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PandasAI v3 maintains backward compatibility for SmartDataframe, SmartDatalake, and Agent. However, we recommend migrating to the new pai.DataFrame() and pai.chat() methods for better performance and features.

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SmartDataframe

SmartDataframe continues to work in v3 with the same API. However, you must configure the LLM globally.

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Using SmartDataframe in v3 (Legacy)

from pandasai import SmartDataframe
import pandasai as pai
import pandas as pd
from pandasai_litellm.litellm import LiteLLM

# Configure LLM globally (required)
llm = LiteLLM(model="gpt-4o-mini", api_key="your-api-key")
pai.config.set({"llm": llm})

# v2 style still works
df = pd.DataFrame({
    "country": ["US", "UK", "France"],
    "sales": [5000, 3200, 2900]
})

smart_df = SmartDataframe(df)
response = smart_df.chat("What are the top countries by sales?")
While SmartDataframe works, we recommend using pai.DataFrame() for better integration with v3 features: 
import pandasai as pai
import pandas as pd

# Configure LLM globally
pai.config.set({"llm": llm})

# Simple approach
df = pd.DataFrame({
    "country": ["US", "UK", "France"],
    "sales": [5000, 3200, 2900]
})
df = pai.DataFrame(df)
response = df.chat("What are the top countries by sales?")
Benefits of pai.DataFrame():
  • Better integration with semantic layer
  • Improved context management
  • Enhanced performance
  • Access to v3-specific features
  • Cleaner API

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SmartDatalake

SmartDatalake still works but is no longer necessary. You can query multiple dataframes directly with pai.chat().

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Using SmartDatalake in v3 (Legacy)

from pandasai import SmartDatalake
import pandasai as pai
import pandas as pd
from pandasai_litellm.litellm import LiteLLM

# Configure LLM globally (required)
llm = LiteLLM(model="gpt-4o-mini", api_key="your-api-key")
pai.config.set({"llm": llm})

# v2 style still works
employees_df = pd.DataFrame({
    "name": ["John", "Jane", "Bob"],
    "department": ["Sales", "Engineering", "Sales"]
})

salaries_df = pd.DataFrame({
    "name": ["John", "Jane", "Bob"],
    "salary": [60000, 80000, 55000]
})

lake = SmartDatalake([
    employees_df,
    salaries_df
])

response = lake.chat("Who gets paid the most?")
Query multiple dataframes directly without SmartDatalake: 
import pandasai as pai

# Configure LLM globally
pai.config.set({"llm": llm})

# Create dataframes
employees = pai.DataFrame(employees_df)
salaries = pai.DataFrame(salaries_df)

# Query across multiple dataframes directly
response = pai.chat("Who gets paid the most?", employees, salaries)
Benefits of pai.chat():
  • No need to instantiate SmartDatalake
  • Cleaner, more intuitive API
  • Better performance
  • Semantic layer support
  • Easier to add/remove dataframes dynamically

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Agent

The Agent class works the same way in v3 as it did in v2. The only requirement is to configure the LLM globally. 
from pandasai import Agent
import pandasai as pai
from pandasai_litellm.litellm import LiteLLM

# Configure LLM globally (required in v3)
llm = LiteLLM(model="gpt-4o-mini", api_key="your-api-key")
pai.config.set({"llm": llm})

# Agent works as before
df1 = pai.DataFrame(sales_data)
df2 = pai.DataFrame(costs_data)

agent = Agent([df1, df2])
response = agent.chat("Analyze the data and provide insights")
Key Change: Configure LLM globally with pai.config.set() instead of passing it per-agent. For detailed information about Agent usage, see the Agent documentation. For information about using Skills with Agent, see the Skills documentation.
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