dlt

Connect an agent via an official MCP server G

Agent access

Drive the product through a documented public API G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Use an official CLI G

Agent access

Build against official SDKs G

Agent access

Run the product headlessly / in CI for automation G

Agent access

Operate the product with natural-language commands G

Agentic features

Set up automations that run autonomously in the background G

Agentic features

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Issue scoped/least-privilege API credentials for an agent G

Agent access

Rely on versioned APIs with a documented deprecation policy G

Api quality

Subscribe to events via webhooks G

Agent access

Test against a sandbox environment without touching production data G

Api quality

A coding agent can scaffold, configure, and run a complete pipeline headlessly through the CLI or API C

Ai build

Self-host the core product G

Syncs move only new and changed records — cursor and state management handled for me, not full reloads C

Incremental

I pick from a broad catalog of maintained connectors for the SaaS APIs, databases, and files my company actually uses C

Catalog

AI drafts a working connector from API documentation — auth, pagination, streams — that I review and ship C

Ai build

Export all of my data in open formats and leave G

Upstream schema changes are detected and propagated by a policy I choose, instead of silently breaking loads C

Evolution

Define rules that trigger actions automatically on events G

I see run status, logs, and row counts per sync, and failures alert me in Slack, email, or a webhook C

Monitoring

I replicate databases with log-based CDC (binlog/WAL) so I capture updates and deletes without hammering the source C

Cdc

Prevent my data from being used to train AI models G

I run and test a pipeline locally against a lightweight destination before it touches production C

Dev loop

My pipelines are plain code and config in my own repository — versioned, reviewed, and portable like any software C

Code first

I build a custom connector for a long-tail API with a supported framework or low-code builder, not a fork C

Custom connectors

Read the product's source under an open license G

Backfill history or resync a single table without rebuilding the whole pipeline C

Backfill

Dbt transformations run against freshly loaded data as part of the pipeline, not on a blind timer C

Dbt

Do everything through the API that I can do in the UI G

I load to the major warehouses and lakes — Snowflake, BigQuery, Databricks, Postgres, object storage — without changing pipelines C

Destinations

Perform bulk operations across many items at once G

An agent can check sync status, diagnose a failed run, and re-trigger it through an API or MCP server C

Ai operate

Schedule recurring jobs or workflows G

Choose where my data is stored (region/residency) G

I define dependencies between pipeline steps and datasets, and the platform orchestrates runs in the right order C

Orchestration

I see end-to-end lineage of my datasets — which sources, steps, and transformations produced each table C

Lineage

Transient failures retry automatically and interrupted syncs resume from checkpoints instead of restarting C

Recovery

Control data retention and deletion G

I control sync frequency per pipeline — from sub-hour schedules to cron expressions and manual triggers C

Scheduling

I sync modeled warehouse data back into SaaS tools (CRM, ads, support) to activate it where teams work C

Reverse etl

The pricing model is published and predictable — I can estimate what a new source costs before connecting it G

Pricing

Opt out of telemetry and usage tracking G

Loaded data lands as typed, deduplicated destination tables ready to query, not raw JSON blobs C

Normalization

Pipelines load into vector stores and LLM-ready formats so my agents can retrieve what was synced C

Ai destinations

Tell how fresh each destination table is and get warned when a pipeline misses its expected cadence C

Freshness

The catalog tells me each connector's maturity, support level, and maintainer before I depend on it C

Catalog

Version, review, and roll back my automations G