Agent with Storage

Code

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.vllm import VLLM
from agno.tools.websearch import WebSearchTools

# Setup the database
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = PostgresDb(db_url=db_url)

agent = Agent(
    model=VLLM(id="Qwen/Qwen2.5-7B-Instruct"),
    db=db,
    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?")

Usage

Install vLLM in a server environment using the installation guide for your hardware. The GPU commands below require a supported Linux accelerator environment. The Agno client can run in a separate Python environment with agno, openai and the example's other dependencies.

Run vllm serve in a dedicated server terminal with its environment active and keep it running. In a client terminal, activate the environment for your saved Python file, export VLLM_API_KEY, and run the agent. The examples use port 8000; for a server on another host, set the matching base_url on VLLM. A server started without --api-key accepts a placeholder key, but Agno still requires a nonempty VLLM_API_KEY.

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

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

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

vllm serve Qwen/Qwen2.5-7B-Instruct \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --dtype float16 \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.9

export VLLM_API_KEY=xxx

python db.py