datalab-python-sdk
SDK for the Datalab document intelligence API
Decision gist · record as of 2026-08-14
Yes, if you need to convert documents to markdown or run document processing workflows and have access to a Datalab API key. The package is actively maintained, has low install friction, carries a permissive MIT license, and integrates well with Python data pipelines. No known vulnerabilities. Main consideration is dependency on an external API service and availability of Datalab credentials.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires DATALAB_API_KEY environment variable or api_key parameter; requires Python 3.10 or later.
- Low install friction with a pure-Python wheel and seven runtime dependencies.
- Actively maintained with recent release; requires Python 3.10 or later.
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, suitable for both open-source and commercial projects.
last release 2026-04-06 (130 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 94,478 downloads/mo, #13,326 on PyPI
Alternatives
Verify before relying
pip install datalab-python-sdk
import os
os.environ['DATALAB_API_KEY'] = 'your_api_key_here'
from datalab_python_sdk import DatalabClient
client = DatalabClient()
result = client.convert('document.pdf')
print(result.markdown)- Whether marker and surya are bundled dependencies or require separate installation.
- Performance characteristics for large documents or batch processing workflows.
- Rate limits or SLA guarantees of the underlying Datalab API service.
What it is and what it does
Datalab SDK is a Python client library for the Datalab document intelligence platform. It provides both a programmatic API and CLI for converting documents to markdown and orchestrating multi-step document processing workflows. The package wraps HTTP calls to a remote Datalab service, handling authentication via API key and managing request/response serialization through pydantic, with built-in retry logic via tenacity and progress reporting via tqdm.
Typical use is to authenticate once with an API key, then call convert() on a document path to retrieve structured output including markdown representation. The SDK also supports chaining multiple processing steps into reusable workflows for more complex document intelligence tasks.
Use it for
- Convert PDF documents to markdown for downstream processing or storage in knowledge bases.
- Extract text and structure from scanned or complex-layout documents using document intelligence.
- Chain multiple document processing steps into reusable workflows for batch automation.
- Automate document conversion in data pipelines or ETL workflows.
- Build document ingestion layers for applications requiring clean markdown from unstructured documents.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to convert documents to markdown or run document processing workflows and have access to a Datalab API key.
The package is actively maintained, has low install friction, carries a permissive MIT license, and integrates well with Python data pipelines. No known vulnerabilities. Main consideration is dependency on an external API service and availability of Datalab credentials.
Install
datalab-python-sdk on PyPI
Before you install
Low install friction with a pure-Python wheel and seven runtime dependencies. Actively maintained with recent release; requires Python 3.10 or later.
Requires DATALAB_API_KEY environment variable or api_key parameter; requires Python 3.10 or later.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions, suitable for both open-source and commercial projects.
Quickstart
pip install datalab-python-sdk
import os
os.environ['DATALAB_API_KEY'] = 'your_api_key_here'
from datalab_python_sdk import DatalabClient
client = DatalabClient()
result = client.convert('document.pdf')
print(result.markdown)Verify before relying
- Whether marker and surya are bundled dependencies or require separate installation.
- Performance characteristics for large documents or batch processing workflows.
- Rate limits or SLA guarantees of the underlying Datalab API service.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesaiohttpclickijsonpydantic-settingspydantictenacitytqdm |
| Maintenance | Actively maintained 130 days since the last release |
| First released | |
| Downloads | 94,478 / month, #13,326 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: datalab_python_sdk-0.5.0-py3-none-any.whl
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