🏗️ Frameworks & Stacks
The tools and frameworks providing the foundation for AI development offer practical solutions for constructing and deploying AI applications. They facilitate the use of collective research, knowledge, and experience in the field of AI solution development.
20 tools
Open-source agent development kit from Google for building multi-agent systems
TypeScript-first AI framework for building agents, RAG pipelines, and workflows
Multi-agent LLM framework using message-based task delegation inspired by the Actor model
Microsoft's SDK for building and orchestrating AI agents in .NET, Python, and Java
Type-safe Python agent framework with Pydantic validation, tool calling, and dependency injection
Open-source TypeScript toolkit for building AI applications with streaming, tool calling, and agents
Low-level framework for building stateful, long-running AI agents with graph-based orchestration
Framework for programming, not prompting, language models with automatic prompt optimization
Structured data extraction from LLMs using Pydantic models with automatic validation and retries
Unified OpenAI-compatible proxy for 100+ LLM providers with cost tracking and load balancing
The open-source AI platform with 500K+ models, inference endpoints, and fine-tuning tools
LangChain gives developers a framework to construct LLM‑powered apps easily.
About Frameworks & Stacks
AI development frameworks and stacks provide the foundational libraries and tools for building LLM-powered applications. These range from orchestration frameworks that chain model calls together to full-stack platforms that handle prompt management, memory, tool use, and deployment in a single package.
The most widely adopted frameworks like LangChain, LlamaIndex, and Vercel AI SDK have become the standard building blocks for AI applications. They abstract away provider differences, handle streaming, and provide patterns for common architectures like RAG, agents, and multi-step workflows.
Choosing the right framework depends on your language ecosystem (Python vs TypeScript), application complexity, and how much control you need over the underlying model interactions. Lightweight libraries suit simple integrations, while full frameworks help manage the complexity of production agent systems.
Frequently Asked Questions
What is an AI development framework?
An AI development framework is a library or platform that provides abstractions, utilities, and patterns for building applications powered by large language models. They handle common tasks like model API calls, prompt templating, output parsing, memory management, and tool integration.
Should I use a framework or call model APIs directly?
For simple, single-model integrations, calling APIs directly is often cleaner. Frameworks become valuable when you need provider abstraction (switching between OpenAI, Anthropic, etc.), complex chains or agents, RAG pipelines, or structured output parsing. Start simple and adopt a framework when the complexity warrants it.
How do I choose between LangChain, LlamaIndex, and other frameworks?
LangChain excels at agent orchestration and complex chains. LlamaIndex specializes in data ingestion and RAG pipelines. Vercel AI SDK is best for TypeScript web applications. Consider your primary use case, language preference, and the framework's approach to abstraction. Try building a small prototype with each to see which fits your mental model.
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