Datris Server

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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

AGPL-3.0