betting that the existing Git workflow is insufficient for agent-generated code, but they take different architectural approaches — Entire focuses on context capture and provenance, while GitButler focuses on a new Git client UX .
• Human-agent collaboration as a first principle. Entire's design philosophy is that humans and AI agents will co-author code, and version control must capture the human's intent and the agent's reasoning together — not just the file diffs . This is a broader industry shift away from human-only Git workflows toward "agent-native" DevOps infrastructure.

What problem it solves for AI coding agents

Entire addresses what Dohmke calls the next bottleneck in software development: the reviewability, auditability, and transparency of AI-generated code . When AI coding agents write code, their session context — the prompts, reasoning, tool calls, and decisions — is ephemeral and disappears when the terminal closes . Traditional Git captures the "what" (code diffs) but not the "why" . Entire means to make agent session context a first-class, versioned, and searchable part of the repository .

Key features and product

The first product is Checkpoints, an open-source CLI (MIT licensed) that hooks into Git workflows :

• Captures agent context automatically — when git commit runs, Entire attaches the full agent session (prompts, responses, tool calls, files touched) to the commit as a permanent checkpoint .
• Searchable provenance — developers can later query "why code changed" and see the full transcript that led to any diff .
• Supports multiple AI coding agents — Claude Code, Codex, Gemini CLI, OpenCode, and others .
• Checkpoint branch — session metadata is stored on a dedicated branch, keeping the user's active branch clean .
• Upcoming context graph — a broader platform layer (beyond the CLI) to navigate agent decision trees across the repo .

The Entire.io website also mentions a repo mirroring feature for "maximum agent throughput," and the GitHub organization (entireio) hosts the open-source CLI code .

Benchmarks announced

The reporting around benchmarks is limited. Available sources describe performance claims in qualitative terms — for example, that Checkpoints makes it "instantly" searchable to recover the context behind any line of code . No standardized quantitative benchmark numbers (e.g., latency, throughput, code review time reduction) were found in the credible sources reviewed. This appears to be a gap in public disclosure as of the launch period.

Funding background

How it fits into the broader trend of decentralized Git hosting

Entire is one of several high-profile moves signaling that the developer tooling stack is being rethought for an AI-agent-heavy world:

• Open-source and independent. Entire is positioning itself as an open, independent platform — not tied to any single AI model vendor. Dohmke has emphasized that openness should sit at the heart of the next developer platform .
• Git-native, not Git-replacement. Entire extends Git rather than replacing it. It treats agent context as first-class Git metadata, which is a different approach from fully decentralized alternatives (like Radicle or GitButler's reimagined Git client) .
• Parallel moves by other GitHub founders. Scott Chacon (co-founder of GitHub) raised $17M from a16z for GitButler, a reimagined Git client for the AI era. Both Dohmke (Entire) and Chacon (GitButler) are betting that the existing Git workflow is insufficient for agent-generated code, but they take different architectural approaches — Entire focuses on context capture and provenance, while GitButler focuses on a new Git client UX .
• Human-agent collaboration as a first principle. Entire's design philosophy is that humans and AI agents will co-author code, and version control must capture the human's intent and the agent's reasoning together — not just the file diffs . This is a broader industry shift away from human-only Git workflows toward "agent-native" DevOps infrastructure.