An intelligent Agentic AI platform that autonomously discovers, evaluates, and submits LinkedIn Easy Apply job applications using Large Language Models, browser automation, and resume-aware reasoning.
AutoAgentHire is a full-stack Agentic AI platform designed to automate the end-to-end job application process on LinkedIn Easy Apply.
The system combines browser automation, Large Language Models (LLMs), resume intelligence, and autonomous decision-making to reduce repetitive manual effort involved in job searching and application submission.
Unlike traditional automation scripts, AutoAgentHire incorporates AI-powered reasoning capabilities that enable it to understand dynamic application forms, generate contextual responses, navigate multi-step workflows, and adapt to varying recruitment processes.
The platform demonstrates how Agentic AI systems can interact with real-world web environments and execute complex tasks with minimal human intervention.
Modern recruitment platforms require candidates to repeatedly provide the same information across multiple applications.
This process is:
- Time-consuming
- Repetitive
- Error-prone
- Difficult to scale
AutoAgentHire addresses this challenge by introducing an intelligent autonomous agent capable of:
- Understanding application context
- Parsing candidate resumes
- Generating AI-assisted responses
- Navigating multi-step forms
- Submitting applications autonomously
The project explores the intersection of:
- Agentic AI Systems
- Large Language Models
- Intelligent Workflow Automation
- Human-AI Collaboration
- Browser-Based Autonomous Agents
- React
- Vite
- TypeScript
Provides:
- User Dashboard
- Profile Management
- Resume Upload
- Automation Monitoring
- FastAPI
- JWT Authentication
- Agent Orchestrator
Handles:
- API Services
- User Management
- Automation Execution
- Status Tracking
- GitHub Models
- Groq
- OpenAI
Responsible for:
- Context Understanding
- Dynamic Form Reasoning
- AI Answer Generation
- Intelligent Decision Making
- Playwright
Responsible for:
- Browser Control
- Form Navigation
- Resume Uploads
- Application Submission
- PostgreSQL
- Supabase
Stores:
- User Profiles
- Automation Results
- Resume Metadata
- User initiates automation.
- LinkedIn Easy Apply jobs are discovered.
- Opportunities are filtered based on preferences.
- Application forms are analyzed.
- Known fields are automatically populated.
- Unknown fields are handled using AI reasoning.
- Validation checks are performed.
- Multi-step workflows are navigated autonomously.
- Applications are submitted.
- Results are stored and displayed.
The Resume Intelligence Engine extracts structured information from uploaded resumes.
- DOCX
- Skills
- Experience
- Education
- Professional Summary
The extracted profile is utilized for:
- Automated field completion
- AI-generated responses
- Context-aware reasoning
- Job matching
- Automatic field population
- Dynamic form understanding
- Context-aware response generation
- AI-assisted reasoning
- Response caching
- GitHub Models
- Groq
- OpenAI
- Unlimited form-page support
- Intelligent navigation
- Validation error recovery
- Resume upload automation
- Safe workflow termination
Prevents:
- Duplicate field processing
- Infinite loops
- Redundant AI requests
- Repeated submissions
Each field is processed only once.
Features:
- JWT Authentication
- bcrypt Password Hashing
- PostgreSQL User Storage
- Secure Environment Variables
- Headless Chromium Execution
- Explicit Wait Strategies
- AI Response Caching
- Field Deduplication
- Subprocess-Based Isolation
| Layer | Technologies |
|---|---|
| Frontend | React, Vite, TypeScript |
| Backend | FastAPI, Python |
| Database | PostgreSQL, Supabase |
| Automation | Playwright |
| AI | GitHub Models, Groq, OpenAI |
| Deployment | Render, Vercel |
| Authentication | JWT, bcrypt |
AutoAgentHire/
│
├── backend/
│ ├── automation/
│ ├── routes/
│ ├── database/
│ ├── llm/
│ ├── parsers/
│ ├── rag/
│ └── utils/
│
├── frontend/
│
├── docker/
├── scripts/
│
├── assets/
│
├── uploads/
├── data/
│
├── requirements.txt
├── render.yaml
├── Procfile
├── .env.example
└── README.mdThe platform successfully demonstrates:
- Autonomous workflow execution
- AI-assisted form reasoning
- Browser-based task automation
- Resume-aware contextual response generation
- Multi-provider LLM orchestration
- Production-ready deployment architecture
- Agentic AI workflow orchestration
- Browser automation at scale
- Resume intelligence integration
- Multi-provider AI routing
- Full-stack cloud deployment
- Secure authentication and data management
Conference Acceptance
- ICRETM 2026
- Paper ID: ICRETM2600507
- Status: Accepted
Research Areas
- Agentic AI Systems
- Large Language Models
- Intelligent Workflow Automation
- Browser Automation
- Software Engineering
- Multi-agent collaboration systems
- Reinforcement learning-based application strategies
- Adaptive job recommendation systems
- Resume optimization using LLMs
- Cross-platform recruitment automation
- Autonomous career assistants
- Agentic AI
- Large Language Models
- Browser Automation
- Full-Stack Development
- FastAPI
- React
- PostgreSQL
- JWT Authentication
- Playwright
- Cloud Deployment
- Intelligent Workflow Automation
- AI System Design
B.Tech Computer Science and Engineering (Artificial Intelligence & Machine Learning)
- Agentic AI
- Machine Learning
- Large Language Models
- Intelligent Automation
- Software Engineering
📧 Email: [email protected]
🔗 GitHub: https://github.com/bennyelmala
MIT License