No bugs can survive the test of fire; not even the ones you wrote into your codebase 🪲.
🔥 flamethrower is an open source, multi-agent, context-intelligent, debugger that utilizes AI superpowers to automate the painful task of debugging. Think a combination of GitHub Copilot's context-awareness in KillianLucas' Open Interpreter packed into a beautiful shell that works out of the box with any existing repo.
Automate: [ Write Code → Run Action → Check Logs → Repeat ] 🚀🚀
Main Differentiators
flamethrowerhttps://github.com/scottsus/flamethrower/assets/88414565/e3c979c0-40ff-4976-aa30-2503a2827742
pip install flamethrower
Or, if you have an existing version and are looking to upgrade to the latest version
pip install --upgrade flamethrower
Navigate to your current workspace, and simply run flamethrower, or ft for the pros.
cd ./unbelievably/complicated/workspace
flamethrower
Use lowercase letters for commands you run in the shell, like python main.py or node server.ts
🔥 flamethrower: Debugging on Autopilot
Instructions:
- ⌨️ Regular shell Use commands like ls, cd, python hello.py
- 🤖 LLM assistance Start command with a Capital letter, try Who are you?
- 📚 Context Intelligent context-awareness from command, files, and stdout logs
- 🪵 Terminal logs All conversation & code output inside flamethrower is logged
...
$ python main.py -> SOME_ERROR
$ Wtf???? # Literally type this in the terminal
An implementation run is initiated with a natural language query that begins with an uppercase letter.
If you say 'Yes', 🔥 flamethrower will debug in the background while you focus on other tasks at hand. It acts similarly to any other human engineer: adding print statements to find the root cause of the issue (which, as we know is the most annoying part). We find this pattern strikingly effective, and is where we believe LAMs have the strongest use case.
If it looks like 🔥 flamethrower is obviously headed in the direction of doom, simply press CTRL+C and give it more suggestions or context.
As long as any shell command or natural language query happens within the context of 🔥 flamethrower, then it is by default captured in the conversation history. That means you can:
Prompts sent to LLM are transparent and easy to observe. All 🔥 flamethrower metadata are neatly kept in a .flamethrower subdirectory, including prompts, conversations, logs, directory info, summaries, and other metadata.
Everytime you send a query, the latest version of your files are sent over, meaning 🔥 flamethrower understands that you changed your files, and are ready to process those changes.
Closed source GitHub Copilot draws context very effectively, and Quick Fix is a neat feature that explains error from stdout logs if the last command returned a non-zero return code.
The Open Interpreter, an open-source gem, specializes in crafting new code from the ground up. It's a favorite among data scientists and those needing sophisticated chart plotting, thanks to its iterative approach to achieving desired results.
🔥 flamethrower combines the agency afforded by Large Action Models (LAM) with the workspace awareness of Copilot, allowing it to take context-specific suggestions and continue iteration until a successful outcome. 🔥 flamethrower is workspace-first, and aims to serve software engineers in complex tasks that need a lot of context management.
🔥 flamethrower is everyone's debugger. Fork it for your own use case, and, one PR at a time we can make the world a more bug-free place ✨ just ping me at scottsus@usc.edu and I'll help you get started.
FAQs
The ultimate debugging experience 🦠🔥
We found that flamethrower demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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