Multi-agent framework: smarter AI, better results

1 + 1 = 3. Sounds impossible? Not when it comes to AI agents.

In 1986, Marvin Minsky introduced The Society of Mind, arguing that intelligence isn’t a single, all-knowing entity but a collection of simple “agents,” each handling a specific task. One agent might recognize shapes, another might process emotions—working together, they create what we call thinking.

Today’s multi-agent systems bring this idea to life. Instead of relying on a single AI agent to do everything, developers train groups of agents to collaborate, compete, and adapt.

The result? Smarter, more flexible systems that achieve more together than any standalone agent ever could.

Minsky saw it in human cognition. Now, AI agents are built on the same principle.

What are multi-agent frameworks?

A multi-agent framework (MAF), often called a self-organized system, is a computer system made up of multiple intelligent agents that interact with each other.

These agent frameworks can tackle problems that are too complex or challenging for a single agent or a traditional system to solve by leveraging the unique problem-solving abilities of different agents.

The agents can use various types of intelligence, including systematic methods, functional approaches, and learning algorithms.

Multi agent interactions are critical for projects requiring sophisticated collaborative capabilities and predefined complex behaviors, particularly in network-heavy and asynchronous operations.

A paper from Microsoft, Pennsylvania State University, and the University of Washington looks at a multi-agent system. It shows that by using multiple agents and focusing on what they do best, we can get better results.

Key Features of Multi-Agent Systems:

• New Intelligence: These systems can create new types of intelligence that handle more complex tasks and achieve better outcomes.
• Collaboration with LLMs: They use LLMs and tools to create intelligent agents that can talk to each other and work together.
• Better Inputs: They provide improved inputs for intelligent agents, helping them do their tasks more effectively.
• Efficient Collaboration: The system allows agents to work together, increasing efficiency and leading to better results for tasks set by people or other applications.

Leading Multi agent Frameworks

Here are some of the top frameworks for building ai agents in 2025. Let’s take a look

Feature	Lyzr	AutoGen	Crew AI	LangGraph
No-Code Agent Building	✅ ✅	❌	❌	❌
Multi-Agent Support	✅	✅	✅	✅
Enterprise-Ready	✅ ✅	✅	❌	❌
Supports Custom Workflows	✅ ✅	✅	✅	✅
Scalable Deployments	✅	✅	❌	✅
API-First Approach	✅	✅	✅	✅
Works with Multiple LLMs	✅	✅	✅	✅

1. Lyzr

Lyzr Automata is a sophisticated multi-agent automation framework designed to keep things simple, with a focus on workflow efficiency and effectiveness.

It enables the creation of multiple agents that are coupled with specific tasks. The agents and tasks can run independently and complete the provided instructions, thus entering a stable state.

2. Autogen

AutoGen specializes in conversational agents, providing a high-level abstraction for multi-agent collaboration. Its design focuses on simulating group discussions, where agents exchange messages to initiate or continue a conversation.

Key Features

• Conversational Engagement: Agents interact in dialogue, sharing messages and insights to accomplish tasks collectively.
• Customization Through Integration: Supports integration with components like LLMs and human inputs, offering flexibility in design.

3. Crew.ai

Building on AutoGen’s autonomy, CrewAI introduces a structured, role-based approach that enhances agent interactions. It’s designed for projects requiring both autonomy and structured workflows, making it suitable for development and production phases. Unlike AutoGen’s message-based interactions, CrewAI offers greater flexibility in how agents collaborate.

Key Features

• Role-Based Agent Design: Customizable agents with predefined roles and goals, enhanced by specialized toolsets.
• Autonomous Inter-Agent Delegation: Agents can autonomously delegate tasks among themselves, streamlining problem-solving and task management.

4. LangGraph

LangGraph is a graph-based framework that enables developers to define complex inter-agent interactions. While not strictly a multi-agent framework, it focuses on building stateful, multi-actor applications, providing fine-grained control over agent interactions.

LangGraph is ideal for custom systems that require detailed scalability and control, often used in workflows based on LLMs. It builds on Langchain, extending its capabilities for tasks involving cycles and repetitions that Langchain alone can’t support.

Key Features

• Stateful Multi-Agent Interactions: Provides detailed control over agent workflows and interactions in stateful environments.
• Integration with Langchain: Expands on Langchain’s capabilities, allowing for more complex and iterative tasks in LLM-based systems.

Behaviors of Multi-agent Frameworks

In a multi-agent system, the behavior of agents often mirrors the complex organizational structures we see in various industries.

Let’s think about traffic management systems for a moment.

Imagine how multiple vehicles (agents) need to work together to keep traffic flowing smoothly and safely. Just like traffic signals help cars and pedestrians coordinate their movements, agents in a multi-agent system must communicate and collaborate to reach a common goal.

These systems have diverse applications, ranging from logistics and supply chain management to healthcare and robotics.

1. Flocking

Flocking is about how agents move in the same direction. To do this, they follow three simple rules:

• Separation: Each agent tries to avoid bumping into others nearby.
• Alignment: Agents aim to match their speed with those around them.
• Cohesion: Agents work to stay close to one another.

For software agents, this coordination is crucial, especially in systems that manage transportation networks, like railroads. Just like birds that need to stay on course, software agents must work together to keep everything running smoothly.

2. Swarming

This is similar to flocking but focuses on how agents position themselves in space. Imagine a group of birds flying together, adjusting their paths based on the movements of their neighbors. In technical terms, swarming is when software agents self-organize without a central controller.

One of the great things about swarming is that it allows one operator to manage a whole group of agents instead of training someone for each individual agent.

This approach is not only less demanding on computer resources but also more dependable, making it easier to control a swarm than to oversee a single agent at a time.

Why the Multi-Agent Framework is a fundamental breakthrough?

Multi-agent systems represent a fundamental breakthrough in artificial intelligence, enabling multiple agents to interact and collaborate to solve complex problems.

Next-gen LLM applications further enhance these capabilities by automating workflows and optimizing the use of foundation models, allowing for more effective task execution both autonomously and with human oversight.

1. Efficiency

Multi-AI agents improve efficiency by dividing tasks among specialized agents. Each agent focuses on what it does best, which leads to faster and more accurate results. This approach reduces delays and makes better use of resources, resulting in smoother operations—similar to a factory assembly line where each worker has a specific job to do.

2. Flexibility

Multi-AI agents are flexible, allowing them to adapt to changing needs and environments. Because each agent works independently, they can quickly respond to market shifts and changing customer demands. This adaptability helps businesses stay competitive in a fast-moving world.

3. Scalability

Multi-AI agent systems are also scalable. When demand increases, it’s easy to add new agents to handle more work without disrupting the existing system. This ability to grow allows businesses to manage larger workloads efficiently and expand their operations smoothly.

Common Pitfalls in a Multi-Agent Framework: From Malfunctions to Coordination Challenges

One of the biggest challenges in multi-agent systems is how complexity increases as the number of agents and their possible actions grow. It becomes tricky to manage without the right algorithms, especially when you want each agent to work independently and avoid complicated communication.

On top of that, the environment is always changing, so the agents need to adapt and update their strategies over time.

1. Agent Malfunctions

When multi-agent systems are built on the same foundational models, they can run into common problems. If one agent malfunctions, it could cause all the other agents to fail or become vulnerable to attacks. This highlights the importance of strong data governance and solid training and testing processes to keep everything running smoothly.

2. Coordination Complexity

A major challenge in building multi-agent systems is ensuring that agents can work together effectively. They need to coordinate and negotiate with each other to make the system function properly. If they can’t communicate and cooperate, the entire system may struggle to achieve its goals.

3. Unpredictable Behavior

In decentralized networks, agents act independently, which can lead to unexpected behavior. This unpredictability makes it difficult to keep the entire system stable and running well. Detecting and managing issues within the larger system can be a real challenge under these conditions.

Lyzr’s Pioneering Approach to Multi Agent Frameworks

Lyzr’s success comes from its strong focus on innovation and a drive to achieve the best results. Using the latest technologies in machine learning, deep learning, and natural language processing, Lyzr has developed a unique system called AgentMesh that expands what AI can do for businesses.

The agent framework at Lyzr is a well-designed ecosystem that brings together various intelligent agents, each created to meet specific business needs. Within this framework, LLM agents collaborate to address complex challenges more effectively by gathering data, analyzing it, and strategizing actions.

By using code generation, Lyzr’s framework can automate complex programming tasks, making them easier to manage by breaking them into smaller subtasks. Additionally, visual design tools are essential resources for developers, simplifying the construction and management of multi-agent systems, particularly in memory and context management for large language model usage.

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