Crash Course: How to Build a Multi-Agent AI System with OpenAI’s SDK

Open AI Agent SDK Crash Course Summary

Key Concepts:

• Agents: LLMs configured with instructions, tools, guardrails, and handoffs.
• Handoffs: A specialized tool call for transferring control between agents.
• Guardrails: Configurable safety checks for input and output validation.
• Tracing: Built-in tracking of agent runs for debugging and optimization.
• Tools: External functions or built-in capabilities (e.g., web search) that agents can utilize.

1. Introduction to Open AI Agent SDK

• The Agent SDK is built on top of the open-source "Swarm" project, now consolidated into a production-ready system.
• The SDK facilitates building multi-agent systems with features like tool calling, handoffs, tracing, and guardrails.

2. Core Concepts

• Agents:
  • Defined by a name, instructions (system prompt), and optional tools.
  • Can be run synchronously or asynchronously using the Runner class.
  • Example: An agent instructed to write a haiku about recursion.
• Handoffs:
  • Enable delegation of tasks between agents, useful when agents specialize in distinct areas.
  • Example: A triage agent handing off to a billing or customer support agent.
• Guardrails:
  • Implement safety checks for input and output validation to prevent unintended behavior.
  • Example: Preventing hateful or abusive input, or limiting output token length.
• Tracing:
  • Provides detailed insights into agent execution, including tool calls, inputs, outputs, and token usage.
  • Accessible through the OpenAI platform, allowing for debugging and optimization.

3. Setting Up and Running Agents

• Installation: Requires installing the openai-agent and openai Python packages.
• API Key: Requires setting the OpenAI API key and optionally enabling tracing export.
• Agent Creation: Uses the Agent class, specifying a name and instructions.
• Agent Execution: Uses the Runner class with the run function, passing the agent and input.

4. Customizing Agents

• LLM Selection: Can customize the LLM used (default is GPT-4o, can use GPT-4o-mini for cost savings).
• Other Parameters: Can customize temperature, maximum tokens, and available tools.

5. Tools: Extending Agent Capabilities

• Built-in Tools: OpenAI provides built-in tools like web search and file search.
• Web Search Tool: Enables agents to perform web searches based on user queries.
  • Example: Asking an agent "What did Nvidia announce today at GTC?"
• Custom Tools:
  • Implemented as Python functions with the @tool decorator.
  • The docstring of the function is crucial for the agent to understand the tool's purpose.
  • Example: A tool to provide the current date and time.
• Combining Tools: Agents can be equipped with multiple tools to handle complex tasks.
  • Example: An agent with both web search and current date/time tools to answer "How has the stock price of Nvidia changed over the last week?"

6. Multi-Agent Systems

• Agents as Tools:
  • Enables hierarchical structures where a coordinator agent orchestrates specialist agents.
  • Example: A productivity assistant with notetaking and task management agents as tools.
  • Different sub-agents can use different LLMs based on their needs.
• Handoffs:
  • Delegation of control from one agent to another.
  • Useful for scenarios where agents specialize in distinct areas.
  • Example: A triage agent handing off to a billing or customer support agent.
  • Custom Handoff Behavior: Can customize behavior during handoffs using the handoff function.

7. Guardrails: Ensuring Safety and Control

• Input Guardrails:
  • Functions decorated with @input_guardrail to monitor and filter user input.
  • Example: Preventing hateful or abusive input.
• Output Guardrails:
  • Functions decorated with @output_guardrail to monitor and filter agent output.
  • Example: Limiting output token length.

8. Building a Multi-Agent Workflow Example

• Three-agent system: research coordinator, web search agent, and summarization agent.
• The research coordinator uses the web search agent to find information and then the summarization agent to summarize the findings.
• The example highlighted a potential issue where the summarization step was ignored, indicating redundancy.

9. Tracing and Observability

• Tracing is a key feature, providing detailed insights into agent execution.
• Allows for debugging and optimization of multi-agent systems.
• Shows tool calls, inputs, outputs, and token usage.

10. Conclusion

• The Open AI Agent SDK is a promising library for building multi-agent systems.
• It offers features like tool calling, handoffs, tracing, and guardrails.
• The quality of agents depends heavily on the underlying LLM used for decision-making.
• Future videos will explore more detailed applications and integrations with other LLMs like Gemini and open-weight models.