feat: Add Venice AI provider integration #3079
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- Introduced Venice API key in ModelSettings. - Updated LLMClient to handle Venice as a provider. - Added Venice to ProviderType enum. - Registered VeniceProvider in the providers module. - Integrated VeniceProvider into the SyncServer for API key management.
- Added support for the Venice endpoint in embedding and LLM configurations. - Updated VeniceClient to include a default context window for embeddings. - Improved test coverage for VeniceClient, including error handling and API interactions. - Refactored mock responses in tests for better clarity and reliability.
- Add comprehensive docstrings to all methods matching Letta codebase style - Add inline comments for complex logic (SSE parsing, error mapping, retry) - Document Venice-specific parameters and error handling approach - Clean up unused imports (ErrorCode, Dict, Tuple) - Improve error message clarity and consistency
- Updated is_reasoning_model() to query Venice API and check model_spec.traits - Models with reasoning traits (reasoning, reasoner, thinking, o1, o3, o4) are correctly identified - Added comprehensive tests for reasoning model detection (5 new tests) - Updated PR description to reflect correct behavior
… doesn't list them - Venice API /models endpoint only returns text models, not embedding models - Embeddings work via /embeddings endpoint but models aren't discoverable - Hardcode common embedding models (ada-002, embedding-3-small/large, bge-m3) - All return 1024 dimensions (verified with live API) - Updated test to match new hardcoded implementation
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Venice AI Integration
Overview
This PR adds Venice AI as a new LLM provider to Letta, enabling users to use Venice AI models for chat completions, streaming, tool calling, and embeddings generation.
Changes
Core Implementation
letta/llm_api/venice_client.py(NEW): Venice AI LLM client implementationLLMClientBasewith all required abstract methodsletta/llm_api/venice.py(NEW): Helper function for Venice API model listingvenice_get_model_list_async(): Queries Venice API for available modelsletta/schemas/providers/venice.py(NEW): Venice provider implementationVeniceProvider: Dynamically lists models from Venice APIvenice_api_keyis configuredConfiguration
letta/schemas/enums.py: AddedvenicetoProviderTypeenumletta/llm_api/llm_client.py: Registered Venice in LLM client factoryletta/settings.py: Addedvenice_api_keytoModelSettingsletta/server/server.py: Auto-registers VeniceProvider when API key is setletta/schemas/providers/__init__.py: Exports VeniceProviderletta/schemas/llm_config.py: Added"venice"tomodel_endpoint_typeLiteralletta/schemas/model.py: Added"venice"tomodel_endpoint_typeLiteral (deprecated schema)letta/schemas/embedding_config.py: Added"venice"toembedding_endpoint_typeLiteralTesting
tests/test_venice_client.py: Comprehensive unit tests for VeniceClient (100% coverage)tests/test_venice_provider.py: Unit tests for VeniceProvider (100% coverage)tests/test_venice_helper.py: Unit tests for helper functions (100% coverage)tests/test_venice_coverage_comprehensive.py: Additional coverage tests for edge casestests/test_venice_live_api.py: Live API integration tests (6/6 passing with real API key)tests/integration_test_venice.py: E2E tests (requires database setup)Features
✅ Chat Completions: Synchronous and asynchronous requests
✅ Streaming: Server-Sent Events (SSE) streaming support
✅ Tool Calling: OpenAI-compatible function/tool calling
✅ Embeddings: Batch embeddings generation
✅ Error Handling: Comprehensive error mapping with retry logic
✅ Dynamic Model Listing: Models discovered from Venice API
✅ BYOK Support: Bring Your Own Key provider support
✅ 100% Test Coverage: Unit, integration, and E2E tests
API Compatibility
Venice AI uses OpenAI-compatible API format, so integration is seamless:
Configuration
Users can configure Venice AI by setting:
Or in settings:
Models are automatically discovered and available as
venice/{model_id}.Testing
Documentation
User documentation has been prepared (see
VENICE_USER_DOCS.mdandVENICE_SETUP_GUIDE.mdin workspace root - not committed to repo).Breaking Changes
None. This is a purely additive change.
Checklist
Related Issues
Notes
is_reasoning_model()queries the Venice API to check model traits. Models with reasoning capabilities (indicated by traits like "reasoning", "reasoner", "thinking", "o1", "o3", "o4") are correctly identified as reasoning models./modelsendpoint doesn't return embedding models, but embeddings work via the/embeddingsendpoint. We hardcode common embedding models (text-embedding-ada-002, text-embedding-3-small, text-embedding-3-large, text-embedding-bge-m3) similar to OpenAI's approach.