Roadmap

We are building AGI. The first step is creating the code generation tooling of the future.

There are three main milestones we believe will 2x gpt-engineer's reliability and capability:

Steps to achieve our roadmap

Continuous evaluation of our progress
- Create a step that asks “did it run/work/perfect” in the end of each run #240
- Run the benchmark multiple times, and document the results for the different "step configs" (STEPS in steps.py) #239
- Document the best performing configs, and feed these learnings into our roadmap
- Collect a dataset for gpt engineer to learn from, by storing code generation runs, and if they fail/succeed (on an opt out basis)
Self healing code
- Feed the results of failing tests back into GPT4 and ask it to fix the code
Let human give feedback
- Ask human for what is not working as expected in a loop, and feed it into GPT4 to fix the code, until the human is happy or gives up
Make code generation become small, verifiable steps
- Ask GPT4 to decide how to sequence the entire generation, and do one prompt for each subcomponent
- For each small part, generate tests for that subpart, and do the loop of running the tests for each part, feeding results into GPT4, and let it edit the code until they pass
LLM tests in CI
- Run very small tests with GPT3.5 in CI, to make sure we don't worsen performance over time
Dynamic planning
- Let gpt-engineer plan which "steps" to carry out itself, depending on the task, by giving it few shot example of what are usually "the right-sized steps" to carry out for other projects

You can:

Submit your first PR to address an issue
Submit PRs to address one of the items in the roadmap
Review your first PR/issue and propose next steps (further review, merge, close)
Sign up to help measure the progress of gpt-engineer towards recursively coding itself

Volunteer work in any of these gets acknowledged.