fix(llms): accept and forward **kwargs in Together/LangChain/Sarvam providers (#5556)

tests/llms/test_langchain.py

@@ -115,6 +115,20 @@ def test_generate_response_with_tools(mock_langchain_model):
     assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
 
 
+def test_generate_response_forwards_extra_kwargs(mock_langchain_model):
+    """Per the LLMBase contract, extra model kwargs must be accepted and forwarded to
+    the underlying LangChain model's ``invoke``."""
+    config = BaseLlmConfig(model=mock_langchain_model, temperature=0.7, max_tokens=100, api_key="test-api-key")
+    llm = LangchainLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    response = llm.generate_response(messages, frequency_penalty=0.5)
+
+    mock_langchain_model.invoke.assert_called_once()
+    assert mock_langchain_model.invoke.call_args.kwargs["frequency_penalty"] == 0.5
+    assert response == "This is a test response"
+
+
 def test_invalid_model():
     """Test that LangchainLLM raises an error with an invalid model."""
     config = BaseLlmConfig(model="not-a-valid-model-instance", temperature=0.7, max_tokens=100, api_key="test-api-key")

tests/llms/test_sarvam.py

@@ -0,0 +1,46 @@
+from unittest.mock import Mock, patch
+
+import pytest
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.sarvam import SarvamLLM
+
+
+@pytest.fixture
+def sarvam_llm():
+    config = BaseLlmConfig(model="sarvam-m", temperature=0.7, max_tokens=100, top_p=1.0, api_key="test-api-key")
+    return SarvamLLM(config)
+
+
+def _mock_post(content="Hello there!"):
+    mock_response = Mock()
+    mock_response.raise_for_status.return_value = None
+    mock_response.json.return_value = {"choices": [{"message": {"content": content}}]}
+    return mock_response
+
+
+def test_generate_response_returns_content(sarvam_llm):
+    with patch("mem0.llms.sarvam.requests.post", return_value=_mock_post("Hi!")) as mock_post:
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello, how are you?"},
+        ]
+        response = sarvam_llm.generate_response(messages)
+
+    assert response == "Hi!"
+    sent_payload = mock_post.call_args.kwargs["json"]
+    assert sent_payload["model"] == "sarvam-m"
+    assert sent_payload["messages"] == messages
+    assert sent_payload["temperature"] == 0.7
+
+
+def test_generate_response_forwards_extra_kwargs(sarvam_llm):
+    """Per the LLMBase contract, extra provider-specific kwargs must be accepted and
+    forwarded into the Sarvam request payload."""
+    with patch("mem0.llms.sarvam.requests.post", return_value=_mock_post("Hi!")) as mock_post:
+        messages = [{"role": "user", "content": "Hello"}]
+        response = sarvam_llm.generate_response(messages, frequency_penalty=0.5)
+
+    assert response == "Hi!"
+    sent_payload = mock_post.call_args.kwargs["json"]
+    assert sent_payload["frequency_penalty"] == 0.5

tests/llms/test_together.py

@@ -84,3 +84,21 @@ def test_generate_response_with_tools(mock_together_client):
     assert len(response["tool_calls"]) == 1
     assert response["tool_calls"][0]["name"] == "add_memory"
     assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
+
+
+def test_generate_response_forwards_extra_kwargs(mock_together_client):
+    """Per the LLMBase contract, extra provider-specific kwargs must be accepted and
+    forwarded to the Together client (matching openai/deepseek/vllm/xai behavior)."""
+    config = BaseLlmConfig(model="mistralai/Mixtral-8x7B-Instruct-v0.1", temperature=0.7, max_tokens=100, top_p=1.0)
+    llm = TogetherLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Hi"))]
+    mock_together_client.chat.completions.create.return_value = mock_response
+
+    response = llm.generate_response(messages, frequency_penalty=0.5)
+
+    assert response == "Hi"
+    call_kwargs = mock_together_client.chat.completions.create.call_args.kwargs
+    assert call_kwargs["frequency_penalty"] == 0.5