Comprehensive Request Statistics Collection With Response Time Percentiles And Failure Tracking

Python load testing framework for APIs and AI endpoints.

Unique: Implements incremental percentile calculation using histogram binning or T-Digest to avoid storing all response times, reducing memory overhead. Failure categorization by error type (timeout, connection error, HTTP status) enables root-cause analysis without post-processing.

vs others: More detailed than simple throughput metrics (requests/sec) because it captures percentile distributions; more memory-efficient than storing all response times because it uses approximate percentile algorithms.

via “real-time request/response metrics collection”

** <img height="12" width="12" src="https://raw.githubusercontent.com/xuzexin-hz/llm-analysis-assistant/refs/heads/main/src/llm_analysis_assistant/pages/html/imgs/favicon.ico" alt="Langfuse Logo" /> - A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and ca

Unique: Transport-agnostic metrics collection integrated into MCP client framework, capturing latency and throughput across stdio, SSE, and HTTP transports without client code changes

vs others: Purpose-built for MCP monitoring vs generic APM tools; understands protocol-specific metrics and integrates with unified dashboard