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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To make an AI integration portable, keep your application’s task instructions, data contracts, and tool behavior independent of any one provider. Then use a provider-specific adapter to translate them into each API’s request and response format, and run the same conformance tests against every supported model. OpenAI, Anthropic, and Google Gemini share concepts, but their schema support and tool-call flows differ; a common JSON Schema file alone does not make requests interchangeable.
What portability means in practice
Portability does not mean sending an identical request to every model API. It means keeping the application’s intended behavior stable while adapting provider-specific details at the boundary. That boundary includes message roles and serialization, output-format controls, tool declarations, continuation formats, and other API parameters.
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Keep three concerns distinct:
- Task instructions: what the model should do, the relevant context, constraints, and examples.
- Data contracts: the shape and meaning of inputs and outputs your application accepts.
- Actions: the tools the application can execute, their arguments, permissions, and side effects.
Represent these in a provider-neutral internal format, then render them through an adapter for the selected provider. This is an engineering pattern, not a standard required by the providers.
Separate structured responses from tool calls
A structured response describes the format of the model’s answer. A tool call asks the application to take an action. They may both involve JSON-shaped data, but they have different purposes and should be handled as different event types.
#1 Best Overall
| Capability | What it is for | What the application must do |
|---|---|---|
| Structured output | Return a response in a requested shape, such as an object with defined fields. | Parse it, validate its schema, and check that its values make sense for the task. |
| Tool or function calling | Propose an interaction with an external system, such as looking up a record or creating an item. | Validate the requested tool and arguments, authorize it, execute it, and return the result using the provider’s continuation format. |
OpenAI distinguishes function calling for connecting models to external systems from structured response formatting. Gemini similarly distinguishes structured output from function calling. Neither a well-formed tool call nor a schema-shaped answer is proof that an external action occurred or that the content is correct. See the OpenAI Structured Outputs documentation and Gemini structured output documentation.
Build a canonical contract before writing adapters
Keep prompts provider-neutral
Store the prompt as meaningful components—intent, context, constraints, examples, and expected behavior—rather than as a serialized provider request. The adapter maps those components to the roles, fields, and controls required by each API. Keep provider-only features such as caching controls or message-specific parameters in the adapter, not in the canonical task definition.
Define tools independently of API syntax
For each application tool, keep a stable internal name and description, a typed input contract, authorization requirements, a side-effect classification, and the application implementation. Version the prompt, schema, and tool definitions with the application release so you can identify exactly which contract a request used.
A provider adapter can translate the internal tool definition into a provider’s declaration format. It should not change what the tool means or grant authority that the application would otherwise withhold.
Use a conservative schema subset and compile per provider
Start with a small common surface: explicit object properties, primitive types, required keys, arrays, and enums where needed. Treat other JSON Schema keywords as provider-specific capabilities until tests show that the exact model and endpoint accept and enforce them.
Do not assume that familiarity with JSON Schema guarantees equivalent behavior. OpenAI strict function arguments require a supported subset and compatible model and request configuration. Anthropic documents limitations for supported schemas. Gemini supports a subset; its documentation says unsupported properties may be ignored and very large or deeply nested schemas may be rejected. Check the current provider documentation for the configuration you deploy: OpenAI function calling, Anthropic structured outputs, and Gemini structured output.
Maintain capability metadata for each adapter rather than silently weakening the canonical contract. If a provider cannot express a constraint, either compile a simpler schema that preserves the same meaning or fail clearly. Silently dropping a constraint that matters to correctness makes the integration appear portable while changing its behavior.
Validate and execute tool calls in application code
A model’s tool call is a proposal, not execution. For custom Gemini functions, Google describes the application-side loop: declare functions, receive a suggested call, execute it in the application, return the result, and continue the conversation. Calls may be chained or parallel; the application still owns execution. See Gemini function calling.
- Parse the provider response and normalize it into an internal event such as
text,tool_call,tool_result,refusal,incomplete, orerror. - Resolve the tool name against the application’s allowlist. Reject unknown tools.
- Validate arguments against the internal contract and application-level rules, not only the schema sent to the model.
- Check authorization and side effects. Apply the user’s permissions and business rules before performing an operation.
- Execute the implementation and record the outcome. Preserve provider call IDs and correlation IDs when supplied, since the provider may need them to continue the interaction.
- Return the result in the provider’s required continuation format, then handle the model’s next response as a new event.
Never treat a generated call as evidence that an operation succeeded. For non-idempotent actions, use application-side deduplication or idempotency protections so retries cannot accidentally repeat the action.
Validate meaning, not just JSON syntax
Validation should happen in layers. First ensure the response can be parsed; next check its schema; then enforce semantic and business invariants. For example, a value can have the correct type and still be outside an allowed range, refer to a nonexistent record, or request an unauthorized action.
- Check ranges, required relationships, and referential integrity.
- Apply authorization and permitted-side-effect checks before tool execution.
- Handle refusals, incomplete outputs, timeouts, malformed responses, unsupported schemas, and tool failures as distinct cases.
- Use bounded retries only when retrying is safe and useful; protect action-taking tools against duplicate execution.
OpenAI’s JSON mode guarantees valid JSON, not conformance to a particular schema; Structured Outputs is intended for schema adherence where supported. Google also recommends semantic validation and application error handling. Provider-side constraints reduce malformed output, but do not replace application checks. See OpenAI Structured Outputs and Gemini structured output.
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Run the same realistic cases through each provider adapter. Include ordinary requests, edge cases, and adversarial inputs; test after changing a provider, model, API version, prompt, schema, or tool definition. A single successful example is not evidence of portability.
Best Value
- Does the provider accept the request and schema?
- Are required fields and enum values produced as expected?
- Do semantic and business-rule checks pass?
- Does the model select a tool only when appropriate?
- Do tool arguments validate and preserve the intended meaning?
- Are sequential and parallel calls represented and continued correctly?
- Are refusals, truncation, and provider errors surfaced distinctly?
- Does the full application task succeed with acceptable latency and cost, if those matter to the product?
These are practical test dimensions derived from documented provider differences, not a published cross-provider benchmark. Track outcomes by adapter and model so a regression is visible rather than masked by an apparently valid response.
Compare provider implementations on the same contract
When selecting a provider or model, compare behavior against your application contract rather than feature names alone. Verify these dimensions for the exact model and endpoint you plan to use:
- Supported schema keywords and how strictly they are enforced.
- Output-format controls and the required request and response envelope.
- Tool-choice controls, call IDs, and response-continuation mechanics.
- Sequential and parallel tool-call behavior.
- Handling of refusals, incomplete outputs, and errors.
- Mapping of your canonical prompt to provider message roles.
- Operational reliability and application-level task success.
Provider features and availability change, so confirm them against current documentation before implementation or deployment. A shared contract makes differences easier to manage; it does not eliminate the need to test them.
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