Overview
Research summary
LlamaIndex is a Python framework centered on connecting language models and agents to application data. It provides abstractions for ingesting and transforming documents, building indexes, retrieving relevant context, composing query pipelines and creating agent or workflow systems that operate over those data sources. Compared with general agent frameworks, its strongest architectural emphasis is the data and retrieval layer: developers can combine structured or unstructured sources, vector stores, retrievers, tools and model calls without making the agent runtime responsible for every integration detail.
The project has expanded beyond its original RAG-oriented scope into agents, workflows and observability integrations, but data remains a first-class concern. The main repository is modular, with a core package and many integrations, and is MIT licensed. Hosted LlamaCloud services and third-party models or databases are governed separately.
Repository summary
- Stars
- 52,188
- Open issues
- Unavailable
- Last push
- 2026-09-15
- Commits, 90 days
- Unavailable
- Repository activity
- Not scored
- Version
- Unavailable
Recorded catalogue figures. View repository data and provenance →
Classification
Pricing & services
Paid services unknown
Whether the provider offers paid products or services has not been established.
Licence scope
Implementation
Recorded implementation details and interfaces for LlamaIndex.
Implementation details
Python core plus modular integration packages
- Languages
- Python
- Repository type
- source
Recorded interfaces and capabilities
Licence scope
Repository
Repository snapshots, release information and recorded maintenance signals.
Repository snapshot
- Stars
- 52,188
- Open issues
- Unavailable
- Last push
- 2026-09-15
- Commits, 90 days
- Unavailable
- Repository activity
- Not scored
- Archived
- Not recorded
Repository activity is a snapshot, not a quality or popularity ranking. It combines recent-push freshness (50%), 90-day commits (30%) and issue pressure (20%).
Maintenance and provenance
- Catalogue snapshot
- 2026-10-06
- Stars source
- Recorded fallback
- Last push source
- Recorded fallback
Documentation
Recorded references and research provenance for this entry.
Recorded sources 8
- https://developers.llamaindex.ai Project page · Research reference
- https://github.com/run-llama/llama_index Linked repository · Research reference
- https://github.com/run-llama/llama_index/blob/main/README.md Research reference
- https://raw.githubusercontent.com/run-llama/llama_index/HEAD/README.md Research reference
- https://www.llamaindex.ai/ Research reference
- https://discord.com/api/v10/invites/dGcwcsnxhU?with_counts=false&with_expiration=true Research reference
- https://www.youtube.com/@LlamaIndex Research reference
- https://api.github.com/repos/run-llama/llama_index Research reference
Research metadata
- Research date
- 2026-09-16
- Catalogue snapshot
- 2026-10-06
Community & social 6 channels
Video
- YouTubeLlamaIndexOfficial
Link details for YouTube — LlamaIndex
- Access
- Public
- Last checked
Communities
- Redditreddit.com/r/LlamaIndexEndorsed
- Discorddiscord.gg/dGcwcsnxhUOfficial
Link details for Discord
- Access
- Public
- Last checked
- GitHub Discussionsgithub.com/run-llama/llama_index/discussionsOfficialDiscussion
Link details for GitHub Discussions
- Access
- Public
- Last checked
Social & updates
- Xx.com/llama_indexOfficial
Link details for X
- Last checked
- LinkedInwww.linkedin.com/company/llamaindexOfficial
Link details for LinkedIn
- Last checked
Related tools
Editorially linked entries to explore alongside LlamaIndex. This is not a ranking.