{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/3"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"},{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks"}],"enrichment":{"capability":"LlamaIndex Core provides foundational abstractions and classes for building LLM applications, particularly retrieval-augmented generation (RAG) systems, with extensible interfaces for LLMs, vector stores, embeddings, and storage.","skillfed_tags":["rag","llm-framework","data-integration"],"use_cases":["Build a document Q&A system that retrieves relevant passages from your knowledge base before sending queries to an LLM.","Create a multi-step RAG pipeline that chains document retrieval, reranking, and LLM synthesis for complex queries.","Prototype LLM applications quickly by swapping vector stores or embedding providers without changing core application code.","Index and query structured data alongside unstructured documents in a unified interface.","Develop agent systems where the LLM can retrieve context from multiple data sources before deciding on actions."],"what_it_does":"LlamaIndex Core is the foundational library for building LLM-powered applications, particularly those using retrieval-augmented generation (RAG). It provides core abstractions\u2014interfaces for LLMs, vector stores, embeddings, and storage backends\u2014that let you connect language models to your data. The library is designed to be extended through subclasses, so you build applications by combining LlamaIndex Core with integrations for specific providers that your application needs.\n\nThe package handles the plumbing between your data and language models: document loading, chunking, embedding, indexing, and retrieval. It abstracts away provider-specific details so you can swap vector stores, embedding models, or LLM backends without rewriting application logic. With 29 runtime dependencies covering async I/O, data serialization, and common utilities, it trades some installation weight for broad compatibility and reduced boilerplate in downstream code.","worth_installing":"Yes. LlamaIndex Core is actively maintained, permissively licensed, and widely adopted (top 5000 PyPI packages by downloads). It has no known vulnerabilities and provides genuine abstraction value for RAG and LLM application development. The 29 dependencies are a real cost, but they enable broad integration support. Install it if you're building LLM applications; skip it only if you need a minimal LLM wrapper without data integration."},"id":"llama-index-core","links":{"html":"https://skillfed.io/packages/llama-index-core","md":"https://skillfed.io/packages/llama-index-core.md","pypi":"https://pypi.org/project/llama-index-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-24","license_spdx":"MIT","license_treatment":"permissive","name":"llama-index-core","python_support":"supports_current","summary":"Interface between LLMs and your data"},"popularity":{"monthly_downloads":16404018,"position":1154,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.14.23"}