AutoGen / CrewAI / LangGraph and other framework research

As a product manager, your core task is to understand what these frameworks can help you solve and where their capabilities are. By running their demos and talking to engineers about their implementation, you’ll be better able to evaluate and choose the right agent orchestration framework for your product needs.

This part is crucial as it is a powerful tool for building complex agent systems and multi-agent collaboration. As AI product managers, we need to be proficient in every line of their code, but we need to have a deep understanding:

1. What are their core design concepts and benefits?What problems do they solve?
2. What scenes are they good at?Which scenarios are suitable for using LangChain, and which are suitable for AutoGen or CrewAI?
3. How do they enable inter-agent collaboration?Understand its communication mechanisms.
4. What can they bring to your current and future Smart Form Agent projects?

Next, I will introduce these three frameworks to you in detail, and analyze and compare them from the perspective of a product manager.

Framework research: AutoGen/CrewAI/LangGraph

These three frameworks represent different genres and focuses of current Agent orchestration.

1. AutoGen (Microsoft Research)

B-end product manager’s ability model and learning improvement
The first challenge faced by B-end product managers is how to correctly analyze and diagnose business problems. This is also the most difficult part, product design knowledge is basically not helpful for this part of the work, if you want to do a good job in business analysis and diagnosis, you must have a solid …

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Core Concept:At the heart of AutoGen is:Multi-Agent Dialogue Framework。 It believes that the best way to solve complex tasks is to have multiple customizable, conversationable AIAgents (even humans) go through like a teamMessaging and conversationsCollaborate until the task is completed. It is particularly emphasizedCode execution and validation。

Key features:

• ConversableAgent:All Agents are subclasses of ConversableAgents, and they communicate with each other via messages (send and receive).
• Role Definition:You can define clear roles for each Agent (e.g., Programmer, Product Manager, Tester).
• Automated Conversations and Human-in-the-Loop:Conversations between agents can be fully automated or paused in specific situations (e.g., when a decision is required and a problem cannot be solved automatically) to request human intervention.
• Tool Integrations:Agents can be given the ability to invoke external tools.
• Code Execution:AutoGen has built-in powerful code execution capabilities, and the code generated by the agent can be automatically executed in a sandbox environment and the execution results are returned as part of the conversation. This is useful for scenarios such as programming, data analysis, task automation, and more.
• LLM agnostic:Support for multiple LLM providers (OpenAI, AzureOpenAI, Google, HuggingFace) as well as native models.
• Use case:Automated software development (automatic writing and testing code by the agent team), data analysis, complex problem solving, research assistance.

Product Manager Perspective:

Advantage:

• Natural team collaboration model:If your product requires simulated team collaboration, multi-role engagement (such as “AI Product Manager” and “AI R&D” collaboration to generate a feature), AutoGen’s conversational mode is intuitive and powerful.
• Automate Code Execution:If your form agent needs to dynamically generate code (such as back-end scripts, front-end logic) or perform complex calculations and data analysis based on user needs, AutoGen has a unique advantage in this regard.
• High degree of automation:Tend to be highly automated, and agents can negotiate and iterate on their own, reducing manual intervention.

Considerations:

• Conversation Overhead:Complex agent conversations may result in high token consumption.
• Debugging Challenges:Debugging the conversation flow between multiple agents can be more complex than linear chains, requiring careful analysis of message logs.
• Process control:While conversation-driven, complex conditional branching and state management may require additional design.

2.CrewAI

Core Concept:CrewAI focuses on building“Smart Team” for multi-agent collaboration, emphasizing the AgentsRole, Task, and Tool。 It provides a more structured way to define agent teams, allowing them to collaborate efficiently on a common goal.

Key features:

• Role Driven:Each Agent is given a clear “role” and “goal”, which helps to limit its behavior and make it more specialized (e.g.: “researcher”, “content creator”, “editor”).
• Task Definition:Break down complex targets into specific “tasks”, each with clear inputs, outputs, and execution agents.
• Process orchestration:It provides a flexible process orchestration mechanism that supports sequential execution, parallel execution, and dynamic collaboration (such as “consensus flow”).
• Tools & Memory:Agents can use tools (integrated through LangChain) and maintain memories.
• Humans in the ring:Support human intervention in task processes.
• Use case:Content creation (research, writing, editorial team), market analysis, sales automation, project management assistance.

Product Manager Perspective:

Advantage:

• Intuitive Role Assignment:If your product scenario can be naturally broken down into different professional roles (such as “form designer”, “form checker”, “form publisher”), CrewAI’s role-driven model is very easy to understand and manage.
• Clear task management:Clearly defined tasks make it easy to track progress and evaluate.
• Process control:Compared to AutoGen’s pure conversation, CrewAI has more clear control over the flow of tasks (sequential, parallel, consensus).

Considerations:

• Flexibility:May not be as free as AutoGen’s pure conversation mode, and some unstructured agent interactions may require customization.
• Ecological dependence:In terms of tools and LLM integration, it relies on LangChain’s ecosystem to a certain extent.

3. LangGraph (extension of LangChain)

Core Concept:LangGraph is an advanced extension of LangChain that focuses on buildingA stateful, backward, and complex loop system for agents。 It introduces the concept of a graph, modeling the Agent’s decision-making and execution process as nodes and edges, enabling it to handle more complex, non-linear workflows than simple chains.

Key features:

• Graph-based orchestration:The state of the agent and the decision-making process are represented as a graph, with nodes being different agent states or operations, and the edges are the transitions between states.
• Status Management:It provides a clear state management mechanism, and the execution of the agent is based on the current state.
• Loops and Backtraces:Support for complex loops (e.g., iterative optimization, multiple attempts) and backtracking (when a path fails, you can go back to the previous state and try again).
• Checkpoints:Checkpoints can be set at specific nodes in the graph for easy debugging and recovery.
• Deep integration with the LangChain ecosystem:Various components of LangChain (LLMs, Tools, Memory, Retrievers, etc.) can be used seamlessly.
• Use case:Agents that require complex decision trees, iterative optimization, multi-step verification, human in-the-loop approval processes, and complex debugging processes.

Product Manager Perspective:

Advantage:

• Ultimate Process Control:If your form generation agent needs to go through a complex decision tree (e.g., dynamically selecting templates based on form types, making multiple iterations based on field validation results), LangGraph provides the most powerful process control capabilities.
• Interpretability:Visualizing the execution path and state transition of an agent through a graph can help you understand the behavior and debugging of the agent.
• Robustness:Support for backtracking and checkpoints, making agents more resilient in the face of complex issues.

Considerations:

• Steep Learning Curve:Compared to other frameworks, the concept of LangGraph is more abstract and requires a deeper understanding to use proficiently.
• Complexity:Suitable for highly complex agent logic, it may be too complex for simple tasks.

How do I select or combine them for your Smart Form Agent project?

Combine your existing “master robot + scene expert robot” architecture and form business background:

1) If you want to quickly implement the core function of “Form AIAgent: User One-sentence Description→ Self-Planning Field→ Automatically Generate Questionnaire→Check Logic→ Send Links”:

• LangChain (Basic) + ReAct Mode:Probably the most direct and fast path. Use LangChain’s Agent module, combined with the ReAct chain of thought, to let the agent independently call your “form generation tool” and “form verification tool”.
• AutoGen (Advanced):If you want the agent to have a more human-like “discussion” process during the “planning field” and “checking logic” stages (e.g., one agent is responsible for understanding the requirements, the other agent is responsible for generating the field, and they are negotiating between each other about the reasonableness of the field), or it needs to be generatedDynamic scriptsTo handle some advanced checks, AutoGen will be very powerful. You can define a “Form Design Expert Agent” and a “Form Validation Expert Agent” to collaborate.

2) If you want to implement the “Unified Framework for Multi-scenario Robots: Automatically Identify User Intent → Dynamically Call Adaptation Agent”:

• LangChain’s AgentRouter(AgentwithTools):This is a classic use case. The master agent receives the user intent and then routes the task to different child agents based on the intent (e.g., your “Form Expert Agent”, “Questionnaire Expert Agent”, “Sign-up Specialist Agent”). Each sub-agent has its own toolset and knowledge.
• LangGraph (more complex routes):If your routing logic is very complex, involving multi-layer nesting, conditional jumping, or even a decision point where one agent needs to go back to another, LangGraph provides a more flexible and powerful graph structure.

3) If you want to create a more robust and self-correcting “Smart Form Generation Agent”:

• LangGraph：Between Generate Questionnaire and Check Logic, the Agent needs to be able to generate results if they don’t meet expectationsBacktrack to the build phase and try again, or according to the verification resultsIteratively optimize fields。 LangGraph’s loop and state management capabilities will be very useful.

Summary suggestions:

• Starting with LangChainAgent, understanding ReAct:This is the foundation for building individual agents and empowering them to use tools.
• When tasks require “team collaboration” and “conversational resolution,” consider AutoGen or CrewAI:Especially when your form creation process requires multiple AI roles to work together like humans (e.g., design, review, publish), AutoGen’s code execution and CrewAI’s role definition will be the highlights.
• When task processes are extremely complex and require state management, looping, and backtracking, dive into LangGraph:For example, after a form is created, it requires multiple rounds of user feedback and iteration, or involves complex permissions and approval processes.