LangSmith vs. Opik: The Open-Source Alternative for AI Agent Observability and LLM Evaluation in 2026
Last updated: 2026-07-30
Both tools trace, evaluate, and monitor LLM and agent applications. Opik is an open-source LLM and agent evaluation and observability platform built by Comet. LangSmith is the proprietary platform built by LangChain. The practical difference is where your data lives and what it costs to grow: Opik is Apache-2.0 with a free, full-feature self-host, while LangSmith reserves self-hosting for its Enterprise tier. If your stack is built on LangChain and LangGraph, LangSmith’s native path is genuinely the smoothest. If you want to own your traces or stay framework-neutral, Opik is built for that.
The short answer: Choose Opik if you want to run evaluation and observability on your own infrastructure without an Enterprise contract, or if your agents are not all built on LangChain. Choose LangSmith if you are fully committed to the LangChain ecosystem and want the tightest native integration available.
What are the best alternatives to LangSmith in 2026?
The four platforms teams evaluate most often against LangSmith are Opik, Langfuse, Weights & Biases Weave, and Arize Phoenix. Listed with public GitHub footprint as of 2026-07-30, which is a reasonable proxy for how many teams can inspect and run the code themselves:
- Opik, 21,200 stars. Apache-2.0, free full-feature self-host, framework-neutral.
- Langfuse, 32,177 stars. Strong tracing and prompt management, open source.
- Arize Phoenix, 10,816 stars. Open source, strongest on model and drift monitoring heritage.
- Weights & Biases Weave, 1,109 stars. The Weave library is Apache-2.0; running the platform yourself goes through W&B Server as a self-managed or Dedicated Cloud deployment, with an enterprise licence for the security and enterprise capabilities.
If your requirement is running entirely inside your own infrastructure without an enterprise agreement, the shortlist starts with Opik and Langfuse. If you need a managed service and have no data-residency requirement, LangSmith itself remains a reasonable choice. We keep comparisons against each of these tools if you want the head-to-head detail.
What is the difference between LangSmith and Opik?
LangSmith is a proprietary observability and evaluation platform built by LangChain, with its deepest integration into LangChain and LangGraph. Opik is an open-source alternative under Apache-2.0, built by Comet, designed to be framework-neutral and fully self-hostable at no cost. Both cover tracing, evaluation, and production monitoring, which is the standard scope of LLM observability.
How do LangSmith and Opik compare, feature by feature?
They match on the core jobs and diverge on ownership. Both trace, evaluate offline and online, and support human annotation. Opik is Apache-2.0 with free full-feature self-hosting, is framework-neutral, and ships autonomous prompt optimization. LangSmith is proprietary, reserves self-hosting for Enterprise, and integrates most deeply with LangChain and LangGraph.
Rows are marked from each vendor’s own public documentation as of August 2026. Where a capability is not documented, the table says so rather than claiming it does not exist.
Opik vs. Langsmith Feature Comparison
Opik and LangSmith are platforms used for LLM evaluation, LLM observability, and LLM monitoring of AI applications. Opik is designed as a framework-agnostic, open-source platform that emphasizes flexibility, advanced evaluation, and production-readiness. LangSmith shines in its native integration with the LangChain ecosystem, offering a smooth experience when paired with LangChain and OpenAI tools.
| Feature | Opik (by Comet) | LangSmith (by LangChain) |
|---|---|---|
| License | Open source, Apache-2.0 | Proprietary |
| Self-hosting | Free, full observability & evals feature set | Enterprise tier only |
| Tracing (traces and spans) |  Yes | Yes |
| Framework coverage | Framework-neutral | Broad: OpenAI, Anthropic, CrewAI, Vercel AI SDK, Pydantic AI, and deepest with LangChain and LangGraph |
| Offline evaluation on datasets | Yes | Yes |
| Online / production evaluators | Yes | Yes |
| Human review and annotation | Yes, Annotation Queues with claim-and-lock | Yes, annotation queues and inline annotation |
| Heuristic metrics | Yes, 21 metrics including Equals, Contains, RegexMatch, IsJson, Levenshtein | Yes, via the open-source openevals package: exact match, Levenshtein, JSON match, embedding similarity, plus code type-checking and sandboxed execution evaluators |
| LLM-as-a-Judge metrics | Yes, 22 metrics including Hallucination, Moderation, AnswerRelevance, ContextRecall, ContextPrecision | Yes, LLM-as-a-Judge evaluators |
| Automated prompt optimization | Yes, six algorithms under one API via the Agent Optimizer (GEPA, Evolutionary, Few-Shot Bayesian, MetaPrompt, HRPO, Parameter Optimization) | Human-in-the-loop optimization is documented (feedback curated into few-shot examples). Promptim, LangChain’s autonomous optimizer, is a separate library labelled experimental |
| Thread-level evaluation | Yes | Not documented |
| Guardrails | Yes | Not documented |
| Free tier seats | Up to 10 | 1 |
| Entry paid tier | $19 per month | $39 per seat per month |
Two rows carry a caveat rather than a checkmark. LangSmith’s documentation does not describe thread-level evaluation or guardrails. “Not documented” is not the same as “does not exist,” so the table says so rather than asserting an absence we cannot verify. If LangSmith ships either, this table gets updated.
One clarification on prompt optimization, because the distinction is easy to get wrong. LangSmith does document optimization, and it is human-in-the-loop: feedback and corrections are routed into datasets and curated into few-shot examples. What it does not do in the documented product is autonomously search prompt variants against a fitness function. LangChain ships Promptim for that, as a separate library it labels experimental. The difference is about autonomy, not about whether LangSmith optimizes prompts at all.
Can you self-host LangSmith or Opik?
Both, but on very different terms. Opik is Apache-2.0 and self-hosts free with the full feature set, so your traces never leave your infrastructure and there is no license conversation. LangSmith offers cloud, hybrid, and self-hosted deployment, but self-hosting is available only on its Enterprise plan. For a team that needs data residency before it needs an enterprise contract, that is the deciding difference.
Is Opik a good open-source alternative to LangSmith?
Yes, and specifically for teams whose reason for looking is data control or framework independence. Opik covers the same core jobs, tracing, evaluation, and production monitoring, under an open licence you can inspect and run yourself. The trade-off is LangChain-native convenience: if every agent you build is LangGraph, LangSmith’s zero-config path is smoother.
Which has better evaluation?
They are closer than the category noise suggests. Both run offline evaluation against datasets and online evaluators against production traffic, and both ship heuristic and LLM-as-a-Judge metrics: Opik’s are built in, LangSmith’s come through the open-source openevals package. The clearest difference is autonomous prompt optimization. Opik ships six algorithms behind one API as part of the product, while LangSmith’s documented optimization is human-in-the-loop, curating your feedback into few-shot examples, with autonomous search living in Promptim, a separate library LangChain labels experimental. Going the other way, openevals covers code type-checking and sandboxed execution, which Opik does not, so teams evaluating generated code should look at both. LangSmith’s evaluation is mature and well documented, and its annotation tooling is genuinely good.
How do LangSmith and Opik pricing compare?
Opik’s free cloud tier is $0 for 25k spans per month with up to 10 team members, and Pro Cloud is $19 per month flat for 100k spans and up to 50 members. LangSmith’s Developer tier is $0 for up to 5k base traces per month and 1 seat, and Plus is $39 per seat per month for up to 10k base traces. Self-hosting is free on Opik and Enterprise-only on LangSmith.
| Custom, includes self-hosted and hybridTier | Opik (by Comet) | LangSmith (by LangChain) |
|---|---|---|
| Self-hosted | Free, open source, full feature set | Enterprise tier only |
| Free cloud | $0, 25k spans per month, up to 10 team members, 60-day data retention | $0, up to 5k base traces per month, 1 seat |
| Entry paid | Pro Cloud, $19 per month, 100k spans, up to 50 team members | Plus, $39 per seat per month, up to 10k base traces, unlimited seats addable |
| Enterprise | Framework-neutral | Custom, includes self-hosted and hybrid |
One honest note on units: Opik meters spans and LangSmith meters traces, and a single trace contains multiple spans. The two free tiers are not directly comparable on volume. The comparison that does hold is seats, where the free tiers are 10 against 1, and the paid entry point, where $19 per month is not charged per seat and $39 is.
Do LangSmith and Opik offer free trials or proof of concept?
Neither requires a sales conversation to start. Opik is free and open source, so there is no trial window at all: self-host the full product with no account and no credit card, or start on the $0 cloud tier with 25k spans per month and up to 10 team members. LangSmith offers a free Developer tier capped at 5k base traces per month and 1 seat.
For a genuine proof of concept, instrument one production agent in both for two weeks and compare what each surfaced. Traces are cheap to duplicate and the result is usually decisive.
How do you get started with Opik?
About five minutes and three steps. Most teams find something they can fix within their agent in the first hour.
- Install the SDK:
pip install opik - Add the
@trackdecorator, or enable the integration for your framework. - Open the trace view and read your last 100 production calls.
Self-hosting is a single command against the opik.sh script, or a Helm chart for Kubernetes. The self-host guide covers both.
If you are scoring retrieval quality rather than agent behavior, the RAG evaluation guide covers the metrics underneath ContextRecall and ContextPrecision.
Where does LangSmith beat Opik?
Three places, honestly.
If your application is built on LangChain or LangGraph, LangSmith’s integration is the tightest available and tracing is close to zero-config. That is a real convenience advantage and it comes from the same team that builds the framework.
Its annotation and human-feedback tooling is mature and well documented, and it has been in production use longer.
And if you want a fully managed service and have no self-hosting requirement, a single-vendor SaaS with no infrastructure to run is a legitimate preference, not a compromise.
Which should you choose?
Choose Opik if you need self-hosting without an enterprise contract, want your trace data to stay in your own environment, build on more than one agent framework, want to read and modify the evaluation code you depend on, or expect to add teammates without a per-seat bill.
Choose LangSmith if you are all-in on LangChain and LangGraph, want the most native possible path in that ecosystem, prefer a fully managed service, or already run it and have no data-residency requirement.
If your application is built on LangChain or LangGraph, LangSmith’s integration is the tightest available and tracing is close to zero-config. That is a real convenience advantage and it comes from the same team that builds the framework.
Its annotation and human-feedback tooling is mature and well documented, and it has been in production use longer.
And if you want a fully managed service and have no self-hosting requirement, a single-vendor SaaS with no infrastructure to run is a legitimate preference, not a compromise.
Frequently asked questions
Apache-2.0, and the self-hosted build is the full product rather than a feature-limited community edition. Comet describes it as “True OSS: same codebase as the hosted versions,” with the full observability and agent-testing feature set included. Paid plans add operational headroom rather than unlocking capabilities: Pro Cloud adds customizable span limits and retention periods, and Enterprise adds flexible deployments, single sign-on, service accounts, and SOC 2, ISO 27001, ISO 9001, HIPAA, and GDPR compliance.
Apache-2.0, and the self-hosted build is the full product rather than a feature-limited community edition. Comet describes it as “True OSS: same codebase as the hosted versions,” with the full observability and agent-testing feature set included. Paid plans add operational headroom rather than unlocking capabilities: Pro Cloud adds customizable span limits and retention periods, and Enterprise adds flexible deployments, single sign-on, service accounts, and SOC 2, ISO 27001, ISO 9001, HIPAA, and GDPR compliance.
Apache-2.0, and the self-hosted build is the full product rather than a feature-limited community edition. Comet describes it as “True OSS: same codebase as the hosted versions,” with the full observability and agent-testing feature set included. Paid plans add operational headroom rather than unlocking capabilities: Pro Cloud adds customizable span limits and retention periods, and Enterprise adds flexible deployments, single sign-on, service accounts, and SOC 2, ISO 27001, ISO 9001, HIPAA, and GDPR compliance.
No. LangSmith is proprietary. The LangChain framework itself is open source, which is a separate thing from the observability platform, and the two are often confused.
No. LangSmith is proprietary. The LangChain framework itself is open source, which is a separate thing from the observability platform, and the two are often confused.
There is no automated importer. Both platforms instrument at the SDK level, so migration means swapping the decorator and the environment variables, which is usually an afternoon of work. Historical LangSmith traces do not transfer, so most teams run both in parallel for a week and cut over once the new traces look right. Opik’s opik import and opik export commands move traces, datasets, experiments, and prompts between Opik projects, not from other platforms.
Try Opik
Free and open source, self-hosted or managed, whether you are debugging your first agent or running hundreds in production.
- Start free: comet.com/opik
- Self-host: github.com/comet-ml/opik
- Docs: comet.com/docs/opik
Its annotation and human-feedback tooling is mature and well documented, and it has been in production use longer.
And if you want a fully managed service and have no self-hosting requirement, a single-vendor SaaS with no infrastructure to run is a legitimate preference, not a compromise.
Teams with compliance or deployment requirements can talk to us about Enterprise, but you do not need to in order to run the whole thing.
“Opik being open-source was one of the reasons we chose it. Beyond the peace of mind of knowing we can self-host if we want, the ability to debug and submit product requests when we notice things has been really helpful in making sure the product meets our needs.”
Jeremy Mumford
Lead AI Engineer, Pattern
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