Repo brief: HKUDS/DeepTutor

Generated by RepoPilot · document generated 2026-09-17 · concise human review Evidence snapshot · analyzed 2026-09-17T00:56:27.128Z · commit 2e0816b090b2

Verdict

Healthy — Strong maintenance signals

• Last commit 1d ago
• 18 active contributors
• Apache-2.0 licensed
• CI configured
• 1 more receipt on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

What it is

DeepTutor is a lifelong personalized tutoring platform that combines AI agents, interactive educational content authoring, and knowledge migration. It enables educators to create adaptive, multi-modal learning experiences (quizzes, videos, interactive modules, chat) through a web interface (TypeScript/JavaScript frontend) backed by Python agents that personalize instruction based on learner progress. Monorepo structure: Python backend (agents, tutoring engine) in root directory; TypeScript/JavaScript frontend in web layer; Dockerfile and Dockerfile.runner indicate containerized microservices; assets/ holds documentation, screenshots, and localized READMEs; documentation files (AGENTS.md,…

Start here

Open these first:

• README.md — Primary entry point documenting DeepTutor's lifelong personalized tutoring vision, features, and architecture overview.
• AGENTS.md — Core agent design patterns and responsibilities that underpin the tutoring system's autonomous reasoning capabilities.
• SKILL.md — Skill definition and management framework essential for personalizing learning paths and tracking mastery.
• KNOWLEDGE_MIGRATION.md — Knowledge base schema and migration patterns critical for maintaining lifelong learning context across versions.
• CONTAINERIZATION.md — Deployment architecture via Docker and container orchestration required for running distributed tutoring agents.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

No package.json, requirements.txt, or setup.py excerpt provided in the data. Before running any setup, verify the repository root for: Python requirements (requirements.txt, setup.py, pyproject.toml) and Node dependenci…

Daily commands:

Cannot be inferred from provided file list. No Makefile, package.json excerpt, or setup command evidence present. Consult README.md and CONTRIBUTING.md in the repository root for verified dev-server startup. Likely: Pyt…

…shortened for this brief.

Key cautions & unknowns

• Concentrated ownership — top contributor handles 59% of recent commits
• Evidence is insufficient to identify hidden traps. Critical unknowns: (1) no dependency manifest visible—package versions, conflicts, and Python/Node version constraints are opaque; (2) no environment variable…
• Published-advisory coverage was unavailable for the captured dependencies.
• Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

• Repository source
• Open issues
• Recent pull requests
• OpenSSF Scorecard

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.

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For the complete agent context, use the CLAUDE.md or Cursor rules export.