fix: working directory should exist, raise error and not try and create one
docs: improved API Lookup instructions
* test added to confirm failures
* chat schema more explicit about file paths
Stay in codex, plan review and fix complicated bugs, then ask it to spawn claude code and implement the plan.
This uses your current subscription instead of API tokens.
The previous commit (f98046c) added shutil.which() to resolve executables,
which broke two tests that only mocked subprocess execution. This commit
adds shutil.which() mocking to both test files to restore test compatibility.
Co-authored-by: Claude <noreply@anthropic.com>
model definitions now support a new `allow_code_generation` flag, only to be used with higher reasoning models such as GPT-5-Pro and-Gemini 2.5-Pro
When `true`, the `chat` tool can now request the external model to generate a full implementation / update / instructions etc and then share the implementation with the calling agent.
This effectively allows us to utilize more powerful models such as GPT-5-Pro to generate code for us or entire implementations (which are either API-only or part of the $200 Pro plan from within the ChatGPT app)
Zen now allows you to define `roles` for an external CLI and delegate work to another CLI via the new `clink` tool (short for `CLI + Link`). Gemini, for instance, offers 1000 free requests a day - this means you can save on tokens and your weekly limits within Claude Code by delegating work to another entirely capable CLI agent!
Define your own system prompts as `roles` and make another CLI do anything you'd like. Like the current tool you're connected to, the other CLI has complete access to your files and the current context. This also works incredibly well with Zen's `conversation continuity`.
feat: Azure OpenAI / Azure AI Foundry support. Models should be defined in conf/azure_models.json (or a custom path). See .env.example for environment variables or see readme. https://github.com/BeehiveInnovations/zen-mcp-server/issues/265
feat: OpenRouter / Custom Models / Azure can separately also use custom config paths now (see .env.example )
refactor: Model registry class made abstract, OpenRouter / Custom Provider / Azure OpenAI now subclass these
refactor: breaking change: `is_custom` property has been removed from model_capabilities.py (and thus custom_models.json) given each models are now read from separate configuration files
fix: restrictions should resolve canonical names for openrouter
fix: tools now correctly return restricted list by presenting model names in schema
fix: tests updated to ensure these manage their expected env vars properly
perf: cache model alias resolution to avoid repeated checks
Adds support for OpenAI's GPT-5-Codex model which uses the new Responses API
endpoint (/v1/responses) instead of the standard Chat Completions API.
Changes:
- Add GPT-5-Codex to MODEL_CAPABILITIES with 400K context, 128K output
- Prioritize GPT-5-Codex for EXTENDED_REASONING tasks
- Add aliases: codex, gpt5-codex, gpt-5-code
- Update tests to expect GPT-5-Codex for extended reasoning
Benefits:
- 40-80% cost savings through Responses API caching
- 3% better performance on coding tasks (SWE-bench)
- Leverages existing dual-API infrastructure
fix: model definition re-introduced into the schema but intelligently and only a summary is generated per tool. Required to ensure CLI calls and uses the correct model
fix: removed `model` param from some tools where this wasn't needed
fix: fixed adherence to `*_ALLOWED_MODELS` by advertising only the allowed models to the CLI
fix: removed duplicates across providers when passing canonical names back to the CLI; the first enabled provider wins
docs: document provider base class
refactor: cleanup custom provider, it should only deal with `is_custom` model configurations
fix: make sure openrouter provider does not load `is_custom` models
fix: listmodels tool cleanup
