LangSmith
LangSmith is agent and LLM observability: trace, monitor, and evaluate AI agents in cloud, BYOC, or self-hosted.
If your agents break in production and you need to know why before your users tell you, LangSmith is the most complete package going — the SmithDB speedups are a real differentiator, not a benchmark exercise. The tradeoff is metering: LSU at $1.00 (and LCU at $1.50 on older tables) is consumed by Deployment, Fleet, Engine, Sandboxes, and Gateway, so cost tracks how chatty your agents are, not headcount. Buy it when debugging agents is a standing job; pass when a single-prompt app and plain
Verified 10h ago · liveness 83/100 · cite: rightaichoice.com/tools/langsmith
- Engineering teams debugging multi-turn, tool-calling agents in production
- Platform teams needing BYOC or self-hosted trace storage for data residency
- AI teams running online evals and LLM-as-judge scoring against live traffic
- Teams on LangChain, LangGraph, or Deep Agents wanting deployment plus observability
- Single-prompt apps at low volume, where plain logging is cheaper and simpler
- Non-technical teams wanting drag-and-drop analytics without reading run trees
- Teams whose trace data legally cannot leave the building and cannot self-host or BYOC
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Skip LangSmith if you're logging a handful of single-prompt calls a day and want per-seat simplicity — the included trace allowance and the LCU/LSU meters only make sense once agent debugging is a recurring, on-call problem.
Past the included traces (5k/mo on Developer, 10k/mo on Plus), overage runs at $1.50 per LCU and $1.00 per LSU, so a verbose agent's bill grows with its tool calls rather than your team size.
The shape fits engineering-led teams where a seat or two rides alongside real trace volume. Developer at $0/seat/mo with 1 seat and 5k base traces is a genuine entry point for solo evaluators; Plus at $39 per seat per month with unlimited seats and 10k base traces suits a five-to-twenty-person platform team. Cheaper-but-narrower comparisons are Langfuse and Arize Phoenix if you only need tracing; you pay LangSmith's LCU/LSU premium for SmithDB query speed, Engine, Sandboxes, and self-hosting.
In short
LangSmith — LangSmith is agent and LLM observability: trace, monitor, and evaluate AI agents in cloud, BYOC, or self-hosted. Best for Engineering teams debugging multi-turn, tool-calling agents in production, Platform teams needing BYOC or self-hosted trace storage for data residency, AI teams running online evals and LLM-as-judge scoring against live traffic. Free to start; paid plans from $39/user/mo.
No head-to-head comparison for LangSmith yet — the alternatives below are the closest matches we list.
What's new in LangSmith
Checked 7 days agoAcross the latest 4 updates: 2 feature updates, 1 changelog entry and 1 news mention.
The Reliability Layer for Healthcare AI: Common LangSmith Use Cases
LangChain details common LangSmith use cases for reliability in healthcare AI, aimed at teams running agents in regulated environments where trace data and evaluation evidence matter.
Jev is now available in LangSmith Evals
Jev is now available in LangSmith Evals, adding a new evaluator option to the platform's scoring and testing tooling.
Jev-as-a-Judge for Agent Evals
LangChain publishes guidance on using Jev as a judge model for agent evaluations in LangSmith.
Building a Harness with Jev
LangChain walks through building an agent harness around Jev, published under its open source and agent architecture categories.
What people actually say about LangSmith — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
58 mentions across 4 sources (Hacker News, Product Hunt, Stack Overflow, Lemmy) · researched Aug 18, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Deep trace visibility into every agent step, including sub-agent token usage.
- +Instant debugging via full-text search and JSON filtering in SmithDB.
- +Live dashboards show cost, latency, and error rates updated in real time.
- +LLM-as-judge evals automate quality scoring on custom criteria without manual labels.
- +Model-agnostic tracing works across OpenAI, Anthropic, Vercel AI, LlamaIndex, and more.
- −Non-LangChain frameworks like CrewAI often fail to trace correctly without manual workarounds.
- −Learning curve for advanced features is steep; dashboard feels overwhelming at first.
- −Usage-based pricing gets expensive fast at high trace volumes.
- −Users report occasional lag when querying very large traces (millions) despite SmithDB's speed.
- −Support is not always responsive, especially on free tier; community forums are hit-or-miss.
- • Overage charges for traces beyond the monthly quota can be significant.
- • Self-hosting SmithDB incurs infrastructure costs (compute, storage) not covered by subscription.
- • Additional seats on Plus plan increase cost for teams; pricing per seat can add up.
Viability Score
How well maintained and how widely used is LangSmith? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Agent tracing with step-by-step runs and message threading for multi-turn chat
- Real-time monitoring dashboards for token usage, cost, latency (P50/P99), errors, and feedback
- Online LLM-as-judge and code evals scoring production traffic continuously
- Offline evaluation suites, dataset collection, annotation queues, and bulk data export
- Insights with unsupervised topic clustering and executive summaries of failure modes
- SmithDB trace database with sub-second queries across millions of traces
- Full-text search, JSON key-path filtering, and trajectory queries on agent runs
- Self-host SmithDB in your VPC: three stateless components on object storage and Postgres
- LangSmith Engine autonomously clusters issues, diagnoses failures, and recommends fixes
- LangSmith Deployment with 1-Click Deploy, scale-to-zero, and an Assistants API
- Sandboxes for ephemeral agent-generated code with configurable TTLs and snapshot/fork
- LLM Gateway for cost controls, rate limiting, model fallbacks, and PII/secret redaction
- Tuned Evaluators including Perceived Error, metered per evaluation run
- Prompt Hub and Playground, annotation queues, and Preview Builds
- OpenTelemetry SDKs for Python, TypeScript, Go, and Java
About LangSmith
LangSmith is an agent and LLM observability platform from the LangChain team, built for engineering and platform teams running agents in production rather than demo scripts. It records every step of a run — inputs, tool calls, model responses, multi-turn message threads — so you can see exactly where latency, cost, and answer quality drift. Native tracing covers the popular agent frameworks, and OpenTelemetry SDKs exist for Python, TypeScript, Go, and Java, plus OpenAI, Anthropic, and Vercel AI SDK instrumentation. On top of tracing sits real-time monitoring: dashboards for token usage, cost, latency at P50 and P99, error rates, and feedback scores, with webhook and PagerDuty alerts when a metric crosses a threshold. Online LLM-as-judge and code evals score production traffic continuously, while offline evaluation suites, dataset collection, annotation queues, and bulk data export handle the pre-ship side. Insights clusters traces with unsupervised topic clustering and returns an executive summary of key findings, and the newer Engine goes further — monitoring traces autonomously, grouping behavior into issues, diagnosing failures, and recommending prompt and code fixes. The infrastructure story is the part general-purpose databases struggle with. Agent traces are deeply nested and payload-heavy, so LangSmith runs SmithDB, a store built for random access on individual runs, full-text search, JSON key-path filtering, and trajectory queries. The pricing page puts trace queries at 860ms before and 71ms on SmithDB (12x), thread queries 9x faster, full-text search 15x, filtering 6x. SmithDB self-hosts inside your VPC as three stateless components on object storage and Postgres. Hosting comes as managed cloud on smith.langchain.com (GCP us-central-1, with US or EU data location on higher tiers), BYOC with a SaaS control plane and self-hosted data plane, or fully self-hosted on your own Kubernetes cluster in AWS, GCP, or Azure. Pricing is freemium and metered:
Behind the Verdict
Where LangSmith earns its keep is the middle of an incident. A tool-calling agent loops, burns $400 of tokens, and returns nonsense — you open the trace, walk the run tree, find the bad tool response at step nine. That is the workflow SmithDB was built for, and the numbers on the pricing page back it: trace queries 860ms down to 71ms, full-text search 6.2s down to 400ms. Once you've filtered a few million traces, going back to a general-purpose store feels broken. Pick LangSmith when agents are core to your product and at least one engineer owns their reliability. Teams on LangChain, LangGraph, or Deep Agents get deployment and observability in one place; teams on generic OpenTelemetry pipelines can still ingest via OTel, and the async callback handler means tracing shouldn't show up in your app's latency budget. Go elsewhere if you're shipping a single-prompt summarizer at low volume. Langfuse and Arize Phoenix both handle basic tracing, and adding a metered platform to a ten-call-a-day app is overhead you'll never amortize. Non-technical stakeholders who want drag-and-drop dashboards without reading run trees will bounce off the trace view. Watch the billing math. Developer gives 5k base traces a month, Plus gives 10k at $39/seat, then you're pay-as-you-go at $1.00 per LSU. Chatty multi-turn agents with heavy payloads chew through units fast, and the newer surfaces — Fleet's 7 LSU on Developer and 37 on Plus, Engine metered in LSUs, Sandboxes at fractions per vCPU-hour, Deployment compute and memory rates — all draw from the same pool. Run the usage calculator before you commit; the estimate depends on your agent's behavior, not your seat count. The newer pieces are worth a look but not a blind rollout. Engine clustering failures and suggesting fixes is the most
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Real-world workflow fit
Concrete scenarios for the personas LangSmith actually fits — and what changes day-one when you adopt it.
An agent's tool-calling loop started returning wrong answers after a prompt change. They open the trace for a failing run, see the exact tool call where the argument went out of schema, compare it against a passing run with the same input, and ship a fix.
Outcome: Root cause found from one trace tree instead of reproducing in staging, and the failing run is saved into an offline eval dataset so the regression is caught next deploy.
Security will not allow trace payloads to leave the customer's cloud. They deploy SmithDB inside the VPC as three stateless components on object storage and Postgres, then point the Python and TypeScript SDKs at the internal endpoint.
Outcome: Engineering keeps full tracing and monitoring while trace data stays inside the customer environment, with no local disks or sharding to operate.
Non-engineers build Fleet agents from templates in plain language, then trigger them via API. Control is centralized: model calls route through the LLM Gateway for rate limits, fallbacks, and PII redaction, and every run lands in the same trace view.
Outcome: The team ships agents without writing code, and the platform team can still answer 'what did it do and what did it cost' from one dashboard.
Use Cases
- Trace agent decision steps to find the root cause of a production failure.
- Score live traffic with online LLM-as-judge evals on the characteristics you care about.
- Deploy long-running agents with cron scheduling, scale-to-zero, and horizontal scaling.
- Watch cost, token usage, and P50/P99 latency across all agents in one dashboard.
- Cluster traces automatically to discover failure patterns nobody thought to look for.
- Run agent-generated code in isolated sandboxes with configurable TTLs.
- Let Engine detect issues and propose prompt and code fixes with eval coverage.
- Route model calls through the LLM Gateway for cost caps, fallbacks, and PII redaction.
Models Under the Hood
as of 2026-10-15
Limitations
- Pricing is usage-metered: Developer is capped at 1 seat and 5k base traces/mo, while Plus is $39 per seat/month with 10k base traces/mo, both pay-as-you-go after that.
- Self-hosted and hybrid deployment are Enterprise-only, along with custom SSO, ABAC, and RBAC.
- Exposure to the underlying AI model stack is indirect — the platform is an observability/evaluation layer rather than a model provider, so it mainly surfaces the Jev evaluator model in its evals tooling.
as of 2026-10-01
Verification history
We have re-verified LangSmith 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 18 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published LangSmith tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Developer
$0/seat/mo
Ideal for
Solo developer or evaluator prototyping an agent who needs tracing and evals before there's a team to onboard.
What this tier adds
Free entry point: $0 per seat per month with a hard cap of 1 seat and 5k base traces/mo before pay-as-you-go.
Plus
$39/seat/mo
Ideal for
A five-to-twenty-person engineering team building and deploying agents that need shared dashboards and evals.
What this tier adds
Adds unlimited seats, 10k base traces/mo, plus Deployment, Engine, and a free Serverless (Small) deployment and 25 free Fleet LCUs/mo.
Enterprise
Custom
Ideal for
Regulated organizations with data-residency, SSO, and support-contract requirements.
What this tier adds
Adds self-hosted and hybrid deployment, custom SSO/ABAC/RBAC, a support SLA, and custom trace ingestion and trace size limits.
Where the pricing makes sense
The company stage and team size where LangSmith's pricing actually pencils out — and where peers do it cheaper.
The shape fits engineering-led teams where a seat or two rides alongside real trace volume. Developer at $0/seat/mo with 1 seat and 5k base traces is a genuine entry point for solo evaluators; Plus at $39 per seat per month with unlimited seats and 10k base traces suits a five-to-twenty-person platform team. Cheaper-but-narrower comparisons are Langfuse and Arize Phoenix if you only need tracing; you pay LangSmith's LCU/LSU premium for SmithDB query speed, Engine, Sandboxes, and self-hosting.
Setup time & first value
How long it actually takes to get something useful out of LangSmith — broken out by persona, not the marketing-page minute.
Solo developer: minutes to first trace — install the SDK, set the API key, and runs land in the project. Engineering team instrumenting an existing agent: an afternoon for OTel or framework tracing, a few days to wire dashboards and online evals. Platform team self-hosting SmithDB: budget days to weeks, since you're standing up three stateless components on object storage and Postgres inside your
Switching to or from LangSmith
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Langfuse: point your existing instrumentation at the LangSmith SDK or send the same OTel spans to LangSmith, then rebuild dashboards.
- →From a general-purpose APM: add the OTel exporter alongside your current telemetry so both receive spans during the transition.
- →From print-statement logging: install the Python or TypeScript SDK and wrap your agent entry point; the tracing quickstart covers the first trace.
- →From a homegrown trace store: export existing runs to a dataset, then use the bulk export and evaluation tooling to re-baseline quality.
- ↗To Langfuse: because LangSmith ingests and emits OTel, keep the OTel exporter and re-point it at the new backend.
- ↗To a general APM: switch the OTel exporter endpoint and let existing dashboards absorb the spans.
- ↗To a self-hosted store: export trace datasets in bulk before cutting over, since evaluation history is the part that's hardest to recreate.
Integrations
Resources & Guides
- Resourcedocs.langchain.com
LangSmith Evaluation - Docs by LangChain
Helpful link from docs.langchain.com
- Resourcedocs.langchain.com
LangSmith Deployment - Docs by LangChain
Deploy and manage agents with durable execution, real-time streaming, and horizontal scaling.
- Resourcedocs.langchain.com
LangSmith Fleet - Docs by LangChain
Create helpful AI agents without code. Start from a template, connect your accounts, and let the agent handle routine work while you stay in control.
- Resourcedocs.langchain.com
Sandboxes overview - Docs by LangChain
Use managed sandboxes to safely execute code and interact with the filesystem in isolated environments.
Tutorials & Learning
YouTube returned 6 videos for “LangSmith”, and we withheld 6: 6 could not be judged, because “LangSmith” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about LangSmith.
Official links
Tools that pair well with LangSmith
Common stack mates teams adopt alongside LangSmith, with the specific reason each pairing earns its keep.
Tokentelemetry
Local, MIT-licensed token and cost observability for AI coding agents — reads the logs your agents already write.
Phoenix
Open-source LLM observability and agent evaluation you can trace, evaluate, and self-host for free.
Langfuse
Open-source LLM observability and agent evals: trace, evaluate, and improve production AI agents with MIT-licensed tooling.
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Tokentelemetry
Local, MIT-licensed token and cost observability for AI coding agents — reads the logs your agents already write.
Frequently Asked Questions
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