refactor: use extract_json for OllamaLLM argument parsing

OllamaLLM.generate_response() never forwarded the tools parameter to
ollama.Client.chat(), and _parse_response() hard-coded tool_calls to an
empty list. This caused graph memory entity extraction to silently return
zero results when using Ollama as the LLM provider.

- Forward tools to client.chat() when provided
- Parse tool_calls from response (supports both dict and object formats)
- Handle string arguments via json.loads

Fixes #4175

Signed-off-by: sxu75374 <imshuaixu@gmail.com>
Signed-off-by: Small <imshuaixu@gmail.com>

mem0/llms/ollama.py

@@ -1,3 +1,4 @@
+import json
 from typing import Dict, List, Optional, Union
 
 try:
@@ -8,6 +9,7 @@ except ImportError:
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.configs.llms.ollama import OllamaConfig
 from mem0.llms.base import LLMBase
+from mem0.memory.utils import extract_json
 
 
 class OllamaLLM(LLMBase):
@@ -61,7 +63,28 @@ class OllamaLLM(LLMBase):
                 "tool_calls": [],
             }
 
-            # Ollama doesn't support tool calls in the same way, so we return the content
+            if isinstance(response, dict):
+                raw_calls = response.get("message", {}).get("tool_calls") or []
+            else:
+                raw_calls = getattr(response.message, "tool_calls", None) or []
+
+            for tool_call in raw_calls:
+                if isinstance(tool_call, dict):
+                    fn = tool_call.get("function", {})
+                    name = fn.get("name", "")
+                    arguments = fn.get("arguments", {})
+                else:
+                    fn = getattr(tool_call, "function", None)
+                    name = getattr(fn, "name", "") if fn else ""
+                    arguments = getattr(fn, "arguments", {}) if fn else {}
+
+                if isinstance(arguments, str):
+                    arguments = json.loads(extract_json(arguments))
+
+                processed_response["tool_calls"].append(
+                    {"name": name, "arguments": arguments}
+                )
+
             return processed_response
         else:
             return content
@@ -113,5 +136,8 @@ class OllamaLLM(LLMBase):
         # Remove OpenAI-specific parameters that Ollama doesn't support
         params.pop("max_tokens", None)  # Ollama uses different parameter names
 
+        if tools:
+            params["tools"] = tools
+
         response = self.client.chat(**params)
         return self._parse_response(response, tools)

tests/llms/test_ollama.py

@@ -32,3 +32,111 @@ def test_generate_response_without_tools(mock_ollama_client):
         model="llama3.1:70b", messages=messages, options={"temperature": 0.7, "num_predict": 100, "top_p": 1.0}
     )
     assert response == "I'm doing well, thank you for asking!"
+
+
+def test_generate_response_with_tools_passes_tools_to_client(mock_ollama_client):
+    """Tools should be forwarded to ollama client.chat()."""
+    config = OllamaConfig(model="llama3.1:70b", temperature=0.1, max_tokens=100, top_p=1.0)
+    llm = OllamaLLM(config)
+    messages = [{"role": "user", "content": "Extract entities from: Alice works at UCSD"}]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "extract_entities",
+                "description": "Extract entities",
+                "parameters": {"type": "object", "properties": {"entities": {"type": "array"}}},
+            },
+        }
+    ]
+
+    mock_response = {
+        "message": {
+            "content": "",
+            "tool_calls": [
+                {
+                    "function": {
+                        "name": "extract_entities",
+                        "arguments": {"entities": [{"name": "Alice"}, {"name": "UCSD"}]},
+                    }
+                }
+            ],
+        }
+    }
+    mock_ollama_client.chat.return_value = mock_response
+
+    response = llm.generate_response(messages, tools=tools)
+
+    # Verify tools were passed to client.chat
+    call_kwargs = mock_ollama_client.chat.call_args
+    assert "tools" in call_kwargs.kwargs or (len(call_kwargs.args) > 0 and "tools" in call_kwargs[1])
+    assert call_kwargs[1]["tools"] == tools
+
+    # Verify tool_calls were parsed correctly
+    assert response["tool_calls"] == [
+        {"name": "extract_entities", "arguments": {"entities": [{"name": "Alice"}, {"name": "UCSD"}]}}
+    ]
+
+
+def test_generate_response_with_tools_no_tool_calls_in_response(mock_ollama_client):
+    """When model returns content without tool_calls, tool_calls should be empty list."""
+    config = OllamaConfig(model="llama3.1:70b", temperature=0.1, max_tokens=100, top_p=1.0)
+    llm = OllamaLLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+    tools = [{"type": "function", "function": {"name": "noop", "parameters": {}}}]
+
+    mock_response = {"message": {"content": "I cannot use tools for this.", "tool_calls": []}}
+    mock_ollama_client.chat.return_value = mock_response
+
+    response = llm.generate_response(messages, tools=tools)
+
+    assert response["content"] == "I cannot use tools for this."
+    assert response["tool_calls"] == []
+
+
+def test_generate_response_with_tools_string_arguments(mock_ollama_client):
+    """When tool_call arguments come as JSON string, they should be parsed."""
+    config = OllamaConfig(model="llama3.1:70b", temperature=0.1, max_tokens=100, top_p=1.0)
+    llm = OllamaLLM(config)
+    messages = [{"role": "user", "content": "test"}]
+    tools = [{"type": "function", "function": {"name": "test_fn", "parameters": {}}}]
+
+    mock_response = {
+        "message": {
+            "content": "",
+            "tool_calls": [
+                {"function": {"name": "test_fn", "arguments": '{"key": "value"}'}}
+            ],
+        }
+    }
+    mock_ollama_client.chat.return_value = mock_response
+
+    response = llm.generate_response(messages, tools=tools)
+
+    assert response["tool_calls"] == [{"name": "test_fn", "arguments": {"key": "value"}}]
+
+
+def test_parse_response_with_tools_object_style(mock_ollama_client):
+    """Test _parse_response with object-style response (non-dict)."""
+    config = OllamaConfig(model="llama3.1:70b")
+    llm = OllamaLLM(config)
+
+    # Simulate object-style response
+    mock_fn = Mock()
+    mock_fn.name = "extract"
+    mock_fn.arguments = {"entities": ["Alice"]}
+
+    mock_tool_call = Mock()
+    mock_tool_call.function = mock_fn
+
+    mock_message = Mock()
+    mock_message.content = ""
+    mock_message.tool_calls = [mock_tool_call]
+
+    mock_response = Mock()
+    mock_response.message = mock_message
+
+    tools = [{"type": "function", "function": {"name": "extract"}}]
+    result = llm._parse_response(mock_response, tools)
+
+    assert result["tool_calls"] == [{"name": "extract", "arguments": {"entities": ["Alice"]}}]