Top 5 LLM Observability Tools

Yaron Friedman

| March 09, 2026 | 15.5 mins |

Introduction

Hallucinations remain one of the biggest reasons LLM apps fail in production, but the “rate” is not a single number. It depends on the task, the prompt shape, the model, and the degree of output constraint. For example, in a summarization-focused benchmark, Vectara’s public hallucination leaderboard shows leading models in the low single digits (with others much higher), and it’s updated regularly so you can track whether things are actually improving over time.

That variability is exactly why observability matters. If you can’t see what the model saw (i.e., the context), what it produced, and how it got there (i.e., tool calls, retrieval steps, and intermediate prompts), you can’t debug failures or prevent them from recurring. In practice, teams reduce hallucinations by combining LLM tracing(to reproduce “why did it say that?”) with LLM telemetry (to catch drift, spikes, and regressions early).

LLM Monitoring vs. Observability vs. Evaluation

When teams first ship an LLM feature, they usually monitor the same metrics they’ve always tracked: latency, error rates, uptime, and cost. That’s monitoring. It answers a practical question: “Is the system healthy right now?” If response time spikes or token usage doubles overnight, monitoring alerts you before customers do.

But LLM systems fail in a different way than traditional APIs. A request can look “healthy” on dashboards yet still produce an incorrect answer because the retrieval step pulled the wrong context, a tool call failed silently, or a prompt change shifted behavior. That’s where observability comes in. Observability helps you trace an outcome through the full path, including the prompt, retrieved context, model call, tool usage, and intermediate steps, so you can explain why something happened, not just that it happened.

Then there’s evaluation, which focuses on quality. Evaluation answers: “Was the output actually good?” That could mean factual accuracy, correct citations, safe behavior, or simply whether the response solved the user’s task. Evaluations can run before release (offline) and after release (online), helping you catch regressions when you swap a model, tweak prompts, or change retrieval settings.

A simple way to think about it is this: monitoring tells you when to worry, observability tells you what went wrong, and evaluation tells you whether the system meets the quality bar in the first place. In practice, teams use all three together because a reliable LLM product isn’t just fast and available; it’s consistently correct, explainable, and improving over time.

1. LangKit

Developed by WhyLabs, LangKit is an open-source toolkit intended to track LLMs by collecting important signals from both generated responses and input prompts. This tracking of LLM behavior guarantees outputs that are correct, relevant, and safe.

Basic characteristics:

LangKit lets users track key metrics, including quality, relevance, sentiment, and security. It provides instant access to telemetry data from LLM answers and prompts to ensure quick setup and seamless workflow integration.
The tool assesses the quality of produced responses by analyzing readability, complexity, and grade level.
LangKit computes similarity ratings between responses and prompts as well as against user-defined themes.
The tool comprises modules to identify patterns indicative of security risks, including jailbreak attempts, prompt injections, and refusals of service.
LangKit provides sentiment analysis to track the emotional tone and possible harm of LLM outputs.

Integration with whylogs

Integration with whylogs represents an important feature of LangKit. This compatibility provides flexibility in how monitoring data is used, enabling the creation of profiles that can be visualized and tracked.

Install LangKit using the Python Package Index (PyPI) as advised here:

pip install langkit[all]

LangKit modules include user-defined functions ( UDFs), which are custom functions designed to process and analyze specific types of data.

import whylogs as why
from langkit import llm_metrics
results = why.log({"prompt": "Hello!", "response": "World!"}, schema=llm_metrics.init())

LangKit allows companies using LLMs to monitor and protect their models by spotting problems such as toxic language, data leaks, hallucinations, and jailbreak attempts.

2. Deepchecks

Deepchecks represents a tool designed to evaluate and monitor LLM applications, ensuring outputs are high-quality and compliant.

Main capabilities:

Deepchecks streamlines the evaluation process by reducing the manual effort required for testing.
It helps identify hallucinations by comparing responses to predefined benchmarks.
Deepchecks includes features for detecting biases, toxicity, and harmful content.
Deepchecks integrates natively with AWS SageMaker, ensuring compatibility with enterprise-scale pipelines.
Built on an open-source ML testing package, Deepchecks benefits from flexibility, adaptability, and transparency.

Deepchecks improves LLM application observability by integrating with tools like Honeycomb.io.

3. Lunary

Designed to improve the deployment and monitoring of LLMs in industrial settings, Lunary provides observability tools and performance evaluation instruments.

Key features:

Lunary offers observability tools that enable programmers to log and examine LLM calls.
The platform features a prompt directory that supports versioning and team collaboration.
Lunary provides an evaluation suite whereby developers may run benchmarks and compare models.

4. OpenLIT

With a specific focus on LLMs, OpenLIT is an open-source platform meant to improve observability in AI engineering.

Key characteristics:

Built on Open Telemetry.
Applications dependent on self-hosted LLMs heavily depend on GPU performance.
OpenLIT offers the means to apply guardrails, ensuring that LLM outputs match ethical standards.
The platform includes features for organizing prompts and securely storing API keys.
OpenLIT provides an interactive environment for developers to test and improve their apps.

5. AgentOps

AgentOps improves the observability of LLM, addressing major AI safety issues.

Main characteristics:

AgentOps finds and tracks many artifacts, including prompts, responses, and intermediate computations.
AgentOps helps with analytics, logging, and monitoring.
AgentOps helps to ensure AI safety by offering detailed insights into LLM agent operations.

6. Langfuse

Langfuse is an open-source LLM engineering and observability platform built for LLM tracing across real applications.

Basic characteristics

End-to-end tracing for LLM apps.
Traces, sessions, and nested observations.
OpenTelemetry-based foundation.
Low-risk instrumentation.
Prompt management and evaluation workflows.

Installation (Python)

Install the Langfuse Python SDK via PyPI:

pip install langfuse

Quick example (Python tracing)

from langfuse import get_client
langfuse = get_client()
with langfuse.start_as_current_observation(as_type="span", name="process-request") as span:
    # Your processing logic here
    span.update(output="Processing complete")
    
    with langfuse.start_as_current_observation(
        as_type="generation",
        name="llm-response",
        model="gpt-3.5-turbo",
    ) as generation:
        # Your LLM call logic here
        generation.update(output="Generated response")

# Flush events in short-lived applications
langfuse.flush()

Langfuse is a strong option if you’re building:

RAG pipelines where retrieval quality often changes.
AI agents with tool calls and retries.
Production LLM features where traceability matters for debugging.

How to Choose the Right LLM Observability Tool

Choosing an LLM observability platform is about choosing the one that fits how your system fails in production. Start by mapping the tool to your application type.

Conclusion

Organizations need to prioritize observability in their AI strategies. The technologies discussed help raise the standards of LLM implementation, ensuring that AI satisfies requirements of responsibility, security, and precision.

FAQs

What is the difference between LLM monitoring and LLM observability?

Monitoring tells you something is wrong. Observability tells you why.

Why is LLM observability critical in production environments?

LLMs can fail silently, producing incorrect answers. Observability provides the evidence to debug and prevent repeats.

How do LLM observability tools help reduce hallucinations?

They help you identify and fix the causes: missing context, poor retrieval, and prompt regressions.

What should I look for when choosing an LLM observability platform?

Prioritize end-to-end tracing, production telemetry, and evaluation support.