A collection of example scripts demonstrating how to build intelligent agents using the Strands framework with local Ollama language models. These scripts showcase different agent capabilities from basic calculations to real-time AWS documentation queries.

This repository contains three progressively advanced examples of Strands Agents:

1. Calculator Agent - Basic mathematical computation capabilities
2. Interactive Conversational Agent - General-purpose question answering
3. AWS Documentation Agent - Real-time AWS documentation queries via MCP

All examples use local Ollama models for privacy and control, with comprehensive documentation and error handling.

• Python 3.8+
• Ollama installed and running locally
• uvx (for AWS documentation script)

1. Install Ollama from ollama.ai
2. Pull a compatible model:

   ollama pull llama2
   # or
   ollama pull mistral

3. Ensure Ollama is running:

   ollama serve

All scripts use inline dependency management. Dependencies are automatically installed when running with uv:

uv run script_name.py

Create a .env file in the repository root:

STRANDS_OLLAMA_HOST=localhost
STRANDS_OLLAMA_MODEL=llama2

Environment Variables:

• STRANDS_OLLAMA_HOST: Hostname where Ollama is running (default: localhost)
• STRANDS_OLLAMA_MODEL: Name of the Ollama model to use

File: calculator_agent.py

A Strands Agent equipped with calculator tools for mathematical computations.

• Integration with calculator tool
• Mathematical problem solving
• Step-by-step calculation explanations

uv run calculator_agent.py

The script automatically asks: "What is the square root of 1764?"

Expected output: Detailed explanation showing the calculation process and result (42).

• Agent: Core Strands agent with calculator tool
• OllamaModel: Local language model integration
• calculator: Pre-built mathematical computation tool

File: interactive_agent.py

A general-purpose conversational agent for open-ended discussions and questions.

• Interactive command-line interface
• General knowledge question answering
• No specialized tools - pure conversation
• Default topic about Agentic AI

uv run interactive_agent.py

Enter a topic to query the LLM about: What is machine learning?

Or press Enter for the default topic: "Tell me about Agentic AI"

• Agent: Basic conversational agent without tools
• OllamaModel: Local language model for responses
• Interactive user input with sensible defaults

File: aws_mcp_agent.py

An advanced agent that provides real-time access to official AWS documentation using Model Context Protocol (MCP).

• Real-time AWS documentation queries
• Official AWS Labs MCP server integration
• Markdown-formatted responses
• Up-to-date service information

• uvx installed: curl -LsSf https://astral.sh/uv/install.sh | sh
• Internet connection for MCP server

uv run aws_mcp_agent.py

Ask a question about aws documentation: How do I configure S3 buckets for static websites?

Or press Enter for default: "Tell me about Amazon Bedrock and how to use it with Python, provide the output in Markdown format"

• Agent: Strands agent with AWS documentation tools
• MCPClient: Model Context Protocol client
• stdio_client: Communication with AWS documentation server
• Real-time documentation access via awslabs.aws-documentation-mcp-server

All scripts share these core architectural elements:

# Environment setup
load_dotenv()
ollama_host = f"https://{os.getenv('STRANDS_OLLAMA_HOST')}:11434"
# Model initialization
ollama_model = OllamaModel(
    host=ollama_host,
    model_id=os.getenv('STRANDS_OLLAMA_MODEL')
)
# Agent creation
agent = Agent(tools=[...], model=ollama_model)

1. Calculator Agent: Basic tool integration
2. Interactive Agent: User interaction patterns
3. AWS MCP Agent: External service integration via MCP

Each script can be executed directly:

# Using uv (recommended)
uv run script_name.py
# Or with traditional Python (after installing dependencies)
python script_name.py

All scripts include comprehensive error handling for:

• Missing environment variables
• Ollama connection issues
• MCP server connectivity (AWS script)
• Invalid user inputs

Each script follows a consistent pattern:

• Environment setup and validation
• Model/client initialization
• User interaction (where applicable)
• Query processing and response handling
• Graceful error management

Start here to understand:

• Basic Strands Agent setup
• Tool integration concepts
• Ollama model configuration

Builds upon basics with:

• User interaction patterns
• Input validation and defaults
• Conversational AI without tools

Demonstrates complex integrations:

• Model Context Protocol usage
• External service integration
• Real-time data access
• Context manager patterns

Ollama Connection Errors:

# Check if Ollama is running
curl https://localhost:11434/api/version
# Start Ollama if needed
ollama serve

Missing Model Errors:

# List available models
ollama list
# Pull required model
ollama pull llama2

AWS MCP Server Issues (AWS script only):

# Ensure uvx is installed
uvx --version
# Test MCP server availability
uvx awslabs.aws-documentation-mcp-server@latest --help

Environment Variable Issues:

• Verify .env file exists and contains required variables
• Check file permissions and syntax
• Ensure no extra spaces or quotes around values

Add debugging to any script by modifying the agent call:

response = agent(query, debug=True)  # If supported

Or add verbose logging:

import logging
logging.basicConfig(level=logging.DEBUG)

1. Fork the repository
2. Create a feature branch
3. Add comprehensive docstrings to new functions
4. Include error handling and examples
5. Test with multiple Ollama models
6. Submit a pull request

This project is licensed under the MIT License - see the LICENSE file for details.

• Strands for the agent framework
• Ollama for local language model hosting
• AWS Labs for the MCP documentation server
• Astral for uvx and uv tools

• Strands Documentation
• Ollama Documentation
• Model Context Protocol
• AWS Documentation MCP Server