fix(llm): forward max_tokens to Ollama num_predict

src/llm/providers/ollama.py

@@ -70,8 +70,13 @@ class OllamaProvider(LLMProvider):
                 "messages": [msg.to_dict() for msg in input.messages],
                 "stream": False,
             }
+            options: dict[str, Any] = {}
             if input.temperature != 1.0:
-                payload["options"] = {"temperature": input.temperature}
+                options["temperature"] = input.temperature
+            if input.max_tokens is not None:
+                options["num_predict"] = input.max_tokens
+            if options:
+                payload["options"] = options
 
             data = json.dumps(payload).encode("utf-8")
             req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})

tests/test_provider_tools.py

@@ -1,3 +1,6 @@
+import json
+import urllib.request
+from io import BytesIO
 from types import SimpleNamespace
 
 import pytest
@@ -5,6 +8,7 @@ import pytest
 from llm.core.types import LLMInput, Message, Role, ToolDefinition
 from llm.providers.claude import ClaudeProvider
 from llm.providers.constants import EMPTY_FILTERED_RESPONSE_ERROR
+from llm.providers.ollama import OllamaProvider
 from llm.providers.openai import OpenAIProvider
 
 
@@ -114,6 +118,56 @@ def test_openai_provider_allows_missing_usage():
     assert output.usage is None
 
 
+@pytest.mark.parametrize(
+    ("max_tokens", "temperature", "expected_options"),
+    [
+        (128, 1.0, {"num_predict": 128}),
+        (128, 0.2, {"temperature": 0.2, "num_predict": 128}),
+        (128, 0.0, {"temperature": 0.0, "num_predict": 128}),
+        (0, 1.0, {"num_predict": 0}),
+        (None, 1.0, {}),
+        (None, 0.2, {"temperature": 0.2}),
+    ],
+)
+def test_ollama_provider_serializes_generation_options(
+    monkeypatch, max_tokens, temperature, expected_options
+):
+    requests = []
+
+    def fake_urlopen(request, timeout):
+        requests.append((request, timeout))
+        return BytesIO(b'{"message": {"content": "ok"}, "done_reason": "stop"}')
+
+    monkeypatch.setattr(urllib.request, "urlopen", fake_urlopen)
+    provider = OllamaProvider(base_url="http://localhost:11434", default_model="llama3.2")
+
+    output = provider.generate(
+        LLMInput(
+            messages=[Message(role=Role.USER, content="hi")],
+            max_tokens=max_tokens,
+            temperature=temperature,
+        )
+    )
+
+    assert len(requests) == 1
+    request, timeout = requests[0]
+    expected_payload = {
+        "model": "llama3.2",
+        "messages": [{"role": "user", "content": "hi"}],
+        "stream": False,
+    }
+    if expected_options:
+        expected_payload["options"] = expected_options
+    assert json.loads(request.data) == expected_payload
+    assert request.full_url == "http://localhost:11434/api/chat"
+    assert request.get_method() == "POST"
+    assert request.get_header("Content-type") == "application/json"
+    assert timeout == 60
+    assert output.content == "ok"
+    assert output.model == "llama3.2"
+    assert output.stop_reason == "stop"
+
+
 def test_claude_provider_serializes_tools_for_messages_api():
     provider = ClaudeProvider(api_key="test")
     client = _AnthropicClient()