ROADMAPPER.AI

RoadMapper.AI is an intelligent Gen-AI Agentic Platform that creates personalized, interactive learning roadmaps for Technical professionals looking to master technical tools or transition into new roles.
Using a multi-agent AI system, it generates customized learning paths complete with project suggestions and detailed milestones.

Features

• Interactive goal clarification and refinement
• Dynamic tool and platform recommendations
• Customized project suggestions with pros and cons
• Detailed project task breakdowns
• Comprehensive stage-based learning roadmaps
• Multiple visualization options for roadmaps

ROADMAPPER.AI Architecture

Agentic Structure

The system uses three specialized AI agents:

1. Clarifier Agent

• Role:- Career Coach specializing in Professional Goals Clarification.
• Purpose:- Helps users clarify their goals and select appropriate tools & platforms.

2. Project Generator Agent

• Role:- Creative Strategist & Innovations Consultant.
• Purpose:- Generating Relevant Project Ideas that when Complete will Demonstrate a a User's Expertise.

3. RoadMapping Agent

• Role:- Learning Journey Architect
• Purpose:- Creation of Detailed, Stage-Based Learning RoadMaps.

Pilot User Journey

• User inputs desired role/tool
• Clarifier Agent generates tool and platform options
• User selects preferred tools and platforms
• Project Generator suggests portfolio projects
• User selects projects
• Roadmapper creates detailed learning stages
• System presents interactive roadmap

Tech Stack

1. Backend:- Python 3.x
2. AI Framework: CrewAI & AgentStack
3. LLM: Gemini 1.5 Flash
4. Agents Tracking & Evals: AgentOps
5. Environment Management: python-dotenv
6. Data Structures: Pydantic for Agent Output data validation
7. Configuration: YAML for agent configurations

Installation

1. Clone the Repository

git clone https://github.com/DMMutua/roadmapper-ai.git
cd roadmapper-ai

2. Create and Activate a Virtual Environment:

python -m venv .venv
source .venv/Scripts/activate

3. Install Dependencies:

pip install -r requirements.txt

4. Set up Environment Variables:

• Create a .env file in the root directory.
• Add API Keys for AgentOps and LLM Providers.

Usage

Basic Usage

To Run the Main Application;

python src/main.py

or

crewai run

Training Mode

Training Crew for Specific Iterations:

python main.py train <n_iterations> <filename>

Replay Mode

For Replaying Specific Task Execution;

python main.py replay <task_id>

Reset Crew Memory

If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature:

crewai reset-memory

Development Approach

• Modular Architecture: Separate agents for different aspects of the roadmap generation process.
• Interactive Design: User input and confirmation at key decision points.
• Structured Data Flow: JSON-based data structures for consistent information transfer.
• Error Handling: Comprehensive error checking and user feedback.
• Configurability: YAML-based agent configurations for easy modification.

License

project is licensed under the MIT License - see the LICENSE file for details.