Storage

Store sessions in a named Postgres table so Llama 4 Maverick keeps history across runs.

At the linked source revision, LlamaOpenAI has a message-formatter signature mismatch and fails before sending a request. Apply the compatible adapter instructions below before running this example.

storage.py
"""Run `uv pip install ddgs sqlalchemy openai` to install dependencies."""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.meta import LlamaOpenAI
from agno.tools.websearch import WebSearchTools

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

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

agent = Agent(
    model=LlamaOpenAI(id="Llama-4-Maverick-17B-128E-Instruct-FP8"),
    db=PostgresDb(db_url=db_url, session_table="llama_openai_sessions"),
    tools=[WebSearchTools()],
    add_history_to_context=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")

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

if __name__ == "__main__":
    pass

Run the Example

Set up your virtual environment

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

Install dependencies

uv pip install -U agno "psycopg[binary]" ddgs llama-api-client openai sqlalchemy

Export your Meta Llama API key

export LLAMA_API_KEY="your_llama_api_key_here"

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

Use the compatible API adapter

Replace the LlamaOpenAI import (or Llama in the byte-image source) with this helper. Then replace every LlamaOpenAI(...) or Llama(...) construction in the saved file with llama_model(...). Keep the existing id, temperature, and any retry options inside those calls.

Compatible model helper
from os import getenv

from agno.models.openai.like import OpenAILike


def llama_model(**kwargs):
    return OpenAILike(
        api_key=getenv("LLAMA_API_KEY"),
        base_url="https://api.llama.com/compat/v1/",
        supports_native_structured_outputs=False,
        supports_json_schema_outputs=True,
        **kwargs,
    )

This uses Meta's OpenAI-compatible endpoint. You need a Meta API account with access to the selected model; check your account's current model catalog before running.

Run the example

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

python storage.py

Full source: cookbook/90_models/meta/llama_openai/storage.py