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Basic Information

llama-github is a Python library designed to retrieve and synthesize relevant GitHub content to provide context for large language models and AI agents. It focuses on Agentic RAG workflows that extract code snippets, issues, READMEs, repository structure and other repository information in response to developer queries. The project is intended for engineers building LLM chatbots, AI agents and automated development assistants who need repository-aware context to solve complex coding tasks. It provides a programmatic interface illustrated by a GithubRAG class that accepts GitHub credentials and optional LLM or embedding API keys. The package is installable via pip and includes architecture diagrams, usage examples and a roadmap to guide integration into production agent stacks.

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Features
The README documents several core features used to power retrieval and context generation. Intelligent GitHub retrieval finds relevant code, issues and repo metadata based on analyzed queries. A repository pool caching mechanism caches readmes, code and issues across threads to speed searches and reduce GitHub API token usage. The codebase emphasizes asynchronous processing to handle concurrent requests efficiently. It uses LLM-powered question analysis to generate search strategies and supports flexible integration with various LLMs, embedding models and rerankers. Authentication supports personal access tokens and GitHub App flows. Additional capabilities include comprehensive logging and error handling and optional use of Jina.ai rerankers and LangChain components.
Use Cases
llama-github helps developers and teams by turning dispersed GitHub content into compact, relevant context that LLMs and agents can use to reason about code and repositories. By automating repository search and context assembly, it reduces manual exploration and accelerates issue resolution and code understanding. The repository caching and asynchronous design improve throughput and lower API token consumption in multi-threaded or production deployments. Integrations with embedding and reranking providers enable higher relevance for retrieved context. The library also powers companion tooling such as an AI PR review assistant, enabling automated, context-aware code reviews and improving developer productivity and code quality when used inside AI-driven development workflows.

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