AI Framework Directory
Compare 15+ AI and LLM frameworks for building intelligent applications. From RAG pipelines to multi-agent systems, find the right framework for your AI project.
LangChain
Free (open source)The most popular LLM application framework for building chains, agents, and RAG pipelines with any model provider.
LlamaIndex
Free (open source) / LlamaCloud for managed ingestionData framework for LLM applications, specialising in data ingestion, indexing, and retrieval for RAG.
LangGraph
Free (open source) / LangGraph Cloud for deploymentFramework for building stateful, multi-step AI agent workflows as directed graphs, built on LangChain.
CrewAI
Free (open source) / CrewAI Enterprise availableFramework for orchestrating multi-agent AI teams with role-based collaboration and task delegation.
AutoGen
Free (open source)Microsoft's framework for building multi-agent conversational AI systems with customisable agent interactions.
Semantic Kernel
Free (open source)Microsoft's SDK for integrating LLMs into applications with plugins, planners, and memory for .NET and Python.
Haystack
Free (open source) / deepset Cloud for managed deploymentEnd-to-end NLP framework by deepset for building production-grade RAG and search pipelines.
Vercel AI SDK
Free (open source)TypeScript-first AI toolkit for building streaming AI interfaces with React, Next.js, and other frameworks.
Instructor
Free (open source)Library for structured output extraction from LLMs using Pydantic models for type-safe AI responses.
DSPy
Free (open source)Stanford framework for programming (not prompting) language models with automatic prompt optimisation.
Pydantic AI
Free (open source)Type-safe, model-agnostic AI agent framework built by the creators of Pydantic for production Python applications.
Mastra
Free (open source)TypeScript-first AI framework for building agents, RAG, and workflows with first-class integrations.
Flowise
Free (open source) / Flowise Cloud availableOpen-source low-code tool for building LLM applications with a visual drag-and-drop interface.
n8n AI
Free (self-hosted) / Cloud from $20/moWorkflow automation platform with AI nodes for building LLM-powered automation without code.
Dify
Free (open source) / Dify Cloud availableOpen-source platform for building, deploying, and managing LLM applications with a visual orchestration interface.
Guide
How to choose
Selecting an AI framework depends on your team's technical capabilities, the type of application you're building, and your programming language preferences. For Python teams building RAG applications, LangChain and LlamaIndex are the most established choices — LangChain for general-purpose LLM applications, LlamaIndex for data-heavy retrieval pipelines. For TypeScript/JavaScript teams, the Vercel AI SDK and Mastra provide first-class support. If you're building multi-agent systems, the landscape is evolving rapidly. LangGraph offers stateful graph-based agent orchestration with LangChain ecosystem compatibility. CrewAI provides an intuitive role-based approach to multi-agent collaboration. AutoGen excels at conversational multi-agent patterns. For simpler agentic workflows, Pydantic AI offers a clean, type-safe approach without the complexity of full orchestration frameworks. For teams without deep programming expertise, low-code options like Flowise, n8n, and Dify enable building LLM applications visually. These tools are excellent for prototyping and for use cases like chatbots, document Q&A, and workflow automation. However, they may become limiting for complex, custom applications where code-first frameworks offer more flexibility and control.
FAQ
Frequently asked questions
For Python developers, start with LangChain — it has the largest community, most tutorials, and broadest model support. For TypeScript developers, start with the Vercel AI SDK. Once you understand the fundamentals, explore specialised frameworks based on your specific needs.
Yes. Despite criticism about complexity, LangChain remains the most widely-used LLM framework with the largest ecosystem. LangChain has also modularised significantly, and LangGraph addresses many of the original complexity concerns for agentic workflows.
LangChain is a general-purpose framework for building LLM applications (chains, agents, tools). LlamaIndex specialises in data ingestion, indexing, and retrieval — making it the better choice for RAG-heavy applications. Many projects use both together.
No. For simple use cases, calling the OpenAI or Anthropic API directly is perfectly fine. Frameworks add value when you need RAG, multi-step agents, tool use, structured outputs, or complex orchestration. Start simple and adopt a framework when your needs grow.
LangChain, LlamaIndex, and Haystack have the most production deployments. For enterprise .NET environments, Semantic Kernel is the natural choice. The key factors for production readiness are observability, error handling, testing support, and community maturity.
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