Phidata is now Agno! We’ve moved! Check out our new home at Agno AGI

​
What is Phidata?

Phidata is a framework for building multi-modal agents and workflows.
  • Build agents with memory, knowledge, tools and reasoning.
  • Build teams of agents that can work together to solve problems.
  • Interact with your agents and workflows using a beautiful Agent UI.

​
Key Features

​
Install

pip install -U phidata

​
Simple & Elegant

Phidata Agents are simple and elegant, resulting in minimal, beautiful code. For example, you can create a web search agent in 10 lines of code.
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("Tell me about OpenAI Sora?", stream=True)

​
Setup

1

Setup your virtual environment

python3 -m venv ~/.venvs/aienv source ~/.venvs/aienv/bin/activate
2

Install libraries

pip install -U phidata openai duckduckgo-search
3

Export your OpenAI key

Phidata works with most model providers but for these examples let’s use OpenAI.
export OPENAI_API_KEY=sk-***
You can get an API key from here.
4

Run the agent

python web_search.py

​
Powerful & Flexible

Phidata agents can use multiple tools and follow instructions to achieve complex tasks. For example, you can create a finance agent with tools to query financial data.
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

​
Multi-Modal by default

Phidata agents support text, images, audio and video. For example, you can create an image agent that can understand images and make tool calls as needed
1

Create an image agent

image_agent.py
from phi.agent import Agent from phi.model.openai import OpenAIChat from phi.tools.duckduckgo import DuckDuckGo  agent = Agent(  model=OpenAIChat(id="gpt-4o"),  tools=[DuckDuckGo()],  markdown=True, )  agent.print_response(  "Tell me about this image and give me the latest news about it.",  images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"],  stream=True, )
2

Run the agent

python image_agent.py

​
Multi-Agent orchestration

Phidata agents can work together as a team to achieve complex tasks.
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

​
Continue reading