- Implement google-vertex provider in packages/ai
- Support ADC (Application Default Credentials) via @google/generative-ai
- Add Gemini model catalog for Vertex AI
- Update packages/coding-agent to handle google-vertex provider
- Renamed AppMessage to AgentMessage throughout
- New agent-loop.ts with AgentLoopContext, AgentLoopConfig
- Removed transport abstraction, Agent now takes streamFn directly
- Extracted streamProxy to proxy.ts utility
- Removed agent-loop from pi-ai (now in agent package)
- Updated consumers (coding-agent, mom) for AgentMessage rename
- Tests updated but some consumers still need migration
Known issues:
- AgentTool, AgentToolResult not exported from pi-ai
- Attachment not exported from pi-agent-core
- ProviderTransport removed but still referenced
- messageTransformer -> convertToLlm migration incomplete
- CustomMessages declaration merging not working properly
- 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 'aborted' as a distinct stop reason separate from 'error'
- Change AssistantMessage.error to errorMessage for clarity
- Update error event to include reason field ('error' | 'aborted')
- Map provider-specific safety/refusal reasons to 'error' stop reason
- Reorganize utility functions into utils/ directory
- Rename agent.ts to agent-loop.ts for better clarity
- Fix error handling in all providers to properly distinguish abort from error
- Switch from Zod to TypeBox for tool parameter schemas
- TypeBox schemas can be serialized/deserialized as JSON
- Use AJV for runtime validation instead of Zod's parse
- Add StringEnum helper for Google API compatibility (avoids anyOf/const patterns)
- Export Type and Static from main package for convenience
- Update all tests and documentation to reflect TypeBox usage
- Replace JSON Schema with Zod schemas for tool parameter definitions
- Add runtime validation for all tool calls at provider level
- Create shared validation module with detailed error formatting
- Update Agent API with comprehensive event system
- Add agent tests with calculator tool for multi-turn execution
- Add abort test to verify proper handling of aborted requests
- Update documentation with detailed event flow examples
- Rename generate.ts to stream.ts for clarity
- Replace createLLM with getModel/getModels/getProviders functions
- Rename PROVIDERS to MODELS (internal only, not exposed)
- Add streamSimple/completeSimple for unified reasoning interface
- Update README with new API examples and comprehensive documentation
- Remove model registration (models are now fixed from build time)
- Add proper TypeScript typing for provider-specific options
- Document context serialization, cross-provider handoffs, and browser usage
- Implement QueuedGenerateStream class that extends AsyncIterable with finalMessage() method
- Add new types: GenerateStream, GenerateOptions, GenerateOptionsUnified, GenerateFunction
- Create generateAnthropic function-based implementation replacing class-based approach
- Add comprehensive test suite for the new generate API
- Support streaming events with text, thinking, and tool call deltas
- Map ReasoningEffort to provider-specific options
- Include apiKey in options instead of constructor parameter
- Changed package name from @mariozechner/ai to @mariozechner/pi-ai
- Fixed generate-models.ts to fetch from models.dev API instead of local file
- Completely rewrote README with practical examples:
- Image input with base64 encoding
- Proper tool calling with context management
- Streaming with completion indicators
- Abort signal usage
- Provider-specific options (reasoning/thinking)
- Custom model definitions for local/self-hosted LLMs
- Environment variables explanation
- Bumped version to 0.5.9 and published
- LLM constructors now take Model objects instead of string IDs
- Added provider field to AssistantMessage interface
- Updated getModel function with type-safe model ID autocomplete
- Fixed Anthropic model ID mapping for proper API aliases
- Added baseUrl to Model interface for provider-specific endpoints
- Updated all tests to use getModel for model instantiation
- Removed deprecated models.json in favor of generated models
- Add Model interface to types.ts with normalized structure
- Create type-safe generic createLLM function with provider-specific model constraints
- Generate models from OpenRouter API and models.dev data
- Strip provider prefixes for direct providers (google, openai, anthropic, xai)
- Keep full model IDs for OpenRouter-proxied models
- Clean separation: types.ts (Model interface), models.ts (factory logic), models.generated.ts (data)
- Remove old model scripts and unused dependencies
- Rename GeminiLLM to GoogleLLM for consistency
- Add tests for new providers (xAI, Groq, Cerebras, OpenRouter)
- Support 181 tool-capable models across 7 providers with full type safety
- Generate models.generated.ts from models.json with proper types
- Categorize providers: OpenAI (Responses), OpenAI-compatible, Anthropic, Gemini
- Create createLLM() factory with TypeScript overloads for type safety
- Auto-detect base URLs and environment variables for providers
- Support 353 models across 39 providers with full autocompletion
- Exclude generated file from git (rebuilt on npm build)
- Remove catch-all [key: string]: any from ModelInfo
- Make all required fields non-optional (attachment, reasoning, etc.)
- Add proper union types for modalities (text, image, audio, video, pdf)
- Mark only cost and knowledge fields as optional
- Export ModalityInput and ModalityOutput types
- Add models script to download latest model information
- Create models.ts module to query model capabilities
- Include models.json in package distribution
- Export utilities to check model features (reasoning, tools)
- Update build process to copy models.json to dist
- Set up @mariozechner/ai package structure following monorepo patterns
- Install OpenAI, Anthropic, and Google Gemini SDK dependencies
- Document comprehensive API investigation for all three providers
- Design minimal unified API with streaming-first architecture
- Add models.dev integration for pricing and capabilities
- Implement automatic caching strategy for all providers
- Update project documentation with package creation guide
