An AI-powered career assistant for resume intelligence, job matching, application tracking, and career insights.
🚀 Live Demo 💻 GitHub Repository
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$ memora boot
Initializing Career Engine...
Loading Resume Intelligence...
Connecting AI Engine...
Checking Career Pipeline...
System Ready.
Welcome back.MemoraOS is a personal AI career operating system designed to organize, analyze, and improve the software engineering job search process.
The project started as a local-first AI memory system focused on Retrieval-Augmented Generation (RAG). It evolved into a practical career assistant that combines:
- Resume intelligence
- Job description analysis
- Resume-to-job similarity matching
- AI-powered career insights
- Application tracking
- Career analytics
The long-term goal is to build a personal AI system that understands a candidate's experience, projects, skills, applications, and professional growth.
The public deployment currently showcases the Resume Analyzer.
Upload:
- Your resume
- A job description
- An optional company name
MemoraOS generates:
- Resume ↔ JD match score
- Strong skill matches
- Missing requirements
- Experience gaps
- Relevant projects
- Resume improvement suggestions
- Interview preparation topics
Demo note: Uploaded documents are processed through the AI inference service configured for the public deployment. Avoid uploading confidential or sensitive documents.
VERSION 1.0 ████████████████████ DEPLOYED ✅
RESUME INTELLIGENCE
✓ Resume Processing
✓ Job Description Processing
✓ PDF / TXT Extraction
✓ Resume ↔ Job Similarity Analysis
✓ Semantic Embeddings
✓ AI-powered Resume Evaluation
✓ Experience Gap Detection
✓ Resume Improvement Suggestions
✓ Interview Preparation
APPLICATION MANAGEMENT
✓ Add Applications
✓ Edit Applications
✓ Delete Applications
✓ Track Application Status
✓ Track Referrals
✓ Store Notes
✓ Track Locations
✓ Manage Follow-ups
AI FOUNDATION
✓ Ollama Local LLM Integration
✓ Hugging Face Inference Support
✓ Embedding-based Retrieval
✓ RAG Architecture Foundation
✓ Modular AI Pipeline
ANALYTICS
✓ Career Dashboard
✓ Application Pipeline Tracking
✓ Career Progress Overview
Resume PDF
|
▼
Document Extraction
|
▼
Smart Chunking
|
▼
Embeddings
|
▼
Semantic Similarity
|
▼
Relevant Context
|
▼
LLM Analysis
|
▼
Structured Results
|
┌────────────┼────────────┐
▼ ▼ ▼
Match Score Experience Resume
Gaps Improvements
│ │ │
└────────────┼────────────┘
▼
Streamlit UI
User
|
▼
Streamlit Interface
|
▼
Application Manager
|
├── Company
├── Role
├── Status
├── Referral
├── Location
├── Notes
└── Follow-up
|
▼
Persistent Storage
Documents
|
▼
RAG Pipeline
|
▼
Vector Database
|
▼
Personal AI Memory
|
▼
Career Intelligence
✓ Upload resumes
✓ Process PDF documents
✓ Process TXT documents
✓ Analyze job descriptions
✓ Generate semantic embeddings
✓ Compare resume against job requirements
✓ Identify strong matches
✓ Identify missing requirements
✓ Identify experience gaps
✓ Recommend resume improvements
✓ Generate interview preparation topics
✓ Track job applications
✓ Store company information
✓ Store role information
✓ Track application status
✓ Track referrals
✓ Store application notes
✓ Track locations
✓ Manage follow-ups
Supported statuses:
Applied
OA Scheduled
OA Completed
Interview
Offer
Rejected
Withdrawn
✓ Application dashboard
✓ Application pipeline
✓ Career progress overview
LANGUAGE
--------
Python
FRONTEND
--------
Streamlit
AI / ML
-------
Hugging Face Inference
Ollama
Semantic Embeddings
Resume Similarity Analysis
Large Language Models
Retrieval-Augmented Generation (RAG)
EMBEDDINGS
----------
BAAI/bge-small-en-v1.5
nomic-embed-text
DOCUMENT PROCESSING
-------------------
PyPDF
Text Extraction
Document Chunking
Document Modeling
DATA / STORAGE
--------------
JSON Persistence
ChromaDB
ENGINEERING
-----------
Modular Python Architecture
Object-Oriented Design
CRUD Architecture
REST/API Integration
Local-first Development
Career information can contain sensitive data:
- Resumes
- Applications
- Personal notes
- Career history
MemoraOS was designed with a local-first architecture so the core system can run locally with locally hosted AI models.
✓ Data ownership
✓ Local experimentation
✓ Offline-capable architecture
✓ Local AI option
✓ Replaceable AI providers
The public demo uses a separate cloud inference configuration so the application can be accessed without requiring users to install local AI models.
During local development, MemoraOS uses Ollama to run AI models locally.
Local Development
MemoraOS
|
▼
Ollama
|
├── Embeddings
└── Local LLM
This allows experimentation with AI models without making the application dependent on a hosted inference API.
The AI layer is designed so that the application can switch between local and hosted inference.
MemoraOS
|
AI Provider
/ \
▼ ▼
Ollama Hugging Face
Local Cloud
This makes the system easier to deploy while preserving the local development workflow.
MemoraOS separates major responsibilities:
Resume
|
▼
Document Processing
|
▼
Chunking
|
▼
Embeddings
|
▼
Retrieval / Similarity
|
▼
AI Analysis
|
▼
Streamlit UI
Application management follows a separate path:
Application
|
▼
Application Manager
|
▼
Storage Layer
|
▼
Analytics
This separation allows new AI capabilities to be added without rewriting the core application.
MemoraOS/
│
├── app/
│ ├── analysis/
│ ├── applications/
│ ├── chatbot/
│ ├── chunking/
│ ├── database/
│ ├── embeddings/
│ ├── indexing/
│ ├── loaders/
│ ├── llm/
│ ├── memory/
│ ├── models/
│ ├── prompts/
│ ├── retrieval/
│ ├── storage/
│ ├── tests/
│ ├── ui/
│ └── app.py
│
├── data/
├── .env.example
├── .gitignore
├── requirements.txt
├── README.md
└── run.py
🏆 UNLOCKED
✓ Resume Intelligence Engine
✓ Semantic Resume Matching
✓ AI-powered Resume Evaluation
✓ Ollama Local AI Integration
✓ Hugging Face Cloud Inference
✓ Career Application Tracker
✓ Dashboard Analytics
✓ CRUD Application Architecture
✓ Modular Python Architecture
✓ Public Streamlit Deployment
────────────────────────────────────
🔒 NEXT UNLOCKS
Resume Optimization
Resume Version Control
AI Job Recommendations
Advanced Interview Coach
Personal Career Memory
Knowledge Graph
v1.0
------
Career Intelligence Foundation
[COMPLETE]
v2.0
------
AI Career Assistant
□ Resume Optimization
□ Resume Version Control
□ AI Job Recommendations
□ Advanced Interview Preparation
□ Career Insights
v3.0
------
Personal AI Memory Operating System
□ Long-term Career Memory
□ Knowledge Graph
□ Project Intelligence
□ Personalized AI Career Companion
COMING SOON
git clone https://github.com/MedlynJacob/MemoraOS.git
cd MemoraOS
python -m venv venv
source venv/bin/activate
# Windows:
# venv\Scripts\activate
pip install -r requirements.txt
python -m streamlit run app/app.pyFor local AI development, configure Ollama and the required local models.
For hosted inference, configure the appropriate environment variables using .env locally or Streamlit Secrets when deploying.
Developer : Medlyn Jacob
Version : v1.0
Current Mode : Career Intelligence
Framework : Streamlit
AI : Ollama / Hugging Face
Embeddings : BGE / Ollama
Storage : JSON / ChromaDB
Status : ONLINE