Top 8 AI Observability Tools for LLM and Agentic Workflows in 2026

Once LLM and agentic workflows are running in production, you need more than availability and request success rates to measure operational reliability. You also need to understand why output quality changed, where response time increased inside the workflow, which model calls drove cost, and whether the system is drifting from expected behavior.

AI observability tools make that behavior traceable. They connect each output to its execution path, so you can review the input context, AI steps, evaluation results, and performance signals behind the result. This guide compares the best AI observability tools for LLM and agentic workflows in 2026.

What to Look for in AI Observability Tools

The right AI observability tool should give you enough evidence to investigate behavior, evaluate outputs, and control how telemetry is captured. Here are the key capabilities to look for.

1. Full-Stack Tracing Across LLM Calls, RAG Pipelines, and Agent Steps: Traces should show how a request moves through the full AI workflow, not only the final model response. For retrieval-augmented generation (RAG), that includes the user input, retrieval step, selected documents, model call, and final output. For agents, it also includes tool calls and intermediate steps, so you can review the path that produced the result.
2. Hallucination Detection and Output Quality Evaluation: An AI observability tool should help you evaluate whether outputs are accurate, grounded, relevant, and safe for the task. This usually means support for automated evaluations, human review, feedback labels, or custom scoring rules. These checks give you a clearer way to review quality than reading isolated outputs one by one.
3. Token Usage, Latency, and Cost Monitoring: The platform should track token usage, response time, and model cost at the workflow level. This matters because expensive or slow AI behavior often comes from a specific prompt, model choice, retrieval step, or repeated agent loop. Good cost and latency views help you find those parts without guessing from aggregate totals.
4. Model Drift and Accuracy Degradation Detection: AI behavior changes when prompts, source data, model versions, user inputs, or retrieval results change. The tool should help you track evaluation scores, feedback trends, and output patterns over time. That makes it easier to catch quality degradation before it becomes a recurring production issue.
5. Agentic Workflow Tracing Across Multi-Step Tool Executions: Agent observability needs more than a single model-call trace. The tool should preserve the sequence of decisions, tool calls, retries, errors, and intermediate outputs across the workflow. That sequence helps you see whether the agent selected the wrong tool, passed the wrong input, or failed after an earlier step succeeded.
6. No-Code or Low-Code Instrumentation: Instrumentation should not slow down adoption across your services. Look for SDK wrappers, decorators, automatic instrumentation, OpenTelemetry support, or runtime capture that reduces custom tracing work. Lower setup effort makes it easier to extend observability beyond a prototype and into production workflows.
7. BYOC or On-Prem Deployment for Data-Sensitive Environments: AI traces often include prompts, responses, retrieved documents, metadata, and user inputs. If those payloads contain sensitive data, deployment control becomes part of the observability decision. Bring your own cloud (BYOC), virtual private cloud (VPC), on-premises, or air-gapped options help you control where telemetry is stored, processed, and accessed.

Let’s now look at the top AI observability tools and how they compare across these criteria.

8 Best AI Observability Tools for Production AI Teams by Category

AI observability tools are built around different parts of the production workflow. Some start with runtime telemetry across services and infrastructure, others focus on LLM traces and datasets, and some lead with evaluations as the primary way to score outputs. Let’s compare the top eight AI observability tools.

Full-Stack and Infrastructure-Native Platforms

Full-stack and infrastructure-native platforms apply AI observability where the workload runs. They connect LLM and agent telemetry with application, Kubernetes, and host signals, so you can debug AI behavior alongside the services and infrastructure behind it.

1. groundcover

groundcover is an AI observability platform for production LLM and agentic workflows. It captures supported LLM API calls at runtime, enriches RAG and agent workflows with OpenTelemetry GenAI traces, and connects AI behavior with Kubernetes, services, hosts, logs, metrics, traces, and events. This gives you AI observability inside the same production context you already use to debug applications and infrastructure.

Workflow Tracing Across LLMs, RAG, and Agents

groundcover uses two capture paths: eBPF auto-capture and OpenTelemetry GenAI instrumentation. eBPF runs at the node level and detects supported AI API calls from workloads running on monitored Kubernetes nodes or Linux hosts. That gives you model names, token counts, cost, latency, prompts, and responses without adding SDK code to the application.

OpenTelemetry GenAI instrumentation adds the full workflow path around those calls. When your app emits GenAI spans through OTLP, groundcover uses core attributes such as `gen_ai.operation.name`, `gen_ai.provider.name`, and `gen_ai.request.model` to identify the operation, provider, and model. It then uses attributes such as `gen_ai.conversation.id` and `gen_ai.agent.name` to connect the call to the conversation, agent, retrieval step, tool call, prompt, response, tool arguments, and tool results.

Output Evaluation and Behavior Drift

groundcover investigates output behavior through the execution context behind each response. You can inspect the prompt, response, retrieved context, tool usage, session history, and related spans in the same trace path. That makes it easier to see whether an output issue started with prompt drift, context drift, a wrong tool input, a failed tool call, or an earlier step that shaped the final response.

Cost, Latency, and Runtime Context

groundcover calculates AI cost from token data on GenAI spans, including input, output, and cached tokens. It also tracks latency, throughput, and errors across LLM interactions and ties those signals to services, Kubernetes workloads, hosts, logs, traces, metrics, and events. Agent Mode builds on that context by querying production data through gcQL, so you can ask investigation questions and generate reusable queries, dashboards, monitors, and other groundcover assets.

Instrumentation, Deployment, and Data Control

groundcover supports eBPF capture for supported providers and OpenTelemetry Protocol (OTLP) for deeper workflow instrumentation. Its bring your own cloud (BYOC) model keeps prompts, responses, logs, metrics, traces, and events in your cloud, while on-premises and air-gapped options support stricter data-control requirements. For sensitive AI payloads, you can configure obfuscation or content stripping before collecting prompts and responses at scale. groundcover MCP also exposes observability context to external AI agents and integrated development environments (IDEs).

2. Arize AI and Phoenix

Arize AI covers AI observability through Phoenix, its open-source tracing and evaluation tool, and Arize AX, its enterprise platform. Phoenix gives you the open-source path for reviewing traces, datasets, prompts, and evaluations. Arize AX adds the production workflow for teams that need the same trace-evaluation loop with managed deployment, private infrastructure, data residency, and team controls.

Workflow Tracing Across LLMs, RAG, and Agents

Arize uses OpenInference on top of OpenTelemetry to trace AI workflows. Your app sends traces over OTLP through framework integrations, auto-instrumentation packages, or manual spans. In a RAG or agent workflow, the trace separates retrieval, model calls, tool invocations, inputs, outputs, latency, and token counts into individual spans.

Output Evaluation and Behavior Drift

Arize connects traces to evaluations at the span, trace, session, and experiment levels. You can score outputs with human review, LLM-as-a-judge evaluators, code evaluators, or external evaluation libraries. In production, evaluation tasks run on incoming traces with filters and sampling, so you can track score changes when prompts, models, retrieval logic, or agent behavior change.

Cost, Latency, and Runtime Context

Arize tracks latency and token usage through OpenInference attributes on each span. It calculates cost from model and provider metadata when a pricing rule exists, or uses cost values sent with the trace. That gives you application-level cost and runtime visibility across spans, traces, and sessions, rather than host or Kubernetes-level context.

Instrumentation, Deployment, and Data Control

Phoenix uses OpenInference instrumentation through framework integrations, auto-instrumentation packages, or manual OpenTelemetry spans sent over OTLP. You can run Phoenix locally, in Docker, on Kubernetes, or in Phoenix Cloud. Arize AX uses the same trace model for managed or self-hosted enterprise deployments with regional data residency and private infrastructure support.

3. Fiddler AI

Fiddler AI is an enterprise AI observability platform for LLM, agentic, and predictive AI applications. It tracks AI behavior from application activity down to sessions, agents, traces, and spans, then connects those traces with metrics, evaluator rules, and alerts.

Workflow Tracing Across LLMs, RAG, and Agents

Fiddler instruments agent applications through LangGraph and LangChain SDKs, the Strands SDK, or OpenTelemetry. Each trace shows agent decision points, LLM calls, tool invocations, API requests, timing, and parent-child relationships. For RAG workflows, its RAG health metrics connect retrieval and generation signals, so you can inspect whether a failure started in query understanding, context retrieval, or response generation.

Output Evaluation and Behavior Drift

Fiddler runs evaluator rules on production traces and spans. A rule maps fields such as prompt, context, response, or tool output into an evaluator, then stores the score with the trace. Its checks cover response quality, RAG faithfulness, context relevance, answer relevance, safety, sentiment, personally identifiable information (PII), protected health information (PHI), and custom judge logic. That gives you score trends you can use to review regressions across agents, prompts, models, and retrieval changes.

Cost, Latency, and Runtime Context

Fiddler tracks end-to-end latency, per-step latency, token usage, API call volume, success rate, tool-call distribution, reasoning-chain length, agent handoffs, retries, and recovery. These signals show whether cost or delay grows from the model call, tool sequence, retry pattern, or longer agent path. Its runtime context stays closer to the AI application layer than Kubernetes or host telemetry.

Instrumentation, Deployment, and Data Control

Fiddler supports SDK-based instrumentation for LangGraph, LangChain, and Strands, with OpenTelemetry for custom frameworks. Deployment options include software as a service (SaaS), virtual private cloud (VPC), on-premises, and air-gapped environments. It also supports role-based access control (RBAC), single sign-on (SSO), encryption, and customer control over telemetry capture.

Full-Lifecycle Platforms

Full-lifecycle platforms connect traces with prompts, datasets, and evaluations. They give you one loop for debugging a production run, testing a prompt or model change, and comparing the result before another release.

4. LangSmith

LangSmith is LangChain’s observability and evaluation platform for LLM and agent applications. It records each request as a trace, then uses that trace data for debugging, production monitoring, datasets, evaluations, and prompt experiments.

Workflow Tracing Across LLMs, RAG, and Agents

LangSmith traces the run tree behind each request. In LangChain and LangGraph apps, `LANGSMITH_TRACING=true` sends runnable and graph executions into LangSmith with no code changes. For custom code, `wrap_openai`, provider wrappers, and `@traceable` record model calls, retrieval functions, tools, inputs, outputs, and nested spans. LangSmith also accepts OpenTelemetry traces through an OTLP endpoint, so non-LangChain services can send spans using GenAI, OpenInference, or LangSmith attributes.

Output Evaluation and Behavior Drift

LangSmith turns traces into evaluation data. You create datasets from curated examples, production traces, or synthetic data, then score outputs with human review, code rules, LLM-as-judge, or pairwise comparison. Online evaluators run on production runs and threads, which lets you track score trends and catch quality degradation after prompt, model, or retrieval changes.

Cost, Latency, and Runtime Context

LangSmith shows latency, token usage, and cost inside the trace tree, project stats, and dashboards. It separates input, output, and other costs, so tool calls and retrieval steps can carry their own cost when you send `usage_metadata`. This gives you application-level runtime visibility rather than Kubernetes or host-level telemetry.

Instrumentation, Deployment, and Data Control

LangSmith supports tracing through environment variables, SDK wrappers, decorators, and OpenTelemetry. Its OpenTelemetry path can map GenAI prompts, completions, token usage, retriever documents, tool arguments, and metadata into LangSmith trace fields. Deployment options include managed cloud, hybrid, and self-hosted Enterprise options for teams with stricter data residency requirements.

5. Langfuse

Langfuse is an open-source observability and evaluation platform for LLM and agentic applications that is now a part of Clickhouse. It connects traces, sessions, prompts, datasets, and evaluations, so you can review production behavior and test changes without moving those workflows into separate tools.

Workflow Tracing Across LLMs, RAG, and Agents

Langfuse records AI workflows as traces with nested observations. A trace can include model calls, retrieval steps, embeddings, tool calls, inputs, outputs, latency, and token usage. Sessions group related traces, which help you review multi-turn conversations and agent runs without treating each request as a separate event.

Output Evaluation and Behavior Drift

Langfuse connects traces with scores, annotations, datasets, and experiments. You can score outputs with LLM-as-a-judge templates, code evaluators, manual labels, or custom scoring workflows. Those scores can attach to observations, traces, or sessions, so you can compare output quality across prompt versions, model changes, and longer conversations, then track regressions over time.

Cost, Latency, and Runtime Context

Langfuse records token usage, latency, and cost for generation and embedding observations. Dashboards and API queries let you inspect cost by model, user, prompt, or workflow. This gives you application-level visibility into AI spend and response time, rather than host or Kubernetes-level runtime context.

Instrumentation, Deployment, and Data Control

Langfuse uses SDKs and OpenTelemetry-based integrations for common LLM providers, frameworks, and agent libraries. You can run it as a managed cloud, self-host it with Docker or Kubernetes, or deploy it in your own cloud or on-premises environment. Self-hosting gives you more control over prompts, responses, traces, scores, and attachments when those records include sensitive data.

6. Opik by Comet

Opik by Comet is an open-source observability and evaluation platform for LLM and agent workflows. It records traces, spans, threads, prompts, and evaluation scores. You can then use that data to debug production behavior and compare changes across prompts, models, and agent versions.

Workflow Tracing Across LLMs, RAG, and Agents

Opik records each agent run as a trace, with spans for model calls, retrieval, function calls, API calls, and other workflow steps. Threads group related traces into conversations, which helps you follow multi-turn behavior instead of reviewing each request in isolation. For supported frameworks, Opik can also render agent graphs that show how the workflow moved through tools and intermediate steps.

Output Evaluation and Behavior Drift

Opik supports online evaluation rules for production traces. You can score outputs with checks for hallucination, moderation, answer relevance, context precision, task completion, and tool behavior. Those scores are stored with traces and spans, so you can track quality changes across prompt versions, model updates, retrieval changes, and agent workflows.

Cost, Latency, and Runtime Context

Opik tracks token usage, estimated cost, and timing across spans and traces. Project dashboards show cost over time, while trace-level views show where latency or spend appeared inside the workflow. When a model is not covered by built-in pricing, you can define model prices or send cost data with the trace.

Instrumentation, Deployment, and Data Control

Opik provides Python and TypeScript SDKs, framework integrations, and OpenTelemetry ingestion. It integrates with agent frameworks such as LangGraph, CrewAI, Pydantic AI, Google ADK, Vercel AI SDK, OpenClaw, and Strands Agents. You can use Opik Cloud or self-host it when you need to keep traces, prompts, responses, and evaluation data in your own environment.

Evaluation-First Tools

Evaluation-first tools use traces as the input for scoring and improving AI behavior. They still capture workflow data, but their main value is in turning production runs, datasets, and evaluations into a repeatable quality loop.

7. Braintrust

Braintrust is an AI observability and evaluation platform for LLM and agent applications. It starts by tracing production runs, then turns those traces into datasets, scorers, dashboards, and release checks. This gives you one workflow for reviewing what happened in production and testing whether the next prompt, model, or agent change improves the result.

Workflow Tracing Across LLMs, RAG, and Agents

Braintrust records each AI run as a trace with nested spans. Those spans can represent model calls, tool invocations, reasoning steps, state changes, and memory operations. Because the spans preserve parent-child relationships, you can follow the workflow from the user input through retrieval, tools, intermediate reasoning, and the final output.

Output Evaluation and Behavior Drift

Braintrust uses traces as the starting point for evaluation. You can turn production failures or curated examples into datasets, define scorers, and run those scorers against prompt, model, or agent changes before release. In production, online scoring applies the same checks to live traces, so regressions show up as score changes tied to the run that caused them.

Cost, Latency, and Runtime Context

Braintrust dashboards aggregate request volume, latency, token usage, and cost from the trace data. You can also create charts for application-specific metrics, such as score trends or failure categories. Because the runtime metrics stay attached to traces, you can review cost, performance, and output quality from the same execution path.

Instrumentation, Deployment, and Data Control

Braintrust provides SDKs for languages such as Python, TypeScript, Go, Ruby, and C#, while OpenTelemetry covers frameworks without a native integration. For deployment, you can use Braintrust-managed SaaS or a hybrid deployment, where the Brainstore data plane runs on your own infrastructure under the Enterprise plan. 

8. Galileo

Galileo is an AI observability, evaluation, and guardrails platform for GenAI and agentic applications. It connects traces with quality metrics, safety checks, datasets, and production monitoring, so you can inspect how an output was produced and how it scored against your evaluation criteria.

Workflow Tracing Across LLMs, RAG, and Agents

Galileo organizes telemetry into log streams, sessions, traces, and spans. Sessions capture multi-turn interactions, traces represent individual operations, and spans record model calls, retrieval steps, tool calls, timing, token usage, and errors. SDKs, framework callbacks, OpenTelemetry, and OpenInference integrations send that data into Galileo, where nested traces show how agent and RAG workflows move across model calls and tools.

Output Evaluation and Behavior Drift

Galileo evaluates outputs with metrics for response quality, RAG behavior, agent performance, safety, compliance, readability, multimodal quality, and model confidence. You can use its built-in metrics, LLM-as-a-judge prompts, or code-based evaluators. Those scores can then be tracked across traces, sessions, and datasets, which helps you review quality changes after prompt, model, retrieval, or agent updates.

Cost, Latency, and Runtime Context

Galileo records timing, token usage, and evaluation results across spans, traces, and sessions. It also tracks model and evaluator cost, which matters when LLM-as-a-judge checks add their own spend. Its runtime context focuses on output quality, safety, and evaluation behavior rather than host or Kubernetes-level debugging.

Instrumentation, Deployment, and Data Control

Galileo supports Python and TypeScript SDKs, LangChain and LangGraph callbacks, OpenTelemetry, OpenInference, and multi-agent tracing paths. Deployment options include SaaS, VPC, and on-premises environments for Enterprise customers. Its access controls include role-based access control (RBAC), single sign-on (SSO), workspace controls, resource permissions, compliant environments, and log export.

AI Observability Tools Comparison Overview

Here’s a quick snapshot of the AI observability tools we've covered, including how each one is deployed, where it is strongest, and what kind of production AI workflow it fits best.

How to Choose the Right AI Observability Tool for Your Team

An AI observability tool can cover the right features and still be the wrong fit for your production AI system. Use these criteria to match each option to the workflows you run, the telemetry you need, and the way you deploy and pay for observability.

• Identify Whether You Need LLM Monitoring, Agentic Tracing, or Both: If your application sends a prompt to a model and returns the answer, model-call monitoring covers the main signals you need: prompt, response, token usage, latency, and cost. Once retrieval, tools, retries, or multi-turn memory influence the answer, those signals are no longer enough. You need agentic tracing to see whether the issue came from the model, the retrieved context, a tool call, or an earlier step in the workflow.
• Assess Instrumentation Overhead and Deployment Complexity: The capture method determines how quickly you can get useful coverage. Runtime capture, gateways, environment variables, SDK wrappers, and auto-instrumentation can reduce setup work for initial visibility. For deeper RAG and agent workflows, look for OpenTelemetry Protocol (OTLP), framework integrations, or manual spans that preserve parent-child relationships across the full trace.
• Evaluate Data Privacy and Residency Requirements: AI observability data often includes prompts, responses, retrieved documents, tool arguments, user identifiers, and evaluation scores. When those records contain customer data, internal documents, or regulated content, the deployment model becomes part of the tool choice. Bring your own cloud (BYOC), virtual private cloud (VPC), on-premises, and air-gapped options give you more control over where traces and evaluation data are stored and processed.
• Check Integration With Your Existing AI Stack and Infrastructure: A tool should match the systems that already shape your AI workflow. If you use LangChain, LangGraph, OpenInference, or OpenTelemetry heavily, full-lifecycle platforms may fit the application layer well. If you need AI telemetry tied to Kubernetes, services, logs, metrics, traces, and events, prioritize a platform that connects LLM and agent behavior with production infrastructure context.
• Consider Pricing Models Based on Token Volume, Hosts, or Data Ingestion: Pricing changes fast when agents move into production. One request can create multiple model calls, tool calls, spans, retries, and evaluator runs. Compare pricing against expected production traffic, since token-based, trace-based, ingestion-based, seat-based, and host-based models scale differently.

Conclusion

AI observability tools add the missing layer between application telemetry and output quality in production AI systems. The right choice depends on whether you need to trace model calls, inspect agent steps, evaluate responses, control data residency, or connect AI behavior with your existing application and infrastructure observability stack.

As LLM and agentic workflows become part of production systems, observability is essential for reliability, cost control, and user trust. Choose a tool that matches the telemetry you require, the workflows you want to trace, and the data control requirements of your environment.