llm-framework-advisor

To choose an LLM agent framework for multi-agent orchestration, evaluate frameworks like LangGraph, CrewAI, and AutoGen against your use case complexity, team size, and hosting requirements. Mapping these decision criteria ensures the selected framework matches your orchestration needs.

The difference between LangChain, CrewAI, and AutoGen lies in their design goals. LangChain offers broad chaining, CrewAI focuses on role-based collaborative agents, and AutoGen targets conversational multi-agent workflows. Comparing them by complexity and language support identifies the right fit.

You can migrate agents from LangChain to LangGraph or CrewAI by following structured migration paths. These involve concrete steps to transition logic, evaluating tradeoffs in state management, and adjusting orchestration patterns to align with the target framework's architecture.

Vendor lock-in and self-hosting heavily affect LLM framework selection. Frameworks like n8n and Dify offer visual hosting options, while Semantic Kernel and Haystack provide deeper self-hosted control. Evaluating these criteria prevents future infrastructure constraints.

You should not use a multi-agent framework when your LLM application has low complexity or a small team size. If your use case involves simple linear chains or basic RAG without inter-agent communication, lighter frameworks like LlamaIndex or Haystack are more efficient.

To compare LLM frameworks for a scalable RAG pipeline, provide your use case, team size, hosting environment, budget, programming language, and LLM provider. These inputs map directly to decision criteria, yielding a targeted framework recommendation for your pipeline.