Introduction to Phidata Agents

Phidata is a powerful framework for building multi-agent AI systems. At the heart of these systems are various types of agents, each with its own specific role and capabilities. In this blog post, we'll dive deep into the world of Phidata agents, exploring their types, functions, and how they work together to create sophisticated AI applications.

The Three Main Agent Types in Phidata

Phidata primarily uses three types of agents:

1. Assistant Agents
2. User Agents
3. Function Agents

Let's examine each of these in detail.

1. Assistant Agents

Assistant agents are the workhorses of Phidata multi-agent systems. They are designed to perform specific tasks, answer questions, and provide information based on their training and knowledge.

Key features of assistant agents:

• Can be specialized for particular domains or tasks
• Interact with users and other agents
• Process and generate natural language
• Access and utilize external knowledge bases

Example:

from phidata import AssistantAgent

finance_expert = AssistantAgent(
    name="Finance Expert",
    description="An AI assistant specialized in financial advice and analysis",
    instructions="Provide accurate financial advice and analysis based on user queries."
)

2. User Agents

User agents represent human users in the multi-agent system. They serve as the interface between human input and the AI system, translating user intentions into actionable requests for other agents.

Key features of user agents:

• Act as proxies for human users
• Interpret and relay user input to other agents
• Can have predefined personas or traits

Example:

from phidata import UserAgent

investor = UserAgent(
    name="Investor",
    description="A user interested in investment advice",
    traits=["risk-averse", "long-term focused"]
)

3. Function Agents

Function agents are specialized agents that perform specific computational tasks or access external resources. They bridge the gap between AI language models and practical, real-world functionalities.

Key features of function agents:

• Execute predefined functions or API calls
• Provide data processing capabilities
• Can interact with databases or external services

Example:

from phidata import FunctionAgent

stock_data_agent = FunctionAgent(
    name="Stock Data Retriever",
    description="Fetches real-time stock market data",
    function=fetch_stock_data
)

def fetch_stock_data(symbol: str):

# Implementation to fetch stock data
    pass

How Agents Work Together

The power of Phidata lies in how these different agent types interact to solve complex problems. Here's a typical workflow:

1. A user agent receives input from a human user.
2. The user agent relays the request to an appropriate assistant agent.
3. The assistant agent processes the request and may call upon function agents for additional data or computations.
4. The assistant agent formulates a response and sends it back to the user agent.
5. The user agent presents the response to the human user.

This collaborative approach allows for handling complex queries that require multiple steps or diverse knowledge sources.

Customizing Agents for Specific Use Cases

One of the strengths of Phidata is the ability to customize agents for specific domains or tasks. For example, you could create a set of agents for a financial advisory system:

finance_assistant = AssistantAgent(
    name="Financial Advisor",
    description="Provides personalized financial advice",
    instructions="Analyze user's financial situation and provide tailored recommendations."
)

risk_calculator = FunctionAgent(
    name="Risk Assessment Tool",
    description="Calculates investment risk based on user profile",
    function=calculate_risk_score
)

market_analyst = AssistantAgent(
    name="Market Analyst",
    description="Provides market insights and trend analysis",
    instructions="Analyze current market conditions and predict trends."
)

These agents can work together to provide comprehensive financial advice, combining personalized recommendations, risk assessment, and market analysis.

Best Practices for Working with Phidata Agents

To get the most out of Phidata's multi-agent systems:

1. Clearly define each agent's role and capabilities.
2. Use descriptive names and instructions for better organization.
3. Leverage function agents for tasks that require precise calculations or data retrieval.
4. Create specialized assistant agents for different domains or subtasks.
5. Design user agents to accurately represent your target users' needs and characteristics.

By following these practices, you can create powerful, efficient, and scalable multi-agent systems that can tackle a wide range of complex tasks and interactions.