Sourcegraph

Amp Code Review 2026: Sourcegraph’s Autonomous Agent Explained Sourcegraph’s Amp has crossed a threshold that most AI coding tools are still approaching: it operates as a genuinely autonomous agent, not a glorified autocomplete engine. Within the first two months of 2026, over 40,000 development teams adopted Amp as their primary agentic coding workflow — a growth rate that puts it firmly in the same conversation as Cursor and Claude Code. Amp plans multi-step tasks, edits files across your entire codebase, runs tests, interprets output, and iterates — without requiring you to break down every instruction into atomic prompts. Built on the foundation Sourcegraph developed for enterprise code intelligence, Amp ships as both a VS Code extension and a standalone CLI, giving developers full flexibility over where and how they work. The 200K token context window means Amp can hold an entire service’s worth of code in working memory simultaneously, which matters enormously once you start tackling refactors that span dozens of files. This review tests Amp’s real capabilities in 2026: what it does well, where it still has rough edges, and who should actually be using it. ...

Sourcegraph Cody is a full-codebase AI code assistant built on Sourcegraph’s enterprise-grade code intelligence platform — offering deep repository context, multi-LLM flexibility, and self-hosted deployment that most AI coding tools can’t match. It’s purpose-built for large, complex codebases where surface-level AI falls short. What Is Sourcegraph Cody? Sourcegraph Cody is an AI code assistant that indexes your entire repository — or your entire organization’s codebase — to deliver context-aware completions, explanations, refactoring, and documentation. Unlike GitHub Copilot (which primarily understands open files) or Cursor (which has good local context but not full-repo indexing), Cody is built on Sourcegraph’s code intelligence platform that has indexed billions of lines of enterprise code since 2013. The key distinction is scope: Cody’s context window isn’t limited to what’s open in your editor — it can reason across your entire repository or even cross-repo, pulling in relevant symbols, functions, and patterns from files you’ve never opened. Cody supports 4+ LLM backends — Claude Sonnet/Opus, GPT-4o, Gemini, and Mixtral — and works across VS Code, JetBrains, Neovim, and Emacs. For developers who live inside large, multi-service repositories, Cody’s architecture is fundamentally different from tools that only understand what you’re currently looking at. That full-repo context is Cody’s defining value proposition in 2026’s crowded AI coding market. ...