Build a Team of Autonomous AI Agents with Phidata

To build a single agent with GPT-4, we first need to give the agent a set of tools and capabilities. We can do this by defining the agent's description, instructions, and access to various tools and assistants.

The key steps are:

1. Define the agent's description and instructions. This sets the tone and expectations for the agent's behavior.
2. Add storage and a knowledge base to the agent, allowing it to retain information and retrieve relevant knowledge.
3. Provide the agent with a set of tools it can use to accomplish tasks, such as accessing web data, running Python scripts, or generating reports.
4. Optionally, give the agent a team of dedicated assistants that it can delegate specific tasks to, such as a data analysis assistant or a research assistant.

With these components in place, the agent can then interact with the user, determining whether to use its own capabilities, leverage a tool, or delegate a task to one of its team members. This allows the agent to tackle a wide range of requests in a flexible and efficient manner.

The code provided in the repository demonstrates how to set up this type of agent using the Fi-data framework. By customizing the agent's description, tools, and team members, you can create a powerful AI assistant tailored to your specific needs.

In this section, we'll explore how to build a team of agents using the new release of the GPT-4 model. We'll start by creating a single agent and then extend it with a team of dedicated agents that can be delegated specific tasks.

The main agent will have access to a set of tools and can directly answer questions, use tools, or delegate tasks to its team members. We'll demonstrate this by asking the agent to write a report on the IBM-HashiCorp acquisition, which it will then delegate to its team.

The team of agents includes:

1. Data Analyst Agent: Capable of analyzing data from various sources like CSV, Parquet, and JSON files.
2. Python Agent: Can write and run Python scripts to perform specific tasks.
3. Research Agent: Generates research reports by leveraging a powerful search engine.
4. Investment Agent: Provides investment recommendations and analysis.

The main agent will orchestrate the work of these specialized agents to complete the requested task. You can customize and extend this team of agents to suit your specific needs.

The code for this application is available in the cookbook/agents folder of the Fi-Data repository. You can fork and clone the repository, then follow the step-by-step instructions in the README to run the application and explore the code.

The code for the agent application showcased in the video is available in the cookbook/agents folder of the Fi-data repository. To access and customize the agent, you can follow these steps:

1. Fork and clone the Fi-data repository from GitHub.
2. Navigate to the cookbook/agents folder within the cloned repository.
3. Open the code in your preferred code editor.

The main file that defines the agent is the agent.py file. In this file, you can find the implementation of the agent, including the instructions, tools, and team members.

To run the agent application, follow the step-by-step instructions provided in the repository's README file. This will guide you through the process of setting up and running the application.

You can customize the agent by modifying the instructions, adding or removing tools, and configuring the team members to suit your specific requirements. The video provides a detailed overview of the agent's capabilities and how to interact with it, which should help you get started with building and extending the agent.

The agent code in the provided example demonstrates a powerful framework for building a team of AI agents that can collaborate to solve complex tasks. Let's break down the key aspects of the code:

1. Agent Description and Instructions: The agent is defined with a description and a set of instructions that outline its capabilities and how it should interact with the user. This includes the ability to determine whether to use a tool, search its knowledge base, or ask for clarification.

2. Storage and Knowledge Base: The agent has access to a persistent storage system to track its interactions and a knowledge base implemented using a vector database for efficient retrieval of relevant information.

3. Tools and Assistants: The agent is equipped with a set of tools, such as the ability to read files, perform web searches, and interact with external services like financial data providers. Additionally, the agent has a team of specialized assistants, including a data analyst, a Python script executor, a research assistant, and an investment advisor.

4. Task Delegation: When the user requests a task that the main agent cannot handle directly, it delegates the task to the appropriate assistant in its team. For example, when asked to write a report on an acquisition, the agent delegates the task to the research assistant, which then uses the provided tools and knowledge to generate the report.

5. Seamless Interaction: The agent is designed to interact with the user in a natural, human-like manner, leveraging the capabilities of powerful language models like GPT-4. It can understand the user's requests, formulate appropriate responses, and coordinate the efforts of its team members to provide comprehensive solutions.

6. Extensibility and Customization: The code is structured in a modular way, allowing for easy customization and extension. Users can add new tools, assistants, and capabilities to the agent as needed, tailoring it to their specific use cases.

This framework demonstrates the power of building AI systems as a collaborative team of specialized agents, each with its own strengths and abilities. By delegating tasks and leveraging the collective knowledge and skills of the team, the main agent can tackle a wide range of complex problems efficiently and effectively.

The provided code serves as a solid foundation for developers interested in exploring the possibilities of multi-agent AI systems and building their own customized solutions.

To run the agent application, follow these steps:

1. Open the README file in the repository. This file contains step-by-step instructions on how to set up and run the application.

2. Ensure that you have the necessary dependencies installed, such as Python, Streamlit, and the required Python packages.

3. Navigate to the agents folder in the repository, where the agent code is located.

4. Run the agent file, as specified in the README instructions. This will start the Streamlit application and allow you to interact with the agent.

5. In the Streamlit application, you can ask the agent questions, request it to perform tasks, and observe how it delegates work to its team of specialized agents.

6. Explore the agent code to understand how the main agent is defined, how it interacts with its tools and team members, and how the overall system is structured.

7. Feel free to customize the agent, add new tools or team members, and experiment with different configurations to suit your specific needs.

Remember, the README file in the repository provides detailed instructions and guidance on running the agent application. Refer to it closely to ensure a smooth setup and execution.

The video has provided a comprehensive overview of building a team of AI agents using the GPT-4 model and the Fi-data framework. The key highlights are:

• The main driver agent is a powerful AI assistant with access to various tools and a team of dedicated agents.
• The driver agent can answer questions directly, use tools, and delegate tasks to its team members.
• The team includes agents for specific tasks like data analysis, Python scripting, research, and investment analysis.
• The agents work together to solve complex problems, with the driver agent orchestrating the collaboration.
• The code for this application is available in the Fi-data repository, and the viewer is encouraged to explore and customize it further.
• The video emphasizes the importance of supporting the Fi-data project by starring its GitHub repository and following the creator on various platforms.

Overall, this video showcases the capabilities of the Fi-data framework in building advanced AI systems with collaborative agents, demonstrating the potential of this technology for various applications.