LangSmith
Observability and evaluation for LLM apps — traces, datasets, A/B testing, and feedback collection.
LangSmith is an observability and evaluation for LLM apps — traces, datasets, A/B testing, and feedback collection. Provides tracing for LLM application calls and chains.
About LangSmith
LangSmith is an observability and evaluation platform for LLM applications, covering traces, datasets, A/B testing, and feedback collection.
It traces LLM calls and chains, lets you build and manage datasets for evaluation runs, compares experiments through A/B testing, and gathers both human and programmatic feedback on outputs.
It suits teams running LLM apps that need to inspect and evaluate behavior in one place. It is less suitable if you need self-hosting or want to avoid vendor lock-in: it is not open source, the standard tiers are not self-hostable, and the free tier caps traces and seats, so production scale requires a paid plan. Its features are also tightly coupled to the LangChain ecosystem.
Compared with other LLM observability tools, it is the LangChain-aligned, hosted option, pairing tracing with built-in evaluation and feedback rather than tracing alone.
Sources: this listing
What it does well
- Provides tracing for LLM application calls and chains.
- Supports dataset creation and management for evaluation runs.
- Includes A/B testing and experiment comparison features.
- Collects human and programmatic feedback on LLM outputs.
Where it falls short
- Free tier imposes limits on traces and seats, requiring paid plans for production scale.
- Not open source and the standard tiers are not self-hostable.
- Observability and evaluation features are tightly coupled to the LangChain ecosystem.
Tagged
- Supports Claude
- Supports GPT
- Supports Gemini
- Supports Local Models
- Web-based
- Enterprise Plan
- Free
- Freemium
Compared with similar things
Picked by shared tags inside the AI Web Apps.
- 01Freemium →OpenRouter
Unified API gateway for 200+ LLMs across providers with usage-based billing on a single account.
- 02Freemium →n8n
Open-source workflow automation with native LLM nodes — self-host on your own infrastructure.
- 03Freemium →Sider
ChatGPT + Claude sidebar for any browser tab — chat about the page, summarise, translate, draw.
- 04Freemium →ChatHub
Use GPT, Claude, Gemini, Bing, and more side-by-side in a single chat interface.
- 05Freemium →Make
Visual automation platform with AI modules for OpenAI, Anthropic, and self-hosted LLMs.
- 06Freemium →Monica
All-in-one AI assistant browser extension — chat, summarise, rewrite, translate, and answer on any site.
Related reading
- How to Get Your AI App Cited by ChatGPT and Perplexity
To get cited by ChatGPT and Perplexity, be the clearest answer in several places at once. Publish answer-first pages with extractable facts, get listed on directories and review sites the engines crawl, and build consistent mentions across Reddit, YouTube, and your own site. Perplexity tends to favour recent content; ChatGPT appears to weight agreement across sources.
Read guide → - Attention Is the New Oil
If you built something with AI, posting is getting hard to ignore. GaryVee's 'interest media' shift is real — more feeds now recommend content by interest, not only by who follows you — so a maker with zero followers can sometimes out-reach an established brand. That's the unlock and the mandate: building got faster, getting seen is the job, and many products struggle to grow without it. Here's the honest first move, written for a builder who would rather ship a feature than post.
Read guide → - The Future of Marketing for People Who Build with AI
When AI makes building a product trivial and floods the web with generic content, marketing inverts: distribution becomes the moat, and distribution increasingly means being the source AI answer engines and buying agents cite and recommend. The durable move before 2027 is to engineer your product to be machine-discoverable and corroborated from day one — even at zero domain authority. Strong in the future means cited, not ranked.
Read guide →
Concepts you should know
- Eval
A reproducible test that measures how an LLM or LLM application performs on a specific task. Golden test sets, rubric grading, A/B comparisons. The closest thing to unit tests for prompts.
- LLM as judge
Using an LLM (often a stronger one than the one being tested) to grade outputs against a rubric. Replaces or supplements human grading for evals at scale. Accuracy of the judge is itself a metric you have to measure.
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</a>Frequently asked questions
- What is LangSmith?
- LangSmith is an observability and evaluation for LLM apps — traces, datasets, A/B testing, and feedback collection.
- Is LangSmith free?
- LangSmith offers a free tier and paid plans with higher limits or premium features.
- What platforms does LangSmith support?
- LangSmith runs on web.
- What category does LangSmith belong to?
- LangSmith is in the AI Web Apps category — Web apps with AI baked in — built for everything from journaling to research. Submitter-shipped products live here.
- What are the downsides of LangSmith?
- Free tier imposes limits on traces and seats, requiring paid plans for production scale. Not open source and the standard tiers are not self-hostable. Observability and evaluation features are tightly coupled to the LangChain ecosystem.