A systematic comparison of orchestration, tool use, and multi-agent coordination
Abstract
Agentic AI frameworks have proliferated rapidly in 2024-2025. This article systematically benchmarks LangChain, CrewAI, and AutoGen across four dimensions: task completion rate, token efficiency, latency, and ease of multi-agent coordination. The goal is to help practitioners choose the right framework for their use case — not to declare a winner.
An agentic framework enables an LLM to plan, use tools, and iterate on its outputs autonomously. The three core capabilities are: (1) tool use — calling external APIs, code executors, or search engines; (2) memory — retaining context across steps; and (3) multi-agent coordination — delegating subtasks to specialised sub-agents.
We benchmark all three frameworks on a standardised set of 20 tasks spanning: information retrieval, code generation, data analysis, and multi-step reasoning. Each task is run 5 times per framework and scored on correctness (0-1), token consumption, and wall-clock latency. GPT-4o is used as the base LLM across all frameworks to isolate framework overhead.
LangChain offers the widest tool ecosystem but the highest latency due to abstraction overhead. CrewAI excels at multi-agent role-playing tasks with cleaner agent definitions. AutoGen is most token-efficient for code-heavy tasks thanks to its built-in code executor and conversation-driven architecture. No single framework dominates all dimensions.