From Black‑Box to Crystal‑Clear: My Hands‑On Guide to LLM Observability

Author(s): Mehdi Zare

Originally published on Towards AI.

I still remember the first time I deployed a complex LangChain app and had no clue why it behaved so unpredictably. It felt like trying to debug a black box. Over time, I learned that LLM observability — the practice of tracing and monitoring our AI app’s inner workings — is a lifesaver. When building with large language models (LLMs), so much happens under the hood: chains of prompts, vector database lookups, external tool calls, and non-deterministic model outputs. Without good observability, you’re essentially flying blind. In the development stage, it becomes challenging to identify the reason behind an agent's incorrect turn or the prompt that triggered an unexpected response. In production, it’s even more critical — you need to catch errors, monitor quality, and ensure performance doesn’t degrade before users notice. The unique challenges of LLMs (their unpredictable outputs and complex multi-step workflows) make traditional monitoring insufficient. That’s why I became somewhat obsessed with finding the right observability tools for my LLM projects.

In this post, I’ll walk through some popular open-source (and free) LLM observability tools I’ve tried, especially focusing on those that play nice with Python frameworks like LangChain and LangGraph. I'll explain each tool's function, integration, hosting,… Read the full blog for free on Medium.

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Published via Towards AI