Refact.ai

Refact.ai sits in the awkward middle ground between Copilot and a full agentic system like Cursor's Agent mode. The autocomplete is solid, occasionally better than GitHub Copilot on Python and Rust in my tests, though it lags on TypeScript. The chat interface works as expected: you ask, it suggests, you apply. Where it tries to differentiate is the agentic RAG layer, which indexes your codebase and supposedly handles multi-file refactors with less hand-holding. In practice, the autonomy is modest. I pointed it at a Flask API that needed endpoint consolidation across four files. It proposed a plan, identified the right files, and generated diffs. But it still required me to review each change before applying, which is sensible but not meaningfully more autonomous than Cursor's multi-file edit mode. The real win is the self-hosted option. If you work in finance, healthcare, or any regulated industry where code cannot leave your infrastructure, Refact.ai is one of the few agents you can actually run on-premise without resorting to brittle local LLMs. The cloud tier uses their own fine-tuned models, which are competent but not state-of-the-art. Failure modes: it occasionally hallucinates imports that don't exist in your dependencies, and the agentic mode can get stuck in loops if your codebase has circular dependencies. The indexing also struggles with monorepos over 500k lines, though that's a common problem. Compared to Cursor, it's less polished and slower to iterate, but Cursor won't run on your own hardware. Compared to Continue.dev, Refact.ai has better out-of-the-box models and a more coherent UX, though Continue.dev offers more flexibility if you want to plug in your own LLM. I'd reach for Refact.ai when I need autocomplete and chat in a self-hosted environment, or when working on a Python or Rust project where the fine-tuned models genuinely outperform the generic alternatives. For everything else, Cursor is faster and Copilot is more reliable.