# Best AI Agent Observability Tools in 2026

*By Nicolas Zeeb · September 8, 2026 · 8 min · LLM basics*

The best AI agent observability tools in 2026 are judged on what a trace still tells you when a run fails. Compare 5 platforms on depth, evals, and pricing.

The best AI agent observability tools in 2026 are judged on what a trace still tells you when a run fails. Compare 5 platforms on depth, evals, and pricing.

## Quick overview

An agent run that calls six tools across three model invocations produces somewhere between twenty and forty spans. When the answer comes back wrong, the cause sits in one of them, and the other thirty-nine look fine. Status codes are 200, latency is normal, and the token count is unremarkable.

The tooling in this category all renders that run as a tree. Where the platforms separate is how much of the surrounding stack ends up inside the same record: the arguments passed to a tool, the context retrieved three steps earlier, the prompt version that was live at the time, and which model actually served the request after a retry.

That is the difference between opening a trace and closing a ticket. The five AI agent observability tools below are compared on how much of a failed run each one lets you reconstruct, what each one costs once volume arrives, and who each one fits.

## The 5 best AI agent observability tools at a glance

1. [Respan](https://www.respan.ai/ai-observability): Tracing, evaluation, prompt management, and model routing on one platform, with the routing decision recorded on the span.
2. [Langfuse](https://langfuse.com/): Open-source tracing and prompt management with online scoring, self-hostable as the full product.
3. [LangSmith](https://www.langchain.com/langsmith-platform): Deep run-level debugging for teams building on LangChain and LangGraph.
4. [Braintrust](https://www.braintrust.dev/): Evaluation-first, with production logs and offline experiments on a shared data layer.
5. [Arize Phoenix](https://arize.com/): OpenTelemetry-native, self-hosted with no feature gates.

## What is AI agent observability?

AI agent observability is the practice of recording what an agent did across a multi-step run, in enough detail that a bad outcome resolves to the step that caused it. It is a narrower job than application monitoring, because the failures are semantic. An agent can return HTTP 200, finish in two seconds, and be wrong.

### What a trace captures across a multi-step run

A trace is a tree of spans. Each LLM call, tool invocation, retrieval, and agent turn becomes a node, nested parent to child, carrying its own input, output, latency, and cost. The tree is what tells you the story, because an individual span rarely explains itself. A tool that returned an empty result is not an error, and it is often the reason the final answer was wrong.

Threading matters as much as nesting. A bad output on turn five of a conversation frequently traces back to context set on turn two, and a platform that treats each turn as an isolated run will not surface that.

### Where reconstruction breaks down

The trail usually goes cold at a boundary the instrumentation did not cross. Tool arguments get recorded as a summary rather than the payload. Retrieved chunks are counted but not stored. The prompt is captured as rendered text with no version attached, so a regression cannot be traced to the change that caused it.

The request path is the boundary most often missed. If routing lives in a separate system, the trace records that a model was called while the record of which model answered after a fallback sits somewhere else, and correlating them by timestamp is the first hour of an incident.

## Why is agent observability important?

- Quality that degrades without moving a single operational metric, so latency, error rate, and token count all stay green while the output gets worse
- Failures that cannot be reproduced locally, because the same input does not reliably produce the same path twice
- Cost that compounds through retries and loops, where one request quietly bills four times
- Spend attribution down to the model, the feature, and the end customer, rather than a single number on an invoice
- Regressions traced to the change behind them, whether a prompt edit, a provider point release, or drift in a retrieval index
- Multi-turn context, where the answer that broke on turn five depends on what was set on turn two
- Tool calls recorded with their actual arguments, since an empty result is not an error and is often the reason the answer was wrong
- A record of which model served the request after a fallback, rather than merely which one was asked for

## What to look for in an agent observability tool

- Instrumentation model. OpenTelemetry and the GenAI semantic conventions mean instrumentation written once can export to more than one backend. Proprietary SDKs carry the highest switching cost.
- What the span carries beyond the LLM call. Tool arguments, retrieved context, prompt version, and the routing decision are the fields that decide whether a failed run is explainable.
- Whether scoring runs on live traffic. Offline evaluation answers whether a change is safe to ship. Online evaluation answers whether the thing you shipped is still working.
- What the meter counts. Across these five the billable unit is variously a span, an observation, a score, a seat, or a gigabyte, and no two vendors define theirs the same way.
- Deployment and retention. Retention windows on entry tiers run from seven to thirty days, which decides whether you can track a quality trend across a quarter.

Note that these criteria are what we use to evaluate and score each of the tools in the list below.

## The 5 best AI agent observability tools in 2026

### 1. Respan

[Respan](https://www.respan.ai/ai-observability) puts tracing, evaluation, prompt management, and model routing on one platform. Every LLM call, tool run, retrieval, and agent turn becomes a span in one trace, with the prompt version and the cost attached to the run that produced it.

**Score:** 94

**Strengths:**

- Full agent traces, nested parent to child, with input, output, latency, and cost on every span
- Threads group multi-turn sessions, so a failure on turn five resolves to context set on turn two
- Online evaluations run on live production spans, filtered by status, customer, or thread and sampled to control cost
- A failing score becomes a dataset row and then an experiment without leaving the platform
- Respan’s [LLM gateway](https://www.respan.ai/ai-gateway) reaches 1,000+ models through one endpoint with automatic failover, retries, and load balancing, adding roughly 10ms at P95, so the model that actually served a request after a fallback is recorded on the span rather than inferred
- Behaviors classify live traffic semantically, with built-in classifiers for frustration, jailbreak, and unsafe output
- Red teaming runs adversarial campaigns against the OWASP Top 10 LLM risks on the same connected agent you already trace
- SOC 2, HIPAA with a BAA, GDPR, and ISO 27001

**Trade-offs:**

- Default retention on the free tier is seven days, which covers evaluation but not a quarterly trend.

**Pricing:** Free covers 100,000 logs, 1,000 scores, and unlimited seats; Team is $199/month billed yearly and lifts retention to 30 days with datasets, evaluators, and prompts unlimited.

**Takeaway:** The strongest fit for teams running agents in production who would rather debug one system than correlate four.

### 2. Langfuse

Open source is the organizing fact about [Langfuse](https://langfuse.com/). The MIT-licensed core self-hosts as the full product with no seat caps or usage limits, and ClickHouse acquired the company in January 2026 with the license, roadmap, and self-hosting parity unchanged [1](https://clickhouse.com/blog/clickhouse-acquires-langfuse-open-source-llm-observability).

**Score:** 88

**Strengths:**

- OpenTelemetry-based ingestion, so instrumentation is portable
- Managed LLM-as-a-Judge and code evaluators score live production observations through rules with filters and sampling rates [2](https://langfuse.com/docs/evaluation/get-started/online)
- Datasets, experiments, and annotation queues sit on the same data model as traces
- Self-hosting is a first-class deployment mode rather than an enterprise concession

**Trade-offs:**

- Routing lives elsewhere. Langfuse traces from the application rather than the proxy and publishes integration guides for LiteLLM, Portkey, OpenRouter, and Vercel AI Gateway [3](https://langfuse.com/resources/engineering/llm-gateway), so failover and spend limits are a second tool
- A unit is any trace, observation, or score, so one agent request with twenty spans and three scorers meters as twenty-four
- Self-hosting in production means operating ClickHouse, Postgres, Redis, and object storage, so the license cost and the total cost are different numbers
- SDKs are Python and TypeScript only

**Pricing:** Hobby is free at 50,000 units; Core is $29/month at 100,000 units, and compliance certifications sit on Pro at $199/month.

**Takeaway:** The natural pick for teams with data residency requirements and the operational capacity to run their own stack.

### 3. LangSmith

[LangSmith](https://www.langchain.com/langsmith-platform) comes from the LangChain team, and the depth follows the framework. Runs render as full execution trees, and a `traceable` wrapper covers code outside LangChain, though the instrumentation is richest inside LangGraph.

**Score:** 84

**Strengths:**

- Run-level debugging for graph-based agents, where the state at each node is visible rather than inferred
- Online LLM-as-a-judge evaluators score production runs at a configurable sampling rate
- Annotation queues route selected runs to human reviewers, and that output feeds evaluation datasets
- An LLM Gateway with named routes and model fallback chains, triggered on configurable HTTP status codes, is in private beta [4](https://docs.langchain.com/langsmith/llm-gateway-fallbacks)

**Trade-offs:**

- Adopting it is partly a bet on a framework, since trace depth outside LangChain and LangGraph is thinner
- Seats and traces meter separately, and there is no read-only seat, so a reviewer who only reads results costs what an engineer costs
- Trace counts include every run inside a chain, so a retrieval, rerank, and generation pipeline bills three times per query
- Self-hosting requires Enterprise

**Pricing:** Developer is free at 5,000 traces on one seat; Plus is $39/seat/month with 10,000 base traces, then $2.50 per 1,000 at 14-day retention or $5.00 per 1,000 at 400-day.

**Takeaway:** Worth it for teams whose stack is LangChain today and will still be LangChain in two years.

### 4. Braintrust

The experiment is the center of [Braintrust](https://www.braintrust.dev/). Change a prompt, run it against a dataset, and see per-scorer deltas against the baseline before the branch merges, with production logs and offline experiments sharing a data layer so a failing production case becomes a test case without an export step.

**Score:** 80

**Strengths:**

- Evaluation results can gate a merge in CI, which catches regressions before they reach traffic
- Autoevals covers LLM-as-judge, heuristic, and statistical scorers out of the box
- Brainstore is purpose-built for deeply nested traces and stays responsive at volume
- A gateway providing one endpoint across OpenAI, Anthropic, Google, and AWS, with automatic caching and logging, is in beta and currently free, with pricing to be announced [5](https://www.braintrust.dev/docs/deploy/gateway)

**Trade-offs:**

- Tracing exists to feed the evaluation workflow, so it is less developed than in platforms built around production debugging
- Billing meters scores rather than traces, so a team running five scorers per output can reach the cap five times faster than a team running one, and the bill tracks evaluation depth instead of traffic
- Retention is 14 days on the free tier and 30 on Pro, which may be short for tracking a trend across a quarter
- SOC 2 attestation, a BAA, audit logging, SAML SSO, and self-hosting are all Enterprise

**Pricing:** Free covers 1 GB and 10,000 scores; Pro is $249/month at 5 GB and 50,000 scores.

**Takeaway:** The right choice where evaluation discipline is the constraint rather than production visibility.

### 5. Arize Phoenix

Two products ship under the Arize name, and the split decides what you get. [Phoenix](https://arize.com/docs/phoenix) is the open-source project under Elastic License 2.0, self-hostable with no feature gates, while Arize AX is the managed platform. Dynatrace signed a definitive agreement to acquire Arize in August 2026, with the transaction not yet closed [6](https://www.dynatrace.com/news/press-release/dynatrace-to-acquire-arize/).

**Score:** 76

**Strengths:**

- Built on OpenTelemetry and OpenInference, so spans land in Phoenix, AX, or any OTel backend from the same instrumentation
- Tracing, span-level evaluation, datasets, experiments, prompt management, and a playground are all in the free self-hosted build
- Online trace evals shipped in the open-source release in July 2026, including agent-specific scorers for tool count per turn and user friction [7](https://github.com/Arize-ai/phoenix/releases/tag/arize-phoenix-v19.5.0)
- Runs locally, in a notebook, or in Docker, which makes it cheap to evaluate before committing

**Trade-offs:**

- Production monitoring, alerting, and compliance live on AX rather than Phoenix, so a team that starts self-hosted and needs monitors later is changing products
- Elastic License 2.0 is more restrictive than MIT or Apache, which matters if you intend to offer it as a service
- Retention on AX runs 15 days on Free and 30 on Pro, and there is nothing published between Pro and Enterprise
- Routing sits outside the platform

**Pricing:** Phoenix is free and self-hosted with no usage caps; AX Free covers 25,000 spans, and AX Pro is $50/month at 50,000 spans and 10 GB.

**Takeaway:** The most portable starting point, particularly for teams already standardized on OpenTelemetry.

## AI agent observability tools compared

| Tool | Request path | Metered by | Evaluation | Best for |
| --- | --- | --- | --- | --- |
| Respan | Gateway, generally available | Logs and scores | Online, on live spans | Agents in production |
| Langfuse | Separate gateway required | Units: trace, observation, score | Online, on observations | Data residency requirements |
| LangSmith | Gateway in private beta | Seats and traces | Online, on runs | LangChain and LangGraph stacks |
| Braintrust | Gateway in beta, free | Scores and data volume | Offline experiments first | Eval-gated CI |
| Arize Phoenix | Separate gateway required | Spans and ingest on AX | Span-level, online since July | OpenTelemetry portability |

## How to choose for your stack

- Data residency first, since traces that cannot leave your infrastructure narrow the shortlist to Langfuse and Phoenix before any feature comparison starts
- LangGraph as the deciding factor, where LangSmith sees node-level state the others infer
- Evaluation discipline over production visibility, which is the case Braintrust is built for
- OpenTelemetry portability, if the priority is instrumenting once and keeping the option to change backends later
- How much of the loop runs inside one system, meaning whether a failing case becomes a dataset without an export and whether a score traces back to the routing decision behind it
- The meter before the sticker price, because score-based billing scales with evaluators per output, unit-based billing counts every span, and seat-based billing scales with headcount independent of traffic
- Spans per request multiplied by scorers per output, which is the shape that decides what any of these actually costs you
- Retention on the tier you can afford, which is what determines whether you can track a quality trend across a quarter

## FAQs

### What is the difference between AI agent observability and LLM observability?

LLM observability records individual model calls: the prompt, the completion, the latency, the cost. Agent observability records the run those calls sit inside, including tool invocations, retrieval steps, and the state carried between turns. A single-call view is enough for a classification endpoint. For an agent that reaches a wrong answer through a wrong tool call and then recovers, the call-level record shows nothing unusual and the run-level record shows the bug.

### Do you need OpenTelemetry to trace an agent?

No, but it decides what a future migration costs. OpenTelemetry and the GenAI semantic conventions mean instrumentation written once can export to more than one backend, so switching platforms is a configuration change rather than a re-instrumentation project. Phoenix and Langfuse are both built on it, and Respan ingests OTLP alongside its own SDKs.

### What should a trace capture for a multi-step agent run?

Every LLM call, tool run, retrieval, and agent turn as its own span, nested parent to child, each carrying input, output, latency, and cost. Beyond that, the fields that decide whether a failure is explainable are the actual tool arguments rather than a summary, the retrieved context rather than a chunk count, the prompt version that was live, and which model served the request after a retry or fallback.

### Is there a free AI agent observability tool?

Several, and they differ mostly in what ends the free tier. Respan's free tier includes tracing, online evaluations, datasets, and human review at 100,000 logs and 1,000 scores with unlimited seats, so the production loop is testable before any spend. Phoenix is free and self-hosted with no usage caps, Langfuse Hobby covers 50,000 units, Braintrust Free covers 1 GB and 10,000 scores, and LangSmith Developer covers 5,000 traces on a single seat.

### What is the best AI agent observability tool?

Respan, for teams running agents in production. Tracing, evaluation, prompt management, and routing sit on one platform, so a failing production score links to the span that produced it, becomes a dataset row, and gets tested as an experiment without an export step, with the model that actually served the request recorded on the span rather than inferred from a second system. The alternative is correlating four tools by timestamp during an incident.

## Citations

[1] ClickHouse. (2026). [ClickHouse acquires Langfuse](https://clickhouse.com/blog/clickhouse-acquires-langfuse-open-source-llm-observability).

[2] Langfuse. (2026). [Evaluate Production Traffic](https://langfuse.com/docs/evaluation/get-started/online).

[3] Langfuse. (2026). [What is an LLM gateway?](https://langfuse.com/resources/engineering/llm-gateway)

[4] LangChain. (2026). [LLM Gateway: Model fallbacks](https://docs.langchain.com/langsmith/llm-gateway-fallbacks).

[5] Braintrust. (2026). [Use the Braintrust gateway](https://www.braintrust.dev/docs/deploy/gateway).

[6] Dynatrace. (2026). [Dynatrace to Acquire AI Observability Leader Arize](https://www.dynatrace.com/news/press-release/dynatrace-to-acquire-arize/).

[7] Arize. (2026). [Phoenix v19.5.0 release notes](https://github.com/Arize-ai/phoenix/releases/tag/arize-phoenix-v19.5.0).