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A local, privacy-first résumé builder using LLMs and Markdown to generate ATS-ready DOCX files with Pandoc — no cloud, no tracking.
| Date | Stars |
|---|---|
| 2026-07-31 | 389 |
| 2026-08-05 | 391 |
| 2026-08-06 | 391 |
Today
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Momentum
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growth rate 0.00%/day
# Resume AI
Resume AI is a local-first resume workspace that helps you:
- track job applications in a Kanban board
- generate and edit resumes for specific roles
- save multiple resume versions to an application card
- export the latest version as PDF or DOCX
The stack runs entirely in Docker Compose and supports three AI provider modes: local Ollama, Ollama Cloud, or any OpenAI-compatible endpoint.
## Quick Start
### Requirements
- Docker and Docker Compose
- An AI provider (see [AI Provider Setup](#ai-provider-setup) below)
### 1. Configure your environment
Copy `.env.example` to `.env` and fill in your provider details:
```bash
cp .env.example .env
```
Choose one profile in `.env`:
| Profile | When to use |
|---------|------------|
| `ollama_cloud` | Dev-friendly — no local model download, uses Ollama Cloud API |
| `ollama` | Full privacy — runs models locally via `ollama serve` |
| `openai` | Any OpenAI-compatible endpoint |
### 2. Start the stack
```bash
docker compose up -d --build
```
| Service | URL |
|---------|-----|
| Frontend | `http://localhost:8080` |
| Node backend | `http://localhost:5001` |
| Kanban API | `http://localhost:8000` |
| MLflow UI | `http://localhost:5002` |
Check the Kanban API health:
```bash
curl -s http://localhost:8000/health
```
## Typical Workflow
1. Open the frontend at `http://localhost:8080`.
2. Create or select an application card in the Kanban board.
3. Open the **Details → Resume** tab.
4. Paste the job description and optional profile notes.
5. Generate a draft, edit it, save it to the card, then export it.
## Core Features
- Kanban board for job application tracking (CRUD, move cards, inline edit)
- AI-assisted board summaries, application tags, and next-step suggestions
- Resume generation with ATS hidden-content warnings
- Resume version history linked to application cards
- PDF and DOCX export through Pandoc
## AI Provider Setup
### Option A — Ollama Cloud (recommended for development)
No local model download or GPU/RAM budget needed.
1. Create an API key at `https://ollama.com/settings/keys`.
2. In your `.env`:
```env
AI_PROVIDER=ollama_cloud
OLLAMA_API_KEY=your-key-here
MODEL_NAME=gemma3:4b
```
Model tags are listed at `https://ollama.com/library`.
### Option B — Local Ollama (full privacy)
1. Install Ollama and start it:
```bash
ollama serve
ollama pull gemma3:1b
```
2. In your `.env`:
```env
AI_PROVIDER=ollama
MODEL_NAME=gemma3:1b
```
Verify Ollama is reachable from the host:
```bash
curl -s http://localhost:11434/api/tags
```
### Option C — OpenAI-compatible endpoint
```env
AI_PROVIDER=openai
OPENAI_BASE_URL=https://api.openai.com/v1
OPENAI_API_KEY=your-key-here
MODEL_NAME=gpt-4o-mini
```
## Kanban and Resume API
Basic Kanban endpoints:
```bash
curl -s http://localhost:8000/kanban/boards
curl -s http://localhost:8000/kanban/boards/1/columns
curl -s http://localhost:8000/kanban/boards/1/applications
```
Create a resume linked to an application card:
```bash
curl -s -X POST http://localhost:8000/resumes \
-H 'Content-Type: application/json' \
-d '{
"application_id": 1,
"job_description": "...",
"input_profile": "...",
"markdown": "# My Resume..."
}'
```
List saved resumes for a card:
```bash
curl -s http://localhost:8000/resumes/applications/1
```
Export the latest saved resume:
```bash
curl -L -o resume.pdf "http://localhost:8000/resumes/applications/1/export?format=pdf"
curl -L -o resume.docx "http://localhost:8000/resumes/applications/1/export?format=docx"
```
## Environment Variables
All variables are set in `.env` at the project root. Docker Compose reads it automatically.
### `kanban_api/`
| Variable | Default | Description |
|----------|---------|-------------|
| `AI_PROVIDER` | `ollama` | `ollama` \| `ollama_cloud` \| `openai` |
| `MODEL_NAME` | `gemma3:1b` | Model tag for the selected provider |
| `OLLAMA_BASE_URL` | `http://host.docker.internal:11434` | Local Ollama URL (provideExcerpt of 5,466 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2e0c7ab9f3021863, llm:topics: ai-resume-builder, local-llm, resume-builder; description: 'A local, privacy-first résumé builder using LLMs and Markdown to generate ATS-ready DOCX files with Pandoc — no cloud, no tracking.'
matched fp:2e0c7ab9f3021863, llm:topics: ai-resume-builder, local-llm, resume-builder; description: 'A local, privacy-first résumé builder using LLMs and Markdown to generate ATS-ready DOCX files with Pandoc — no cloud, no tracking.'
matched fp:2e0c7ab9f3021863, llm:topics: ai-resume-builder, local-llm, resume-builder; description: 'A local, privacy-first résumé builder using LLMs and Markdown to generate ATS-ready DOCX files with Pandoc — no cloud, no tracking.'