Examples
- Examples
- Getting Started
- Agents
- Teams
- Workflows
- Applications
- Streamlit Apps
- Evals
Agent Concepts
- Reasoning
- Multimodal
- RAG
- User Control Flows
- Knowledge
- Memory
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Context
- Embedders
- Agent State
- Observability
- Miscellaneous
Models
- Anthropic
- AWS Bedrock
- AWS Bedrock Claude
- Azure AI Foundry
- Azure OpenAI
- Cerebras
- Cerebras OpenAI
- Cohere
- DeepInfra
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- IBM
- LM Studio
- LiteLLM
- LiteLLM OpenAI
- Meta
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Perplexity
- Together
- XAI
- Vercel
- vLLM
vLLM
Agent with Storage
Code
cookbook/models/vllm/storage.py
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from agno.agent import Agent
from agno.models.vllm import vLLM
from agno.storage.postgres import PostgresStorage
from agno.tools.duckduckgo import DuckDuckGoTools
DB_URL = "postgresql+psycopg://ai:ai@localhost:5532/ai"
agent = Agent(
model=vLLM(id="Qwen/Qwen2.5-7B-Instruct"),
storage=PostgresStorage(table_name="agent_sessions", db_url=DB_URL),
tools=[DuckDuckGoTools()],
add_history_to_messages=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")
Ensure Postgres database is running.
Usage
1
Create a virtual environment
Open the Terminal
and create a python virtual environment.
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python3 -m venv .venv
source .venv/bin/activate
2
Install Libraries
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pip install -U agno openai vllm sqlalchemy psycopg[binary] duckduckgo-search
3
Start Postgres database
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./cookbook/scripts/run_pgvector.sh
4
Start vLLM server
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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
5
Run Agent
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python cookbook/models/vllm/storage.py
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