fix: avoid sending both temperature and top_p to Anthropic API (#4471)

Co-authored-by: utkarsh240799 <utkarsh240799@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

mem0/configs/llms/anthropic.py

@@ -16,7 +16,7 @@ class AnthropicConfig(BaseLlmConfig):
         temperature: float = 0.1,
         api_key: Optional[str] = None,
         max_tokens: int = 2000,
-        top_p: float = 0.1,
+        top_p: Optional[float] = None,
         top_k: int = 1,
         enable_vision: bool = False,
         vision_details: Optional[str] = "auto",
@@ -32,7 +32,7 @@ class AnthropicConfig(BaseLlmConfig):
             temperature: Controls randomness, defaults to 0.1
             api_key: Anthropic API key, defaults to None
             max_tokens: Maximum tokens to generate, defaults to 2000
-            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_p: Nucleus sampling parameter, defaults to None (omitted to avoid conflict with temperature)
             top_k: Top-k sampling parameter, defaults to 1
             enable_vision: Enable vision capabilities, defaults to False
             vision_details: Vision detail level, defaults to "auto"

mem0/llms/anthropic.py

@@ -40,6 +40,31 @@ class AnthropicLLM(LLMBase):
         api_key = self.config.api_key or os.getenv("ANTHROPIC_API_KEY")
         self.client = anthropic.Anthropic(api_key=api_key)
 
+    def _get_common_params(self, **kwargs) -> Dict:
+        """Get common parameters, avoiding sending both temperature and top_p together.
+
+        Anthropic rejects requests that include both temperature and top_p.
+        When both are set, we keep temperature and drop top_p.
+        """
+        params = {}
+
+        if self.config.max_tokens is not None:
+            params["max_tokens"] = self.config.max_tokens
+
+        has_temperature = self.config.temperature is not None
+        has_top_p = self.config.top_p is not None
+
+        if has_temperature and has_top_p:
+            # Anthropic forbids both; prefer temperature
+            params["temperature"] = self.config.temperature
+        elif has_temperature:
+            params["temperature"] = self.config.temperature
+        elif has_top_p:
+            params["top_p"] = self.config.top_p
+
+        params.update(kwargs)
+        return params
+
     def generate_response(
         self,
         messages: List[Dict[str, str]],

tests/llms/test_anthropic.py

@@ -0,0 +1,100 @@
+from unittest.mock import Mock, patch
+
+import pytest
+
+pytest.importorskip("anthropic", reason="anthropic package not installed")
+
+from mem0.configs.llms.anthropic import AnthropicConfig
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.anthropic import AnthropicLLM
+
+
+@pytest.fixture
+def mock_anthropic_client():
+    with patch("mem0.llms.anthropic.anthropic") as mock_anthropic:
+        mock_client = Mock()
+        mock_anthropic.Anthropic.return_value = mock_client
+        yield mock_client
+
+
+def test_default_config_omits_top_p(mock_anthropic_client):
+    """Default AnthropicConfig should not set top_p to avoid conflict with temperature."""
+    config = AnthropicConfig(model="claude-3-5-sonnet-20240620", api_key="test-key")
+    assert config.top_p is None
+    assert config.temperature == 0.1
+
+
+def test_generate_response_does_not_send_top_p_by_default(mock_anthropic_client):
+    """Anthropic API rejects temperature and top_p together; top_p must be omitted by default."""
+    config = AnthropicConfig(model="claude-3-5-sonnet-20240620", api_key="test-key")
+    llm = AnthropicLLM(config)
+
+    mock_response = Mock()
+    mock_response.content = [Mock(text="Hello!")]
+    mock_anthropic_client.messages.create.return_value = mock_response
+
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hi"},
+    ]
+
+    llm.generate_response(messages)
+
+    call_kwargs = mock_anthropic_client.messages.create.call_args[1]
+    assert "top_p" not in call_kwargs
+    assert call_kwargs["temperature"] == 0.1
+
+
+def test_generate_response_sends_top_p_alone_when_no_temperature(mock_anthropic_client):
+    """When user sets only top_p (no temperature), top_p should be sent."""
+    config = AnthropicConfig(model="claude-3-5-sonnet-20240620", api_key="test-key", top_p=0.9, temperature=None)
+    llm = AnthropicLLM(config)
+
+    mock_response = Mock()
+    mock_response.content = [Mock(text="Hello!")]
+    mock_anthropic_client.messages.create.return_value = mock_response
+
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hi"},
+    ]
+
+    llm.generate_response(messages)
+
+    call_kwargs = mock_anthropic_client.messages.create.call_args[1]
+    assert call_kwargs["top_p"] == 0.9
+    assert "temperature" not in call_kwargs
+
+
+def test_both_set_prefers_temperature_over_top_p(mock_anthropic_client):
+    """When both temperature and top_p are set, temperature wins and top_p is dropped."""
+    config = AnthropicConfig(model="claude-3-5-sonnet-20240620", api_key="test-key", top_p=0.9, temperature=0.5)
+    llm = AnthropicLLM(config)
+
+    mock_response = Mock()
+    mock_response.content = [Mock(text="Hello!")]
+    mock_anthropic_client.messages.create.return_value = mock_response
+
+    messages = [{"role": "user", "content": "Hi"}]
+    llm.generate_response(messages)
+
+    call_kwargs = mock_anthropic_client.messages.create.call_args[1]
+    assert call_kwargs["temperature"] == 0.5
+    assert "top_p" not in call_kwargs
+
+
+def test_base_config_conversion_does_not_send_both(mock_anthropic_client):
+    """BaseLlmConfig defaults both temperature=0.1 and top_p=0.1; Anthropic must not send both."""
+    base_config = BaseLlmConfig(model="claude-3-5-sonnet-20240620", api_key="test-key")
+    llm = AnthropicLLM(base_config)
+
+    mock_response = Mock()
+    mock_response.content = [Mock(text="Hello!")]
+    mock_anthropic_client.messages.create.return_value = mock_response
+
+    messages = [{"role": "user", "content": "Hi"}]
+    llm.generate_response(messages)
+
+    call_kwargs = mock_anthropic_client.messages.create.call_args[1]
+    assert "temperature" in call_kwargs
+    assert "top_p" not in call_kwargs