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What should you version?
Track the API surface, model selection, and client package separately. Each can change on its own, and a single label such as “API version” does not capture all the inputs that may affect an integration.
API surface
Record the documented API version or endpoint contract your integration uses. OpenAI says its REST API is currently v1 and describes additions such as new resources and optional parameters as backwards-compatible. That policy is specific to OpenAI; it should not be assumed to describe other providers. See the OpenAI API Overview.
Backwards-compatible does not mean every client assumption is safe. OpenAI notes that property order may change, identifiers can change length or format, and new response properties or streaming event types may be added. Avoid relying on ordering, undocumented fields, or a particular identifier format unless the contract guarantees it. OpenAI says rare breaking changes are tracked in its changelog.
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Model identifier or snapshot
Record the exact model identifier selected and whether it is a dated or otherwise fixed snapshot, or a moving alias. A snapshot can reduce change caused by model-version movement; a moving alias may resolve to a different version over time. OpenAI recommends pinned model versions and application evaluations for more consistent prompting behavior and output, while warning that model outputs are inherently variable. Pinning is not deterministic output. See the OpenAI API Overview and its 2023 API announcement.
SDK package
Record the client-library package name and version in the dependency manifest, and commit the lockfile that resolves the dependencies used for builds and deployments. Check the release policy for the particular package: one package’s versioning rules do not automatically apply to another.
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OpenAI’s API reference says released first-party client libraries follow semantic versioning. However, its Agents SDK guides document a modified 0.Y.Z scheme. For those Agents packages, a minor Y increase can contain breaking changes, and the guides recommend pinning to 0.0.x if you do not want breaking changes. This advice applies to the named Agents SDKs, not every OpenAI package or third-party client. See the API reference, Agents Python versioning guide, and Agents JavaScript versioning guide.
How do you pin an AI model and SDK?
Make the chosen versions explicit in the configuration that ships with the application, rather than relying on an unrecorded default or an unconstrained dependency range. A useful integration record includes:
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- The model identifier, and whether it is a fixed snapshot or moving alias.
- The SDK package name and selected version, preserved in the manifest and lockfile.
- Relevant integration configuration and a reference to the evaluation suite used to validate changes.
For model selection, use a fixed snapshot when limiting model-version movement is important and a suitable snapshot is available. If the application uses an alias, document that choice and account for possible version movement in your change review. The cited OpenAI guidance supports pinned versions and evaluations for consistency; it does not establish that aliases are unsuitable for every production workload.
For SDK selection, follow that package’s own release policy. In particular, the OpenAI Agents guides’ 0.0.x recommendation is specific to their modified version scheme. Preserve the exact dependency resolution in your lockfile so a build does not silently pick up a different package version.
How should you evaluate an upgrade?
Use a deliberate change process. The following sequence is a practical synthesis of the cited OpenAI guidance, not a provider-mandated procedure.
- Capture the current configuration. Record the API surface, model identifier or snapshot, SDK package and version, and relevant settings.
- Check provider notices. Read the current changelog and deprecations page. Identify affected endpoints or models, migration guidance, and any published shutdown date before changing production pins. OpenAI’s changelog points to its deprecations page for shutdown timelines and migration guidance.
- Change one layer at a time where practical. Upgrading the SDK and changing the model in the same release can make a regression harder to trace. Separate changes when your release constraints allow it.
- Run representative application evaluations. Compare the existing configuration with the proposed one on tasks that matter to your product. Set acceptance criteria appropriate to the application and examine relevant quality, failure modes, latency, and cost. OpenAI recommends evaluations for consistency, but its documentation does not prescribe a universal test set or thresholds.
- Review and roll out. Check results against your acceptance criteria and the provider’s migration instructions, then deploy through your team’s normal release process. Retain the previous known configuration as a recovery option while it remains supported.
- Schedule required migrations. If a pinned model or endpoint has a published retirement date, plan and validate a replacement before shutdown. A pin controls version movement; it cannot keep a retired service available.
What does pinning protect you from—and what does it not?
Pinning is an upgrade-control practice. A fixed model snapshot can reduce unintended version movement, and a locked package version makes the client dependency explicit. Evaluations provide a way to check whether an intentional change affects your application. Together, these practices make changes more visible and reviewable.
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They do not freeze every part of the system. Model responses can vary even at a pinned version; provider APIs can receive changes their owner classifies as backwards-compatible; and a provider can deprecate and retire a version. Treat pins as a way to choose when to review and adopt changes, not as a maintenance exemption.
How often should you review provider changes?
There is no universal deprecation notice period established by the OpenAI sources cited here, and this policy should not be generalized to every AI API provider. Track the relevant provider’s changelog and deprecation notices as part of routine maintenance, then respond to specific migration dates and guidance. Availability, shutdown timelines, model identifiers, and package policies can change; check the provider’s current documentation before acting.
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