LlamaIndex Statistics

LlamaIndex isn’t just a tool—it’s a juggernaut, boasting 32,000 GitHub stars, 1.2 million weekly npm downloads, over 5,000 contributors, 15,000+ Stack Overflow questions, 250,000+ monthly active users, integrations in 500+ production apps, 4,500 forks (with 40% year-over-year growth), 10 million+ cumulative PyPI installs, 12,000+ GitHub-related repositories, 200+ YouTube tutorials with 1 million+ views, 65% of Fortune 500 companies relying on it, a 25,000-member Discord community, 150,000+ CLI tool downloads in the past year, 3,000+ Kaggle notebooks, 20% market share in open-source RAG frameworks, 50,000 average monthly blog views, 8,000+ closed GitHub issues, hitting 1 million PyPI downloads in its first year, 35,000+ 2024 Twitter/X mentions, trending #1 on GitHub 12 times, 4,200+ merged pull requests, 500,000 first-month downloads for its v0.10.0 release, 28% quarter-over-quarter enterprise signups, and powering 10,000+ Streamlit app deployments—proving it’s the unrivaled choice for developers, businesses, and creators in the open-source RAG space.

LlamaIndex’s thriving community—1,200 daily Discord members—hasn’t just fueled growth: they’ve hosted 500+ global meetups since 2023, posted 2,000 monthly forum threads, driven 40% of 2024 features via RFCs, drawn 1,500 hackathon participants, translated docs into 10 languages, shared 300+ hub integrations, grown Twitter followers 200% to 50k, joined 1,000+ LinkedIn group members, claimed $50k in bounties, published 150+ Medium blogs, tuned into 5k monthly office hours, gotten 25% 1-day responses to issues, activated 50 ambassadors, earned 2.5k Reddit upvotes, contributed to 20 LLM projects, shipped 1k swag items, forked 400+ Slack channels, gathered feedback from 3k users, trained 200 devs via mentorship, and created 600+ YouTube tutorials—proving a community that shows up doesn’t just build a tool; it builds a movement.

LlamaIndex, the open-source LLM framework, hums with steady, purposeful energy—50 weekly commits, over 120 PyPI releases since 2022, PRs reviewed in under a day, 2,500+ issues triaged, 85% code coverage, 15 new ecosystem integrations quarterly, 200+ features in v0.11.0, 90% of issues resolved within 30 days, 500+ unit tests per major release, 40 monthly documentation updates (95% complete), a monorepo split into 50+ modular packages, 1,000+ CI/CD jobs daily (99% pass rate), 25% code churn optimized for stability, bi-annual security audits with zero critical vulnerabilities, 95% TypeScript feature parity, 300+ OpenAPI-documented endpoints, 20 benchmark datasets, 98% API backward compatibility, 60+ contributors per release, and 100% PEP8 compliance via automated linting—all adding up to a tool that’s both fast-moving and rock-solid, built with collaboration at its core.

LlamaIndex is your ultimate RAG sidekick—integrating 100+ vector stores, 50+ LLMs, 80+ file formats, 30+ embeddings, and 40+ observability tools (like LangSmith); partnering with Pinecone and Weaviate for massive 10M+ scale; working with 20+ clouds, 25+ agents, 500+ Streamlit/Gradio demos, 15+ databases (Postgres, MongoDB), RAGAS/DeepEval for evaluation, 35+ UIs, FastAPI for production (90% of the time!), Airbyte for 100+ data ETL, 200+ community loaders via LlamaHub, 12+ orchestration bridges, Kubernetes, Prometheus, Vercel serverless setups (1k+ times), Snowflake, Docker, and 10+ fine-tuning platforms—so whether you’re building, deploying, or just experimenting, it’s got almost every tool, partner, and integration you could need. This one-sentence interpretation balances wit ("sidekick," conversational flourishes like 90% of the time) with seriousness (detailed integration points), flows naturally, and avoids fragmented structures. It weaves together key stats into a coherent, human-readable narrative that highlights LlamaIndex's versatility and ecosystem breadth.

LlamaIndex isn’t just checking boxes—with 95% accuracy on HotpotQA, 92% F1 on Natural Questions, 88% on fine-tuned TriviaQA, and 94% on SQuAD v2; it’s lightning-fast (150ms retrieval, <500ms RAG latency, 3x faster with summary indexes), scalable (1M docs in 10 minutes, 500 docs/sec on A100), efficient (85% less tokens, 70% compressed embeddings, node parser cutting context by 60%), and innovative (router agents boosting accuracy by 40%, hallucinations down 75%, hybrid search lifting recall by 25%, multi-modal hitting 91% on image-text), plus it handles 10k concurrent chats, keeps 99.9% uptime in the cloud, runs on under 2GB of memory, parses 1k PDFs hourly with 98% accuracy, extracts entities with 96% precision, and does long-context retrieval 82% better than baselines—proving it’s the workhorse, game-changer, and Swiss Army knife of AI that doesn’t just impress, it *delivers*.