RepoAgent is an open-source AI assistant framework tailored for software developers to interact with code repositories. It leverages large language models to provide context-aware code exploration, on-demand Q&A, automated documentation and summarization, test case generation, and issue analysis. By indexing project files and maintaining repository context, it helps teams accelerate code understanding, improve documentation quality, and streamline development workflows.
RepoAgent is an open-source AI assistant framework tailored for software developers to interact with code repositories. It leverages large language models to provide context-aware code exploration, on-demand Q&A, automated documentation and summarization, test case generation, and issue analysis. By indexing project files and maintaining repository context, it helps teams accelerate code understanding, improve documentation quality, and streamline development workflows.
RepoAgent is an AI framework that transforms any code repository into an interactive knowledge base. It indexes source files, functions, classes, and documentation into a vector store, enabling fast retrieval and context-aware responses. Developers can ask natural language questions about code functionality, architecture, or dependencies. It supports automated code summarization, documentation generation, and test case creation by integrating with LLMs. RepoAgent also analyzes issues, pull requests, and commit history to provide insights on code quality and potential bugs. Its modular design allows customization of retrieval pipelines, model selection, and output formatting. By embedding directly into CI/CD pipelines or IDEs, RepoAgent streamlines development, reduces onboarding time, and boosts team productivity.
Who will use RepoAgent?
Software Developers
Code Reviewers
DevOps Engineers
Technical Writers
QA Engineers
How to use the RepoAgent?
Step1: Install RepoAgent via pip and clone your repository.
Step2: Configure the repository path and model settings in config.yaml.
Step3: Run the indexing command to build the document store.
Step4: Launch the interactive agent CLI or integrate with your IDE.
Step5: Ask natural language queries to explore code, generate summaries, and create tests.