7 Best LLM Observability Tools to Monitor & Eval AI Agents

7 LLM Observability Tools to Monitor & Eval AI Agents

When an AI agent hallucinates in production, your error logs will not tell you. Traditional monitoring tracks latency, throughput, and HTTP status codes. None of that captures whether the model fabricated a citation, leaked personally identifiable information, or subtly drifted from the tone your users expect. The failure is semantic, not infrastructural, and it slips through every conventional alarm.

LLM observability tools exist to close that gap. The best ones go beyond logging prompts and completions. They trace every step of an agentic pipeline, score outputs against quality dimensions like groundedness, relevance, and policy compliance, and surface regressions before they reach users. Some even intervene in real time, blocking or retrying a response that fails validation before it is returned.

We evaluated seven platforms across depth of tracing, evaluation capabilities, real-time guardrails, and the feedback loops they create between monitoring and improvement. Here is what we found.

The best LLM observability tools at a glance

What to look for in an LLM observability tool

Tracing depth. Surface-level request/response logging is table stakes. The tool should trace every intermediate step in an agent or chain: tool calls, retrieval results, prompt assembly, retries, and branching logic. Without step-level visibility, you cannot isolate which link in the chain caused a bad output.

Evaluation beyond accuracy. Accuracy benchmarks tell you whether a model can answer correctly on average. Production systems need evaluation across multiple quality dimensions simultaneously: groundedness, relevance, tone, policy compliance, PII leakage, and structural consistency. The best tools let you define and score these dimensions automatically on every trace.

Real-time intervention. Observability that only tells you about failures after they reach users is incomplete. Guardrails that validate outputs before they are returned, and optionally retry or block failing responses, transform monitoring from a passive record into an active safety net.

Closing the feedback loop. The most powerful observability platforms do not just surface problems. They connect monitoring insights to improvement actions: retraining signals, prompt iteration, regression alerts, and deployment gating based on quality scores. The feedback loop is what separates a logging tool from an engineering platform.

1. Trainly

What is Trainly?

Trainly is an AI observability platform built around a single idea: monitoring should not be passive. Beyond tracing every step in your LLM pipeline, Trainly evaluates outputs in real time using a scorer ecosystem and enforces quality with guardrails that can stop, retry, or flag failing agent steps before they reach users.

Integration requires two lines of code. Add the @observe decorator to any Python function. Trainly captures input, output, latency, token usage, and metadata automatically. Enable guards=True and every call is validated against the scorers you configure in the dashboard, with no code changes needed when you update rules.

Who should use Trainly?

• Teams shipping AI agents to production who need real-time quality enforcement, not just post-hoc dashboards.
• Organizations that require SLA-like guarantees on model behavior through reliability contracts.
• Developers who want deep observability with minimal integration effort (two lines of code).

Standout features

• Real-time guardrails: the @observe decorator with guards=True stops or retries agent steps that fail validation before the response is returned. No other tool in this list does this at the decorator level.
• Reliability contracts: define SLA-like pass-rate thresholds on specific scorers. Trainly tracks compliance over time, takes automated snapshots when contracts are violated, and alerts your team.
• Scorer ecosystem: 12+ built-in scorers covering hallucination detection, relevance, tone consistency, PII leakage, and more. Add custom LLM-as-judge scorers with natural-language criteria.
• Semantic observability: anomaly detection and clustering across traces surface behavioral drift that individual trace inspection would miss.
• Dashboard-defined rules: change guardrail thresholds, add scorers, or update validation logic from the dashboard. No redeployment required.

2. LangSmith

What is LangSmith?

LangSmith is the observability and evaluation platform from LangChain. It provides deep tracing for LangChain and LangGraph applications, annotation queues for human review, and an evaluation framework that supports LLM-as-judge, code scorers, and dataset-driven testing.

Who should use LangSmith?

• Teams already building on the LangChain or LangGraph ecosystem who want native tracing.
• Organizations with human review workflows who need annotation queues and feedback collection.
• Developers who rely heavily on dataset-driven offline evaluations.

Standout features

• First-class LangChain/LangGraph tracing with automatic span capture across chains, tools, and retrievers.
• Annotation queues for human-in-the-loop review and labeling.
• LLM-as-judge evaluators with customizable criteria and reference-free scoring.
• Dataset management and regression testing across prompt iterations.
• Prompt versioning with A/B deployment support.

3. Langfuse

What is Langfuse?

Langfuse is an open-source LLM engineering platform that combines observability, prompt management, and evaluation in a single tool. Its MIT license and self-hosting support make it a popular choice for teams that need full data control.

Who should use Langfuse?

• Teams that require self-hosted or on-premise observability for compliance reasons.
• Developers who want an open-source core they can extend and contribute to.
• Organizations looking for integrated prompt management alongside tracing.

Standout features

• Fully open source under MIT license with Docker self-hosting.
• Prompt management with versioning and deployment directly from the platform.
• Framework-agnostic tracing with integrations for LangChain, LlamaIndex, OpenAI SDK, and more.
• Cost tracking and analytics broken down by model, user, and feature.
• Evaluation pipelines with custom scoring functions.

4. Helicone

What is Helicone?

Helicone is an LLM observability platform and AI gateway. Instead of an SDK that instruments your code, Helicone works as a proxy layer. You change a single base URL in your API calls and all requests flow through Helicone, giving you logging, caching, rate limiting, and analytics with near-zero integration effort.

Who should use Helicone?

• Teams that want observability without changing application code beyond a base URL.
• Organizations that need an AI gateway with built-in caching and rate limiting.
• Developers who prioritize low-latency proxy architectures.

Standout features

• Proxy-based architecture: change one URL to enable full observability.
• Built-in response caching that reduces cost and latency for repeated queries.
• Rate limiting and retry logic at the gateway level.
• Cost analytics with per-request and per-user breakdowns.
• Open source under Apache-2.0 with self-hosting support.

5. Braintrust

What is Braintrust?

Braintrust is an AI engineering platform that combines evaluation, logging, and a prompt playground. It emphasizes experiment-driven development: run evaluations against datasets, compare results across prompt versions, and use the built-in proxy for cost-optimized model routing.

Who should use Braintrust?

• Teams with a strong evaluation-first workflow who run experiments before shipping prompt changes.
• Developers who want a prompt playground alongside their observability tooling.
• Organizations looking for cost-optimized model routing through an AI proxy.

Standout features

• Experiment-based evaluation with side-by-side comparison of prompt variants.
• Prompt playground for interactive testing and iteration.
• AI proxy with model routing and cost optimization.
• Production logging with real-time monitoring dashboards.
• Partially open source (MIT) with SDK and evaluation libraries.

6. Arize Phoenix

What is Arize Phoenix?

Arize Phoenix is a local-first observability and evaluation tool designed to run alongside your development environment. It launches in a notebook or as a standalone app, ingests traces via OpenTelemetry, and provides visualization, evaluation, and dataset management. Phoenix is the open-source counterpart to Arize's commercial cloud platform.

Who should use Arize Phoenix?

• Data scientists and ML engineers who want observability integrated into their notebook workflow.
• Teams that prefer local-first tools they can run without cloud dependencies during development.
• Organizations already using OpenTelemetry who want LLM-specific visualization on top of it.

Standout features

• Runs locally in a notebook or as a standalone app with zero cloud dependency.
• OpenTelemetry-based instrumentation for standardized tracing.
• Embedding visualization with UMAP projections for retrieval analysis.
• Built-in evaluation with LLM-as-judge and retrieval relevance scoring.
• Dataset management for curating evaluation sets from production traces.

7. Datadog LLM Observability

What is Datadog LLM Observability?

Datadog LLM Observability is an add-on to the Datadog platform that extends its infrastructure monitoring to cover LLM workloads. It traces LLM calls, tracks token usage and cost, and correlates LLM performance with the rest of your infrastructure metrics, all within the Datadog UI you already know.

Who should use Datadog LLM Observability?

• Organizations already on the Datadog platform who want LLM monitoring alongside infrastructure metrics.
• Enterprise teams that need unified dashboards for APM, infrastructure, and LLM observability.
• Companies with existing Datadog contracts where adding LLM observability is a natural extension.

Standout features

• Unified platform: LLM traces live alongside APM, logs, and infrastructure metrics.
• Correlation between LLM latency/errors and underlying infrastructure health.
• Token usage and cost tracking with per-model breakdowns.
• Integration with Datadog Monitors for alerting on LLM-specific metrics.
• Support for major LLM providers and orchestration frameworks.