Redesigning Source Control

Redesigning source control at Anthropic \ Anthropic Skip to main content Updated October 15 to include more detail on the projects that pioneered many of the techniques we use in Scion.

At Anthropic, coding is no longer the bottleneck to building systems or doing research. Claude is able to autonomously iterate on research loops, micro-optimize complex programs, and build new services from the ground up at a speed and scale we haven’t seen before. The bottleneck, now, is all of the developer infrastructure built for human scale: source control, builds, and CI. In May 2026, Anthropic averaged around 2,500 code changes merged per day; as of September, we merged over 30,000 per day. We don’t expect this rate of growth to slow down anytime soon; the latent demand for merging code is practically infinite, and every bottleneck we remove immediately leads to a further increase in the amount of code we produce. Changes merged to our monorepo per day, from January to September 2026.

In order to support this growth, we built Scion, an in-house version control system, and fully migrated our monorepo from git in five months. We also built and shipped a code review surface (called Antique), code search, a rich source control IAM system, novel ways of managing and landing large amounts of reviewed code, and more, all in that same time period. In this post, we’ll focus on how we built Scion specifically. It makes for an interesting case study: Claude’s ability to write code was both what put us in this situation in the first place and how we were able to quickly build a high-quality, scalable system to deal with it. Growing our monorepo Before we explore what we built, we should acknowledge how unusual our workload is. Unsurprisingly, Anthropic uses Claude to write software far more than almost any other company, so we’re often the first to see where tools built for human scale start to strain. Source control is just one example of this. While we don’t expect many organizations to have these problems in the very near term, we suspect it won’t be too long before others who use AI coding tools experience these same pressure points, which is why we wanted to share what we built and what we learned. Git is a decentralized system: every clone is a complete, self-contained repository that can compute diffs, read history and blame, and check out any revision while fully offline. For the vast majority of repositories, this is the right design, and it’s a large part of why git became the standard. As our monorepo grows, it increasingly doesn’t fit that design space. The history of our main branch alone is almost 100 GB, and a compressed working copy is over 25 GB. We perform on the order of 25 million code checkouts per day for CI, before counting developer environments, agent sandboxes, and research jobs. Fetched naively, that would be over 600 PB of git traffic per day. Git offers tools for working with a slice of a repository, such as shallow clones, sparse checkouts, and promisor remotes. Beyond these, there are standard workarounds for large monorepos, like internal git mirrors and repository snapshots (seeds) kept in blob storage. We’ve used all of them—at our peak, we operated several hundred internal git mirrors to deal with the load. This bought us time, but they became harder to operate as our merge rate increased. Seeds fell further from the tip of main between refreshes, and it took more and more care to keep operations that depend on history, like diff and blame, working on partial clones. The typical setup for scaling git checkouts.

Taking a step back, we noticed that, like most companies, we were using git as a centralized system with one authoritative copy, but nearly all of our clients were agents and jobs that are online by definition. The properties that make git excellent for distributed, offline work were actively undermining our ability to scale as the repo grew. So we asked ourselves: if we designed source control with the fact that agents are always online in mind from the start, what would it look like? Principles for agent-scale source control With agentic coding, many common assumptions about how software is developed are evolving. We embraced these changes when building Scion, which allowed us to take a fundamentally different approach than git on several core principles. Source control for huge monorepos has prior art, notably Google’s Piper and Meta’s Sapling , 2 so we were able to build Scion with their hard-won lessons in mind. Our principles are: Clients are always online. Git assumes that clients work offline and need a complete copy of the repository. Since nearly all of our clients are agents and jobs, we designed Scion’s client and protocols around a server that is always reachable, and have clients fetch data when they need it. Client-server asymmetry. In git, every client is capable of being a full-fledged server. In Scion, the server answers questions about file history and blame, creates commits, performs merges and rebases, and so on. The client maintains a bounded local object cache, lays out the working copy files on disk, monitors for filesystem changes, and gives users an ergonomic way to work with the server. It can compute very little on its own but, in exchange, it requires much less data locally. A comprehensive source control service can power many novel applications. With git, a CI job or tool that needs to ask a question about the repository, such as who last changed a line or how two commits are related, has to maintain a local copy of the repo or hope that vendor-specific APIs provide the answer. With Scion, all of the source control queries the client uses are also exposed via API, so in many cases jobs don’t need a local repository at all. Many internal tools, such as our automated bisect and revert pipelines, wouldn’t be practical at scale without this cheap, programmatic access to source control. Everything must be horizontally scalable. At our peak, we ran several hundred git mirrors, which significantly strained capacity and no longer scaled once the repo outgrew the mirrors’ disks or memory limits. In Scion, the repository’s data is distributed, and every component is independently scalable; there is no fully materialized copy of the repo anywhere, even on the server. There is no equivalent of packfiles (git’s compressed bundles of objects), so no repository maintenance is needed. Commits must be cheap, incremental, and lock-free. In...