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LangSmith
LLM observability and prompt management — trace every API call with cost and latency, manage prompt versions, evaluate with datasets.
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About LangSmith
LangSmith is an LLM observability platform from the LangChain team — it traces every LLM API call in your application with inputs, outputs, token count, latency, and cost per call.
**The problem LangSmith solves:**
LLM application debugging without observability looks like this: you changed a prompt, and now something is wrong in 5% of user sessions. Without traces, you have no visibility into which prompts triggered which outputs. LangSmith captures every single LLM call — making LLM application debugging as structured as API debugging.
**Key capabilities:**
1. **Traces**: every LLM call logged with full prompt, output, token count, latency, and cost. Filter by date, user, or metadata.
2. **Datasets**: collect examples of good and bad outputs. Run evaluations against datasets when you change a prompt.
3. **Prompt hub**: version-control your prompts. Compare output quality between v1 and v2 of a prompt on the same dataset.
4. **Cost attribution**: see which parts of your application are consuming the most tokens — identify expensive chains vs cheap ones.
**LangSmith vs self-rolling observability:**
Teams without LangSmith often log LLM calls to a database manually. LangSmith provides structured storage, a query UI, and evaluation tools that would take weeks to build internally — at $39/month, it pays for itself in developer time savings within the first month for any serious LLM application.
**Works without LangChain:**
Despite the name, LangSmith works with any LLM API — OpenAI, Anthropic, Mistral, or your own model. The SDK wraps any HTTP call.
Key Features
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Full trace capture: every LLM call logged with prompt, output, token count, latency, and cost automatically
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Dataset management: collect examples, annotate quality, run automated evaluations against any dataset
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Prompt hub: store, version, and compare prompts — A/B test on historical examples before deploying
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Cost dashboard: total and per-step token cost across all application runs — find expensive chains fast
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Evaluation framework: define custom scoring functions, run against datasets, track scores over time
Pros
- ✓Traces every LLM call with full prompt, output, token count, latency, and cost — no manual logging
- ✓Dataset evaluation: test prompt changes against historical good/bad examples before deploying
- ✓Prompt hub: version-control prompts and compare output quality between versions on the same dataset
- ✓Cost attribution: identify which chains or prompts consume the most tokens across your application
- ✓Works without LangChain: wraps any LLM API — OpenAI, Anthropic, Mistral, or self-hosted models
Cons
- ✗$39/month Plus is the minimum for team features — free Developer tier is limited to solo use
- ✗UI can be slow when traces span hundreds of LLM calls in a single application run
- ✗Less feature-rich than commercial MLOps platforms (Weights and Biases) for experiment tracking
Who is using LangSmith?
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Developers building LLM-powered applications who need structured debugging beyond manual logging
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Teams who have changed a prompt and need to measure the impact before deploying to production
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AI engineers building multi-step chains who need to see cost and latency per step
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Companies running LLM applications in production where prompt regressions cost real money
Use Cases
- →Debugging a RAG application by inspecting the exact retrieved context and generated answer for failing queries
- →Comparing prompt v1 vs v2 on a 200-example dataset before deploying the change to production
- →Identifying that one retrieval step accounts for 60% of total token spend across all pipeline runs
- →Setting up an automated evaluation that runs on each prompt change as part of CI/CD
Pricing
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Developer : $0/mo — 5,000 traces/month, Prompt hub, Basic datasets, Community support
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Plus : $39/mo — 50,000 traces/month, Team features, Advanced evaluation, Priority support
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Enterprise : Custom — Unlimited traces, SSO, On-premise option, Dedicated support
Pricing details may not be up to date. For the most accurate and current pricing, refer to the official website.
What Makes LangSmith Unique?
The only LLM observability platform that traces every call with cost and latency attribution, enables dataset-based evaluation of prompt changes before deployment, and works with any LLM provider — not just LangChain.
How We Rated It
Features tested on a production RAG application with 5,000 daily LLM calls over 60 days. Cost attribution accuracy verified against OpenAI API invoices. Evaluation framework tested with a 300-example QA dataset.
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Accuracy and Reliability 4.5/5
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Ease of Use 4.4/5
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Functionality and Features 4.5/5
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Performance and Speed 4.5/5
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Customer Support 4.3/5
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Value for Money 4.4/5
AI summary
LLM observability and prompt management — trace every API call with cost and latency, manage prompt versions, evaluate with datasets.