SWE-Agent by SWE-Agent | AI Agents

Specialized agent

•

By SWE-Agent

SWE-Agent is a cutting-edge AI-driven coding assistant developed by a team at Princeton University. This innovative tool is designed to autonomously resolve bugs and issues within real-world GitHub repositories, significantly enhancing the efficiency and productivity of software development workflows. By leveraging advanced language models like GPT-4, SWE-Agent provides a robust solution for developers to automate the debugging process, reduce time spent on fixing bugs, and improve overall code quality.

Features

SWE-Agent boasts a comprehensive set of features aimed at making software development more efficient and effective. Below is a detailed overview of its functionalities:

Feature	Description
Autonomous Bug Resolution	Replicates issues from GitHub, identifies root causes, proposes fixes, and submits them as pull requests.
Custom File Viewer and Editor	Displays manageable chunks of code (up to 100 lines) with scrolling and search functionality.
Agent-Computer Interface (ACI)	Enhances interactions between the language model and codebase for efficient browsing and editing.
Integration with Docker and Miniconda	Streamlines setup and usage, simplifying Python environment management.
Built-in Linting Tool	Ensures syntactical correctness of code edits before applying them.
Cost Efficiency	Imposes cost limits on running tasks with GPT-4 to prevent excessive expenditure.
Performance Metrics	Achieved a benchmark score of 12.29% in the SWE-bench test, indicating strong autonomous issue resolution capabilities.
Open-Source and Community Engagement	Allows developers to modify and extend functionality, promoting community involvement and continuous improvement.

Use cases

SWE-Agent can be leveraged in various scenarios, including:

Bug Fixing: Developers can use SWE-Agent to automatically detect and fix bugs in their codebase, reducing the time and effort traditionally required for debugging.
Code Review: The agent can propose changes to code based on identified issues, allowing teams to evaluate and integrate solutions more collaboratively.
Onboarding New Developers: New team members can utilize SWE-Agent to familiarize themselves with existing codebases by receiving assistance in navigating and understanding the code.
Continuous Integration: SWE-Agent can be integrated into CI/CD pipelines to automatically handle bug resolutions as part of the deployment process.

How to get started

Getting started with SWE-Agent involves a few straightforward steps:

Installation and Setup: First, set up Docker and Miniconda. For a quick start, developers can use GitHub Codespaces for a one-click deployment.
Using SWE-Agent: After setup, SWE-Agent can be invoked directly within GitHub. Developers can enter a GitHub issue, and the agent will generate a proposed solution, including a pull request.
Review and Integration: The proposed solution can be reviewed by team members for accuracy and effectiveness before integration into the main codebase.

Pricing for SWE-Agent

The pricing for SWE-Agent is structured as follows:

Default Cost Limit: $2 per task
Average Cost per Solved Task: To be specified in the upcoming technical paper

Note: The technical paper is expected to outline cost optimization strategies in detail, including the average cost per solved task.

Features

SWE-Agent boasts a comprehensive set of features aimed at making software development more efficient and effective. Below is a detailed overview of its functionalities:

Feature

Description

Autonomous Bug Resolution

Replicates issues from GitHub, identifies root causes, proposes fixes, and submits them as pull requests.

Custom File Viewer and Editor

Displays manageable chunks of code (up to 100 lines) with scrolling and search functionality.

Agent-Computer Interface (ACI)

Enhances interactions between the language model and codebase for efficient browsing and editing.

Integration with Docker and Miniconda

Streamlines setup and usage, simplifying Python environment management.

Built-in Linting Tool

Ensures syntactical correctness of code edits before applying them.

Cost Efficiency

Imposes cost limits on running tasks with GPT-4 to prevent excessive expenditure.

Performance Metrics

Achieved a benchmark score of 12.29% in the SWE-bench test, indicating strong autonomous issue resolution capabilities.

Open-Source and Community Engagement

Allows developers to modify and extend functionality, promoting community involvement and continuous improvement.

Use cases

SWE-Agent can be leveraged in various scenarios, including:

Bug Fixing: Developers can use SWE-Agent to automatically detect and fix bugs in their codebase, reducing the time and effort traditionally required for debugging.

Code Review: The agent can propose changes to code based on identified issues, allowing teams to evaluate and integrate solutions more collaboratively.

Onboarding New Developers: New team members can utilize SWE-Agent to familiarize themselves with existing codebases by receiving assistance in navigating and understanding the code.

Continuous Integration: SWE-Agent can be integrated into CI/CD pipelines to automatically handle bug resolutions as part of the deployment process.

How to get started

Getting started with SWE-Agent involves a few straightforward steps:

Installation and Setup: First, set up Docker and Miniconda. For a quick start, developers can use GitHub Codespaces for a one-click deployment.

Using SWE-Agent: After setup, SWE-Agent can be invoked directly within GitHub. Developers can enter a GitHub issue, and the agent will generate a proposed solution, including a pull request.

Review and Integration: The proposed solution can be reviewed by team members for accuracy and effectiveness before integration into the main codebase.

Pricing for SWE-Agent

The pricing for SWE-Agent is structured as follows:

Default Cost Limit: $2 per task

Average Cost per Solved Task: To be specified in the upcoming technical paper

Note: The technical paper is expected to outline cost optimization strategies in detail, including the average cost per solved task.