Agents are different from chatbots. They make multi-step decisions, call tools, retrieve data, and chain reasoning over many calls — and any one of those steps can quietly fail in production. Generic LLM monitoring shows you that something went wrong; agent observability shows you which step, why, and what to do about it.
We tested every agent observability tool we could get an account for. Here are the five worth your shortlist.
Eval-driven dev platform combining traces, datasets, scorers, and a playground in one product.
The best agent observability tool we've used, by a clear margin. Complete traces with expandable tree views — inputs, outputs, timing, costs at every decision point — plus the same evaluation scorers running in CI, in the playground, and on production traffic. That continuity is the part that separates real eval-driven development from theater, and Braintrust is the only tool that delivers it cleanly.
Loop turns log analysis into a cross-functional task: PMs ask questions about agent behavior in plain English, generate test datasets from production traces in seconds, and create custom scorers without touching Python. Multi-agent workflows with nested spans, GitHub Actions auto-evaluation on every commit, native integrations with LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, and Vercel AI SDK — the surface area is broad and the depth is real.
Notion went from fixing 3 issues per day to 30 after adopting it. The customer list (Stripe, Vercel, Airtable, Instacart, Zapier) reflects the same outcome at scale. For teams shipping production agents, this is the default pick.
Agent reliability platform with cheap, fast evaluators that can run on every request in production.
The pick for high-volume agents where every request matters. Luna-2 evaluators are cheap enough to score live traffic — not just a sample — which means safety and task-completion checks on every interaction instead of statistical extrapolation from a 10% sample.
Failure clustering with root-cause hints saves real time on triage. Younger product than Braintrust or Langfuse, but the unit economics for online evaluation are uniquely strong.
Visual workflow builder with built-in observability for low-code agent development.
The pick when agent design needs cross-functional input. The visual workflow canvas means PMs and domain experts can read and modify the same agent engineers build, with online evals running against the same graph used to design it.
Engineers who want pure code-first agents will find the visual model fights them. But for orgs where agent quality lives partly outside engineering, Vellum is the most coherent answer we've seen.
Enterprise ML governance platform extended to LLMs and generative AI, with audit-ready traces and in-environment evaluations.
The pick for regulated industries. Hierarchical traces, in-environment evaluation execution, audit-ready trace lineage, and SOC 2 compliance — built for finance, healthcare, and government AI teams that need governance, not just dashboards.
Real enterprise software with a real enterprise setup curve. Overkill outside compliance-driven use cases, but indispensable inside them.
Proxy-based LLM observability — drop in by changing the base URL, no SDK changes needed.
The pick for the entry point. Proxy-based, zero-code integration, multi-provider — gets you from zero observability to real cost and request data in minutes. For agent-specific debugging it's not the answer; the proxy can't see reasoning steps or tool calls without additional instrumentation.
We'd start here on day one of an agent project, then layer Braintrust, Langfuse, or Galileo on top once tracing depth becomes the bottleneck.
What "agent observability" should actually do
Four pieces, in our view:
- Tracing — the full path from user input through every reasoning step, tool call, and external API hit, with timing.
- Logging — the literal prompts, responses, tool inputs/outputs, and errors at each step.
- Metrics — latency, token use, cost per request, error rate, success rate by task type.
- Evaluation — automated quality scoring on traces, ideally with the same scorers used in CI.
Most of the tools in this list cover three of those four. The ones that cover all four — and connect them — are the ones we'd recommend for a team building agents that have to actually work in production.
How to choose
- Default answer: Braintrust. If your team is shipping a production agent and you don't have a hard requirement that disqualifies it, this is the pick.
- Hard requirement to self-host? Langfuse (not on this list because it leans observability-first rather than agent-specific, but the right answer if OSS is non-negotiable).
- High request volume needing online eval on every request? Galileo, layered with Braintrust for the dev-time loop.
- PMs and domain experts deeply involved in agent design? Vellum.
- Regulated industry needing audit trails and in-environment evaluation? Fiddler.
- Day one, just want any visibility? Helicone, then graduate within weeks.
The biggest risk in this category isn't picking the wrong tool — it's not adopting one until after a customer-visible failure. By the time you need an agent observability tool, you've usually already needed it for two months.