How to Build Multi-Agent AI Systems with OpenAI Swarm & Secure Them Using Portkey

Nerding I/O
29 Oct 202423:12

TLDRIn this Nerding.IO episode, JD explores OpenAI Swarm, an educational multi-agent system, and its integration with Portkey, an AI Gateway for enhanced observability and security. They discuss orchestrating handoffs between agents using OpenAI, setting up observability, and leveraging Portkey's features for security, including canary tests and load balancing. The episode demonstrates a complex trip planning example, showcasing agent communication and handoffs, and highlights Portkey's ability to manage API keys and perform A/B testing.

Takeaways

  • ๐Ÿค– OpenAI Swarm is an educational and experimental framework introduced by OpenAI for exploring multi-agent systems.
  • ๐Ÿ”— Connecting OpenAI Swarm to Portkey, an AI Gateway, enhances observability and security, and allows for advanced configurations like Canary tests.
  • ๐Ÿ“ In a multi-agent system, agents can hand off tasks to each other, with OpenAI handling the orchestration and handoffs.
  • ๐Ÿ› ๏ธ Agents in Swarm are set up with a name, instructions (prompts), functions (tools), and a run (message history).
  • ๐Ÿ‘๏ธโ€๐Ÿ—จ๏ธ The Swarm client is crucial as it allows customization of the base URL and different models compliant with the OpenAI specification.
  • ๐Ÿšช Portkey acts as a proxy or middleman, providing out-of-the-box observability and various security features.
  • ๐Ÿ”‘ Portkey is an open-source AI Gateway that can be self-hosted, offering a control panel for AI apps and analytics.
  • ๐Ÿ“Š Portkey logs show the total cost of tokens, the sequence of requests, and the flow of interactions within the multi-agent system.
  • ๐ŸŒ A complex example in the script demonstrates a trip planner with agents handling weather checks, flight bookings, and hotel bookings.
  • ๐Ÿ”„ Agents can operate independently or be compiled together for communication, showcasing the flexibility of multi-agent architecture.
  • ๐Ÿ“ˆ Portkey's observability features allow for the monitoring of request histories, handoffs, and the ability to provide feedback on response quality.

Q & A

  • What is OpenAI Swarm?

    -OpenAI Swarm is an educational multi-agent system designed to facilitate experimentation with multi-agent frameworks using OpenAI's tools.

  • How does Portkey enhance the functionality of OpenAI Swarm?

    -Portkey acts as an AI Gateway, providing observability, security features, and the ability to run configurations such as Canary tests, thereby enhancing the overall system's functionality.

  • What are the key components of an agent in OpenAI Swarm?

    -An agent in OpenAI Swarm is defined by its name, instructions (or prompts), functions (tools), and message history.

  • How can you customize the OpenAI client in Swarm?

    -You can customize the OpenAI client in Swarm by passing your own client configuration, allowing you to set a base URL and choose different models that comply with OpenAI specifications.

  • What type of operations can you perform with the Portkey AI Gateway?

    -With Portkey, you can monitor AI requests, manage logs, and implement security measures such as virtual keys for API management.

  • What example use case does the script provide for using multiple agents?

    -The script provides a trip planner use case where different agents handle tasks like weather checking, flight booking, and hotel reservations, showcasing agent handoffs.

  • What is the significance of agent handoffs in multi-agent systems?

    -Agent handoffs allow for efficient task delegation and collaboration among agents, enabling them to leverage each other's capabilities to complete complex tasks.

  • How does Portkey support observability?

    -Portkey supports observability by providing a control panel where you can view logs, analyze request patterns, and monitor token usage, enhancing transparency in operations.

  • What are Canary tests, and how can they be implemented with Portkey?

    -Canary tests are a method to test new features or configurations with a small subset of users before a full rollout. In Portkey, you can configure this by setting specific percentages for different keys or parameters.

  • What role does the triage agent play in the trip planner example?

    -The triage agent serves as a supervisor that manages the flow of requests between different specialized agents, ensuring that the user's needs are addressed by the appropriate agent.

Outlines

00:00

๐ŸŒŸ Introduction to OpenAI Swarm and Portkey

In this opening segment, JD introduces OpenAI Swarm, an educational multi-agent system, and its connection to Portkey, an AI Gateway. He outlines the benefits of observability and security it offers, particularly for configurations like Canary tests. The discussion includes the purpose of OpenAI Swarm as an experimental framework for multi-agent operations, with a focus on the importance of agent handoffs and setting up observability through the core file of Swarm.

05:06

๐Ÿ” Exploring Portkey's Functionality

This paragraph delves into the functionality of Portkey as an AI Gateway, highlighting its open-source nature and control panel features for AI applications. JD illustrates the practical implications by reviewing logs from OpenAI requests processed through Portkey, emphasizing its role in tracking token costs and providing a detailed sequence of requests, thereby enhancing system visibility and control.

10:09

๐Ÿš€ Complex Example: Trip Planner

JD transitions to a more complex application involving a trip planner. He outlines the setup of multiple agents for specific tasks such as getting weather updates and booking flights or hotels. By structuring agents with designated functions, the system can dynamically interact and transfer responsibilities. The paragraph discusses the concept of creating reusable agent classes for flexibility in various application contexts.

15:10

๐Ÿ“… Running the Trip Planner

This section focuses on executing the trip planner, showcasing its capabilities to interactively gather user input for trip planning based on weather conditions. JD demonstrates how the agents communicate and hand off tasks, from checking the weather to booking flights and hotels. He highlights the importance of context and the dynamic nature of agent interactions, allowing for seamless transitions and maintaining relevant information throughout the process.

20:11

๐Ÿ”’ Security and Observability Features

The final paragraph discusses advanced features in Portkey, including security measures like virtual keys and load balancing across multiple API keys. JD explains how these features facilitate smoother key rotation and allow for robust A/B testing and Canary testing configurations. This segment underscores the value of Portkey in enhancing system security and operational efficiency while providing a user-friendly interface for managing these processes.

Mindmap

Keywords

๐Ÿ’กMulti-Agent System

A multi-agent system consists of multiple interacting intelligent agents that work together to achieve a common goal. In the context of the video, OpenAI Swarm serves as an educational framework for building such systems, allowing agents to communicate and transfer tasks seamlessly. This concept is essential as it enables complex problem-solving scenarios, like trip planning, where multiple agents specialize in different tasks.

๐Ÿ’กOpenAI Swarm

OpenAI Swarm is a framework designed for experimenting with multi-agent systems using OpenAI's technologies. It provides a structure for defining agents with specific roles and capabilities, facilitating the orchestration of tasks among them. The video highlights its use in transferring tasks between agents, showcasing its practical application in coordinating complex interactions.

๐Ÿ’กPortkey

Portkey is an AI Gateway that enhances observability and security for AI applications. It acts as a middleware that connects agents to different AI models, enabling features like monitoring and security configurations. The video discusses how integrating Portkey allows for real-time analytics and logging, which are crucial for understanding the behavior of multi-agent systems.

๐Ÿ’กObservability

Observability refers to the ability to monitor and analyze the performance and behavior of a system. In the video, Portkey provides tools for observability, allowing users to track the requests and responses within the multi-agent setup. This capability is important for diagnosing issues and optimizing agent interactions, ensuring a smooth operational flow.

๐Ÿ’กAgent Handoff

Agent handoff is the process of transferring control or tasks from one agent to another within a multi-agent system. The video illustrates this concept through examples where tasks, such as booking flights or hotels, are passed between specialized agents. This flexibility allows the system to leverage the strengths of each agent, improving efficiency and effectiveness in task execution.

๐Ÿ’กFunction Call

A function call is a command that invokes a specific piece of code to perform a task. In the video, agents utilize function calls to execute their designated roles, such as retrieving weather data or booking accommodations. This technical aspect is crucial for the agents' operations, demonstrating how they can perform complex actions based on user input.

๐Ÿ’กCanary Tests

Canary tests are a technique used to validate new features or changes in a system by exposing a small subset of users to them before a full rollout. The video mentions this concept in relation to Portkey, which allows developers to test variations in their models. This approach minimizes risk by ensuring that only a small percentage of traffic interacts with the new features initially.

๐Ÿ’กSecurity Aspects

Security aspects in AI systems involve measures to protect data and ensure safe interactions between agents and external APIs. The video discusses how Portkey adds security features, allowing for better management of API keys and protecting sensitive information. This is particularly relevant in a multi-agent context, where multiple data exchanges can introduce vulnerabilities.

๐Ÿ’กAgent Roles

Agent roles define the specific functions and responsibilities assigned to each agent within a multi-agent system. In the video, different agents are designated roles such as the triage agent for initial inquiries and the booking agents for specific tasks. This specialization allows for more efficient processing of requests and enhances the overall performance of the system.

๐Ÿ’กDynamic Function Building

Dynamic function building refers to the ability of a system to create or modify functions at runtime based on the context and requirements. The video showcases this when the agents adapt their responses and actions based on user input and previous interactions. This flexibility is vital for creating responsive and intelligent systems that can handle a variety of scenarios effectively.

Highlights

Introduction to OpenAI Swarm, an educational multi-agent system.

Connecting OpenAI Swarm to Portkey, an AI Gateway for observability and security.

OpenAI Swarm's ability to orchestrate and handoff different routines among agents.

Setting up an agent in Swarm with a name, instructions, and functions.

The importance of the Swarm client for observability setup.

Customizing the base URL and models in the Swarm core file.

Using Portkey as a proxy to hit the AI Gateway instead of going directly to a model.

Portkey's features for observability and security in multi-agent systems.

Defining the model or client in Portkey and passing it to Swarm.

Executing a simple agent handoff using Portkey.

Portkey as an open-source AI Gateway with a control panel for AI apps.

Analyzing logs in Portkey to observe the cost of tokens and sequence of requests.

Understanding the system's behavior through Portkey's analytics.

A complex example of a trip planner using multi-agent architecture.

Agents in the trip planner having specific functions like getting weather or booking flights and hotels.

Transferring between agents in the trip planner example.

The ability to isolate agents in different classes and call them individually or in a multi-agent setup.

Incorporating payment APIs like Stripe into the booking process.

Observability of the system through Portkey after executing the trip planner.

Guard rails in Portkey for response moderation and bias detection.

Virtual keys in Portkey for secure key management and rotation.

Load balancing and Canary tests in Portkey for managing multiple keys and testing new models.

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