wildguard ai

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built WildGuard AI, an AI-powered multi-agent wildlife identification and safety assistant.

I built it for a friend who enjoys spending time outdoors, where encountering unfamiliar wildlife can quickly become a safety concern. The goal was to create something they could use when they encounter an animal they don't recognize.

A user can upload a wildlife image, and WildGuard AI coordinates multiple specialized agents to:

• Identify the species
• Verify whether the identification is geographically plausible
• Assess the potential risk
• Provide emergency first-aid guidance
• Explain the animal's habitat, behavior, and ecological importance
• Generate a structured wildlife safety report

Instead of relying on one general-purpose agent, WildGuard divides the problem into specialized agents coordinated by an Orchestrator.

Demo

Live frontend:

https://satyakamspc.github.io/Wildguard-AI/

The project is currently deployed using GitHub Pages.

Code

WildGuard AI — GitHub Repository

How I Built It

WildGuard AI is built around Google ADK (Agent Development Kit) as the agent framework, with Gemini providing the model intelligence.

The system uses a multi-agent architecture consisting of:

• Orchestrator Agent — coordinates the complete workflow
• Species Identification Agent — identifies wildlife from the uploaded image
• Geographic Verification Agent — checks geographic plausibility
• Risk Assessment Agent — evaluates potential danger
• First Aid Agent — generates emergency guidance
• Knowledge Agent — provides ecological and educational information
• Report Agent — combines the results into a structured report

The application is built with:

• React + Vite for the frontend
• Django for the backend
• Google ADK for agent orchestration
• Gemini API for AI inference
• SQLite for local data storage
• Pydantic for structured data validation
• Pillow for image processing
• GitHub Actions + GitHub Pages for frontend deployment

The project also uses dedicated Agent Skills for the AI/ML, backend, frontend, and database parts of the application.

Why Does Open Innovation Matter?

WildGuard AI uses the open-source Google ADK as the foundation for its multi-agent architecture.

This was important because wildlife analysis is not a single task. Identification, geographic verification, risk assessment, first aid, and ecological education are different responsibilities that benefit from being separated into independently understandable agents.

Using an open agent framework allowed me to structure the application around specialized, inspectable components rather than putting the entire workflow inside a single opaque AI prompt.

The model layer is separated from the agent architecture as well. Gemini currently provides the model intelligence, while ADK handles the agent orchestration and workflow.

This separation makes the system easier to extend: individual agents can be modified, tested, or replaced without redesigning the entire application.

Prize Categories

• GitHub — Best Use of GitHub Copilot
  • WildGuard AI uses GitHub Actions to automate the frontend build and deployment workflow to GitHub Pages.