9 Best AI Agent Observability Tools in 2026: How Each Shows a Multi-Step Run

What Are AI Agent Observability Tools?

AI agent observability tools are platforms that store each multi-step agent run as a nested trace of model calls, tool calls and sub-agent handoffs, group the runs of one conversation into a session or thread, and score them with evaluators. An LLM tracing tool is built around the single request; these are built around the run: which sub-agent acted, which tool it called, and what the run cost.

For the concept and what to trace, see the guide to AI agent observability; for span structure and context propagation, the guide to AI agent tracing. Each tool below is checked for:

• Run-level view - a trajectory, transcript, agent graph or timeline that reads a run above the single span.
• Sub-agents and handoffs - whether a delegation shows as its own element in the trace.
• Sessions or threads - grouping of the traces that belong to one conversation.
• MCP and A2A boundaries - what the tool records when a run calls an MCP server or another agent over the Agent2Agent protocol, and whether the far side joins the same trace.
• Agent-level evaluation - evaluators for tool calls, a trajectory, a whole trace or a whole session.
• Cost per run - token cost rolled up from spans to the trace or session.
• Framework coverage - a documented integration for each of eight frameworks: LangGraph, the OpenAI Agents SDK, the Claude Agent SDK, CrewAI, Google ADK, AutoGen, Pydantic AI and the Vercel AI SDK.
• License and hosting - where trace data can live, and on what terms.

The unit these tools have to show is a short run that a person will read. Measuring Agents in Production (arXiv, revised June 2026), built on 20 case studies and a survey of 86 practitioners across 26 domains, reports that 68% of production agents execute at most 10 steps before human intervention and 74% depend primarily on human evaluation.

How These Tools Were Selected and Compared

A tracer is in this list when its own documentation, as read on October 5, 2026, shows all of the following:

• A multi-step run is stored as a nested trace with tool-call spans, which rules out gateways that see model calls alone.
• A run can be read or grouped above the single span, through sessions or threads, an agent graph, or a transcript or trajectory view.
• Evaluation is aimed at agent behavior, meaning tool calls, a trajectory, or a whole trace or session.
• The vendor has not announced maintenance mode, and where the platform code is public, its repository shows a push in the last 90 days.

Eight tracers met every condition and appear in alphabetical order. Their agent features overlap too much for a 1-to-8 ranking to be more than opinion, so the order says nothing about quality. TestMu AI Agent Testing is included under a different rule: it is not a code-level tracer, and it is here for voice and phone agents, where a run is a call.

Helicone and AgentOps are left out. Helicone, an AI gateway and LLM observability platform, announced on March 3, 2026, that its services continue in maintenance mode after its acquisition by Mintlify. AgentOps does not meet the 90-day condition, as the output below shows.

This comparison of AI agent observability tools rests on documentation: no product was run against a common agent, and no prices are quoted, so check each vendor's site for those. MLflow Tracing, W&B Weave, OpenLLMetry and Fiddler AI are in the roundup of AI observability tools, which also covers how each tool collects traces, so they are not repeated here.

Phoenix, Laminar, Langfuse and Opik publish their platform code, so to check their license and activity, along with the two tools left out, a Node.js script was run at 17:10 UTC on October 5, 2026. It sends one unauthenticated request per repository to the GitHub REST API, reads the first line of each license file, and asks the PyPI JSON API for the latest AgentOps release. Its console output, unedited:

GitHub REST API, one unauthenticated request per repository, run at 2026-10-05T17:10:25Z (rate limit remaining: 54)

repository            article   license (GitHub)  archived  last push (UTC)       days ago  stars
Arize-ai/phoenix      in list   NOASSERTION       false     2026-10-05T17:04:54Z  0         11714
lmnr-ai/lmnr          in list   Apache-2.0        false     2026-10-05T16:36:20Z  0         3299
langfuse/langfuse     in list   NOASSERTION       false     2026-10-05T17:07:34Z  0         35403
comet-ml/opik         in list   Apache-2.0        false     2026-10-05T17:07:34Z  0         22387
AgentOps-AI/agentops  left out  MIT               false     2026-06-25T08:25:03Z  102       5886
Helicone/helicone     left out  Apache-2.0        false     2026-09-16T19:29:27Z  18        6199

First non-empty line of each license file (raw.githubusercontent.com, default branch):
Arize-ai/phoenix   LICENSE    Elastic License 2.0 (ELv2)
lmnr-ai/lmnr       LICENSE.md Apache License
langfuse/langfuse  LICENSE    Copyright (c) 2023-2026 ClickHouse, Inc.
comet-ml/opik      LICENSE    Copyright (c) Comet ML, Inc

PyPI JSON API, package agentops: latest version 0.4.21, uploaded 2025-08-29

GitHub's license detector returns NOASSERTION for Phoenix and Langfuse: Phoenix's file is the Elastic License 2.0, and Langfuse's is an MIT license with a carve-out for its ee folders. If you are shortlisting open-source AI agent observability tools, read the license file, since the label on a repository page does not settle it.

In that output, all four listed repositories were pushed on the day of the check. AgentOps's last push, on June 25, 2026, was 102 days earlier, and the latest PyPI release of its Python SDK is dated August 29, 2025. Helicone's last push was 18 days earlier, so its exclusion rests on its own announcement.

The 9 Best AI Agent Observability Tools in 2026

In the AgentPProf paper (arXiv, September 2026), the authors write that existing agent observability tools "focus on per-execution debugging and tracing rather than cross-run, long term profiling". So judge each tool first on how it shows one run, then on how far its sessions and cost roll-up reach beyond it.

Run views, handoffs, sessions and protocol boundaries by tool

Evaluation level, cost roll-up, frameworks and hosting by tool

Every cell reports the vendor's own documentation or product page as it read on October 5, 2026. A capability or framework missing from a cell is one not found in that documentation, which is no statement that the product lacks it, and each vendor documents frameworks beyond these eight.

How to Choose an AI Agent Observability Platform

Whichever tools you shortlist, plan for a person to read the failed run. In When Agentic Executions Fail (arXiv, August 2026), researchers injected ten types of operational fault into five applications that coordinate agents over the Agent2Agent protocol and call tools through the Model Context Protocol. On a dataset of 275 traces, the strongest zero-shot baseline named the fault type from a single trace 24.8% of the time, and naming the type together with its location topped out at 22%.

• Start from your framework - the second table names which of eight agent frameworks each tool documents an integration for. Then read what that integration records: Braintrust's Handoff spans, for one, are documented for the OpenAI Agents SDK.
• Pick the unit you debug - for one run at a time, compare a transcript in Laminar, a trajectory in LangSmith, an agent graph in Langfuse or Opik, and the trace layouts in Braintrust. For whole conversations, compare sessions and threads.
• List every boundary the run crosses - sub-agents, MCP servers and A2A peers. Check whether the tool joins the trace through an integration, as Datadog's MCP integration and Phoenix's MCP instrumentor do, or through code you write, as with Langfuse's _meta propagation.
• Decide the level you need scores at - a tool call, a trace or a session. If scores matter more to you than traces, read the roundup of AI agent evaluation tools as well.
• Settle where trace data is stored - self-hosted under an open license, customer-hosted on a vendor's Enterprise plan, or SaaS. The License and hosting column of the second table shows which tools offer each.

Once a tool is in place, alerting on what it records is the next job, and the guide to AI agent monitoring covers baselines and drift alerts.

Pre-Release Checks for Tool Calls, Handoffs and MCP Servers With Agent Assurance

The eight tracers above show what instrumentation reported. The AgentSight paper (arXiv, August 2025) describes a related gap: its authors write that existing tools observe either an agent's high-level intent, through LLM prompts, or its low-level actions, such as system calls, without correlating the two. In the rebooking run, a trace records that the fare-search tool was called and what it returned; whether the booking system now holds the right flight is a separate check.

That separate check is the job of TestMu AI's Agent Assurance, which tests how your agents actually behave across workflows, tools, and actions. It is not an observability or tracing product: it runs suites before release against an agent you point it at, and sees no live traffic. For the rebooking agent, that means scenarios written from its code or spec, the real agent called in staging, and each acceptance criterion judged on evidence from the run:

• Tool calls - each call the profile hands back is compared with the agent's own tool declarations, and a criterion can state that a tool must never be called.
• Sub-agents and handoffs - the fares sub-agent is reachable only through the planner, so it is tested through the planner, and the handoff is checkable only when the profile returns the calls that show it.
• MCP servers - discovery asks each configured MCP server which tools it really has, and a judge can confirm the rebooked flight through a read-only MCP tool you approve. Only stdio servers connect in the current release.
• Effects - what changed on disk under the declared paths, which artifacts the run left, and whether a record exists, confirmed with a read-only query through a tool you approve.
• Unverified criteria - every criterion is Pass, Fail or Unable to Verify, and Unable to Verify stays out of the pass rate, counted neither as a pass nor as a failure.

Most eval and observability tools score what your agent said and recorded. Agent Assurance checks what the run changed, and reports what it could not verify. It uses model judges too and runs in CI, so the contrast is about evidence: a claimed action never counts as proof.

It runs before release and leaves your production tracer in place. That tracer's instrumentation helps here too: a profile can return the tool calls, token usage and traces it produces, and the more it returns, the fewer criteria come back Unable to Verify.

Agent Assurance is pre-alpha and publicly installable: expect its command surface and file formats to shift from one release to the next. It is driven from the terminal by Rook CLI, which installs on macOS and Linux, and 64-bit Windows through npm or WSL; the npm route below needs Node.js 22 or newer:

npm install -g @testmuai/rook

The skill installer below adds a /rook skill to Claude Code, and the request after it is written for the rebooking agent:

npx @testmuai/rook-skill@latest install --agent claude-code

/rook Use the staging profile for the rebooking agent in this repository. Show me the write tools it declares before any scenario runs. Then propose at most three scenarios in which the planner delegates to the fares sub-agent, and in each one require that every tool call the profile returns matches a tool the agent declares and that a read-only booking lookup shows the rebooked flight.

The booking lookup in that request is a read-only tool you supply and approve; without one, that criterion comes back Unable to Verify. Use a staging booking system: the agent makes its bookings for real during a run, and Agent Assurance cannot undo them.

Note: Check tool calls, handoffs and the effects of a run in staging before release, with unverified criteria reported apart from the pass rate. Get started with Agent Assurance

Conclusion

Start with the last agent run that went wrong and write down what it needed answered: which sub-agent acted, which tool call ran with which arguments, and what the run cost. Then put that run through a trial of each of the AI agent observability tools on your shortlist, and drop any that cannot answer all of it without custom code.

Before the fix ships, run the scenario behind that incident against staging and check the record it was supposed to change. Rook CLI is the terminal surface for that check, and the Agent Assurance results and evidence guide shows how each criterion's verdict and evidence appear in a report, including what could not be verified.