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Open-source AI pair-programmer that lives in your terminal, uses Git for every change, and works with GPT, Claude, Gemini, DeepSeek, and local models.
Free tier available
✓ Free plan available
GPT Engineer is the open-source precursor project created by Anton Osika in 2023. Its success directly led Osika to co-found Lovable.dev, a commercial, hosted, browser-based product that applies the same prompt-to-codebase concept with a polished UI, live preview, and team features. The GitHub repo explicitly describes itself as a 'Precursor to: https://lovable.dev' — GPT Engineer remains experimental and community-driven while Lovable receives the commercial development focus.
The GPT Engineer software itself is 100% free and released under the MIT license. However, you pay for the underlying LLM API calls — typically OpenAI GPT-4 usage, which can cost anywhere from cents to several dollars per project depending on size. If you run fully local models via Ollama or llama.cpp, the entire workflow is free but generation quality will depend on the local model you choose.
Install via pip with `pip install gpt-engineer`, set your OpenAI (or alternative provider) API key as an environment variable, create a project folder containing a `prompt` text file describing what you want to build, and run `gpte <project-folder>`. The CLI will ask clarifying questions, generate the code, and optionally execute it. The `gpte --improve` flag lets you iterate on an existing project.
The repository is still open and accepting community contributions, but commits have slowed significantly since 2024 as the original creator's focus shifted to Lovable. It's best thought of as a stable experimental platform and reference implementation rather than an actively evolving product. For day-to-day coding work most users will get more value from actively maintained alternatives like Aider, Cursor, or Claude Code.
Yes. GPT Engineer supports Anthropic's Claude models, Open Router (which proxies dozens of providers), and locally hosted models through llama.cpp or Ollama. This is configured via environment variables and makes it one of the more model-agnostic options among prompt-to-codebase tools, which is valuable for privacy-sensitive work or cost optimization.
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