IntelliFlow: An AI Learning Path Generation System Based on Multi-Agent Workflow

IntelliFlow is an AI learning path generation system based on Multi-Agent Workflow. It generates structured Markdown learning paths through collaboration among three agents—Planner, Researcher, and Writer—combined with Tavily search enhancement. It addresses the pain points of learning planning in the era of information explosion: traditional methods are time-consuming and lack systematicness, while LLM question-answer interactions are difficult to customize.

In the era of information explosion, formulating a reasonable learning path faces problems of being time-consuming and difficult to ensure systematicness; traditional solutions (official documents, blogs, communities) are inefficient; simple LLM question-answer interactions can only give general suggestions and lack customization. IntelliFlow attempts to solve these pain points using multi-agent workflow + Search Augmented Generation.

Core agent division of labor: Planner parses the user's goals, basic level, and timeline, then breaks down learning stages; Researcher extracts topics and calls Tavily to search for resources; Writer integrates results to generate Markdown paths. The tech stack includes Python3.10+, FastAPI, LangGraph, OpenAI SDK, Tavily Search API, etc. Main workflow: User input → Planner → Researcher (Tavily search) → Writer → Output learning path.

Practical application scenarios: 1. Quick start for new technologies (e.g., learning Redis from scratch in two weeks, generating paths with stage division, materials, and practical projects); 2. Dynamic resource supplementation (Chat Assistant identifies search intent and retrieves real-time resources); 3. Continuous iterative optimization (integrating suggestions into the path via the revise-plan interface).

Current limitations: No user login/permission system; no database persistence (results stored locally); no long-term memory/cross-session user profiles; no learning progress tracking; no deduplication and credibility scoring for search resources; no asynchronous task queue (interfaces execute synchronously).

Future directions: Introduce a database to save paths and revision history; add progress tracking and stage review; deduplication and quality scoring of search results; optimize long-time requests with asynchronous task queues; unit test and end-to-end test coverage; Docker deployment support.

Technical insights: 1. Multi-Agent Workflow division allows each agent to focus on specific tasks, improving output quality; 2. Search Augmented Generation solves the problem of LLM knowledge timeliness; 3. Good engineering practices such as clear module division, interface design, and graceful degradation mechanisms.

IntelliFlow is a well-designed multi-agent learning assistant that solves learning planning problems by combining LLMs, search enhancement, and workflow orchestration. Its architecture, practices, and engineering design provide references for similar AI applications. For developers learning LangGraph, multi-agent, or Search Augmented Generation technologies, it is an open-source case worth in-depth study—with clear code and complete documentation, suitable for beginners.