Local Model Integration with Ollama

Prerequisites

Virtual Environment Setup

setup.sh

python3 -m venv .venv
source .venv/bin/activate
pip install "xpander-sdk[agno]"

Environment Setup

The .env file is created when you download your agent code:

xpander init

This downloads your agent code from the platform with the .env file pre-configured with your keys.

Ollama Setup

Install and start Ollama, then pull a model:

setup_ollama.sh

# Install Ollama (follow instructions at https://ollama.ai)
# Pull a model
ollama pull gpt-oss:20b

from dotenv import load_dotenv load_dotenv() from xpander_sdk import Backend from agno.agent import Agent from agno.models.ollama import Ollama # Initialize backend backend = Backend() # Override model with local Ollama agno_agent = Agent(**backend.get_args(override={ 'model': Ollama(id="gpt-oss:20b") })) # Test the local model agno_agent.print_response(message="How to install xpander-cli?")

How to Run

Make sure Ollama is running with your chosen model

Run the example:

local_model_example.py

python local_model_example.py

This example shows model override capabilities where you can use local Ollama models while maintaining all Xpander backend benefits including configuration management, conversation memory, and storage. The hybrid architecture provides local privacy for inference while keeping cloud-based features for everything else.

Start

Build

Types of Agents in xpander

Manage

Local Model Integration with Ollama

Virtual Environment Setup

Environment Setup

Ollama Setup

How to Run

Production Deployment

​Virtual Environment Setup

​Environment Setup

​Ollama Setup

​How to Run

​Production Deployment

Virtual Environment Setup

Environment Setup

Ollama Setup

How to Run

Production Deployment