Specify what you want it to build, the AI asks for clarification, and then builds it.

GPT Engineer is made to be easy to adapt, extend, and make your agent learn how you want your code to look. It generates an entire codebase based on a prompt.

• Demo

• Simple to get value
• Flexible and easy to add new own "AI steps". See steps.py.
• Incrementally build towards a user experience of:
  1. high level prompting
  2. giving feedback to the AI that it will remember over time
• Fast handovers, back and forth, between AI and human
• Simplicity, all computation is "resumable" and persisted to the filesystem

Choose either stable or development.

For stable release:

• python -m pip install gpt-engineer

For development:

• git clone https://github.com/AntonOsika/gpt-engineer.git
• cd gpt-engineer
• python -m pip install -e .
  • (or: make install && source venv/bin/activate for a venv)

API Key Either just:

• export OPENAI_API_KEY=[your api key]

Or:

• Create a copy of .env.template named .env
• Add your OPENAI_API_KEY in .env

Or:

• (advanced) Use a local model (or azure). See docs.

Check the Windows README for windows usage.

• Create an empty folder for your project
  • If inside the repo, you can run: cp -r projects/example/ projects/my-new-project
• Create a file called prompt (no extension) and fill it with instructions
• gpt-engineer <project_dir>
  • For example: gpt-engineer projects/my-new-project

By running gpt-engineer you agree to our terms.

Results

Check the generated files in projects/my-new-project/workspace

Workflow

gpt-engineer --help lets you see all available options.

For example:

• To improve any existing project, use the flag: -i
• To give feedback to/improve a gpt-engineer generated project, use: --steps use_feedback

Alternatives

You can check Docker instructions to use Docker, or simply do everything in your browser:

You can specify the "identity" of the AI agent by editing the files in the preprompts folder.

Editing the preprompts, and evolving how you write the project prompt, is how you make the agent remember things between projects.

Each step in steps.py will have its communication history with GPT4 stored in the logs folder, and can be rerun with scripts/rerun_edited_message_logs.py.

You can also run with open source models, like WizardCoder. See the documentation for example instructions.

The gpt-engineer community is building the open platform for devs to tinker with and build their personal code-generation toolbox.

If you are interested in contributing to this, we would be interested in having you.

If you want to see our broader ambitions, check out the roadmap, and join discord to get input on how you can contribute to it.

We are currently looking for more maintainers and community organizers. Email [email protected] if you are interested in an official role.