12 Best AI Agent Observability Tools in 2026 (Tracing, Evals, Cost Tracking)

12 Best AI Agent Observability Tools in 2026 (Tracing, Evals, Cost Tracking)

The best AI agent observability tools in 2026 are Langfuse (open-source, best for LLM traces) and Arize Phoenix (best for evals). AI agent observability is now a core operations requirement, not a nice-to-have. Once an agent runs in production, it makes dozens of model calls, retrieval steps, and tool invocations per request. When something breaks, slows down, or quietly burns budget, you need to see exactly which step failed and why. The tools in this guide give you that visibility.

This is an ops-buyer guide. It compares twelve real platforms used by engineering teams in 2026 to trace agent runs, evaluate output quality, and track token cost. Each one solves the same core problem from a different angle, so the right pick depends on whether you self-host, who owns evaluation, and how much existing infrastructure you want to reuse.

We focus on three things ops teams actually pay for: distributed tracing of every agent step, evaluations that catch quality regressions, and cost tracking that attributes spend to models, users, and features. For each tool we cover what it tracks, who it fits, and whether it is open-source or SaaS.

How we ranked them: tracing depth counts most, then evals, then cost precision, then how easy it is to self-host. Every claim comes from the vendor's own docs, product page or GitHub README, and the four tools added in the September 2026 update (Opik, Pydantic Logfire, W&B Weave, AgentOps) were checked on September 26, 2026. No hands-on testing claimed for this list.

If you run agents at scale and want help wiring observability into a production stack, our team builds and maintains these pipelines through automation maintenance and support.

TL;DR

• Langfuse and Arize Phoenix lead the open-source field for self-hosted tracing plus evals, both built on OpenTelemetry.
• Helicone is the fastest path to LLM cost tracking, since it sits as a proxy and needs almost no code change.
• LangSmith and Braintrust are the strongest commercial platforms when evaluation quality drives your roadmap.
• Traceloop OpenLLMetry and OpenLIT are open standards that pipe agent telemetry into tools you already run, like Datadog or Grafana.
• Datadog LLM Observability fits teams that want agent monitoring inside an existing enterprise observability platform.
• Opik from Comet is the open-source pick with the most built-in eval metrics (30-plus) and guardrails in the same tool.
• Pydantic Logfire suits Python teams that want agent traces next to API and database spans, all queryable in SQL.
• W&B Weave fits ML teams already on Weights & Biases, and AgentOps focuses on replaying multi-agent runs in frameworks like CrewAI.
• Pick on three axes: open-source versus SaaS, depth of evals, and how precise the cost attribution needs to be.

What is AI agent observability?

AI agent observability is the practice of capturing, storing, and analyzing every step an AI agent takes so you can debug failures, measure quality, and control cost. It extends classic application observability (logs, metrics, traces) to the specifics of large language models and multi-step agents.

A single agent request can fan out into many operations: a planning call, several tool calls, retrieval from a vector database, and a final synthesis call. Observability tooling records each of these as a span inside one trace, so you can replay the full run and see inputs, outputs, latency, and token counts at every node.

Most modern tools build on OpenTelemetry, the vendor-neutral telemetry standard. That matters because it lets you instrument once and send the same data to different backends, which protects you from lock-in. Many platforms add an LLM-specific layer (often called OpenInference or OpenLLMetry) that knows how to capture prompts, completions, and model metadata.

Why it matters (cost, latency, quality)

Three failure modes push teams to adopt observability. The first is cost. Token spend scales with usage, and a single inefficient prompt or runaway agent loop can multiply a bill overnight. Cost tracking attributes spend to specific models, users, and features so you can find and fix the expensive paths.

The second is latency. Agents chain calls, so total response time is the sum of every step. Without tracing, a slow retrieval or a redundant model call hides inside an opaque request. Span-level timing shows you exactly where the seconds go.

The third is quality. Models drift, prompts regress, and edge cases slip through. Evaluations score outputs against datasets or LLM-as-judge rubrics, both offline before you ship and online in production. This is the difference between guessing your agent works and proving it. If quality and reliability are your priority, our AI agent development practice bakes evals in from the first sprint.

1. Langfuse

Langfuse is the most widely adopted open-source LLM engineering platform, and it has become the default choice for teams that want self-hosted observability with a full feature set. Its core is MIT-licensed and covers end-to-end tracing, prompt management, evaluations, datasets, and a playground.

What it tracks: full agent traces with nested spans, token usage and cost per call, prompt versions, dataset-based and LLM-as-judge evals, and user-level metrics. It integrates with OpenTelemetry, LangChain, the OpenAI SDK, and LiteLLM.

Best for: teams that want an all-in-one, self-hostable platform and value owning their data. It scales from a local Docker setup to large production deployments.

Open-source vs SaaS: both. The core is open-source and free to self-host, with a managed cloud tier for teams that prefer not to run infrastructure.

2. Helicone

Helicone is an open-source observability platform built around a proxy model, which makes it the fastest tool here to switch on. You change a base URL or add one line, and every request flows through Helicone with no further instrumentation.

What it tracks: every request automatically, with detailed cost tracking across providers, latency, usage trends, top models, and per-user analytics. Its model registry prices 300-plus models, so cost numbers are precise rather than estimated.

Best for: teams whose first priority is LLM cost tracking and request logging with minimal engineering effort. The proxy approach suits products that route through many providers.

Open-source vs SaaS: both. The platform is open-source and self-hostable, with a hosted cloud option.

3. Arize Phoenix

Arize Phoenix is an open-source AI observability and evaluation tool that runs entirely on your machine with a single function call, with no API keys or cloud account required. It captures traces through OpenTelemetry and OpenInference auto-instrumentation.

What it tracks: distributed traces of every LLM call, retrieval, and agent step, plus LLM-based evaluations through its phoenix.evals library. It adds dataset management, RAG-specific metrics, and embeddings analysis for deeper retrieval debugging.

Best for: teams that want LangSmith-level tracing and evals without sending data to a third party, and engineers who do heavy RAG work.

Open-source vs SaaS: open-source for Phoenix itself, with a separate commercial Arize platform for enterprise-scale monitoring.

4. LangSmith

LangSmith is the commercial observability and agent engineering platform from the LangChain team, though it is framework-agnostic and works without LangChain. It is built to take an agent from prototype to production with strong evaluation tooling.

What it tracks: every step of an agent run, production-wide performance metrics, and a unified cost view across full agent workflows. Its evaluation suite ships 30-plus templates covering safety, response quality, trajectory, and multimodal checks, usable for both offline experiments and online monitoring.

Best for: teams that want a polished, managed platform where evaluation is a first-class workflow, and especially teams already in the LangChain ecosystem.

Open-source vs SaaS: primarily SaaS, hosted at the LangSmith cloud with enterprise self-hosting available.

5. Braintrust

Braintrust is a commercial platform that tightly couples observability and evaluations in one workflow, positioning itself as the quality layer for production AI. It is used by teams at companies like Stripe, Vercel, and Notion.

What it tracks: production traces, output quality through custom and built-in scorers, and prompt and model iteration. Its assistant, Loop, analyzes production traces in natural language, generates eval datasets, and recommends scorers. A purpose-built data store keeps queries fast at scale.

Best for: teams where rigorous, continuous evaluation drives the roadmap and where engineers and domain experts collaborate on quality.

Open-source vs SaaS: SaaS, with SDK, OpenTelemetry, and proxy integration methods for flexible onboarding.

6. Opik

Opik is an open-source platform from Comet for tracing, evaluating and monitoring LLM apps and agents. It is Apache 2.0 licensed, and the full platform is free to self-host.

What it tracks: traces of LLM calls, conversations and agent activity, plus token usage and model cost. Its eval library ships 30-plus metrics, among them answer relevance, context precision, hallucination and task completion, alongside LLM-as-judge metrics, datasets and experiments. Online evaluation rules score live traffic, guardrails block PII exposure and policy violations, and a PyTest integration runs evals in CI.

Best for: teams that want Langfuse-style self-hosting with more ready-made eval metrics and guardrails in the same tool. The README lists 60-plus framework integrations, including LangChain, LlamaIndex, CrewAI, AutoGen and Google ADK.

Drawback: the feature list is wide (tracing, evals, guardrails, an agent optimizer SDK and an AI debugging assistant), so a small team that only wants request logs gets more product than it needs. Skip it if your only goal is a quick cost number, where Helicone is lighter.

Open-source vs SaaS: both. Self-host with Docker Compose or Kubernetes and Helm, or use the managed Comet cloud, which has a free tier with no credit card.

7. Traceloop OpenLLMetry

Traceloop OpenLLMetry is an open-source SDK that extends OpenTelemetry to LLMs, maintained by Traceloop. Rather than being a destination, it is the instrumentation layer that produces standardized agent telemetry.

What it tracks: LLM-specific data on top of standard OpenTelemetry traces and metrics, including model name and version, prompt and completion tokens, temperature, latency, and errors. It ships instrumentations for OpenAI, Anthropic, Cohere, vector databases like Pinecone, and frameworks like LangChain and Haystack.

Best for: teams that already run an observability backend and want to add LLM visibility without adopting a new platform. Because it uses the OpenTelemetry protocol, you can pipe data into Datadog, New Relic, Sentry, or Honeycomb.

Open-source vs SaaS: open-source under Apache 2.0, with a commercial Traceloop platform for managed reliability.

8. Pydantic Logfire

Pydantic Logfire is an OpenTelemetry-native observability platform from the team behind Pydantic. It covers the whole application, so model calls, agent steps, API requests and database queries land in one trace.

What it tracks: LLM and agent spans including tool calls, retrieval and reasoning steps, with token usage and cost wherever the framework's telemetry reports them. Evals combine programmatic checks, LLM judges and human review attached to traces. Every span is queryable with Postgres-flavored SQL in the UI or over MCP. SDKs exist for Python, JavaScript/TypeScript and Rust, and other languages send data through standard OpenTelemetry.

Best for: Python teams, especially on Pydantic AI or FastAPI, who want agent traces next to the rest of their backend telemetry. Pricing checked September 26, 2026: 10 million logs, spans and metrics a month free, Team at $49 a month, Growth at $249 a month, and $2 per extra million records.

Drawback: the SDKs are MIT licensed but the platform (UI and backend) is closed source, and self-hosting is only on the Enterprise plan. Skip it if you need a fully open-source, self-hosted stack.

Open-source vs SaaS: SaaS, with open-source SDKs that can also export to any OpenTelemetry-compatible backend.

9. Datadog LLM Observability

Datadog LLM Observability brings agent monitoring into the established Datadog platform, which suits organizations already standardized on Datadog for infrastructure and APM. It is generally available and has expanded to cover agentic workflows.

What it tracks: end-to-end agent and LLM traces, operational performance, and evaluations for quality, privacy, and safety. Because it lives inside Datadog, you correlate agent behavior with the rest of your system metrics, logs, and APM data in one place.

Best for: enterprise teams that want LLM observability unified with existing monitoring, governance, and security tooling rather than a separate point solution.

Open-source vs SaaS: SaaS, as part of the broader Datadog platform.

10. W&B Weave

W&B Weave is the observability and evaluation platform from Weights & Biases for agents and LLM applications. It ships Python and TypeScript libraries and an OpenTelemetry-compatible SDK.

What it tracks: traces of LLM calls and any function you instrument, with versioning. For supported integrations such as OpenAI, Anthropic, Cohere and Mistral it records token usage and calculates cost per call automatically. For fine-tuned or self-hosted models you register prices with add_cost(). Evaluations run with LLM judges and custom scorers.

Best for: ML teams that already use Weights & Biases for training experiments and want LLM app traces and evals in the same workspace.

Drawback: cost tracking for models outside the supported integrations needs manual add_cost() setup. Skip it if you have no Weights & Biases footprint and want a self-hosted open-source platform, where Langfuse or Opik fit better.

Open-source vs SaaS: the Weave library is open-source under Apache 2.0 and logs to the Weights & Biases platform.

11. OpenLIT

OpenLIT is an open-source, OpenTelemetry-native AI engineering platform that auto-instruments your stack with a single line of code. It positions itself as a unified observability layer that also handles GPU monitoring, guardrails, and prompt management.

What it tracks: distributed traces, token usage, response times, and request flows in real time, plus online and offline evaluations through its UI and SDKs. It auto-instruments 50-plus LLM providers, vector databases, and agent frameworks.

Best for: teams that want an open standard, broad provider coverage, and extras like GPU monitoring in one self-hosted package.

Open-source vs SaaS: open-source and free to self-host, built on OpenTelemetry so its data flows into compatible backends.

12. AgentOps

AgentOps is a developer platform built around agent sessions rather than single LLM calls. You install it with pip install agentops, and its main feature is replaying whole agent runs.

What it tracks: step-by-step execution graphs with "time travel" debugging to rewind and replay a run, LLM calls, tool calls and multi-agent interactions, token spend with model providers, and a log of errors and prompt injection attempts. Native integrations cover CrewAI, AG2 (AutoGen), Agno, LangGraph and the OpenAI Agents SDK.

Best for: teams building multi-agent systems on CrewAI or AutoGen who need to see why a run went sideways. Pricing checked September 26, 2026: free up to 5,000 events a month, Pro from $40 a month, and custom Enterprise plans.

Drawback: the product page and README we checked list no built-in evaluation suite or datasets. Skip it if offline evals and regression datasets are your main need, where Braintrust, LangSmith or Opik go deeper.

Open-source vs SaaS: both. The AgentOps app (dashboard and API backend) is open-source under MIT and can run on your own machine, with a hosted cloud option.

Comparison table

If you are setting up production observability for an AI agent system, our AI agent development team includes Langfuse or Helicone integration as a standard part of every production build. Book a free architecture review to see how we structure monitoring and alerting.

Market update: Cisco's acquisition of Galileo

The observability market consolidated further in 2026. Cisco announced its intent to acquire Galileo, an AI agent observability and evaluation platform, on April 9, 2026, and closed the deal on May 22, 2026 (Cisco Blogs). Galileo's tracing, guardrails, and eval tooling are being folded into Splunk Observability Cloud's existing AI Agent Monitoring capabilities, giving Cisco a single instrumentation layer across the full agent development lifecycle.

For buyers, the practical read is this: agent observability is no longer a category of standalone startups only. Established infrastructure vendors are acquiring their way in, which means teams already standardized on Cisco or Splunk will see native agent tracing and evals show up inside tools they already run, not just as a new line item to procure. It does not change the buyer framework above (self-host versus SaaS, eval ownership, cost precision, existing infrastructure), but it does add "does our existing observability vendor already offer this" as a question worth asking before adding a new tool.

How to choose

Start with one question: do you need to self-host? If data residency, privacy, or budget rules out sending traces to a vendor, focus on Langfuse, Opik, Arize Phoenix, or OpenLIT. All four are open-source and free to self-host. If your agents lean on retrieval, pair that choice with a solid RAG pipeline architecture so the traces show clean retrieval spans.

Next, decide who owns evaluation. If continuous, rigorous quality measurement drives your roadmap and non-engineers help define scorers, Braintrust or LangSmith give you the deepest eval workflows. If you want strong evals but prefer to self-host, Opik, Phoenix and Langfuse are the closest open-source match. Our AI agent evals guide covers how to write the scorers these tools run.

Then weigh cost precision. If your immediate pain is an unpredictable token bill, Helicone gets you accurate per-model, per-user cost tracking with almost no code. Its proxy model is the lowest-effort way to start measuring spend across providers.

Finally, consider existing infrastructure. If you already run Datadog, Datadog LLM Observability keeps everything in one pane. If you run another backend, Traceloop OpenLLMetry or OpenLIT instrument your agents and ship the data wherever you already look. Many teams pair an open standard like OpenLLMetry with a backend of choice for maximum flexibility.

Three narrower fits round out the list. Pydantic Logfire makes sense when your agents live inside a Python backend and you want agent spans, API calls and database queries in one trace. W&B Weave makes sense when your ML team already works in Weights & Biases. AgentOps makes sense when you build on CrewAI or AutoGen and the hardest question is why a multi-agent run went off course.

A practical pattern: instrument with an OpenTelemetry-based layer, route traces to a self-hosted platform like Langfuse or Phoenix, and add a proxy like Helicone if cost attribution is the priority. Before go-live, run the AI agent production readiness audit checklist to confirm tracing and alerting actually cover every step. If you need help designing and running that stack, our MCP server development services and engineer placement options put production-grade observability expertise on your team.

For the exact process we run in production, see our AI agent deployment workflow: steps, tools, and when not to use it.

Ready to ship an observable, production-ready AI agent? Our AI agent development service covers framework selection, observability tooling, and ongoing support through our automation maintenance service. Book a free scoping call.

FAQ

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

LLM observability tracks individual model calls, while AI agent observability tracks full multi-step runs. Agents chain planning calls, tool calls, and retrieval, so agent observability captures the whole trace and the relationships between steps, not one prompt and completion alone.

Which AI agent observability tool is best for cost tracking?

Helicone is the strongest pure cost-tracking option because its proxy model captures every request automatically and prices 300-plus models precisely. Langfuse and OpenLIT also track cost well if you prefer a self-hosted, full-platform approach.

Are open-source observability tools good enough for production?

Yes. Langfuse, Arize Phoenix, and OpenLIT all run in production at scale and offer tracing, evals, and cost tracking. Open-source tools give you data ownership and no per-seat lock-in, at the cost of running and maintaining the infrastructure yourself.

What is the best open-source AI agent observability tool?

Langfuse is the broadest all-in-one open-source option, with an MIT core covering tracing, prompts and evals. Opik (Apache 2.0) fits if you want 30-plus built-in eval metrics and guardrails. Arize Phoenix fits RAG-heavy work and runs locally with no account. All three are free to self-host.

Which observability tool works best for CrewAI or multi-agent systems?

AgentOps is built around agent sessions, with native integrations for CrewAI, AG2 (AutoGen), LangGraph and the OpenAI Agents SDK, plus step-by-step replay of each run. If you also need deep evals next to the traces, Opik lists CrewAI and AutoGen among its 60-plus framework integrations.

How much do AI agent observability tools cost?

Several start free. Pydantic Logfire includes 10 million records a month free, then Team costs $49 a month. AgentOps is free up to 5,000 events a month, with Pro from $40 a month. Langfuse, Phoenix, Opik and OpenLIT are free to self-host, so your main cost is infrastructure. Prices checked September 26, 2026.

Do I need OpenTelemetry for AI agent observability?

You do not strictly need it, but it helps. OpenTelemetry is a vendor-neutral standard, so instrumenting with it (often through OpenLLMetry or OpenInference) lets you switch or combine backends without re-instrumenting your code. Most leading tools in 2026 support it.

What are evals in the context of agent observability?

Evals are automated scores that measure output quality against a dataset or an LLM-as-judge rubric. You run them offline before shipping to catch regressions and online in production to monitor live quality. Braintrust and LangSmith offer the deepest eval workflows.

Can I use more than one observability tool together?

Yes, and many teams do. A common setup pairs an OpenTelemetry instrumentation layer like Traceloop OpenLLMetry with a backend such as Langfuse or Datadog, then adds Helicone as a proxy for precise cost attribution. The standards-based approach makes combining tools straightforward.

How much engineering effort does it take to add observability?

It ranges from one line to a short integration. Proxy tools like Helicone and auto-instrumenting platforms like OpenLIT need almost no code. SDK-based platforms like Langfuse, Phoenix, and Braintrust take a bit more setup but give you finer control over what you capture.

For the Claude API integration that most of these tools observe, the Claude Fable 5 API tutorial covers streaming, tool use, and prompt caching setup.

Sources: Langfuse, Helicone, Arize Phoenix, LangSmith, Braintrust, Traceloop OpenLLMetry, Datadog LLM Observability, OpenLIT, Opik, Pydantic Logfire, W&B Weave, AgentOps