A curated index of the tools developers actually use to build with AI. No models — just the tooling around them: frameworks, vector databases, inference, observability, agents and more.
Frameworks and SDKs are the building blocks of an LLM app: prompt handling, streaming, retrieval, structured output and agents. These are the libraries developers reach for first, from full agent frameworks to focused tools for typed outputs.
The de facto framework for building LLM applications.
LangChain gives you composable primitives — prompt templates, chains, agents, retrievers and hundreds of integrations — to build anything from a chatbot to a multi-step agent pipeline, in Python or JavaScript. It shines when many components need to talk to each other.
The data framework for connecting your data to LLMs.
LlamaIndex specializes in ingestion and retrieval: it reads documents, chunks and indexes them, and gives you query engines with citations out of the box. If you are building RAG, it is the fastest path from raw files to a working question-answering system.
The TypeScript toolkit for shipping AI-powered products.
The AI SDK provides one streaming interface to every major provider — OpenAI, Anthropic, Google, Mistral, Ollama and more — plus React hooks like useChat that make streaming UIs trivial. It has become the standard choice for AI features in web apps.
Structured, validated outputs from any LLM.
Instructor patches popular model clients so calls return typed objects — Pydantic models or Zod schemas — instead of raw JSON strings, with automatic retries when validation fails. It removes most of the pain of getting reliable structured output.
Inference & model routing →Vector databases →AI coding tools →LLM observability & evals →Data & agent tooling →
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