Horizontal evaluation of AI Agent framework: Comparison of MCP integration capabilities of LangChain, LangGraph, n8n, Dify, CrewAI, and Coze
For MCP integration scenarios, we horizontally compare the capabilities and applicable groups of six mainstream Agent frameworks: LangChain, LangGraph, n8n, Dify, CrewAI, and Coze, and provide selection recommendations.
Evaluation background
MCP has become the standard interface for Agents to connect tools and data sources. This article is aimed at developers and teams who want to connect to MCP and build Agent, and make a horizontal comparison of 6 mainstream frameworks.
represents tool comparison table
| Framework | Positioning | Development methods | MCP integration | Typical scenarios | Suitable for the crowd |
|---|---|---|---|---|---|
| LangChain | LLM application development framework | Python/JS | Support | Complex chaining and tool calling | Engineer in-depth customization |
| LangGraph | Agent orchestration framework | Python / JS | Support | Stateful, controllable multi-step Agent | Teams that need fine control of the process |
| n8n | Workflow Automation | Visualization + Code | Support | Business Workflow Series Agent | Operations/Automation Staff |
| Dify | LLM application platform | Low-code visualization | Support | Knowledge base + Agent application rapid construction | Product/business team |
| CrewAI | Multi-agent collaboration | Python | Support | Role-based multi-Agent collaboration | Want to quickly build a multi-Agent team |
| Coze | No-code Bot platform | No-code / low-code | Support | For C-side / B-side Bot applications | No-code players and operations |
Note: The above is a compilation of the official positioning and platform collection data of each framework. The specific MCP access methods and version differences are subject to the real-time page of each official document.
Interpretation of key dimensions
- Controllability: LangGraph has the most detailed control over the stateful graph structure; LangChain is flexible but requires deeper engineering capabilities.
- Get started quickly: Coze and Dify can be connected to MCP tools without code, and are the fastest to implement.
- Engineering: LangChain/LangGraph has the most complete ecosystem and is suitable for core systems that require deep customization and long-term evolution.
- Automation: n8n is good at embedding MCP tools into daily business workflows.
Selection suggestions
- Requires deep customization of Agent core system: LangChain + LangGraph combination.
- Knowledge base/Agent application needs to be launched quickly: Dify.
- Need to quickly build and verify without code: Coze.
- Need to embed Agent into business process automation: n8n.
- Requires multi-agent role collaboration: CrewAI.
Applicable people
- Developer/Engineer: LangChain, LangGraph
- Products/Business/Operations: Dify, n8n, Coze
- Evaluation criteria: controllability, speed of getting started, ecological integrity, cost
Pitfall avoidance reminder
- The number of MCP tools is exploding but the quality varies. Verify the server stability and authentication before connecting to production.
- The framework version iterates quickly, and the official long-term support version is locked first.
- Don’t use multiple agents at the beginning. Start with a single agent + MCP to open the closed loop and then expand.
Reference sources
- LangGraph documentation: https://langchain-ai.github.io/langgraph
- n8n: https://n8n.io
- Dify: https://dify.ai
- CrewAI: https://www.crewai.com
- Coze: https://www.coze.com
Copyright: Content sourced from
Official documents of each framework and AIStarMap tool data
. This platform has compiled and organized this content for informational purposes and learning exchange only. If there are any copyright concerns, please contact us for resolution.
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