LlamaIndex
🛠️ Developer Toolsfree
★4.4
Data framework for LLM applications
frameworkdatarag
Try LlamaIndex →Use Cases
- •Build production RAG pipelines that connect LLMs to private enterprise data sources
- •Parse and extract structured data from complex PDFs, invoices, and documents using LlamaParse
- •Create multi-agent systems that orchestrate multiple AI agents with shared knowledge bases
Integrations
OpenAI, Anthropic, Cohere, and 50+ LLM providersPinecone, Weaviate, Qdrant, Chroma, Milvus (20+ vector stores)LangChain (bidirectional integration)Notion, Google Drive, Slack, GitHub (300+ data connectors via LlamaHub)
Pros
- +Core framework is fully open-source with an extremely active community and 300+ integrations
- +LlamaParse is one of the best document parsers available for complex PDFs with tables and charts
- +Highly flexible architecture supports everything from simple RAG to complex multi-agent workflows
Cons
- -Steep learning curve — the abstractions and APIs change frequently between versions
- -LlamaCloud pricing can get expensive at scale since credits are consumed per page parsed
- -Documentation, while extensive, can be hard to navigate and sometimes lags behind API changes
Quick Start
1. Install the framework with `pip install llama-index`
2. Set your OpenAI API key as an environment variable (or configure another LLM provider)
3. Load your documents using a SimpleDirectoryReader or one of 300+ data connectors
4. Build an index with `VectorStoreIndex.from_documents(documents)` to create searchable embeddings
5. Query the index with `index.as_query_engine().query('your question')` to get RAG-powered answers
Pricing
Framework (llama-index): Free and open-source (MIT license). LlamaCloud Free: 10K credits/mo, 1 user. LlamaCloud Starter: $50/mo — 50K credits, 5 users, 5 data sources. LlamaCloud Pro: $500/mo — 500K credits, 10 users, 25 data sources. Enterprise: Custom pricing — dedicated support, VPC deployment. 1,000 credits = ~$1.