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# Former GitHub CEO launches Entire, a distributed Git network built for AI agent workloads

**[Collections](https://daily.dev/sources/collections)** · 2 min read · 1 upvotes · 0 comments

## Summary

Thomas Dohmke, former GitHub CEO, has launched Entire — a distributed Git network designed for AI agent workloads. The platform mirrors GitHub repositories across regional nodes (US, EU, Australia) to handle concurrent traffic from fleets of AI coding agents, reporting ~570,000 clones/hour and ~586 pushes/second. Benchmarks show roughly 40x faster push throughput versus GitHub when running 16 agents simultaneously. Beyond raw performance, Entire adds a semantic memory layer that logs agent sessions, prompts, and tool calls directly into the repository, enabling agents to learn from past sessions. Features include Entire Blame (linking code lines to agent sessions), Entire Review (multi-agent PR analysis), and semantic code search by intent. It integrates with Claude Code, Codex, Cursor, and Copilot. The company raised a $60M seed round at a $300M valuation and plans to open source its Git backend and benchmark suite.

## Content

Thomas Dohmke, who stepped down as GitHub's CEO last year, has launched a preview of Entire — a distributed Git network built to handle the kind of concurrent traffic that fleets of AI coding agents generate.

The core problem Entire is solving is straightforward: when dozens or hundreds of agents are cloning, pulling, and pushing to the same repository simultaneously, centralized Git hosts hit rate limits fast. Entire mirrors GitHub repositories across regional nodes in the US, EU, and Australia, so agents read from local copies instead of hammering a single origin server.

The company rewrote its Git server from scratch, treating Git more like a database than a version control tool. Self-reported benchmarks show around 570,000 clones per hour and roughly 586 pushes per second from a single repository. A more direct comparison: running 16 agents pushing commits for one minute, Entire sustained 331 pushes per second versus 8 per second on GitHub — about 40x faster in that test.

Beyond raw throughput, Entire has been building a semantic memory layer that logs agent sessions, prompts, and tool calls directly into the repository alongside the code. The idea is that agents can learn from previous sessions rather than repeating the same mistakes, and developers get a clearer picture of *why* code was written a certain way, not just what changed.

A few features sit on top of this:

- **Entire Blame** — links any line of code back to the specific agent session and prompt that produced it
- **Entire Review** — runs multi-agent diff analysis on pull requests
- **Semantic search** — lets you search code history by intent rather than just text

The memory layer already integrates with Claude Code, Codex, Cursor, and Copilot. Native repositories that don't require GitHub underneath are planned for later this year.

Entire plans to open source both the Git backend and its benchmark suite. Longer-term, the company is working toward full decentralization with data residency and sovereignty controls.

The company raised a $60M seed round at a $300M valuation in February 2026 and has grown from 15 to over 40 employees across nine countries since then.

## Similar posts on daily.dev

- [GitHub’s former CEO launches a developer platform for the age of agentic coding](https://daily.dev/posts/github-s-former-ceo-launches-a-developer-platform-for-the-age-of-agentic-coding-dphwu4bkt) · The New Stack · 1 upvotes · 0 comments

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Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#git](https://daily.dev/tags/git), [#distributed-systems](https://daily.dev/tags/distributed-systems), [#version-control](https://daily.dev/tags/version-control), [#vector-search](https://daily.dev/tags/vector-search)

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