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Virtual Environment Setup

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

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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.

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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
local_model_example.py
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?")

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How to Run

  1. Make sure Ollama is running with your chosen model
  2. 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.

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Production Deployment

For creating and deploying agents to production, see the Setup and Deployment Guide.