Comparing AI agent frameworks: CrewAI, LangGraph, and BeeAI

AI agent frameworks are platforms for building autonomous systems that perceive, reason, and act. They abstract low-level complexities (vs. traditional explicit coding) and accelerate development (prototyping in days, not months) while managing scale (single agents to enterprise systems). Built-in tools handle memory, learning, and integration—enabling tasks from customer service bots to code generators. Developers focus on customization; frameworks ensure security and reliability.

This article provides a comprehensive analysis of the capabilities, features, and implementation considerations of leading AI agent frameworks focused on multi-agent collaboration and orchestration. Based on official documentation and reliable sources, this article provides a detailed comparison of CrewAI, LangGraph, and IBM's BeeAI frameworks.

Use the insights from this comparison to help make informed decisions when selecting appropriate frameworks for your projects that require sophisticated AI agent implementations.

There are several emerging frameworks in the industry now, we will be covering three most popular here.

CrewAI: Orchestrating role-based collaborative intelligence

CrewAI represents a production-grade AI agent framework that is specifically designed for orchestrating role-playing, autonomous AI agents. Developed as a standalone solution without dependencies on other agent frameworks, CrewAI enables the creation of collaborative AI systems where agents work together as a cohesive unit to tackle complex challenges. The framework focuses on enabling human-like collaboration between specialized agents, each fulfilling distinct roles with specific tools and clearly defined goals.

CrewAI has emerged as one of the most popular Python frameworks for intelligent multi-agent collaboration, fundamentally transforming how developers approach complex AI workflows. Unlike traditional single-agent systems that operate in isolation, CrewAI introduces autonomous AI agents that collaborate as a team, with each agent performing specialized functions toward collective objectives.

LangGraph: Building stateful multi-actor applications

LangGraph provides a library for developing stateful, multi-actor applications with large language models (LLMs), specifically designed for creating agent and multi-agent workflows. LangGraph is built by LangChain Inc. but can operate independently of the LangChain framework. The library excels in providing fine-grained control over both the flow and state of agent applications through a central persistence layer.

LangGraph powers production-grade agents trusted by major enterprises including LinkedIn, Uber, Klarna, and GitLab, demonstrating its effectiveness in real-world applications. By standardizing critical components such as memory and human-in-the-loop capabilities, LangGraph enables developers to focus on agent behavior rather than infrastructure concerns.

BeeAI: Building, deploying, and serving scalable agent workflows

BeeAI is an open-source framework developed by IBM for building, deploying, and serving scalable agent-based workflows with various AI models. BeeAI enables developers to construct production-ready multi-agent systems in both Python and TypeScript. The framework is designed to perform robustly with IBM Granite and Llama 3.x models, but it supports integration with numerous other LLM providers.

The BeeAI framework emphasizes flexibility in agent architecture, seamless model and tool integration, and production-grade controls for enterprise deployments. BeeAI is part of a broader ecosystem that includes tools for visual interaction with agents and telemetry collection.

Architecture and design philosophy of AI agent frameworks

Let’s begin our comparison by examining the architecture and design philosophy of these three AI agent frameworks.

CrewAI's collaborative intelligence approach

CrewAI adopts a comprehensive approach to agent collaboration, structuring its architecture around crews, agents, tasks, and execution processes. The framework is built from the ground up without dependencies on Langchain or other agent frameworks, giving developers complete control over system behavior. At its core, CrewAI enables agents to assume specific roles within a crew, share goals, and operate as a cohesive unit.

The architecture follows a modular design that separates the concerns of agent creation, task definition, and process orchestration. This separation allows for precise customization at every level while maintaining a clean abstraction layer that simplifies development. CrewAI's standalone nature provides developers with greater flexibility and control over agent behavior and interaction patterns.

CrewAI implements a programming model centered around crews, agents, and tasks. Developers can create new projects using the CrewAI Command Line Interface (CLI), which generates a structured project folder with configuration files for agents and tasks. The framework supports YAML-based configuration, making it accessible for developers with varying levels of expertise.

The typical development workflow involves defining agents in an agents.yaml file, specifying tasks in a tasks.yaml file, and implementing custom logic, tools, and arguments in a crew.py file. This approach enables clear separation of concerns while maintaining flexibility for customization.

LangGraph's state-centric framework

LangGraph implements a graph-based architecture focused on managing application state through a central persistence layer. This architecture draws inspiration from established distributed computing models like Pregel and processing frameworks like Apache Beam, with a public interface reminiscent of NetworkX. The framework's design emphasizes stateful execution, allowing applications to maintain context across interactions.

The core architectural component in LangGraph is the StateGraph, which enables developers to define nodes (processing steps) and edges (transitions between steps) to create sophisticated workflows. This state-centric approach allows for checkpointing execution states, making it possible to implement features like memory persistence and human-in-the-loop interventions.

LangGraph adopts a state graph programming model where developers define nodes (processing steps) and edges (transitions between steps) to create workflows. The framework uses a StateGraph class initialized with a state schema, typically the prebuilt MessagesState for handling conversations.

A typical LangGraph implementation involves defining tools for the agent to use, creating nodes for agent logic and tool execution, establishing edges and conditional paths between nodes, and compiling the graph into a runnable application. The framework provides both high-level abstractions and low-level APIs for detailed customization.

BeeAI's modular and flexible architecture

BeeAI implements a modular architecture focused on flexibility and scalability for multi-agent systems. The framework offers multiple approaches to agent development, from pre-built agent types like ReAct Agent to fully customizable architectures using its Workflows system.

BeeAI's Workflows feature provides a structured way to build multi-agent systems where different agents can collaborate on complex tasks. The framework's architecture includes robust event tracking for full agent workflow visibility, telemetry collection, diagnostic logging, and well-defined exception handling. This comprehensive approach enables the development of distributed agent networks that can scale effectively in production environments.

BeeAI offers a versatile development experience with support for both Python and TypeScript, providing full library parity across languages. The framework's programming model supports multiple paradigms, from workflow-based development to direct agent implementation. This flexibility allows developers to choose the approach that best fits their use case and expertise level.

For workflow-based development, BeeAI provides the AgentWorkflow class that simplifies multi-agent orchestration. As demonstrated in the official documentation, developers can create multiple agents with different roles and tools, then orchestrate their collaboration within a coherent workflow.

The framework also supports various memory strategies for optimizing token usage, structured output generation, and sandboxed code execution for safe evaluation of generated code. These features, combined with comprehensive error handling and diagnostic logging, create a development experience focused on building production-ready agent systems.

Key features comparison

Integration capabilities

Use cases and applications

Deployment and scalability comparison

Strengths and limitations

CrewAI strengths and limitations

Strengths:

• Deep customization capabilities from inner prompts to low-level APIs
• Autonomous inter-agent delegation for complex problem-solving
• Flexible task management with granular control
• Production-grade architecture with robust error handling
• Support for both high-level abstractions and low-level customization
• Model flexibility with support for OpenAI and open-source models
• Event-driven flows for complex, real-world workflows

Limitations:

• As a standalone framework, it may lack some integrations available in ecosystem-based solutions
• Enterprise features require a separate commercial solution
• Learning curve associated with the crew-based programming model

LangGraph strengths and limitations

Strengths:

• Central persistence layer enabling sophisticated state management
• Built-in support for memory and human-in-the-loop capabilities
• Powers production-grade agents at major enterprises
• Seamless integration with LangChain and LangSmith
• Graph-based programming model for flexible workflow definition
• Comprehensive platform for development, deployment, and monitoring

Limitations:

• May require familiarity with graph-based programming concepts
• Some advanced features are part of the commercial LangGraph Platform
• Potential dependency on LangChain ecosystem for certain functionalities

BeeAI strengths and limitations

Strengths:

• Dual language support with full parity between Python and TypeScript
• Flexible multi-agent patterns through workflow-based development.
• Production-grade controls for enterprise deployment.
• Wide model support including IBM Granite, Llama 3.x, and DeepSeek R1
• Seamless integration with IBM's ecosystem of AI products and services.
• Memory optimization strategies for efficient token usage.
• Comprehensive error handling with clear, well-defined exceptions.

Limitations:

• Relatively new framework with Python library (in alpha stage as of February 2025).
• Stronger integration with IBM ecosystem may influence architecture decisions.
• In-progress roadmap items including standalone documentation site and more reference implementations.
• May have a learning curve for developers not familiar with IBM's AI ecosystem.

Conclusion

CrewAI, LangGraph, and BeeAI each offer powerful capabilities for implementing AI agent systems, with distinct advantages based on specific requirements and use cases.

CrewAI excels in scenarios requiring role-based collaboration among specialized agents, with its autonomous delegation capabilities and flexible task management making it ideal for complex business applications. The framework's standalone nature provides developers with deep customization options and fine-grained control over agent behavior.

LangGraph shines in applications requiring sophisticated state management, memory persistence, and human-in-the-loop capabilities. Its graph-based programming model enables flexible workflow definition with precise control over execution paths, making it suitable for production-grade applications with complex decision logic.

BeeAI, IBM's agent framework, delivers a comprehensive solution for building production-ready multi-agent systems with dual language support. Its integration with IBM's AI ecosystem, particularly watsonx.ai and Granite models, makes it especially valuable for enterprises already leveraging IBM technologies. The framework's flexible architecture accommodates various agent patterns while providing production-grade controls for reliability and scalability.

Choosing an AI agent framework

So, what AI agent framework should you choose?

Choose CrewAI when:

• Implementing systems requiring role-specific expertise and collaboration
• Building applications with complex task delegation requirements
• Needing deep customization of agent behavior and interaction patterns
• Developing standalone solutions without dependencies on other frameworks
• Implementing event-driven workflows with complex orchestration patterns

Start using CrewAI and watsonx to build smart AI agents.

Choose LangGraph when:

• Building applications requiring sophisticated state management
• Implementing systems with human-in-the-loop validation and correction
• Developing agents for enterprises with existing LangChain investments
• Creating applications requiring memory persistence across interactions
• Implementing complex decision trees with conditional execution paths

Start using LangGraph and watsonx.ai flows engine to build a tool calling agent.

Choose BeeAI when:

• Developing multi-agent systems requiring both Python and TypeScript support
• Building applications that integrate with IBM's AI ecosystem, particularly watsonx.ai
• Implementing enterprise-grade agent systems with production-ready controls
• Creating solutions that utilize IBM Granite, Llama 3.x, or DeepSeek R1 models
• Developing competitive analysis workflows or multi-specialist virtual assistants

Set up and run the BeeAI framework to get started building AI agents.

Next steps

Check out this article, "Implementing AI agents with AI agent frameworks," which provides a hands-on look at implementing an AI agent in each of these three AI agent frameworks.