phidata

Build multi-modal Agents with memory, knowledge, tools and reasoning.

What is phidata?

Phidata is a framework for building multi-modal agents, use phidata to:

• Build multi-modal agents with memory, knowledge, tools and reasoning.
• Build teams of agents that can work together to solve problems.
• Chat with your agents using a beautiful Agent UI.

Install

pip install -U phidata

Key Features

• Simple & Elegant
• Powerful & Flexible
• Multi-Modal by default
• Multi-Agent orchestration
• A beautiful Agent UI to chat with your agents
• Agentic RAG built-in
• Structured Outputs
• Reasoning Agents
• Monitoring & Debugging built-in
• Demo Agents

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, create a file web_search.py

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

web_agent = 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)

Install libraries, export your OPENAI_API_KEY and run the Agent:

pip install phidata openai duckduckgo-search

export OPENAI_API_KEY=sk-xxxx

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, create a file 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)

Install libraries and run the Agent:

pip install yfinance

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, create a file 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,
)

Run the Agent:

python image_agent.py

Multi-Agent orchestration

Phidata agents can work together as a team to achieve complex tasks, create a file 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],
    model=OpenAIChat(id="gpt-4o"),
    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)

Run the Agent team:

python agent_team.py

A beautiful Agent UI to chat with your agents

Phidata provides a beautiful UI for interacting with your agents. Let's take it for a spin, create a file playground.py

[!NOTE] Phidata does not store any data, all agent data is stored locally in a sqlite database.

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.storage.agent.sqlite import SqlAgentStorage
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.yfinance import YFinanceTools
from phi.playground import Playground, serve_playground_app

web_agent = Agent(
    name="Web Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGo()],
    instructions=["Always include sources"],
    storage=SqlAgentStorage(table_name="web_agent", db_file="agents.db"),
    add_history_to_messages=True,
    markdown=True,
)

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"],
    storage=SqlAgentStorage(table_name="finance_agent", db_file="agents.db"),
    add_history_to_messages=True,
    markdown=True,
)

app = Playground(agents=[finance_agent, web_agent]).get_app()

if __name__ == "__main__":
    serve_playground_app("playground:app", reload=True)

Authenticate with phidata by running the following command:

phi auth

or by exporting the PHI_API_KEY for your workspace from phidata.app

export PHI_API_KEY=phi-***

Install dependencies and run the Agent Playground:

pip install 'fastapi[standard]' sqlalchemy

python playground.py

• Open the link provided or navigate to http://phidata.app/playground
• Select the localhost:7777 endpoint and start chatting with your agents!

Agentic RAG

We were the first to pioneer Agentic RAG using our Auto-RAG paradigm. With Agentic RAG (or auto-rag), the Agent can search its knowledge base (vector db) for the specific information it needs to achieve its task, instead of always inserting the "context" into the prompt.

This saves tokens and improves response quality. Create a file 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)

Install libraries and run the Agent:

pip install lancedb tantivy pypdf sqlalchemy

python rag_agent.py

Structured Outputs

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

Create a file 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"),
    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")

• Run the structured_output.py file

python structured_output.py

• The output is an object of the MovieScript class, here's how it looks:

MovieScript(
│   setting='A bustling and vibrant New York City',
│   ending='The protagonist saves the city and reconciles with their estranged family.',
│   genre='action',
│   name='City Pulse',
│   characters=['Alex Mercer', 'Nina Castillo', 'Detective Mike Johnson'],
│   storyline='In the heart of New York City, a former cop turned vigilante, Alex Mercer, teams up with a street-smart activist, Nina Castillo, to take down a corrupt political figure who threatens to destroy the city. As they navigate through the intricate web of power and deception, they uncover shocking truths that push them to the brink of their abilities. With time running out, they must race against the clock to save New York and confront their own demons.'
)

Reasoning Agents (experimental)

Reasoning helps agents work through a problem step-by-step, backtracking and correcting as needed. Create a file reasoning_agent.py.

from phi.agent import Agent
from phi.model.openai import OpenAIChat

task = (
    "Three missionaries and three cannibals need to cross a river. "
    "They have a boat that can carry up to two people at a time. "
    "If, at any time, the cannibals outnumber the missionaries on either side of the river, the cannibals will eat the missionaries. "
    "How can all six people get across the river safely? Provide a step-by-step solution and show the solutions as an ascii diagram"
)

reasoning_agent = Agent(model=OpenAIChat(id="gpt-4o"), reasoning=True, markdown=True, structured_outputs=True)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

Run the Reasoning Agent:

python reasoning_agent.py

[!WARNING] Reasoning is an experimental feature and will break ~20% of the time. It is not a replacement for o1.

It is an experiment fueled by curiosity, combining COT and tool use. Set your expectations very low for this initial release. For example: It will not be able to count ‘r’s in ‘strawberry’.

Demo Agents

The Agent Playground includes a few demo agents that you can test with. If you have recommendations for other demo agents, please let us know in our community forum.

Monitoring & Debugging

Monitoring

Phidata comes with built-in monitoring. You can set monitoring=True on any agent to track sessions or set PHI_MONITORING=true in your environment.

[!NOTE] Run phi auth to authenticate your local account or export the PHI_API_KEY

from phi.agent import Agent

agent = Agent(markdown=True, monitoring=True)
agent.print_response("Share a 2 sentence horror story")

Run the agent and monitor the results on phidata.app/sessions

# You can also set the environment variable
# export PHI_MONITORING=true

python monitoring.py

View the agent session on phidata.app/sessions

Debugging

Phidata also includes a built-in debugger that will show debug logs in the terminal. You can set debug_mode=True on any agent to track sessions or set PHI_DEBUG=true in your environment.

from phi.agent import Agent

agent = Agent(markdown=True, debug_mode=True)
agent.print_response("Share a 2 sentence horror story")

Getting help

• Read the docs at docs.phidata.com
• Post your questions on the community forum
• Chat with us on discord

More examples

Agent that can write and run python code

from phi.agent.python import PythonAgent
from phi.model.openai import OpenAIChat
from phi.file.local.csv import CsvFile

python_agent = PythonAgent(
    model=OpenAIChat(id="gpt-4o"),
    files=[
        CsvFile(
            path="https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
            description="Contains information about movies from IMDB.",
        )
    ],
    markdown=True,
    pip_install=True,
    show_tool_calls=True,
)

python_agent.print_response("What is the average rating of movies?")

python python_agent.py

Agent that can analyze data using SQL

import json
from phi.model.openai import OpenAIChat
from phi.agent.duckdb import DuckDbAgent

data_analyst = DuckDbAgent(
    model=OpenAIChat(model="gpt-4o"),
    markdown=True,
    semantic_model=json.dumps(
        {
            "tables": [
                {
                    "name": "movies",
                    "description": "Contains information about movies from IMDB.",
                    "path": "https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
                }
            ]
        },
        indent=2,
    ),
)

data_analyst.print_response(
    "Show me a histogram of ratings. "
    "Choose an appropriate bucket size but share how you chose it. "
    "Show me the result as a pretty ascii diagram",
    stream=True,
)

pip install duckdb

python data_analyst.py

Check out the cookbook for more examples.

Contributions

We're an open-source project and welcome contributions, please read the contributing guide for more information.

Request a feature

• If you have a feature request, please open an issue or make a pull request.
• If you have ideas on how we can improve, please create a discussion.

Telemetry

Phidata logs which model an agent used so we can prioritize features for the most popular models.

You can disable this by setting PHI_TELEMETRY=false in your environment.

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