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AI Agent-Native Data Platform — ingest, validate, transform, and query data.
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AI Agent-Native Data Platform — ingest, validate, transform, and query data.
Security Report
3.0
Use Caution3.0High RiskValid MCP server (3 strong, 1 medium validity signals). 7 known CVEs in dependencies (0 critical, 7 high severity) Package registry verified. Imported from the Official MCP Registry.
13 files analyzed · 8 issues found
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What You'll Need
Set these up before or after installing:
URL of the Datris pipeline server
Environment variable: PIPELINE_URL
API key for the Datris pipeline server (if key validation is enabled)Required
Environment variable: PIPELINE_API_KEY
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-datris-datris": {
"env": {
"PIPELINE_URL": "your-pipeline-url-here",
"PIPELINE_API_KEY": "your-pipeline-api-key-here"
},
"args": [
"datris-mcp-server"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
Datris — The First AI Agent-Native Data Platform
datris.ai · Try Hosted Free · Documentation · MCP Registry · PyPI
Ingest, validate, transform, store, and retrieve your data — whether you're an AI agent talking through MCP or a developer writing config. One platform for both.
Why Datris?
- Agent-native — Built-in MCP server with 35+ tools. Claude, Cursor, OpenClaw, and any MCP-compatible agent can operate pipelines through natural conversation
- Taps — AI-generated Python scripts that fetch data from external sources (APIs, web scraping, databases) and push it into pipelines. Describe what you want, Datris generates the script. Includes AI diagnosis, CRON scheduling, and credentials via Vault
- AI at every stage — AI data quality, AI transformations, AI schema generation, AI profiling, AI error explanation, natural language queries, RAG
- No vendor lock-in — 100% open-source infrastructure (MinIO, PostgreSQL, MongoDB, Kafka, Vault). Runs anywhere Docker does
- Configuration-driven — Define pipelines through JSON. No code required
Quick Start
git clone https://github.com/datris/datris-platform-oss.git
cd datris-platform-oss
cp .env.example .env # Add your ANTHROPIC_API_KEY and/or OPENAI_API_KEY
docker compose up -d
UI: http://localhost:4200 · API: http://localhost:8080
Connect an AI Agent
Add to your MCP client config (Claude Desktop, Cursor, etc.):
{
"mcpServers": {
"datris": {
"command": "uvx",
"args": ["datris-mcp-server"],
"env": {
"PIPELINE_URL": "http://localhost:8080"
}
}
}
}
CLI
brew tap datris/tap
brew install datris
datris ingest data.csv --dest postgres
datris ingest sales.csv --ai-validate "prices > 0" --ai-transform "convert dates to YYYY/MM/DD"
datris query "SELECT * FROM sales"
datris search "quarterly revenue" --store pgvector
datris tap create "Fetch S&P 500 daily prices from yfinance" --pipeline stocks
datris taps
What It Does
Source (File Upload / MinIO Event / Database Pull / Kafka)
→ Preprocessor (optional REST endpoint)
→ Data Quality (AI rules, header validation, schema validation)
→ Transformation (AI transformation, destination schema)
→ Destinations (in parallel):
PostgreSQL, MongoDB, MinIO (Parquet/ORC), Kafka, ActiveMQ,
REST Endpoint, Qdrant, Weaviate, Milvus, Chroma, pgvector
→ Notifications (ActiveMQ topic)
AI-Powered Features
| Feature | Description |
|---|---|
| MCP Server | 30+ tools for AI agents — pipeline CRUD, upload, query, search, profiling |
| AI Data Quality | Plain English validation rules — AI generates and runs a validation script |
| AI Transformation | Plain English transformations — AI generates and runs a transformation script |
| AI Schema Generation | Upload a file, get a complete pipeline config |
| AI Data Profiling | Upload a file, get statistics + suggested validation rules |
| AI Error Explanation | Job failures explained in plain English |
| Natural Language Query | Ask questions in English, get SQL results |
| RAG Pipeline | Chunk, embed, and search across 5 vector databases |
Supported Formats
CSV, JSON, XML, Excel, PDF, Word, PowerPoint, HTML, email, EPUB, plain text, .zip/.tar/.gz archives
AI Providers
Anthropic Claude (Opus 4.6, Sonnet 4.6, Haiku) · OpenAI (GPT-5, GPT-4.1, o3) · Ollama (local models)
Architecture
| Service | Purpose |
|---|---|
| MinIO | S3-compatible object store for file staging and data output |
| MongoDB | Configuration store, job status tracking, metadata |
| ActiveMQ | File notification queue, pipeline event notifications |
| HashiCorp Vault | Secrets management (database credentials, API keys) |
| Apache Kafka | Optional streaming source and destination |
| Apache Spark | Local Spark for writing Parquet/ORC to MinIO |
Documentation
Full documentation at docs.datris.ai or locally at docs/.
License
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