Refact.ai

Free

Refact.ai is an open-source AI coding agent that works inside your IDE. It is a coding assistant for software developers who want autonomous coding, in-IDE chat, and real-time completions without sending code to a vendor cloud.

You can run it entirely local with your own models via Ollama, LM Studio, or vLLM. Or you can bring an API key from OpenAI, Anthropic, or other providers.

It fits teams with strict data rules. It is a poor fit if you want a polished, managed service with guaranteed uptime.

Open it if you need local AI coding with full control. Skip it if you want someone else to run the infrastructure.

It helps most when you need privacy and control more than convenience.

• Developers prioritizing open-source and local execution: You keep code on your machine. No external indexing, no cloud uploads.
• Teams evaluating AI agents against SWE-bench tasks: You get benchmark tooling built in. You see how it scores against engineering task standards.
• Regulated industries requiring data residency: You meet finance, healthcare, or defense rules. Cloud AI is off-limits by policy.
• Solo developers running local Ollama setups: You already run local models. This plugs into your stack without new infra.
• Engineering teams wanting to fine-tune on proprietary code: You train models on your own patterns. Enterprise plans include LLM fine-tuning options.
• Developers working large codebases who need context-aware completions: The RAG system indexes your full repo. Suggestions match your existing code, not generic patterns.
• Users who want fine-grained control over LLM context: Five @-commands let you pick what goes to the model. You send only what matters.
• Teams comfortable with self-hosting and infrastructure management: You run Docker, manage GPUs, and handle updates. The trade-off is worth the control.

These are the parts you notice after a few tries.

• True local execution with no cloud dependency: Code never leaves your machine. Even indexing runs via a Rust-based LSP server you control.
• Task planner with isolated git worktrees: It breaks features into cards and runs agents in parallel. You see the plan before it writes code.
• RAG-enhanced completions via dual indexing: AST parsing plus semantic search. It understands structure and meaning, then ranks usefulness line by line.
• Transparent reasoning with step-by-step previews: You see how it makes decisions. You verify before you accept, or learn from its logic.
• Broad provider support including local runtimes: Swap between Ollama, OpenAI, Anthropic, or your own endpoint. You are not locked to one vendor.

Skip it when the fit is wrong.

• Original cloud service shut down in 2026: The company pivoted hard to open-source. No commercial support or SLA exists now.
• Development now relies on a single maintainer: The main repo is archived. Community forks carry the work. Long-term stability is uncertain.
• Background process and terminal are early preview: The IDE plugins are marked stable, but newer features break or change without warning.
• Self-hosting requires real DevOps skills: You manage Docker, GPU drivers, and model downloads. The one-line install script is the easy part.
• Documentation is limited and scattered: Wiki-format docs, no API reference, and outdated website copy. You troubleshoot from GitHub issues.