The College Admissions Strategist is an agentic AI framework built to revolutionize the university application process. By leveraging a swarm of specialized AI agents, the system provides personalized college shortlists, financial aid strategies, and deep-dive essay critiques that mimic high-end human admissions consulting.

• Personalized College Matchmaking: Analyzes GPA, SAT/ACT, and extracurriculars to find "Reach," "Match," and "Safety" schools.
• Financial Aid Optimization: Strategizes for merit-based and need-based scholarships.
• Essay Critique Engine: High-level feedback on narrative flow, tone, and impact.
• Multi-Agent Orchestration: Uses a coordinated team of agents (Researcher, Strategist, and Editor) to ensure balanced advice.

This project utilizes a Collaborative Agentic Loop:

1. The Admissions Researcher: Scrapes and analyzes current university data, rankings, and acceptance trends.
2. The Strategic Consultant: Evaluates student profiles against historical admission benchmarks.
3. The Financial Advisor: Specializes in FAFSA, CSS Profile navigation, and scholarship hunting.
4. The Chief Editor: Refines the final output for tone and professional clarity.

• Core Framework: LangGraph, FAST API.
• LLM: llama-3.1-8b-instant, llama-3.3-70b-versatile
• Scraping and search tools: crawl4ai, serper
• Frontend: chainlit

• Python 3.10+
• API Keys: GROQ, Serper (for web search)

1. Clone the repo:

   git clone [https://github.com/coderkun12/College-Admissions-Strategist.git](https://github.com/coderkun12/College-Admissions-Strategist.git)
   cd College-Admissions-Strategist

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: create a .env file: GROQ_API_KEY=your_key_here SERPER_API_KEY=your_key_here
4. Run the application:

   python main.py

1. User is prompted to enter university name, program name, level of study and background of user.
2. After user enters the information, /run-agent makes a llm call to the llm defined in utils.py to extract the data in JSON format.
3. After the data is extracted in JSON format, filename is created as: CourseName-ProgramName.docx.
4. After step 3, the agentic framework is initiated to perform search program details user seeks and a strategy user can pursue. It works as follows: Manager (determines steps to take) -> Scraper (searches for top sources and scrapes their data) -> Strategist (Prepares the strategy and documentation for the user).
5. After documentation text is returned, a word file is created with strategy and course details for the user. User is provided a file to download in the chat.

Contributions are welcome! Please open an issue or submit a pull request for any feature enhancements.