feat: token usage tracking in responses api

[phact]

Token Usage Tracking Implementation

1. Added Usage model to Properties schema (src/lfx/src/lfx/schema/properties.py and src/backend/base/langflow/schema/properties.py):
   - New Usage class with input_tokens, output_tokens, total_tokens fields
   - Added usage field to Properties class
2. Added usage extraction in LCModelComponent (src/lfx/src/lfx/base/models/model.py):
   - New extract_usage() method to extract token usage from AIMessage response_metadata
   - Supports OpenAI format (token_usage.prompt_tokens/completion_tokens/total_tokens)
   - Supports Anthropic format (usage.input_tokens/output_tokens)
   - Modified _get_chat_result() to set usage on returned Message for non-streaming
3. Added streaming usage support (src/lfx/src/lfx/custom/custom_component/component.py):
   - Modified _stream_message() to return usage data from final chunk
   - Modified send_message() to set usage on message properties after streaming
4. Updated OpenAI Responses API endpoint (src/backend/base/langflow/api/v1/openai_responses.py):
   - Non-streaming: Extract usage from component_output.results Message properties
   - Streaming: Capture usage from add_message event with state=complete
   - Added response.completed event with usage in streaming responses
5. Fixed integration tests (src/backend/tests/integration/test_openai_responses_integration.py):
   - Added programmatic model configuration for LanguageModelComponent
   - Set API key directly in template (workaround for global variable lookup issue in tests)
   - Added usage validation assertions

Summary by CodeRabbit

Release Notes

• New Features
  • Added token usage metrics to API responses, displaying input tokens, output tokens, and total tokens consumed for each request
  • Token usage is now captured and available in both streaming and non-streaming response formats
  • Usage data is extracted from language model responses and included in all API response payloads

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Walkthrough

This pull request introduces per-response usage metrics (input_tokens, output_tokens, total_tokens) throughout the codebase. A new Usage model is added to schema definitions. Usage data is captured from OpenAI-like responses in both streaming and non-streaming paths and propagated through Message properties and API responses.

Changes

Cohort / File(s)	Summary
Schema definitions src/backend/base/langflow/schema/properties.py, src/lfx/src/lfx/schema/properties.py	New public Usage model introduced with optional fields input_tokens, output_tokens, and total_tokens. New optional usage field added to Properties model in both backend and lfx packages.
API response handling src/backend/base/langflow/api/v1/openai_responses.py	Usage data extraction and propagation added to both streaming and non-streaming paths. Streaming path captures usage from completed messages and emits response.completed event. Non-streaming path extracts usage from result outputs. Public OpenAIResponsesResponse signature updated to accept usage field.
LLM component integration src/lfx/src/lfx/base/models/model.py	New public method extract_usage(message: AIMessage) added to parse token usage from OpenAI and Anthropic response metadata. Usage data populated in _get_chat_result flow and attached to Message properties.
Custom component streaming src/lfx/src/lfx/custom/custom_component/component.py	Stream handling return types updated to tuple[str, dict | None] for _stream_message and _handle_async_iterator. Usage metadata extraction added across streaming chunks with fallback to response_metadata. Captured usage propagated to stored_message properties.
Integration tests src/backend/tests/integration/test_openai_responses_integration.py	Programmatic configuration of LanguageModelComponent added. Non-streaming test expanded to validate usage object and token fields. Streaming test adds validation for response.completed event containing usage data.

Sequence Diagram

sequenceDiagram
    participant Client
    participant OpenAIAPI as OpenAI API
    participant Handler as OpenAI Response Handler
    participant Schema as Message Properties
    participant Response as OpenAI Response Object

    Client->>OpenAIAPI: Send prompt (streaming or non-streaming)
    OpenAIAPI->>Handler: Return response with usage metadata
    
    alt Streaming Path
        loop For each chunk
            OpenAIAPI-->>Handler: Stream chunk with usage_metadata
            Handler->>Schema: Extract usage (input, output, total tokens)
            Handler->>Handler: Accumulate usage_data
        end
        Handler->>Handler: Message reaches "complete" state
        Handler->>Response: Emit completion event with usage_data
        Response-->>Client: Send response.completed with usage
    else Non-Streaming Path
        Handler->>Schema: Extract usage from result outputs
        Handler->>Handler: Construct usage_data dict
        Handler->>Response: Populate OpenAIResponsesResponse with usage field
        Response-->>Client: Send response with usage_data
    end
    
    Response-->>Client: Final response includes usage metrics

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

🚥 Pre-merge checks | ✅ 5 | ❌ 2

❌ Failed checks (1 error, 1 warning)

Check name	Status	Explanation	Resolution
Test Coverage For New Implementations	❌ Error	PR adds new Usage model classes and extract_usage() method but lacks unit tests for critical implementations including Usage model validation, extract_usage() method, modified streaming methods, and usage extraction logic.	Add unit tests for Usage model schema validation, extract_usage() method parsing, streaming method return types, and openai_responses.py usage extraction logic to complement existing integration tests.
Test Quality And Coverage	⚠️ Warning	Test coverage for token usage tracking is incomplete: no unit tests for Usage schema, extract_usage() method, or streaming usage modifications despite integration tests being added.	Add unit tests for Usage model, extract_usage() method, and streaming usage; expand integration tests to include error scenarios, edge cases, and async error handling validation.

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Docstring Coverage	✅ Passed	Docstring coverage is 80.00% which is sufficient. The required threshold is 80.00%.
Test File Naming And Structure	✅ Passed	Test file follows all specified patterns: proper naming convention, pytest structure with decorators, descriptive test function names, logical organization with helper functions, comprehensive coverage of positive/negative scenarios and edge cases.
Excessive Mock Usage Warning	✅ Passed	Integration tests use real component configuration and API interactions without evidence of excessive mock usage, following proper test design practices.
Title check	✅ Passed	The title 'feat: token usage tracking in responses api' clearly and concisely describes the main change—adding token usage tracking to the responses API. It is specific, relates to the core functionality introduced across multiple files, and accurately summarizes the primary objective of the changeset.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch openai-responses-usage

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[codecov]

Codecov Report

❌ Patch coverage is 28.23529% with 61 lines in your changes missing coverage. Please review.
✅ Project coverage is 35.45%. Comparing base (5369b63) to head (f44d1fa).
⚠️ Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
...c/lfx/src/lfx/custom/custom_component/component.py	22.22%	25 Missing and 3 partials ⚠️
src/lfx/src/lfx/base/models/model.py	9.09%	20 Missing ⚠️
...c/backend/base/langflow/api/v1/openai_responses.py	26.66%	11 Missing ⚠️
src/lfx/src/lfx/base/models/unified_models.py	0.00%	2 Missing ⚠️

❌ Your patch status has failed because the patch coverage (28.23%) is below the target coverage (40.00%). You can increase the patch coverage or adjust the target coverage.
❌ Your project status has failed because the head coverage (41.87%) is below the target coverage (60.00%). You can increase the head coverage or adjust the target coverage.

Additional details and impacted files

@@            Coverage Diff             @@
##             main   #11302      +/-   ##
==========================================
+ Coverage   35.38%   35.45%   +0.07%     
==========================================
  Files        1527     1527              
  Lines       73484    73563      +79     
  Branches    11041    11059      +18     
==========================================
+ Hits        25999    26080      +81     
+ Misses      46073    46068       -5     
- Partials     1412     1415       +3     

Flag	Coverage Δ
backend	56.29% <45.00%> (+0.29%)	⬆️
frontend	16.98% <ø> (ø)
lfx	41.87% <23.07%> (-0.06%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
src/backend/base/langflow/schema/properties.py	91.42% <100.00%> (+1.42%)	⬆️
src/lfx/src/lfx/schema/properties.py	82.85% <100.00%> (+2.85%)	⬆️
src/lfx/src/lfx/base/models/unified_models.py	30.27% <0.00%> (-0.13%)	⬇️
...c/backend/base/langflow/api/v1/openai_responses.py	41.53% <26.66%> (+29.08%)	⬆️
src/lfx/src/lfx/base/models/model.py	20.00% <9.09%> (-1.31%)	⬇️
...c/lfx/src/lfx/custom/custom_component/component.py	57.94% <22.22%> (-1.43%)	⬇️

... and 9 files with indirect coverage changes

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[edwinjosechittilappilly]

Tested in LF. LGTM

[github-actions]

Frontend Unit Test Coverage Report

Coverage Summary

Lines	Statements	Branches	Functions
	18.8% (6095/32404)	12.25% (3097/25275)	12.64% (879/6952)

Unit Test Results

Tests	Skipped	Failures	Errors	Time
2310	0 💤	0 ❌	0 🔥	32.971s ⏱️

[edwinjosechittilappilly]

@HimavarshaVS @RamGopalSrikar Please coordinate with QA and Merge this PR as soon as available?