Phidata is a framework for building agentic systems, engineers use phidata to:
- Build Agents with memory, knowledge, tools and reasoning. examples
- Build teams of Agents that can work together. example
- Chat with Agents using a beautiful Agent UI. example
- Monitor, evaluate and optimize Agents. example
- Build agentic systems i.e. applications with an API, database and vectordb.
pip install -U phidataLet's start by building a simple agent that can search the web, 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(
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)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.pyLets create another agent that can 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.pyNow lets create a team of agents using the agents above, 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.pyReasoning is an experimental feature that 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.pyWarning
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’.
Tip
If using tools with reasoning=True, set structured_outputs=False because gpt-4o doesnt support tools with structured outputs.
Instead of always inserting the "context" into the prompt, the RAG Agent can search its knowledge base (vector db) for the specific information it needs to achieve its task.
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.pyPhidata 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:
phi auth
Note
If phi auth fails, you can set the PHI_API_KEY environment variable by copying it from phidata.app
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:7777endpoint and start chatting with your agents!
AgentPlayground.mp4
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.
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.pyView the agent session on phidata.app/sessions
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")
- Read the docs at docs.phidata.com
- Post your questions on the community forum
- Chat with us on discord
Show code
The PythonAgent can achieve tasks by writing and running python code.
- Create a file
python_agent.py
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?")- Run the
python_agent.py
python python_agent.pyShow code
The DuckDbAgent can perform data analysis using SQL.
- Create a file
data_analyst.py
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,
)- Install duckdb and run the
data_analyst.pyfile
pip install duckdb
python data_analyst.pyShow code
One of our favorite LLM features is generating structured data (i.e. a pydantic model) from text. Use this feature to extract features, generate data etc.
Let's create a Movie Agent to write a MovieScript for us, 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-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")- Run the
structured_output.pyfile
python structured_output.py- The output is an object of the
MovieScriptclass, 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.'
)Check out the cookbook for more examples.
We're an open-source project and welcome contributions, please read the contributing guide for more information.
- 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.
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.