Comparing Multi-agent AI frameworks

https://sajalsharma.com/posts/overview-multi-agent-fameworks/

A Comparative Overview

To better understand the differences and applications of these frameworks, let’s examine them in a comparative table:

Feature	AutoGen	CrewAI	LangGraph
Type of Framework	Conversational Agents	Role-Playing Agents	Graph-Based Agents
Autonomy	Highly Autonomous	Highly Autonomous	Conditionally Autonomous
Collaboration	Centralized group chat	Autonomous agents with roles and goals	Condition-based, cycling graphs
Execution	Managed by a dedicated agent	Dynamic delegation, but possible to define hierarchical processes	All agents perform functions
Use Cases	Experimentation, prototyping, use cases that beget conversational patterns	Development to production	Detailed control scenarios

All of the above frameworks allow you to customize which LLMs to use per agent, or in the case of LangGraph, per execution node.

https://www.rungalileo.io/blog/mastering-agents-langgraph-vs-autogen-vs-crew

Comparison Summary

Woah, that was a lot of information! Here's a quick summary to make it easier to digest.

 

Criteria	LangGraph	Autogen	Crew AI	Final Verdict
Ease of Usage	❌	✅	✅	Autogen and Crew AI are more intuitive due to their conversational approach and simplicity.
Multi-Agent Support	✅	✅	✅	Crew AI excels with its structured role-based design and efficient interaction management among multiple agents.
Tool Coverage	✅	✅	✅	LangGraph and Crew AI have a slight edge due to their extensive integration with LangChain.
Memory Support	✅	✅	✅	LangGraph and Crew AI are advanced in memory support features, ensuring contextual awareness and learning over time.
Structured Output	✅	✅	✅	LangGraph and Crew AI have strong support for structured outputs that are versatile and integrable.
Documentation	✅	✅	✅	LangGraph and Crew AI offer extensive and well-structured documentation, making it easier to get started and find examples.
Multi-Agent Pattern Support	✅	✅	✅	LangGraph stands out due to its graph-based approach which makes it easier to visualize and manage complex interactions.
Caching	✅	✅	✅	LangGraph and Crew AI lead with comprehensive caching mechanisms that enhance performance.
Replay	✅	❌	✅	LangGraph and Crew AI have inbuilt replay functionalities, making them suitable for thorough debugging.
Code Execution	✅	✅	✅	Autogen takes the lead slightly with its innate code executors but others are also capable.
Human in the Loop	✅	✅	✅	All frameworks provide effective human interaction support and hence, are equally strong in this criterion.
Customization	✅	✅	✅	All the frameworks offer high levels of customization, serving various requirements effectively.
Scalability	✅	✅	✅	All frameworks are capable of scaling effectively, recommend experimenting with each to understand the best fit.
Open source LLMs	✅	✅	✅	All frameworks support open source LLMs.

 

 

https://www.concision.ai/blog/comparing-multi-agent-ai-frameworks-crewai-langgraph-autogpt-autogen

Using LLMs in your applications can be significantly enhanced by adopting multi-agent frameworks. While many of us are used to interacting with an LLM directly, or employing methods such as RAG to improve relevance and context, these strategies provide access to human-like cognition but mimic engaging with a single, all-around "individual."

Multi-agent frameworks introduce the concept of emulating a diverse team, comprised of both generalists and specialists, working together to achieve a particular objective. These agents operate essentially as cycles that utilize LLM outputs to activate other software functions (such as data retrieval), and then integrate the findings back into the LLM to fulfill the overarching goal. They prove particularly beneficial under certain conditions: When the specific tools required are uncertain in advance (for example, based on user input, you might need to employ RAG, conduct a web search, use both, or deploy another strategy), when the LLM may require several attempts to deliver an accurate response, and the correct solutions can be validated independently of an LLM (such as in the generation of functional executable code).

Opting for a multi-agent strategy might lead you to develop your own framework, but for many, leveraging an established one is more practical. Let's explore some of the top multi-agent frameworks and discuss the main advantages and disadvantages of each.

THREE MAIN STREAM

https://github.com/langchain-ai/langgraph

https://github.com/microsoft/autogen

https://github.com/crewAIInc/crewAI-examples/tree/main

OTHER

https://github.com/geekan/MetaGPT

https://github.com/Significant-Gravitas/AutoGPT

CHATDEV 实现自己的agent

https://github.com/OpenBMB/ChatDev