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EvoScientist/tests/test_llm.py
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"""Tests for EvoScientist LLM module."""
from unittest.mock import patch
import pytest
# Side-effect import: applies module-level monkey-patches (e.g.,
# _patch_openai_capture_reasoning_content) before tests reference patched
# functions from langchain_openai.
import EvoScientist.llm.patches # noqa: F401
from EvoScientist.llm import (
DEFAULT_MODEL,
MODELS,
get_chat_model,
get_model_info,
get_models_for_provider,
list_models,
)
from EvoScientist.llm.models import _MODEL_ENTRIES
# =============================================================================
# Test MODELS registry
# =============================================================================
class TestModelsRegistry:
def test_models_is_dict(self):
"""Test that MODELS is a dictionary."""
assert isinstance(MODELS, dict)
def test_entries_has_all_providers(self):
"""Test that _MODEL_ENTRIES covers all registered providers."""
providers = {p for _, _, p in _MODEL_ENTRIES}
assert "anthropic" in providers
assert "openai" in providers
assert "google-genai" in providers
assert "minimax" in providers
assert "nvidia" in providers
assert "siliconflow" in providers
assert "openrouter" in providers
assert "zhipu" in providers
assert "zhipu-code" in providers
assert "volcengine" in providers
assert "dashscope" in providers
assert "dashscope-code" in providers
assert "deepseek" in providers
assert "moonshot" in providers
assert "kimi-coding" in providers
def test_entries_are_valid_tuples(self):
"""Test that _MODEL_ENTRIES contains valid (name, model_id, provider) tuples."""
valid_providers = {
"anthropic",
"openai",
"google-genai",
"minimax",
"nvidia",
"siliconflow",
"openrouter",
"zhipu",
"zhipu-code",
"volcengine",
"dashscope",
"dashscope-code",
"custom-openai",
"custom-anthropic",
"deepseek",
"moonshot",
"kimi-coding",
}
for entry in _MODEL_ENTRIES:
assert len(entry) == 3, f"Entry {entry} doesn't have 3 elements"
name, model_id, provider = entry
assert isinstance(name, str)
assert isinstance(model_id, str)
assert provider in valid_providers, (
f"Unknown provider '{provider}' for '{name}'"
)
def test_get_models_for_provider(self):
"""Test that get_models_for_provider returns correct models."""
anthropic_models = get_models_for_provider("anthropic")
assert len(anthropic_models) > 0
for name, model_id in anthropic_models:
assert isinstance(name, str)
assert isinstance(model_id, str)
# Third-party providers now have registered models
openrouter_models = get_models_for_provider("openrouter")
assert len(openrouter_models) > 0
siliconflow_models = get_models_for_provider("siliconflow")
assert len(siliconflow_models) > 0
# =============================================================================
# Test DEFAULT_MODEL
# =============================================================================
class TestDefaultModel:
def test_default_model_exists_in_registry(self):
"""Test that DEFAULT_MODEL is a valid model in MODELS."""
assert DEFAULT_MODEL in MODELS
def test_default_model_is_anthropic(self):
"""Test that default model uses Anthropic."""
_, provider = MODELS[DEFAULT_MODEL]
assert provider == "anthropic"
# =============================================================================
# Test list_models
# =============================================================================
class TestListModels:
def test_returns_list(self):
"""Test that list_models returns a list."""
result = list_models()
assert isinstance(result, list)
def test_returns_all_model_names(self):
"""Test that list_models returns all model names."""
result = list_models()
assert set(result) == set(MODELS.keys())
def test_list_is_not_empty(self):
"""Test that the list is not empty."""
assert len(list_models()) > 0
# =============================================================================
# Test get_model_info
# =============================================================================
class TestGetModelInfo:
def test_returns_tuple_for_valid_model(self):
"""Test that get_model_info returns tuple for valid model."""
result = get_model_info("claude-sonnet-4-6")
assert result is not None
assert isinstance(result, tuple)
assert len(result) == 2
def test_returns_none_for_invalid_model(self):
"""Test that get_model_info returns None for invalid model."""
result = get_model_info("nonexistent-model")
assert result is None
def test_returns_correct_info(self):
"""Test that get_model_info returns correct info."""
model_id, provider = get_model_info("gpt-5-nano")
assert model_id == "gpt-5-nano"
assert provider == "openai"
# =============================================================================
# Test get_chat_model
# =============================================================================
class TestGetChatModel:
@patch("EvoScientist.llm.models.init_chat_model")
def test_uses_default_model_when_none(self, mock_init):
"""Test that get_chat_model uses default model when model=None."""
mock_init.return_value = "mock_model"
get_chat_model()
mock_init.assert_called_once()
call_kwargs = mock_init.call_args[1]
# Default model should be resolved from MODELS
expected_model_id, expected_provider = MODELS[DEFAULT_MODEL]
assert call_kwargs["model"] == expected_model_id
assert call_kwargs["model_provider"] == expected_provider
@patch("EvoScientist.llm.models.init_chat_model")
def test_resolves_short_name(self, mock_init):
"""Test that get_chat_model resolves short names correctly."""
mock_init.return_value = "mock_model"
get_chat_model("claude-opus-4-8")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "claude-opus-4-8"
assert call_kwargs["model_provider"] == "anthropic"
@patch("EvoScientist.llm.models.init_chat_model")
def test_resolves_openai_short_name(self, mock_init):
"""Test that get_chat_model resolves OpenAI short names."""
mock_init.return_value = "mock_model"
get_chat_model("gpt-5-mini")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "gpt-5-mini"
assert call_kwargs["model_provider"] == "openai"
@patch("EvoScientist.llm.models.init_chat_model")
def test_uses_full_model_id(self, mock_init):
"""Test that get_chat_model accepts full model IDs."""
mock_init.return_value = "mock_model"
get_chat_model("claude-3-opus-20240229")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "claude-3-opus-20240229"
# Should infer anthropic from the model prefix
assert call_kwargs["model_provider"] == "anthropic"
@patch("EvoScientist.llm.models.init_chat_model")
def test_provider_override(self, mock_init):
"""Test that provider can be overridden."""
mock_init.return_value = "mock_model"
get_chat_model("claude-sonnet-4-6", provider="custom_provider")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "custom_provider"
@patch("EvoScientist.llm.models.init_chat_model")
def test_passes_kwargs(self, mock_init):
"""Test that additional kwargs are passed through."""
mock_init.return_value = "mock_model"
get_chat_model("gpt-5-nano", temperature=0.7, max_tokens=1000)
call_kwargs = mock_init.call_args[1]
assert call_kwargs["temperature"] == 0.7
assert call_kwargs["max_tokens"] == 1000
@patch("EvoScientist.llm.models.init_chat_model")
def test_infers_openai_from_gpt_prefix(self, mock_init):
"""Test that OpenAI is inferred from gpt- prefix."""
mock_init.return_value = "mock_model"
get_chat_model("gpt-4-turbo-preview")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
@patch("EvoScientist.llm.models.init_chat_model")
def test_infers_openai_from_o1_prefix(self, mock_init):
"""Test that OpenAI is inferred from o1 prefix."""
mock_init.return_value = "mock_model"
get_chat_model("o1-preview")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
@patch("EvoScientist.llm.models.init_chat_model")
def test_infers_google_from_gemini_prefix(self, mock_init):
"""Test that google-genai is inferred from gemini prefix."""
mock_init.return_value = "mock_model"
get_chat_model("gemini-2.0-flash")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "google-genai"
@patch("EvoScientist.llm.models.init_chat_model")
def test_defaults_to_anthropic_for_unknown(self, mock_init):
"""Test that anthropic is default for unknown model prefixes."""
mock_init.return_value = "mock_model"
get_chat_model("some-unknown-model")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "anthropic"
# =============================================================================
# Test Ollama provider
# =============================================================================
class TestOllamaProvider:
"""Ollama models are not in the static registry (detected dynamically).
All tests use explicit provider or ollama: prefix."""
@patch("EvoScientist.llm.models.init_chat_model")
def test_explicit_provider(self, mock_init):
"""Test that explicit provider='ollama' routes correctly."""
mock_init.return_value = "mock_model"
get_chat_model("llama3.1:8b", provider="ollama")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "llama3.1:8b"
assert call_kwargs["model_provider"] == "ollama"
@patch("EvoScientist.llm.models.init_chat_model")
def test_ollama_base_url_passthrough(self, mock_init, monkeypatch):
"""Test that OLLAMA_BASE_URL env var is passed to kwargs."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OLLAMA_BASE_URL", "http://gpu-cluster:11434")
get_chat_model("llama3.1:8b", provider="ollama")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["base_url"] == "http://gpu-cluster:11434"
assert call_kwargs["model_provider"] == "ollama"
@patch("EvoScientist.llm.models.init_chat_model")
def test_ollama_no_base_url_when_unset(self, mock_init, monkeypatch):
"""Test that base_url is not set when OLLAMA_BASE_URL is empty."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
get_chat_model("llama3.1:8b", provider="ollama")
call_kwargs = mock_init.call_args[1]
assert "base_url" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_reasoning_auto_enabled_for_ollama(self, mock_init, monkeypatch):
"""Test that reasoning is auto-enabled for Ollama models."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
get_chat_model("llama3.1:8b", provider="ollama")
call_kwargs = mock_init.call_args[1]
assert "thinking" not in call_kwargs
assert call_kwargs["reasoning"] is True
@patch("EvoScientist.llm.models.init_chat_model")
def test_reasoning_not_overridden_for_ollama(self, mock_init, monkeypatch):
"""Test that explicit reasoning=False is not overridden for Ollama."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
get_chat_model("llama3.1:8b", provider="ollama", reasoning=False)
call_kwargs = mock_init.call_args[1]
assert call_kwargs["reasoning"] is False
def test_no_static_registry_entries(self):
"""Test that Ollama has no static registry entries (models detected dynamically)."""
ollama_models = get_models_for_provider("ollama")
assert len(ollama_models) == 0
@patch("EvoScientist.llm.models.init_chat_model")
def test_ollama_prefix_inference(self, mock_init, monkeypatch):
"""Test that ollama: prefix infers ollama provider."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OLLAMA_BASE_URL", raising=False)
get_chat_model("ollama:phi3:mini")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "phi3:mini"
assert call_kwargs["model_provider"] == "ollama"
# =============================================================================
# Test slash model ID no longer routes to nvidia
# =============================================================================
class TestSlashModelIdFallback:
@patch("EvoScientist.llm.models.init_chat_model")
def test_slash_model_id_defaults_to_anthropic(self, mock_init):
"""Unregistered model IDs containing '/' should NOT route to nvidia.
They fall through to the default 'anthropic' provider, consistent
with how all other unknown model IDs are handled.
"""
mock_init.return_value = "mock_model"
get_chat_model("some-org/some-model")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "some-org/some-model"
assert call_kwargs["model_provider"] == "anthropic"
# =============================================================================
# Test third-party provider routing
# =============================================================================
class TestThirdPartyRouting:
@patch("EvoScientist.llm.models.init_chat_model")
def test_siliconflow_routes_through_openai(self, mock_init, monkeypatch):
"""SiliconFlow provider should route through OpenAI with correct base_url."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("SILICONFLOW_API_KEY", "sf-key-123")
get_chat_model("Pro/zai-org/GLM-5", provider="siliconflow")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
assert call_kwargs["base_url"] == "https://api.siliconflow.cn/v1"
assert call_kwargs["api_key"] == "sf-key-123"
# SiliconFlow should disable thinking
assert call_kwargs["extra_body"]["enable_thinking"] is False
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_uses_native_provider(self, mock_init, monkeypatch):
"""OpenRouter should use native 'openrouter' provider via init_chat_model."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key-456")
# Assert the DEFAULT effort, so isolate from any leaked env override.
monkeypatch.delenv("EVOSCIENTIST_REASONING_EFFORT", raising=False)
get_chat_model("x-ai/grok-4.3", provider="openrouter")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openrouter"
assert call_kwargs["api_key"] == "or-key-456"
assert call_kwargs["reasoning"] == {"effort": "high", "summary": "auto"}
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_reasoning_user_override(self, mock_init, monkeypatch):
"""User-supplied reasoning config should not be overridden."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
get_chat_model(
"x-ai/grok-4.3",
provider="openrouter",
reasoning={"effort": "low"},
)
call_kwargs = mock_init.call_args[1]
assert call_kwargs["reasoning"] == {"effort": "low"}
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_reasoning_effort_from_env(self, mock_init, monkeypatch):
"""Reasoning effort should be configurable via env var."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.setenv("EVOSCIENTIST_REASONING_EFFORT", "medium")
get_chat_model("x-ai/grok-4.3", provider="openrouter")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["reasoning"] == {"effort": "medium", "summary": "auto"}
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_anthropic_prompt_cache_disabled_by_default(
self, mock_init, monkeypatch
):
"""OpenRouter Anthropic prompt caching should be opt-in."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.delenv(
"EVOSCIENTIST_OPENROUTER_ANTHROPIC_PROMPT_CACHE", raising=False
)
get_chat_model("claude-sonnet-4.6", provider="openrouter")
call_kwargs = mock_init.call_args[1]
assert "cache_control" not in call_kwargs
assert "cache_control" not in call_kwargs.get("model_kwargs", {})
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_anthropic_prompt_cache_opt_in(self, mock_init, monkeypatch):
"""The opt-in flag should declare caching for OpenRouter Claude models."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.setenv("EVOSCIENTIST_OPENROUTER_ANTHROPIC_PROMPT_CACHE", "true")
get_chat_model("claude-sonnet-4.6", provider="openrouter")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openrouter"
assert call_kwargs["model"] == "anthropic/claude-sonnet-4.6"
assert call_kwargs["model_kwargs"]["cache_control"] == {"type": "ephemeral"}
@patch("EvoScientist.llm.models.init_chat_model")
def test_prompt_cache_opt_in_skips_non_anthropic_openrouter(
self, mock_init, monkeypatch
):
"""OpenRouter models with implicit caching should be left alone."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.setenv("EVOSCIENTIST_OPENROUTER_ANTHROPIC_PROMPT_CACHE", "true")
get_chat_model("x-ai/grok-4.3", provider="openrouter")
call_kwargs = mock_init.call_args[1]
assert "cache_control" not in call_kwargs
assert "cache_control" not in call_kwargs.get("model_kwargs", {})
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_anthropic_prompt_cache_preserves_top_level_override(
self, mock_init, monkeypatch
):
"""The default should not duplicate a caller's cache_control kwarg."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.setenv("EVOSCIENTIST_OPENROUTER_ANTHROPIC_PROMPT_CACHE", "true")
override = {"type": "ephemeral", "ttl": "1h"}
get_chat_model(
"claude-sonnet-4.6",
provider="openrouter",
cache_control=override,
)
call_kwargs = mock_init.call_args[1]
assert call_kwargs["cache_control"] == override
assert "cache_control" not in call_kwargs.get("model_kwargs", {})
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_anthropic_prompt_cache_preserves_model_kwargs_override(
self, mock_init, monkeypatch
):
"""The default should not duplicate model_kwargs cache_control."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.setenv("EVOSCIENTIST_OPENROUTER_ANTHROPIC_PROMPT_CACHE", "true")
override = {"type": "ephemeral", "ttl": "1h"}
get_chat_model(
"claude-sonnet-4.6",
provider="openrouter",
model_kwargs={"cache_control": override},
)
call_kwargs = mock_init.call_args[1]
assert "cache_control" not in call_kwargs
assert call_kwargs["model_kwargs"]["cache_control"] == override
@patch("EvoScientist.llm.models.init_chat_model")
def test_openrouter_anthropic_prompt_cache_warns_on_invalid_model_kwargs(
self, mock_init, monkeypatch
):
"""Invalid model_kwargs shape should warn and skip cache injection."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.setenv("EVOSCIENTIST_OPENROUTER_ANTHROPIC_PROMPT_CACHE", "true")
with pytest.warns(UserWarning, match="model_kwargs` is not a dict"):
get_chat_model(
"claude-sonnet-4.6",
provider="openrouter",
model_kwargs="bad",
)
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_kwargs"] == "bad"
@patch("EvoScientist.llm.models.init_chat_model")
def test_custom_routes_through_openai(self, mock_init, monkeypatch):
"""Custom provider should route through OpenAI with env-configured base_url."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("CUSTOM_OPENAI_BASE_URL", "https://my-llm.example.com/v1")
monkeypatch.setenv("CUSTOM_OPENAI_API_KEY", "custom-key-789")
get_chat_model("my-custom-model", provider="custom-openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
assert call_kwargs["base_url"] == "https://my-llm.example.com/v1"
assert call_kwargs["api_key"] == "custom-key-789"
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_base_url_override(self, mock_init, monkeypatch):
"""Anthropic provider should support base_url override (e.g. ccproxy)."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("ANTHROPIC_BASE_URL", "http://localhost:8000/api/v1")
monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-dummy")
get_chat_model("claude-sonnet-4-6", provider="anthropic")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "anthropic"
assert call_kwargs["base_url"] == "http://localhost:8000/api/v1"
assert call_kwargs["api_key"] == "sk-dummy"
# Proxy mode: thinking skipped (history round-trip causes 422)
assert "thinking" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_no_base_url_when_unset(self, mock_init, monkeypatch):
"""Anthropic provider should not set base_url when env var is empty."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("ANTHROPIC_BASE_URL", raising=False)
monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-real")
get_chat_model("claude-sonnet-4-6", provider="anthropic")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "anthropic"
assert "base_url" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_third_party_no_reasoning(self, mock_init, monkeypatch):
"""Third-party providers routed through OpenAI should NOT get auto-reasoning."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("SILICONFLOW_API_KEY", "sf-key")
get_chat_model("deepseek-v3", provider="siliconflow")
call_kwargs = mock_init.call_args[1]
assert "reasoning" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_volcengine_routes_through_openai(self, mock_init, monkeypatch):
"""Volcengine provider should route through OpenAI with correct base_url."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("VOLCENGINE_API_KEY", "ve-key-123")
get_chat_model("doubao-seed-1.6", provider="volcengine")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
assert call_kwargs["base_url"] == "https://ark.cn-beijing.volces.com/api/v3"
assert call_kwargs["api_key"] == "ve-key-123"
@patch("EvoScientist.llm.models.init_chat_model")
def test_dashscope_routes_through_openai(self, mock_init, monkeypatch):
"""DashScope provider should route through OpenAI with correct base_url."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("DASHSCOPE_API_KEY", "ds-key-456")
get_chat_model("qwen-max", provider="dashscope")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
assert (
call_kwargs["base_url"]
== "https://dashscope.aliyuncs.com/compatible-mode/v1"
)
assert call_kwargs["api_key"] == "ds-key-456"
@patch("EvoScientist.llm.models.init_chat_model")
def test_dashscope_code_routes_through_openai(self, mock_init, monkeypatch):
"""DashScope-Code (sk-sp-* subscription keys) routes through OpenAI
with the coding.dashscope.aliyuncs.com base URL, reusing DASHSCOPE_API_KEY.
"""
mock_init.return_value = "mock_model"
monkeypatch.setenv("DASHSCOPE_API_KEY", "sk-sp-key-789")
get_chat_model("qwen3-coder", provider="dashscope-code")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
assert call_kwargs["base_url"] == "https://coding.dashscope.aliyuncs.com/v1"
assert call_kwargs["api_key"] == "sk-sp-key-789"
@patch("EvoScientist.llm.models.init_chat_model")
def test_minimax_routes_through_anthropic(self, mock_init, monkeypatch):
"""MiniMax provider should route through Anthropic with correct base_url."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key-123")
get_chat_model("MiniMax-M2.5", provider="minimax")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "anthropic"
assert call_kwargs["base_url"] == "https://api.minimaxi.com/anthropic"
assert call_kwargs["api_key"] == "mm-key-123"
@patch("EvoScientist.llm.models.init_chat_model")
def test_minimax_base_url_env_override(self, mock_init, monkeypatch):
"""MINIMAX_BASE_URL env var should override the default base URL."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key-123")
monkeypatch.setenv("MINIMAX_BASE_URL", "https://api.minimax.io/anthropic")
get_chat_model("MiniMax-M2.5", provider="minimax")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["base_url"] == "https://api.minimax.io/anthropic"
@patch("EvoScientist.llm.models.init_chat_model")
def test_minimax_gets_thinking(self, mock_init, monkeypatch):
"""MiniMax provider should get auto-thinking (thinking-capable via Anthropic)."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key")
get_chat_model("MiniMax-M2.5", provider="minimax")
call_kwargs = mock_init.call_args[1]
assert "thinking" in call_kwargs
assert "reasoning" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_minimax_short_name_resolution(self, mock_init, monkeypatch):
"""MiniMax short names should resolve to correct model IDs."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key")
get_chat_model("minimax-m2.5", provider="minimax")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "MiniMax-M2.5"
assert call_kwargs["model_provider"] == "anthropic"
@patch("EvoScientist.llm.models.init_chat_model")
def test_minimax_highspeed_model(self, mock_init, monkeypatch):
"""MiniMax M2.5-highspeed model should resolve correctly."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("MINIMAX_API_KEY", "mm-key")
get_chat_model("minimax-m2.5-highspeed", provider="minimax")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model"] == "MiniMax-M2.5-highspeed"
assert call_kwargs["model_provider"] == "anthropic"
assert call_kwargs["base_url"] == "https://api.minimaxi.com/anthropic"
@patch("EvoScientist.llm.models.init_chat_model")
def test_custom_anthropic_via_routed_dict(self, mock_init, monkeypatch):
"""custom-anthropic should work via _ANTHROPIC_ROUTED_PROVIDERS dict."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("CUSTOM_ANTHROPIC_BASE_URL", "https://my-claude.example.com")
monkeypatch.setenv("CUSTOM_ANTHROPIC_API_KEY", "ca-key-789")
get_chat_model("claude-sonnet-4-6", provider="custom-anthropic")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "anthropic"
assert call_kwargs["base_url"] == "https://my-claude.example.com"
assert call_kwargs["api_key"] == "ca-key-789"
# custom-anthropic is NOT thinking-capable → thinking skipped
assert "thinking" not in call_kwargs
# =============================================================================
# Test MiniMax provider
# =============================================================================
class TestMiniMaxProvider:
def test_minimax_in_anthropic_routed_providers(self):
"""MiniMax should be registered in _ANTHROPIC_ROUTED_PROVIDERS."""
from EvoScientist.llm.models import _ANTHROPIC_ROUTED_PROVIDERS
assert "minimax" in _ANTHROPIC_ROUTED_PROVIDERS
base_url, api_key_env = _ANTHROPIC_ROUTED_PROVIDERS["minimax"]
assert base_url == "https://api.minimaxi.com/anthropic"
assert api_key_env == "MINIMAX_API_KEY"
def test_minimax_not_in_openai_routed_providers(self):
"""MiniMax should NOT be in _OPENAI_ROUTED_PROVIDERS (moved to Anthropic)."""
from EvoScientist.llm.models import _OPENAI_ROUTED_PROVIDERS
assert "minimax" not in _OPENAI_ROUTED_PROVIDERS
def test_minimax_models_registered(self):
"""MiniMax should have 5 direct model entries in _MODEL_ENTRIES."""
minimax_models = get_models_for_provider("minimax")
assert len(minimax_models) == 5
model_names = {name for name, _ in minimax_models}
assert "minimax-m3" in model_names
assert "minimax-m2.7" in model_names
assert "minimax-m2.7-highspeed" in model_names
assert "minimax-m2.5" in model_names
assert "minimax-m2.5-highspeed" in model_names
def test_minimax_model_ids_correct(self):
"""MiniMax model IDs should match the official API model names."""
minimax_models = get_models_for_provider("minimax")
model_dict = dict(minimax_models)
assert model_dict["minimax-m2.7"] == "MiniMax-M2.7"
assert model_dict["minimax-m2.5"] == "MiniMax-M2.5"
assert model_dict["minimax-m2.5-highspeed"] == "MiniMax-M2.5-highspeed"
def test_minimax_short_name_in_models_dict(self):
"""MiniMax short names should be accessible via the MODELS dict."""
# Note: MODELS dict uses last-entry-wins, so direct minimax entries
# may be overridden by nvidia/siliconflow/openrouter entries.
# Use get_models_for_provider() for provider-specific lookups.
minimax_models = get_models_for_provider("minimax")
assert len(minimax_models) > 0
# =============================================================================
# Test _flatten_message_content
# =============================================================================
class TestFlattenMessageContent:
"""Tests for the content-flattening utility used by OpenAI-compatible providers."""
def test_string_passthrough(self):
from EvoScientist.llm.patches import _flatten_message_content
assert _flatten_message_content("hello") == "hello"
def test_non_list_passthrough(self):
from EvoScientist.llm.patches import _flatten_message_content
assert _flatten_message_content(42) == 42
assert _flatten_message_content(None) is None
def test_text_blocks(self):
from EvoScientist.llm.patches import _flatten_message_content
content = [
{"type": "text", "text": "Hello"},
{"type": "text", "text": "World"},
]
assert _flatten_message_content(content) == "Hello\n\nWorld"
def test_skips_thinking_blocks(self):
from EvoScientist.llm.patches import _flatten_message_content
content = [
{"type": "thinking", "text": "Let me think..."},
{"type": "text", "text": "The answer is 42"},
{"type": "reasoning", "text": "internal reasoning"},
{"type": "reasoning_content", "text": "more reasoning"},
]
assert _flatten_message_content(content) == "The answer is 42"
def test_string_blocks(self):
from EvoScientist.llm.patches import _flatten_message_content
content = ["hello", "world"]
assert _flatten_message_content(content) == "hello\n\nworld"
def test_mixed_blocks(self):
from EvoScientist.llm.patches import _flatten_message_content
content = [
{"type": "thinking", "text": "skip me"},
"plain string",
{"type": "text", "text": "dict text"},
]
assert _flatten_message_content(content) == "plain string\n\ndict text"
def test_empty_list(self):
from EvoScientist.llm.patches import _flatten_message_content
assert _flatten_message_content([]) == ""
def test_only_thinking_blocks(self):
from EvoScientist.llm.patches import _flatten_message_content
content = [{"type": "thinking", "text": "thought"}]
assert _flatten_message_content(content) == ""
def test_preserves_image_block(self):
from EvoScientist.llm.patches import _flatten_message_content
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
assert _flatten_message_content([img]) == [img]
def test_preserves_image_url_block(self):
from EvoScientist.llm.patches import _flatten_message_content
img = {"type": "image_url", "image_url": {"url": "data:image/png;base64,AAA"}}
assert _flatten_message_content([img]) == [img]
def test_preserves_file_block(self):
# PDF/document files are preserved (capable models read them).
from EvoScientist.llm.patches import _flatten_message_content
f = {"type": "file", "base64": "FFF", "mime_type": "application/pdf"}
assert _flatten_message_content([f]) == [f]
def test_unsupported_media_dropped(self):
# video/audio are NOT in the allowlist -> dropped, not crashing
# (langchain-openai raises ValueError on `video`).
from EvoScientist.llm.patches import _flatten_message_content
for block in (
{"type": "video", "base64": "VVV", "mime_type": "video/mp4"},
{"type": "audio", "base64": "ZZZ", "mime_type": "audio/wav"},
):
assert _flatten_message_content([block]) == ""
def test_non_image_media_dropped_keeps_text(self):
from EvoScientist.llm.patches import _flatten_message_content
content = [
{"type": "text", "text": "hi"},
{"type": "video", "base64": "VVV", "mime_type": "video/mp4"},
]
# Video dropped, text kept -> plain string (no media list).
assert _flatten_message_content(content) == "hi"
def test_consolidates_text_and_image(self):
from EvoScientist.llm.patches import _flatten_message_content
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
content = [{"type": "text", "text": "a photo"}, img]
assert _flatten_message_content(content) == [
{"type": "text", "text": "a photo"},
img,
]
def test_multiple_text_blocks_with_image(self):
from EvoScientist.llm.patches import _flatten_message_content
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
content = [
{"type": "text", "text": "a"},
{"type": "text", "text": "b"},
img,
]
assert _flatten_message_content(content) == [
{"type": "text", "text": "a\n\nb"},
img,
]
def test_preserves_text_media_ordering(self):
# Text after an image must stay AFTER it (not consolidated to the front).
from EvoScientist.llm.patches import _flatten_message_content
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
content = [
{"type": "text", "text": "before"},
img,
{"type": "text", "text": "after"},
]
assert _flatten_message_content(content) == [
{"type": "text", "text": "before"},
img,
{"type": "text", "text": "after"},
]
def test_thinking_dropped_image_kept(self):
from EvoScientist.llm.patches import _flatten_message_content
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
content = [{"type": "thinking", "text": "hmm"}, img]
assert _flatten_message_content(content) == [img]
def test_pure_text_still_returns_string(self):
from EvoScientist.llm.patches import _flatten_message_content
content = [{"type": "text", "text": "x"}, {"type": "text", "text": "y"}]
result = _flatten_message_content(content)
assert result == "x\n\ny"
assert isinstance(result, str)
def test_unknown_nontext_block_still_dropped(self):
from EvoScientist.llm.patches import _flatten_message_content
content = [{"type": "tool_use", "id": "1", "name": "foo"}]
assert _flatten_message_content(content) == ""
# =============================================================================
# Test _patch_openai_compat_content (all 4 paths)
# =============================================================================
class TestPatchOpenAICompatContent:
"""Verify content flattening covers _generate, _agenerate, _stream, _astream."""
def _make_model(self):
"""Create a minimal mock model with all 4 methods."""
from unittest.mock import AsyncMock, MagicMock
model = MagicMock()
model._generate = MagicMock(return_value="gen_result")
model._agenerate = AsyncMock(return_value="agen_result")
model._stream = MagicMock(return_value=iter(["chunk1"]))
model._astream = AsyncMock()
return model
def test_generate_flattened(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model)
msg = HumanMessage(content=[{"type": "text", "text": "hello"}])
model._generate([msg])
called_msgs = orig.call_args[0][0]
assert called_msgs[0].content == "hello"
@pytest.mark.anyio
async def test_agenerate_flattened(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._agenerate
_patch_openai_compat_content(model)
msg = HumanMessage(content=[{"type": "text", "text": "hello"}])
await model._agenerate([msg])
called_msgs = orig.call_args[0][0]
assert called_msgs[0].content == "hello"
def test_stream_flattened(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._stream
_patch_openai_compat_content(model)
msg = HumanMessage(content=[{"type": "text", "text": "hello"}])
list(model._stream([msg]))
called_msgs = orig.call_args[0][0]
assert called_msgs[0].content == "hello"
@pytest.mark.anyio
async def test_astream_flattened(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
received_msgs = []
async def _fake_astream(messages, *args, **kwargs):
received_msgs.extend(messages)
for chunk in ["c1", "c2"]:
yield chunk
model._astream = _fake_astream
_patch_openai_compat_content(model)
msg = HumanMessage(content=[{"type": "text", "text": "hello"}])
chunks = []
async for c in model._astream([msg]):
chunks.append(c)
assert chunks == ["c1", "c2"]
assert received_msgs[0].content == "hello"
def test_generate_preserves_media(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model)
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
msg = HumanMessage(content=[{"type": "text", "text": "see"}, img])
model._generate([msg])
called_msgs = orig.call_args[0][0]
assert called_msgs[0].content == [{"type": "text", "text": "see"}, img]
@pytest.mark.anyio
async def test_agenerate_preserves_media(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._agenerate
_patch_openai_compat_content(model)
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
msg = HumanMessage(content=[{"type": "text", "text": "see"}, img])
await model._agenerate([msg])
called_msgs = orig.call_args[0][0]
assert called_msgs[0].content == [{"type": "text", "text": "see"}, img]
def test_stream_preserves_media(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._stream
_patch_openai_compat_content(model)
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
msg = HumanMessage(content=[{"type": "text", "text": "see"}, img])
list(model._stream([msg]))
called_msgs = orig.call_args[0][0]
assert called_msgs[0].content == [{"type": "text", "text": "see"}, img]
@pytest.mark.anyio
async def test_astream_preserves_media(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
received_msgs = []
async def _fake_astream(messages, *args, **kwargs):
received_msgs.extend(messages)
for chunk in ["c1", "c2"]:
yield chunk
model._astream = _fake_astream
_patch_openai_compat_content(model)
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
msg = HumanMessage(content=[{"type": "text", "text": "see"}, img])
chunks = []
async for c in model._astream([msg]):
chunks.append(c)
assert chunks == ["c1", "c2"]
assert received_msgs[0].content == [{"type": "text", "text": "see"}, img]
def test_toolmessage_image_hoisted_to_human(self):
from langchain_core.messages import ToolMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model) # hoist_tool_media=True (OpenAI-compat)
# deepagents read_file emits this exact shape for an image file.
tm = ToolMessage(
content_blocks=[
{"type": "image", "base64": "AAA", "mime_type": "image/png"}
],
tool_call_id="tc1",
name="read_file",
)
model._generate([tm])
called_msgs = orig.call_args[0][0]
# Tool content becomes a string placeholder (OpenAI-compat requirement) ...
assert isinstance(called_msgs[0].content, str)
# ... and the image is hoisted into a following HumanMessage.
assert len(called_msgs) == 2
hoisted = called_msgs[1]
assert hoisted.type == "human"
assert any(
isinstance(b, dict) and b.get("type") == "image" for b in hoisted.content
)
def test_toolmessage_image_kept_inline_when_no_hoist(self):
from langchain_core.messages import ToolMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model, hoist_tool_media=False) # Anthropic-routed
tm = ToolMessage(
content_blocks=[
{"type": "image", "base64": "AAA", "mime_type": "image/png"}
],
tool_call_id="tc1",
name="read_file",
)
model._generate([tm])
called_msgs = orig.call_args[0][0]
# No hoisting: image stays inline in the tool message content.
assert len(called_msgs) == 1
content = called_msgs[0].content
assert isinstance(content, list)
assert any(isinstance(b, dict) and b.get("type") == "image" for b in content)
def test_parallel_tool_images_hoisted_after_tools(self):
from langchain_core.messages import AIMessage, ToolMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model)
ai = AIMessage(
content="",
tool_calls=[
{"id": "c1", "name": "read_file", "args": {}},
{"id": "c2", "name": "read_file", "args": {}},
],
)
t1 = ToolMessage(
content_blocks=[
{"type": "image", "base64": "AAA", "mime_type": "image/png"}
],
tool_call_id="c1",
name="read_file",
)
t2 = ToolMessage(
content_blocks=[
{"type": "image", "base64": "BBB", "mime_type": "image/png"}
],
tool_call_id="c2",
name="read_file",
)
model._generate([ai, t1, t2])
called_msgs = orig.call_args[0][0]
# Tool results stay consecutive; one hoisted HumanMessage follows them.
assert [m.type for m in called_msgs] == ["ai", "tool", "tool", "human"]
assert isinstance(called_msgs[1].content, str)
assert isinstance(called_msgs[2].content, str)
imgs = [b for b in called_msgs[3].content if b.get("type") == "image"]
assert len(imgs) == 2
def test_assistant_text_still_flattened_to_string(self):
from langchain_core.messages import AIMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model)
msg = AIMessage(
content=[
{"type": "text", "text": "hi"},
{"type": "thinking", "text": "t"},
]
)
model._generate([msg])
called_msgs = orig.call_args[0][0]
assert called_msgs[0].content == "hi"
def test_tool_media_flushed_before_next_human(self):
from langchain_core.messages import HumanMessage, ToolMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model)
tm = ToolMessage(
content_blocks=[
{"type": "image", "base64": "AAA", "mime_type": "image/png"}
],
tool_call_id="tc1",
name="read_file",
)
nxt = HumanMessage(content="thanks")
model._generate([tm, nxt])
called = orig.call_args[0][0]
# tool(placeholder), hoisted image (human), then the original human msg
assert [m.type for m in called] == ["tool", "human", "human"]
assert isinstance(called[0].content, str)
assert any(b.get("type") == "image" for b in called[1].content)
assert called[2].content == "thanks"
def test_tool_message_text_and_image_split(self):
from langchain_core.messages import ToolMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model)
tm = ToolMessage(
content=[
{"type": "text", "text": "chart description"},
{"type": "image", "base64": "AAA", "mime_type": "image/png"},
],
tool_call_id="tc1",
name="read_file",
)
model._generate([tm])
called = orig.call_args[0][0]
# Tool keeps the text as its string content; image hoisted to a human msg.
assert called[0].content == "chart description"
assert any(b.get("type") == "image" for b in called[1].content)
def test_tool_message_interleaved_text_not_lost(self):
# Interleaved [text, image, text] in a tool result: BOTH text runs must
# survive the hoisting split (not just the first).
from langchain_core.messages import ToolMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
orig = model._generate
_patch_openai_compat_content(model)
tm = ToolMessage(
content=[
{"type": "text", "text": "before"},
{"type": "image", "base64": "AAA", "mime_type": "image/png"},
{"type": "text", "text": "after"},
],
tool_call_id="tc1",
name="read_file",
)
model._generate([tm])
called = orig.call_args[0][0]
# both text runs preserved in the tool placeholder; image hoisted
assert "before" in called[0].content
assert "after" in called[0].content
assert any(b.get("type") == "image" for b in called[1].content)
# =============================================================================
# Test no-vision fallback (models that reject image input)
# =============================================================================
class TestNoVisionFallback:
"""Verify image-rejecting models fall back to a text placeholder."""
def _img_tool(self):
from langchain_core.messages import ToolMessage
return ToolMessage(
content_blocks=[
{"type": "image", "base64": "AAA", "mime_type": "image/png"}
],
tool_call_id="t1",
name="read_file",
)
def _make_model(self):
from unittest.mock import MagicMock
model = MagicMock()
model._agenerate = None
model._stream = None
model._astream = None
return model
def test_media_error_types(self):
from EvoScientist.llm.patches import (
_FILE_CONTENT_TYPES,
_IMAGE_CONTENT_TYPES,
_is_http_400,
_media_error_types,
)
# marker identifies the specific modality
assert (
_media_error_types(Exception("No endpoints found that support image input"))
>= _IMAGE_CONTENT_TYPES
)
assert (
_media_error_types(Exception("file input is not supported"))
== _FILE_CONTENT_TYPES
)
# DeepSeek-style maps to all media (generic "expected text")
assert (
_media_error_types(
Exception("unknown variant `image_url`, expected `text`")
)
>= _IMAGE_CONTENT_TYPES
)
# non-media errors implicate nothing
assert _media_error_types(Exception("rate limit exceeded")) == set()
assert (
_media_error_types(Exception("No endpoints found for some/model")) == set()
)
# bare "expected text" (non-media schema error) must NOT match
assert (
_media_error_types(
Exception("tool schema validation failed: expected text")
)
== set()
)
class _E(Exception):
status_code = 400
assert _is_http_400(_E("bad request"))
assert not _is_http_400(Exception("rate limit exceeded"))
def test_media_types_in(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _media_types_in
img = {"type": "image", "base64": "A", "mime_type": "image/png"}
f = {"type": "file", "base64": "F", "mime_type": "application/pdf"}
assert _media_types_in([HumanMessage(content=[img, f])]) == {"image", "file"}
assert _media_types_in([HumanMessage(content="hi")]) == set()
def test_strip_media_types_replaces_only_given(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _strip_media_types
img = {"type": "image", "base64": "AAA", "mime_type": "image/png"}
f = {"type": "file", "base64": "FFF", "mime_type": "application/pdf"}
msg = HumanMessage(content=[{"type": "text", "text": "see"}, img, f])
# Strip only files -> image survives, file becomes a placeholder block.
out = _strip_media_types([msg], {"file"})
types = [b.get("type") for b in out[0].content if isinstance(b, dict)]
assert "image" in types # image preserved
assert "file" not in types # file stripped
assert any(
b.get("type") == "text" and "omitted" in b.get("text", "").lower()
for b in out[0].content
)
def test_strip_media_types_preserves_position(self):
# Stripped block is replaced IN PLACE; surrounding text/kept media keep
# their order (placeholder where the image was, file stays last).
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _strip_media_types
img = {"type": "image", "base64": "A", "mime_type": "image/png"}
f = {"type": "file", "base64": "F", "mime_type": "application/pdf"}
msg = HumanMessage(
content=[
{"type": "text", "text": "t1"},
img,
{"type": "text", "text": "t2"},
f,
]
)
out = _strip_media_types([msg], {"image"}) # block only image
content = out[0].content
assert all(b.get("type") != "image" for b in content) # image gone
# order preserved: t1, placeholder (where image was), t2, file
assert content[0]["text"] == "t1"
assert content[1]["type"] == "text"
assert "omitted" in content[1]["text"].lower()
assert content[2]["text"] == "t2"
assert content[3]["type"] == "file" # file kept at its original position
def test_strip_media_types_dedups_consecutive(self):
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _strip_media_types
a = {"type": "image", "base64": "A", "mime_type": "image/png"}
b = {"type": "image", "base64": "B", "mime_type": "image/png"}
msg = HumanMessage(content=[a, b])
out = _strip_media_types([msg], {"image"})
# two adjacent stripped blocks collapse into ONE placeholder
assert len(out[0].content) == 1
assert "omitted" in out[0].content[0]["text"].lower()
def test_profile_no_vision_strips_upfront(self):
# Proactive: profile says image_inputs is False -> strip from the start,
# no failing first request.
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
model.profile = {"image_inputs": False}
calls = []
def _gen(msgs, *a, **k):
calls.append(msgs)
return "ok"
model._generate = _gen
_patch_openai_compat_content(model)
assert model._generate([self._img_tool()]) == "ok"
assert len(calls) == 1 # no failed attempt
assert all(isinstance(m.content, str) for m in calls[0])
assert any("omitted" in m.content.lower() for m in calls[0])
def test_profile_with_vision_does_not_strip(self):
# Profile says image_inputs is True -> normal preserve path (no upfront strip).
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
model.profile = {"image_inputs": True}
calls = []
def _gen(msgs, *a, **k):
calls.append(msgs)
return "ok"
model._generate = _gen
_patch_openai_compat_content(model)
assert model._generate([self._img_tool()]) == "ok"
# Image preserved (hoisted), not replaced by a placeholder.
assert any(
isinstance(m.content, list)
and any(b.get("type") == "image" for b in m.content)
for m in calls[0]
)
def test_generate_falls_back_and_caches(self):
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
calls = []
state = {"raised": False}
def _gen(msgs, *a, **k):
calls.append(msgs)
if not state["raised"]: # fail exactly once, ever
state["raised"] = True
raise Exception("unknown variant `image_url`, expected `text`")
return "ok"
model._generate = _gen
_patch_openai_compat_content(model)
tm = self._img_tool()
# 1st turn: preserve attempt fails once -> strip -> ok
assert model._generate([tm]) == "ok"
assert len(calls) == 2
retry = calls[1]
assert all(isinstance(m.content, str) for m in retry)
assert any("omitted" in m.content.lower() for m in retry)
# 2nd turn: cached no-vision -> straight to stripped, single call (no failure)
calls.clear()
assert model._generate([tm]) == "ok"
assert len(calls) == 1
assert all(isinstance(m.content, str) for m in calls[0])
def test_non_image_error_not_retried(self):
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
calls = []
def _gen(msgs, *a, **k):
calls.append(msgs)
raise Exception("rate limit exceeded")
model._generate = _gen
_patch_openai_compat_content(model)
with pytest.raises(Exception, match="rate limit"):
model._generate([self._img_tool()])
assert len(calls) == 1
def test_stream_falls_back(self):
from unittest.mock import MagicMock
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
model._generate = MagicMock(return_value="g")
calls = []
def _stream(msgs, *a, **k):
calls.append(msgs)
if len(calls) == 1:
raise Exception("No endpoints found that support image input")
yield from ["x", "y"]
model._stream = _stream
_patch_openai_compat_content(model)
out = list(model._stream([self._img_tool()]))
assert out == ["x", "y"]
assert len(calls) == 2
@pytest.mark.anyio
async def test_astream_falls_back(self):
from unittest.mock import MagicMock
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
model._generate = MagicMock(return_value="g")
calls = []
async def _astream(msgs, *a, **k):
calls.append(msgs)
if len(calls) == 1:
raise Exception("No endpoints found that support image input")
for c in ["x", "y"]:
yield c
model._astream = _astream
_patch_openai_compat_content(model)
out = [c async for c in model._astream([self._img_tool()])]
assert out == ["x", "y"]
assert len(calls) == 2
def test_unrelated_400_retry_fails_not_cached(self):
# A non-media 400 (e.g. tool schema) whose stripped retry ALSO fails must
# surface the original error and must NOT permanently flip to no-media.
from EvoScientist.llm.patches import _patch_openai_compat_content
class _E(Exception):
status_code = 400
model = self._make_model()
calls = []
def _gen(msgs, *a, **k):
calls.append(msgs)
raise _E("invalid tool schema") # 400, not media; fails every time
model._generate = _gen
_patch_openai_compat_content(model)
tm = self._img_tool()
with pytest.raises(_E):
model._generate([tm])
assert len(calls) == 2 # preserve attempt + stripped retry (both fail)
# Not cached: the next call attempts preserve again (not straight-to-stripped)
calls.clear()
with pytest.raises(_E):
model._generate([tm])
assert len(calls) == 2
def test_pdf_rejection_does_not_disable_images(self):
# Per-modality: a PDF/file rejection caches only file types; a later
# image must still be preserved (not stripped).
from langchain_core.messages import HumanMessage, ToolMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
calls = []
state = {"raised": False}
def _gen(msgs, *a, **k):
calls.append(msgs)
has_file = any(
isinstance(m.content, list)
and any(
isinstance(b, dict) and b.get("type") == "file" for b in m.content
)
for m in msgs
)
if has_file and not state["raised"]:
state["raised"] = True
raise Exception("file input is not supported")
return "ok"
model._generate = _gen
_patch_openai_compat_content(model)
pdf_tm = ToolMessage(
content_blocks=[
{"type": "file", "base64": "F", "mime_type": "application/pdf"}
],
tool_call_id="t1",
name="read_file",
)
assert model._generate([pdf_tm]) == "ok" # file rejected -> stripped -> ok
# Now an image: must still be preserved (images not blocked by a PDF reject)
calls.clear()
img_msg = HumanMessage(
content=[{"type": "image", "base64": "A", "mime_type": "image/png"}]
)
assert model._generate([img_msg]) == "ok"
assert len(calls) == 1 # single attempt, no failure
assert any(
isinstance(m.content, list)
and any(isinstance(b, dict) and b.get("type") == "image" for b in m.content)
for m in calls[0]
)
def test_bare_400_recovers_but_not_cached(self):
# A bare 400 with NO media marker recovers this request (stripped retry)
# but must NOT cache (no permanent degradation) — High #1.
from EvoScientist.llm.patches import _patch_openai_compat_content
class _E(Exception):
status_code = 400
model = self._make_model()
calls = []
state = {"raised": False}
def _gen(msgs, *a, **k):
calls.append(msgs)
if not state["raised"]:
state["raised"] = True
raise _E("transient bad request") # 400, no media marker
return "ok"
model._generate = _gen
_patch_openai_compat_content(model)
tm = self._img_tool()
assert model._generate([tm]) == "ok" # bare 400 -> stripped retry -> ok
assert len(calls) == 2
# NOT cached: the next call still attempts preserve (image kept, not stripped)
calls.clear()
assert model._generate([tm]) == "ok"
assert len(calls) == 1
assert any(
isinstance(m.content, list)
and any(isinstance(b, dict) and b.get("type") == "image" for b in m.content)
for m in calls[0]
)
def test_mixed_modality_caches_only_culprit(self):
# image+file message; provider rejects only the file -> cache file only,
# images stay preserved on later turns — High #2.
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
calls = []
state = {"raised": False}
def _gen(msgs, *a, **k):
calls.append(msgs)
if not state["raised"]:
state["raised"] = True
raise Exception("file input is not supported")
return "ok"
model._generate = _gen
_patch_openai_compat_content(model)
mixed = HumanMessage(
content=[
{"type": "image", "base64": "A", "mime_type": "image/png"},
{"type": "file", "base64": "F", "mime_type": "application/pdf"},
]
)
assert model._generate([mixed]) == "ok" # file rejected -> retry -> cache file
# later image-only request: image must still be preserved
calls.clear()
img = HumanMessage(
content=[{"type": "image", "base64": "A", "mime_type": "image/png"}]
)
assert model._generate([img]) == "ok"
assert len(calls) == 1
assert any(
isinstance(m.content, list)
and any(isinstance(b, dict) and b.get("type") == "image" for b in m.content)
for m in calls[0]
)
def test_stream_empty_retry_raises_original(self):
# If the stripped streaming retry yields ZERO chunks, surface the
# original error instead of silently returning an empty stream.
from unittest.mock import MagicMock
from EvoScientist.llm.patches import _patch_openai_compat_content
model = self._make_model()
model._generate = MagicMock(return_value="g")
calls = []
def _stream(msgs, *a, **k):
calls.append(msgs)
if len(calls) == 1:
raise Exception("No endpoints found that support image input")
return # retry yields nothing
yield # pragma: no cover (makes this a generator)
model._stream = _stream
_patch_openai_compat_content(model)
with pytest.raises(Exception, match="support image"):
list(model._stream([self._img_tool()]))
assert len(calls) == 2
# =============================================================================
# Test _patch_deepseek_reasoning_passback
# =============================================================================
class TestPatchDeepseekReasoningPassback:
"""Verify reasoning_content is injected into DeepSeek payload assistant messages.
This patch fixes the 400 error from DeepSeek V4 thinking mode in multi-turn
+ tool_use scenarios. See langchain PR #34516 for upstream reference.
"""
def _make_model(self, model_name="deepseek-v4-pro", payload_messages=None):
"""Create a mock ChatOpenAI-like model for the DeepSeek base URL."""
from unittest.mock import MagicMock
if payload_messages is None:
payload_messages = [
{"role": "user", "content": "hi"},
{"role": "assistant", "content": "hello"},
{"role": "user", "content": "ok"},
]
model = MagicMock()
model.model_name = model_name
class _Wrapped:
def __init__(self, msgs):
self._msgs = msgs
def to_messages(self):
return self._msgs
model._convert_input = lambda x: _Wrapped(x)
model._get_request_payload = MagicMock(
return_value={"messages": payload_messages}
)
return model
def test_injects_reasoning_content_from_additional_kwargs(self):
from langchain_core.messages import AIMessage, HumanMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
model = self._make_model()
_patch_deepseek_reasoning_passback(model)
messages = [
HumanMessage("hi"),
AIMessage(
content="hello",
additional_kwargs={"reasoning_content": "let me think..."},
),
HumanMessage("ok"),
]
payload = model._get_request_payload(messages)
assert payload["messages"][1]["reasoning_content"] == "let me think..."
def test_empty_reasoning_for_reasoner_model(self):
from langchain_core.messages import AIMessage, HumanMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
model = self._make_model(model_name="deepseek-reasoner")
_patch_deepseek_reasoning_passback(model)
messages = [
HumanMessage("hi"),
AIMessage(content="hello"), # no reasoning_content
HumanMessage("ok"),
]
payload = model._get_request_payload(messages)
assert payload["messages"][1]["reasoning_content"] == ""
def test_empty_fallback_for_non_reasoner_without_rc(self):
from langchain_core.messages import AIMessage, HumanMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
model = self._make_model(model_name="deepseek-v4-pro")
_patch_deepseek_reasoning_passback(model)
messages = [
HumanMessage("hi"),
AIMessage(content="hello"), # no reasoning_content
HumanMessage("ok"),
]
payload = model._get_request_payload(messages)
# Empty-string fallback applies to ALL DeepSeek models (not just
# reasoner) so cross-provider history doesn't trigger 400.
assert payload["messages"][1]["reasoning_content"] == ""
def test_handles_multiple_ai_messages(self):
from langchain_core.messages import AIMessage, HumanMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
model = self._make_model(
payload_messages=[
{"role": "user", "content": "q1"},
{"role": "assistant", "content": "a1"},
{"role": "user", "content": "q2"},
{"role": "assistant", "content": "a2"},
{"role": "user", "content": "q3"},
]
)
_patch_deepseek_reasoning_passback(model)
messages = [
HumanMessage("q1"),
AIMessage(content="a1", additional_kwargs={"reasoning_content": "rc1"}),
HumanMessage("q2"),
AIMessage(content="a2", additional_kwargs={"reasoning_content": "rc2"}),
HumanMessage("q3"),
]
payload = model._get_request_payload(messages)
assert payload["messages"][1]["reasoning_content"] == "rc1"
assert payload["messages"][3]["reasoning_content"] == "rc2"
def test_real_world_tool_use_flow(self):
"""The real scenario this patch was built for: AI thinks → tool_call →
ToolMessage → next turn must carry reasoning_content from prior AI msg.
This mirrors what happens in /tmp/verify_deepseek.py and what the user
actually triggers via 'create file then read it' in EvoSci CLI.
"""
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
# Mock payload that mirrors what langchain-openai produces:
# user → assistant (with tool_calls) → tool_result → user (next turn)
model = self._make_model(
payload_messages=[
{"role": "user", "content": "Read hello.txt"},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {"name": "read_file", "arguments": "{}"},
}
],
},
{"role": "tool", "content": "file contents", "tool_call_id": "call_1"},
{"role": "user", "content": "now what?"},
]
)
_patch_deepseek_reasoning_passback(model)
messages = [
HumanMessage("Read hello.txt"),
AIMessage(
content="",
additional_kwargs={"reasoning_content": "I should call read_file"},
tool_calls=[{"name": "read_file", "args": {}, "id": "call_1"}],
),
ToolMessage(content="file contents", tool_call_id="call_1"),
HumanMessage("now what?"),
]
payload = model._get_request_payload(messages)
# The assistant message (index 1) must carry reasoning_content
assistant_msg = payload["messages"][1]
assert assistant_msg["role"] == "assistant"
assert assistant_msg["reasoning_content"] == "I should call read_file"
# tool_calls preserved
assert "tool_calls" in assistant_msg
# ToolMessage (index 2) untouched
assert "reasoning_content" not in payload["messages"][2]
def test_mixed_ai_messages_with_and_without_rc(self):
"""Some AIMessages have reasoning_content, some don't (e.g., legacy turns
before patch was deployed). Each should be handled independently."""
from langchain_core.messages import AIMessage, HumanMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
model = self._make_model(
model_name="deepseek-v4-pro",
payload_messages=[
{"role": "user", "content": "q1"},
{"role": "assistant", "content": "a1"}, # no rc
{"role": "user", "content": "q2"},
{"role": "assistant", "content": "a2"}, # has rc
{"role": "user", "content": "q3"},
],
)
_patch_deepseek_reasoning_passback(model)
messages = [
HumanMessage("q1"),
AIMessage(content="a1"), # no reasoning_content
HumanMessage("q2"),
AIMessage(
content="a2",
additional_kwargs={"reasoning_content": "rc2"},
),
HumanMessage("q3"),
]
payload = model._get_request_payload(messages)
# First AI msg: no rc → empty-string fallback (covers cross-model switch)
assert payload["messages"][1]["reasoning_content"] == ""
# Second AI msg: has rc → injected
assert payload["messages"][3]["reasoning_content"] == "rc2"
def test_handles_responses_api_payload(self):
"""Payload without 'messages' key (e.g. Responses API) should not crash."""
from unittest.mock import MagicMock
from langchain_core.messages import HumanMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
model = MagicMock()
model.model_name = "deepseek-v4-pro"
class _Wrapped:
def __init__(self, msgs):
self._msgs = msgs
def to_messages(self):
return self._msgs
model._convert_input = lambda x: _Wrapped(x)
# Simulate Responses API payload (no 'messages' key)
model._get_request_payload = MagicMock(
return_value={"input": [{"role": "user", "content": "hi"}]}
)
_patch_deepseek_reasoning_passback(model)
# Should not raise, just return the payload as-is
payload = model._get_request_payload([HumanMessage("hi")])
assert "input" in payload
assert "messages" not in payload
def test_cross_provider_switch_history(self):
"""User chats with Anthropic/OpenAI then switches to DeepSeek V4 Pro.
Historical AI messages have no reasoning_content (the previous
provider never produced it). The patch must inject an empty-string
fallback so DeepSeek doesn't 400 on
"reasoning_content must be passed back to the API".
"""
from langchain_core.messages import AIMessage, HumanMessage
from EvoScientist.llm.patches import _patch_deepseek_reasoning_passback
model = self._make_model(
model_name="deepseek-v4-pro",
payload_messages=[
{"role": "user", "content": "earlier question to anthropic"},
{"role": "assistant", "content": "anthropic answer"},
{"role": "user", "content": "now ask deepseek pro"},
],
)
_patch_deepseek_reasoning_passback(model)
messages = [
HumanMessage("earlier question to anthropic"),
AIMessage(content="anthropic answer"), # no reasoning_content
HumanMessage("now ask deepseek pro"),
]
payload = model._get_request_payload(messages)
assert payload["messages"][1]["reasoning_content"] == ""
# =============================================================================
# Test _patch_openai_capture_reasoning_content (module-level monkey-patch)
# =============================================================================
class TestPatchOpenAICaptureReasoningContent:
"""Verify reasoning_content is captured into AIMessage.additional_kwargs.
This patch is applied at import time and globally affects langchain-openai's
_convert_dict_to_message and _convert_delta_to_message_chunk.
"""
def test_capture_from_non_streaming_response(self):
"""reasoning_content in OpenAI response dict → AIMessage.additional_kwargs."""
from langchain_openai.chat_models.base import _convert_dict_to_message
msg = _convert_dict_to_message(
{
"role": "assistant",
"content": "hi",
"reasoning_content": "let me think...",
}
)
assert msg.additional_kwargs.get("reasoning_content") == "let me think..."
def test_capture_absent_when_field_missing(self):
"""No reasoning_content in response → not added to additional_kwargs."""
from langchain_openai.chat_models.base import _convert_dict_to_message
msg = _convert_dict_to_message({"role": "assistant", "content": "hi"})
assert "reasoning_content" not in msg.additional_kwargs
def test_capture_from_streaming_chunk(self):
"""reasoning_content delta is captured onto the chunk's additional_kwargs."""
from langchain_core.messages import AIMessageChunk
from langchain_openai.chat_models.base import (
_convert_delta_to_message_chunk,
)
chunk = _convert_delta_to_message_chunk(
{"role": "assistant", "content": "", "reasoning_content": "thinking"},
AIMessageChunk,
)
assert chunk.additional_kwargs.get("reasoning_content") == "thinking"
def test_capture_does_not_affect_other_fields(self):
"""Existing tool_calls / function_call extraction unaffected."""
from langchain_openai.chat_models.base import _convert_dict_to_message
msg = _convert_dict_to_message(
{
"role": "assistant",
"content": "calling tool",
"tool_calls": [
{
"id": "call_1",
"type": "function",
"function": {"name": "get_weather", "arguments": "{}"},
}
],
"reasoning_content": "use the tool",
}
)
assert len(msg.tool_calls) == 1
assert msg.tool_calls[0]["name"] == "get_weather"
assert msg.additional_kwargs.get("reasoning_content") == "use the tool"
class TestIsResponsesReasoningItem:
"""_is_responses_reasoning_item flags encrypted OpenAI-Responses items."""
def test_rs_id_is_responses_item(self):
from EvoScientist.llm.patches import _is_responses_reasoning_item
assert _is_responses_reasoning_item({"id": "rs_09363d42", "type": "x"})
def test_encrypted_data_is_responses_item(self):
from EvoScientist.llm.patches import _is_responses_reasoning_item
assert _is_responses_reasoning_item({"data": "gAAAAAB...", "type": "x"})
def test_plain_text_reasoning_is_not_responses_item(self):
from EvoScientist.llm.patches import _is_responses_reasoning_item
assert not _is_responses_reasoning_item(
{"type": "reasoning.text", "text": "thinking", "index": 0}
)
assert not _is_responses_reasoning_item("not a dict")
class TestPatchOpenrouterStripResponsesReasoning:
"""OpenAI-Responses encrypted reasoning items (`rs_` id / encrypted data)
are stripped from outgoing OpenRouter assistant messages, preventing the
multi-turn "Item with id 'rs_...' not found" 400 (store=false; #37777).
"""
def _apply(self):
import langchain_openrouter.chat_models as mod
import EvoScientist.llm.patches as patches
orig = mod._convert_message_to_dict
orig_flag = patches._openrouter_reasoning_strip_patched
patches._openrouter_reasoning_strip_patched = False
patches._patch_openrouter_strip_responses_reasoning()
return patches, mod, orig, orig_flag
@staticmethod
def _restore(patches, mod, orig, orig_flag):
mod._convert_message_to_dict = orig
patches._openrouter_reasoning_strip_patched = orig_flag
def test_strips_encrypted_item_drops_key_when_empty(self):
from langchain_core.messages import AIMessage
patches, mod, orig, orig_flag = self._apply()
try:
msg = AIMessage(
content="done",
additional_kwargs={
"reasoning_details": [
{
"type": "reasoning.summary",
"format": "openai-responses-v1",
"id": "rs_09363d42b054",
"data": "gAAAAAB...",
"summary": "real reasoning text",
"index": 0,
}
],
},
)
result = mod._convert_message_to_dict(msg)
# sole entry was an rs_ item → reasoning_details removed entirely.
assert "reasoning_details" not in result
finally:
self._restore(patches, mod, orig, orig_flag)
def test_keeps_plain_text_reasoning(self):
from langchain_core.messages import AIMessage
patches, mod, orig, orig_flag = self._apply()
try:
msg = AIMessage(
content="done",
additional_kwargs={
"reasoning_details": [
{"type": "reasoning.text", "text": "thinking", "index": 0},
{"id": "rs_abc", "data": "blob", "index": 1},
],
},
)
result = mod._convert_message_to_dict(msg)
kept = result["reasoning_details"]
assert len(kept) == 1
assert kept[0]["type"] == "reasoning.text"
finally:
self._restore(patches, mod, orig, orig_flag)
def test_does_not_mutate_original_message(self):
from langchain_core.messages import AIMessage
patches, mod, orig, orig_flag = self._apply()
try:
details = [{"id": "rs_abc", "data": "blob"}]
msg = AIMessage(
content="x", additional_kwargs={"reasoning_details": details}
)
mod._convert_message_to_dict(msg)
# stored history untouched — we filter a fresh list, not in place.
assert details == [{"id": "rs_abc", "data": "blob"}]
finally:
self._restore(patches, mod, orig, orig_flag)
def test_patch_is_idempotent(self):
patches, mod, orig, orig_flag = self._apply()
try:
wrapper = mod._convert_message_to_dict
# Second call is guarded by the flag → must not re-wrap.
patches._patch_openrouter_strip_responses_reasoning()
assert mod._convert_message_to_dict is wrapper
finally:
self._restore(patches, mod, orig, orig_flag)
def test_non_dict_entry_is_kept(self):
from langchain_core.messages import AIMessage
patches, mod, orig, orig_flag = self._apply()
try:
msg = AIMessage(
content="done",
additional_kwargs={
"reasoning_details": [
"opaque", # non-dict slipped in → kept, not crashed on
{"id": "rs_abc", "data": "blob", "index": 1},
],
},
)
result = mod._convert_message_to_dict(msg)
assert result["reasoning_details"] == ["opaque"]
finally:
self._restore(patches, mod, orig, orig_flag)
# =============================================================================
# Test _apply_auto_config
# =============================================================================
class TestAutoConfig:
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_4_5_thinking(self, mock_init, monkeypatch):
"""Anthropic 4-5 models get enabled thinking with budget."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("ANTHROPIC_BASE_URL", raising=False)
get_chat_model("claude-haiku-4-5")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["thinking"] == {"type": "enabled", "budget_tokens": 10000}
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_4_6_adaptive_thinking(self, mock_init, monkeypatch):
"""Anthropic 4-6 models get adaptive thinking with max effort."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("ANTHROPIC_BASE_URL", raising=False)
get_chat_model("claude-sonnet-4-6")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["thinking"] == {"type": "adaptive", "display": "summarized"}
assert call_kwargs["effort"] == "max"
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_4_8_adaptive_thinking(self, mock_init, monkeypatch):
"""Anthropic 4-8 models get adaptive thinking with max effort."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("ANTHROPIC_BASE_URL", raising=False)
get_chat_model("claude-opus-4-8")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["thinking"] == {"type": "adaptive", "display": "summarized"}
assert call_kwargs["effort"] == "max"
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_4_6_proxy_no_thinking(self, mock_init, monkeypatch):
"""Anthropic 4-6 models via proxy skip thinking (history round-trip 422)."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("ANTHROPIC_BASE_URL", "http://127.0.0.1:8000")
monkeypatch.setenv("ANTHROPIC_API_KEY", "ccproxy-oauth")
get_chat_model("claude-sonnet-4-6")
call_kwargs = mock_init.call_args[1]
assert "thinking" not in call_kwargs
assert "effort" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_4_5_proxy_no_thinking(self, mock_init, monkeypatch):
"""Anthropic 4-5 models via proxy also skip thinking (history round-trip 422)."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("ANTHROPIC_BASE_URL", "http://127.0.0.1:8000")
monkeypatch.setenv("ANTHROPIC_API_KEY", "ccproxy-oauth")
get_chat_model("claude-haiku-4-5")
call_kwargs = mock_init.call_args[1]
assert "thinking" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_4_6_no_proxy_no_downgrade(self, mock_init, monkeypatch):
"""Anthropic 4-6 models without proxy still get adaptive thinking."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("ANTHROPIC_BASE_URL", "https://api.anthropic.com")
monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-real")
get_chat_model("claude-sonnet-4-6")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["thinking"] == {"type": "adaptive", "display": "summarized"}
assert call_kwargs["effort"] == "max"
@patch("EvoScientist.llm.models.init_chat_model")
def test_anthropic_thinking_not_overridden(self, mock_init):
"""User-supplied thinking config should not be overridden."""
mock_init.return_value = "mock_model"
custom_thinking = {"type": "enabled", "budget_tokens": 500}
get_chat_model("claude-sonnet-4-6", thinking=custom_thinking)
call_kwargs = mock_init.call_args[1]
assert call_kwargs["thinking"] == custom_thinking
@patch("EvoScientist.llm.models.init_chat_model")
def test_openai_reasoning_xhigh(self, mock_init, monkeypatch):
"""gpt-5.4+ and codex models get xhigh reasoning."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
get_chat_model("gpt-5.4", provider="openai")
assert mock_init.call_args[1]["reasoning"] == {
"effort": "xhigh",
"summary": "auto",
}
get_chat_model("gpt-5.3-codex", provider="openai")
assert mock_init.call_args[1]["reasoning"] == {
"effort": "xhigh",
"summary": "auto",
}
get_chat_model("gpt-5.5", provider="openai")
assert mock_init.call_args[1]["reasoning"] == {
"effort": "xhigh",
"summary": "auto",
}
@patch("EvoScientist.llm.models.init_chat_model")
def test_openai_reasoning_high_fallback(self, mock_init, monkeypatch):
"""Other OpenAI models get high reasoning effort."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
get_chat_model("gpt-5-nano")
assert mock_init.call_args[1]["reasoning"] == {
"effort": "high",
"summary": "auto",
}
get_chat_model("gpt-5.2", provider="openai")
assert mock_init.call_args[1]["reasoning"] == {
"effort": "high",
"summary": "auto",
}
@patch("EvoScientist.llm.models.init_chat_model")
def test_openai_base_url_override(self, mock_init, monkeypatch):
"""OpenAI provider should support base_url override (e.g. ccproxy Codex)."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENAI_BASE_URL", "http://127.0.0.1:8000/codex/v1")
monkeypatch.setenv("OPENAI_API_KEY", "ccproxy-oauth")
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
assert call_kwargs["base_url"] == "http://127.0.0.1:8000/codex/v1"
assert call_kwargs["api_key"] == "ccproxy-oauth"
# Proxy mode: reasoning skipped (ccproxy untested)
assert "reasoning" not in call_kwargs
# Proxy mode: Responses API (bypasses format chain), streaming ON
assert call_kwargs["use_responses_api"] is True
assert "streaming" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_openai_localhost_non_ccproxy_not_downgraded(self, mock_init, monkeypatch):
"""Local OpenAI-compatible endpoints (vLLM, etc.) are not affected by ccproxy workarounds."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENAI_BASE_URL", "http://127.0.0.1:8080/v1")
monkeypatch.setenv("OPENAI_API_KEY", "sk-local-key")
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["base_url"] == "http://127.0.0.1:8080/v1"
# NOT ccproxy: reasoning should be applied, no forced Chat Completions
assert call_kwargs["reasoning"] == {"effort": "high", "summary": "auto"}
assert "use_responses_api" not in call_kwargs
assert "streaming" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_openai_codex_path_but_wrong_key_not_ccproxy(self, mock_init, monkeypatch):
"""ccproxy detection requires both /codex/ path AND ccproxy-oauth key."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENAI_BASE_URL", "http://127.0.0.1:8000/codex/v1")
monkeypatch.setenv("OPENAI_API_KEY", "sk-real-key")
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["reasoning"] == {"effort": "high", "summary": "auto"}
assert "use_responses_api" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_openai_ccproxy_key_but_wrong_path_not_ccproxy(
self, mock_init, monkeypatch
):
"""ccproxy detection requires both /codex/ path AND ccproxy-oauth key."""
mock_init.return_value = "mock_model"
monkeypatch.setenv("OPENAI_BASE_URL", "http://127.0.0.1:8000/v1")
monkeypatch.setenv("OPENAI_API_KEY", "ccproxy-oauth")
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["reasoning"] == {"effort": "high", "summary": "auto"}
assert "use_responses_api" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_openai_no_base_url_when_unset(self, mock_init, monkeypatch):
"""OpenAI provider should not set base_url when env var is empty."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.setenv("OPENAI_API_KEY", "sk-real")
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["model_provider"] == "openai"
assert "base_url" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_google_thoughts(self, mock_init):
"""Google GenAI models get include_thoughts=True by default."""
mock_init.return_value = "mock_model"
get_chat_model("gemini-2.5-flash")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["include_thoughts"] is True
@patch("EvoScientist.llm.models.init_chat_model")
def test_use_responses_api_false(self, mock_init, monkeypatch):
"""use_responses_api=false forces Chat Completions and drops reasoning."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.setenv("EVOSCIENTIST_USE_RESPONSES_API", "false")
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["use_responses_api"] is False
assert "reasoning" not in call_kwargs
@patch("EvoScientist.llm.models.init_chat_model")
def test_use_responses_api_true(self, mock_init, monkeypatch):
"""use_responses_api=true explicitly enables the Responses API."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.setenv("EVOSCIENTIST_USE_RESPONSES_API", "true")
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["use_responses_api"] is True
assert call_kwargs["reasoning"] == {"effort": "high", "summary": "auto"}
@patch("EvoScientist.llm.models.init_chat_model")
def test_use_responses_api_default_unchanged(self, mock_init, monkeypatch):
"""Empty use_responses_api preserves default behavior (no kwarg set)."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.delenv("EVOSCIENTIST_USE_RESPONSES_API", raising=False)
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert "use_responses_api" not in call_kwargs
assert call_kwargs["reasoning"] == {"effort": "high", "summary": "auto"}
@pytest.mark.parametrize("env_value", ["FALSE", " false ", "False"])
@patch("EvoScientist.llm.models.init_chat_model")
def test_use_responses_api_false_normalization(
self, mock_init, monkeypatch, env_value
):
"""Case/whitespace variants of 'false' are normalized correctly."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.setenv("EVOSCIENTIST_USE_RESPONSES_API", env_value)
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["use_responses_api"] is False
assert "reasoning" not in call_kwargs
@pytest.mark.parametrize("env_value", ["TRUE", " true ", "True"])
@patch("EvoScientist.llm.models.init_chat_model")
def test_use_responses_api_true_normalization(
self, mock_init, monkeypatch, env_value
):
"""Case/whitespace variants of 'true' are normalized correctly."""
mock_init.return_value = "mock_model"
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.setenv("EVOSCIENTIST_USE_RESPONSES_API", env_value)
get_chat_model("gpt-5-nano", provider="openai")
call_kwargs = mock_init.call_args[1]
assert call_kwargs["use_responses_api"] is True
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