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.

Trinity-RFT (Retrieval Fine-Tuning) is a unified open-source framework designed to enhance model accuracy and efficiency by combining retrieval and fine-tuning workflows. Users can prepare a corpus, build a retrieval index, and plug the retrieved context directly into training loops. It supports multi-modal retrieval for text, images, and video, integrates with popular vector stores, and offers evaluation metrics and deployment scripts for rapid prototyping and production deployment.

CAMEL-AI is a Python-based framework that allows developers and researchers to build, configure, and run multiple autonomous AI agents powered by LLMs. It offers built-in support for retrieval-augmented generation (RAG), external tool usage, agent communication, memory and state management, and scheduling. With modular components and easy integration, teams can prototype complex multi-agent systems, automate workflows, and scale experiments across different LLM backends.