Multi-Agent Collaboration Networks: Agent Binod and His Squad of Clever Agents!

Agents as Team Players:

Picture each agent as a unique team member with a specific job. Some agents are “assistants” who specialize in certain tasks, while others are “managers” overseeing everything. Together, they form a strong, well-organized team where everyone knows their role.

Connections Between Agents:

The agents communicate along pathways that connect them, ensuring each one can work with others in an organized, logical way.

Topological Ordering:

The DAG determines the order in which the agents interact with one another. In order to keep things flowing smoothly and prevent needless noise, it makes sure that every bit of information is presented in a systematic order, preventing a scenario in which everyone is yelling at once.

Order of Interactions:

A particular setup, called a directed acyclic graph (DAG), arranges how agents interact. This arrangement ensures information flows smoothly, with no confusion or overlap.

Types of Structures: This system tries different setups, like chains, trees, or networks:

• Chains are great for tasks that need a step-by-step approach, like following a process.
• Mesh structures work best for complex tasks needing lots of back-and-forth, like making logical decisions.

A fascinating effect, similar to real-life social networks, shows that teams with close connections tend to perform better!

Scaling the System:

Adding agents improves the team’s solutions up to a point. After a certain number, though, adding more agents doesn’t make a big difference.

System Workflow

Interactive Thinking:

Each agent builds on ideas shared by others, much like passing around notes with new insights added at each step.

Using Memory:

Agents use short-term memory for tasks they’re working on right now, and long-term memory to save the best solutions, helping avoid overload.

Steps to Build This System

1. Choose a Model:

• Use a smart model like GPT-4 for complex reasoning and teamwork.

2. Pick a Structure:

• Choose a setup that fits the task — chains for step-by-step tasks, or graphs for group work.

3. Assign Agent Roles:

Split roles between helpers (assistants) and leaders (managers), making the team more scalable.

4. Set Up Communication:

Give agents a way to communicate. Each agent will:

• Take on tasks or parts of a solution
• Improve the work
• Pass it along to others

5.Manage Memory:

Use short-term memory for active tasks and save top solutions in long-term memory.

6. Scale Carefully:

1. Start with a few agents and add more if needed, checking how well the team performs as it grows.

Tools and Technologies:

• LLMs (like GPT-4): These serve as the thinking core for each agent.
• Graph Databases or DAG structures: To keep track of the network.
• Distributed Systems (like AWS Lambda): For managing teamwork across many agents.

Example Implementation of a Multi-Agent System

Here’s an example of how you can implement a simple multi-agent system with task automation using GPT-4 and Neo4j for memory management.

1. Setting Up OpenAI and Neo4j Connections

import openai
import networkx as nx
from neo4j import GraphDatabase

openai.api_key = 'sk-**'uri = "bolt://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "your-neo4j-password"))

Explanation:

• openai.api_key is used to authenticate with OpenAI's GPT-4 API.
• GraphDatabase.driver connects to the Neo4j instance where agent memories will be stored.

2. Storing Agent Memory in Neo4j

def store_short_term_memory(agent, interaction, result):
    with driver.session() as session:
        query = (
            "MERGE (a:Agent {name: $agent_name}) "
            "CREATE (m:Memory {type: 'short_term', interaction: $interaction, result: $result}) "
            "CREATE (a)-[:GENERATED]->(m) "
        )
        session.run(query, agent_name=agent, interaction=interaction, result=result)

def store_long_term_memory(agent, final_solution):
    with driver.session() as session:
        query = (
            "MERGE (a:Agent {name: $agent_name}) "
            "CREATE (m:Memory {type: 'long_term', final_solution: $solution}) "
            "CREATE (a)-[:GENERATED]->(m) "
        )
        session.run(query, agent_name=agent, solution=final_solution)

Explanation:

• store_short_term_memory: Logs short-term interactions like intermediate summaries.
• store_long_term_memory: Stores final conclusions as long-term memory for future reference.

3. Retrieving Long-Term Memory for Fact-Checking

def retrieve_long_term_memory(query_string):
    with driver.session() as session:
        query = (
            "MATCH (m:Memory {type: 'long_term'}) "
            "WHERE m.final_solution CONTAINS $query_string "
            "RETURN m.final_solution AS solution"
        )
        result = session.run(query, query_string=query_string)
        for record in result:
            return record["solution"]
    return None

Explanation:

• This function searches for stored long-term memories based on a query and returns matching records, if any.

4. Using GPT-4 for Text Summarization

def agent_summarize(text):
    response = openai.chat.completions.create(
      model="gpt-4",
      messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": f"Summarize the following text: {text}"}
      ]
    )
    return response.choices[0].message.content

Explanation:

• agent_summarize: Summarizes the input text using GPT-4's language model.

5. Combining Summaries Into a Final Conclusion

def agent_combine_summaries(summary1, summary2):
    combined_text = f"Summary 1: {summary1}\nSummary 2: {summary2}\nBased on these, provide a combined conclusion."
    response = openai.chat.completions.create(
      model="gpt-4",
      messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": combined_text}
      ]
    )
    return response.choices[0].message.content

Explanation:

• This function takes two summaries as input, combines them, and asks GPT-4 to generate a final conclusion based on the combined text.

6. Fact-Checking the Final Conclusion Using Stored Knowledge

def agent_fact_check_final(conclusion, query_string):
    long_term_memory = retrieve_long_term_memory(query_string)
    
    if long_term_memory:
        # Use the retrieved long-term memory to fact-check the final conclusion
        fact_check_prompt = f"Here is a final conclusion: {conclusion}\nCheck it against this prior knowledge: {long_term_memory}\nIs the conclusion accurate? If not, provide the correct information."
        response = openai.chat.completions.create(
          model="gpt-4",
          messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": fact_check_prompt}
          ]
        )
        fact_check_result = response.choices[0].message.content
        return fact_check_result  
    else:
        return conclusion

Explanation:

• agent_fact_check_final: Retrieves the relevant memory from the database and asks GPT-4 to fact-check the final conclusion.

7. Example Workflow Using a Directed Acyclic Graph (DAG)

# Create a directed acyclic graph (DAG)
G = nx.DiGraph()

# Add nodes (LLM agents)
G.add_node('Agent_1', task='Summarize first text')
G.add_node('Agent_2', task='Summarize second text')
G.add_node('Agent_3', task='Combine summaries and conclude')
G.add_node('Agent_4', task='Fact-check the final conclusion')# Add directed edges (instruction flow)
G.add_edges_from([('Agent_1', 'Agent_3'), ('Agent_2', 'Agent_3'), ('Agent_3', 'Agent_4')])

Explanation:

• nx.DiGraph(): A directed acyclic graph representing the flow of tasks among agents.
• Nodes represent agents, and edges represent task dependencies.

8. Bringing It All Together (Main Execution)

text1 = "Large language models like GPT-4 are transforming industries by automating tasks that require understanding of natural language."
text2 = "Recent advancements in AI have enabled machines to learn complex tasks, making them valuable in fields like healthcare and finance."

summary1 = agent_summarize(text1)
summary2 = agent_summarize(text2)store_short_term_memory("Agent_1", "Summarize text1", summary1)
store_short_term_memory("Agent_2", "Summarize text2", summary2)final_conclusion = agent_combine_summaries(summary1, summary2)store_short_term_memory("Agent_3", "Combine summaries", final_conclusion)
fact_checked_final_conclusion = agent_fact_check_final(final_conclusion, "language models")store_long_term_memory("Agent_4", fact_checked_final_conclusion)
print("Summary by Agent 1:", summary1)
print("Summary by Agent 2:", summary2)
print("Final Conclusion by Agent 3:", final_conclusion)
print("Fact-Checked Final Conclusion by Agent 4:", fact_checked_final_conclusion)
stored_solution = retrieve_long_term_memory("final")
print(f"Retrieved long-term memory for future use: {stored_solution}")

Output

Summary by Agent 1: Large language models such as GPT-4 are revolutionizing various industries by automating tasks that necessitate comprehension of natural language.
Summary by Agent 2: Recent AI developments have allowed machines to learn intricate tasks, proving beneficial in areas such as healthcare and finance.
Final Conclusion by Agent 3: The advent of advanced AI systems like GPT-4 has significantly transformed multiple sectors. These large language models, capable of understanding and interpreting natural language, have automated various complex tasks which have proved to be extremely beneficial in industries like healthcare and finance. The recent advancements in AI not only underline the promising potential of these technologies but also pave the way for the future role of AI in revolutionizing and improving efficiency in numerous fields.
Fact-Checked Final Conclusion by Agent 4: Yes, the conclusion is accurate. It correctly highlights the significant impact of advanced AI systems like GPT-4 across various industries, including healthcare and finance. The conclusion aligns well with the provided prior knowledge too, emphasizing the role of AI in automating complex tasks that require natural language comprehension. Both the conclusion and the prior knowledge point to the transformative potential of AI for various fields.

References

1. https://arxiv.org/pdf/2406.07155
2. https://www.deeplearning.ai/the-batch/agentic-design-patterns-part-5-multi-agent-collaboration/?ref=dl-staging-website.ghost.io

About the Author:

Nitin is a Senior Developer at Codestax. Has deep interest and experience in building highly configurable, scalable and cost-effective no-code/low-code software platforms.

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