Crewai vs. LangGraph: Which multi agent framework should you use?

While there are different ways to build an AI agent from scratch, it’s great that you are taking the efficient approach of using multi agent frameworks.

You’re probably here because you’ve shortlisted Crew and LangGraph and want to decide which one is right for you.

You’re in the right place. 

In this blog, we will compare the two in detail — on their features, benefits, and ideal use cases, including how agents connect to establish communication and interoperability.

To get started, we need to have a basic understanding of multi agent systems. So, let’s first get a few basics out of the way. 

Why do you need a multi agent framework?

As your AI systems scale and you add multiple agents with different capabilities, the complexity of these applications grow. As the complexity grows, you will need a structured environment that orchestrates the agent activities, including the technical steps and requirements involved in building agents.

That is where agentic frameworks come in.

Multi agent frameworks provide you with a foundational structure for developing autonomous systems, and define parameters and protocols to handle interactions between multiple specialized agents.

These frameworks also incorporate agent actions, which are fundamental components within a node-based AI framework, facilitating the execution of complex tasks. An agentic application can significantly enhance user experience and system efficiency by streamlining user interaction through minimal input and adaptive responses.

Single agent systems

These systems are autonomous but rely on one agent to handle a wide range of tasks — like a jack of all trades. For example, here’s how it works when requested for a sales pipeline report:

As you can see, one agent carries out a series of tasks to accomplish the requested outcome. Single agent systems are great for specialized tasks where the problem is well defined and the scope is limited. But as your environment and context evolves, they fall short.

Multi agent systems

Multi agent systems, on the contrary, consist of multiple AI agents working together to achieve common goals. Let’s look at the same example of requesting to email the sales report, and see how the multi agent architecture manages specialized agents to execute it.

Instead of one agent accomplishing all the tasks, the tasks are broken down into smaller components where each agent specializes in a specific task – such as planning, integrating, analyzing, and more like a team of specialists working together, where each brings their unique expertise to the table.

And because these AI agents can collaborate dynamically and run these tasks in parallel, they can tackle more complex problems where the environment is always changing and evolving. Multi agent interactions allow these AI agents to communicate within the system, monitor and debug in real-time, and handle handoffs efficiently, enabling dynamic workflows.

The better you understand how these multi agents interact, the better you can optimize these systems and scale your operations, without worrying about bottlenecks or performance issues.

And that’s exactly what multi agent frameworks help you with. Additionally, these frameworks give you a set of pre-packaged tools and features to help you quickly build any type of agent systems — be it knowledge oriented, process oriented or predictive.

In short, agentic frameworks are the backbone of scalable, efficient and autonomous AI operations.

With that said let’s get to the comparison.

What is Crewai?

It is an open-source multi agent orchestration framework, that helps you build multi agent systems, and integrate them with the latest LLMs and your codebase.

The framework automates multi-agent workflows, enables them to communicate and collaborate on tasks as a team, and make decisions autonomously. Their modular design includes a range of tools such as agents, tools, tasks, processes, and an agent development kit, to engage, collect information, handle complex tasks, and manage their operations through tool calls.

Crew’s hierarchical process generates a supervisor agent to oversee task execution and agent coordination. 

The agent engine facilitates the transition from prototype to production by managing various complexities including infrastructure, security, and performance evaluation, while integrating seamlessly with existing frameworks. With asynchronous tool execution, agents can work on different business processes concurrently, increasing productivity.

Crew is primarily designed for research and quick prototypes.

What is LangGraph?

LangGraph is an open-source AI framework to develop, deploy and manage advanced workflows for generative AI agents. Using graph-based architecture LangGraph handles the relationships between different components of the AI workflow. 

LangGraph applications offer robust features like scalable infrastructure and self-hosted deployment options that enhance workflow management and processing without adding overhead.

Built on LangChain, a Python framework for AI applications, LangGraph excels at managing large language models. Function calling is integral to enhancing the capabilities of AI applications, particularly when combined with structured outputs and fine-tuning techniques. 

The platform provides various deployment options, including self-hosted solutions, cloud SaaS, and the Bring Your Own Cloud (BYOC) option, catering to different user needs and preferences.

With LangGraph Studio, a visual interface to develop workflows, users can reduce coding. One of LangGraph’s key features is its state management system which acts like a memory bank, storing information and enabling better state management and debugging. Users can run LangGraph Platform entirely on their own infrastructure as part of a Self-Hosted Enterprise solution, allowing companies to maintain control over their data while outsourcing the management of some services.

The integrated development environment in LangGraph Studio is specifically tailored for AI agent development, offering powerful tools for visualization, real-time interaction, and debugging, which enhances the overall development experience for users working with agent workflows.

This is crucial for graph execution as it allows managing individual agents as graph nodes in the system. LangGraph supports a human-in-the-loop approach, so human input can enhance AI decision making and user interactions.

LangGraph is primarily designed for engineering teams and production.

Architecture and design philosophy

Architecture and design of agent frameworks play a huge role in determining their effectiveness to support development of complex AI applications. A well-designed framework ideally provides a modular and flexible structure, allowing you to easily integrate various components and tools.

Additionally, the framework should support seamless interaction between multiple agents, making it easy to build sophisticated workflows and applications. 

Crew’s approach

Crew is an agent framework that adopts a collaborative intelligence approach, helping you create multi-agent systems where specialized agents work together to achieve shared objectives. This approach is particularly useful for applications requiring dynamic task allocation, large language models, and multi-agent orchestration.

LangGraph’s framework

LangGraph is an agent framework that takes a state-centric approach, providing a graph-based architecture to enable creation of complex workflows and applications. This approach is ideal for applications requiring sophisticated state management, knowledge retrieval, and distributed systems.

How to choose between Crewai and LangGraph?

Both are multi agent frameworks and they provide plenty of resources to get you started including tutorials, documentation, pre-built agents and tools.

With Crew, you can start by creating a simple agent and gradually add more complexity and functionality as you get more comfortable with the framework.

LangGraph has a different approach where you can design graph based workflows that can be deployed in a scalable and secure environment. This visual interface simplifies the process of building and managing complex workflows. 

But before pulling the trigger and making a choice, let’s see how to evaluate these two.

Key evaluation factors

When choosing between the two for multi-agent systems several key evaluation factors come into play.

1. Ease of use

This is arguably one of the most important aspects that will make or break adoption of the multi-agent framework in your organization. So here are some questions to consider:

Operational questions:

• How easy is it to create and coordinate a group of specialized agents towards a common goal?
• Is the framework more linear/step-by-step or event/state-driven for agent workflows?
• How much upfront config/customization to get a basic multi-agent system up and running?
• Are there easy ways to visualize and debug agent interactions during dev and after deployment?

Technical questions:

• Can I build agent workflows using simple role/task definitions or does it require deeper flow control (e.g. custom graphs, nodes, edges)?
• Is there built-in support for common patterns like delegation, collaboration and task handoff between agents?
• How easy is it to customize agent behavior without rewriting the whole system?
• Are there templates or predefined structures for common multi-agent workflows?

2. Scalability

Operational questions:

• Can the framework naturally support adding more agents and complexity without a lot of management overhead?
• Does the framework have best practices or tools for scaling across different workloads (simple tasks vs dynamic interactions)?
• How well does it support going from a few agents to a complex system with dozens or hundreds of agents, utilizing parallel processing to manage multiple agents and tasks efficiently?

Technical questions:

• Does the architecture support asynchronous execution, parallelism and distributed deployments?
• Are there limits in the framework’s design that would restrict the number of agents or agent memory/state?
• Can I partition large agent systems into smaller reusable modules or graphs?
• How does the framework handle coordination bottlenecks or message-passing at scale?

3. Performance

Operational questions:

• What are the response times for agent interactions in medium to large deployments?
• How well does the system perform when agent workflows require complex state tracking vs simple task execution, especially considering the memory management needed for handling data complexities within multi-agent systems?
• Does the framework have guidance on performance tuning for different use cases (e.g. real-time decisions vs batch processing)?

Technical questions:

• How does the framework manage agent state, transitions and message passing?
• Does it natively support fine-grained control over retries, timeouts and failure recovery at agent or workflow level?
• How much overhead does the orchestration layer add under high load?
• Are there tools or hooks for profiling and optimizing agent performance?
• How does the framework handle application control flow in managing complex workflows, particularly in multi-agent architectures?

4. Cost and Community Support

Operational questions:

• Is it fully open-source, partially open-source with proprietary layers or commercial-only?
• How active and mature is the community around the framework (plugins, integrations, example projects)?
• Are there established support channels (forums, Slack/Discord communities, professional support)?
• What is the release frequency for updates, new features and bug fixes?

Technical questions:

• Are there enough libraries, connectors and extension points to integrate the framework with external systems (APIs, databases, vector stores etc.)?
• How fast do bugs or feature requests get addressed by maintainers or the community?
• Is there clear documentation on best practices for cost optimization (compute resources, memory usage) when scaling?
• Can I self-host and run the framework without relying on a specific cloud or SaaS service?

With that context, let's now compare head-to-head.

Evaluating Crewai and LangGraph: Head-to-head 

TL;DR

Crew is a multi-agent orchestration framework that is beginner friendly with easy but limited flexibility. It is typically used in the research phase for creating quick prototypes and testing outcomes. It leverages autonomous agents to handle specific tasks, making it easier to manage and test various scenarios.

On the other hand, LangGraph built on LangChain provides more control. It’s a great choice for software development teams and engineering teams to develop complex workflows for specific use cases. LangGraph's agent capabilities allow agents to manage tasks independently while a supervisor orchestrates their interactions, enhancing overall efficiency and scalability.

1. Ease of use

Crewai: Easy to set up

It is known for its user-friendly interface and ease of use, making it ideal for beginners who want to build quick MVPs and test agent delegation scenarios. Crew makes it very easy to build agents and set up agent roles, tasks, and teams within the framework. It’s focused on sequential or goal-driven workflow, and its simple setup allows you to get started with minimal hassle.

LangGraph: Comes with a steep learning curve

LangGraph was created by LangChain as an open-source AI agent framework, showcasing its versatility in automating tasks. LangGraph requires you to have a deeper understanding of graph structures (nodes, edges, state transitions). It is for engineering teams in the production stage wanting to model complex, non–linear agent interactions. 

So, more effort is needed for the initial set up and configuration, and comes with a steeper learning curve.

2. Scalability

Crewai: Designed for research

It can scale well and handle a moderate number of agents and task-based workflows, however, conditional logic within workflows can be tricky. Crew is a better fit when independent agents don’t need to constantly talk to each other, and not for real-time interaction-heavy use cases.

LangGraph: Designed for engineers

LangGraph, on the contrary, is designed for scale—with asynchronous and distributed systems in mind. It can easily handle conditional logic within workflows and is a great fit for highly interconnected agents and large graphs. 

If you’re running a complex, dynamic multi-agent setup that constantly evolves, then LangGraph is your best bet. 

Additionally, LangGraph leverages directed acyclic graphs (DAGs) to create node-based workflows that enhance user interaction and support complex decision-making processes in various applications.

3. Performance

Crewai: Performs well for simple, linear workflows

It performs well when you’re running simple, linear, or team-based workflows, where the orchestration demands are well-defined and predictable. However, the performance might struggle when agents need to solve complex tasks that require frequent, complex state syncs or events.

LangGraph: Built for large volumes and highly tunable

LangGraph manages concurrent, stateful agent workflows with high efficiency as it is built to handle large volumes of simultaneous agent events with great control. 

While LangGraph is highly tunable, you need to actively optimize it for best performance. Streaming intermediate steps can enhance user experience by providing real-time updates on agent reasoning and actions.

4. Cost and community support

Crewai: Open-source and expanding community

It is an open source framework with a growing but relatively young community compared to broader ecosystems. Given its low operational overhead, it is definitely a cost-effective option for small to medium systems. While it has fewer native integrations today, its ecosystem is expanding steadily.

LangGraph: Open-source with a more mature ecosystem

LangGraph is open-source (via LangChain), backed by a large and mature ecosystem of community and contributors. While it can be heavier to self-host and optimize, it offers rich access to third-party tools, and plugins. 

LangGraph is currently in a strong momentum, catering to a wide range of use cases, and also providing a robust foundation to scale advanced systems. 

The LangGraph Platform is a powerful service designed for deploying and scaling agent applications efficiently, benefiting engineering teams in crafting user experiences, managing complex workflows, and offering various deployment options.

But what are both missing? Why go for Zams instead?

While Crewai is beginner-friendly and easy-to-use, it is limited in flexibility. On the other hand, LangGraph offers great control and flexibility but is not easy to quickly set up and get going.

Both these platforms are primarily for software development teams.

When engineering teams want to research and quickly prototype, they go for Crew. And during production—they prefer LangGraph to develop agents for complex and detailed workflows.

When you have a burning problem to fix —  causing operational inefficiencies such as revenue leak or customer churn, and you know that fixing it can save/make millions of dollars — should the UI be an impediment to implementing AI?

No, right? That’s where Zams comes in. 

Zams: Designed for easy scalability and enterprise data

Zams is a multi-agent orchestration platform where the easy-to-use UI helps you build the agents you want, without having to spend a lot of engineering time.

Just simply define what you want your agent to do.

Set the objective for your agent and give a context prompt – that’s it. You’re on your way to building your agent.

Simply put — with Zams, you can build workflows for the most nuanced operations with a few clicks and prompts — whereas all the complexities of building the agent quietly happen under the hood.

Final verdict

To sum up, Crew and Langgraph are both powerful frameworks, each with its unique strengths and weaknesses. Choosing the right framework ultimately depends on your specific needs and expertise.

For beginners or those looking for a simple multi-agent orchestration framework Crew is the way to go. Its user friendly and modular design makes it easy to get started.

On the other hand, LangGraph is more suitable for advanced users and software developers. Using LangGraph can significantly enhance your workflow and collaboration by leveraging its visual interface and platform features.

However, the size of your engineering team or their bandwidth shouldn’t stop you from implementing the right solutions in the right time. And if that’s you — we highly recommend you to take a look at Zams or schedule a demo with us.

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