Bubble and OpenAI

[final schooner]

How should I build an agent swarm with openai assistants api?

[fickle locust]

Hi @final schooner. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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Hmm, I don't know enough to give you a confident answer yet. However, you can explore the Assistants API overview to understand how to build AI assistants using OpenAI's tools and models. Additionally, you might find the Assistants API quickstart helpful for step-by-step integration. If you're interested in orchestrating multiple agents, you might also want to look into the concept of orchestrating agents and structured outputs for multi-agent systems from the OpenAI Cookbook.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

@final schooner Kapa.ai is still learning and improving, please let me know how I did by reacting below.

[final schooner]

@fickle locust I wanto to connect two or more agents toogether, how should I do this?

[fickle locust]

Let me check the knowledge base for you again. One moment! :books:

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To connect two or more agents together, you can use the concept of routines and handoffs as described in the OpenAI Cookbook. This involves orchestrating multiple agents in a way that they can pass tasks or information between each other effectively.

Steps to Connect Agents

1. Define Agents: Create classes or structures for each agent, specifying their roles, models, and tools they can use. For example:

   class Agent(BaseModel):
       name: str = "Agent"
       model: str = "gpt-4o-mini"
       instructions: str = "You are a helpful Agent"
       tools: list = []
   

2. Implement Handoffs: Allow agents to hand off tasks to each other. This can be done by defining functions that enable one agent to transfer control to another when necessary.

   def run_full_turn(agent, messages):
       # Logic to handle agent's task and decide on handoff
       # ...
       for tool_call in message.tool_calls:
           result = execute_tool_call(tool_call, tools_map)
           # Handle the result and decide if a handoff is needed
   

3. Execute Tasks: Use a function to execute tasks and manage the flow of information between agents. This involves processing user queries and routing them to the appropriate agents.

   def handle_user_message(user_query, conversation_messages=[]):
       # Logic to handle user input and route to agents
       # ...
       for agent in agents:
           if agent == "Data Processing Agent":
               handle_data_processing_agent(query, conversation_messages)
           elif agent == "Analysis Agent":
               handle_analysis_agent(query, conversation_messages)
           elif agent == "Visualization Agent":
               handle_visualization_agent(query, conversation_messages)
   

4. Use Structured Outputs: Leverage structured outputs to ensure that the communication between agents follows a strict schema, which can help in maintaining consistency and reliability.

For more detailed examples and code snippets, you can refer to the Orchestrating Agents and Structured Outputs for Multi-Agent Systems sections in the OpenAI Cookbook. These resources provide comprehensive guidance on setting up and managing multi-agent systems.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: