Enhanced node structure by including `thoughtSignature` for inline data parts in Gemini OpenAI, Gemini CLI, and Antigravity request handlers to improve traceability of thought processes.
Fixed incorrect boundary logic for `message_delta` emission, ensuring proper handling of usage updates and `emitMessageStopIfNeeded` within the response loop.
Implemented `Fork` flag in `ModelNameMapping` to allow aliases as additional models while preserving the original model ID. Updated the `applyOAuthModelMappings` logic, added tests for `Fork` behavior, and updated documentation and examples accordingly.
Introduced `tokenRefreshTimeout` constant for token refresh operations and enhanced context propagation for `refreshToken` by embedding roundtrip information if available. Adjusted `refreshAuth` to ensure default context initialization and handle cancellation errors appropriately.
Replaced file-based auth entry counting with `TokenStore`-backed implementation, enhancing flexibility and context-aware token management. Updated related logic to reflect this change.
Added support for external hooks to observe model registry events using the `ModelRegistryHook` interface. Implemented thread-safe, non-blocking execution of hooks with panic recovery. Comprehensive tests added to verify hook behavior during registration, unregistration, blocking, and panic scenarios.
Refactored `applyPayloadConfig` to `applyPayloadConfigWithRoot`, adding support for default rule validation against the original payload when available. Updated all executors to use `applyPayloadConfigWithRoot` and incorporate an optional original request payload for translations.
Improved consistency across OpenAI, Claude, and Gemini handlers by replacing initial `select` statement with a `for` loop for better readability and error-handling robustness.
Refined header assignment to use `x-api-key` for Anthropic API requests, ensuring correct authorization behavior based on request attributes and URL validation.
Updated Antigravity, Gemini, and Gemini-CLI translators to process `systemResult` of type `string` for system instructions. Ensures properly formatted JSON with dynamic content assignment.
fix(antigravity): validate function arguments before serialization
Ensure `function.arguments` is a valid JSON before setting raw bytes, fallback to setting as parameterized content if invalid.
feat: handle array input for system instructions in translators
Enhanced Gemini, Gemini-CLI, and Antigravity translators to process array content for system instructions. Adds support for assigning roles and handling multiple content parts dynamically.
Added comprehensive tests for `FillFirstSelector` and `RoundRobinSelector` to ensure proper behavior, including deterministic, cyclical, and concurrent scenarios. Introduced dynamic routing strategy updates in `service.go`, normalizing strategies and seamlessly switching between `fill-first` and `round-robin`. Updated `Manager` to support selector changes via the new `SetSelector` method.
Optimized the handling of JSON serialization and deserialization by replacing redundant `json.Marshal` and `json.Unmarshal` calls with `sjson` and `gjson`. Introduced a `marshalJSONValue` utility for compact JSON encoding, improving performance and code simplicity. Removed unused `encoding/json` imports.
This update enhances the `FileRequestLogger` by introducing support for spooling large request and response bodies to temporary files, reducing memory consumption. It adds atomic requestLogID generation for sequential log naming and new methods for non-streaming/streaming log assembly. Also includes better error handling during logging and temp file cleanups.
fix: unify response field naming across translators
Standardize `text` to `delta` and add missing `output` field in all response payloads for consistency across OpenAI, Claude, and Gemini translators.
This commit introduces several improvements to the AMP (Advanced Model Proxy) module:
- **Model Mapping Logic:** The `FallbackHandler` now uses a more robust approach for model mapping. It includes the extraction and preservation of dynamic "thinking suffixes" (e.g., `(xhigh)`) during mapping, ensuring that these configurations are correctly applied to the mapped model. A new `resolveMappedModel` function centralizes this logic for cleaner code.
- **ModelMapper Verification:** The `ModelMapper` in `model_mapping.go` now verifies that the target model of a mapping has available providers *after* normalizing it. This prevents mappings to non-existent or unresolvable models.
- **Gemini Thinking Configuration Cleanup:** In `gemini_thinking.go`, unnecessary `generationConfig.thinkingConfig.include_thoughts` and `generationConfig.thinkingConfig.thinkingBudget` fields are now deleted from the request body when applying Gemini thinking levels. This prevents potential conflicts or redundant configurations.
- **Testing:** A new test case `TestModelMapper_MapModel_TargetWithThinkingSuffix` has been added to `model_mapping_test.go` to specifically cover the preservation of thinking suffixes during model mapping.
This commit introduces a new configuration option `logs-max-total-size-mb` that allows users to set a maximum total size (in MB) for log files in the logs directory. When this limit is exceeded, the oldest log files will be automatically deleted to stay within the specified size. Setting this value to 0 (the default) disables this feature. This change enhances log management by preventing excessive disk space usage.
Refactors the reasoning effort conversion logic for Gemini models.
The update specifically addresses how `reasoning_effort` is translated into Gemini 3 specific thinking configurations (`thinkingLevel`, `includeThoughts`) and ensures that numeric budgets are not incorrectly applied to level-based models.
Changes include:
- Differentiating conversion logic for Gemini 3 models versus other models.
- Handling `none`, `auto`, and validated thinking levels for Gemini 3.
- Maintaining existing conversion for models not using discrete thinking levels.
The prompt for the gpt-5.2 codex model has been updated with more comprehensive instructions. This includes detailed guidelines on general usage, editing constraints, the plan tool, sandboxing configurations, handling special user requests, frontend task considerations, and final message presentation. The updates aim to improve the model's understanding and execution of complex coding tasks by providing clearer directives and constraints.
This change introduces specific logic to load and use instructions for the 'gpt-5.2-codex' model variant by recognizing the 'gpt-5.2-codex_prompt.md' filename. This ensures the correct prompts are used when the '5.2-codex' model is identified, complementing the recent addition of its definition.
Luis Pater