llama-index-core
Interface between LLMs and your data
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Most use cases will also require external LLM and embedding provider credentials or local model setup.
- Low friction installation with a pure Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most production and research contexts.
last release 2026-06-24 (51 days) · last repo commit 2026-08-14 · 51,641 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 16,404,018 downloads/mo, #1,154 on PyPI
Alternatives
Verify before relying
pip install llama-index-core
from llama_index_core import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)- Whether the 29 runtime dependencies are all strictly necessary for basic usage or if many are optional for specific integrations.
- Performance characteristics and memory footprint when indexing large document collections.
- Specific version compatibility constraints with popular LLM providers and vector databases.
- Whether SimpleDirectoryReader is available in core or requires a separate integration package.
What it is and 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—interfaces for LLMs, vector stores, embeddings, and storage backends—that 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
llama-index-core on PyPI
Before you install
Low friction installation with a pure Python wheel. Active maintenance with recent commits and a large repository footprint (51641 stars). Requires Python 3.10 or later and brings in 29 runtime dependencies including common data-handling and async libraries.
Requires Python 3.10 or later. Most use cases will also require external LLM and embedding provider credentials or local model setup.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most production and research contexts.
Quickstart
pip install llama-index-core
from llama_index_core import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)Verify before relying
- Whether the 29 runtime dependencies are all strictly necessary for basic usage or if many are optional for specific integrations.
- Performance characteristics and memory footprint when indexing large document collections.
- Specific version compatibility constraints with popular LLM providers and vector databases.
- Whether SimpleDirectoryReader is available in core or requires a separate integration package.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 29 packagesaiohttpaiosqlitebanksdataclasses-jsondeprecateddirtyjsoneval-type-backportfiletypefsspechttpxllama-index-workflowsnest-asyncionetworkxnltknumpypillowplatformdirspydanticpyyamlrequestssetuptoolssqlalchemytenacitytiktokentinytagtqdmtyping-extensionstyping-inspectwrapt |
| Maintenance | Actively maintained 51 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 16,404,018 / month, #1,154 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Application FrameworksTopic :: Software Development :: Libraries :: Python Modules |
Evidence: llama_index_core-0.14.23-py3-none-any.whl
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See also llama-index · llama-index-legacy · llama-index-vector-stores-qdrant · llama-index-cli · llama-index-vector-stores-milvus · llama-index-vector-stores-redis · llama-index-embeddings-azure-openai · needle-python · cocoindex · npmai