🛠️

Tool

Graphsignal Profiler

Name: Graphsignal Profiler
Author: graphsignal

by graphsignal graphsignal/graphsignal-profiler

Free2AITools Nexus Index

59.8

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 55

P: Popularity 45

R: Recency 99

Q: Quality 70

Tech Context

Vital Performance —

Source →

Python Lang

Open Source 205 Stars

Alpha Reliability

Tool Information Summary
Entity Passport
Registry ID	graphsignal/graphsignal-profiler
License	Apache-2.0
Provider	github

📜

Cite this tool

Academic & Research Attribution

BibTeX

@misc{gh_tool_graphsignal_graphsignal_profiler,
  author = {graphsignal},
  title = {Graphsignal Profiler Tool},
  year = {2026},
  howpublished = {\url{https://github.com/graphsignal/graphsignal-profiler}},
  note = {Accessed via Free2AITools.}
}

APA Style

graphsignal. (2026). Graphsignal Profiler [Tool]. Free2AITools. https://github.com/graphsignal/graphsignal-profiler

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🐙 GitHub Clone

git clone https://github.com/graphsignal/graphsignal-profiler

🐍 PIP Install

pip install graphsignal-profiler

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 55

Popularity (P) 45

Recency (R) 99

Quality (Q) 70

💬 Index Insight

FNI V2.0 for Graphsignal Profiler: Authority (A:55), Popularity (P:45), Recency (R:99), Quality (Q:70). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📋 Specs

Language: Python
License: Apache-2.0
Version: —

Technical Documentation

Graphsignal: Inference Profiler

Graphsignal is an inference profiling platform that helps developers accelerate and troubleshoot AI systems. It provides essential visibility across the inference stack, including:

Continuous, high-resolution profiling timelines exposing operation durations and resource utilization across inference workloads.
LLM generation tracing with per-step timing, token throughput, and latency breakdowns for major inference frameworks.
System-level metrics for inference engines and hardware (CPU, GPU, accelerators).
Error monitoring for device-level failures, runtime exceptions, and inference errors.
Inference telemetry for AI agents to identify bottlenecks and drive targeted improvements across the inference stack.

Learn more at graphsignal.com.

Install

bash

uv tool install 'graphsignal[cu12]'   # CUDA 12.x
# or
uv tool install 'graphsignal[cu13]'   # CUDA 13.x

Profile

Wrap your launch command with graphsignal-run:

bash

export GRAPHSIGNAL_API_KEY=
graphsignal-run vllm serve  --port 8001

Environment variables read by the profiler:

Variable	Purpose
`GRAPHSIGNAL_API_KEY` (required)	Your account API key.
`GRAPHSIGNAL_TAG_<KEY>=<value>`	Arbitrary tag attached to all signals (e.g. `GRAPHSIGNAL_TAG_DEPLOYMENT=us-prod`).

Sign u

Social Proof

GitHub Repository

205Stars

11Forks

Repo Issues

🐙 Data Source: GitHub ↗

🔄 Updated daily

Source summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Tool Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: gh-tool--graphsignal--graphsignal-profiler
slug: graphsignal--graphsignal-profiler
source: github
author: graphsignal
license: Apache-2.0
tags: machine-learning, deep-learning, huggingface, inference, pytorch, debugging, monitoring, python, artificial-intelligence, langchain, langchain-python, tracer, ai-agents, observability, openai-api

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag: other

📊 Engagement & Metrics

downloads: 0
stars: 205
forks: 11
github stars: 205

Data indexed from public sources. Updated daily.