Datris — The Data Control Plane for AI Agents

Name: Datris
Author: datris

datris.ai · Documentation · MCP Registry · PyPI

Agents ask Datris for data. Datris finds it, acquires it, validates it, lands it in the stores you already run, and returns it with provenance — over MCP, without ever holding your keys. It sits beside your warehouse and lake; it doesn't replace them.

Why Datris?

Your agents already acquire, validate, and load data. Without a control plane, they do it badly. Datris puts that work behind one governed surface:

One MCP door — 63 capabilities behind a single MCP server. Claude, Cursor, and any MCP-compatible agent learn one interface instead of 63 integrations
Vault-brokered credentials — the agent references a secret by name and never holds a key; agent-written code runs in an isolated container with no keys inside
Every run recorded — job state, row counts, and provenance for every run; every generated script versioned in git
Durable state — pipelines and sync bookmarks live in the platform, not the chat, so regenerating a script never loses its place
The operating loop — Acquire (AI-generated taps) → Validate (plain-English rules) → Land (multi-destination pipelines) → Observe (provenance and job state) → Explain & Repair (AI error explanation), with the same audit trail every time
Self-host anywhere — on-prem, any cloud, or your laptop; 100% open-source infrastructure (MinIO, PostgreSQL, MongoDB, Kafka, Vault), AGPL-3.0, no managed service

Quick Start

You only need Docker. This pulls pre-built images and runtime files, seeds a .env, and starts the stack into ./datris — no git checkout required:

curl -fsSL https://get.datris.ai/install.sh | sh

The install.sh installer is a POSIX shell script (macOS/Linux). On Windows, run it from WSL2 or Git Bash, or use the single-file Compose option below, which works natively in PowerShell.

Single file, no installer (works on Windows)

A fully self-contained Compose file — the init scripts and config are inlined, so nothing else is needed (requires Docker Compose ≥ 2.23):

# macOS / Linux
curl -O https://get.datris.ai/docker-compose.standalone.yml
ANTHROPIC_API_KEY=sk-ant-... docker compose -f docker-compose.standalone.yml up -d

# Windows (PowerShell) — use curl.exe, and set the key with $env:
curl.exe -O https://get.datris.ai/docker-compose.standalone.yml
$env:ANTHROPIC_API_KEY="sk-ant-..."
docker compose -f docker-compose.standalone.yml up -d

From a git clone

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 (or the AZURE_OPENAI_* trio, XAI_API_KEY, or AI_PROVIDER=bedrock)
docker compose up -d

UI: http://localhost:4200 · API: http://localhost:8080

Connect an AI Agent

Add to your MCP client config (Claude Desktop, Claude Code, Cursor, etc.). With the Docker stack running, the npx mcp-remote stdio bridge connects to the bundled MCP server on port 3000 — your client appears in the Datris UI Agent Monitor tab with live tool-call streaming:

{
  "mcpServers": {
    "datris": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:3000/sse", "--transport", "sse-only"]
    }
  }
}

Paste-and-go for the default local setup — no API key required when USE_API_KEYS=false (the OSS default). If your instance enables auth (USE_API_KEYS=true or hosted/multi-tenant), append "--header", "x-api-key:<your-key>" to the args array. The Configuration → Connect Your Agent page generates the snippet for you and adds the header automatically when you paste your key.

Requires Node.js on your PATH (brew install node). For a stdio alternative without Docker, or full Claude Desktop / Claude Code / Cursor walkthroughs, see Configuring Claude.

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	63 tools for AI agents — pipeline CRUD, upload, query, search, profiling, taps
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 (DOCX), plain text

AI Providers

Anthropic Claude (Opus 4.8 default for chat and CodeGen) · OpenAI (GPT-5.5) · Azure OpenAI (bring your Azure resource; models by deployment name) · Amazon Bedrock (Claude through your AWS account — IAM auth, AWS billing, IAM-role support with zero stored keys) · Grok (xAI's models through their OpenAI-compatible API) · Ollama (local models, optional). Embeddings via OpenAI text-embedding-3-small (recommended when you have an OpenAI key), Azure OpenAI, the bundled TEI sidecar (BAAI/bge-m3 — fully local, no API key), or Ollama.

Architecture

Service	Purpose
MinIO	S3-compatible object store for file staging and data output
PostgreSQL	Default structured destination, also hosts pgvector for RAG
MongoDB	Configuration store, job status tracking, metadata
ActiveMQ	File notification queue, pipeline event notifications
HashiCorp Vault	Secrets management (database credentials, API keys)
TEI	Text Embeddings Inference sidecar (BAAI/bge-m3) — local vector embeddings when you're not using OpenAI embeddings
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