Aimlapi Image Agent With Memory

Analyze an image through AIMLAPI and retain it for a follow-up using a database.

These retained examples come from the source revision linked below. Use the installation and model configuration in the AI/ML API guide, or use the compatible OpenAILike adaptation below with an older package.

Current main adds SqliteDb(db_file="tmp/aimlapi_image_agent.db") and uses gpt-5.6-luna. The retained source below is the earlier implementation; apply its database and model setup instructions before running it.

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

The source enables history but omits a database. Before running your saved copy, add from agno.db.in_memory import InMemoryDb to its imports and db=InMemoryDb(), inside its Agent(...) constructor. Keep the existing history settings and reuse the same agent and session across turns. This retains history only for the lifetime of the process; use a persistent database to resume after a restart.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno openai

Export your AI/ML API key

export AIMLAPI_API_KEY="your_aimlapi_api_key_here"

Use the working gateway adapter

In the saved Python file, replace the AIMLAPI import with:

from os import environ
from agno.models.openai.like import OpenAILike

Replace the entire AIMLAPI(...) model expression with:

OpenAILike(
    id="openai/gpt-5-2",
    api_key=environ["AIMLAPI_API_KEY"],
    base_url="https://api.aimlapi.com/v1",
)

Keep the surrounding Agent(...) settings and run calls. This route supports image input as well as text; use the provider's exact IDs when choosing another model.

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