AI Agent Automatically Codes WITH TOOLS - SWE-Agent Tutorial ("Devin Clone")
TLDRDiscover the SWE-Agent, a revolutionary coding assistant from Princeton that excels in debugging and resolving real-world issues on GitHub. This AI, which uses a combination of advanced tools like Docker and Miniconda, showcases near-human performance in software engineering tasks. With its ability to replicate issues, edit code, and submit PRs effectively, SWE-Agent represents a significant leap in AI-assisted coding. The tutorial demonstrates its installation, setup, and practical application, highlighting its efficiency and the cutting-edge integration of AI with traditional development environments.
Takeaways
- 🚀 The SWE-Agent, developed by a team at Princeton, is a new AI-driven coding assistant that focuses on fixing real-world bugs from GitHub repositories by analyzing issues, replicating them, and submitting fixes as pull requests.
- 🌟 SWE-Agent is generating significant interest, quickly gaining popularity with over 3,500 stars on GitHub shortly after its release.
- 📊 It showcases impressive performance with a benchmark score of 12.29%, closely rivaling Devon's 13.84%, using the power of GPT-4 in software engineering tasks.
- 🛠️ Special features of SWE-Agent include a custom file viewer and editor, designed to handle large codebases effectively by displaying manageable chunks of code and allowing for easy navigation within files.
- 🔍 The tool uses a simplified command system and feedback format that optimizes language models' ability to interact with code repositories, which enhances their effectiveness in browsing, viewing, editing, and executing code.
- 📝 It integrates tools like Docker and Miniconda to streamline the setup and usage process, reducing the typical complexities associated with Python environment and package management.
- ⚙️ The SWE-Agent comes equipped with a built-in linting tool that ensures code edits meet syntactical correctness before being applied.
- 💻 The project illustrates practical AI applications in software development by demonstrating an AI that not only identifies and understands issues within a codebase but also proposes and applies fixes autonomously.
- 🆕 The video tutorial covers the installation and setup process for SWE-Agent, detailing steps involving Docker, Miniconda, and Visual Studio Code, along with troubleshooting tips for common setup issues.
- 📈 SWE-Agent represents a significant advance in AI-assisted coding, promising to enhance efficiency in managing and resolving coding issues, with potential future upgrades to include local model support for cost-free operations.
Q & A
What is SWE-Agent and what makes it unique?
-SWE-Agent is a software engineering language model developed by a team at Princeton that specializes in fixing real-world bugs and issues on GitHub. It automatically replicates issues, fixes them, and submits a PR (pull request), making it stand out for its ability to interact directly with code repositories.
How does SWE-Agent perform in terms of effectiveness compared to other models?
-SWE-Agent, using GPT-4, has a reported effectiveness of 12.29% on the SWE Bench test, which is nearly as good as Devon's performance of 13.84%. This is notable considering it was recently released and is based on open-source technology.
What specific functionalities does SWE-Agent include to enhance its interaction with codebases?
-SWE-Agent is equipped with a custom file viewer that displays only 100 lines at a time, a full directory string search command, and a file editor with scrolling and search commands. These features are designed to make it easier for the language model to understand and navigate large codebases effectively.
Why is the use of Universal C tags important in projects like SWE-Agent?
-Universal C tags are crucial because they provide a simplified way for language models to search through large codebases. This functionality is vital in understanding and connecting different parts of a complex code structure, which typical models struggle with.
What measures does SWE-Agent take to ensure code edits are syntactically correct?
-SWE-Agent includes a linter that checks the syntax before allowing any edit commands to go through. This ensures that the code modifications are not only functionally correct but also adhere to coding standards.
How does the user interact with SWE-Agent to resolve GitHub issues?
-The user provides a GitHub issue URL to SWE-Agent, which then replicates the issue, searches relevant files, and suggests or makes necessary code changes to resolve the issue.
What challenges did the narrator face during the installation of SWE-Agent and how were they addressed?
-The narrator faced issues with Miniconda on macOS with Apple silicon, which were unresolved. Instead, they switched to using a platform called Lightning.a, which came pre-installed with Docker and Conda, to successfully install and run SWE-Agent.
What are the key advantages of the file viewer and editor built into SWE-Agent?
-The custom file viewer and editor built into SWE-Agent help limit the information overload by displaying manageable chunks of code and providing navigation tools like scroll and search. This custom IDE-like environment is tailored to optimize the model's code interaction.
How does SWE-Agent handle situations where its actions exceed predefined cost limits?
-SWE-Agent has cost limits set for running tasks with GPT-4, like $2 by default, to prevent excessive expenditure. If the cost exceeds this limit during an operation, the task is halted, which ensures that usage remains cost-effective.
What future enhancements does the narrator anticipate for SWE-Agent?
-The narrator speculates that future versions of SWE-Agent might include support for local models to eliminate costs associated with cloud-based model usage and improve efficiency and accessibility for all users.
Outlines
🚀 Introducing the Swe-AI Agent for Code Fixes
The video introduces a new coding assistant called Swe-AI Agent, developed by a team at Princeton. It is a standout tool for software engineering, capable of fixing real-world bugs and issues on GitHub. The assistant is already highly regarded, with over 3,500 stars shortly after its release. The Swe-AI Agent excels at diagnosing issues from a provided GitHub issue URL, replicating the problem, fixing it, and then submitting the correction as a pull request. It has shown impressive performance in benchmarks, nearly matching that of Devon, another leading model. The project's success is attributed to its design, which includes simple language model-centric commands and a feedback format that facilitates easier codebase navigation, viewing, editing, and execution.
🛠️ Setting Up and Using the Swe-AI Agent
The video provides a step-by-step guide on how to install and use the Swe-AI Agent. It covers the initial setup process, which involves installing Docker and Miniconda, and then cloning the Swe-AI Agent repository from GitHub. The guide also explains how to set up a conda environment and run a setup script to build the Docker image. However, the presenter encounters an error related to Miniconda on Mac OS with Apple silicon, which they are unable to resolve. As a workaround, they switch to Lightning, which has Docker and conda pre-installed, and successfully complete the setup. The video also covers creating a keys file for environment variables, including GitHub token and API keys for OpenAI, Anthropic, and Together, which are optional. Finally, the presenter demonstrates how to run the Swe-AI Agent using a command in the terminal and provides a meta example of the agent attempting to fix an issue from its own repository.
🔍 Debugging and Cost Management in Swe-AI Agent
The video showcases the debugging process of the Swe-AI Agent as it attempts to fix an issue. It highlights the agent's ability to locate and inspect code, make necessary changes, and apply edits. The agent successfully identifies and corrects an error related to a 'base commit' in a large code file. However, during the process, the video points out an 'cost limit exceeded' error, indicating that the agent's use of GPT had surpassed a preset cost limit of $2. The presenter appreciates the feature that allows setting a cost limit and suggests the possibility of using a local model to avoid costs. The video concludes with a full demo by one of the authors of the Swe-AI Agent, showing an end-to-end resolution of a GitHub issue. The demo includes reproducing the bug, searching the repository for the function causing the issue, applying a fix, and confirming the solution's effectiveness through testing.
Mindmap
Keywords
💡SWE-Agent
💡GitHub
💡Pull Request (PR)
💡GPT 4
💡Language Model
💡Codebase
💡Linter
💡File Viewer
💡IDE (Integrated Development Environment)
💡Docker
💡Miniconda
Highlights
SWE-AGENT is a coding assistant developed by a team at Princeton, specializing in fixing real-world bugs and issues on GitHub.
It has gained significant attention, accumulating over 3,500 stars shortly after its release.
SWE-AGENT performs nearly as well as Devon, a leading model in the field.
The tool replicates issues from GitHub, fixes them, and submits the solution as a pull request.
SWE-AGENT uses GPT 4 and has demonstrated impressive performance in the SWE bench test.
The project introduces simple language model-centric commands and a feedback format for easier code interaction.
It includes a linter to ensure 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 full directory string searching command is integrated for efficient codebase navigation.
The tool provides clear messaging for commands with empty outputs, enhancing user experience.
Installation is streamlined with Docker and Miniconda, reducing environment setup complexity.
SWE-AGENT comes with a conda environment setup for ease of use.
The tool is designed to work with large codebases, which are typically challenging for language models.
AER AI DR is highlighted as a project that also handles large codebases effectively using Universal C tags.
The potential for using a local model in the future is discussed, which could eliminate costs associated with using GPT.
A full demo is provided by one of the authors, showcasing end-to-end resolution of a GitHub issue.
The demo includes a step-by-step process of identifying, editing, and testing a code solution.
The tool's ability to understand and interact with code is demonstrated through its actions in the demo.
The project has been well-received and shows the potential of AI in assisting with complex software engineering tasks.
