feat: integrated litellm for tracing

[shah-siddd]

Pull Request

Summary

Add comprehensive LiteLLM integration to the OpenLayer Python SDK, enabling automatic tracing and monitoring of completions across 100+ LLM providers through LiteLLM's unified interface.

Changes

• Core Integration: Added litellm_tracer.py with full support for streaming and non-streaming completions
• Public API: Added trace_litellm() function to openlayer.lib for easy integration
• Streaming Support: Implemented proper streaming usage data collection using stream_options={"include_usage": True}
• Provider Detection: Multi-tier provider detection supporting all LiteLLM-compatible services
• Example Documentation: Added comprehensive Jupyter notebook (litellm_tracing.ipynb) with multi-provider examples
• Test Coverage: Complete test suite with 12 passing tests covering all functionality
• Dependency Management: Added LiteLLM as optional dependency with conditional imports
• Data Parity: Ensured 100% data consistency between streaming and non-streaming modes

Context

LiteLLM is a popular library that provides a unified interface to call 100+ LLM APIs (OpenAI, Anthropic, Google, AWS Bedrock, etc.) using the same input/output format. This integration allows users to:

• Monitor multiple providers through a single integration point
• Switch between LLM services without changing tracing setup
• Compare performance across different providers and models
• Reduce integration complexity by supporting all providers at once

This addresses the need for comprehensive LLM monitoring across diverse model providers in production environments.

Testing

• Unit tests: All 12 LiteLLM integration tests passing
• Integration tests: Conditional imports and dependency management tests passing
• Core functionality: 21 core tracing tests passing (no regressions)
• Manual testing:
  • ✅ Verified streaming vs non-streaming data parity (100% match)
  • ✅ Tested multi-provider support (OpenAI, Anthropic, Groq)
  • ✅ Validated cost calculation and token counting accuracy
  • ✅ Confirmed proper error handling and graceful fallbacks

Test Results:

tests/test_litellm_integration.py - 12/12 PASSED ✅
tests/test_integration_conditional_imports.py - 3/3 PASSED ✅ 
tests/test_tracing_core.py - 21/21 PASSED ✅

Key Technical Achievements:

• 🎯 100% data parity between streaming and non-streaming modes
• ⚡ Proper streaming implementation using official LiteLLM streaming usage API
• 🔍 Comprehensive provider detection with multiple fallback strategies
• 🛡️ Robust error handling maintaining system stability
• 📊 Complete metadata capture including costs, tokens, latency, and provider info