AccidentguardAi Crew

Welcome to the AccidentguardAi Crew project, powered by crewAI. This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.

Installation

Ensure you have Python >=3.10 <3.14 installed on your system. This project uses UV for dependency management and package handling, offering a seamless setup and execution experience.

First, if you haven't already, install uv:

pip install uv

Next, navigate to your project directory and install the dependencies:

(Optional) Lock the dependencies and install them by using the CLI command:

crewai install

Customizing

Add your OPENAI_API_KEY into the .env file

• Modify src/accidentguard_ai/config/agents.yaml to define your agents
• Modify src/accidentguard_ai/config/tasks.yaml to define your tasks
• Modify src/accidentguard_ai/crew.py to add your own logic, tools and specific args
• Modify src/accidentguard_ai/main.py to add custom inputs for your agents and tasks

Running the Project

To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:

$ crewai run

This command initializes the accidentguard_ai Crew, assembling the agents and assigning them tasks as defined in your configuration.

This example, unmodified, will run the create a report.md file with the output of a research on LLMs in the root folder.

Understanding Your Crew

The accidentguard_ai Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in config/tasks.yaml, leveraging their collective skills to achieve complex objectives. The config/agents.yaml file outlines the capabilities and configurations of each agent in your crew. What's New Core Pipeline — 9-Agent Automated Accident Response System A full end-to-end multi-agent system that detects road incidents and coordinates emergency + insurance response automatically.

Agents shipped:

🛡️ Media Integrity & Fraud Detection — Deepfake guardrail with score-based rejection (>0.65 halts pipeline) 🎥 Road Incident Detection — AI vision classification: COLLISION, FIRE, MEDICAL, MINOR, FALSE ALARM 📱 Manual SOS Processing — Fallback for user-submitted alerts when CCTV fails 🪪 Biometric & Vehicle Identification — OCR plate recognition + face detection + insurance DB lookup 🔧 Damage Assessment — Per-part damage % estimation, MAJOR/MODERATE/MINOR/TOTAL LOSS classification 🚑 Emergency Dispatch Coordinator — Ambulance, towing, police, toll booth, and driver SMS dispatch 📋 Insurance Claims Automation — Auto-initiates vehicle, medical, and fire claims simultaneously 📡 SOS Emergency Alert & Live GPS — Configurable authority endpoint + 3-ping live GPS broadcast (90s) 📄 Final Incident Report — Full JSON + Markdown dossier with 5 stakeholder sections 🔌 Input Connectivity Modes Mode Trigger CCTV Auto Roadside IP camera snapshot/stream Mobile Manual Alert User SOS via app IoT Sensor Vehicle impact/vibration payload Webhook/API Push Traffic mgmt or toll system push Flashlight SOS Triple-tap mobile hardware trigger Buzzer SOS 3-second hold mobile hardware trigger 🧪 Trial Mode — "Trigger and Go" Set trial_mode = TRUE to run the full pipeline with zero real API dependencies Real AI vision analysis on a public accident image All downstream steps (dispatch, claims, SOS, report) run with realistic preset mock data No real emergency services, APIs, or registries are contacted

Support

For support, questions, or feedback regarding the AccidentguardAi Crew or crewAI.

• Visit our documentation
• Reach out to us through our GitHub repository
• Join our Discord
• Chat with our docs

Let's create wonders together with the power and simplicity of crewAI.