🌍 ClimateGuard — AI-Powered Climate Awareness Agent

ClimateGuard is an AI agent built with Fetch.ai’s uAgents framework and powered by the ASI:One (asi1-mini) large language model. It engages users in natural conversations about climate change, carbon footprint reduction, sustainable living, and environmental policies — delivering personalized, science-based, and actionable advice.

This repository contains the agent implementation for ClimateGuard to be deployed on the decentralized Fetch.ai Agentverse platform or locally for testing.

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Quick Start

Prerequisites

• Python 3.9 or higher
• Fetch.ai uagents framework
• OpenAI Python SDK compatible with ASI:One API

Setup

1. Clone this repo:

git clone https://github.com/riyaarah/Climate-Guard.git
cd climateguard

2. Install dependencies:

pip install -r requirements.txt

3. Replace the OpenAI API key placeholder in agent.py with your ASI:One API key:

api_key='sk_67703b522cef404ba12cdb5c9b9e152e9aa86fa175d44ac79944a03e588ea3c3'

4. Run the agent:

python agent.py

The agent will start on http://127.0.0.1:8000/submit and listen for chat messages.

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Agent Overview

• Agent name: climate_guard

• Port: 8000

• Seed phrase: climate_guard_secret_phrase

• Protocol: Chat protocol based on Fetch.ai uAgents chat_protocol_spec

• LLM: ASI:One asi1-mini model

• Key features:

  • Handles conversational queries about climate science, carbon footprint, sustainability
  • Responds with practical, science-based advice
  • Acknowledges incoming messages and sends chat responses
  • Handles errors gracefully and informs the user

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Technical Details

• Uses uagents to manage the agent lifecycle and messaging

• Uses OpenAI Python SDK with a custom base URL to connect to ASI:One LLM

• Implements message handlers for:

  • ChatMessage — receives user queries, calls LLM, returns answers
  • ChatAcknowledgement — no-op in this implementation

• Maintains conversational context by processing message contents

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Environment Variables

For security, consider setting your API key as an environment variable and reading it in agent.py instead of hardcoding.

Example:

import os
api_key = os.getenv("sk_67703b522cef404ba12cdb5c9b9e152e9aa86fa175d44ac79944a03e588ea3c3")

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Future Improvements

• Add persistent user context for multi-turn personalized conversations
• Integrate localized climate data sources for more tailored responses
• Enable voice interaction support
• Implement analytics dashboard for user engagement insights

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License

This project is licensed under the MIT License.

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