AI Agent Automatically Codes WITH TOOLS - SWE-Agent Tutorial ("Devin Clone")
TLDRThe video introduces SWE-Agent, a groundbreaking coding assistant developed by a team at Princeton. This tool is designed to fix real-world bugs and issues on GitHub. It works by analyzing a given GitHub issue URL, replicating the issue, and then submitting a fix as a pull request. The SWE-Agent has gained significant attention, with over 3,500 stars shortly after its release. It performs nearly as well as Devon, another prominent model, with a SWE bench test performance of 12.29% using GPT 4. The project's success is attributed to its simple language model-centric commands and feedback format, which facilitate easier codebase navigation, viewing, editing, and execution. Features include a linter, a custom file viewer, and a file editor with scrolling and search capabilities. The tool also has a full directory string searching command and provides clear messages for empty outputs. The installation process involves setting up Docker, Miniconda, and cloning the SWE-Agent repository. The video also demonstrates the tool resolving an issue from its own repository and highlights the potential for using a local model in the future.
Takeaways
- 🌟 SWE-Agent is a new coding assistant developed by a team at Princeton, specializing in fixing real-world bugs and issues on GitHub.
- 🔍 The tool can analyze a GitHub issue URL, replicate the issue, fix it, and submit the solution as a pull request.
- 📈 SWE-Agent has gained significant popularity, with over 3,500 stars shortly after its release.
- 💻 It uses GPT 4 and has shown impressive performance, nearly matching that of Devon in benchmarks.
- 🛠️ The project includes features like a linter for syntax checking, a custom file viewer, and a file editor with search capabilities.
- 🔬 SWE-Agent uses a special command and feedback format to help the language model navigate and understand large codebases.
- 📚 It provides a full directory string searching command and handles empty command outputs gracefully.
- 🚀 The setup process is streamlined with Docker and miniconda, making it easier for users to get started.
- 💡 The tool can be enhanced with a local model in the future, potentially eliminating costs associated with using cloud-based models.
- 📝 SWE-Agent includes a keys configuration file for environment variables, including GitHub token and optional API keys for OpenAI, Anthropic, and Together.
- 🚧 There's an option to set a cost limit for using GPT, which is useful for controlling expenses on model usage.
- 🎬 The video includes a full demo by one of the authors, showing an end-to-end resolution of a GitHub issue using SWE-Agent.
Q & A
What is the name of the coding assistant introduced in the script?
-The coding assistant introduced in the script is called SWE-AGENT.
What university's team developed SWE-AGENT?
-SWE-AGENT was developed by a team at Princeton.
What is the primary function of SWE-AGENT?
-The primary function of SWE-AGENT is to fix real-world bugs and issues on GitHub by replicating the issue, fixing it, and submitting a fix as a pull request (PR).
How does SWE-AGENT perform in comparison to Devon?
-SWE-AGENT performs nearly as well as Devon, with a SWE bench test performance of 12.29% using GPT 4, which is very impressive considering it was just released.
What are some of the features that SWE-AGENT has?
-SWE-AGENT features include a linter that ensures syntactical correctness, a custom file viewer, commands for scrolling and searching within files, and a full directory string searching command.
How does SWE-AGENT make it easier for the language model to understand large codebases?
-SWE-AGENT designs simple language model-centric commands and feedback formats, which allows the language model to more easily browse, view, edit, and execute code files within a repository.
What are the prerequisites for installing SWE-AGENT?
-To install SWE-AGENT, you need to have Docker installed and also Miniconda. Additionally, you should have Visual Studio Code for cloning the GitHub repository.
How does SWE-AGENT handle the process of fixing an issue?
-SWE-AGENT handles the process of fixing an issue by first reproducing the bug, searching the repository for the function causing the issue, analyzing the code, generating an edit, applying the edit, and then re-running the reproduction code to check the output.
What is the significance of the 'environment.yml' file in setting up SWE-AGENT?
-The 'environment.yml' file contains the definition of the environment necessary for SWE-AGENT, which helps reduce the amount of guesswork and simplifies the process of setting up the environment.
How does SWE-AGENT handle cost management during its operations?
-SWE-AGENT allows for setting a cost limit to manage the expenses associated with using the GPT model. If the cost limit is exceeded, the operation is stopped.
What is the potential future enhancement to SWE-AGENT that the speaker is interested in?
-The speaker is interested in the potential future enhancement of powering SWE-AGENT with a local model, which would eliminate the cost associated with using cloud-based models.
Can SWE-AGENT be used to solve issues from its own repository?
-Yes, SWE-AGENT can be used to solve issues from its own repository, as demonstrated when the speaker used an issue from the SWE-AGENT repository to test the tool.
Outlines
🌟 Introduction to SWe-Agent
The video introduces a new coding assistant named SWe-Agent, developed by a team at Princeton, designed to fix real-world software bugs via GitHub issues. It uniquely interfaces with software engineering models to replicate and resolve issues, then submits fixes as pull requests. Impressively, SWe-Agent has garnered significant attention with its benchmark performance close to Devon, another model, by leveraging OpenAI's GPT-4. The tool simplifies the interaction with code repositories through specialized commands, enabling the language model to browse, view, edit, and execute code files more effectively. This capability is highlighted as its unique selling point compared to other models which struggle with large and interconnected codebases.
🔧 Setting Up and Troubleshooting SWe-Agent
The setup process for SWe-Agent involves installing Docker and Miniconda, tools commonly used in the video's channel. The installation includes a built-in Cond environment for easier management of Python dependencies. Despite initial ease, the presenter encounters a problem specific to macOS with Apple silicon, leading to an error that is unresolved in the video. Switching to Lightning.a, which has Docker and Cond pre-installed, resolves the setup issues. Further steps include configuring a 'keys' file with necessary API tokens and running a setup script to build the Docker image. A demonstration follows, showing how SWe-Agent addresses an issue from its own repository, highlighting the tool’s potential to autonomously debug and suggest code fixes.
🛠️ Demonstration of SWe-Agent’s Capabilities
The video culminates with a demonstration of SWe-Agent by one of its creators, Carlos, who showcases the tool resolving a real issue from the Simpai repository on GitHub. The agent first reproduces the reported bug, confirms it, and then proceeds to debug and suggest a fix by modifying the code directly. This process demonstrates the agent's ability to interact with code files, identify problems, and apply corrections effectively. The edit is successfully applied, although a cost limit for GPT-4 usage is exceeded. The demo underscores the model's potential for AI-driven coding assistance, concluding with a hopeful outlook on the continuous advancements in AI coding helpers.
Mindmap
Keywords
💡SWE-Agent
💡GitHub
💡Pull Request (PR)
💡GPT
💡Swe-bench Test Performance
💡Language Model
💡Linter
💡File Viewer
💡IDE (Integrated Development Environment)
💡Docker
💡Miniconda
Highlights
SWE-Agents is a new coding assistant developed by a team at Princeton.
It specializes in fixing real-world bugs and issues on GitHub.
SWE-Agents has received significant attention, with over 3,500 stars shortly after release.
The tool can replicate and fix issues by submitting a pull request (PR).
Performance on the SWE Bench Test is close to that of Devon, a leading model.
SWE-Agents uses GPT 4 and is open source, achieving a 12.29% success rate.
The assistant uses simple language model-centric commands and feedback format for easier code navigation.
It includes a linter that ensures syntactical correctness before code execution.
A custom file viewer is provided, displaying 100 lines at a time for optimal comprehension.
The file editor includes commands for scrolling and searching within the file.
A special built full directory string searching command is included for efficient codebase navigation.
The tool provides clear messaging for commands with empty output.
Installation is straightforward with Docker and Miniconda, and a conda environment is included.
The setup script builds the Docker image, simplifying environment and dependency management.
Users can set a cost limit for the use of GPT to manage expenses.
The tool has the potential to be powered by a local model in future versions.
Carlos, one of the authors, demonstrates resolving a GitHub issue using SWE-Agents.
The assistant successfully identifies and fixes a matrix operation issue in a GitHub repository.
The tool confirms the issue, makes the necessary code changes, and retests to ensure the fix is effective.
The demonstration shows the end-to-end capability of SWE-Agents in resolving coding issues.