OpenPipeline
From SELECT * to plain English.
OpenPipeline turns every dbt model into a story anyone can read. Every change, from a teammate or an AI agent, goes to the data's owner before it lands.
Website · Live demo (read-only, nothing to install) · Docs
Agents propose, owners approve. AI agents are starting to write SQL. OpenPipeline
lets them, safely: an agent reads your project, checks its idea on sample rows, and
files a proposal. The owner sees it on the graph, in plain words and business terms,
and accepts or rejects it. Nothing touches your dbt files until a person says yes.
It opens a dbt project you already have. It never runs dbt, never connects to your
warehouse, and reads and writes plain dbt files, so you can stop using it at any time.
How a change lands
- Propose. A teammate opens a pull request, or an agent calls
propose_change
through the MCP server.
- Understand. OpenPipeline shows what the change does in plain English (or in
your own business words), which models and columns it touches downstream, and
which business rules and owners are involved.
- Approve. The owner accepts or rejects it, with comments. Only then is a file
written.
Try it in one minute
pip install openpipeline
openpipeline demo
This copies the bundled jaffle_shop example to a temporary folder, files one
pending proposal from an imaginary agent, and starts the server at
http://127.0.0.1:8484. Open the Proposals tab and press Show on graph to
see a review.
What you get
- Model graph. Every model and source as an interactive map. Click one to
light up everything it reads from and everything that depends on it.
- Story view. Each model explained step by step in plain words: where the data
comes from, how it is joined and filtered, what one row means, and how each
column is worked out.
- AI explanations (optional). Bring your own Anthropic key to get a friendlier
write-up and suggested descriptions. Nothing is sent unless you click the button.
- Editable docs. Fix descriptions in the UI. They are saved into your own YAML
files and the comments in them are kept.
- Sample rows. Run a model on small local sample data in an in-memory DuckDB and
see real rows. No warehouse involved.
- Search and column lineage. Press
/ to find any model, source, or column.
Pick a column to see where it comes from and what uses it.
- Business rules. Name the rules buried in your SQL, give each an owner, and link
them to the models and columns that carry them.
- PR summaries. A GitHub Action comments on pull requests with what changed in
business terms, what is affected downstream, and which owners to loop in.
- Safe changes from AI agents. An MCP server lets agents such as Claude read
the project and propose changes. You review each proposal on the graph, comment,
and accept or reject it. Nothing is written until a person accepts.
Use it on your own project
openpipeline serve path/to/your/dbt/project
# open http://127.0.0.1:8484
With Docker:
docker run --rm -p 8484:8484 -v "$PWD:/project" ghcr.io/pawanpatil19/openpipeline
docker logs <container> # prints the write token and a link that opens with it
The container listens on all interfaces, so changes (editing docs, accepting
proposals, comments, AI calls) need a token. It makes one up and prints it, or use
-e OPENPIPELINE_TOKEN=.... Add -e OPENPIPELINE_READ_ONLY=1 for a server that
refuses every write. See Write access and security.
You do not need dbt installed or warehouse credentials. OpenPipeline reads
target/manifest.json when it exists (run dbt compile for the most accurate
results) and otherwise reads your SQL and YAML files directly.
Optional extras:
pip install 'openpipeline[ai]' # AI explanations, with your own Anthropic key
pip install 'openpipeline[preview]' # sample rows, run locally with DuckDB
Connect an AI agent
claude mcp add openpipeline -- openpipeline mcp /path/to/dbt/project
The agent can look at everything, but it can only propose edits. It reads with
explain, search, column_lineage, impact_of, and list_rules, checks its work
with preview_change and sample_rows, and files changes with propose_change
or propose_docs, and suggests glossary terms with propose_glossary_term. See Working with AI agents.
Command line
openpipeline demo # the example project plus a proposal to review
openpipeline serve [PROJECT] # web UI and API on :8484 (--token, --read-only)
openpipeline explain customers [PROJECT] # explain one model (add --ai for an AI write-up)
openpipeline lineage customers lifetime_value [PROJECT]
openpipeline sample customers [PROJECT] # a few rows, on sample data
openpipeline rules [PROJECT] # business rules, grouped by owner
openpipeline review BASE HEAD # Markdown summary of a change, for a PR
openpipeline mcp [PROJECT] # MCP server for agents
PROJECT defaults to the current folder or $OPENPIPELINE_PROJECT_DIR. Run any
command with --help for its options.
Documentation
Guides, the command line, HTTP API, and configuration reference are at
https://openpipeline.vercel.app/docs/.
How it is built
Read docs/architecture.md for how the parts fit.
Status
Early (0.x). The features above work and are covered by tests, including on real
public dbt projects, but names and options may still change before 1.0. Bug reports and
ideas are welcome in the issue tracker.
Contributing
CONTRIBUTING.md explains how to set up the project and run the
checks. In short:
make install # Python and web dependencies, plus git hooks
make dev # API with reload on :8484, web dev server on :5173
make lint typecheck test
License
Apache 2.0
