AI Coding Frameworks and Libraries

An overview of frameworks and libraries that power AI-assisted development — from LangChain to Vercel AI SDK.

The AI Development Ecosystem

Beyond IDEs and chat interfaces, a rich ecosystem of frameworks enables developers to build AI-powered applications. These range from high-level SDK wrappers to low-level model serving infrastructure.

Application Frameworks

Vercel AI SDK

A TypeScript toolkit for building AI-powered web applications. Provides streaming responses, tool calling, multi-model support, and React hooks for AI chat interfaces. Best for Next.js applications that need AI features.

LangChain

The most popular framework for building LLM applications, available in Python and JavaScript. Provides abstractions for chains, agents, memory, and tool use. Best for complex AI pipelines with multiple steps and data sources.

LlamaIndex

Specialized in data indexing and retrieval for LLMs. If your application needs to query large document sets, codebases, or databases through natural language, LlamaIndex provides the indexing and retrieval infrastructure.

Model Serving

Ollama

Run open-source LLMs locally with a single command. Provides an OpenAI-compatible API, making it a drop-in replacement for cloud models in development. Supports quantized models optimized for consumer hardware.

vLLM

High-throughput, low-latency model serving for production deployments. Uses PagedAttention for efficient GPU memory management. Best for teams serving models at scale with strict latency requirements.

Choosing a Framework