The Best Agent Observability Tools
2026 Market Guide
A criteria-based guide to the agent observability tools, maintained by Prefactor, which builds one of them, with every claim about the other tools checked against their own sites.
How we compared: 7 criteria
- Agent-level tracing: full multi-step trajectories, not just single LLM calls
- Evaluation: quality scoring built in or cleanly integrated
- Cost & token attribution, per agent, per session, over time
- Who can write and tune evaluations: engineers only, or product managers and subject matter experts too
- Ingestion: native framework SDKs, OpenTelemetry, or both
- Framework coverage: LangChain, LangGraph, CrewAI, OpenAI Agents, and others
- Open source / self-hosting option
What matters when evaluating agents in production?
- Evaluating every run. A test set before release shows how the agent handles known cases. Scoring each completed run in production shows how it handles real inputs, which is where most of the useful signal is.
- Judging against the outcome the business wants. Generic metrics such as faithfulness or toxicity say whether an answer is well formed. Criteria drawn from what the business counts as a good outcome say whether the agent did the job.
- Who can write and tune evaluations. The people who know what good looks like are often product managers and subject matter experts. Check whether they can write and adjust evaluations themselves, or whether every change goes through an engineer.
- How results lead to changes in the agent. An evaluation earns its place when it changes the agent. Look at how results reach the people who own the agent, and whether a change can be checked against the same evaluations afterwards.
Agent evaluation tools compared
Updated October 2026. Each value was checked against the tool's own site, and the sources are linked on every row. Prefactor publishes this page and is listed alphabetically with the rest.
| Tool | Best for | How it evaluates | Deployment | Pricing model | Sources |
|---|---|---|---|---|---|
| Arize AX | Improving agents in production (Arize is now part of Dynatrace) | LLM-as-judge, agent-as-judge and code evaluators label production traces continuously; datasets and experiments compare changes; human annotation calibrates scores. | Cloud; self-hosted on Enterprise | Free tier, then monthly plan metered on spans, ingestion and retention | Pricing · Docs |
| Arize Phoenix | Local-first tracing, evaluation and experiments you run yourself | LLM-based response and retrieval evals and code evaluators, run over versioned datasets and experiments. | Self-hosted (Elastic License 2.0, source-available) | Free; Arize AX is the paid hosted option | GitHub · Pricing |
| Braintrust | Experiments and evals for teams improving AI products | Scorers (built-in, LLM-as-judge or custom code) run on datasets as experiments, in CI and on production logs. | Cloud; self-hosted data plane on Enterprise | Free tier, then platform fee plus usage; unlimited users | Pricing · Evaluate docs · Self-hosting |
| DeepEval (Confident AI) | Pytest-style evals in CI, with a hosted platform on top | 50+ metrics, including LLM-as-judge, agent, RAG and multi-turn, run as unit tests; Confident AI adds online evals on traces and human annotation. | DeepEval runs locally (Apache 2.0); Confident AI is cloud, on-premises on Enterprise | DeepEval free; Confident AI free tier, then flat plans plus trace usage | GitHub · Pricing |
| Galileo (Splunk Agent Observability) | Evaluating and monitoring enterprise AI apps and agents, now within Splunk | LLM-as-judge, small Luna evaluation models and code-based metrics, applied in experiments and to production logs. | Cloud (Splunk Observability Cloud) or self-hosted | Free edition, then annual pricing based on span volume | Metrics docs · Acquisition · Pricing |
| Langfuse | Open source tracing and evals you can self-host (now part of ClickHouse) | LLM-as-judge, code evaluators, user feedback and manual labelling on traces, plus datasets for experiments. | Cloud or self-hosted (MIT, apart from licensed add-ons) | Free to self-host; cloud free tier, then monthly plan plus usage units | Pricing · Self-hosting · GitHub |
| LangSmith | Teams building agents, especially on LangChain or LangGraph | Offline experiments on datasets plus online evaluation of production traces, using LLM-as-judge, code rules, pairwise comparison and human review. | Cloud (US or EU); hybrid or self-hosted on Enterprise | Free single-seat plan, then per seat plus pay-as-you-go traces | Pricing · Evaluation docs |
| Opik (Comet) | Open source LLM and agent observability with evaluation | LLM-as-judge and heuristic metrics on datasets, online evaluation rules on production traces, feedback scores and a PyTest integration for CI. | Cloud or self-hosted (Apache 2.0) | Free to self-host; cloud free tier, then flat monthly plans metered on spans | Pricing · GitHub |
| Prefactor | Evaluating every agent run against the company's own knowledge and context | Judges score each completed run against criteria your team writes, or built-in ones such as correctness and hallucination, and record a pass or fail verdict with an explanation on the run. Product managers and subject matter experts can write and tune criteria in the web app, not only engineers. Runtime policies, approval holds and a kill switch can act on what the judges find. | Cloud; self-hosted on Enterprise. Judges can also run in your own infrastructure | Free tier, then usage-based plans metered on spans | Evals docs · Pricing |
| Ragas | Moving from spot checks to systematic LLM and RAG evaluation in code | LLM-based and traditional metrics, custom LLM-judged metrics and synthetic test set generation, run as experiments in code. | Python library you run yourself (Apache 2.0) | Free; no published paid tier | Docs · GitHub |
| W&B Weave | Tracing and evaluating AI agents (Weights & Biases is now part of CoreWeave) | Evaluation pipelines with LLM-judge and custom scorers over datasets; monitors score production traces automatically. | Cloud (CoreWeave-hosted); dedicated or private hosting on Enterprise | Free tier, then plan with seat caps plus ingestion overage | Pricing · Monitors docs · GitHub |
Prefactor
Observability and evaluation of every agent runClosed sourceBest for Seeing every agent run and judging it against what the business needs
Records every run of your agents through native SDKs or OpenTelemetry, with LLM calls, tool calls, inputs, outputs and token usage. Judges then score each completed run against criteria drawn from the company's own knowledge and context, and record a pass or fail verdict with an explanation on the run. Criteria are written in the web app, so product managers and subject matter experts can write and tune evaluations, not only engineers. It can also act on what the judges find, with runtime policies, approval holds and a kill switch for a run, an agent or a team.
Visit Prefactor →Arize Phoenix
Source-available tracing & evaluationPartial OSSBest for OpenTelemetry-native teams who want self-hosted tracing plus evals
A source-available observability and evaluation tool (Elastic License 2.0) built on OpenInference/OpenTelemetry conventions; Dynatrace completed its acquisition of Arize on 1 October 2026. Strong for tracing, RAG analysis, and running evaluations locally, with a hosted option via Arize AX for production scale.
Visit Arize Phoenix →Braintrust
Commercial evaluation-first platformClosed sourceBest for Teams practising eval-driven development and experimentation
An evaluation and experimentation platform that pairs logging with eval harnesses and scoring functions for multi-step tasks. Leans toward the offline experimentation and regression-testing side of the workflow.
Visit Braintrust →Datadog Agent Observability
APM suite add-onClosed sourceBest for Organisations already standardised on Datadog
Datadog Agent Observability (formerly LLM Observability) brings LLM and agent tracing into the broader Datadog APM platform, so AI telemetry sits alongside the rest of your infrastructure monitoring. Best when consolidating tooling matters more than agent-specific depth.
Visit Datadog Agent Observability →Helicone
Proxy-based gateway & tracingOpen sourceBest for Fast, low-code logging via a proxy
An AI gateway and observability tool that captures requests through a proxy, quick to add logging, caching and basic cost tracking. As of 20 October 2026 it has been in maintenance mode since March 2026, with new cloud sign-ups disabled, so it suits existing users more than new deployments. Lighter on multi-step agent trajectories.
Visit Helicone →Langfuse
Open-source LLM engineering platformOpen sourceBest for Teams wanting open-source tracing they can self-host
A widely adopted open-source platform for tracing, prompt versioning, and evaluation, with both cloud and self-hosted deployments; ClickHouse acquired it in January 2026 with no licence changes announced. A common default for teams that want OSS and broad framework coverage.
Visit Langfuse →LangSmith
Commercial tracing & evaluationClosed sourceBest for Teams building on LangChain or LangGraph
LangChain's commercial platform for tracing, evaluation, and prompt engineering. Tightest integration with the LangChain/LangGraph ecosystem, and a strong all-round option for debugging and evals.
Visit LangSmith →Frequently asked questions
What are agent observability tools?
Agent observability tools capture what AI agents actually do in production, every reasoning step, tool call, retrieval and token, plus the quality and cost of the result, and present it per agent, per session and over time. They differ from LLM observability tools, which trace single model calls, and from general application monitoring, which sees a 200 OK but not whether the agent chose the right tool or stayed grounded.
The tools in this guide fall into three groups: open-source tracing platforms you can self-host (Langfuse, Arize Phoenix), commercial suites that pair tracing with evaluation (LangSmith, Braintrust, Datadog Agent Observability, Helicone), and platforms built around evaluating each run against what the business needs, which is where Prefactor sits.
How do I choose an agent observability tool?
Start from the gap you actually have, not the longest feature list. If you have no tracing yet, an open-source platform like Langfuse or Arize Phoenix gets you visibility fast and can be self-hosted. If you live in the LangChain ecosystem, LangSmith's native integration is hard to beat. If you've standardised on Datadog, its Agent Observability keeps AI telemetry next to everything else.
Once tracing exists, the next question is usually whether a specific agent is doing the job: is it improving or regressing, and where does it need to change. That takes evaluation against what the business counts as a good outcome, ideally written by the people who know what good looks like. That is where Prefactor focuses. Many teams run a tracing tool and an evaluation layer together rather than choosing one.
What is the difference between agent observability and LLM observability?
LLM observability traces single model calls: the prompt, completion, tokens, and latency of one request. Agent observability covers the multi-step reality on top: full trajectories, tool calls, retrievals, sub-agents, and the actions an agent takes across a whole session.
Most tools in this guide do both, but they differ in emphasis. Proxy and gateway tools lean toward per-call logging; agent-focused platforms reconstruct the full trajectory so you can attribute a failure to the right step. For the full explanation, see our guide to what agent observability is.
Does the ingestion method (native SDK vs OpenTelemetry) matter?
Yes: it's one of the bigger practical differences. OpenTelemetry gives you a vendor-neutral way to ship spans, but on its own it captures generic GenAI telemetry, not framework-native agent structure (graph nodes, sub-agents, the real tool-call tree) unless you add instrumentation. Native framework SDKs capture that richer structure directly.
The strongest position is both: native SDKs for the frameworks you use, plus OpenTelemetry for the closed tools that can only emit it. That combination is how Prefactor ingests. Check which frameworks each tool has a native SDK for before you choose.
Is observability enough, or do I also need evaluation?
Observability tells you what happened; evaluation tells you whether it was any good. A trace shows that an agent called a tool and returned an answer, but not whether the answer did the job, and an agent soon handles more runs than anyone can read.
Evaluation scores runs against criteria. The useful question is whose criteria: generic metrics catch malformed or ungrounded answers, while criteria drawn from the company's own knowledge and context show whether a well-formed answer was right for the business. Prefactor focuses on the second, and adds the acting half on top: runtime policies, approval holds and a kill switch for a run, an agent or a team.
Are open-source agent observability tools good enough?
For most teams, yes: open-source tools like Langfuse and Arize Phoenix cover tracing and evaluation well, and self-hosting keeps sensitive prompts and data inside your own environment. The trade-offs are the operational cost of running them and, depending on the project, less mature production scoring and alerting.
A common pattern is open-source tracing for breadth plus a specialised layer for evaluating each agent against what the business needs.
See how Prefactor records and evaluates every agent run
Prefactor evaluates agents using your company's own knowledge of what good looks like, so you know they're doing the job and where they need to improve. It records every run, judges score each one against the criteria your team sets, and runtime policies act on what they find.
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