LlamaIndex
LLM data framework for connecting custom data sources to language models. Best-in-class RAG, data connectors, and query engines. Python and TypeScript.
LlamaIndex is the go-to framework for connecting your own data to large language models. It provides data connectors (LlamaHub) for ingesting from PDFs, databases, APIs, Notion, Slack, and hundreds of other sources. The indexing layer chunks, embeds, and stores your data in any vector database. Query engines handle retrieval-augmented generation with support for recursive retrieval, sub-question decomposition, and multi-document synthesis. It also includes an agent framework with tool use and multi-step reasoning. Available in both Python and TypeScript (LlamaIndex.TS). If your use case is primarily about making LLMs smarter with your own data rather than building autonomous agents, LlamaIndex is more focused and mature than LangChain for that specific problem.
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LLM data framework for connecting custom data sources to language models. Best-in-class RAG, data connectors, and query engines. Python and TypeScript.
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Vercel AI SDK
The TypeScript toolkit for building AI apps. Unified API across OpenAI, Anthropic, Google. Streaming, tool calling, structured output, multi-step agents. 50K+ GitHub stars.
Claude Agent SDK
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LangChain / LangGraph
Most popular LLM framework. 100K+ GitHub stars. Chains, RAG, vector stores, tool use. LangGraph adds stateful multi-agent workflows with cycles and persistence.
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