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Multi Agent LLM: Key Frameworks & Applications
CEO of Label Your Data
TL;DR
What Are Multi-Agent LLMs?
A multi-agent LLM is an AI system that consists of more than one specialized agent. Each of these perform different tasks to achieve a common goal. These types of LLMs are different to traditional ones in that they distribute the tasks. This improves efficiency, accuracy, and adaptability.
Why AI/ML Teams Use Multi-Agent LLMs
Real-world AI applications often involve complex workflows that traditional systems struggle with. Multi-agent large language models serve as a dynamic solution to improve:
Automation
Decision-making
Large-scale model interaction
Key Advantages Over Single-Agent Systems
Now let’s look at why these systems are the better option:
Task specialization: Agents handle specific subtasks, enhancing performance.
Collaborative accuracy: Agents verify and refine outputs, reducing errors.
Streamlined workflows: Breaking down complex tasks simplifies decision-making.
How a Multi Agent LLM Works
Think of an LLM multi agent like a specialist on a team. You put together people with the right skills to get the project done. The same is true here.
According to research, the Chain-of-Agents approach is as much as 10% more efficient than RAG LLM. In addition, we’re seeing AI leaders like OpenAI, Google DeepMind, and Meta actively developing multi-agent systems LLM. They’re using these across various domains.
Core Components of a Multi-Agent System
Let’s analyze a multi-agent LLM framework in more detail.
Agent Roles and Specialization
This system works because each player has a specific role:
Planner: Breaks down tasks into subtasks and assigns them.
Evaluator: Verifies outputs and ensures accuracy.
Executor: Performs the actual computations or actions.
Communication and Decision-Making
You’ll need to set up structured messaging protocols, so your agents can coordinate efficiently and execute their tasks in a timely manner.
Memory and Context Handling
How do we reduce hallucination with so many agents working together? You can ensure continuity and improve accuracy by setting up a shared memory.
Common Architectures of Multi-Agent Systems
Supervisor Model
With this model, one central agent oversees all the tasks and splits the work into subtasks. It then delegates to other agents.
Hierarchical Multi-Agent Models
Structured layers of agents handle tasks at different levels, optimizing decision-making efficiency.
Decentralized Systems
With this type of multi agent framework, LLM systems use autonomous agents. Each operates on its own, and coordinates with other agents. There’s no supervisor here.
Hierarchical Multi-Agent Models
In this LLM multi agent framework, tasks are divided by complexity and assigned to specialized agents for optimal efficiency.
Managing our remote SEO team at FATJOE, I’ve seen multi-agent LLMs function like specialists, each handling a different aspect of an SEO campaign. Last month, three AI agents collaborated on keyword research, content optimization, and backlink analysis, cutting project time by 40% while maintaining quality.
CEO at FATJOETop Frameworks and Tools for a Multi Agent LLM
Now let’s look at some of the top options on the market today.
AutoGen
Microsoft created this multi agent architecture LLM, and it’s one of the best. It allows you to create sociable AI assistants that can work well together and use tools. Best of all, it supports human-in-the-loop models and is completely customizable.
A lot of developers are using this tech, making it a good option if you want support or to collaborate.
LangGraph
This multi-agent LLM aims to create a structured environment for interaction modeling. It uses a graph-based system that means that your workflows can move in more than just one way.
Think of it like Google Maps. The system gives you a few ways to get to your destination, and you choose the best one. In this case, you’re giving AI good directions.
What’s special here is that you can use increasingly complex workflows and scale them up easily. If one agent is busy, the system can reroute to another easily.
CrewAI
This framework aims to optimize your AI team’s efficiency. You can put together a team of AI agents and tools to build the skills you need. Want to learn more about it? The company’s CEO has a free course about multi-agent systems, with a focus on CrewAI.
MetaGPT
Think of MetaGPT as the assembly line of the multi-agent LLM world. You start with standard operating procedures and assign each agent a predefined operating role. It’s an ideal way to create workflows for software development.
AutoGPT
This system is great at finishing off lists of instructions. It’ll work through each step exactly as you instruct it. It’s got a great memory and some interesting tools that make it a good option for long-term agent collaboration.
Applications of Multi-Agent LLMs in AI and ML
Automated Data Labeling and Annotation
These systems make automation more efficient, allowing you to complete tasks like high-volume LLM data labeling faster and more accurately. You can assign different data annotation roles to different agents.
For example, you can task one agent with object detection and image recognition and another with text recognition in unlabeled data. You could then assign a third one with more specialized tasks, like geospatial annotation. This can significantly reduce data annotation pricing.
I worked on a data labeling project with a massive, diverse dataset. Using a multi-agent LLM setup, we improved efficiency by assigning specialized agents—one for text classification, another for image recognition, and others for tasks like entity extraction—rather than relying on a single, general model.
Senior Software Engineer at Netflix AI Research Assistants for Model Training
Fine-tuning a machine learning algorithm is part science and part art. Sometimes you need to experiment to see what works best. Multi-agent systems let you test theories on a large scale to support OCR deep learning.
Code Generation and Software Development
AI systems thrive on good structure. Providing them with a collaborative environment with clear rules makes it easier for them to generate good code.
AI-Powered Decision Support Systems
Having several agents working together to analyze complex datasets improves the recommendations the system makes. Think about it this way. You see an investment advisor about estate planning. They analyze financial datasets and industry news about the best trades and investments. They might draw on the expertise of others to find the best fit for your needs.
In effect, you have a team of advisors, all with a certain level of input. The same applies with AI.
Multi-Agent LLMs in Robotics and Autonomous Systems
Sometimes machines have to make on-the-spot decisions. Say, for example, you run a manufacturing plant and one of the machines is overheating. Your multi-agent LLM would have to reroute production to give the system time to cool off.
When you have multiple agents, it becomes easy to make these real-time decisions. This is useful in robotics and self-driving vehicles in particular.
Our multi-agent LLMs at FuseBase streamline SaaS workflow automation by dividing tasks—one manages client communications, another optimizes scheduling and resource allocation. This collaboration helps catch issues early, like scheduling conflicts or unusual resource requests, making our platform 40% more efficient than single AI systems.
Challenges and Limitations of Using a Multi Agent LLM
So, why aren’t single systems obsolete yet? We still have some teething problems to overcome, including:
Coordination and Scalability
On paper, the system works extremely well. The more complex the workflow and large-scale the environment, the greater the chance of errors creeping in. You may also experience issues managing agent interactions.
Computational Costs and Resource Allocation
These systems can be resource-intensive. You’ll have to make trade-offs between performance and efficiency. It can be tricky to find the right balance.
Security and Privacy Risks
You have to be sure that the system is secure, so there’s no chance of mishandling data between the different agents.
We’ve covered these very briefly in this post. If you want a more in-depth explanation, check out this paper.
Multi-agent LLMs enhance AI teamwork by assigning specialized tasks to different agents. Rather than one AI handling everything, multiple agents collaborate, reducing errors and speeding up problem-solving. At Parachute, we use this approach to filter out false positives in cybersecurity alerts—one agent flags threats, another analyzes patterns, and a third cross-checks vulnerabilities—resulting in faster, more accurate threat detection.
CEO at ParachuteFuture Prediction for Multi-Agent LLMs
So, where to from here?
Reinforcement Learning for Multi-Agent Coordination
As AI advances, it’ll get better at making the right choices. We can speed up this process by reviewing those decisions and reinforcing the right ones. Over time, the agents will also learn from one another, making the systems even more efficient. We’ll likely see different LLMs working together for mutual benefit.
Hybrid Models Combining Human and AI Agents
A lot of the initial focus during development was to make AI as autonomous as possible. Today, we’re realizing that human oversight can be a vital part of the equation. AI, much like a child, sometimes needs guidance in finding the right answers, and we need to support its learning.
In the future, we’ll see an increase in human-in-the-loop and other hybrid models.
Optimizing Multi-Agent Workflows for Enterprise AI
One of the biggest issues with LLMs is that they’re resource-intensive. Going forward, developers will focus on making multi-agent AI more cost-effective and easy to scale.
We’re already seeing examples where companies are choosing high-quality datasets over sheer volume. Some firms are even choosing to supplement their training data synthetically to create a more focused set. This reduces the number of resources we need and speeds up the development process.
About Label Your Data
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FAQ
What is multi-agent in LLM?
A multi-agent LLM uses several AI agents to collaborate on tasks. At the simplest level, it’s breaking a complex process up into steps.
What is the difference between single agent and multi-agent LLM?
Single-agent LLMs work independently, while multi-agent models divide the tasks between specialized agents to improve performance.
What is multiple LLM?
A multiple LLM is a system that uses more than one large language model. They can work independently or collaboratively.
What is the difference between single agent and multi-agent reinforcement learning?
Single-agent reinforcement learning centers on optimizing one AI’s decision-making. Multi-agent reinforcement learning helps multiple AI entities learn to cooperate or compete.
Written by
CEO of Label Your Data
Karyna is the CEO of Label Your Data, a company specializing in data labeling solutions for machine learning projects. With a strong background in machine learning, she frequently collaborates with editors to share her expertise through articles, whitepapers, and presentations.