all_providers.py
"""
Interchange Model: All 5 Providers
Cycles through OpenAI Chat, OpenAI Responses, Claude, Gemini, and AWS Claude.
Tool calls happen on every turn, then the history is summarized from a different provider.
"""
import os
from random import randint
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.anthropic import Claude
from agno.models.aws import Claude as AWSClaude
from agno.models.google import Gemini
from agno.models.openai import OpenAIChat, OpenAIResponses
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"The weather in {city} is sunny and {randint(-10, 35)}C."
def main() -> None:
db_url = os.getenv(
"AGNO_POSTGRES_URL",
"postgresql+psycopg://ai:ai@localhost:5532/ai",
)
db = PostgresDb(db_url)
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
db=db,
add_history_to_context=True,
num_history_runs=10,
tools=[get_weather],
debug_mode=True,
introduction="You are a weather agent that can check the weather in different cities.",
)
# Turn 1 — OpenAI Chat (call_* IDs)
agent.print_response("What is the weather in Paris?")
# Turn 2 — OpenAI Responses (fc_* IDs)
agent.model = OpenAIResponses()
agent.print_response("What is the weather in London?")
# Turn 3 — Claude (toolu_* IDs)
agent.model = Claude()
agent.print_response("What is the weather in Tokyo?")
# Turn 4 — Gemini (UUID-style IDs)
agent.model = Gemini()
agent.print_response("What is the weather in New York?")
# Turn 5 — Back to OpenAI Chat to summarize all history
agent.model = OpenAIChat(id="gpt-4o")
agent.print_response("Summarize all the weather we checked.")
# Turn 6 — Claude summarizes (sees history from all providers)
agent.model = Claude()
agent.print_response("Which city had the best weather?")
# Turn 7 — AWS Claude
agent.model = AWSClaude()
agent.print_response("What is the weather in Beijing?")
# Turn 8 — OpenAI Responses (fc_* IDs)
agent.model = OpenAIResponses()
agent.print_response("What is the weather in London?")
if __name__ == "__main__":
main()
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno "anthropic[bedrock]" "psycopg[binary]" aioboto3 boto3 google-genai openai sqlalchemy
3
Export environment variables
export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
export AWS_ACCESS_KEY_ID="your_aws_access_key_id_here"
export AWS_REGION="your_aws_region_here"
export AWS_SECRET_ACCESS_KEY="your_aws_secret_access_key_here"
export GOOGLE_API_KEY="your_google_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:ANTHROPIC_API_KEY="your_anthropic_api_key_here"
$Env:AWS_ACCESS_KEY_ID="your_aws_access_key_id_here"
$Env:AWS_REGION="your_aws_region_here"
$Env:AWS_SECRET_ACCESS_KEY="your_aws_secret_access_key_here"
$Env:GOOGLE_API_KEY="your_google_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
docker run -d `
-e POSTGRES_DB=ai `
-e POSTGRES_USER=ai `
-e POSTGRES_PASSWORD=ai `
-e PGDATA=/var/lib/postgresql `
-v pgvolume:/var/lib/postgresql `
-p 5532:5432 `
--name pgvector `
agnohq/pgvector:18
5
Run the example
Save the code above as
all_providers.py, then run:python all_providers.py