Tencent/WeSmartFlow

Health v2 · maintenance only

Flags: no_releases young no_license

Adoption not part of the score

1020 stars · 7 forks observed · 2026-09-01

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

WeSmartFlow is an agent-native adaptive learning framework that models the full learning process with ReAct-based tutoring agents, graph memory knowledge tracking, and multi-agent courseware generation. It combines knowledge graph mastery modeling, SM-2 spaced repetition, and automated content pipelines (outlines, slides, quizzes, visuals, audio) into an education-focused agent engineering stack.

Use cases

• build an AI tutor that tracks learner mastery over time
• generate complete course packages from a single topic
• maintain a personal knowledge graph with spaced repetition review
• orchestrate multi-agent workflows for educational content creation
• create interactive knowledge cards and visualizations for learning
• run adaptive quizzes with automatic mastery updates

When to choose

• you need an agent framework purpose-built for education and learning-state modeling
• you want long-term learner memory via knowledge graphs rather than stateless chat
• you need automated multi-agent generation of slides, quizzes, and audio lessons

When to avoid

• you just need a simple chatbot or Q&A assistant without learning-state tracking
• you need a production-grade LMS rather than an experimental agent framework
• your project has no education or tutoring component

Facets

framework · maturity active

agent-framework rag llm-inference chatbot data-visualization search-engine tts prompt-engineering artificial-intelligence education large-language-models python cross-platform adaptive-learning knowledge-graph multi-agent react-agent spaced-repetition tutoring graph-memory courseware-generation fastapi vue3 ai-agents natural-language-processing web-server macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Tencent/WeSmartFlow")