* feat: add GPT-5 model support
- Added GPT-5 models (gpt-5-2025-08-07, gpt-5-mini-2025-08-07, gpt-5-nano-2025-08-07)
- Added nectarine-alpha-new-reasoning-effort-2025-07-25 experimental model
- Set gpt-5-2025-08-07 as default OpenAI Native model
- Implemented GPT-5 specific handling with streaming and reasoning effort support
* fix: remove hardcoded temperature from GPT-5 handler
- Updated handleGPT5Message to use configurable temperature
- Now uses this.options.modelTemperature ?? OPENAI_NATIVE_DEFAULT_TEMPERATURE
- Maintains consistency with other model handlers
* feat: add reasoning effort support for all OpenAI models
* fix: update test to expect new default model gpt-5-2025-08-07
* feat: increase GPT-5 models context window to 400,000
- Updated context window from 256,000 to 400,000 for gpt-5-2025-08-07
- Updated context window from 256,000 to 400,000 for gpt-5-mini-2025-08-07
- Updated context window from 256,000 to 400,000 for gpt-5-nano-2025-08-07
- Updated context window from 256,000 to 400,000 for nectarine-alpha-new-reasoning-effort-2025-07-25
As requested by @daniel-lxs in PR #6819
* revert: remove GPT-5 models, keep only nectarine experimental model
- Removed gpt-5-2025-08-07, gpt-5-mini-2025-08-07, gpt-5-nano-2025-08-07
- Kept nectarine-alpha-new-reasoning-effort-2025-07-25 experimental model
- Reverted default model back to gpt-4o
- Updated tests and changeset accordingly
* feat: add GPT-5 models with updated context windows
- Added gpt-5-2025-08-07, gpt-5-mini-2025-08-07, gpt-5-nano-2025-08-07 models
- All GPT-5 models configured with 400,000 context window
- Updated nectarine model context window to 256,000
- All models configured with reasoning effort support
- Set gpt-5-2025-08-07 as default OpenAI Native model
- Added GPT-5 model handling in openai-native.ts
- Updated tests to reflect new default model
* fix: restore reasoning effort support for o1 series models
- Added supportsReasoningEffort: true to o1, o1-preview, and o1-mini models
- This restores the ability to use reasoning effort parameters with these models
- The existing code in openai-native.ts already handles reasoning effort correctly
* Revert "fix: restore reasoning effort support for o1 series models"
This reverts commit 7251237ae8.
* fix: restore reasoning effort support for o3 and o4 models
- Added supportsReasoningEffort: true to o3, o3-high, o3-low models
- Added supportsReasoningEffort: true to o4-mini, o4-mini-high, o4-mini-low models
- Added supportsReasoningEffort: true to o3-mini, o3-mini-high, o3-mini-low models
- These models have both supportsReasoningEffort and reasoningEffort properties
* Revert "fix: restore reasoning effort support for o3 and o4 models"
This reverts commit a75a2b8a69.
* fix: restore reasoning effort support for o3 and o4 models
- Added supportsReasoningEffort: true to o3, o3-high, o3-low models
- Added supportsReasoningEffort: true to o4-mini, o4-mini-high, o4-mini-low models
- Added supportsReasoningEffort: true to o3-mini, o3-mini-high, o3-mini-low models
* fix: adjust reasoning effort support for o3/o4 models
- Keep supportsReasoningEffort only for base o3, o4-mini, and o3-mini models
- Remove supportsReasoningEffort from -high and -low variants
- Position supportsReasoningEffort right before reasoningEffort property
* fix: remove nectarine experimental model
- Removed nectarine-alpha-new-reasoning-effort-2025-07-25 from openai.ts
- Removed nectarine handling from openai-native.ts (renamed to handleGpt5Message)
- Removed associated changeset file
- Keep GPT-5 models with developer role handling
* feat: implement full GPT-5 support with verbosity and minimal reasoning
- Add all three GPT-5 models with accurate pricing (.25/0 for gpt-5, /bin/sh.25/ for mini, /bin/sh.05//bin/sh.40 for nano)
- Implement verbosity control (low/medium/high) that passes through to API
- Add minimal reasoning effort support for fastest response times
- GPT-5 models use developer role instead of system role
- Set gpt-5-2025-08-07 as default OpenAI Native model
- Add Responses API infrastructure for future migration
- Update tests to verify all GPT-5 features
- All 27 tests passing
Note: UI controls for verbosity still need to be added in a follow-up PR
* feat: add verbosity setting for GPT-5 models
- Add VerbosityLevel type definition to model types
- Add verbosity field to ProviderSettings schema
- Create Verbosity UI component for settings
- Add verbosity labels to all localization files
- Integrate verbosity handling in model parameters transformation
- Update OpenAI native handler to support verbosity for GPT-5
- Add comprehensive tests for verbosity setting
- Update existing GPT-5 tests to use verbosity from settings
* Delete .roorules
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Co-authored-by: Roo Code <roomote@roocode.com>
Co-authored-by: hannesrudolph <hrudolph@gmail.com>
Co-authored-by: Daniel Riccio <ricciodaniel98@gmail.com>
Co-authored-by: Daniel <57051444+daniel-lxs@users.noreply.github.com>
* feat: add GLM-4.5 and OpenAI gpt-oss models to Fireworks provider
- Added GLM-4.5 (355B/32B active) and GLM-4.5-Air (106B/12B active) models from Z.ai
- Added gpt-oss-20b and gpt-oss-120b models from OpenAI
- All models configured with 128K context window
- Added comprehensive test coverage for all new models
Fixes#6753
* fix: update GLM-4.5 model IDs to use p instead of hyphen
- Changed glm-4-5 to glm-4p5
- Changed glm-4-5-air to glm-4p5-air
- Updated corresponding test cases
---------
Co-authored-by: Roo Code <roomote@roocode.com>
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
Co-authored-by: Daniel <57051444+daniel-lxs@users.noreply.github.com>
Co-authored-by: Daniel Riccio <ricciodaniel98@gmail.com>
* feat: add prompt caching support for LiteLLM (#5791)
- Add litellmUsePromptCache configuration option to provider settings
- Implement cache control headers in LiteLLM handler when enabled
- Add UI checkbox for enabling prompt caching (only shown for supported models)
- Track cache read/write tokens in usage data
- Add comprehensive test for prompt caching functionality
- Reuse existing translation keys for consistency across languages
This allows LiteLLM users to benefit from prompt caching with supported models
like Claude 3.7, reducing costs and improving response times.
* fix: improve LiteLLM prompt caching to work for multi-turn conversations
- Convert system message to structured format with cache_control
- Handle both string and array content types for user messages
- Apply cache_control to content items, not just message level
- Update tests to match new message structure
This ensures prompt caching works correctly for all messages in a conversation,
not just the initial system prompt and first user message.
* fix: resolve TypeScript linter error for cache_control property
Use type assertion to handle cache_control property that's not in OpenAI types
* feat: Adding more settings and control over Gemini
- with topP, topK, maxOutputTokens
- allow users to enable URL context and Grounding Research
* feat: Adding parameter titles and descriptions + translation to all languages
* feat: adding more translations
* feat: adding `contextLimit` implementation from `maxContextWindow` PR + working with profile-specific thresholding
* feat: max value for context limit to model's limit + converting description and titles to settings for translation purposes
* feat: all languages translated
* feat: changing profile-specific threshold in context management setting will also change in Gemini context management
- sync between Context Management Settting <-> Gemini Context Management with regards to thresholding
* feat: max value of maxOutputTokens is model's maxTokens + adding more tests
* feat: improve unit tests and adding `data-testid` to slider and checkbox components
* fix: small changes in geminiContextManagement descriptions + minor fix
* fix: Switching from "Gemini Context Management" to "Token Management
- better naming and correct purpose
* fix: input field showed NaN -> annoying UX
* fix: Removing redundant "tokens" after the "set context limit"'s checkbox + removing the lengthy description
* fix: Changing the translation to be consistent with the english one
* fix: more translations
* fix: translations
* fix: removing contextLimit and token management related code
- due to the decision in: https://github.com/RooCodeInc/Roo-Code/issues/3717
* fix: removing `contextLimit` test and removing token management in translations
* fix: changing from `Advanced Features` to `Tools` to be consistent with Gemini docs/AI studio
* fix: adding `try-catch` block for `generateContentStream`
* feat: Include citations + improved type safety
* feat: adding citation for streams (generateContextStream)
* fix: set default values for `topP`, `topK` and `maxOutputTokens`
* fix: changing UI/UX according to the review/feedback from `daniel-lxs`
* fix: updating the `Gemini.spec.tsx` unit test
- testing when it is hidden
- testing when users click on the collapsible trigger and model configuration appears
* fix: more changes from the feedback/review from `daniel-lxs`
* fix: adding sources at the end of the stream to preserve
* fix: change the description for grounding with google search and url context
* fix: adding translations
* fix: removing redundant extra translations - a mistake made by the agent
* fix: remove duplicate translation keys in geminiSections and geminiParameters
- Fixed duplicate keys in 13 localization files (es, fr, hi, id, it, ja, ko, nl, pl, pt-BR, ru, tr, vi)
- Removed second occurrence of geminiSections and geminiParameters keys
- Kept first occurrence which contains more comprehensive descriptions
- All JSON files validated for syntax correctness
- Translation completeness verified with missing translations script
Resolves duplicate key issue identified in PR #4895
* fix: delete topK, topP and maxOutputTokens from Gemini
* fix: deleting topK, topP and maxOutputTokens from translations/locales
* fix: adjust spacing between labels and descriptions + sentence casing
* fix: adding maxOutputTokens back and removing unknown type
* fix: internalizing error Gemini error message
* fix: updating tests in Gemini and Vertex to adjust to the new error logging
* fix: address PR review feedback for Gemini tools feature
- Fix Hindi translation grammatical error in settings.json
- Internationalize 'Sources:' string and error messages in gemini.ts
- Add comprehensive error scenario tests to gemini-handler.spec.ts
- Remove unused currentModelId prop from Gemini component
- Update all locale files with new translation keys
---------
Co-authored-by: Roo Code <roomote@roocode.com>
Co-authored-by: Matt Rubens <mrubens@users.noreply.github.com>
Co-authored-by: Daniel Riccio <ricciodaniel98@gmail.com>
refactor: move HuggingFace models API to providers/fetchers
- Moved getHuggingFaceModels functionality from src/api/huggingface-models.ts to src/api/providers/fetchers/huggingface.ts
- Added getHuggingFaceModelsWithMetadata function to maintain the same API interface
- Updated import in webviewMessageHandler.ts to use the new location
- Deleted the now redundant src/api/huggingface-models.ts file
This consolidates all HuggingFace-related API logic into a single location within the providers/fetchers directory structure.
* add more details
* format details better
* fix tests
* fix: address PR #6190 review feedback
- Move huggingface-models.ts to src/api/providers/fetchers/huggingface.ts
- Remove 'any' types and add proper TypeScript interfaces
- Add missing i18n keys and translations for all languages
- Replace magic numbers with named constants
- Add JSDoc documentation for HuggingFaceModel interface
- Improve error handling in API endpoint
- Update model capabilities display to match other providers
- Remove tool calling display (not used)
- Add comprehensive test coverage for new UI features
* fix: preserve HuggingFace provider details in model response
- Store raw HuggingFace models in cache to preserve provider information
- Export getCachedRawHuggingFaceModels to retrieve full model data
- Update huggingface-models.ts to return cached raw models when available
- Include provider name in model descriptions
- Always add provider-specific variants to show all available providers
- Remove console.log statements from fetcher
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Co-authored-by: Daniel Riccio <ricciodaniel98@gmail.com>
* basic hugging face provider
* fetch hf models and providers
* save provider to config
* Update translations
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Co-authored-by: Thomas G. Lopes <26071571+TGlide@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Co-authored-by: Daniel Riccio <ricciodaniel98@gmail.com>
- Update parseOpenRouterModel to always use actual max_completion_tokens from OpenRouter API
- Remove artificial restriction that only reasoning budget and Anthropic models get their actual max tokens
- Fall back to 20% of context window when max_completion_tokens is null
- Update getModelMaxOutputTokens to use same fallback logic for consistency
- Update tests to reflect new behavior
- Fixes issue where reserved tokens showed ~209k instead of actual model limits (e.g. GPT-4o: 16,384)
* chore: adding x-title header and testing for litellm
* chore: indentation fi and headers order fix
* chore: spacing fix
* chore: removed white space
* fix: allow user headers to override default headers and clean up formatting
- Reorder header spread in router-provider.ts so user-provided openAiHeaders can override DEFAULT_HEADERS
- Remove unnecessary blank lines after imports for consistency
- This matches the pattern used in openai.ts where DEFAULT_HEADERS come first
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Co-authored-by: Daniel Riccio <ricciodaniel98@gmail.com>
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
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Co-authored-by: Daniel <57051444+daniel-lxs@users.noreply.github.com>
* fix: resolve Claude Code token counting inefficiency and enable caching (#5104)
- Remove 1.5x fudge factor from Claude Code token counting
- Enable prompt caching support for all Claude Code models
- Add comprehensive tests for token counting and caching
- Update existing tests to reflect accurate token counting
This fixes the extreme token inefficiency where simple messages would
jump from ~40k to over 60k tokens, causing API hangs when approaching
the artificial 120k limit. Claude Code now properly utilizes its full
200k context window with accurate token counting.
* fix: address PR review comments
- Extract IMAGE_TOKEN_ESTIMATE as a named constant for clarity
- Update token counting tests to use exact counts instead of ranges for deterministic testing
- Fix test expectations to match actual tokenizer output
* Remove token counting changes, keep only cache support
- Removed custom countTokens override from claude-code.ts
- Deleted claude-code-token-counting.spec.ts test file
- Kept cache token collection and reporting functionality
- Kept supportsPromptCache: true for all Claude Code models
- Kept claude-code-caching.spec.ts tests
This focuses the PR on enabling cache support without modifying token counting behavior.
* fix: update webview test to expect supportsPromptCache=true for Claude Code models
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Co-authored-by: Daniel Riccio <ricciodaniel98@gmail.com>
* Fix temperature parameter error for Azure OpenAI reasoning models
* Fix tests: Update O3 family model tests to expect temperature: undefined
- Updated failing tests in openai.spec.ts to expect temperature: undefined for O3 models
- This aligns with the PR changes that remove temperature parameter for Azure OpenAI o1, o3, and o4 models
- All 4 previously failing tests now pass
---------
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fix: handle null families field in Ollama model details schema
- Updated OllamaModelDetailsSchema to make families field nullable and optional
- Made all unused properties optional in Ollama schemas to prevent validation errors
- Added test cases to verify handling of null families field
- Only required properties that are actually used in the code are now mandatory
- Fixes Zod validation error when Ollama returns null for families array
