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… env var Replace provider-specific environment variable checks (OPENAI_API_KEY, ANTHROPIC_API_KEY) and skip markers (requires_openai, requires_anthropic) with a single EFFECTFUL_LLM_MODEL environment variable that controls which model is used for all LLM integration tests. - Remove hardcoded model names from all test files in favor of LLM_MODEL read from EFFECTFUL_LLM_MODEL env var - Replace requires_openai/requires_anthropic markers with requires_llm - Remove model parametrization that was cross-provider; tests now use whichever model the env var specifies - Use litellm.supports_vision() to conditionally skip vision tests - Remove default model from LiteLLMProvider (make model required) - Update CI workflow to pass EFFECTFUL_LLM_MODEL as a matrix parameter, making it easy to add parallel CI stages for different providers - Rename/remove fixture files to match updated test names Closes #589
…ename test - Move LLM_MODEL and requires_llm definitions to tests/conftest.py; all four LLM test files now import from there - Fix import ordering in test_handlers_llm_encoding.py (stdlib before local) - Rename test_agent_tool_names_are_openai_compatible_integration to test_agent_tool_names_are_valid_integration since it's no longer OpenAI-specific
- Restore LiteLLMProvider default model via env var fallback so existing
callers (template.py, test_handlers_llm_template.py, notebook) are not
broken: model=os.environ.get("EFFECTFUL_LLM_MODEL", "gpt-4o")
- Move LLM_MODEL/requires_llm from conftest.py to tests/_llm_helpers.py
since conftest.py should not be imported directly
- Fix import placement in test_handlers_llm_provider.py (was between
module-level constants instead of grouped with imports)
- Remove redundant @requires_llm on vision tests where the skipif
condition already covers the not-LLM_MODEL case
The env var belongs in test infrastructure, not the library API. LiteLLMProvider should have a clean, explicit default.
Remove provider-specific environment variable checks and skip markers from LLM tests in favor of a single EFFECTFUL_LLM_MODEL env var. - Add LLM_MODEL and requires_llm to tests/conftest.py; LLM_MODEL defaults to "gpt-4o-mini" and is overridable via EFFECTFUL_LLM_MODEL - Live tests (tool calling, encoding, agent tool names) use LLM_MODEL and are gated by requires_llm which checks for any provider API key - Integration tests use make_provider() which returns a live LiteLLMProvider when API keys are available, else falls back to ReplayLiteLLMProvider for offline replay from fixtures - Replay-only tests (simple_prompt_multiple_models, cross_endpoint, caching) keep hardcoded model names and always run since they never call the API - Update CI workflow to pass EFFECTFUL_LLM_MODEL as a matrix parameter for easy parallel stages with different providers Closes #589
…asisresearch/effectful into dn-fully-parametric-model-test
…asisresearch/effectful into dn-fully-parametric-model-test
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Seems like this refractoring uncovered that we never tested encoding integration for real after putting some fixtures there. Tuple encoding is failing again. Will try to fix. |
Three fixes in encoding.py: 1. TupleEncodable.encode() now returns a TupleItems model instance (not a raw tuple), and deserialize() returns the model directly. This fixes pydantic validation in litellm integration tests for NamedTuple and fixed-tuple types. 2. Add _TupleSafeJsonSchema that overrides pydantic's tuple_schema() to produce object schemas (item_0, item_1 properties) instead of prefixItems arrays. Applied via _BoxEncoding.model_json_schema() so dataclasses containing tuple fields produce OpenAI-compatible schemas. 3. SequenceEncodable.encode() returns a list (not tuple) to preserve encode idempotency — nested_type on a list dispatches to the sequence encoder, avoiding a mismatch with TupleEncodable. Also adds test_handlers_llm_encoding.py back to CI workflow.
Revert encoding.py to master and remove encoding tests from CI workflow. Tuple schema fixes will be in a dedicated PR.
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closes #589