Who AI recommends, and when it changes.
Data as of Apr 23, 2026 · Based on 19 AI answers · A buyer need in LLM Agent Frameworks and Tooling. · See how Parse measures this
Recommendation share
LangChain leads at 49% of AI recommendations; CrewAI follows at 16%.
By platform
Both platforms lead with LangChain.
Representative prompts behind this market ranking, and how AI tends to answer.
Buyer needs that sit next to this one in the same market.
When buyers ask for a framework to build multi-step, stateful LLM agent workflows, AI assistants consistently direct them to 
Where a different pick wins:
An extremely lightweight, experimental option focused on quick multi-agent demos via agent handoffs. · 1 source
LlamaIndexTailored for intensive document retrieval and augmentation workflows rather than general agent orchestration. · 1 source
AUTOGENIts actor/message model and concurrency support suit complex multi-agent team-of-agents patterns. · 1 source
Why here: Role-based crew abstraction enables rapid prototyping with built-in memory and guardrails. · 2 sources
Why here: Praised for conversational multi-agent collaboration and distributed agent workflows. · 2 sources
“I want to build a complex LLM agent that can use multiple tools and APIs. What agentic framework is best for managing multi-step, stateful tasks?”
AI assistants most frequently recommend LangGraph for explicit state control and production durability, often mentioning CrewAI for role-based teams and
“We are struggling to orchestrate multi-step tasks across different autonomous agents. Who offers a "multi-agent orchestration" framework?”
Responses highlight LangGraph as the primary choice for stateful orchestration, with CrewAI and 
