Master GPT Engineer with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install via pip
: `pip install gpt
engineer` and ensure Python
10+ environment is ready for CLI usage
Set up LLM provider
: Configure API keys for OpenAI, Anthropic, or your preferred model provider in environment variables
Write project specification
: Create a clear, detailed prompt describing the software you want to build — be specific about requirements and tech stack
Run generation
engineer <project
path>` to generate code and follow the interactive clarification process
Iterate and improve
i flag for iterative improvements and customize preprompts for consistent coding standards
💡 Quick Start: Follow these 13 steps in order to get up and running with GPT Engineer quickly.
Explore the key features that make GPT Engineer powerful for coding agents workflows.
Write a project spec in plain English and GPT Engineer creates the complete directory structure, implementation files, configuration, and runnable code. The AI asks clarifying questions before generating to improve output quality.
Rapidly prototyping a REST API with Flask by describing requirements in natural language and getting a complete, runnable project in minutes.
Works with OpenAI (GPT-4, GPT-4o), Anthropic Claude, Azure OpenAI, and open-source models like WizardCoder. Switch models by changing configuration without modifying workflow.
Using GPT-4o for complex full-stack projects, Claude for long-context specifications, and a local WizardCoder instance for air-gapped or cost-sensitive environments.
Define the AI agent's coding conventions, preferred frameworks, error handling patterns, and documentation style through customizable preprompt files that persist across sessions.
Setting team-wide coding standards so every generated project follows consistent patterns without repeating instructions.
The -i flag enables improvement mode where GPT Engineer reads existing code and applies targeted changes based on new instructions, supporting ongoing development rather than one-shot generation.
Adding authentication to an existing generated Flask API by describing the new requirements without regenerating the entire project.
Accepts image inputs (architecture diagrams, wireframes, UX mockups) alongside text prompts when using vision-capable models, bridging design and code.
Feeding a Figma wireframe screenshot into GPT Engineer to generate a frontend matching the design layout.
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.
Now that you know how to use GPT Engineer, it's time to put this knowledge into practice.
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Tutorial updated March 2026