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AI agents are only as good as
the context they can reach.

Marmot is the open source context layer for agents and humans. Catalog every service, API, queue, topic, database and pipeline then expose real, governed context to your AI agents and your team.

Get StartedLive Demo

Plugins

Iceberg

Populate

Terraform
Pulumi
Populate
Marmot
MarmotContext layer
Discover

AI Agents

Integrations

Stop hardcoding context

Why you need a context layer

This is how most agents get data today, and why it doesn't last. A context layer replaces the hand wired map with one that stays current.

Hardcoded today
  • Schemas pasted into prompts that quietly go stale
  • A new MCP server to wire up for every source
  • No ownership, meaning or lineage, so the model guesses
  • Every team rebuilds the same context plumbing
With Marmot
  • One governed context layer across every asset
  • One MCP endpoint every agent shares
  • Ownership, definitions and lineage built in
  • Always live, never a stale copy

You don't move your databases to Marmot. You stop hardcoding the context around them. See Marmot for agents

Context for AI and engineers

Catalog every data asset, enrich it with the context that matters and make it accessible to your team and your AI tools.

Discover

One place for agents and humans to find every service, API, queue, topic and database.

Understand

Trace how data flows and what depends on what with lineage.

Contextualize

Ownership, business definitions and custom fields that give AI the full picture.

Share

Expose certified context through MCP, the API and the UI.

Built-in MCP server

Ask your AI assistant about your business and get answers backed by your actual catalog.

Works with any MCP-compatible client.

Set up MCP

What tables do we have related to customer orders?

discover_data

I found 3 assets matching "customer orders": the orders table in the warehouse, an orders_raw Kafka topic, and a daily_orders_summary view.

Who owns the orders table?

find_ownership

The orders table is owned by the Data Platform team. Sarah Chen is the primary contact.

What does "GMV" mean in the order_gmv column?

lookup_term

GMV stands for Gross Merchandise Value — the total sales revenue before deductions.

Traditional catalog
ElasticsearchSearch
KafkaEvents
FrontendUI
APIBackend
Neo4jGraph
MySQLMetadata
AirflowOrchestration
7+ servicesHours to deploy
Marmot
Marmot
MarmotSingle binary
PostgreSQLSearch, storage & graphs
2 servicesMinutes to deploy

Less infrastructure, same power

Traditional data catalogs need an entire platform team. Marmot needs a database you probably already run.

Quick start

Deploy in under five minutes

Marmot runs as a single binary backed by PostgreSQL - the only dependency you need to start cataloging your data.

Follow the Quick Start guide
~ / marmot
$docker compose up -d
[+] Container postgres-1 Started
[+] Container marmot-1 Started
Marmot is running at http://localhost:8080

Connect to your data sources

Growing ecosystem of plugins

PostgreSQL

MySQL

MongoDB

ClickHouse

BigQuery

Kafka

Airflow

dbt

S3

DuckDB

SQS

OpenAPI

View all plugins

Don't see your data source? Open an issue to request a plugin.

Built to scale

Simple architecture doesn't mean limited. Marmot handles real workloads on modest infrastructure.

Load tested on real infrastructure
500k+
Assets
100+
Concurrent users
<50ms
Avg response time
Read more

Only metadata. Your data stays put.

A context layer needs to know about your assets, not to hold their contents. Marmot is built so the data itself never leaves your systems.

Metadata, not your data

Marmot catalogs schemas, ownership, descriptions, lineage and statistics. The rows, messages and payloads inside your systems never enter Marmot.

Deploy it your way

Use managed Cloud, or run the open source build yourself. Run it yourself and even the metadata stays inside your own VPC and under your own controls.

Open source and auditable

MIT licensed and built in the open. Read exactly what Marmot collects, how it connects to a source, and what it stores, line by line.

Talk to us

Ready to give your AI the context it needs?

Try the live demo or explore the open source, self-hostable solution. MIT licensed with a flexible API.

View Live DemoRead the Docs

Have questions? Get in touch