dsa-agent: Automated DSA Problem Recommendation and Tracking

Описание и применение

Разработчик создал dsa-agent для автоматизации выбора задач по алгоритмам. Агент анализирует историю решений в Firestore и рекомендует следующую задачу, обновляя трекер в Notion. Архитектура использует двух агентов для надежности.

Задача

Students practicing Data Structures and Algorithms (DSA) spend excessive time deciding which problem to solve next. Generic AI assistants lack context of solved problems, and static trackers (like spreadsheets) cannot recommend next steps, leading to inefficiency.

Результат

The dsa-agent successfully automates the selection of the next DSA problem based on personal history, reducing decision fatigue. It integrates with GitHub for automatic tracking and uses a two-agent architecture to ensure reliable output formatting.

Рабочий процесс

• Developer commits a solution to the repository via terminal.
• GitHub webhook triggers a FastAPI backend.
• Backend logs the solved problem to Firestore (persistent storage).
• A two-agent ADK pipeline is initiated.
• Agent 1 reads the solve history from Firestore and determines the next recommended problem.
• Agent 2 formats the recommendation into clean output (separated to avoid schema/tool conflicts).
• The recommendation, including a dynamically generated LeetCode link, is displayed on the UI.
• The Notion tracker is automatically updated to mark the problem as solved.

Доказательства

When I commit on the terminal, it fires a GitHub webhook that hits a FastAPI backend. The backend logs the solved problem to Firestore...

Источник

Splitting it into two agents one focused purely on reasoning over history, the other purely on formatting fixed that.

Источник