Top 5 Multi Agent Frameworks

Think of multi agent frameworks as the infrastructure, the scaffolding you use to set up and manage multiple AI agents working together. 

This is about defining roles for different agents, setting their tasks, how they talk to each other, coordinating, and how they plug into the big language models, other tools, etc. 

Moving beyond that single agent to multi agent systems, which one's right for you? Maybe for automating customer service, or streamlining workflows, or even building that next big AI app.

We will unpack some of the main frameworks out there, look at their differences, their strengths, and where they really shine. Hopefully, it will clarify this complex area so that you can figure out a path forward.

Why this focus on multi agent systems now?

Well, it feels like we're hitting a new phase in enterprises. It's less about just proving AI can work, more about how to scale that intelligence across real business workflows.

Businesses are realizing that to really make a difference, they often need more than one AI working in isolation. They need them coordinated.

Trying to build these coordinated AI systems from scratch is really complex. Why? Because of these hurdles:

• Orchestration -  Getting AI agents to work together smoothly, not trip over each other. 
• Memory - How do they remember what's happened before? 
• Communication - Setting up reliable ways for them to talk. 
• Error handling  - What happens when one agent messes up?

And just observing them, understanding what's going on inside, building all that foundation yourself is a massive job. This is where these frameworks come in. 

So these multi agent frameworks are like pre-built foundations. These are like reusable building blocks for these agent ecosystems. Instead of reinventing the wheel for communication or memory, you use the framework's tools. It lets you focus on the actual application, the roles, and the tasks. 

It speeds things up massively, both for getting started, prototyping, and then actually deploying them.

Where are enterprises using multi-agent systems effectively?

There are several multi-agent system use cases that companies have. 

Think about supply chains. Imagine an agent for each key point, like logistics, demand forecasting, etc. They could all be talking in real time. So if there's a delay somewhere as bad weather hits a port, the logistics agent flags it, communicates with the shipping agent. 

Maybe they automatically reroute a shipment. Or they adjust inventory levels based on sudden demand spikes. It makes the whole chain much more responsive and resilient. That's way beyond traditional methods.

Finance is another big one. Automating really complex processes, such as reconciliation, finding anomalies in transactions, and generating reports. You could have one agent that just extracts data from invoices, another that compares it to payment records, a third that flags discrepancies, and maybe even starts the review process. 

And they can run 24/7. This cuts down processing time, reduces errors, and thus has a big impact on the operational workflows. 

Read more on use cases

What are the top 5 multi agent frameworks?

Let's get into the frameworks themselves.

LangChain 

LangChain helps you build LLM-powered apps, but stringing together actions in a sequence, like a chain. It’s a common pattern to retrieve some data, then summarize that data, and then use the summary to answer a question.

Retrieve, summarize, and answer. That's your chain - a defined sequence of steps. It's very modular. LangChain gives you all these pre-built components, document loaders to get data in, text splitters to break it up, prompt components to talk to the LLM, the LLM connection itself, memory components, etc.

Key features 

1. Agent and tool abstraction layers: LangChain provides high-level abstractions for creating agents and tools. So you don't have to sweat the small stuff when it comes to interacting with different LLMs or external services.
2. Built-in memory: It offers various memory implementations, from simple conversation buffers to more sophisticated memory networks, allowing agents to maintain context over multiple turns.
3. Vector store integration: For RAG, LangChain seamlessly integrates with numerous vector databases.  This simplifies the process of indexing and querying knowledge bases to ground the LLM's responses in relevant data.
4. Supports OpenAI, Anthropic, Hugging Face, and more: LangChain is designed to be LLM-agnostic. It provides integrations with a wide range of popular LLM providers, giving developers the flexibility to choose the best model for their specific needs.
5. Tool routing and agent decision-making logic: It provides mechanisms for agents to decide which tool to use based on the current context and objective. This involves defining prompting strategies and decision-making processes that guide the agent's actions.

Who’s it for 

LangChain is a great option for developers and teams building LLM-powered workflows that are moderately complex. If you need more than just basic LLM calls but aren't quite ready to tackle highly intricate, multi-agent collaborations, LangChain is a good place to start. Its modularity allows you to gradually build complexity as you go. 

Best suited for (use case)

Building intelligent chatbots with knowledge retrieval, automating multi-step customer service workflows, and integrating AI with various business tools.

CrewAI

‍CrewAI 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.

CrewAI is all about simplifying how you get multiple agents to work together and achieve a shared goal. With CrewAI, you can set up and manage these collaborative interactions between your agents without any fuss. 

Its main angle is simplicity and speed. It’s easy to get started and uses simple YAML files to set up role-based agents, handles task delegation, and has built-in agent chat. 

You can spin up a team of collaborative agents,  like for marketing tasks or finance bots, with just a few lines of code.

It's built on top of Langchain, which can be good leverage as that ecosystem, but also means sometimes you inherit Langchain's dependencies and structure. 

Key features 

1. Lightweight agent management: CrewAI allows you to define agents, their roles, goals, and tools using simple YAML configuration files. This makes it incredibly easy to define and manage multiple agents without extensive coding.
2. Role-based agents and task delegation: The core concept of CrewAI revolves around assigning specific roles to agents (e.g., "Researcher," "Writer," "Editor"). Tasks can then be delegated to these agents based on their defined responsibilities.
3. Agent-to-agent chat built in: CrewAI facilitates direct communication between agents. This allows them to collaborate, share information, and coordinate their efforts to achieve the overall objective.
4. CLI and cloud-ready: CrewAI offers a command-line interface (CLI) for easy interaction and management. Its design also makes it suitable for deployment in cloud environments.

Who’s it for 

CrewAI is your go-to framework if you're a team that needs to get a collaborative agent system up and running quickly, but you don't want to deal with a ton of complex infrastructure management or extensive coding.

Best suited for (use case)

Quickly setting up AI-powered teams for marketing campaigns, financial reporting, content creation workflows, and internal process automation. 

AutoGen (by Microsoft)

AutoGen’s main focus area is collaboration. It's designed to create teams of agents that can chat with each other to solve problems. You could think of brainstorming, critiquing code, and refining ideas through conversation. So these agents are working together almost like a human team.

It's strong for complex multi-step tasks, especially code generation. They have Autogen Studio to visualize and manage it.  Python and .NET support is there, but maybe less developed. AutoGen works best with newer LLMs.

AutoGen is great for building advanced multi-agent conversation systems. It's all about making interactions between agents, and even with humans, feel natural and dynamic.

Key features 

• Designed for multi-agent conversation frameworks: AutoGen's core strength lies in managing complex, multi-turn conversations between multiple agents. It provides the underlying infrastructure for agents to interact and reason together.
• Conversation history: AutoGen carefully manages the history of conversations between agents, and organizes turn-taking to ensure smooth interactions. It also provides strong support for agents using tools during their conversations.
• Human-in-the-loop workflows: A key aspect of AutoGen is its support for incorporating human intervention into the agent workflows. Humans can provide feedback, guide the conversation, or take over control at any point.
• Seamless with Azure/OpenAI: Being a Microsoft product, AutoGen has strong integrations with Azure services and OpenAI models, making it a natural choice for users within that ecosystem.

Who’s it for 

AutoGen is a great choice for enterprises and researchers who need to manage complex projects that require a lot of back-and-forth between AI agents and humans.

Best suited for (use case)

Automating intricate financial analysis requiring collaboration between AI analysts, generating complex documents with multiple AI reviewers, and managing multi-stage project workflows with AI coordination. 

Semantic Kernel 

Semantic Kernel takes a plugin-based, modular approach to building intelligent agents. It focuses on connecting AI "skills" (LLM-powered functions or native code) to real-world APIs and services.

Its strength is its plugin-based architecture. And it has really strong C-sharp and .NET support. It’s ideal for companies already deep in the Microsoft stack. Semantic is great for building agents as services, internal co-pilots, etc., within a Microsoft-centric environment.

Key features 

1. Plugin-based, modular approach to agents: It encourages breaking down agent capabilities into reusable "plugins." These plugins can be semantic (powered by LLMs) or native (traditional code), promoting modularity and reusability.
2. Connects AI skills to real-world APIs: A core strength of Semantic Kernel is its ability to easily integrate LLM-powered skills with external APIs and services. This allows agents to perform actions in the real world based on their reasoning.
3. Great for integrating reasoning + actions: It helps in combining the reasoning capabilities of LLMs with the ability to execute actions through plugins, enabling agents to not just think but also act.
4. Strong C#/.NET support: Developed by Microsoft, Semantic Kernel has excellent support for the C# and .NET ecosystems, making it a preferred choice for developers working within that environment.

Who’s it for 

Enterprise developers with legacy infrastructure or Microsoft-heavy stacks will find Semantic Kernel particularly attractive because of its strong .NET support and focus on integration. It's great for working with existing Microsoft technologies and potentially integrating AI into legacy systems.

Best suited for (use case)

Developing internal AI assistants that can access and interact with various company systems (e.g., scheduling, ticketing), automating business processes by connecting AI skills to existing APIs, and building AI-powered services within a Microsoft ecosystem.

LlamaIndex

LlamaIndex focuses on data connectors, indexing, and retrieval strategies to enhance the knowledge and context of LLMs when building LLM applications that work with your data.

It has query planning agents and smart ways to route requests based on context. LlamaIndex is tool-agnostic. It is powerful for knowledge-heavy stuff, enterprise search, assistance that need to understand lots of internal documents, and Q&A over specific knowledge bases.

Key features 

1. Data connectors and retrieval pipelines: LlamaIndex has many data connectors to get data from different places, like documents, databases, and APIs. It also has advanced tools to index and organize this data so it can be easily found when needed.
2. Query planning agents + context routing: It includes agents specifically designed for query planning and context routing. These agents can analyze user queries, determine the relevant data sources, and route the query to the appropriate retrieval mechanism.
3. Tool-agnostic with OpenAI & more.: While focused on data, LlamaIndex is designed to be compatible with various LLM providers, allowing you to leverage its data capabilities with your preferred LLM.
4. Strong ecosystem for knowledge-heavy use cases: LlamaIndex has a rich ecosystem of integrations and tools specifically tailored for applications that require deep understanding and retrieval from large amounts of knowledge.

Who’s it for 

LlamaIndex is the go-to framework for developers who need to build applications that can answer questions or perform tasks based on specific, often large and complex, datasets. If you're building RAG agents or working with complex enterprise data, LlamaIndex is the framework for you!

Best suited for (use case)

Creating enterprise-wide knowledge search platforms, building internal Q&A systems for documentation and policies, and developing AI-powered insights engines from large datasets.

So, which is the best multi-agent framework?

There's no single best framework.

As with everything else, it depends! It depends on your project, your team, and your priorities. Like ease of use versus control, scalability, and community support. You need to match the framework's strengths to your specific needs. 

Let’s recap the multi agent frameworks quickly. 

✅ LangChain is a versatile and adaptable framework, offering a wide array of tools and abstractions. This makes it a great choice for various applications, especially those that require flexibility in chaining different LLM calls and integrating diverse data sources and tools. 

If you're experimenting with different ideas or need a reliable foundation with numerous integrations, LangChain is a strong option.

✅ CrewAI is all about getting those collaborative agents up and running quickly. Need to create teams of AI agents that can chat and delegate tasks, especially for specific business functions?

CrewAI's simplicity and YAML-based configuration make it a very appealing choice. It's perfect for scenarios where speed of deployment and clear role-based collaboration are key.

✅ AutoGen truly excels when handling intricate, multi-step conversations between AI agents, and even those that involve human participation. It's specifically designed to orchestrate these complex interactions while seamlessly maintaining the conversation's state.  

So, if your particular use case involves sophisticated reasoning that demands precise control over how agents communicate, particularly within the Microsoft ecosystem, AutoGen definitely warrants a closer look.

✅ If your enterprise relies heavily on the .NET ecosystem and you need to integrate AI deeply with existing systems and APIs, Semantic Kernel is your go-to. 

Its plugin-based architecture encourages modularity and reusability, making it ideal for building AI-powered services and internal copilots that can interact with your core business applications.

✅ When your application relies heavily on retrieving and reasoning over large amounts of your data, LlamaIndex is the specialist. Its focus on data connectors, indexing, and retrieval makes it the prime choice for building knowledge-intensive applications like enterprise search engines, internal Q&A systems, and insights platforms.

And yet frameworks alone don’t cut it, here’s why

You can build a smart agent using LangChain to automate customer support queries. It can understand questions, look up information, and give helpful answers. Great!

But what happens when you want to deploy dozens of these intelligent agents? What if you also want agents helping with sales, marketing, or other internal operations? Suddenly, you have a whole team of AI agents working on different things, potentially interacting with each other and your existing business systems.

Choosing the right framework is only half the battle. The real challenge? Operationalizing multi-agent systems at scale — across teams, systems, and workflows.

That’s where Zams comes in.

Think of Zams as the UI for AI, like the way browsers are for the internet.

While frameworks are aimed primarily at engineering teams, Zams is a multi-agent orchestration platform designed specifically for teams across functions. You can quickly build your AI agent to automate operations with just a few prompts, with security and governance built-in.

While frameworks like LangChain give you the foundation, Zams gives you:

📈 Visual interfaces for designing and monitoring agent workflows

🔍 Built-in observability to track decisions, errors, and interactions

🔗 Enterprise connectors for real-time integration with ERPs. 

🙂 Security and access controls that meet enterprise-grade compliance

Most importantly, Zams adapts to every business nuance — helping teams orchestrate agents across the messy, ambiguous realities of finance, supply chains, and risk.

Other frameworks get you started. Zams gets you to the outcome — without the headache.

Final thoughts…

This world of AI agents and multi agent frameworks is moving incredibly fast. It's opening up really powerful new ways to build intelligent autonomous systems. Understanding the concepts and different frameworks gives you the power to make smart choices.

Take a moment to think about a workflow or a problem you're dealing with. Could one of these multi-agent ideas, one of these frameworks, offer a totally new angle or a big improvement? 

Try a small project. Engage with the communities. There's so much potential here. And honestly, we're just scratching the surface of what multi-agent AI will do for work and, well, everything else.