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Datris — The Data Control Plane for AI Agents

datris.ai · Documentation · MCP Registry · PyPI

⭐ If Datris is useful to you, star the repo — it helps other people find it.

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.

See it in 90 seconds — describe the data you need, and the Assistant builds the tap, runs it, and lands the data:

https://github.com/user-attachments/assets/4f03dc12-e9d7-4f93-8ba4-d712ab7d3733

🔊 Sound is off by default — click the speaker icon in the player.

Why Datris?

https://github.com/user-attachments/assets/ba3de886-1413-4d92-92bb-99018cfaff38

🔊 Sound is off by default — click the speaker icon in the player, or watch on YouTube.

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 — 90 tools behind a single MCP server. Claude, Cursor, and any MCP-compatible agent learn one interface instead of 90 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
• Scoped keys, approval gates, audit log — each agent gets its own API key limited to the capabilities you grant; an agent policy decides per action whether an agent runs it, waits for a person to approve, or is refused; an audit log, once you turn it on (USE_AUDIT_LOG=true), records who did what, best effort by default and without loss in strict mode
• Every run recorded — job state and row counts for every run, provenance on its rows with stamping on; 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 once the audit log is on (see strict mode)
• Self-host anywhere — on-prem, any cloud, or your laptop; 100% open-source infrastructure (MinIO, PostgreSQL, MongoDB, Kafka, Vault), Apache-2.0, no managed service

Quick Start

You need Docker and one AI provider key (Anthropic, OpenAI, Grok, Azure OpenAI, or Amazon Bedrock). This pulls pre-built images and runtime files, seeds a .env, and starts the stack into ./datris — no git checkout required. The installer asks for the key:

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

Installing from a coding agent or a CI job with no terminal attached? Export the key first (ANTHROPIC_API_KEY, OPENAI_API_KEY, or another provider's): the installer asks nothing and reads it from the environment. Then check http://localhost:8080/api/v1/version answers before going on. Full steps: Install for Agents.

Minimal install (laptop-friendly). The default stack runs about ten containers and the bundled embedding server downloads a 2.2 GB model on first boot. None of that is required. Three settings in .env cut it to nine small containers, no download, and roughly 3.5 GB of memory (4 GB of Docker memory is enough; the full stack wants 8 GB):

TEI_ENABLED=0               # skip the local embedding server and its 2.2 GB download
EMBEDDING_PROVIDER=openai   # semantic search via OpenAI (needs OPENAI_API_KEY); omit if you have no OpenAI key
POSTGRES_ENABLED=0          # optional: skip bundled Postgres; MongoDB stays as the destination

The installer sets the first two when you choose OpenAI embeddings. The datris server itself runs on a 2 GB heap by default. Details: Installation → Minimal install.

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.

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

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 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.

To teach the coding agents in your own projects (Claude Code, Codex, and similar) to use Datris for data work, install the datris-platform skill from skills/ — see Agent Skill.

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 --collection financial_docs
datris tap create "Fetch S&P 500 daily prices from yfinance" --pipeline stocks
datris taps
datris doctor               # operational self-check; prints the fix for every finding

What It Does

Source (Tap / File Upload / MinIO Event / Database Pull / Kafka)
  → Preprocessor (optional REST endpoint)
  → Field Protection (pseudonymize, mask, redact, encrypt, or drop sensitive fields)
  → Data Quality (AI rules, header validation, schema validation)
  → Transformation (AI transformation, destination schema)
  → Destinations (in parallel):
      PostgreSQL, MongoDB, Snowflake, Databricks, Microsoft SQL Server / Azure SQL,
      MinIO / S3 (Parquet, ORC, Apache Iceberg), Kafka, ActiveMQ,
      REST Endpoint, Qdrant, Weaviate, Milvus, Chroma, pgvector
  → Notifications (ActiveMQ topic)

AI-Powered Features

Feature	Description
MCP Server	90 tools for AI agents — pipeline CRUD, upload, query, search, profiling, taps, catalogs, provenance
Taps	Describe a source in plain English — AI generates a Python script that fetches it on demand or on a schedule, run in an isolated container
Assistant	Conversational tap and pipeline creation, operations, and configuration in the UI
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
Field Protection	Pseudonymize, mask, redact, encrypt, or drop sensitive fields before any AI stage or destination sees them

Supported Formats

CSV, JSON, XML, Excel, PDF, Word (DOCX), PowerPoint, HTML, email, EPUB, plain text

AI Providers

Anthropic Claude (Opus 5.5 default for chat and CodeGen) · OpenAI (GPT-5.6 Sol default for chat and CodeGen) · 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, and Iceberg tables to MinIO / S3
Tap runner	Isolated container that runs AI-generated tap scripts with no platform credentials and no route to internal services
CodeGen runner	Second isolated container, built from the same image, that runs AI-generated data-quality and transformation scripts
MCP server	Bundled MCP endpoint (SSE on port 3000) for AI agents

Documentation

Full documentation at docs.datris.ai or locally at docs/. Going to production? Start with Security Architecture and SECURITY.md.

License

Apache License 2.0. See NOTICE for attribution.