# Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI

arXiv: https://arxiv.org/abs/2411.18241 · HTML: https://mlsanity.com/paper/2411.18241/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 7 of 7)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [Reflective Linguistic Programming (RLP): A Stepping Stone in Socially-Aware AGI (SocialAGI)](https://mlsanity.com/paper/2305.12647/) (2023)
- [A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph](https://mlsanity.com/paper/2407.19994/) (2024)

## Built on by (most cited first, 30 of 35)

- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025)
- [Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topics](https://mlsanity.com/paper/2505.00675/) (2025)
- [The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis](https://mlsanity.com/paper/2602.10453/) (2026)
- [AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities](https://mlsanity.com/paper/2508.11126/) (2025)
- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation](https://mlsanity.com/paper/2504.08621/) (2025)
- [Agentic AI Frameworks: Architectures, Protocols, and Design Challenges](https://mlsanity.com/paper/2508.10146/) (2025)
- [From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration](https://mlsanity.com/paper/2603.04474/) (2026)
- [Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective](https://mlsanity.com/paper/2508.01443/) (2025)
- [HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration](https://mlsanity.com/paper/2507.04067/) (2025)
- [From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context](https://mlsanity.com/paper/2506.03546/) (2025)
- [SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization](https://mlsanity.com/paper/2506.03548/) (2025)
- [A Scenario-Driven Cognitive Approach to Next-Generation AI Memory](https://mlsanity.com/paper/2509.13235/) (2025)
- [AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Models](https://mlsanity.com/paper/2511.06804/) (2025)
- [Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems](https://mlsanity.com/paper/2503.04827/) (2025)
- [Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems](https://mlsanity.com/paper/2506.05370/) (2025)
- [Training-Free Multimodal Large Language Model Orchestration](https://mlsanity.com/paper/2508.10016/) (2025)
- [Hybrid Agentic AI and Multi-Agent Systems in Smart Manufacturing](https://mlsanity.com/paper/2511.18258/) (2025)
- [AAFLOW: Scalable Patterns for Agentic AI Workflows](https://mlsanity.com/paper/2605.02162/) (2026)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- [Scene-Aware Vectorized Memory Multi-Agent Framework with Cross-Modal Differentiated Quantization VLMs for Visually Impaired Assistance](https://mlsanity.com/paper/2508.18177/) (2025)
- [ScheduleMe: Multi-Agent Calendar Assistant](https://mlsanity.com/paper/2509.25693/) (2025)
- [BIOGEN: Evidence-Grounded Multi-Agent Reasoning Framework for Transcriptomic Interpretation in Antimicrobial Resistance](https://mlsanity.com/paper/2510.16082/) (2025)
- [Agentic Educational Content Generation for African Languages on Edge Devices](https://mlsanity.com/paper/2511.07437/) (2025)
- [A2E : An End-to-End Agent Auditing Engine](https://mlsanity.com/paper/2608.07346/) (2026)
- [OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows](https://mlsanity.com/paper/2608.08340/) (2026)
- [Defending Large Language Models Against Jailbreak Exploits with Responsible AI Considerations](https://mlsanity.com/paper/2511.18933/) (2025)
- [Declarative by Design, Assistable Only by Convention: Benchmarking Multi-Agent Frameworks for AI-Assistability](https://mlsanity.com/paper/2602.11198/) (2026)
- [Policy-Guided Threat Hunting: An LLM enabled Framework with Splunk SOC Triage](https://mlsanity.com/paper/2603.23966/) (2026)

## Abstract (arXiv)

With the rapid development of large model technology, the application of agent technology in various fields is becoming increasingly widespread, profoundly changing people's work and lifestyles. In complex and dynamic systems, multi-agents achieve complex tasks that are difficult for a single agent to complete through division of labor and collaboration among agents. This paper discusses the integrated application of LangGraph and CrewAI. LangGraph improves the efficiency of information transmission through graph architecture, while CrewAI enhances team collaboration capabilities and system performance through intelligent task allocation and resource management. The main research contents of this paper are: (1) designing the architecture of agents based on LangGraph for precise control; (2) enhancing the capabilities of agents based on CrewAI to complete a variety of tasks. This study aims to delve into the application of LangGraph and CrewAI in multi-agent systems, providing new perspectives for the future development of agent technology, and promoting technological progress and application innovation in the field of large model intelligent agents.