Autonomous AI Study Notes: A Multi-Agent System with LangGraph and Streamlit

A developer built an autonomous multi-agent system using LangGraph, LangChain, Tavily Search, and Playwright that researches complex topics and renders handwritten-style study notes as PNG screenshots. The system, deployed as a Streamlit app, uses a stateful graph with researcher, note renderer, and critic agents to validate output completeness. It is designed to help students and educators create visual study guides quickly.

This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK. I built an Autonomous Multi-Agent Handwritten Notes Generator . Students and educators often need clean, visual study guides that resemble real handwritten notes, but manually summarizing technical subjects and formatting them takes hours. This system solves that by combining autonomous web research, structured note extraction, and headless browser rendering. You enter any topic or question, and a coordinated team of AI agents researches the concept, formats it into a notebook layout using Google handwriting fonts Caveat , and captures a high-resolution .png notebook page screenshot. Deployment & Repository Links: An autonomous multi-agent system built with LangGraph, Tavily, and Playwright that researches complex topics and renders handwritten-style student study notes into PNG screenshots. An autonomous multi-agent workflow built using LangGraph , LangChain , Tavily Search , and Playwright . The system researches complex technical concepts and dynamically compiles the findings into styled, handwritten-notebook PNG screenshots. User Input / Prompt │ ▼ Researcher Node ── Tavily Web Search & Summarization │ ▼ Note Renderer Node ── HTML/CSS + Google Caveat Font + Playwright Screenshot │ ▼ Critic Node ── Validation Check: Is Output Complete? │ Approved? ──► No ──► Researcher Node │ Yes ▼ PNG Screenshot Saved Live Application: https://multi-agent-handwritten-notes-uyc8am8kwydzwgjufxo3tp.streamlit.app/ https://multi-agent-handwritten-notes-uyc8am8kwydzwgjufxo3tp.streamlit.app/ The system uses a stateful multi-agent graph with discrete responsibilities: Researcher Agent researcher node : 0.0 for deterministic, accurate factual extraction. Note Renderer Agent note renderer node : .png screenshot. Critic Agent critic node : APPROVED . If validation fails, it routes back for reflection. User Prompt │ ▼ Researcher Agent ──► Tavily Search + Summarization │ ▼ Note Renderer Agent ──► HTML/CSS + Playwright Screenshot │ ▼ Critic Agent ──► Approved? ──► Output PNG Saved │ │ └─────── No ────────┘ packages.txt with Chromium alongside standard Python requirements.