🐛 Fix: Critical Streaming Usage Metadata & Advanced Model Support

[imranarshad]

🐛 Fix: Critical Streaming Usage Metadata & Advanced Model Support

📋 Summary

This PR fixes 6 critical bugs in the langchain-litellm package that affect streaming, usage tracking, and advanced AI model features. The primary fix addresses Issue #20 - missing usage metadata in streaming responses.

🔥 Critical Bugs Fixed

1. Missing Usage Metadata in Streaming Responses (Issue #20)

• Problem: Streaming responses don't include token usage metadata
• Impact: Impossible to track costs during streaming operations
• Fix: Added stream_options={"include_usage": True} and usage extraction logic

2. Missing reasoning_content Support for Thinking Models

• Problem: reasoning_content lost for o1, Claude thinking, Gemini reasoning models
• Impact: Loss of valuable reasoning insights users pay premium for
• Fix: Added reasoning_content handling in _convert_dict_to_message

3. Streaming Crashes with Dictionary Deltas

• Problem: AttributeError when LiteLLM returns dict deltas instead of Delta objects
• Impact: Unexpected crashes during streaming
• Fix: Added robust type checking and handling for both formats

4. Tool Call Processing Failures

• Problem: KeyError when providers return different tool call formats
• Impact: Tool calling failures with certain providers
• Fix: Added defensive programming with fallbacks

5. Incomplete Usage Metadata

• Problem: Missing cache tokens and reasoning tokens in usage details
• Impact: Incomplete cost tracking and debugging info
• Fix: Enhanced _create_usage_metadata to extract advanced details

6. Async Streaming Reliability

• Problem: Incorrect async completion call pattern
• Impact: Potential async streaming failures
• Fix: Corrected async streaming method call

🧪 Testing

Before Fix (Broken):

from langchain_litellm import ChatLiteLLM
from langchain_core.messages import HumanMessage

llm = ChatLiteLLM(model="gpt-4o", streaming=True)
chunks = list(llm.stream([HumanMessage(content="Hello")]))
usage_found = any(hasattr(chunk, 'usage_metadata') and chunk.usage_metadata for chunk in chunks)
print(f"Streaming usage metadata found: {usage_found}")  # False ❌

After Fix (Working):

from langchain_litellm import ChatLiteLLM
from langchain_core.messages import HumanMessage

llm = ChatLiteLLM(model="gpt-4o", streaming=True)
chunks = list(llm.stream([HumanMessage(content="Hello")]))
usage_found = any(hasattr(chunk, 'usage_metadata') and chunk.usage_metadata for chunk in chunks)
print(f"Streaming usage metadata found: {usage_found}")  # True ✅

# Get usage details
final_chunk = chunks[-1]
print(f"Input tokens: {final_chunk.usage_metadata.input_tokens}")    # 12
print(f"Output tokens: {final_chunk.usage_metadata.output_tokens}")  # 5
print(f"Total tokens: {final_chunk.usage_metadata.total_tokens}")    # 17

Advanced Model Testing:

# Testing reasoning_content support
llm = ChatLiteLLM(model="vertex_ai/gemini-2.5-flash")
result = llm.invoke("What is 2+2?", thinking={"type": "enabled", "budget_tokens": 1024})
print(f"Reasoning content available: {bool(result.additional_kwargs.get('reasoning_content'))}")  # True ✅
print(f"Reasoning tokens: {result.usage_metadata.output_token_details.get('reasoning', 0)}")  # 457

📊 Impact Assessment

Feature	Before	After	Status
Streaming Usage Metadata	❌ Missing	✅ Working	Fixed
Reasoning Content (o1, Claude, Gemini)	❌ Lost	✅ Preserved	Fixed
Streaming Stability	❌ Crashes	✅ Robust	Fixed
Tool Call Compatibility	❌ Failures	✅ Reliable	Fixed
Advanced Usage Details	❌ Basic	✅ Complete	Enhanced
Async Streaming	❌ Unreliable	✅ Stable	Fixed

🔧 Technical Details

Key Changes in langchain_litellm/chat_models/litellm.py:

1. Enhanced _default_params:

   # Add stream_options when streaming is enabled
   if self.streaming:
       params["stream_options"] = {"include_usage": True}

2. Fixed _stream method:

   # Extract and attach usage metadata from chunks
   if "usage" in chunk and chunk["usage"]:
       usage_metadata = _create_usage_metadata(chunk["usage"])
       message_chunk.usage_metadata = usage_metadata

3. Added reasoning_content support:

   # Add reasoning_content support for thinking-enabled models
   if _dict.get("reasoning_content"):
       additional_kwargs["reasoning_content"] = _dict["reasoning_content"]

4. Robust delta handling:

   # Handle both Delta objects and dicts
   if isinstance(delta, dict):
       role = delta.get("role")
       content = delta.get("content") or ""
       # ... handle dict format
   else:
       role = delta.role
       content = delta.content or ""
       # ... handle Delta object format

5. Enhanced usage metadata:

   # Extract advanced usage details
   if "cache_read_input_tokens" in token_usage:
       input_token_details["cache_read"] = token_usage["cache_read_input_tokens"]
   
   # Reasoning tokens for o1 models, Claude thinking, etc.
   completion_tokens_details = token_usage.get("completion_tokens_details", {})
   if completion_tokens_details and "reasoning_tokens" in completion_tokens_details:
       output_token_details["reasoning"] = completion_tokens_details["reasoning_tokens"]

🚀 Benefits

• ✅ Production Ready: Real-time cost tracking for streaming applications
• ✅ Advanced AI Support: Full support for o1, Claude thinking, Gemini reasoning
• ✅ Provider Compatibility: Robust handling across OpenAI, Anthropic, Google, etc.
• ✅ Backward Compatible: No breaking changes to existing code
• ✅ Comprehensive: Fixes multiple related issues in one PR

🔗 Related Issues

• Fixes Bug: Streaming responses missing usage metadata for cost tracking #20: Streaming responses missing usage metadata
• Addresses reasoning_content support for thinking models
• Improves streaming reliability and provider compatibility
• Enhances usage tracking for cost optimization

📦 Commits

• 5201401: feat: Add comprehensive streaming usage metadata support
• af41ab9: feat: add reasoning_content support for thinking-enabled models

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Ready for Review 🎉 This PR addresses fundamental issues that affect production usage tracking and advanced AI model features. All fixes maintain backward compatibility while adding essential missing functionality.

[Akshay-Dongare]

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[Akshay-Dongare]

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