- Add setOAuthStorage() and resetOAuthStorage() to pi-ai for custom storage backends
- Configure coding-agent to use its own configurable OAuth path via getOAuthPath()
- Model selector (/model command) now only shows models from --models scope when set
- Rewrite OAuth documentation in pi-ai README with examples
Fixes#255
When a user interrupts a tool call flow (sends a message without providing
tool results), APIs like OpenAI Responses and Anthropic fail because:
- OpenAI requires tool outputs for function calls
- OpenAI requires reasoning items to have their following items
- Anthropic requires non-empty content for error tool results
Instead of filtering out orphaned tool calls (which breaks thinking signatures),
we now insert synthetic empty tool results with isError: true and content
'No result provided'. This preserves the conversation structure and satisfies
all API requirements.
- Add OAuth handler with PKCE flow and local callback server
- Automatic project discovery via loadCodeAssist/onboardUser endpoints
- Store credentials with projectId for API calls
- Encode token+projectId as JSON for provider to decode
- Register as 'google-cloud-code-assist' OAuth provider
- Add new API type 'google-cloud-code-assist' for Gemini CLI / Antigravity auth
- Extract shared Google utilities to google-shared.ts
- Implement streaming provider for Cloud Code Assist endpoint
- Add 7 models: gemini-3-pro-high/low, gemini-3-flash, claude-sonnet/opus, gpt-oss
Models use OAuth authentication and have sh cost (uses Google account quota).
OAuth flow will be implemented in coding-agent in a follow-up.
- Add supportsXhigh() function to ai package for checking xhigh support
- Clamp xhigh to high for OpenAI models that don't support it
- Update coding-agent to use centralized supportsXhigh()
- gpt-5.2, gpt-5.2-codex now show xhigh in thinking selector
Closes#236
Add getApiKey hook to AgentLoopConfig that resolves API keys dynamically
before each LLM call. This allows short-lived OAuth tokens (e.g. GitHub
Copilot, Anthropic OAuth) to be refreshed between turns when tool
execution takes a long time.
Previously, the API key was resolved once when ProviderTransport.run()
was called and passed as a static string to the agent loop. If the loop
ran for longer than the token lifetime (e.g. 30 minutes for Copilot),
subsequent LLM calls would fail with expired token errors.
Changes:
- Add getApiKey hook to AgentLoopConfig (packages/ai)
- Call getApiKey before each LLM call in streamAssistantResponse
- Update ProviderTransport to pass getApiKey instead of static apiKey
- Update web-ui ProviderTransport with same pattern
Implement Agent Skills standard (https://agentskills.io/specification):
- Validate name (must match parent dir, lowercase, max 64 chars)
- Validate description (required, max 1024 chars)
- Warn on unknown frontmatter fields
- Warn on name collisions (keep first)
- Change prompt format to XML structure
- Remove {baseDir} placeholder (use relative paths)
- Add tests and update documentation
fixes#231
Previously, when using 'google-generative-ai' API with a custom baseUrl
in models.json, the baseUrl was ignored and requests always went to the
default Google endpoint.
Now the provider correctly passes model.baseUrl to the SDK's
httpOptions.baseUrl, enabling use of custom endpoints or API proxies.
Fixes#216
- Fix tool result format for Gemini 3 Flash Preview compatibility
- Use 'output' key for successful results (not 'result')
- Use 'error' key for error results (not 'isError')
- Per Google SDK documentation for FunctionResponse.response
- Improve type safety in google.ts provider
- Add ImageContent import and use proper type guards
- Replace 'as any' casts with proper typing
- Import and use Schema type for tool parameters
- Add proper typing for index deletion in error handler
- Add comprehensive test for Gemini 3 Flash tool calling
- Tests successful tool call and result handling
- Tests error tool result handling
- Verifies fix for issue #213Fixes#213
* use the correct Gemini 3 Flash Preview thinking levels
* fix a build error
* add changelog entry
* regenerate models
* make less assumptions about future models
