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https://github.com/modal-labs/devlooper
A program synthesis agent that autonomously fixes its output by running tests!
https://github.com/modal-labs/devlooper
agent gpt-4 language-model llm modal python
Last synced: 17 days ago
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A program synthesis agent that autonomously fixes its output by running tests!
- Host: GitHub
- URL: https://github.com/modal-labs/devlooper
- Owner: modal-labs
- License: mit
- Created: 2023-07-31T18:20:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-15T03:18:46.000Z (12 months ago)
- Last Synced: 2024-08-08T00:43:02.235Z (4 months ago)
- Topics: agent, gpt-4, language-model, llm, modal, python
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 422
- Watchers: 8
- Forks: 25
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- project-awesome - modal-labs/devlooper - A program synthesis agent that autonomously fixes its output by running tests! (Python)
README
# 🐥 devlooper
`devlooper` is a program synthesis agent that autonomously fixes its output by running tests!
Here's `devlooper` in action, taking 11 iterations to create a Python library that generates voronoi diagrams:
## ⚙️ How it works
This project extends [smol developer](https://github.com/smol-ai/developer) by giving it access to a [sandbox](https://modal.com/docs/guide/sandbox) to run tests in. The agent iterates until all tests pass, by updating the code and fixing the environment (installing packages).
### 📦 Environment templates
The project uses environment "templates" to define the basic setup and test harness for a given language/framework. For now, three templates are provided:
- React + Jest
- Python
- Rust
However, any language/framework should work, as long as it can be installed within a container. Contributions for more templates are welcome (see [`env_templates.py`](https://github.com/modal-labs/devlooper/blob/main/src/env_templates.py)).
### 🏖️ Sandbox
We use [Modal](http://modal.com/)'s new [Sandbox](https://modal.com/docs/guide/sandbox) primitive to run tests in an isolated environment and fetch the output. This allows us to construct the image incrementally as well (similar to building up a Dockerfile in layers that are cached).
### 🤖 Debug loop
In each iteration, the agent runs the test command for the environment. If a non-zero exit code is received, the agent passes the `stdout` and `stderr` from the sandbox to the LLM to diagnose the error. This diagnosis is used in a separate step to generate a `DebugPlan` consisting of three types of actions:
1. Inspect and fix a file
2. Install a package in the image
3. Run commands in the image
More types of actions can be implemented pretty easily — once again, contributions are welcome!
Running the diagnosis as a separate step seems to boost model accuracy by quite a bit (instead of immediately predicting the `DebugPlan`). We suspect the benefits are similar to why Chain-of-Thought prompting works so well.
## 🧑🚀 Usage
### Set up
- Create a [Modal](http://modal.com/) account ([reach out to us](mailto:[email protected]) if you are still on the waitlist!)
- Install `modal` in your current Python environment
```bash
pip install modal
```
- Create a Modal token
```bash
modal token new
```
- Create an [OpenAI](https://openai.com/) account and get an API key
- [Create a Modal secret](https://modal.com/secrets/create) named `openai-secret`
### Generate!
You're ready to generate! From the root directory of this repo, `modal run` the program with your choice of `prompt` and `template`:
```bash
modal run src.main --prompt="a simple 2D graphics library" --template="rust"
```
```bash
modal run src.main --prompt="a todo-list app" --template="react"
```
```bash
modal run src.main --prompt="a webscraper that checks if there are new reservations for a given restaurant on Resy" --template="python"
```
Once all tests pass, the output will be written to `output/` in the same directory by default. This can be overridden using `--output-path`.
## ✨ Showcase
_Coming soon_
## 🔮 Future directions
This project is mostly a proof of concept, and there's a lot of cool additions that will make this better. Here are some ideas:
- Allowing feedback from users in the loop, or accepting an existing project + plan as input and making suggested changes to it.
- Making the debugging prompt better with relevant parts of the code, retrieved using embeddings.
- Go out and fetch the documentation for a package if needed.
- Using previous edits in the prompt somewhere to prevent the model from going into a loop.
- Synthesizing `EnvTemplate`s from scratch.
- Generalizing this to more LLMs, including open-source ones!