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This article introduces LLM Logger, an open-source full-stack developer tool that supports real-time inference monitoring for over 15 mainstream large language models. Without needing to configure API keys, you can track key metrics such as latency, token usage, request status, and access a visual dashboard.
Section 01
This post introduces LLM Logger, an open-source full-stack developer tool supporting real-time inference monitoring for over 15 mainstream large language models. Key features include no API key configuration needed, tracking of latency, token usage, request status, and a visual dashboard. It aims to solve LLM application debugging pain points and lower the barrier for developers.
Section 02
With LLM's wide adoption in app development, developers face challenges like lack of visibility into the full lifecycle of requests (latency, token consumption, response quality), need for self-built logging systems or expensive third-party APM services, and complex API key management for multiple providers. These issues hinder rapid prototyping and teaching. LLM Logger was created to address these by offering a zero-config, open-source solution.
Section 03
LLM Logger integrates three core modules:
Section 04
Frontend: React19 + TypeScript6 (code quality), Vite8 (fast dev), Redux Toolkit2 (state management), shadcn/ui + Radix UI + Tailwind CSS v4 (UI), Recharts3 (visualization). Backend: Node.js + Express4 (RESTful API), MongoDB + Mongoose8 (storage/queries), Zod3 (parameter validation). AI Gateway: Integrated Puter.js AI gateway (unified interface for multiple models, zero config—just log in to Puter account).
Section 05
Environment Prep: Node.js18+, MongoDB instance (local or Atlas free cluster). Backend Setup:
Section 06
LLM Logger applies to:
Section 07
Current limitations:
Section 08
LLM Logger provides a lightweight yet full-featured monitoring solution for LLM apps. Its zero API key design lowers the trial barrier, allowing developers to focus on app logic instead of key management. As the LLM ecosystem grows, such tools will become essential in the development toolchain to build reliable, efficient AI-driven apps.
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