# GradeOps: An AI Automatic Grading System Based on Multimodal Large Models and LangGraph > GradeOps is an open-source AI grading engine that combines vision-language models with LangGraph agent workflows to enable automatic recognition, scoring, and manual review processes for handwritten exam papers. - 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm) - 发布时间: 2026-06-14T14:14:25.000Z - 最近活动: 2026-06-14T14:21:00.507Z - 热度: 152.9 - 关键词: AI阅卷, LangGraph, 视觉语言模型, 教育科技, FastAPI, 人机协作, OCR, Gemini, Nemotron - 页面链接: https://www.zingnex.cn/en/forum/thread/gradeops-langgraphai - Canonical: https://www.zingnex.cn/forum/thread/gradeops-langgraphai - Markdown 来源: floors_fallback --- ## GradeOps: Guide to the AI Automatic Grading System Based on Multimodal Large Models and LangGraph GradeOps is an open-source AI grading engine that combines vision-language models with LangGraph agent workflows to enable automatic recognition, scoring, and manual review processes for handwritten exam papers. The project uses FastAPI to build the backend, and adopts the "AI initial grading + manual review" human-machine collaboration model to improve efficiency and quality, providing a modular and engineering-practical reference implementation for the edtech field. ## Project Background and Problem Scenarios ## Project Background and Problem Scenarios Traditional exam paper grading faces dual challenges of efficiency and accuracy: difficulty in handwritten recognition, insufficient consistency in subjective question scoring, and slow grading speed for large-scale exams. The maturity of large language models and vision-language models provides possibilities to solve these problems. GradeOps Backend is designed for this scenario, with the core being the "AI initial grading + manual review" human-machine collaboration model, which not only improves efficiency but also ensures quality. It is an edtech engineering example built with FastAPI + LangGraph. ## Core Architecture Design ## Core Architecture Design ### Two-Stage AI Pipeline The first stage uses NVIDIA Nemotron vision-language model (OpenRouter service) to handle OCR recognition of handwritten text and mathematical formulas; the second stage uses Google Gemini to score based on nested JSON grading standards and provide reasoning explanations. The two stages are decoupled to facilitate component upgrades and replacements. ### LangGraph Agent Orchestration Using LangGraph to model the grading lifecycle as a state machine. When manual intervention is needed, it automatically pauses and persists the state to SQLite via SqliteSaver, ensuring the review queue survives across requests. ### Human-Machine Collaboration Mechanism Bridging the AI pipeline and frontend via REST API. Teaching assistants can view AI scores, adjust and confirm them, and the results are written back to the LangGraph state. Teachers hold the final decision-making power. ## Detailed Tech Stack ## Detailed Tech Stack ### Backend Framework - FastAPI: High-performance asynchronous Python web framework that supports automatic OpenAPI documentation generation ### AI and Orchestration - LangGraph: Agent workflow framework for building grading state machines - LangChain: Integrates various model providers ### Model Configuration - Google Gemini (gemini-3.1-flash-lite): Scoring reasoning and grading - NVIDIA Nemotron (nvidia/nemotron-nano-12b-v2-vl:free): Handwritten OCR service provided by OpenRouter ### Data Layer - PostgreSQL: Stores user information, exam paper metadata, etc. - SQLite: LangGraph state checkpoints (checkpoints.db) - SQLAlchemy: ORM layer for unified data access ### Security and Tools - PyJWT: Session token signing - pwdlib[argon2]: Password hashing - PyMuPDF: PDF exam paper parsing ## Key Design Highlights ## Key Design Highlights ### Asynchronous Background Processing Put OCR recognition and scoring calculation into FastAPI background tasks. The upload interface responds quickly, and model inference is executed in the background, ensuring API responsiveness and real-time dashboard updates. ### Role-Based Access Control Distinguish between teacher and teaching assistant permissions. Authentication status is passed via httponly cookies to reduce the risk of XSS attacks. ### Relational Data Optimization Optimize data queries for large-scale exam scenarios, efficiently aggregate batch scoring statistics, and avoid performance bottlenecks as data grows. ## Deployment and Usage Guide ## Deployment and Usage The project provides a complete local development guide: 1. Environment configuration: Manage sensitive information such as database connections and API keys via .env 2. Virtual environment: Supports Windows, macOS, Linux multi-platforms 3. Dependency management: Detailed requirements.txt and supplementary package instructions 4. Database initialization: PostgreSQL creation script 5. API documentation: Automatically generated Swagger UI supports interactive testing The accompanying React frontend (gradeops-frontend repository) forms a complete educational grading solution. ## Summary and Reflections ## Summary and Reflections GradeOps demonstrates the application of cutting-edge AI technology in traditional education scenarios, and its design philosophy is worth learning: 1. Human-machine collaboration rather than replacement: AI handles initial grading and standardized processing, while humans retain final decision-making power 2. Modular architecture: The two-stage model and pluggable LangGraph state machine reflect good decoupling 3. Engineering practicality: Technical choices such as httponly cookies and asynchronous task queues serve actual deployment needs It provides developers exploring AI educational applications with a fully functional and clearly structured reference implementation.