Welcome to the world of Moatless Tools, a unique hobby project designed to explore how Language Learning Models (LLMs) can assist in editing code within extensive existing codebases. This article will guide you on utilizing Moatless Tools effectively, and share troubleshooting tips along the way.
What is Moatless Tools?
Moatless Tools is a project focused on building efficient tools that integrate the right context into prompts and accurately handle responses. Unlike traditional methods relying solely on reasoning agents, the project champions the creation of robust tools that streamline coding tasks.
Understanding the SWE-Bench
To validate the concepts behind Moatless Tools, I utilize the SWE-bench benchmark. Currently, this project holds a position in sixth place on the SWE-Bench Lite Leaderboard. The latest tools are:
- GPT-4o: Moatless Tools 0.0.1 achieves a solve rate of 24%, with a cost of around $0.13 per benchmark instance. Running 300 instances of the SWE Bench Lite dataset would average approximately $40. Try it out in Google Colab.
- Claude 3.5 Sonnet: Version 0.0.2 boasts a 26.7% solve rate, costing around $0.17 per instance. Try the Claude 3.5 evaluation set up on Google Colab.
Running Moatless Tools
Though my project is still a work in progress, you’re welcome to clone the repository and run the notebook. Here’s how:
How Moatless Tools Works
Think of the process behind Moatless Tools as navigating through an adventure game where you solve puzzles at each level. The tool functions as an agentic loop, transitioning between different states as it resolves the challenge of code editing.
- Search: Similar to searching for treasure, this loop uses function calling to pinpoint relevant code based on various parameters such as query, class name, or function name. The semantic search method leverages a vector index, chunking code into manageable parts for better retrieval.
- Identify: Once the treasure is found, this state determines whether all necessary code has been gathered. Suppose it hasn’t, it sends you back to the Search state.
- PlanToCode: Here, the request for code changes is broken into smaller, more digestible parts, akin to slicing a large cake into pieces for easier serving.
- ClarifyChange: If the proposed changes appear intimidatingly large, the system will refine its focus, ensuring adjustments are manageable.
- EditCode: The final stage edits the code using predefined search-replace blocks, minimizing the potential for errors by confirming what changes will occur prior to their implementation.
Troubleshooting Tips
While experimenting with Moatless Tools, you may encounter a few bumps along the road. Here are some troubleshooting ideas to help you:
- Ensure all necessary libraries are installed correctly to avoid import errors.
- If the tool seems to be stuck during the search state, consider refining your query or verifying the parameters used.
- For any unexplained behavior, reviewing logs will offer insights into what went wrong.
- Feeling lost? Don’t hesitate to reach out to the community for support and suggestions.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Final Thoughts
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
