API Documentation Agent (LLM)
Project Overview
The system ingests documentation, retrieves context with vector search, and uses LLMs to emit structured API calls and code.
About This Project
Autonomous agent that converts unstructured API documentation into executable integration snippets.
Tech Stack
Team
Mentors
- Radhika Sharda
- Sanstuti Mishra
- Pramod Budhiraja
Mentees
- Nabeel
- Rahul
- Umar
- Rushi
- Pranav
Methodology
The project follows a structured implementation approach that includes Scrapy-based ingestion of docs, LangChain chunking and ChromaDB embeddings, LLM reasoning to structured API JSON, Jinja2 conversion to Python/cURL, and React + TypeScript UI and FastAPI backend. These steps are executed iteratively to validate assumptions, improve performance, and ensure reliable delivery of the final solution.
Expected Outcome
By the end of this project, the team is expected to deliver Lower friction API onboarding, and Executable code generation from docs. Together, these outcomes reflect both technical feasibility and practical value for demos, evaluation, and future scaling.