Multi-Agent vs Single Agent
Understanding the Limitations of Individual LLM Agents and the Superiority of Multi-Agent Collaboration.
Large language models (LLMs) have emerged as powerful tools capable of performing a variety of tasks, from generating human-like text to providing detailed answers to complex questions.
Despite their impressive capabilities, individual LLM agents face significant limitations that hinder their effectiveness in real-world applications.
This blog aims to explore these limitations, specifically focusing on five major problems: hallucinations, limited context windows, single-threaded processing, lack of collaboration, and substantial size and cost.
Additionally, we will demonstrate how multi-agent collaboration offers a superior solution to these issues and showcase the Swarms framework as a simple yet powerful tool for building multi-agent applications.
Learn more from the supporting research papers here:
And, join the multi-agent community here!
1. Hallucinations: The Challenge of Inaccuracy
One of the most prominent issues with individual LLM agents is their tendency to hallucinate.
Hallucination occurs when an LLM generates incorrect or nonsensical information.
This problem arises because LLMs, despite their training on vast datasets, can produce outputs that seem plausible but are factually incorrect or illogical.
The reliability of these models is compromised when they provide misleading or false information, which is particularly problematic in applications requiring high accuracy, such as medical diagnosis, legal advice, or financial analysis.
In the paper “Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems” [PDF LINK], researchers explore the limitations of LLMs in producing accurate responses.
They found that even with increased computational power and more extensive training, LLMs still exhibit hallucination tendencies, indicating a fundamental limitation in their design.
By employing a multi-agent framework such as swarms, we can mitigate the issue of hallucinations.
In such a setup, multiple agents can cross-verify information, significantly reducing the risk of incorrect outputs.
Each agent can specialize in different aspects of a problem, ensuring a more comprehensive and accurate response. This collaborative approach leverages the strengths of individual agents while minimizing their weaknesses.
Supporting Research
- “Learning to Decode Collaboratively with Multiple Language Models” [PDF LINK] demonstrates how multiple LLMs working together can improve the accuracy and reliability of the generated content.
- “SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents” [PDF LINK] highlights the benefits of collaborative learning among agents in producing more accurate and contextually appropriate responses.
2. Limited Context Windows: The Challenge of Memory
Another significant limitation of individual LLM agents is their restricted context windows.
Context windows refer to the amount of text an LLM can process at one time.
Most current LLMs have a fixed context window size, which limits their ability to handle long documents or conversations that require maintaining context over extended text spans.
This limitation is particularly detrimental in applications such as legal document analysis, lengthy customer support interactions, or complex narrative generation.
The paper “Chain of Agents: Large Language Models Collaborating on Long-Context Tasks” [PDF LINK] discusses the challenges posed by limited context windows and how they impede the ability of LLMs to maintain coherence over long documents.
The study shows that single-agent systems often lose track of context, leading to disjointed or irrelevant responses.
Multi-agent frameworks offer a solution by distributing the processing load among multiple agents.
Each agent can handle a segment of the context, and through inter-agent communication, they can maintain a coherent understanding of the entire document or conversation.
This approach effectively extends the context window, allowing for better handling of long texts and improving the overall coherence of responses.
Supporting Research
- “LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration” [PDF LINK] illustrates how multi-agent systems can be used to process extended contexts, enhancing the capabilities of LLMs in handling long-form content.
- “Scaling Instructable Agents Across Many Simulated Worlds” [PDF LINK] provides evidence of the effectiveness of multi-agent systems in maintaining context over long interactions, thereby improving the quality and relevance of the generated outputs.
3. Single-Threaded Processing: The Challenge of Efficiency
Most individual LLMs operate in a single-threaded manner, meaning they can only process one task at a time.
This limitation leads to inefficiency and slower response times, particularly in applications that require handling multiple queries or tasks simultaneously.
In a business setting, this inefficiency can translate to longer wait times for customers, reduced productivity, and ultimately, a negative impact on the bottom line.
The paper “Mixture-of-Agents Enhances Large Language Model Capabilities” [PDF LINK] explores the inefficiencies of single-threaded processing in LLMs and highlights the potential benefits of using multiple agents to handle parallel tasks.
The study shows that single-threaded LLMs struggle with multitasking and often exhibit degraded performance under high load conditions.
Multi-agent collaboration inherently support parallel processing, where multiple agents can work on different tasks simultaneously.
This capability significantly improves efficiency and response times, making it possible to handle multiple queries or tasks concurrently without compromising performance.
By leveraging parallel processing, businesses can achieve higher throughput and provide faster, more reliable services to their customers.
Get started with mixture of agents now with the swarms framework:
And, the documentation:
https://docs.swarms.world/en/latest/swarms/structs/moa/
Supporting Research
- “AgentScope: A Flexible yet Robust Multi-Agent Platform” [PDF LINK] demonstrates the advantages of multi-agent systems in handling parallel tasks and improving overall efficiency.
- “Automated Unit Test Improvement using Large Language Models” [PDF LINK] showcases how multi-agent frameworks can enhance the efficiency of software development processes by enabling parallel task execution and reducing the time required for testing and debugging.
4. Lack of Collaboration
Individual LLMs lack the ability to collaborate, which is crucial for solving complex problems that require diverse expertise and perspectives.
This limitation is particularly evident in applications that involve interdisciplinary knowledge or multifaceted problem-solving, such as scientific research, strategic planning, or comprehensive data analysis.
The paper “Human-level play in the game of Diplomacy by combining language models with strategic reasoning” [PDF LINK] highlights the limitations of individual LLMs in collaborative scenarios.
The study shows that single-agent systems struggle to coordinate and strategize effectively, leading to suboptimal outcomes in complex, multi-agent environments.
Multi-agent systems enable collaboration among agents, each bringing their unique expertise and perspectives to the table.
By working together, these agents can tackle complex problems more effectively than a single LLM could.
This collaborative approach allows for the integration of diverse knowledge and skills, leading to more comprehensive and innovative solutions.
Get started with this multi-agent architecture with swarms now:
- “More agents is all you need” [PDF LINK] emphasizes the importance of collaboration among agents in achieving superior problem-solving capabilities and highlights the benefits of multi-agent frameworks in complex scenarios.
- “Experiential Co-Learning of Software-Developing Agents” [PDF LINK] provides evidence of the effectiveness of collaborative learning among agents in improving their overall performance and problem-solving abilities.
5. Substantial Size and Cost: The Challenge of Accessibility
Advanced LLMs require significant computational resources, making them costly and less accessible to many organizations.
The size and cost of these models pose a barrier to entry for businesses that may benefit from AI but cannot afford the infrastructure required to deploy and maintain large LLMs.
This limitation restricts the democratization of AI and hinders the widespread adoption of advanced language models.
The paper [ LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration ] [ PDF LINK] discusses the high costs associated with deploying individual LLMs and the challenges faced by organizations in accessing these advanced technologies.
The study highlights the need for more cost-effective solutions that can deliver similar or superior performance.
Multi-agent frameworks can be optimized to use resources more efficiently, reducing the overall computational cost.
By distributing tasks among multiple agents, the system can achieve better performance with lower resource requirements.
This approach makes advanced AI technologies more accessible to a broader range of organizations, enabling them to leverage the benefits of LLMs without the prohibitive costs.
Lower your costs and increase performance with the swarms framework now:
Supporting Research
- “MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution” [PDF LINK] showcases the cost-efficiency of multi-agent frameworks in handling complex tasks with reduced computational overhead.
- “Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration” [PDF LINK] demonstrates how multi-agent systems can be deployed on resource-constrained devices, providing advanced capabilities without the need for extensive computational resources.
Introducing Swarms Framework
To address the aforementioned limitations of individual LLM agents, we present the Swarms framework.
Swarms is a simple yet powerful tool designed to facilitate the development of multi-agent applications.
It enables the creation of collaborative, efficient, and cost-effective AI solutions, making it an ideal choice for organizations looking to leverage the full potential of AI.
Get started with a simple pip install:
Now you can build agents like this:
from swarms import Agent, OpenAIChat
def calculate_profit(revenue: float, expenses: float):
"""
Calculates the profit by subtracting expenses from revenue.
Args:
revenue (float): The total revenue.
expenses (float): The total expenses.
Returns:
float: The calculated profit.
"""
return revenue - expenses
def generate_report(company_name: str, profit: float):
"""
Generates a report for a company's profit.
Args:
company_name (str): The name of the company.
profit (float): The calculated profit.
Returns:
str: The report for the company's profit.
"""
return f"The profit for {company_name} is ${profit}."
# Initialize the agent
agent = Agent(
agent_name="Accounting Assistant",
system_prompt="You're the accounting agent, your purpose is to generate a profit report for a company!",
agent_description="Generate a profit report for a company!",
llm=OpenAIChat(),
max_loops=1,
autosave=True,
dynamic_temperature_enabled=True,
dashboard=False,
verbose=True,
streaming_on=True,
# interactive=True, # Set to False to disable interactive mode
# stopping_token="<DONE>",
# saved_state_path="accounting_agent.json",
# tools=[calculate_profit, generate_report],
# docs_folder="docs",
# pdf_path="docs/accounting_agent.pdf",
)
agent.run(
"We're the Swarm Corporation, our total revenue is $100,000 and our total expenses are $50,000."
)And, then orchestrate them like this:
from swarms import MixtureOfAgents, Agent, OpenAIOpenAIChat
# Define agents
director = Agent(
agent_name="Director",
system_prompt="Directs the tasks for the accountants",
llm=OpenAIChat(),
max_loops=1,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
state_save_file_type="json",
saved_state_path="director.json",
)
# Initialize accountant 1
accountant1 = Agent(
agent_name="Accountant1",
system_prompt="Prepares financial statements",
llm=OpenAIChat(),
max_loops=1,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
state_save_file_type="json",
saved_state_path="accountant1.json",
)
# Initialize accountant 2
accountant2 = Agent(
agent_name="Accountant2",
system_prompt="Audits financial records",
llm=OpenAIChat(),
max_loops=1,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="<DONE>",
state_save_file_type="json",
saved_state_path="accountant2.json",
)
# Initialize the MixtureOfAgents
moe_swarm = MixtureOfAgents(agents=[director, accountant1, accountant2], final_agent=director)
# Run the swarm
history = moe_swarm.run(task="Perform task X.")
print(history)Benefits of Swarms:
- Ease of Use: Swarms provides a user-friendly interface that simplifies the development and deployment of multi-agent systems. Its intuitive design allows developers to quickly build and integrate multi-agent frameworks into their applications.
- Collaboration and Specialization: Swarms enables agents to collaborate and specialize in different tasks, improving the overall performance and accuracy of the system. Each agent can focus on its area of expertise, contributing to a more comprehensive solution.
- Scalability: The framework supports the scalability of multi-agent systems, allowing organizations to expand their AI capabilities as needed. Swarms can handle an increasing number of agents and tasks without compromising performance.
- Cost Efficiency: By optimizing resource usage, Swarms reduces the computational cost associated with deploying advanced AI technologies. This makes it accessible to a wider range of organizations, enabling them to benefit from AI without significant financial investment.
- Flexibility: Swarms is designed to be flexible and adaptable, supporting a variety of applications and use cases. Whether it’s customer support, data analysis, or strategic planning, Swarms can be tailored to meet the specific needs of different industries.
Learn more now:
And, book an onboarding call with me to get get you setup!
Learn more about Swarms
To get started with the Swarms framework, you can visit the official GitHub repository Swarms on GitHub.
The repository provides comprehensive documentation, tutorials, and examples to help you build and deploy multi-agent applications using Swarms.
By leveraging this framework, you can overcome the limitations of individual LLM agents and unlock the full potential of AI in your organization.
Conclusion
Individual LLM agents, despite their advanced capabilities, face significant limitations that hinder their effectiveness in real-world applications.
Issues such as hallucinations, limited context windows, single-threaded processing, lack of collaboration, and substantial size and cost pose challenges that need to be addressed.
Multi-agent collaboration offers a robust solution to these problems, enabling more accurate, efficient, and cost-effective AI systems.
By leveraging frameworks like Swarms, organizations can build and deploy multi-agent applications that overcome the inherent limitations of individual LLMs and unlock new levels of AI performance and scalability.
As the field of AI continues to evolve, embracing multi-agent frameworks will be crucial for staying competitive and innovative in a rapidly changing landscape.
Get started now with the swarms framework:
Resources
Multi-Agent Papers List:
Swarms Framework:
Swarms Community:
Subscribe to my blog:
My github: https://github.com/kyegomez
Paper References
- K-Level Reasoning with Large Language Models
- More agents is all you need
- LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
- Automated Unit Test Improvement using Large Language Models
- Human-level play in the game of Diplomacy by combining language models with strategic reasoning
- AgentScope: A Flexible yet Robust Multi-Agent Platform
- Experiential Co-Learning of Software-Developing Agents
- Communicative Agents for Software Development
- Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Match Human Crowd Accuracy
- Generative Agents: Interactive Simulacra of Human Behavior
- Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
- Learning to Decode Collaboratively with Multiple Language Models
- SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents
- Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
- LLM Agent Operating System
- MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution
- AllHands: Ask Me Anything on Large-scale Verbatim Feedback via Large Language Models
- Scaling Instructable Agents Across Many Simulated Worlds
- Evolutionary Optimization of Model Merging Recipes
- Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
- Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
- CulturePark: Boosting Cross-cultural Understanding in Large Language Models
- Constitutional AI: Harmlessness from AI Feedback
- Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
- Mixture-of-Agents Enhances Large Language Model Capabilities
- AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
- CODER: ISSUE RESOLVING WITH MULTI-AGENT AND TASK GRAPHS
For the full list of references:
By addressing the limitations of individual LLM agents through multi-agent collaboration and utilizing frameworks like Swarms, organizations can unlock new possibilities in AI, driving innovation, efficiency, and accuracy in their operations.
