fix(llma): gemini missing cached and reasoning tokens (#323)

* fix(llma): Gemini missing cached and reasoning tokens

* chore(llma): bump version

* chore(llma): run ruff

Author: Radu-Raicea

CHANGELOG.md

@@ -1,3 +1,7 @@
+# 6.7.3 - 2025-09-04
+
+- fix: missing usage tokens in Gemini
+
 # 6.7.2 - 2025-09-03
 
 - fix: tool call results in streaming providers

posthog/ai/anthropic/anthropic.py

@@ -10,7 +10,7 @@
 import uuid
 from typing import Any, Dict, List, Optional
 
-from posthog.ai.types import StreamingContentBlock, ToolInProgress
+from posthog.ai.types import StreamingContentBlock, TokenUsage, ToolInProgress
 from posthog.ai.utils import (
     call_llm_and_track_usage,
     merge_usage_stats,
@@ -126,7 +126,7 @@ def _create_streaming(
         **kwargs: Any,
     ):
         start_time = time.time()
-        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
         accumulated_content = ""
         content_blocks: List[StreamingContentBlock] = []
         tools_in_progress: Dict[str, ToolInProgress] = {}
@@ -210,14 +210,13 @@ def _capture_streaming_event(
         posthog_privacy_mode: bool,
         posthog_groups: Optional[Dict[str, Any]],
         kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
         latency: float,
         content_blocks: List[StreamingContentBlock],
         accumulated_content: str,
     ):
         from posthog.ai.types import StreamingEventData
         from posthog.ai.anthropic.anthropic_converter import (
-            standardize_anthropic_usage,
             format_anthropic_streaming_input,
             format_anthropic_streaming_output_complete,
         )
@@ -236,7 +235,7 @@ def _capture_streaming_event(
             formatted_output=format_anthropic_streaming_output_complete(
                 content_blocks, accumulated_content
             ),
-            usage_stats=standardize_anthropic_usage(usage_stats),
+            usage_stats=usage_stats,
             latency=latency,
             distinct_id=posthog_distinct_id,
             trace_id=posthog_trace_id,

posthog/ai/anthropic/anthropic_async.py

@@ -11,7 +11,7 @@
 from typing import Any, Dict, List, Optional
 
 from posthog import setup
-from posthog.ai.types import StreamingContentBlock, ToolInProgress
+from posthog.ai.types import StreamingContentBlock, TokenUsage, ToolInProgress
 from posthog.ai.utils import (
     call_llm_and_track_usage_async,
     extract_available_tool_calls,
@@ -131,7 +131,7 @@ async def _create_streaming(
         **kwargs: Any,
     ):
         start_time = time.time()
-        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
         accumulated_content = ""
         content_blocks: List[StreamingContentBlock] = []
         tools_in_progress: Dict[str, ToolInProgress] = {}
@@ -215,7 +215,7 @@ async def _capture_streaming_event(
         posthog_privacy_mode: bool,
         posthog_groups: Optional[Dict[str, Any]],
         kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
         latency: float,
         content_blocks: List[StreamingContentBlock],
         accumulated_content: str,

posthog/ai/anthropic/anthropic_converter.py

@@ -14,7 +14,6 @@
     FormattedMessage,
     FormattedTextContent,
     StreamingContentBlock,
-    StreamingUsageStats,
     TokenUsage,
     ToolInProgress,
 )
@@ -164,7 +163,38 @@ def format_anthropic_streaming_content(
     return formatted
 
 
-def extract_anthropic_usage_from_event(event: Any) -> StreamingUsageStats:
+def extract_anthropic_usage_from_response(response: Any) -> TokenUsage:
+    """
+    Extract usage from a full Anthropic response (non-streaming).
+
+    Args:
+        response: The complete response from Anthropic API
+
+    Returns:
+        TokenUsage with standardized usage
+    """
+    if not hasattr(response, "usage"):
+        return TokenUsage(input_tokens=0, output_tokens=0)
+
+    result = TokenUsage(
+        input_tokens=getattr(response.usage, "input_tokens", 0),
+        output_tokens=getattr(response.usage, "output_tokens", 0),
+    )
+
+    if hasattr(response.usage, "cache_read_input_tokens"):
+        cache_read = response.usage.cache_read_input_tokens
+        if cache_read and cache_read > 0:
+            result["cache_read_input_tokens"] = cache_read
+
+    if hasattr(response.usage, "cache_creation_input_tokens"):
+        cache_creation = response.usage.cache_creation_input_tokens
+        if cache_creation and cache_creation > 0:
+            result["cache_creation_input_tokens"] = cache_creation
+
+    return result
+
+
+def extract_anthropic_usage_from_event(event: Any) -> TokenUsage:
     """
     Extract usage statistics from an Anthropic streaming event.
 
@@ -175,7 +205,7 @@ def extract_anthropic_usage_from_event(event: Any) -> StreamingUsageStats:
         Dictionary of usage statistics
     """
 
-    usage: StreamingUsageStats = {}
+    usage: TokenUsage = TokenUsage()
 
     # Handle usage stats from message_start event
     if hasattr(event, "type") and event.type == "message_start":
@@ -329,26 +359,6 @@ def finalize_anthropic_tool_input(
             del tools_in_progress[block["id"]]
 
 
-def standardize_anthropic_usage(usage: Dict[str, Any]) -> TokenUsage:
-    """
-    Standardize Anthropic usage statistics to common TokenUsage format.
-
-    Anthropic already uses standard field names, so this mainly structures the data.
-
-    Args:
-        usage: Raw usage statistics from Anthropic
-
-    Returns:
-        Standardized TokenUsage dict
-    """
-    return TokenUsage(
-        input_tokens=usage.get("input_tokens", 0),
-        output_tokens=usage.get("output_tokens", 0),
-        cache_read_input_tokens=usage.get("cache_read_input_tokens"),
-        cache_creation_input_tokens=usage.get("cache_creation_input_tokens"),
-    )
-
-
 def format_anthropic_streaming_input(kwargs: Dict[str, Any]) -> Any:
     """
     Format Anthropic streaming input using system prompt merging.

posthog/ai/gemini/gemini.py

@@ -3,6 +3,8 @@
 import uuid
 from typing import Any, Dict, Optional
 
+from posthog.ai.types import TokenUsage
+
 try:
     from google import genai
 except ImportError:
@@ -294,7 +296,7 @@ def _generate_content_streaming(
         **kwargs: Any,
     ):
         start_time = time.time()
-        usage_stats: Dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
+        usage_stats: TokenUsage = TokenUsage(input_tokens=0, output_tokens=0)
         accumulated_content = []
 
         kwargs_without_stream = {"model": model, "contents": contents, **kwargs}
@@ -350,12 +352,11 @@ def _capture_streaming_event(
         privacy_mode: bool,
         groups: Optional[Dict[str, Any]],
         kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
         latency: float,
         output: Any,
     ):
         from posthog.ai.types import StreamingEventData
-        from posthog.ai.gemini.gemini_converter import standardize_gemini_usage
 
         # Prepare standardized event data
         formatted_input = self._format_input(contents)
@@ -368,7 +369,7 @@ def _capture_streaming_event(
             kwargs=kwargs,
             formatted_input=sanitized_input,
             formatted_output=format_gemini_streaming_output(output),
-            usage_stats=standardize_gemini_usage(usage_stats),
+            usage_stats=usage_stats,
             latency=latency,
             distinct_id=distinct_id,
             trace_id=trace_id,

posthog/ai/gemini/gemini_converter.py

@@ -10,7 +10,6 @@
 from posthog.ai.types import (
     FormattedContentItem,
     FormattedMessage,
-    StreamingUsageStats,
     TokenUsage,
 )
 
@@ -283,29 +282,71 @@ def format_gemini_input(contents: Any) -> List[FormattedMessage]:
     return [_format_object_message(contents)]
 
 
-def extract_gemini_usage_from_chunk(chunk: Any) -> StreamingUsageStats:
+def _extract_usage_from_metadata(metadata: Any) -> TokenUsage:
+    """
+    Common logic to extract usage from Gemini metadata.
+    Used by both streaming and non-streaming paths.
+
+    Args:
+        metadata: usage_metadata from Gemini response or chunk
+
+    Returns:
+        TokenUsage with standardized usage
+    """
+    usage = TokenUsage(
+        input_tokens=getattr(metadata, "prompt_token_count", 0),
+        output_tokens=getattr(metadata, "candidates_token_count", 0),
+    )
+
+    # Add cache tokens if present (don't add if 0)
+    if hasattr(metadata, "cached_content_token_count"):
+        cache_tokens = metadata.cached_content_token_count
+        if cache_tokens and cache_tokens > 0:
+            usage["cache_read_input_tokens"] = cache_tokens
+
+    # Add reasoning tokens if present (don't add if 0)
+    if hasattr(metadata, "thoughts_token_count"):
+        reasoning_tokens = metadata.thoughts_token_count
+        if reasoning_tokens and reasoning_tokens > 0:
+            usage["reasoning_tokens"] = reasoning_tokens
+
+    return usage
+
+
+def extract_gemini_usage_from_response(response: Any) -> TokenUsage:
+    """
+    Extract usage statistics from a full Gemini response (non-streaming).
+
+    Args:
+        response: The complete response from Gemini API
+
+    Returns:
+        TokenUsage with standardized usage statistics
+    """
+    if not hasattr(response, "usage_metadata") or not response.usage_metadata:
+        return TokenUsage(input_tokens=0, output_tokens=0)
+
+    return _extract_usage_from_metadata(response.usage_metadata)
+
+
+def extract_gemini_usage_from_chunk(chunk: Any) -> TokenUsage:
     """
     Extract usage statistics from a Gemini streaming chunk.
 
     Args:
         chunk: Streaming chunk from Gemini API
 
     Returns:
-        Dictionary of usage statistics
+        TokenUsage with standardized usage statistics
     """
 
-    usage: StreamingUsageStats = {}
+    usage: TokenUsage = TokenUsage()
 
     if not hasattr(chunk, "usage_metadata") or not chunk.usage_metadata:
         return usage
 
-    # Gemini uses prompt_token_count and candidates_token_count
-    usage["input_tokens"] = getattr(chunk.usage_metadata, "prompt_token_count", 0)
-    usage["output_tokens"] = getattr(chunk.usage_metadata, "candidates_token_count", 0)
-
-    # Calculate total if both values are defined (including 0)
-    if "input_tokens" in usage and "output_tokens" in usage:
-        usage["total_tokens"] = usage["input_tokens"] + usage["output_tokens"]
+    # Use the shared helper to extract usage
+    usage = _extract_usage_from_metadata(chunk.usage_metadata)
 
     return usage
 
@@ -417,22 +458,3 @@ def format_gemini_streaming_output(
 
     # Fallback for empty or unexpected input
     return [{"role": "assistant", "content": [{"type": "text", "text": ""}]}]
-
-
-def standardize_gemini_usage(usage: Dict[str, Any]) -> TokenUsage:
-    """
-    Standardize Gemini usage statistics to common TokenUsage format.
-
-    Gemini already uses standard field names (input_tokens/output_tokens).
-
-    Args:
-        usage: Raw usage statistics from Gemini
-
-    Returns:
-        Standardized TokenUsage dict
-    """
-    return TokenUsage(
-        input_tokens=usage.get("input_tokens", 0),
-        output_tokens=usage.get("output_tokens", 0),
-        # Gemini doesn't currently support cache or reasoning tokens
-    )

posthog/ai/openai/openai.py

@@ -2,6 +2,8 @@
 import uuid
 from typing import Any, Dict, List, Optional
 
+from posthog.ai.types import TokenUsage
+
 try:
     import openai
 except ImportError:
@@ -120,7 +122,7 @@ def _create_streaming(
         **kwargs: Any,
     ):
         start_time = time.time()
-        usage_stats: Dict[str, int] = {}
+        usage_stats: TokenUsage = TokenUsage()
         final_content = []
         response = self._original.create(**kwargs)
 
@@ -171,14 +173,13 @@ def _capture_streaming_event(
         posthog_privacy_mode: bool,
         posthog_groups: Optional[Dict[str, Any]],
         kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
         latency: float,
         output: Any,
         available_tool_calls: Optional[List[Dict[str, Any]]] = None,
     ):
         from posthog.ai.types import StreamingEventData
         from posthog.ai.openai.openai_converter import (
-            standardize_openai_usage,
             format_openai_streaming_input,
             format_openai_streaming_output,
         )
@@ -195,7 +196,7 @@ def _capture_streaming_event(
             kwargs=kwargs,
             formatted_input=sanitized_input,
             formatted_output=format_openai_streaming_output(output, "responses"),
-            usage_stats=standardize_openai_usage(usage_stats, "responses"),
+            usage_stats=usage_stats,
             latency=latency,
             distinct_id=posthog_distinct_id,
             trace_id=posthog_trace_id,
@@ -316,7 +317,7 @@ def _create_streaming(
         **kwargs: Any,
     ):
         start_time = time.time()
-        usage_stats: Dict[str, int] = {}
+        usage_stats: TokenUsage = TokenUsage()
         accumulated_content = []
         accumulated_tool_calls: Dict[int, Dict[str, Any]] = {}
         if "stream_options" not in kwargs:
@@ -387,15 +388,14 @@ def _capture_streaming_event(
         posthog_privacy_mode: bool,
         posthog_groups: Optional[Dict[str, Any]],
         kwargs: Dict[str, Any],
-        usage_stats: Dict[str, int],
+        usage_stats: TokenUsage,
         latency: float,
         output: Any,
         tool_calls: Optional[List[Dict[str, Any]]] = None,
         available_tool_calls: Optional[List[Dict[str, Any]]] = None,
     ):
         from posthog.ai.types import StreamingEventData
         from posthog.ai.openai.openai_converter import (
-            standardize_openai_usage,
             format_openai_streaming_input,
             format_openai_streaming_output,
         )
@@ -412,7 +412,7 @@ def _capture_streaming_event(
             kwargs=kwargs,
             formatted_input=sanitized_input,
             formatted_output=format_openai_streaming_output(output, "chat", tool_calls),
-            usage_stats=standardize_openai_usage(usage_stats, "chat"),
+            usage_stats=usage_stats,
             latency=latency,
             distinct_id=posthog_distinct_id,
             trace_id=posthog_trace_id,