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Creates a new chat session to have multi-turn conversations with the model.

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Method Signature

client.chats.create(
    model: str,
    config: Optional[GenerateContentConfigOrDict] = None,
    history: Optional[list[ContentOrDict]] = None
) -> Chat

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Parameters

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model
string
required
The model to use for the chat session.Example: 'gemini-2.0-flash' or 'gemini-1.5-pro'
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config
GenerateContentConfig
Configuration for the generate content requests in this chat session.This config will be used as the default for all messages sent in the chat unless overridden in individual send_message() or send_message_stream() calls.Common config options:
  • temperature: Controls randomness (0.0 to 2.0)
  • max_output_tokens: Maximum tokens in response
  • top_p: Nucleus sampling parameter
  • top_k: Top-k sampling parameter
  • system_instruction: System-level instructions
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history
list[Content]
Previous conversation history to initialize the chat with.Each Content object should have:
  • role: Either "user" or "model"
  • parts: List of content parts (text, images, etc.)
If not provided, starts with an empty history.

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Returns

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Chat
Chat
A Chat object that maintains conversation state and provides methods to send messages:
  • send_message(): Send a message and get the complete response
  • send_message_stream(): Send a message and stream the response
  • get_history(): Get the conversation history

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Examples

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Basic Chat Session

from google import genai

client = genai.Client(api_key='your-api-key')

# Create a new chat session
chat = client.chats.create(model='gemini-2.0-flash')

# Send messages
response = chat.send_message('Hello! How are you?')
print(response.text)

response = chat.send_message('Tell me a joke')
print(response.text)

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Chat with Configuration

# Create chat with custom config
chat = client.chats.create(
    model='gemini-1.5-pro',
    config={
        'temperature': 0.7,
        'max_output_tokens': 1024,
        'system_instruction': 'You are a helpful coding assistant.'
    }
)

response = chat.send_message('How do I write a Python decorator?')
print(response.text)

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Chat with History

from google.genai import types

# Resume a previous conversation
history = [
    types.Content(role='user', parts=[types.Part(text='What is Python?')]),
    types.Content(role='model', parts=[types.Part(text='Python is a high-level programming language...')]),
]

chat = client.chats.create(
    model='gemini-2.0-flash',
    history=history
)

# Continue the conversation
response = chat.send_message('What are its main features?')
print(response.text)

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Async Chat Session

import asyncio
from google import genai

client = genai.Client(api_key='your-api-key')

async def chat_example():
    # Create async chat
    chat = client.aio.chats.create(model='gemini-2.0-flash')
    
    # Send message asynchronously
    response = await chat.send_message('Hello!')
    print(response.text)

asyncio.run(chat_example())

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API Availability

This method is available in both Gemini API and Vertex AI.

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