* Add Novita as an LLM provider
Registers Novita (novita.ai) as an OpenAI-routed provider, following the
same pattern as Requesty/Atlas Cloud/SiliconFlow: a base_url + API key env
var entry in _OPENAI_ROUTED_PROVIDERS, a handful of model registry entries
(DeepSeek/Qwen/GLM), onboarding wizard support (constants/steps/wizard/
helpers), a key validator using the auth-preflight sentinel pattern (Novita's
/v1/models endpoint returns the public catalog even for an invalid key, so
auth must be checked via a chat completion instead), and a host-to-provider
mapping entry for error attribution.
* Recommend Novita's current flagship models
The models listed for Novita were older ids that no longer reflect what
the platform leads with. Point the recommendations at the three current
flagships instead, each verified against api.novita.ai:
moonshotai/kimi-k3 1M context, native vision
zai-org/glm-5.2 1M context, long-horizon agentic work
deepseek/deepseek-v4-flash-0731 1M context, cheapest of the three
Context windows, output limits, input modalities and pricing were taken
from the live /openai/v1/models response rather than carried over.
* Keep branch CI workflow files unchanged (no workflow OAuth scope)
Co-authored-by: multica-agent <github@multica.ai>
* ci: restore workflow files to match main
---------
Co-authored-by: jax-novita <jax-novita@users.noreply.github.com>
Co-authored-by: multica-agent <github@multica.ai>
Co-authored-by: Dinos Papakostas <dinospk1999@gmail.com>
Co-authored-by: Xi Zhang <106144707+X-iZhang@users.noreply.github.com>
* Add Requesty as an LLM provider
* Address review: Requesty prompt caching, model ordering, key validation
- Declare Anthropic-style prompt caching for Requesty Claude models by
default (mirroring the OpenRouter behavior), with an opt-out flag
EVOSCIENTIST_REQUESTY_ANTHROPIC_PROMPT_CACHE. Requesty is an OpenAI-routed
provider, so the caching check now uses the original provider name.
- Move the Requesty model entries above OpenRouter so Requesty no longer
overrides native/OpenRouter models for names it shares with them
(the MODELS dict is last-entry-wins); drop the outdated gpt-4o-mini entry.
- Fix validate_requesty_key: Requesty's /v1/models returns 200 even for an
invalid/missing key (public catalog), so it cannot validate a key. Use a
minimal authenticated /v1/chat/completions request instead (200 = valid,
403 = invalid), verified against the live endpoint.
- Add tests for Requesty prompt caching (default on, opt-out, non-Anthropic skip).
* Validate Requesty key against auth layer, not a specific model
The onboarding validator probed /v1/chat/completions with a hardcoded
real model (openai/gpt-4o-mini), which tied key validation to that model
staying available upstream. The router resolves auth before the model, so
probe a deliberately nonexistent sentinel model (requesty/auth-preflight)
instead: a valid key yields 404 (model-not-found, auth passed), an invalid
key yields 401/403, and 429/5xx stay inconclusive so a transient outage
does not reject a good key. Add unit tests covering each case.
---------
Co-authored-by: X-iZhang <zacharyzhang2022@gmail.com>
* Add onboarding wizard with style and validation components
- Introduced `style.py` for shared visual elements used in the onboarding wizard.
- Created `validators.py` for input validation, including integer and choice validators, and API key validation functions for various providers.
- Implemented `wizard.py` as the entry point for the onboarding process, managing user prompts and configuration steps.
- Added progress rendering and autosave functionality to enhance user experience during the onboarding process.
* feat(onboarding): enhance validation and configuration for onboarding wizard
- Added validation for UI backends, workspace modes, and providers in the onboarding command.
- Updated channel definitions to include secret field handling for sensitive tokens.
- Improved user prompts for required fields, ensuring sensitive data is masked.
- Introduced constants for valid providers, UI backends, and workspace modes to maintain consistency.
- Implemented tests to ensure alignment between constants and interactive choices in onboarding steps.
* feat(onboarding): improve WeChat account ID prompt and validation for newly enabled channels
* feat(onboarding): enhance WeChat backend credential prompts and validation
* feat(onboarding): refine WeChat backend credential prompts for wecom and wechatmp
* Refactor onboarding package for improved structure and clarity
- Simplified the onboarding package by removing unnecessary re-exports and consolidating public API to only include `run_onboard`.
- Updated `install_back_keys` to `install_navigation_keys` for clarity and consistency in the prompter module.
- Enhanced the `NonInteractivePrompter` class to support strict mode, allowing for better handling of non-interactive prompts.
- Adjusted the onboarding steps to utilize the new navigation keys installation method.
- Improved the `run_onboard` function to handle section implications based on user flags, enhancing the onboarding experience.
- Updated tests to reflect changes in imports and ensure compatibility with the new structure.
* feat(onboarding): enhance validation logic for non-interactive prompts
* refactor(onboarding): streamline onboarding module structure and enhance validation error handling
* refactor(onboarding): enhance config revert logic to preserve original file state
* refactor(onboarding): enhance tavily key validation and error handling in onboarding process
jax-novita