Check out your Company Bowl for anonymous work chats.
AI added new ways to search code, but not all of them apply to every problem. Here’s how to choose between Code Search, Deep Search, and MCP.
Check out The Sourcegraph guide to surviving Big Code! In it you'll find 100 use cases for navigating, understanding, and evolving enterprise code at scale.
Sourcegraph and Amp are becoming two separate companies, pushing the frontier of software development in code search and coding agents, respectively. Dan Adler will step into the role of CEO at Sourcegraph. Quinn Slack and Beyang Liu are founding Amp Inc. Our board investors Craft, Redpoint, Sequoia, Goldcrest, and a16z will continue to serve on the boards of both companies.
When you’re fixing a bug, building a feature, or digging into a service you didn’t write, the hardest part is often understanding what the existing code is doing - tracking down definitions, usages, and dependencies across files or repositories. Code Search helps with that every day. But when you don’t know what to search for - or you’re trying to understand how something works, not just where it’s used - Deep Search offers a faster way in. Deep Search lets you ask natural-language questions about your codebase and returns a structured, navigable answer. It uses Sourcegraph’s underlying search and code navigation to explore the code for you, running queries, following references, and summarizing its findings with links, code snippets, and context.
Since the dawn of code, larger dev teams and larger codebases = slower progress. This is why enterprise software development is hard, expensive, and risky. AI can’t automate all dev work, but AI is very good at automating the repetitive parts of coding. This is industrialization: breaking a complex process into 100 smaller tasks, then automating the soul-crushing parts and letting humans focus on the ones they’re good at. By industrializing software development, devs will move faster, not slower, as the codebase and team grow. We believe AI coding agents are best suited to automate the repetitive, mind-numbing parts of enterprise software development, not to try (and fail) to replace humans.
Have you ever wondered how an AI coding assistant works? We’ve been working on our AI coding assistant, Cody, for a while now, and over time we’ve learned that really, context is king.
Sourcegraph has been named one of Fast Company's Next Big Things in Tech! Get Cody for free today at cody.dev!
Cody for Sourcegraph 5.1 brings AI to every part of your coding workflow. Let's go over some of the highlights: 🪄 Better autocomplete with a more powerful LLM (Anthropic Claude) and broader context. Free for devs. 🧪 New and improved recipes: like explain code, fix code smells, optimize performance, and more. 💬 Inline chat: for easy fixes, refactors, and questions on specific regions of code files. 🌐 Multi-repository context: Cody looks beyond just the current repository for knowledge when writing/fixing code and answering questions. ➕ Support for JetBrains IDEs, IntelliJ, PyCharm, WebStorm, etc. 💻 The Cody desktop app makes it easy to use Cody on your private code in your editor and chat UI. No server deployment needed. Thank you to everyone who has been using Cody, contributing to Cody on GitHub, and making feature suggestions for Cody. This one is for you.
The AI landscape has gone completely nutso. It’s a 100-foot wave, crashing towards us. And here at Sourcegraph we’re basically trying to surf it. We are making sure that no matter what happens, we’re building the best tool out there.
Sourcegraph 4.0 is now available! What’s new? A dozen new features to grok code, spend more time in flow, and execute big decisive refactors across your codebase.
