Platform

One governed loop, every step recorded.

Datris is the open-source data control plane for AI agents. An agent asks for data; the platform acquires it, validates it, lands it in the stores you already run, and returns it with a receipt.

Have an engineer try it Commercial licensing

Introduction

More walkthroughs →

The operating loop

One agent-driven loop

Acquire, validate, land, observe, explain, repair. Same loop every run, same audit trail every time — so the agent's job is reasoning about the data, not improvising infrastructure.

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Acquire

AI-Generated Taps

Describe a source in plain English — an API, a vendor drop, a folder of PDFs. Datris generates a Python tap, wires up scheduling, and brokers credentials through Vault. Schema is auto-generated from a sample.

✓

Validate

Plain-English Rules

Data quality rules described in natural language and executed on every row. Bad records quarantine at the door — not after they pollute your warehouse.

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Land

Multi-Destination Pipelines

One pipeline lands the same records in Postgres, MongoDB, Snowflake, Databricks, S3, and vector stores in parallel. Idempotent by key — reruns don't double-write.

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Observe

Provenance & Job State

Every run gets a durable record: which script, which commit, which inputs, which rows landed. Agents poll job status through MCP; humans see it in the UI. The transcript is not the system of record.

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Explain & Repair

AI Error Explanation

When something fails, Datris analyzes the error chain and explains the root cause in plain English. The agent reads that, adjusts, and retries — instead of pasting stack traces into another chat.

Where Datris fits

Agents don't need a new data platform. They need a way into yours.

Datris sits beside the warehouse and the lake, never in front of them. It is the intake valve: the agent asks, Datris acquires and validates, and the rows land in the stores your teams already query. Nothing moves out of your stack and nothing new becomes the system of record.

Agents Don't Need a New Data Platform →
Load the Lake. Don't Live in It. →

The write path

SnowflakeAnalytics warehouse
DatabricksLakehouse
PostgreSQLOperational store
MongoDBDocuments
S3 / MinIOObject storage, Parquet
pgvector · Qdrant · Weaviate · Milvus · ChromaVector search

One pipeline lands the same validated records in several of these in parallel, idempotent by key. Keep the intake layer neutral and every future architecture decision stays yours.

Governance

Enforced by the platform, not by the prompt

The controls a risk committee asks about, in the open-source build, on by default. This is the part of Datris a head of data repeats to an auditor.

Agent Policy

Per-action allow, approve, or refuse, enforced by the platform rather than by a prompt. An agent that is not allowed to delete a pipeline cannot, no matter what it was told. A schedule can only point at a script that has passed a test run, whoever edited it.

The AI approval you already have →

Audit log

Every create, change, run, delete, login, and denied request, attributed to a login or API key. Export to CSV or mirror to your SIEM.

Stop giving agents master keys →

Provenance

Every landed row cites the pipeline, the run, the script commit, and the source. Agent-written scripts are versioned in git, so what ran is what you can diff.

If an agent wrote it, git should remember it →

Lineage

Where each dataset came from and where it went, across every destination a pipeline writes to.

Validate once, deliver everywhere →

Commercial licensing Security overview

Live Read

Not every answer needs a table

Most of what an agent asks for is a one-off: what a source returns for these ids, whether today's file is clean, what a transformation would produce. Live Read runs the full governed pipeline and hands the rows back to the caller instead of landing them. If the rows turn out to be worth keeping, switch the destination. The tap, its schedule, and its sync bookmark do not move.

Land it

Data you will want again

Rows land beside your lake, in the stores your teams already query. Observed, catalogued, refreshed on a schedule, and every row cites its source.

Read it live

An answer you need once

The same run, but no loader. Validated rows go straight back to the agent, are held briefly, and expire. Nothing is written, nothing new becomes a system of record.

What stays the same in both lanes

The tap, generated or hand-written, with credentials brokered through Vault
Data quality rules and the transformation chain
Agent Policy, the audit log, and the run record
The agent never holds a key, in either lane

The built-in Assistant picks the lane for you: a one-off question goes to Live Read, not a new table. Live Read in the docs →

Model Context Protocol

Your AI agents are
first-class pipeline operators

Datris ships with a native MCP server. Claude, Cursor, OpenClaw, and any MCP-compatible AI agent can register pipelines, trigger jobs, and query your structured, document, and vector data in real time — all through natural conversation.

Transports: stdioSSE (Server-Sent Events)Streamable HTTP

Compatible agents:

MCP Capabilities

Register pipelines and generate schemas from sample data
Create, schedule, and run AI-generated taps
Ingest documents into vector databases (extract → chunk → embed)
Upload data for processing
Trigger and monitor pipeline jobs
Profile data and get AI insights
Semantic search across vector databases
Query PostgreSQL, MongoDB, Snowflake, and Databricks directly
Get an answer without landing a table — Live Read pipelines return validated rows straight to the agent
Run the built-in doctor to diagnose the platform before retrying a failed run
Manage credentials via Vault — without ever holding the key

Example prompt

"Generate a tap for our prime broker margin API, schedule it daily, validate that 'all account_ids must be present and balances must be non-negative', and load into Postgres."

Formats

Speaks every data language

Ingest structured data, unstructured documents, and archives. Output to vector stores, structured stores, or optimized columnar formats.

Format	Input	Default Destination
CSV		SQL DB
JSON / NDJSON		NoSQL DB
XML		NoSQL DB
Excel (.xlsx)		SQL DB
PDF		Vector DB
Word (.docx)		Vector DB
PowerPoint (.pptx)		Vector DB
HTML		Vector DB
Email (.eml)		Vector DB
EPUB		Vector DB
Archives (.zip, .tar, .gz)		Unpacked, routed
Plain Text		Vector DB

Destinations are fully configurable. Route any format to any target — SQL databases, NoSQL stores, vector databases, REST endpoints, Kafka topics, or ActiveMQ queues. Object-store destinations write Parquet, ORC, or Apache Iceberg tables with atomic commits, upserts by key, partitioning, and schema evolution. Or skip landing entirely: a Live Read pipeline runs the same validation and transformation chain and hands the rows straight back to the caller.

RAG Pipeline

Full RAG pipeline built in

Extract, chunk, embed, and upsert documents into any major vector database. Build retrieval-augmented generation workflows without leaving your pipeline.

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5 Vector Databases

Qdrant Weaviate Milvus Chroma pgvector

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Chunking Strategies

Choose the right chunking strategy for your use case:

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Embedding Providers

Generate embeddings with cloud or local models:

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Document Extraction

Extract text from virtually any document format:

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Document Ledger

Content-hashed ledger of every document seen. Re-runs skip unchanged files automatically — no re-embedding the same folder every night, no surprise OpenAI bill.

RAG Pipeline Flow

1 Document Extraction

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2 Chunking

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3 Embeddings

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4 Vector Upsert