fix(provider): preserve litellm usage details (#2246)

src/langbot/pkg/provider/modelmgr/requester.py

@@ -12,6 +12,19 @@ import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
+LLM_USAGE_QUERY_VARIABLE = '_llm_usage'
+STREAM_USAGE_QUERY_VARIABLE = '_stream_usage'
+
+
+def _store_llm_usage(query: pipeline_query.Query | None, usage_info: dict | None) -> None:
+    """Store the latest provider usage on the query for upstream action handlers."""
+    if query is None or not usage_info:
+        return
+    if query.variables is None:
+        query.variables = {}
+    query.variables[LLM_USAGE_QUERY_VARIABLE] = dict(usage_info)
+
+
 class RuntimeProvider:
     """运行时模型提供商"""
 
@@ -67,6 +80,7 @@ class RuntimeProvider:
             if isinstance(result, tuple):
                 msg, usage_info = result
                 if usage_info:
+                    _store_llm_usage(query, usage_info)
                     input_tokens = usage_info.get('prompt_tokens', 0)
                     output_tokens = usage_info.get('completion_tokens', 0)
                 return msg
@@ -146,11 +160,12 @@ class RuntimeProvider:
             if query:
                 if query.variables is None:
                     query.variables = {}
-                if '_stream_usage' in query.variables:
+                if STREAM_USAGE_QUERY_VARIABLE in query.variables:
-                    usage_info = query.variables['_stream_usage']
+                    usage_info = query.variables[STREAM_USAGE_QUERY_VARIABLE]
+                    _store_llm_usage(query, usage_info)
                     input_tokens = usage_info.get('prompt_tokens', 0)
                     output_tokens = usage_info.get('completion_tokens', 0)
-                    del query.variables['_stream_usage']
+                    del query.variables[STREAM_USAGE_QUERY_VARIABLE]
         except Exception as e:
             status = 'error'
             error_message = str(e)

src/langbot/pkg/provider/modelmgr/requesters/litellmchat.py

@@ -262,32 +262,82 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
         - dict with the same keys
         - missing ``total_tokens`` (derived from prompt + completion)
         - ``None`` / partially-populated usage (defaults to 0)
+        - provider-specific token details, including cache token counters
         """
-        if usage is None:
-            return {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
 
-        def _get(key: str) -> typing.Any:
+        def _plain_value(value: typing.Any) -> typing.Any:
-            if isinstance(usage, dict):
+            if value is None:
-                return usage.get(key)
+                return None
-            return getattr(usage, key, None)
+            if isinstance(value, dict):
+                return {k: _plain_value(v) for k, v in value.items() if v is not None}
+            if isinstance(value, (list, tuple)):
+                return [_plain_value(v) for v in value]
 
-        prompt_tokens = _get('prompt_tokens') or 0
+            model_dump = getattr(value, 'model_dump', None)
-        completion_tokens = _get('completion_tokens') or 0
+            if callable(model_dump):
-        total_tokens = _get('total_tokens') or 0
+                try:
+                    dumped = model_dump()
+                    if isinstance(dumped, dict):
+                        return _plain_value(dumped)
+                except Exception:
+                    pass
+
+            return value
+
+        def _usage_dict(value: typing.Any) -> dict[str, typing.Any]:
+            if value is None:
+                return {}
+            plain = _plain_value(value)
+            if isinstance(plain, dict):
+                return plain
+
+            def _is_mock_attr(attr: typing.Any) -> bool:
+                return type(attr).__module__.startswith('unittest.mock')
+
+            data: dict[str, typing.Any] = {}
+            for key in (
+                'prompt_tokens',
+                'completion_tokens',
+                'total_tokens',
+                'prompt_tokens_details',
+                'completion_tokens_details',
+                'cache_creation_input_tokens',
+                'cache_read_input_tokens',
+                'input_token_details',
+                'output_token_details',
+            ):
+                attr_value = getattr(value, key, None)
+                if attr_value is not None and not _is_mock_attr(attr_value):
+                    data[key] = _plain_value(attr_value)
+            return data
+
+        def _to_int(value: typing.Any) -> int:
+            try:
+                return int(value or 0)
+            except (TypeError, ValueError):
+                return 0
+
+        normalized = _usage_dict(usage)
+
+        prompt_tokens = _to_int(normalized.get('prompt_tokens'))
+        completion_tokens = _to_int(normalized.get('completion_tokens'))
+        total_tokens = _to_int(normalized.get('total_tokens'))
 
         # Some providers omit total_tokens in streaming usage; derive it.
         if not total_tokens:
             total_tokens = prompt_tokens + completion_tokens
 
-        return {
+        normalized['prompt_tokens'] = prompt_tokens
-            'prompt_tokens': int(prompt_tokens),
+        normalized['completion_tokens'] = completion_tokens
-            'completion_tokens': int(completion_tokens),
+        normalized['total_tokens'] = total_tokens
-            'total_tokens': int(total_tokens),
+        return normalized
-        }
 
-    def _extract_usage(self, response) -> dict:
+    def _extract_usage(self, response) -> dict | None:
         """Extract usage info from a non-streaming LiteLLM response."""
-        return self._normalize_usage(getattr(response, 'usage', None))
+        usage = getattr(response, 'usage', None)
+        if usage is None:
+            return None
+        return self._normalize_usage(usage)
 
     @staticmethod
     def _as_dict(value: typing.Any) -> dict:
@@ -486,7 +536,7 @@ class LiteLLMRequester(requester.ProviderAPIRequester):
                     if query is not None:
                         if query.variables is None:
                             query.variables = {}
-                        query.variables['_stream_usage'] = usage_info
+                        query.variables[requester.STREAM_USAGE_QUERY_VARIABLE] = usage_info
 
                 if not hasattr(chunk, 'choices') or not chunk.choices:
                     continue

tests/unit_tests/provider/test_litellmchat.py

@@ -115,6 +115,15 @@ class TestExtractUsage:
         assert result['prompt_tokens'] == 0
         assert result['completion_tokens'] == 0
 
+    def test_extract_usage_without_provider_usage(self):
+        """Missing provider usage is not treated as authoritative zero usage."""
+        requester = litellmchat.LiteLLMRequester(ap=Mock(), config={})
+
+        response = Mock()
+        response.usage = None
+
+        assert requester._extract_usage(response) is None
+
 
 class TestNormalizeUsage:
     """Test _normalize_usage helper covering real-world usage shapes"""
@@ -131,6 +140,22 @@ class TestNormalizeUsage:
         )
         assert result == {'prompt_tokens': 12, 'completion_tokens': 8, 'total_tokens': 20}
 
+    def test_preserves_token_details(self):
+        """Provider token details such as cache counters are preserved."""
+        result = litellmchat.LiteLLMRequester._normalize_usage(
+            {
+                'prompt_tokens': 12,
+                'completion_tokens': 8,
+                'total_tokens': 20,
+                'prompt_tokens_details': {'cached_tokens': 7},
+                'completion_tokens_details': {'reasoning_tokens': 3},
+            }
+        )
+
+        assert result['prompt_tokens'] == 12
+        assert result['prompt_tokens_details'] == {'cached_tokens': 7}
+        assert result['completion_tokens_details'] == {'reasoning_tokens': 3}
+
     def test_missing_total_is_derived(self):
         """When total_tokens is absent/zero it is derived from prompt + completion"""
         usage = Mock()
@@ -166,9 +191,7 @@ class TestInvokeLLMStreamUsage:
         if has_choice:
             choice = Mock()
             delta = Mock()
-            delta.model_dump = Mock(
+            delta.model_dump = Mock(return_value={'role': 'assistant', 'content': content, 'tool_calls': tool_calls})
-                return_value={'role': 'assistant', 'content': content, 'tool_calls': tool_calls}
-            )
             choice.delta = delta
             choice.finish_reason = finish_reason
             chunk.choices = [choice]
@@ -313,7 +336,8 @@ class TestInvokeLLMStreamUsage:
 
         with patch.object(litellmchat, 'acompletion', new=AsyncMock(side_effect=lambda **kw: _aiter())):
             collected = [
-                chunk async for chunk in requester.invoke_llm_stream(
+                chunk
+                async for chunk in requester.invoke_llm_stream(
                     query=query,
                     model=model,
                     messages=messages,
@@ -788,7 +812,9 @@ class TestInvokeRerank:
         with patch('httpx.AsyncClient', return_value=mock_client):
             # arerank must NOT be called on the openai-compatible path
             with patch.object(
-                litellmchat, 'arerank', new_callable=AsyncMock,
+                litellmchat,
+                'arerank',
+                new_callable=AsyncMock,
                 side_effect=AssertionError('arerank must not be used for openai-compatible provider'),
             ):
                 results = await requester.invoke_rerank(
@@ -1068,8 +1094,7 @@ class TestScanModels:
 
         with patch.object(litellmchat.litellm, 'supports_function_calling') as mock_supports_function_calling:
             mock_supports_function_calling.side_effect = (
-                lambda model, custom_llm_provider=None: model == 'moonshot/kimi-k2.6'
+                lambda model, custom_llm_provider=None: model == 'moonshot/kimi-k2.6' and custom_llm_provider is None
-                and custom_llm_provider is None
             )
 
             assert requester._supports_function_calling('kimi-k2.6') is True