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What is phidata?

Phidata is a framework for building agentic systems, engineers use phidata to:

  • Build Agents with memory, knowledge, tools and reasoning.
  • Build teams of Agents that can work together.
  • Chat with Agents using a beautiful Agent UI.
  • Monitor, evaluate and optimize Agents.
  • Build agentic systems i.e. applications with an API, database and vectordb.

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Let’s build some agents

1

Setup your virtual environment

2

Install libraries

3

Export your OpenAI key

Phidata works with every LLM but for these examples let’s use OpenAI.

You can get an API key from here.

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Web Search Agent

Let’s build a simple agent that can search the web, create a file web_search.py

1

Create a web search agent

web_search.py
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.duckduckgo import DuckDuckGo

web_agent = Agent(
    name="Web Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGo()],
    instructions=["Always include sources"],
    show_tool_calls=True,
    markdown=True,
)
web_agent.print_response("Whats happening in France?", stream=True)
2

Run the agent

Install libraries

pip install duckduckgo-search

Run the agent

python web_search.py

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Financial Agent

Lets create another agent that can query financial data, create a file finance_agent.py

1

Create a finance agent

finance_agent.py
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.yfinance import YFinanceTools

finance_agent = Agent(
    name="Finance Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
    instructions=["Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)
finance_agent.print_response("Summarize analyst recommendations for NVDA", stream=True)
2

Run the agent

Install libraries

pip install yfinance

Run the agent

python finance_agent.py

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Team of Agents

A team of agents can work together to solve complex problems, create a file agent_team.py

1

Create an agent team

agent_team.py
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.yfinance import YFinanceTools

web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGo()],
    instructions=["Always include sources"],
    show_tool_calls=True,
    markdown=True,
)

finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
    instructions=["Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)

agent_team = Agent(
    team=[web_agent, finance_agent],
    instructions=["Always include sources", "Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)

agent_team.print_response("Summarize analyst recommendations and share the latest news for NVDA", stream=True)
2

Run the agent team

Run the agent team

python agent_team.py

Agent teams are non-deterministic and are not recommended for production systems, we recommend using workflows instead.

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Agentic RAG

Instead of always inserting the “context” into the prompt, we give our Agent a tool to search its knowledge base (vector db) for the information it needs.

This saves tokens and improves response quality. Create a file rag_agent.py

1

Create a RAG agent

rag_agent.py
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.embedder.openai import OpenAIEmbedder
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.lancedb import LanceDb, SearchType

# Create a knowledge base from a PDF
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    # Use LanceDB as the vector database
    vector_db=LanceDb(
        table_name="recipes",
        uri="tmp/lancedb",
        search_type=SearchType.vector,
        embedder=OpenAIEmbedder(model="text-embedding-3-small"),
    ),
)
# Comment out after first run as the knowledge base is loaded
knowledge_base.load()

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    # Add the knowledge base to the agent
    knowledge=knowledge_base,
    show_tool_calls=True,
    markdown=True,
)
agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
2

Run the agent

Install libraries

pip install lancedb tantivy pypdf sqlalchemy

Run the agent

python rag_agent.py

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Structured Outputs

Agents can return their output in a structured format as a Pydantic model.

Create a file structured_output.py

1

Create a structured output agent

structured_output.py
from typing import List
from pydantic import BaseModel, Field
from phi.agent import Agent
from phi.model.openai import OpenAIChat

# Define a Pydantic model to enforce the structure of the output
class MovieScript(BaseModel):
    setting: str = Field(..., description="Provide a nice setting for a blockbuster movie.")
    ending: str = Field(..., description="Ending of the movie. If not available, provide a happy ending.")
    genre: str = Field(..., description="Genre of the movie. If not available, select action, thriller or romantic comedy.")
    name: str = Field(..., description="Give a name to this movie")
    characters: List[str] = Field(..., description="Name of characters for this movie.")
    storyline: str = Field(..., description="3 sentence storyline for the movie. Make it exciting!")

# Agent that uses JSON mode
json_mode_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You write movie scripts.",
    response_model=MovieScript,
)
# Agent that uses structured outputs
structured_output_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"),
    description="You write movie scripts.",
    response_model=MovieScript,
    structured_outputs=True,
)

json_mode_agent.print_response("New York")
structured_output_agent.print_response("New York")
2

Run the agent

python structured_output.py

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Next Steps

  • Chat with your Agents using a beautiful Agent UI.
  • Learn how to monitor and debug your Agents.
  • For more advanced cases, build deterministic, stateful, multi-agent workflows.

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