Yandex automated Chromium updates with an LLM agent

Every four weeks, Yandex Browser transitions to a new version of Chromium. For end users, this happens transparently, but developers must resolve thousands of code conflicts and fix just as many compilation errors. Yandex decided to automate this routine and embedded an LLM agent into the upstream synchronization process.

The Scale of Updates That Consume Months

Each update cycle encounters approximately 10,000 commits from the main Chromium repository and roughly 1,500 proprietary changes accumulated in Yandex's fork. The result: over 1,000 merge conflicts, thousands of compilation errors, numerous dependencies that no longer work with the new version. The process involves dozens of developers with varying skill levels. The total effort for one cycle amounts to several person-months. This is time the team cannot spend on features, optimization, or user bugs. Pure routine that blocks development.

Why Simple Chat with an LLM Doesn't Work

One might think: feed an error to ChatGPT, get a fix. In reality, this doesn't work. Yandex doesn't deal with isolated errors—it's about regularly updating a large fork with deep change history. Thousands of problems need to be handled simultaneously, considering both upstream context and proprietary modifications. A standard LLM query is useless here: there isn't enough context, solutions contradict each other, and validation is needed at every step. That's why Yandex went further—not just feeding errors into a chat, but embedding an LLM agent that understands the project structure and can analyze and resolve conflicts autonomously.

How the Agent Resolves Conflicts

The agent works in two stages. In the first, it analyzes the conflict: what changed in the upstream, what exists in the fork, why they're incompatible, which dependencies are broken. In the second, it proposes a resolution, considering both sides.

Here are the main tasks the agent handles:

• Resolving merge conflicts by analyzing changes on both sides
• Fixing compilation errors (type errors, linking errors, missing includes)
• Validating solutions: ensuring nothing broke in adjacent modules

The agent isn't perfect—complex cases still require manual work. But it handles 70-80% of the work independently. Developers shift to code review mode instead of writing code, which is much faster and more efficient.

What This Means for Browser Development

Large fork vendors (browsers, operating systems, embedded kernels) now see a real path to less painful upstream synchronization. LLM agents find their place not in flashy demos and presentations, but in routine industrial processes where they genuinely reduce costs. This is a sign of LLM maturity in development: not a replacement for developers, but an intelligent assistant in routine work that frees up time for creativity and new features. Yandex is getting the most out of what was added to open models—not hallucinations, but systematic work organization.