dbt-sl-sdk
A client for dbt's Semantic Layer
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries no known vulnerabilities, and fills a clear need for Python developers working with dbt's Semantic Layer. The sync/async flexibility and pyarrow integration are well-suited to modern data workflows. The only caveat is that license treatment is unclear, so verify licensing implications for your use case before committing to production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- You must provide valid dbt Semantic Layer credentials (environment_id, auth_token, host).
- Low install friction with a pure-wheel distribution and five runtime dependencies.
License · maintenance · safety
(unclear)
last release 2026-05-27 (79 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 116,274 downloads/mo, #12,213 on PyPI
Alternatives
Verify before relying
pip install "dbt-sl-sdk[sync]"
from dbt_sl_sdk import SemanticLayerClient
client = SemanticLayerClient(
environment_id=123,
auth_token="<your-token>",
host="semantic-layer.cloud.getdbt.com",
)
with client.session():
metrics = client.metrics()
table = client.query(metrics=[metrics[0].name], group_by=["metric_time"])
print(table)- Whether the package's telemetry collection (platform information sent to dbt Labs by default) poses privacy concerns for your use case.
- Performance characteristics when working with large metric definitions under lazy loading mode.
- Specific license terms and any restrictions on commercial or proprietary use.
What it is and what it does
dbt-sl-sdk is a Python client for dbt's Semantic Layer, a centralized metrics platform accessible through a REST API. The package wraps that API in a straightforward Python interface, handling connection pooling via session context managers and supporting both synchronous and asynchronous workflows. All query results come back as pyarrow tables, which you can then convert to other formats if needed.
The library is designed for developers who want to pull metric data into Python applications without writing raw HTTP calls. It includes features like lazy loading to avoid fetching nested object lists for large projects, optional telemetry that can be disabled, and a consistent API between sync and async variants. The package requires Python 3.9 or later and depends on pyarrow, mashumaro, adbc-driver-flightsql, adbc-driver-manager, and typing-extensions.
Use it for
- Query dbt metrics and dimensions programmatically from a Python application without manual API calls.
- Build data pipelines that fetch metric data and convert pyarrow results to other formats.
- Integrate dbt Semantic Layer queries into async applications using async variants for non-blocking I/O.
- Explore metric definitions and their nested dimensions via lazy-loading mode for large projects.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and fills a clear need for Python developers working with dbt's Semantic Layer. The sync/async flexibility and pyarrow integration are well-suited to modern data workflows. The only caveat is that license treatment is unclear, so verify licensing implications for your use case before committing to production.
Install
dbt-sl-sdk on PyPI
Before you install
Low install friction with a pure-wheel distribution and five runtime dependencies. Actively maintained with a recent release within the last 79 days.
Requires Python 3.9 or later. You must provide valid dbt Semantic Layer credentials (environment_id, auth_token, host).
Quickstart
pip install "dbt-sl-sdk[sync]"
from dbt_sl_sdk import SemanticLayerClient
client = SemanticLayerClient(
environment_id=123,
auth_token="<your-token>",
host="semantic-layer.cloud.getdbt.com",
)
with client.session():
metrics = client.metrics()
table = client.query(metrics=[metrics[0].name], group_by=["metric_time"])
print(table)Verify before relying
- Whether the package's telemetry collection (platform information sent to dbt Labs by default) poses privacy concerns for your use case.
- Performance characteristics when working with large metric definitions under lazy loading mode.
- Specific license terms and any restrictions on commercial or proprietary use.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release <3.14,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesadbc-driver-flightsqladbc-driver-managermashumaropyarrowtyping-extensions |
| Maintenance | Actively maintained 79 days since the last release |
| First released | |
| Downloads | 116,274 / month, #12,213 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dbt_sl_sdk-0.13.4-py3-none-any.whl
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See also cube_dbt · dbt-metricflow · dbt-semantic-interfaces · dbt-mcp · metricflow · dbt-fabricspark · dbt-snowflake · dbt · dbt-core · dbt-metabase