Aimlapi Image Agent With Memory

Analyze an image with a Llama vision model on AIMLAPI, then answer a follow-up using chat history.

image_agent_with_memory.py

"""
Aimlapi Image Agent With Memory
===============================

Cookbook example for `aimlapi/image_agent_with_memory.py`.
"""

from agno.agent import Agent
from agno.media import Image
from agno.models.aimlapi import AIMLAPI

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

agent = Agent(
    model=AIMLAPI(id="meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo"),
    markdown=True,
    add_history_to_context=True,
    num_history_runs=3,
)

agent.print_response(
    "Tell me about this image",
    images=[
        Image(
            url="https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg"
        )
    ],
    stream=True,
)

agent.print_response("Tell me where I can get more images?")

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    pass

Run the Example

1. Set up your virtual environment

   Mac

   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 openai
   
3. Export your AI/ML API API key

   Mac/Linux

   export AIMLAPI_API_KEY="your_aimlapi_api_key_here"
   

   $Env:AIMLAPI_API_KEY="your_aimlapi_api_key_here"
   
4. Run the example

   Save the code above as image_agent_with_memory.py, then run:

   python image_agent_with_memory.py
   

Full source: cookbook/90_models/aimlapi/image_agent_with_memory.py