[PR #633] [CLOSED] Add Plot Ark AI Curriculum Engine — multi-agent pipeline with Tavily + LightRAG + GPT-4o-mini #1971

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GiteaMirror · 2026-04-18T19:36:15-05:00

GiteaMirror commented

2026-04-18 19:36:15 -05:00

📋 Pull Request Information

Original PR: https://github.com/Shubhamsaboo/awesome-llm-apps/pull/633
Author: @Schlaflied
Created: 3/23/2026
Status: ❌ Closed

Base: main ← Head: add-plot-ark-curriculum-engine

📝 Commits (2)

3b209a6 Add Plot Ark agentic curriculum engine
4217c17 Add Plot Ark AI Curriculum Engine — multi-agent pipeline with Tavily + LightRAG + GPT-4o-mini

📊 Changes

4 files changed (+696 additions, -264 deletions)

View changed files

📝 README.md (+265 -264)
➕ advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/README.md (+87 -0)
➕ advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/ai_curriculum_engine.py (+339 -0)
➕ advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/requirements.txt (+5 -0)

📄 Description

What this adds

A self-contained tutorial for an agentic curriculum generation pipeline built on top of Plot Ark, an open-source EdTech project.

Folder: advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/

How it works

Three agents run in sequence:

Research Agent (Tavily) — searches academic papers, video lectures, and expert articles for the course topic before generating anything, so the curriculum is grounded in real sources
Knowledge Graph Agent (LightRAG) — ingests retrieved sources and builds a per-course entity-relationship graph; hybrid retrieval extracts key concept relationships that inform module design
Curriculum Agent (GPT-4o-mini) — generates structured modules with Bloom's Taxonomy-aligned learning objectives (level-aware: Beginner → L1-2, Intermediate → L3-4, Advanced → L5-6), resources, and assessments

Files

File	Description
`ai_curriculum_engine.py`	Main Streamlit app — all three agents + UI
`requirements.txt`	`streamlit`, `openai`, `tavily-python`, `lightrag-hku`
`README.md`	Setup, architecture diagram, example output

Run it

pip install -r requirements.txt
streamlit run ai_curriculum_engine.py

API keys: OpenAI + Tavily (enter in sidebar or set as env vars).

What makes this different

Most LLM apps generate course content from a prompt alone. This pipeline researches first, then generates — Tavily finds real academic sources, LightRAG extracts concept relationships, and the LLM uses both as grounding context. The result is curricula with real citations instead of hallucinated references.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/Shubhamsaboo/awesome-llm-apps/pull/633 **Author:** [@Schlaflied](https://github.com/Schlaflied) **Created:** 3/23/2026 **Status:** ❌ Closed **Base:** `main` ← **Head:** `add-plot-ark-curriculum-engine` --- ### 📝 Commits (2) - [`3b209a6`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/3b209a669417212e2baa890731615413e8488aa5) Add Plot Ark agentic curriculum engine - [`4217c17`](https://github.com/Shubhamsaboo/awesome-llm-apps/commit/4217c17c131e13eefe66d78bc1c12602fefaace9) Add Plot Ark AI Curriculum Engine — multi-agent pipeline with Tavily + LightRAG + GPT-4o-mini ### 📊 Changes **4 files changed** (+696 additions, -264 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+265 -264) ➕ `advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/README.md` (+87 -0) ➕ `advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/ai_curriculum_engine.py` (+339 -0) ➕ `advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/requirements.txt` (+5 -0) </details> ### 📄 Description ## What this adds A self-contained tutorial for an agentic curriculum generation pipeline built on top of **[Plot Ark](https://github.com/Schlaflied/Plot-Ark)**, an open-source EdTech project. **Folder:** `advanced_ai_agents/multi_agent_apps/ai_curriculum_engine/` ## How it works Three agents run in sequence: 1. **Research Agent (Tavily)** — searches academic papers, video lectures, and expert articles for the course topic before generating anything, so the curriculum is grounded in real sources 2. **Knowledge Graph Agent (LightRAG)** — ingests retrieved sources and builds a per-course entity-relationship graph; hybrid retrieval extracts key concept relationships that inform module design 3. **Curriculum Agent (GPT-4o-mini)** — generates structured modules with Bloom's Taxonomy-aligned learning objectives (level-aware: Beginner → L1-2, Intermediate → L3-4, Advanced → L5-6), resources, and assessments ## Files | File | Description | |------|-------------| | `ai_curriculum_engine.py` | Main Streamlit app — all three agents + UI | | `requirements.txt` | `streamlit`, `openai`, `tavily-python`, `lightrag-hku` | | `README.md` | Setup, architecture diagram, example output | ## Run it ```bash pip install -r requirements.txt streamlit run ai_curriculum_engine.py ``` API keys: OpenAI + Tavily (enter in sidebar or set as env vars). ## What makes this different Most LLM apps generate course content from a prompt alone. This pipeline **researches first, then generates** — Tavily finds real academic sources, LightRAG extracts concept relationships, and the LLM uses both as grounding context. The result is curricula with real citations instead of hallucinated references. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2026-04-18 19:36:15 -05:00

GiteaMirror closed this issue

2026-04-18 19:36:16 -05:00

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Reference: github-starred/awesome-llm-apps#1971