How to Switch Between Different Models

When working with Agno, you may need to switch between different models. While Agno supports 20+ model providers, switching between different providers can cause compatibility issues. Switching models within the same provider is generally safer and more reliable.

Recommended Approach

Safe: Switch models within the same provider (OpenAI → OpenAI, Google → Google)
Risky: Switch between different providers (OpenAI ↔ Google ↔ Anthropic)

Cross-provider switching is risky because message history between model providers are often not interchangeable, as some require messages that others don’t. However, we are actively working to improve interoperability.

Safe Model Switching

The safest way to switch models is to change the model ID while keeping the same provider:

from uuid import uuid4
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai.chat import OpenAIChat

db = SqliteDb(db_file="tmp/agent_sessions.db")

session_id = str(uuid4())
user_id = "[e-mail]"

# Create initial agent with expensive model
expensive_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    instructions="You are a helpful assistant for technical discussions.",
    db=db,
    add_history_to_context=True,
)

# Have a conversation
expensive_agent.print_response(
    "Explain quantum computing basics", session_id=session_id, user_id=user_id
)
expensive_agent.print_response(
    "What are the main applications?", session_id=session_id, user_id=user_id
)

# Switch to budget model within same provider (safe)
budget_agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    instructions="You are a helpful assistant for technical discussions.",
    db=db,
    add_history_to_context=True,
)

# Continue conversation - history shared via session_id
budget_agent.print_response(
    "Can you summarize our discussion so far?", session_id=session_id, user_id=user_id
)

Cross-Provider Switching

Switching between providers can cause compatibility issues and is not recommended for production use without thorough testing:

from uuid import uuid4
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai.chat import OpenAIChat
from agno.models.google import Gemini

db = SqliteDb(db_file="tmp/cross_provider_demo.db")

session_id = str(uuid4())
user_id = "[e-mail]"

# Create OpenAI agent
openai_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    instructions="You are a helpful assistant.",
    db=db,
    add_history_to_context=True,
)

# Have a conversation with OpenAI
openai_agent.print_response(
    "Explain machine learning basics", session_id=session_id, user_id=user_id
)
openai_agent.print_response(
    "What about deep learning?", session_id=session_id, user_id=user_id
)

# Now switch to Google Gemini using same session
gemini_agent = Agent(
    model=Gemini(id="gemini-2.0-flash-exp"),
    instructions="You are a helpful assistant.",
    db=db,
    add_history_to_context=True,
)

# This may work but with unpredictable results due to message format differences
gemini_agent.print_response(
    "Continue our discussion about machine learning", session_id=session_id, user_id=user_id
)
# Note: Gemini may not properly interpret OpenAI's message history format

Learn More

• All supported models
• Environment variables setup