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App preview:

A modern, elegant application to analyze your CSV files with Artificial Intelligence using multiple AI providers (OpenAI, Anthropic, Google, Mistral, and more). Privacy-first: the app does not store your data. Use a self-hosted/custom endpoint to keep processing entirely local; otherwise API calls go to the selected provider. Self-hostable with Docker.

• Drag & Drop or file selection
• Automatic detection of delimiters (comma, semicolon, tab)
• Configurable settings: delimiter, header row, encoding
• Type inference for columns (text, number, date)

• Interactive table with sorting and pagination
• Data preview with automatic formatting
• Smooth navigation for large datasets

• Multiple AI providers: OpenAI, Anthropic Claude, Google Gemini, Mistral, and more
• Custom endpoint support: Connect to Ollama, LM Studio, vLLM, or any OpenAI-compatible API
• Intelligent analysis of your data with AI-generated insights
• Markdown rendering: AI responses rendered with full Markdown support (headings, lists, tables, syntax-highlighted code blocks)
• Streaming with Markdown: Real-time streaming responses are rendered progressively as formatted Markdown
• Smart chart suggestions tailored to your dataset
• Chart types: Bar, Line, Pie, Scatter, Area

• Local or direct-to-provider: the app does not store your data. Processing can happen in your browser when using a self-hosted/custom endpoint; otherwise API calls go directly to the chosen AI provider.
• API keys stored locally in your browser
• No tracking or third-party cookies

• Docker ready: Deploy in seconds with a single command
• Custom AI endpoints: Use your own LLM server (Ollama, LM Studio, vLLM, etc.)
• Full control: Host on your own infrastructure

# Clone the repo
git clone https://github.com/maxgfr/csv-ai-analyzer.git
cd csv-ai-analyzer

# Install dependencies
pnpm install

# Run in development
pnpm dev

The application will be accessible at http://localhost:3000

This project ships a static public/models.json that contains the models catalog fetched from https://models.dev/api.json.

• You can regenerate the file locally with:

pnpm run fetch-models

• A GitHub Action is configured to run this script once per day and automatically commit public/models.json if it changes.

  • Workflow: .github/workflows/update-models.yml
  • Runs daily (UTC 06:00) and is also triggerable manually with workflow_dispatch.

If you'd like the file to be refreshed more/less often you can update the cron schedule in the workflow file.

# Build the Docker image
docker build -t csv-ai-analyzer .

# Run the container
docker run -p 3000:3000 csv-ai-analyzer

Or use Docker Compose:

# docker-compose.yml
version: '3.8'
services:
  csv-ai-analyzer:
    build: .
    ports:
      - "3000:3000"
    restart: unless-stopped

docker compose up -d

The application will be accessible at http://localhost:3000

Or use the live version directly!

Drag and drop your CSV file or click to select a file.

If automatic detection doesn't work perfectly, adjust the settings:

• Custom delimiter
• Header row choice
• File encoding

Click the ⚙️ icon to configure your AI provider:

• Choose a provider: OpenAI, Anthropic, Google, Mistral, or others
• Enter your API key: Each provider has its own API key format
• Custom Endpoint: Enable "Use Custom Endpoint" to connect to local/self-hosted OpenAI-compatible APIs (Ollama, LM Studio, vLLM, etc.)

1. Toggle "Use Custom Endpoint" in settings
2. Enter your API Base URL (e.g., http://localhost:11434/v1 for Ollama)
3. Enter your model name (e.g., llama3.2, mistral, codellama)
4. API key is optional for most local servers

Example configurations:

Click "Run Complete Analysis" and the AI will analyze your data, detect anomalies, and suggest relevant visualizations.

The chart components are extracted into a standalone npm package @maxgfr/csv-charts, available for use in any React project.

pnpm add @maxgfr/csv-charts

Peer dependencies: react, recharts, lucide-react.

import { ChartDisplay } from "@maxgfr/csv-charts";

const data = {
  headers: ["Category", "Sales"],
  rows: [["Electronics", "1200"], ["Clothing", "800"]],
  columns: [
    { name: "Category", type: "string", index: 0 },
    { name: "Sales", type: "number", index: 1 },
  ],
  rowCount: 2,
};

const charts = [{
  id: "1",
  type: "bar",
  title: "Sales by Category",
  description: "Compare sales across categories",
  xAxis: "Category",
  yAxis: "Sales",
  aggregation: "sum",
}];

<ChartDisplay data={data} charts={charts} />

Chart types: bar, line, area, scatter, pie. Aggregations: sum, avg, count, min, max, none.

See the full documentation in packages/csv-charts/README.md.

csv-ai-analyzer/
├── src/                          # Next.js application
│   ├── app/_components/          # React components
│   ├── lib/                      # Services, parsers, stores
│   └── styles/                   # Global CSS
├── packages/
│   └── csv-charts/               # @maxgfr/csv-charts npm package
│       ├── src/                   # Package source (ChartDisplay, SingleChart, etc.)
│       ├── dist/                  # Built output (ESM + .d.ts)
│       └── package.json
├── pnpm-workspace.yaml           # Monorepo workspace config
└── package.json                  # Root app

MIT - Use as you wish!