What is Planning Agent

Planning-Agent is an intelligent learning assistant system that provides personalized learning planning based on user profiles, utilizing FastAPI for its backend.

Use cases

Use cases include creating tailored study plans for students, generating practice questions for exam preparation, and providing learning path recommendations based on user profiles.

How to use

To use Planning-Agent, clone the repository, install the required Python packages, configure your LLM API key in the .env file, and run the backend and frontend locally.

Key features

Key features include collecting user information, receiving learning prompts, and generating personalized study paths, which encompass learning goals, detailed learning plans, and custom test questions.

Where to use

Planning-Agent can be used in educational settings, tutoring services, and personalized learning applications to enhance user learning experiences.

Planning Agent

简体中文

Project Overview

Planning-Agent is an intelligent learning assistant system built with FastAPI. It provides personalized learning planning based on user profiles. Core features include:

• Collecting basic user information (user_portrait) (not implemented yet)
• Receiving user learning prompts (prompt)
• Generating personalized study paths, including:
  • Learning goals (learning_goal)
  • Detailed learning plans (learning_plan)
  • Custom test questions (exam_questions)

Agent Graph

---
config:
  flowchart:
    curve: linear
---
graph TD;
        __start__([<p>__start__</p>]):::first
        input(input)
        planner(planner)
        evaluator(evaluator)
        examiner(examiner)
        __end__([<p>__end__</p>]):::last
        __start__ --> input;
        evaluator --> planner;
        input --> planner;
        planner -.-> evaluator;
        planner -.-> examiner;
        examiner --> __end__;
        classDef default fill:#f2f0ff,line-height:1.2
        classDef first fill-opacity:0
        classDef last fill:#bfb6fc

Environment Setup

Backend

To clone the entire repository including submodules, run:

git clone --recursive https://github.com/Blattvorhang/Planning-Agent.git

If you’ve already cloned the repo without --recursive, initialize the submodules manually:

git submodule update --init --recursive

Install the required Python packages:

pip install -r requirements.txt
uv pip install -e "./graph/tools/arxiv-mcp-server/[test]"

Then, configure your LLM API key in the .env file. Use .env.example as a reference.

Frontend

Install dependencies by running:

cd frontend
npm install

Running the Project

Currently, the project supports local deployment only. Backend runs at localhost:8000, and the frontend is available at localhost:3000.

Start the Backend

You can launch the FastAPI backend using either of the following commands:

uvicorn main:app --reload
# or
python main.py

Start the Frontend

Run:

cd frontend && npm run dev

API Reference

Learning Plan Endpoint

• Endpoint: POST /api/learn

• Request Body:

  {
    "prompt": "Description of the learning goal"
  }
  

• Response:

  {
    "learning_goal": "Generated learning goal",
    "answer": "Learning plan",
    "exam_questions": "Test questions"
  }
  

Project Structure

Planning-Agent/
├── LICENSE
├── README.md
├── README_zh.md
├── app
│   ├── README.md
│   ├── __init__.py
│   ├── api.py         # FastAPI route definitions
│   ├── deps.py        # Dependency injection
│   ├── models.py      # Data models
│   ├── question.py    # Data structure for questions
│   └── service.py     # Core service logic
├── assets
│   └── demo.mp4
├── chatbot
├── frontend
├── graph
│   ├── agents
│   ├── tools          # Tool integrations
│   └── workflow
├── main.py            # Entry point for the FastAPI app
└── requirements.txt   # Dependency requirements

Extended Features

This project integrates with the arxiv-mcp-server tool, enabling:

• Academic paper search
• Paper content downloads
• Research material analysis

For detailed usage, refer to tools/arxiv-mcp-server/README.md.