Philosophy
The core design approach behind each AI application framework.
Vercel AI SDK
Treats AI integration as a full-stack platform problem, offering broad model and streaming primitives with optional (but not required) deeper integration into Vercel’s own platform, like its AI Gateway.
TanStack AI
Treats AI as a library composition problem — every piece is an importable, tree-shakeable building block you compose as needed, with no platform association, service fees, or vendor lock-in.
LangChain
A general-purpose orchestration framework aiming to be the common abstraction layer over the whole LLM app stack — prompts, chains, memory, tools, and retrieval — backed by the broadest integration catalog in the category.
LangGraph
An extension of the LangChain ecosystem focused specifically on graph-based, stateful agent workflows, prioritizing deterministic control, auditability, and human-in-the-loop checkpoints over free-form chains.
LlamaIndex
Purpose-built around connecting LLMs to your own data, treating retrieval and indexing as the core problem to solve rather than general-purpose orchestration.
Mastra
A batteries-included, TypeScript-first agent framework bundling agents, workflows, memory, and tool calling into one strongly-typed package, prioritizing developer experience and production reliability out of the box.
CrewAI
Built around a role-based mental model where a multi-agent system is a “crew” of agents, each with a persona, tools, and a task, prioritizing speed of initial multi-agent prototyping.
Semantic Kernel (Microsoft Agent Framework)
Originally a lightweight SDK for embedding AI orchestration into existing enterprise apps across languages; as of its 2026 merger with AutoGen into Microsoft Agent Framework 1.0, it now unifies single-agent and multi-agent orchestration in one Microsoft-backed framework.
Haystack
Purpose-built for document processing and RAG pipelines, prioritizing clean, composable pipeline components for chunking, embedding, and retrieval over general-purpose agent orchestration.