LangSmith Alternatives (2026): 8 LLM Tracing and Eval Tools

Open source or platform: that's the first fork for anyone leaving LangSmith. Self-hosting a tracer keeps prompts, outputs and user data on your own servers, but somebody on your team owns the upgrades. A module inside a bigger monitoring platform saves that work, yet ties your agent traces to a vendor that may treat language models as one feature among many.

The eight alternatives below are listed in no particular order. They're grouped by shape rather than by size, and each one is described from its own website, with its claims labelled as the vendor's own.

Self-host the tracer, or rent it?

Traces from a language-model app aren't like ordinary logs. They hold the user's question, the retrieved documents, the prompt you built and the model's answer, so they often carry the most sensitive data in the whole system. That's the strongest reason teams pick an open-source tool they can run inside their own network.

The case for a hosted platform is just as plain. You don't have to keep a database of spans healthy, you get dashboards on day one, and your on-call engineers already know where to look. Neither choice is wrong. It depends on who'll look after the tool at two in the morning.

Open-source tracers you can run yourself

Arize Phoenix is an open-source tool for tracing, evaluating and debugging agents and LLM applications, and you can run it locally or self-host it. Its site makes a point of running experiments and comparing results in your own environment, which matters if prompts carry customer data.

Opik by Comet presents itself as an end-to-end observability platform for testing, shipping and improving agentic systems. The vendor says it logs every step an agent takes, from the user's message to each tool call, with automated evaluation workflows on top.

Laminar is open source too, and pitched squarely at agents. The vendor says it catches agent failures automatically at scale and captures model calls, tool calls and sub-agents, which is the kind of detail you need when a multi-step run quietly goes wrong.

Tracing inside a wider monitoring or ML platform

Datadog LLM Observability brings offline experiments and production tracing into one platform, and the company says a single trace can connect an AI system from backend services to the end-user experience. It's most useful where the rest of your stack already reports there.

MLflow is an open-source AI platform for agents, LLMs and models, backed by the Linux Foundation. It makes most sense where a team already tracks classic machine learning experiments and wants its language-model work in the same place. Tracing is one part of a much wider tool.

SDKs and monitors built around the app

AgentOps is an SDK and dashboard for tracking model calls, tools and multi-agent interactions from prototype to production. The vendor says logs, errors and prompt injection attempts are kept in one data trail, alongside spend tracking.

Lunary combines observability with prompt management, so you can see how your models perform in real time and how users interact with them. Traceloop is built on OpenTelemetry and ships with OpenLLMetry, an open-source SDK; the vendor frames that as transparency without lock-in, and it turns evals and monitors into a continuous feedback loop. For more options in the same space, see my Langfuse alternatives.

Check how each one hooks into your code

Your current platform comes from the LangChain team. If your agents are built on LangChain, tracing may have arrived with very little work, and that's easy to forget until you try to switch. So before anything else, check how each tool here instruments your stack.

Several tools on this page are built on open standards or open-source SDKs, which makes moving traces in and out much easier. A few are general machine learning or infrastructure monitors that treat language-model tracing as one feature among many. Ask three questions of each one:

• How does it capture a run? A decorator, a callback handler, an OpenTelemetry exporter or a proxy all behave differently once you're in production.
• Can you export what it stores? If traces sit in an open format, a later move is a weekend job rather than a quarter.
• Does it understand agents? A tool built for single prompts may flatten a multi-step run into one long line, which hides the step that failed.

Eight tracing tools at a glance

Tool	Shape	Where it helps
AgentOps	Agent tracing SDK	Tracking multi-agent runs from prototype to production
Arize Phoenix	Open-source LLM tracing	Traces and evals you run locally or self-host
Datadog LLM Observability	Module in a monitoring platform	Teams already monitoring their backend in one platform
Laminar	Open-source agent observability	Catching agent failures across tool calls and sub-agents
Lunary	LLM observability and prompts	Live model performance alongside prompt management
MLflow	Open-source AI platform	Teams running classic ML models and agents together
Opik by Comet	Open-source AI observability	Logging every agent step with automated eval workflows
Traceloop	OpenTelemetry LLM monitoring	Keeping traces on an open standard you can move

Every tracer, A to Z

Listed alphabetically, one line each.

AgentOps

Best for: Developers who want an SDK and dashboard tracking LLM calls, tools and multi-agent interactions, with errors and prompt injection attempts kept in one data trail.

Arize Phoenix

Best for: Teams that want to trace, evaluate and debug agents and LLM apps on their own machines or servers, running experiments in their own environment.

Datadog LLM Observability

Best for: Engineering groups already on Datadog who want production tracing and offline experiments for agents in the same platform as their backend monitoring.

Laminar

Best for: AI developers wanting an open-source way to trace, evaluate and debug agents, with LLM calls, tool calls and sub-agents captured automatically.

Lunary

Best for: Smaller app teams who want observability and prompt management together, with a real-time view of model performance and how users interact with it.

MLflow

Best for: Machine learning teams who want one open-source platform covering models, LLMs and agents, backed by the Linux Foundation.

Opik by Comet

Best for: Builders of agentic systems who want every step logged, from user input to tool calls, with automated evaluation workflows for testing and shipping.

Traceloop

Best for: Engineers who want LLM monitoring built on OpenTelemetry through the open-source OpenLLMetry SDK, with evals and monitors feeding one loop.

My advice for AI teams switching tracers

Start with where your traces are allowed to live. If prompts carry customer data and your security team wants it kept in-house, shortlist the self-hostable tools and stop there. If not, look hard at what you already run: a module in your existing monitoring platform often wins on convenience, even if a specialist tool has sharper agent views.

Then weigh lock-in honestly. A tool built on OpenTelemetry won't be the most polished on day one, but it'll be the easiest to leave. For more options in this group, my guide to the best LLM observability tools goes wider.

Push one failing agent run through two tools

1. Pick a real agent workflow that failed recently, with tool calls and at least one retrieval step.
2. Instrument it with two tools from your shortlist, side by side, on the same code.
3. Replay the failure and time how long it takes each tool to show you the step that broke.
4. Ask the engineer who'll be on call which view they'd trust under pressure.

The one that makes the failed step obvious is the one you'll keep using. Polished dashboards matter far less than that.

Do you make LLM observability or tracing software? Tool makers can ask to be considered for a featured place on this list.

Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.

Frequently asked questions

What does an LLM observability tool do?

It records each run of a language-model app or agent: the input, the prompts, any retrieved data, tool calls and the output. That lets you debug failures, run evaluations and watch quality once real users arrive.

Is open-source tracing good enough for production?

It can be, if someone on the team is happy to run the service, keep its storage healthy and apply updates. The open-source tools here are used for production work, but they need an owner.

Can I keep using LangChain with a different tracer?

Usually, yes. Many tracers offer LangChain integrations or work through OpenTelemetry, but check how deep that support goes for agents and multi-step chains before you commit.

Why does OpenTelemetry matter here?

It's an open standard for traces. Tools that use it make it easier to send the same data to more than one place, or to switch later without re-instrumenting your code.

Do I need evaluations as well as traces?

Traces tell you what happened; evaluations tell you whether it was any good. Most teams end up wanting both, so check whether a tool runs evals on live traffic or only on test sets.

Are these tracing tools ranked?

No. They're in no particular order and grouped by shape, because the right one depends on your stack and where your data can live.