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.

CrewAI

Ideal for environments that require production-grade applications with methodical task distribution and dependable implementation, and where the integration of framework analytics is not a concern.

Pros:

• Production Orientation: Tailored for production environments, CrewAI emphasizes well-structured code and practical usability.

• Agent Distribution: Promotes an organized method for distributing responsibilities among agents through structured task delegation.

  
  

Cons:

• Restrictions on Re-delegation: Limits on re-delegating tasks and incorporating external agents may restrict the flexibility in task management and execution.

• Data Usage: Collects anonymized usage data, potentially raising privacy concerns for some teams.

More information: https://www.crewai.com/

LangGraph

Ideal for handling complex task interdependencies—its graph-based approach excels in visualizing task interdependencies and agent relationships.

Pros:

• Specialized Agent Focus: Facilitates the division of complex problems into manageable tasks that specialized agents can target, boosting efficiency.

• Graph Representation: Utilizes a graph structure to outline agent interactions, providing a clear and scalable method for managing multi-agent systems.

Cons:

• Complex Setup: The graph-based configuration may be challenging for developers not well-versed in graph theory or those seeking simpler implementations.

• Focused Task Limitation: Although effective in specialized tasks, it struggles with broader or highly interconnected tasks that demand extensive agent collaboration beyond simple graph structures.

More Information: https://python.langchain.com/docs/langgraph/

AutoGPT

Ideal for developers who want to utilize visual design tools for easy construction and management of multi-agent systems, with a focus on memory and context management in LLM use.

Pros:

• Memory and Context Management: Shares strengths with AutoGen in memory and context capabilities, enhancing its utility in LLM applications that demand these features.

Cons:

• Visual Builder Dependency: Depends on visual builders for designing applications, which may restrict teams that prefer to script their designs directly in code.

More Information: https://autogpt.net/

AutoGen

Ideal for developers who value community-driven support and want a robust framework for complex, large-scale LLM applications that integrate multiple agents, tools, and human feedback. It excels in environments demanding dynamic, customizable agent interactions across various application domains.

Pros:

• Well-established: Boasts an active community, providing substantial support and collaboration opportunities for developers. Customizable

• Agents: Offers highly flexible task execution with agents that can be customized to integrate LLMs, various tools, and human feedback.

Cons:

• Complexity: Presents a considerable complexity, posing a challenge for newcomers who must overcome a steep learning curve to effectively harness its capabilities.

• Less Structured: Compared to other frameworks, some developers may find AutoGen less structured, potentially complicating implementation efforts.

More Information: https://microsoft.github.io/autogen/

Update: Read our post on Multi-Agent LLM Frameworks — Which applications are possible?