Datris

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One Control Plane, Every Agent

• One MCP door — 73 capabilities behind a single MCP server (stdio and SSE). Claude, Cursor, OpenClaw, and any MCP-compatible agent learn one interface instead of 73 integrations
• Vault-brokered credentials — the agent references a secret by name; it 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 in the agent’s context, so a regenerated script picks up where the old one left off
• Explain and repair — when a run fails, AI Error Explanation returns the root cause in plain English instead of a stack trace

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The Operating Loop

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AI-Powered Features

• MCP server (AI agent integration) - Built-in MCP server lets AI agents (Claude, Cursor, OpenClaw, custom frameworks) natively interact with the pipeline — register pipelines, upload files, trigger jobs, profile data, run semantic searches, and query databases. Supports stdio and SSE transports
• AI-powered data quality - Validate with plain English rules via aiRule. Examples:
  • “Validate that all email addresses are properly formatted and all phone numbers contain 7–15 digits”
  • “Ensure price is positive, quantity is a whole number, and discount never exceeds price”
  • “Check that start_date is before end_date and both are in YYYY-MM-DD format”
  • “Verify that country codes are valid ISO 3166-1 alpha-2 codes”
  • “Flag any row where revenue minus cost does not equal profit within a 0.01 tolerance”
• AI transformations - Describe transformations in natural language. Examples:
  • “Convert all dates to YYYY-MM-DD format and normalize phone numbers to E.164”
  • “Categorize transactions as ‘small’ under $100, 'medium'$100–$1000, or 'large' over$1000”
  • “Extract city and state from the address column into separate columns”
  • “Standardize company names — remove Inc, LLC, Corp suffixes and trim whitespace”
  • “Convert all currency amounts from EUR to USD using a rate of 1.08”
• Assistant - Conversational tap and pipeline creation. Describe a source (“yfinance daily prices for the S&P 500”) and Datris generates the tap script, builds the pipeline, and runs it — grouped into a Data Catalog for organization. The fastest way to onboard a new external data source
• Taps - AI-generated Python scripts that fetch data from external sources (APIs, web scraping, databases) and push it into pipelines. Describe what data you want in plain English, and Datris generates the script. Includes AI diagnosis when scripts fail, CRON scheduling, and credentials management via Vault secrets
• Datris CLI - Command-line interface for ingesting data, running queries, and managing pipelines. datris ingest data.csv --ai-validate "prices > 0" --ai-transform "convert dates to YYYY/MM/DD"
• AI schema generation - Upload any data and receive a complete, ready-to-register pipeline configuration — field names and types inferred automatically
• AI data profiling - Upload a data and get summary statistics, quality issues, and suggested validation rules — all powered by AI analysis
• AI error explanation - When jobs fail, AI analyzes the error chain and explains the root cause in plain English. No more digging through stack traces
• AI providers - Anthropic Claude (choose any model), OpenAI (chose any model, plus embeddings models), Azure OpenAI (the same OpenAI models served from your Azure resource), Amazon Bedrock (Claude through your AWS account with IAM auth), Grok (xAI’s models through their OpenAI-compatible API), or local models via Ollama (Llama, Mistral, Phi). No vendor lock-in — switch providers without changing your pipeline config

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RAG Pipeline

• 5 vector databases - Qdrant, Weaviate, Milvus, Chroma, pgvector (PostgreSQL)
• Chunking strategies - Fixed-size, sentence, paragraph, recursive
• Embedding providers - OpenAI (the recommended default when you have an OpenAI key), Azure OpenAI (an embedding deployment on your Azure resource), bundled TEI (bge-m3 — runs locally, no API key), or Ollama (local models)
• Document extraction - PDF, Word, PowerPoint, Excel, HTML, email, EPUB, plain text

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Key Features

• Configuration-driven - Define pipelines entirely via MCP, the Datris UI, or directly with JSON. Extend it with AI instructions for data quality and transformations. You even have the option of defining your own preprocessor via a REST endpoint
• Multiple ingestion methods - MCP, data upload API, MinIO bucket events, database polling, Kafka streaming
• Data quality - AI rules (LLM-generated), and/or JSON/XML schema validation
• Transformations - AI transformations (LLM-generated), destination schema (drop/rename/retype columns) for structure data
• Multiple destinations - Write to PostgreSQL, MongoDB, Snowflake, Databricks, Kafka, ActiveMQ, REST endpoints, Qdrant, Weaviate, Milvus, Chroma, pgvector, or MinIO/S3 (Parquet/ORC) in parallel
• Event notifications - Subscribe to pipeline processing events via ActiveMQ topics

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Architecture

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Processing Flow

Source (Data Upload / MinIO Event / Database Pull / Kafka)
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  v
Preprocessor (optional REST endpoint)
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  v
Data Quality (AI rules, header validation, schema validation)
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  v
Transformation (AI transformations)
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  v
Destinations (executed in parallel)
  ├── Object Store (MinIO or AWS S3 - Parquet, ORC)
  ├── PostgreSQL (COPY bulk insert)
  ├── MongoDB (document upsert)
  ├── Snowflake (PUT + COPY INTO / MERGE into the user's account)
  ├── Databricks (Unity Catalog managed Delta tables via SQL warehouse)
  ├── Kafka (topic producer)
  ├── ActiveMQ (queue)
  ├── REST Endpoint (HTTP POST)
  ├── Qdrant (vector database - chunking, embeddings, RAG)
  ├── Weaviate (vector database - chunking, embeddings, RAG)
  ├── Milvus (vector database - chunking, embeddings, RAG)
  ├── Chroma (vector database - chunking, embeddings, RAG)
  └── pgvector (PostgreSQL vector database - chunking, embeddings, RAG)
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  v
Notifications (published to ActiveMQ topic)

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Retrieval Flow

Client (AI Agent via MCP / Developer via REST API)
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  v
Interface
  ├── MCP Server (stdio or SSE)
  └── REST API (POST /api/v1/query/* and /api/v1/search/*)
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  v
Query Source
  ├── PostgreSQL (read-only SQL SELECT queries)
  ├── MongoDB (document queries with filters and projections)
  ├── Snowflake (pipeline-scoped read-only SQL: SELECT, SHOW, DESCRIBE)
  ├── Databricks (pipeline-scoped read-only SQL: SELECT, SHOW, DESCRIBE)
  ├── Qdrant (semantic search)
  ├── Weaviate (semantic search)
  ├── Milvus (semantic search)
  ├── Chroma (semantic search)
  ├── pgvector (semantic search via PostgreSQL)
  ├── Pipeline Configurations (list/get)
  ├── Job Status (by pipeline token or pipeline name)
  ├── AI Schema Generation (from uploaded files)
  └── AI Data Profiling (statistics, quality issues, suggested rules)

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Supported Data Formats

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Quick Links

• Installation - Get running with Docker Compose
• Quick Start - End-to-end walkthrough
• Data Catalog - Organize related taps and pipelines into named groups
• Datris CLI - Command-line interface for ingesting, querying, and managing pipelines
• Pipeline Configuration - Full JSON configuration reference
• Preprocessor - External preprocessing via REST endpoints
• API Reference - REST API documentation
• AI Schema Generation - Generate pipeline configs from files using AI
• AI Configuration - Configure AI providers (Anthropic, OpenAI, Azure OpenAI, Amazon Bedrock, Grok, Ollama)
• AI Data Quality - Natural language validation
• AI Transformation - Natural language transformation
• AI Data Profiling - Profile data files and get recommended rules
• AI Error Explanation - Automatic plain-English error analysis
• Qdrant Destination - Vector database for RAG with chunking, embeddings, and metadata
• Weaviate Destination - Vector database for RAG with chunking, embeddings, and metadata
• Milvus Destination - Scalable vector database for RAG
• Chroma Destination - Lightweight vector database for RAG — single container
• pgvector Destination - PostgreSQL vector database for RAG — no separate server required
• Query API - Query PostgreSQL and MongoDB via REST API
• Search API - Semantic search across vector databases via REST API
• MCP Server - AI agent integration via Model Context Protocol
• Example Applications - Vector store chat, Kafka loader, preprocessor, and more