awesome-llm-observability

Name: awesome-llm-observability Review
Item: awesome-llm-observability
Rating: 2.0
Author: Aaron Matthews

Skill Development Poor

Works inClaude Code

Poor Scanned — metadata only

LLM applications introduce opacity and unpredictability that traditional monitoring misses.

50+ curated LLM observability tools PLUS 26 Agent Skills (several with runnable, unit-tested scripts) to build, evaluate, debug, secure & monitor reliable LLM apps. Tracing, evals, guardrails, LLMOps.

Find observability tools to trace and debug LLM application behavior in production
Discover evaluation frameworks to measure and improve your AI agent performance
Implement guardrails and security checks across your LLM deployment pipeline

26 starsFreeQuick setup

Below standard — Significant caveats apply. Not recommended without careful review of the security and evaluation sections.

Why it matters

LLM applications introduce opacity and unpredictability that traditional monitoring misses. This curated collection accelerates adoption of observability practices—tracing, evals, guardrails—reducing production incidents and debugging cycles for teams shipping LLM features at scale.

Best for

Engineering teams building or maintaining LLM-powered applications requiring production visibility and reliability.

Works in

Claude Code Claude Cowork Claude Chat

Source

https://github.com/ContextJet-ai/awesome-llm-observability

By ContextJet-ai

How to Get It

Option 1: Claude Desktop App (Code Mode)Click the + button next to the prompt box → Plugins → Add plugin. Search and click Install. Skills work in Claude Code only.

Option 2: Paste into Claude CodeCopy the command below and paste it into your conversation. Claude will install it.

Command

claude plugins install ContextJet-ai/awesome-llm-observability

Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.

Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.

First thing to try

After installing, paste this into Claude:

Help me find observability tools to trace and debug LLM application behavior in production

CostFree

Trust Signals Auto-scanned

Stars26Last updated2026-08-03Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-08-03 · scanner v1

Community Pulse New

No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.

Reviewer notes

Auto-scanned review. These are observations, not a security certification.

Scored from trust signals (evidence-eval-v1): 26 GitHub stars; contributors unknown; last commit 0d ago; license no license.

Things to check

Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.

How to evaluate tools before deploying →

Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.

Evaluation Scores

Evaluation

Ease of Use

1/5

Versatility

2/5

Reliability

3/5

Security

3/5

Overall score2.05 / 5.00 PoorEvaluatedAug 2026

Scored from trust signals (evidence-eval-v1): 26 GitHub stars; contributors unknown; last commit 0d ago; license no license.

← Back to the Claude Observatory