📊 CSV AI Analyzer

🚀 Try it live →

App preview:

A modern, elegant application to analyze your CSV and Excel (.xlsx) 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.

✨ Features

📁 File Upload & Parsing

Drag & Drop or file selection — supports CSV and Excel (.xlsx) files
Automatic detection of delimiters (comma, semicolon, tab) for CSV files
Configurable settings: delimiter, header row, encoding
Type inference for columns (text, number, date)

📋 Data Visualization

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

🤖 AI-Powered Analysis

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

🔒 Privacy

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

🐳 Self-Hostable

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

🚀 Installation

Option 1: Local Development

# 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

Auto-updating the models catalog

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.

Option 2: Docker (Self-Hosting)

# 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!

🎮 Usage

1. Upload a File

Drag and drop your CSV or Excel (.xlsx) file, or click to select a file.

2. Configure Parsing (Optional)

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

Custom delimiter
Header row choice
File encoding

3. Configure your AI Provider

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.)

Using Custom Endpoints (Ollama, LM Studio, vLLM, etc.)

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

Example configurations:

Provider	Base URL	Model Examples
Ollama	`http://localhost:11434/v1`	`llama3.2`, `mistral`, `codellama`
LM Studio	`http://localhost:1234/v1`	Model name from LM Studio
vLLM	`http://localhost:8000/v1`	Your loaded model name
OpenRouter	`https://openrouter.ai/api/v1`	`openai/gpt-4o`, `anthropic/claude-3`

4. Analysis & Charts

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

📦 `csv-charts-ai` — Standalone Library

The core of this project is published as a standalone npm package csv-charts-ai — a full-featured library for AI-powered CSV analysis, chart generation, and interactive visualization. It works with any LLM provider (OpenAI, Anthropic, Google, Mistral, Ollama, or any OpenAI-compatible endpoint).

Use it in React apps, Node.js scripts, APIs, or CLI tools.

Installation

pnpm add csv-charts-ai

Core utilities are bundled. AI providers are pluggable — install only the ones you need (@ai-sdk/openai, @ai-sdk/anthropic, etc.). Optional peer dependencies (for React chart components only): react, recharts.

Quick Start — Full AI Analysis

import { registerProvider, fromSDK, parseCSV, analyzeData } from "csv-charts-ai";
import { createOpenAI } from "@ai-sdk/openai";

// 1. Register your AI provider(s) — once, at app startup
registerProvider("openai", fromSDK(createOpenAI));

// 2. Parse any CSV string (auto-detects delimiter, infers types)
const data = parseCSV(csvString);

// 3. Run full analysis: summary + anomalies + charts in parallel
const result = await analyzeData({
  model: { apiKey: "sk-...", model: "gpt-4o" },
  data,
});

console.log(result.summary.keyInsights);
console.log(`Found ${result.anomalies.length} anomalies`);
console.log(`Generated ${result.charts.length} chart suggestions`);

React Components

Chart components live in a separate entry point (csv-charts-ai/charts) so the core stays React-free:

import { ChartDisplay, defaultDarkTheme } from "csv-charts-ai/charts";

// Display AI-generated charts with interactive toolbar (zoom, sort, trendline, export)
<ChartDisplay data={data} charts={charts} theme={defaultDarkTheme} />

// Unstyled mode — strip all Tailwind classes and style it yourself
<ChartDisplay data={data} charts={charts} unstyled className="my-charts" />

Multi-Provider Support

import { registerProvider, fromSDK } from "csv-charts-ai";
import { createOpenAI } from "@ai-sdk/openai";
import { createAnthropic } from "@ai-sdk/anthropic";

registerProvider("openai", fromSDK(createOpenAI));
registerProvider("anthropic", fromSDK(createAnthropic));

// OpenAI (default)
{ apiKey: "sk-...", model: "gpt-4o" }

// Anthropic
{ apiKey: "sk-ant-...", model: "claude-sonnet-4-20250514", provider: "anthropic" }

// Ollama / vLLM / LM Studio (local, uses openai provider)
{ apiKey: "", model: "llama3", baseURL: "http://localhost:11434/v1" }

// Any Vercel AI SDK LanguageModel instance (no registration needed)
const anthropic = createAnthropic({ apiKey: "sk-ant-..." });
suggestCharts({ model: anthropic("claude-sonnet-4-20250514"), data });

AI Functions Reference

Function	Description
`analyzeData()`	Full pipeline: summary + anomalies + charts in parallel
`suggestCharts()`	Generate 2–4 chart suggestions from data
`suggestCustomChart()`	Generate a single chart from a text prompt
`repairChart()`	Fix a chart config that failed to render
`summarizeData()`	AI-generated data summary with key insights
`detectAnomalies()`	Find outliers, missing values, type mismatches
`askAboutData()`	Ask natural-language questions about data
`streamAskAboutData()`	Streaming version with `onChunk` callback
`suggestQuestions()`	Suggest interesting questions to ask about the data

React Components Reference (`csv-charts-ai/charts`)

Component	Description
`ChartDisplay`	Multi-chart container with optional card wrapper and theme
`SingleChart`	Individual chart with toolbar (sort, zoom, trendline, CSV/PNG export)
`ChartToolbar`	Standalone toolbar component
`ChartThemeProvider`	React context for chart theming
`ChartIconProvider`	React context for pluggable icons (default: built-in SVGs)
`defaultDarkTheme` / `defaultLightTheme`	Built-in themes

Utilities Reference

Utility	Description
`parseCSV(csv, options?)`	Parse CSV string into `TabularData` with auto-delimiter detection
`parseXLSX(file, options?)`	Parse XLSX file into `TabularData` (browser)
`computeDiff(dataA, dataB, options)`	Compare two datasets (index, key, or content matching)
`generateDataSummary(data)`	Detailed human-readable data summary
`registerProvider()` / `fromSDK()`	Register AI providers (pluggable)
`createModel()` / `createAppModel()`	Create a `LanguageModel` from config
`processChartData()`	Process and aggregate chart data
`COLORS`	Default 8-color palette

Chart types: bar, line, area, scatter, pie. Aggregations: sum, avg, count, min, max, none. Multi-series supported via groupBy.

See the full documentation with all options and examples in packages/csv-charts-ai/README.md.

🛠️ Tech Stack

Technology	Usage
Next.js	React Framework with App Router
TailwindCSS	Styling and design system
PapaParse	Client-side CSV parsing
read-excel-file	Lightweight XLSX parsing (~35 KB)
Recharts	React charting library
react-markdown	Markdown rendering for AI responses
rehype-highlight	Syntax highlighting in code blocks
Lucide React	Modern icons
js-cookie	Secure local persistence
tsup	Package bundling for `csv-charts-ai`
semantic-release	Automated npm publishing via CI

📁 Project Structure

csv-ai-analyzer/
├── src/                          # Next.js application
│   ├── app/_components/          # React components
│   ├── lib/                      # Services, parsers, stores
│   └── styles/                   # Global CSS
├── packages/
│   └── csv-charts-ai/            # csv-charts-ai 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

📝 License

MIT - Use as you wish!

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📊 CSV AI Analyzer

✨ Features

📁 File Upload & Parsing

📋 Data Visualization

🤖 AI-Powered Analysis

🔒 Privacy

🐳 Self-Hostable

🚀 Installation

Option 1: Local Development

Auto-updating the models catalog

Option 2: Docker (Self-Hosting)

🎮 Usage

1. Upload a File

2. Configure Parsing (Optional)

3. Configure your AI Provider

Using Custom Endpoints (Ollama, LM Studio, vLLM, etc.)

4. Analysis & Charts

📦 `csv-charts-ai` — Standalone Library

Installation

Quick Start — Full AI Analysis

React Components

Multi-Provider Support

AI Functions Reference

React Components Reference (`csv-charts-ai/charts`)

Utilities Reference

🛠️ Tech Stack

📁 Project Structure

📝 License

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

📊 CSV AI Analyzer

✨ Features

📁 File Upload & Parsing

📋 Data Visualization

🤖 AI-Powered Analysis

🔒 Privacy

🐳 Self-Hostable

🚀 Installation

Option 1: Local Development

Auto-updating the models catalog

Option 2: Docker (Self-Hosting)

🎮 Usage

1. Upload a File

2. Configure Parsing (Optional)

3. Configure your AI Provider

Using Custom Endpoints (Ollama, LM Studio, vLLM, etc.)

4. Analysis & Charts

📦 csv-charts-ai — Standalone Library

Installation

Quick Start — Full AI Analysis

React Components

Multi-Provider Support

AI Functions Reference

React Components Reference (csv-charts-ai/charts)

Utilities Reference

🛠️ Tech Stack

📁 Project Structure

📝 License

📦 `csv-charts-ai` — Standalone Library

React Components Reference (`csv-charts-ai/charts`)