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.
- 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)
- 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 devThe 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.jsonif it changes.- Workflow:
.github/workflows/update-models.yml - Runs daily (UTC 06:00) and is also triggerable manually with
workflow_dispatch.
- Workflow:
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-analyzerOr use Docker Compose:
# docker-compose.yml
version: '3.8'
services:
csv-ai-analyzer:
build: .
ports:
- "3000:3000"
restart: unless-stoppeddocker compose up -dThe application will be accessible at http://localhost:3000
Or use the live version directly!
Drag and drop your CSV or Excel (.xlsx) 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.)
- Toggle "Use Custom Endpoint" in settings
- Enter your API Base URL (e.g.,
http://localhost:11434/v1for 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 |
Click "Run Complete Analysis" and the AI will analyze your data, detect anomalies, and suggest relevant visualizations.
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.
pnpm add csv-charts-aiCore 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.
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`);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" />Register only the providers you use, then pass config objects or LanguageModel instances:
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 });| 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 |
| 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 |
| 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.
| 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 |
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
MIT - Use as you wish!