The Best Agent Observability Tools — 2026 Market Guide
A vendor-led, criteria-based guide to the serious agent observability tools — maintained by Prefactor and refreshed monthly, with a candid view of where Prefactor leads and where others are the better fit.
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
- Enforcement — acting on agent issues at runtime (guardrails, policy, controls), not just observing and evaluating them
- Ingestion — native framework SDKs, OpenTelemetry, or both
- Framework coverage — LangChain, LangGraph, CrewAI, OpenAI Agents, and others
- Open source / self-hosting option
| Tool | Category | Best for | Open source |
|---|---|---|---|
| Prefactor | Agent-quality & risk layer on your existing traces | Continuous agent-level evaluation, cost tracking, and runtime risk management in production | No |
| Arize Phoenix | Open-source tracing & evaluation | OpenTelemetry-native teams who want OSS tracing plus evals | Yes |
| Braintrust | Commercial evaluation-first platform | Teams practising eval-driven development and experimentation | No |
| Datadog LLM Observability | APM suite add-on | Organisations already standardised on Datadog | No |
| Helicone | Proxy-based gateway & tracing | Fast, low-code logging via a proxy | Partial |
| Langfuse | Open-source LLM engineering platform | Teams wanting open-source tracing they can self-host | Yes |
| LangSmith | Commercial tracing & evaluation | Teams building on LangChain or LangGraph | No |
Prefactor
Agent-quality & risk layer on your existing tracesWorks on top of the traces you already collect to add a per-agent view: continuous evals on sampled production traffic, cost attribution, and quality trends across sessions, sub-agents, and tool calls. The difference from measurement-only tools: Prefactor doesn't just observe and evaluate — it enforces, with runtime guardrails, policy, and controls that catch and contain issues rather than just charting them. Works across any framework via native SDKs and OpenTelemetry.
Visit Prefactor →Arize Phoenix
Open-source tracing & evaluation Open sourceBest for OpenTelemetry-native teams who want OSS tracing plus evals
An open-source observability and evaluation library built on OpenInference/OpenTelemetry conventions. Strong for tracing, RAG analysis, and running evaluations locally, with a hosted option via Arize AX for production scale.
Phoenix is OSS tracing and eval you run yourself. Prefactor works on top of traces like Phoenix's — adding continuous agent-level evals, per-agent cost, and, unlike measurement-only tools, runtime risk management that acts on issues rather than just surfacing them — with native SDKs plus OTel.
Braintrust
Commercial evaluation-first platform Closed 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.
Braintrust is strongest offline, for experiments and regression tests. Prefactor focuses on production — scoring live traffic, tracking per-agent quality and cost, and managing risk at runtime — and complements an offline harness.
Datadog LLM Observability
APM suite add-on Closed sourceBest for Organisations already standardised on Datadog
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.
Datadog fits if you're consolidating on its APM. Prefactor is agent-native — agent-level evals, sub-agent and tool-call fidelity, cost per agent, and runtime risk management — rather than LLM telemetry inside a broader infrastructure suite.
Helicone
Proxy-based gateway & tracing Partial OSSBest for Fast, low-code logging via a proxy
An AI gateway and observability tool that captures requests through a low-latency proxy, making it quick to add logging, caching, and basic cost tracking with minimal code changes. Lighter on multi-step agent trajectories.
Helicone is quick proxy-based per-call logging. Prefactor reconstructs full multi-step agent trajectories, scores agent quality, and manages risk at runtime — well beyond logging individual calls at the gateway — via native SDKs and OTel.
Langfuse
Open-source LLM engineering platform Open sourceBest for Teams wanting open-source tracing they can self-host
A widely adopted open-source platform for tracing, prompt management, and evaluation, with both cloud and self-hosted deployments. A common default for teams that want OSS and broad framework coverage.
Langfuse is popular OSS tracing you can self-host. Prefactor layers continuous agent-level evals, cost attribution, and runtime risk management on top of the traces you already collect — including Langfuse's — so you add the agent-quality and risk view without switching stacks.
LangSmith
Commercial tracing & evaluation Closed 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.
LangSmith is the tightest fit if you're all-in on LangChain/LangGraph. Prefactor is framework-agnostic — native SDKs plus OTel across LangGraph, CrewAI, OpenAI Agents and more — and centres on production agent quality, cost, and runtime risk management.
Frequently asked questions
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 LLM Observability keeps AI telemetry next to everything else.
The gap most teams hit once tracing exists is the agent-level question — is this specific agent improving or regressing, what is it costing, and what do we do when it misbehaves — across sessions, sub-agents, and tool calls. That spans evaluation, cost, and risk management on top of traces, which is where Prefactor focuses. Many teams run a tracing tool and an agent-quality-and-risk 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 OTel for the closed tools that can only emit OpenTelemetry. That combination — which is how Prefactor ingests — is why the ingestion column is in the comparison above.
Is observability enough, or do I also need risk management?
Observability tells you what happened; risk management does something about it. Tracing and evals surface that an agent called the wrong tool, leaked context, or ran up cost — but on their own they don't stop it next time. Risk management adds the runtime side: guardrails, policy, and controls that catch and contain issues as they happen.
Most tools in this guide are measurement-only by design. Prefactor adds the acting half on top of the measuring half, which is the main reason it sits in a different category from pure observability or eval tools.
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, risk controls, and alerting.
A common pattern is open-source tracing for breadth plus a specialised layer for the agent-level evaluation, cost, and risk management that open-source projects cover less deeply.
See how Prefactor adds agent-level evals and risk management to the traces you already collect
Prefactor helps teams observe, evaluate, and improve their AI agents in production — across every framework and provider.
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