AI Frameworks for Production - Best Choices for 2026
Compare AI frameworks for production: LangChain, LlamaIndex, Haystack and more. Discover which framework best fits RAG, agents, and LLM applications.
AI frameworks speed up development of LLM applications, RAG systems, and AI agents. They abstract boilerplate around API calls, prompt management, retrieval, and agents. In this guide we compare the main AI frameworks for production in 2026.
Ranking criteria
- Maturity and stability for production use cases
- RAG and retrieval support
- Agent and tool-calling capabilities
- Community, documentation, and ecosystem
1. LangChain
The most popular framework for LLM applications. Offers chains, agents, tools, and a large ecosystem of integrations. Suitable for RAG, agents, and complex workflows.
Pros
- +Extensive ecosystem and integrations
- +Chains, agents, and tool-calling out-of-the-box
- +LangSmith for observability and debugging
Cons
- -Steep learning curve due to many abstractions
- -API changes between versions
- -Can be overkill for simple use cases
2. LlamaIndex
Framework focused on data indexing and retrieval for LLM applications. Excellent for RAG, document QA, and knowledge-augmented applications.
Pros
- +Data-first design: indexing, chunking, retrieval
- +Extensive document loaders and vector stores
- +Eval tools for RAG quality
Cons
- -Less agent focus than LangChain
- -Smaller community
- -Overlap with LangChain in use cases
3. Haystack
Framework for NLP pipelines and search from deepset. Focus on production-ready search, QA, and summarisation. Good fit for enterprise.
Pros
- +Production-ready with monitoring and scaling
- +Modular pipeline architecture
- +Strong document processing and search
Cons
- -Less LLM-native than LangChain/LlamaIndex
- -Smaller community in the LLM space
- -More focused on traditional NLP
4. Semantic Kernel
Microsoft's framework for AI applications with plugins, planning, and agents. Strongly integrated with Azure and .NET.
Pros
- +Enterprise focus and Microsoft integration
- +Planner for multi-step reasoning
- +Plugin-based architecture
Cons
- -Less popular in Python-first ecosystem
- -Reliance on Microsoft stack
- -Smaller community than LangChain
5. CrewAI
Framework for multi-agent systems. Agents collaborate with roles, goals, and tasks. Ideal for complex agent workflows.
Pros
- +Multi-agent collaboration out-of-the-box
- +Clear role-task model
- +Simple setup for agent teams
Cons
- -More niche than LangChain
- -Less mature
- -Less suitable for simple RAG
Our pick
For most RAG and LLM projects we recommend LangChain or LlamaIndex. LangChain for broad range and agents, LlamaIndex for data-heavy RAG. For enterprise with existing Microsoft stack, Semantic Kernel is a strong option.
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