Graphsignal

## Overview Graphsignal is an inference observability platform designed to assist developers in optimizing and troubleshooting AI systems. It provides visibility across the inference stack through continuous, high-resolution profiling timelines, LLM generation tracing, system-level metrics, and error monitoring. The platform aims to expose operation durations and resource utilization, including GPU behavior, at a granular level. The Graphsignal SDK integrates into application code to automatically measure and record operations in both single-run scripts and long-running server applications. Performance data is then sent to Graphsignal servers for processing and analysis. The platform supports various inference frameworks and hardware, including NVIDIA GPUs, PyTorch, Hugging Face, vLLM, and SGLang. ## Key Features * **Continuous Inference Profiling:** Offers high-resolution profiling timelines that detail operation durations and resource utilization across inference workloads. This includes insights into internal runtime and GPU behavior. * **LLM Tracing:** Provides per-step timing, token throughput, and latency breakdowns for LLM generation across major inference frameworks. * **System Metrics:** Collects system-level metrics for inference engines and hardware components such as CPU, GPU, and other accelerators. * **Error Monitoring:** Monitors for device-level failures, runtime exceptions, and general inference errors. * **AI Optimization Integration:** Enables AI coding agents (e.g., Cursor, Claude Code, Codex, Gemini) to fetch and analyze Graphsignal data in natural language, facilitating tasks like identifying root causes of latency spikes or performance bottlenecks. * **Low-Overhead SDK:** The SDK is designed to have minimal impact on production performance, with profiling using low-overhead APIs and tracing typically adding under 100 microseconds per trace. ## Who It's For Graphsignal is intended for AI engineers, developers, and teams who need to debug, optimize, and monitor the performance of their AI inference systems in production environments. This includes those working with large language models (LLMs), GPU-accelerated workloads, and complex AI stacks where understanding internal operations and resource utilization is critical for performance improvement and error resolution. ## Notable Strengths Graphsignal's strengths include its granular, millisecond-level observability into inference systems, which can reveal details often missed by traditional monitoring tools. The platform's ability to integrate with AI coding agents for natural language data analysis streamlines the debugging and optimization workflow. Its support for a range of popular AI frameworks and hardware, coupled with a low-overhead SDK, makes it suitable for production deployment. The "autodebug" concept, an autonomous agent that continuously optimizes inference services, demonstrates a forward-looking approach to AI system management.