Best LLM observability tools in 2026

Quick Answer

• Best overall open-source LLM observability platform: Langfuse
• Best LangChain and LangGraph-native workflow: LangSmith
• Best gateway-first request and cost monitoring: Helicone
• Best OpenTelemetry-centered AI observability option: Arize Phoenix
• Best eval-first quality workflow: Braintrust
• Best gateway control plane plus observability: Portkey
• Best enterprise eval and monitoring branch: Galileo
• Best open-source agent observability challenger: Laminar
• Best prompt and agent iteration workspace: Latitude
• Best full-stack OpenTelemetry observability branch: SigNoz

Summary Table

How To Choose LLM Observability Software

Start with the system you are shipping. A simple chat feature, a RAG workflow, a multi-step agent, a coding assistant, and an internal copilot all produce different debugging problems. The right tool should show the full chain of model calls, retrieval steps, tool calls, user sessions, latency, token usage, cost, inputs, outputs, and evaluation results without forcing engineers to stitch together screenshots from multiple dashboards.

1. Decide whether tracing, evals, gateway control, or full-stack telemetry is the center

Tracing-first tools help developers see what happened inside one request or agent run. Eval-first tools help teams decide whether a model, prompt, retriever, or agent change is better than the last version. Gateway-first tools help platform teams route traffic, enforce policies, cache responses, retry failures, and control provider usage. Full-stack observability tools help SRE and platform teams connect LLM behavior to application latency, errors, logs, metrics, and infrastructure.

Most teams need more than one of these jobs, but one branch usually matters most. Choose around that primary job instead of buying the longest feature list.

2. Require traces that match real agent behavior

Useful LLM traces should show more than prompt and completion text. For agents and RAG systems, look for nested spans across model calls, retrieval, reranking, tool calls, function calls, memory, custom business logic, external APIs, and user sessions. Engineers should be able to filter by cost, latency, provider, model, prompt version, user, session, environment, and failure type.

3. Treat evals as production infrastructure

LLM evals should not be a one-off spreadsheet. The strongest teams build datasets, run regression checks before release, score live samples, inspect failures, and keep human review in the loop for edge cases. Braintrust, Langfuse, LangSmith, Arize Phoenix, Galileo, Laminar, and Latitude all speak to pieces of this workflow, but the best choice depends on how mature your evaluation process already is.

4. Make prompt management auditable

Prompt changes can break production behavior as easily as code changes. If prompts live inside the observability platform, check versioning, deployment workflow, rollback, labels, experiments, approvals, and how prompt changes connect to trace and eval results. If prompts stay in code, make sure traces still capture the deployed version.

5. Connect cost and latency to product behavior

LLM costs are rarely evenly distributed. A small set of users, prompts, tools, retrievers, models, or agent loops can drive most spend. Good observability should expose token usage, model cost, latency, retries, cache hit rates, error rates, and quality metrics by route, tenant, environment, session, and feature.

6. Do not ignore deployment and data control

Traces can include prompts, retrieved documents, customer data, personally identifiable information, source code, security context, and proprietary workflow details. Before choosing a hosted platform, check redaction controls, retention, access control, audit logs, regional hosting, self-hosting, SOC 2, ISO, HIPAA eligibility, and data export. Open-source and self-hosted options are attractive when trace data is sensitive, but they still require operational ownership.

Evaluation Criteria

ClawNewbie ranked these tools for engineering teams shipping LLM apps and agents, not for generic analytics dashboards:

• Trace depth: Can the platform capture nested model calls, retrieval, tool use, custom logic, user sessions, and agent runs?
• Evaluation workflow: Does it support datasets, experiments, scoring, regression checks, human review, and production monitoring?
• Prompt management: Can teams version, test, deploy, compare, and roll back prompts in a controlled workflow?
• Cost and latency monitoring: Can engineers see token usage, model cost, provider latency, retries, errors, and expensive paths?
• Gateway and policy controls: Does it help with routing, caching, guardrails, retries, provider abstraction, or traffic governance?
• OpenTelemetry and integrations: Can it work with existing frameworks, model providers, agent frameworks, and observability stacks?
• Deployment control: Does it support self-hosting, open-source inspection, data export, regional controls, and enterprise security needs?
• Team workflow: Can product, engineering, evaluation, support, and operations teams collaborate without losing source context?

Best LLM Observability Tools In 2026

1. Langfuse

Langfuse is the best overall default for teams that want one open-source platform to connect LLM traces, prompt management, evals, experiments, annotation, cost monitoring, and production iteration. Its official positioning is not just "see prompts and completions." It frames the platform as a full LLM engineering loop for debugging, improving, and monitoring AI applications and agents.

Langfuse is especially strong when the team wants flexibility: self-hosting, OpenTelemetry support, broad integrations, prompt management, eval workflows, human annotation, and cost/latency dashboards. It fits teams that do not want an AI-only black box and prefer a platform they can inspect, host, export from, and connect to common frameworks.

The caution is ownership. A full-cycle platform is only valuable if someone owns traces, prompt versions, evaluation datasets, annotation queues, dashboards, and review loops. Langfuse can be lightweight to start, but it becomes most valuable when engineering treats it as part of the release process.

Choose Langfuse if:

• You want an open-source LLM observability platform with tracing, prompts, evals, experiments, and cost tracking.
• You need self-hosting or stronger control over AI trace data.
• Your product includes agents, RAG workflows, copilots, or multi-step LLM features.

Skip Langfuse if:

• You only need a managed enterprise eval workflow and do not want to operate any open-source option.
• Your team is fully standardized on LangChain/LangGraph and wants the most native ecosystem workflow.
• You mainly need an AI gateway rather than an engineering platform.

Pricing note: Langfuse has hosted and self-hosted paths. Publisher should recheck current free-tier, cloud, and enterprise packaging before import.

Read next: Best AI agent platforms, best AI code review tools, and Claude Code vs Devin.

2. LangSmith

LangSmith is the most natural pick for teams already building with LangChain or LangGraph. It focuses on the problems those teams face every day: tracing agent behavior, debugging chains, building datasets, running evals, monitoring production quality, and improving prompts and workflows over time.

The product belongs near the top of this list because LangChain and LangGraph are common choices for agentic systems. If your team is building multi-step agents, the observability tool should understand that shape without forcing you to model every span manually.

The caution is ecosystem fit. LangSmith can still be useful outside LangChain, but buyers should test how much of the workflow feels native if their production stack is built on other frameworks.

Choose LangSmith if:

• Your LLM app or agent stack already uses LangChain or LangGraph.
• You need trace debugging, datasets, evals, monitoring, and prompt iteration in one managed workflow.
• Agent behavior is complex enough that raw logs are no longer enough.

Skip LangSmith if:

• You want open-source/self-hosted control as the main buying criterion.
• You prefer a gateway-first layer for cost, routing, and provider control.
• Your stack is not tied to LangChain/LangGraph and another tool integrates more cleanly.

Pricing note: Recheck LangSmith's current developer, team, and enterprise packaging before publishing exact pricing.

Read next: AI browser agents and Devin.

3. Helicone

Helicone is the clearest pick for teams that want gateway-first LLM observability. It is strongest when the first pain is operational visibility: which requests came in, which model handled them, what they cost, how long they took, where failures happened, and how prompt or session behavior changed over time.

That gateway angle makes Helicone useful for teams that want fast installation and request-level cost monitoring across providers. It can be easier to reason about than a full eval platform if the immediate problem is uncontrolled usage, missing request logs, and no way to inspect production behavior.

The caution is that gateway observability and eval infrastructure are different jobs. Helicone is a good starting point for request visibility and cost control, but teams with serious quality gates should test how its prompt and experiment workflows compare with eval-first tools.

Choose Helicone if:

• You want gateway/proxy observability with fast request logging and cost tracking.
• Your team needs visibility into model usage, latency, sessions, prompts, and production requests.
• You want a practical operational layer before building a heavy evaluation program.

Skip Helicone if:

• Your main need is deep offline eval experimentation.
• You want a LangChain/LangGraph-native trace workflow.
• You need full-stack OpenTelemetry observability as the primary platform.

Pricing note: Recheck hosted, open-source, and enterprise packaging before import.

4. Arize Phoenix

Arize Phoenix is the best branch for teams that want AI observability and evaluation to line up with OpenTelemetry and production tracing practices. It is especially relevant for RAG systems and agents because it can represent model calls, retrieval, tool calls, framework spans, and custom logic rather than flattening the workflow into one prompt record.

Phoenix is also attractive when a team wants an open-source-friendly path but still cares about enterprise observability discipline. It fits buyers who are already thinking in spans, traces, evaluations, and instrumentation rather than dashboards alone.

The caution is integration planning. If the company already has a mature observability platform, decide whether Phoenix becomes the AI-specific layer, the main LLM trace store, or part of a broader OpenTelemetry pipeline.

Choose Arize Phoenix if:

• You want OpenTelemetry-centered tracing for LLM calls, retrieval, tools, and custom logic.
• Your product includes RAG, agents, or complex AI workflows that need explainable traces.
• You care about evals and experiments alongside observability.

Skip Arize Phoenix if:

• You mainly want a lightweight gateway request monitor.
• You need LangChain-native workflows above broader instrumentation.
• Your team is not ready to instrument applications and maintain trace quality.

Pricing note: Phoenix has open-source and managed/enterprise context through Arize. Publisher should recheck packaging and deployment options.

5. Braintrust

Braintrust is the strongest eval-first recommendation. It is built around the idea that AI quality should be measured systematically: define datasets, run experiments, score outputs, compare versions, monitor production, and iterate in a playground with enough context to understand failures.

This is the right branch when the team already knows that traces alone will not solve the problem. If model changes, prompt edits, retrieval adjustments, or agent logic updates are creating regressions, the buyer needs repeatable evaluation workflows, not just request logs.

The caution is process maturity. Braintrust is most valuable when teams can define what "good" means, build test sets, write or choose scorers, review failures, and use eval results in release decisions.

Choose Braintrust if:

• You need systematic evaluations, experiments, datasets, scoring, and production monitoring.
• Prompt and model changes need regression checks before release.
• Product and engineering teams need a shared workflow for quality review.

Skip Braintrust if:

• You only need basic tracing and cost logs.
• You want a gateway layer for routing, caching, and retries.
• Your team is not ready to maintain evaluation datasets or scoring logic.

Pricing note: Recheck current plan, usage, and enterprise terms before publishing exact pricing.

6. Portkey

Portkey is the best fit when the buyer wants an AI gateway and observability layer together. It is not only about seeing what happened; it also helps platform teams control how model traffic moves through providers, retries, caching, routing, guardrails, and analytics.

That makes Portkey relevant for companies standardizing model access across products or teams. If engineering needs a shared control plane for OpenAI, Anthropic, open-source models, agents, and internal policies, observability is only one part of the buying decision.

The caution is focus. If the real problem is trace analysis, eval datasets, or prompt experiments, a gateway-control platform may need to be paired with a tracing/eval-first tool.

Choose Portkey if:

• You need an AI gateway with routing, retries, caching, guardrails, and analytics.
• Platform teams want provider control and visibility in one layer.
• You need observability tied to model traffic governance.

Skip Portkey if:

• You do not want a gateway architecture.
• You need eval-first workflows above routing and policy controls.
• Your team wants an open-source LLM engineering platform as the main home for prompts and traces.

Pricing note: Recheck current hosted, enterprise, and open-source packaging before import.

7. Galileo

Galileo belongs on the shortlist for teams looking for a managed AI observability and evaluation platform with an enterprise quality angle. Its positioning centers on evaluating, monitoring, experimenting with, and improving production AI systems.

It is most relevant when the buyer wants a mature quality workflow and is prepared to treat evals and monitoring as part of the AI release process. Teams comparing Galileo should test it with real failure cases, not just demo examples: retrieval misses, hallucinations, unsafe responses, agent loops, stale context, and quality regressions after a model change.

The caution is fit and cost. Publisher should avoid specific plan claims unless rechecked, and buyers should compare Galileo against Braintrust, Langfuse, LangSmith, and Phoenix using the same evaluation dataset.

Choose Galileo if:

• You want managed AI evaluation and observability workflows.
• You need production quality monitoring and experimentation.
• Your team has enterprise procurement and support expectations.

Skip Galileo if:

• You need open-source or self-hosted control as the deciding factor.
• You want gateway-first request monitoring.
• You are not ready to define eval datasets and quality metrics.

8. Laminar

Laminar is an open-source agent observability option to watch. It fits teams that are building agentic workflows and want traces, evaluations, datasets, and debugging without committing immediately to the largest platforms in the category.

The appeal is developer focus. Agent builders need to understand tool calls, intermediate steps, failures, and changes in behavior across versions. Laminar's positioning speaks directly to that need.

The caution is maturity and ecosystem footprint. Teams should verify hosted availability, self-hosting expectations, integrations, support, and how it behaves under production volume before ranking it above the category leaders.

Choose Laminar if:

• You want open-source agent observability and evaluation workflows.
• Your team is experimenting with agent traces and wants a developer-centered platform.
• You are comfortable validating a smaller or newer market entrant.

Skip Laminar if:

• You need a more established enterprise platform today.
• You need gateway controls as the core buying criterion.
• You want a full-stack observability platform for all application telemetry.

9. Latitude

Latitude fits teams that treat prompt and agent iteration as a product workflow. It is relevant for teams that need observability, prompt work, evals, and collaboration around agent behavior rather than only raw infrastructure traces.

That makes it most useful when product, engineering, and AI teams need to inspect agent runs, improve prompts, and coordinate iteration. It can be a better fit than a generic observability dashboard when the core question is "why did this agent behave this way, and how do we improve it?"

The caution is category coverage. Buyers should test whether Latitude covers the observability depth, eval workflow, data control, and production monitoring they need, especially if they are comparing it with Langfuse, LangSmith, Phoenix, or Braintrust.

Choose Latitude if:

• Prompt and agent iteration are central to your workflow.
• Product and engineering need a shared workspace around agent behavior.
• You want observability tied closely to improvement loops.

Skip Latitude if:

• You need full-stack OpenTelemetry observability.
• Gateway traffic control is the main requirement.
• You want the broadest open-source platform footprint.

10. SigNoz

SigNoz is different from the AI-native tools in this roundup. It is a full-stack open-source observability platform, not a dedicated LLM eval suite. That distinction is exactly why it belongs here for some teams.

If your platform already runs on OpenTelemetry and engineers want LLM spans beside application traces, metrics, logs, infrastructure signals, and service performance, SigNoz can be the observability home for the broader system. That matters when an AI feature fails because of retrieval latency, a downstream API, a queue, a model provider, or normal application infrastructure.

The caution is AI workflow depth. SigNoz can be part of an LLM observability stack, but it will not replace purpose-built prompt management, eval datasets, human review, or LLM-specific quality workflows for many teams.

Choose SigNoz if:

• You want open-source full-stack observability with OpenTelemetry.
• LLM traces need to sit beside logs, metrics, application traces, and infrastructure data.
• Your SRE/platform team wants one operational view across AI and non-AI systems.

Skip SigNoz if:

• You need prompt management and eval workflows as first-class features.
• You want a dedicated LLM engineering platform.
• You need gateway-specific controls for provider routing and guardrails.

Best Tool By Use Case

• Best default for open-source LLM engineering: Langfuse
• Best for LangChain/LangGraph agents: LangSmith
• Best for gateway-first monitoring: Helicone
• Best for OpenTelemetry AI traces: Arize Phoenix
• Best for evaluation programs: Braintrust
• Best for AI gateway control: Portkey
• Best for enterprise AI quality workflows: Galileo
• Best emerging open-source agent option: Laminar
• Best prompt and agent iteration workspace: Latitude
• Best for full-stack observability teams: SigNoz

Implementation Checklist

Before buying, run a proof of concept with a real production-like workflow:

1. Trace a normal user request, a failed request, a slow request, and a high-cost request.
2. Trace a full agent run with tool calls, retrieval, retries, and custom business logic.
3. Create an evaluation dataset from real examples and run it against two prompt or model versions.
4. Inspect how cost, latency, errors, and quality metrics break down by route, tenant, user, provider, and model.
5. Verify prompt versioning, rollback, permissions, audit logs, and deployment workflow.
6. Confirm data retention, redaction, export, self-hosting, regional hosting, and compliance needs.
7. Decide who owns eval datasets, dashboards, prompt releases, and production review queues.

Common Mistakes

• Choosing a trace viewer when the real problem is missing evals.
• Choosing an eval platform when the real problem is gateway cost and provider control.
• Logging prompts without redaction, access control, or retention planning.
• Treating screenshots from a demo as proof that the tool can capture your actual agent spans.
• Ignoring prompt versioning and then being unable to connect quality regressions to release changes.
• Measuring average cost while missing expensive outlier sessions, agent loops, and retriever failures.
• Keeping AI traces separate from application telemetry when outages depend on both.

Staged Roadmap Note

Do not publish the follow-on pages in this pass. After Publisher validates and publishes the main hub, the next cluster expansion should prioritize:

• /compare/langfuse-vs-langsmith-2026
• /compare/langfuse-vs-helicone-2026
• /compare/langsmith-vs-helicone-2026
• /compare/langfuse-vs-arize-phoenix-2026
• /tools/langfuse
• /tools/langsmith
• /tools/helicone
• /tools/arize-phoenix
• /tools/braintrust

FAQ

What is LLM observability?

LLM observability is the practice of tracing, measuring, evaluating, and debugging how LLM applications behave in production. It usually includes model calls, prompts, responses, retrieval, tool calls, agents, costs, latency, errors, user sessions, and quality signals.

What is the difference between LLM observability and evals?

Observability helps teams see what happened in production. Evals help teams decide whether behavior is good enough, improving, or regressing. Production LLM teams usually need both: traces to diagnose failures and evals to prevent bad changes from shipping.

Do I need an LLM gateway to observe my app?

No. Tracing SDKs and OpenTelemetry instrumentation can capture LLM behavior without routing every request through a gateway. A gateway is useful when you also need provider routing, retries, caching, guardrails, usage controls, or centralized model access.

Should LLM traces go into my existing observability platform?

Sometimes. If LLM behavior is tightly connected to normal services, OpenTelemetry and full-stack observability matter. But AI-specific tools often provide better prompt, eval, dataset, annotation, and quality workflows. Many teams use both.

What should I test before choosing a tool?

Test a real user session, a real agent run, a RAG failure, a slow request, a high-cost request, a prompt change, and a release candidate against an evaluation dataset. Also verify data controls before sending sensitive prompts or documents.

Which LLM observability tool is best for agents?

Langfuse, LangSmith, Arize Phoenix, Laminar, and Latitude are all strong candidates for agent workflows. Choose LangSmith if you are deep in LangChain/LangGraph, Langfuse if you want an open-source full-cycle platform, Phoenix if OpenTelemetry instrumentation matters most, and Laminar or Latitude if their agent-focused workflows match your stack.

If prompt changes are part of your production incident pattern, pair observability with a dedicated prompt management workflow for versioning, labels, and rollback. prompt management and versioning.

Use these comparison pages when the shortlist has narrowed to adjacent LLM evaluation, observability, or gateway platforms.

• Langfuse vs LangSmith 2026
• Langfuse vs Arize Phoenix 2026
• Braintrust vs Promptfoo
• Braintrust vs LangSmith
• Helicone vs Portkey