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@codeflash-ai codeflash-ai bot commented Oct 29, 2025

📄 32% (0.32x) speedup for Logging.initialize_standard_built_in_tools_params in litellm/litellm_core_utils/litellm_logging.py

⏱️ Runtime : 8.03 microseconds 6.10 microseconds (best of 35 runs)

📝 Explanation and details

The optimized code achieves a 31% speedup by eliminating redundant function calls and unnecessary dictionary traversals in tool extraction logic.

Key optimizations:

  1. Eliminated redundant _get_tools_from_kwargs calls: The original code called this helper function multiple times with different tool_type parameters, but the function ignored the parameter and always returned kwargs.get("tools"). The optimized version directly calls kwargs.get("tools") once, removing function call overhead.

  2. Consolidated tool type checking: Instead of making separate calls to search for "web_search_preview" and "web_search" tools, the optimized version retrieves the tools list once and checks for multiple tool types (web_search_preview, web_search, or presence of search_context_size) in a single loop iteration.

  3. Simplified tool matching logic: The optimized code inlines the tool type checking directly in the loop, avoiding additional function calls to _is_web_search_tool_call and _is_file_search_tool_call.

Performance impact from profiler data:

  • _get_web_search_options execution time reduced from 31.3μs to 5.9μs (81% improvement)
  • _get_file_search_tool_call execution time reduced from 5.6μs to 3.0μs (47% improvement)
  • Overall function execution time reduced from 74.7μs to 42.1μs

These optimizations are particularly effective for scenarios where the tools parameter is frequently accessed during logging initialization, as they reduce both CPU cycles and memory allocations from repeated dictionary lookups and function calls.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 3 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⏪ Replay Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_pytest_teststest_litellmresponseslitellm_completion_transformationtest_litellm_completion_responses___replay_test_0.py::test_litellm_litellm_core_utils_litellm_logging_Logging_initialize_standard_built_in_tools_params 8.03μs 6.10μs 31.6%✅

To edit these changes git checkout codeflash/optimize-Logging.initialize_standard_built_in_tools_params-mhbuqgqe and push.

Codeflash

The optimized code achieves a **31% speedup** by eliminating redundant function calls and unnecessary dictionary traversals in tool extraction logic.

**Key optimizations:**

1. **Eliminated redundant `_get_tools_from_kwargs` calls**: The original code called this helper function multiple times with different `tool_type` parameters, but the function ignored the parameter and always returned `kwargs.get("tools")`. The optimized version directly calls `kwargs.get("tools")` once, removing function call overhead.

2. **Consolidated tool type checking**: Instead of making separate calls to search for "web_search_preview" and "web_search" tools, the optimized version retrieves the tools list once and checks for multiple tool types (`web_search_preview`, `web_search`, or presence of `search_context_size`) in a single loop iteration.

3. **Simplified tool matching logic**: The optimized code inlines the tool type checking directly in the loop, avoiding additional function calls to `_is_web_search_tool_call` and `_is_file_search_tool_call`.

**Performance impact from profiler data:**
- `_get_web_search_options` execution time reduced from 31.3μs to 5.9μs (81% improvement)
- `_get_file_search_tool_call` execution time reduced from 5.6μs to 3.0μs (47% improvement)
- Overall function execution time reduced from 74.7μs to 42.1μs

These optimizations are particularly effective for scenarios where the `tools` parameter is frequently accessed during logging initialization, as they reduce both CPU cycles and memory allocations from repeated dictionary lookups and function calls.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 29, 2025 10:27
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 29, 2025
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

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