Top 3 Trending Agentic AI Frameworks: LangGraph vs AutoGen vs Crew AI
Top 3 Agentic AI Solutions in 2024
Agentic AI is the hot new topic, and naturally, agentic AI solutions have emerged to ride the latest generative AI hype wave (see my predictions for agentic AI business impact in 2025). But do you need any of these solutions to implement agentic AI patterns in your generative AI applications? Absolutely not.
As an AI strategist who’s helped translate AI industry trends for the world’s largest enterprises over the past decade, I’ll help you demystify agentic AI and help you understand what it is and why you need it. I’ll also help you understand when it might make sense to try an agentic AI framework vs implementing agentic AI patterns on your own. And if an agentic AI solution makes sense for your use case, I’ve completed the quick-start tutorials for the top 3 trending agentic AI solutions, so maybe you don’t have to. I’ll share my thoughts on each one and hopefully save you some time identifying the best agentic AI framework for you. Here’s what you need to know.
Agentic AI Overview
While it sounds complicated, Agentic AI is a design pattern for orchestrating multiple autonomous AI agents that can collaborate to make decisions and take actions with minimal human intervention. Each agent is specialized for a specific task and accepts input from the user or another agent. Then, it uses tools and a generative AI model to complete its task and sends an output to either the user or another agent.
Agentic AI helps us overcome a common limitation when we ask a generative AI model to do multiple things in a single prompt. Similar to small children, the more we ask them to do at once, the less likely they are to do all we ask. Conversely, if we ask a child to complete one simple task at a time, they are more likely to complete each task successfully. The same is true of generative AI models.
Agentic AI lets us break down complex tasks into simple subtasks, which are then assigned to specialized agents. The agents work together to complete the complex task, similar to how we might collaborate to achieve a common goal. Put another way, if we split up our single model prompt with multiple requirements into multiple model requests and daisy chain them together, we are far more likely to get the results we are looking for.
For example, if I ask an OpenAI model to generate an image for an article title in a specific style without misspellings, the model is unlikely to generate an image that meets all my requirements. I encountered this limitation when building my AI news app when submitting requests to OpenAI’s DALL-E 3 model to create images for each article title. The generated images frequently included text with misspellings. Before there was an available agentic AI solution to overcome this challenge, I learned I could reduce the number of images with misspellings if I broke down the task into two model requests:
Agentic AI Example for Image Spellcheck
DALLE-3 image requests to generate the image based on the article title, then send the returned image to the GPT4-V vision model
GPT4-V vision requests to visually inspect the DALLE-3 image and look for misspellings. If misspellings are found, trigger a retry of the DALLE-3 image request. Only exit this loop and return the final image when no misspellings are detected.
I unknowingly had created what the industry would call an “agentic AI” workflow that significantly improved the quality of my images. I implemented this pattern in Python code generated by OpenAI’s models in response to my natural language prompts. I did not use any “agentic AI” solutions because they didn’t yet exist.
As agentic AI solutions emerged, I began tinkering with them to see if they could somehow make my life easier. I extended my AI News application to implement many similar patterns to create consistent-length summaries and generate summaries of summaries of summaries (check out the 7-day rolling super-summary subject line at the top of my AI News, generated by a fine-tuned OpenAI model and updated daily).
I discovered that deploying agentic AI patterns in code was always faster and easier for my specific needs than relying on an agentic AI platform. This is due in large part to the fact that I have not been a software developer throughout my career. I have only recently gained software development superpowers due to generative AI foundation models from OpenAI and Anthropic that do an excellent job of converting my natural language prompts into code.
Agentic AI frameworks provided the key benefit of achieving the same results I was getting with less code. Agentic AI solutions provide a layer of abstraction that reduces the amount of code I have to write. For my skillset, however, this perceived key benefit turned out to be a major drawback and the reason I abandoned agentic AI frameworks to help with my application.
I abandoned agentic AI frameworks because I discovered that the leading foundation models have very little knowledge (training data) about these emerging agentic AI solutions. To begin using an agentic AI framework, I would have to start writing code myself, which is something I don’t have much experience in.
Additionally, the benefit of delivering the same results in less code was negated by the fact that I don’t actually write any code. I simply ask an LLM in the Cursor AI Code Editor for what I want, and the model converts my request into code. The quantity of code generated is simply not that important to me because I didn’t have to write any of it.
To be clear, my agentic AI workflows are relatively simple. I chain multiple agent API calls for quality control and stepwise summarization. For my purposes, I don’t need advanced agentic AI capabilities like memory or human-in-the-loop. If your use case requires more sophisticated workflows, perhaps an agentic AI platform is a good fit for you.
If you need to simplify the process of making multiple requests to generative AI models to accomplish complex tasks, these are the leading agentic AI frameworks in 2024:
For the purposes of this article, however, we’ll focus on the top 3 trending agentic AI frameworks as indicated by search volume in Google Trends.
Top 3 trending agentic AI frameworks in 2024, along with key differentiators:
Focuses on graph-based state management and workflow control
Best for complex, stateful applications requiring precise control over agent interactions
Strongest visualization of agent relationships and workflow paths
Most mature and production-ready
Excels at code execution and sandbox environments
Strong error handling and reliability features
Best documentation and community support
Prioritizes developer experience and ease of use
Built-in project structure and YAML configuration
Strong testing and CLI tools
Best for rapid prototyping and getting started quickly
LangGraph vs AutoGen vs CrewAI
LangGraph
LangGraph stands out for its focus on complex workflows through its graph-based approach to orchestrating AI agents. Unlike AutoGen and CrewAI, LangGraph's core strength lies in managing intricate, multi-step processes where the sequence and flow of agent interactions are critical.
Think of LangGraph as the traffic control system for your AI agents. While all three frameworks enable multi-agent collaboration, LangGraph provides unmatched visibility and control over how information and tasks flow between agents. Its graph-based architecture enables complex workflow design with sophisticated state tracking and process management capabilities.
Key differentiators:
Complex workflow orchestration through graph visualization
Fine-grained control over agent interaction patterns
Advanced error handling with state-based recovery
Sophisticated workflow management system
LangGraph is ideal when you need to implement complex, multi-step workflows with precise control over agent interactions. However, it has a steeper learning curve than CrewAI's user-friendly configuration.
Choose LangGraph when complex workflows and precise control over agent communication patterns are critical to your application.
AutoGen
Microsoft’s AutoGen distinguishes itself as the enterprise-focused framework in the agentic AI space. While LangGraph excels at complex workflows and CrewAI prioritizes rapid prototyping, AutoGen shines in providing robust infrastructure for enterprise teams.
Think of AutoGen as the enterprise-grade solution for AI agents. It provides battle-tested infrastructure for deploying agents in production environments, with features like advanced error handling and extensive logging capabilities. While all frameworks support basic agent interactions, AutoGen's reliability features make it particularly well-suited for enterprise deployments.
Key differentiators:
Enterprise-ready infrastructure
Advanced error handling and recovery mechanisms
Extensive documentation and enterprise support
Proven reliability at scale
AutoGen is ideal when you need enterprise-grade reliability and robust error handling. While it may require more setup compared to CrewAI's streamlined configuration, it offers the most comprehensive solution for enterprise deployments.
Choose AutoGen when enterprise-grade reliability and advanced error handling are your top priorities.
CrewAI
CrewAI sets itself apart through its focus on rapid prototyping and developer experience. While AutoGen focuses on enterprise teams and LangGraph specializes in complex workflows, CrewAI prioritizes getting developers up and running quickly with an intuitive project structure.
Think of CrewAI as the rapid prototyping toolkit for AI agents. It provides a streamlined development experience that makes it particularly accessible for those new to agentic AI development. While all frameworks enable multi-agent systems, CrewAI's focus on rapid development makes it the fastest path to a working prototype.
Key differentiators:
Rapid prototyping capabilities
Streamlined development experience
Easy-to-use workflow management
Quick setup and iteration
CrewAI is ideal when you need to prototype and iterate on multi-agent systems quickly. While it may not match AutoGen's enterprise features, it offers the fastest path from concept to working implementation.
Choose CrewAI when rapid prototyping and ease of use are your primary concerns and you want to quickly experiment with multi-agent architectures.
Making the Right Choice: Agentic AI Implementation Strategy
Whether you choose to implement agentic AI patterns in your own code or leverage one of these frameworks depends entirely on your specific needs and constraints. If you're comfortable with LLM-assisted coding and need simple agent workflows, direct implementation might be your best path forward, as it was in my case.
However, if your use case requires more sophisticated capabilities, these frameworks offer distinct advantages:
Choose LangGraph when you need to orchestrate complex, multi-step workflows with precise visualization and control
Consider AutoGen for enterprise environments where reliability and advanced error handling are mission-critical
Opt for CrewAI when rapid prototyping and development speed are your primary concerns
Remember, there's no one-size-fits-all solution in the agentic AI space. The key is to first understand your specific requirements around workflow complexity, team expertise, and production needs. Then, whether you choose direct implementation or one of these frameworks, you can make an informed decision that best serves your objectives.
The most important takeaway? Don't let the hype around agentic AI frameworks drive your decision. Start with your use case, and let that guide your implementation strategy.
Want to learn more? Check out my predictions for agentic AI business impact in 2025.
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