API, SDK & CLI
Build production Unitlab automations with the Python SDK, CLI, and authenticated HTTP API.
Choose the integration surface by runtime and operating model. All three surfaces address the same authenticated Unitlab resources.
Outcome: Build production Unitlab automations with the Python SDK, CLI, and authenticated HTTP API.
This reference is generated from the current unitlab-sdk-codebase package at version 3.0.0, its public source, executable command tree, and tests. It does not reuse previous documentation.
Choose a surface
Typed application integration
Python SDK
Shell, CI, or operator runbook
CLI with --json
Non-Python runtime
Authenticated HTTP API
Coverage
Projects, Data Units, Batch Queues, Assets, folders, cloud storage, grouping, datasets and versions, embedding spaces, ontologies, workflow stages and tasks, releases, errors, and production automation patterns.
The installed SDK and CLI help are authoritative for the pinned package version. Test upgrades against representative create, upload, attach, workflow, embedding, and release operations before changing production jobs.
Operating contract
Execution surface
Pinned unitlab==3.0.0 application environment on Python 3.10+.
Identity
Least-privilege API key supplied through approved configuration.
Target resolution
Stable resource IDs and an explicitly bounded target set.
Success evidence
Typed return fields, server-side state, and downstream acceptance of the result.
Failure and recovery boundary
Authentication or authorization fails
Stop, correct the service identity or access model, rotate exposed credentials, and rerun a read-only check.
Validation or entitlement rejects the operation
Correct the input or entitlement; do not retry an unchanged request.
A request times out
Inspect remote state before repeating a mutation because the server may have accepted it.
Asynchronous processing exceeds its deadline
Preserve the Batch Queue or release ID, continue bounded monitoring, and inspect item-level failures.
Only part of a batch succeeds
Keep successful identifiers, isolate failed rows, and retry only the corrected subset.
Pin and test the production SDK version. Use stable IDs, keep secrets out of logs and command history, and record the correlation ID, target IDs, counts, final state, and redacted error details for material state changes.
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