LangSmith
AI agent observability for tracing, monitoring, and evaluating LLM apps
LangSmith is the most purpose-built observability platform for AI agents, especially if you use LangChain or LangGraph. The new LangSmith Engine autonomously detects issues and suggests fixes, and SmithDB delivers sub-second full-text search. However, costs scale with trace volume—watch your usage for simpler apps.
- Teams building and deploying AI agents in production
- Developers using LangChain or LangGraph
- Engineering teams monitoring LLM cost, latency, and error rates
- Platform teams requiring self-hosted observability for data residency
- Simple single-prompt LLM apps where basic logging suffices
- Teams on a very tight budget due to pay-as-you-go overages
- Non-technical teams preferring no-code analytics dashboards
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Skip LangSmith if you only need basic logging for simple single-prompt LLM apps without multi-step agent behavior.
Each extra base trace beyond 5k/month on Developer is pay-as-you-go; costs can add up quickly at high volume.
LangSmith's freemium model suits solo developers with 5k free traces, but scaling teams quickly hit overage charges. Plus at $39/seat is competitive for mid-sized teams, but Enterprise (custom) is needed for SSO and self-hosting. Compared to Datadog or Grafana, LangSmith is purpose-built for agents but may be pricier per trace. Startups can get discounted rates through the LangSmith for Startups program.
In short
LangSmith — AI agent observability for tracing, monitoring, and evaluating LLM apps. Best for Teams building and deploying AI agents in production, Developers using LangChain or LangGraph, Engineering teams monitoring LLM cost, latency, and error rates. Free to start; paid plans from $39/mo.
What's new in LangSmith
Checked 12 days agoAcross the latest 9 updates: 4 feature updates, 1 changelog entry and 4 news mentions.
Your coding agent bill doubled. Here’s how to fix it.
Article on reducing coding agent costs using LangSmith observability and evaluation.
How Pendo used LangSmith to trace Novus from user behavior to code fixes
Case study on Pendo using LangSmith for tracing and debugging agents.
Running Untrusted Agent Code Without a Sandbox
Guide on safely executing untrusted code in agents without sandboxing.
Harbor x LangChain: A Unified Stack for Evaluating Agents
Partnership with Harbor to provide a unified evaluation stack for agents.
How Candidly Built State-Aware Agent Harnesses with LangSmith
Case study on Candidly using LangSmith for state-aware agent monitoring.
Introducing Dynamic Subagents in Deep Agents
New feature for creating subagents dynamically within Deep Agents framework.
Prompt Caching with Deep Agents
Introducing prompt caching to reduce latency and cost in Deep Agents.
Full Text Search in SmithDB: Constructing and Querying our Inverted Index (Pt. 2)
Technical deep-dive on SmithDB's full-text search capabilities.
How To Give Your Agent Memory
Tutorial on implementing memory in agents using LangChain tools.
Viability Score
How likely is LangSmith to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Trace agent executions step by step
- Monitor real-time dashboards with cost tracking
- Online LLM-as-judge evals for quality scoring
- Automated insights with unsupervised topic clustering
- SmithDB purpose-built for agent traces
- Sub-second query performance across millions of traces
- Full-text search with inverted index
- JSON key-path filtering and trajectory queries
- Self-host SmithDB inside your VPC
- LangSmith Engine for autonomous issue detection and fixes
- Deploy and scale agents with LangSmith Deployment
- Sandboxes for safe agent-generated code execution
- Fleet agents for no-code agent creation
- Supports Python, TypeScript, Go, Java SDKs
- OpenTelemetry support
About LangSmith
LangSmith is an observability platform built for AI agents and LLM applications, designed by the team behind LangChain. It provides end-to-end visibility into agent behavior by tracing every step from user input to tool calls and model responses. LangSmith is framework-agnostic, supporting any LLM framework including OpenAI SDK, Anthropic SDK, Vercel AI SDK, and LlamaIndex, as well as custom implementations via Python, TypeScript, Go, or Java SDKs or OpenTelemetry. Key features include real-time monitoring dashboards with cost tracking, online LLM-as-judge evals, PagerDuty alerts, and automated insights like unsupervised topic clustering and error analysis. SmithDB, a purpose-built database for agent traces, delivers sub-second query performance across millions of traces with full-text search and JSON key-path filtering. The platform also offers evaluation capabilities to score and improve agent performance, and deployment features to ship agents in production. As of June 2026, LangSmith Engine autonomously monitors traces, clusters issues, and recommends fixes to prompts and code, reducing manual debugging effort. LangSmith’s agent-focused design gives it an edge over general-purpose observability tools for teams building and deploying AI agents in production. It offers managed cloud, BYOC, and self-hosted options for data residency.
Behind the Verdict
LangSmith is the most purpose-built observability platform for AI agents. If your team lives in LangChain or LangGraph, the integration is seamless. The new LangSmith Engine is a standout—autonomous issue detection and fix suggestions save hours of manual debugging. SmithDB, the custom-built trace database, delivers sub-second queries that general-purpose databases can't match. But LangSmith comes with important caveats. Pricing scales with trace volume; the free tier only includes 5,000 base traces per month. For high-volume agents, costs can add up quickly—be mindful of overages. The platform is designed for developers, so non-technical teams may find the learning curve steep. If you just need basic logging for simple single-prompt LLM apps, something simpler and cheaper will suffice. Compared to alternatives like LangFuse or Weights & Biases Prompts, LangSmith offers deeper agent-specific tracing and deployment features, but at a potentially higher cost. For teams shipping complex agents in production, LangSmith’s agent-first architecture and autonomous engine make it a strong choice—just budget carefully.
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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 fails to retrieve the correct data after a tool call. The engineer uses LangSmith tracing to step through the execution, inspect the model's intermediate reasoning, and see the exact API response.
Outcome: The engineer pinpoints the issue (a malformed query parameter) in minutes, fixes the prompt, and redeploys with confidence.
The team configures LangSmith monitoring dashboards to track token usage, latency P99, and cost per agent. They set up PagerDuty alerts for when latency exceeds 5 seconds.
Outcome: They get real-time visibility across all agents, catch a cost spike early, and optimize model choices to reduce spending by 20%.
The researcher uses LangSmith evaluation to score retrieval accuracy with LLM-as-judge evals on a dataset of 10k queries. They run online evals to monitor changes after updating the embedding model.
Outcome: They identify a drop in retrieval quality due to the new embeddings, roll back the change, and save weeks of manual testing.
Use Cases
- Trace agent decision steps to identify failure root causes in production.
- Evaluate agent outputs with LLM-as-judge evals during development.
- Deploy long-running agents with cron scheduling and horizontal scaling.
- Monitor cost and latency across all agents in real-time dashboards.
- Automatically cluster traces to discover unknown failure patterns.
- Run agent-generated code safely in isolated sandboxes.
- Autonomously detect and fix agent issues with LangSmith Engine.
Models Under the Hood
as of 2026-07-05
Limitations
- Free tier caps at 5k base traces/month; overage charges apply per trace.
- Plus plan adds 10k base traces but still incurs overage.
- Sandboxes, deployment runs, Fleet runs have separate usage-based charges.
- Self-hosting and SSO are Enterprise-only.
- Trace ingestion and event limits apply per hour.
- Overage costs can escalate at scale.
as of 2026-07-01
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/month
Ideal for
Solo developers building personal projects or prototypes with low trace volume (up to 5k base traces/month).
What this tier adds
Free entry point; single seat, community support, no deployment or Fleet beyond 1 agent/50 runs.
Plus
$39/seat/month
Ideal for
Teams actively building and deploying agents with moderate usage — up to 10k base traces/month, unlimited seats.
What this tier adds
Adds unlimited seats, email support, 1 free dev deployment, unlimited Fleet agents, 500 Fleet runs/month.
Enterprise
Custom
Ideal for
Large organizations requiring custom trace volume, self-hosted/hybrid deployment, SSO, RBAC, and support SLAs.
What this tier adds
Custom pricing; self-hosted and hybrid options, custom SSO/RBAC, support SLA, custom seats and workspaces.
Where the pricing makes sense
The company stage and team size where LangSmith's pricing actually pencils out — and where peers do it cheaper.
LangSmith's freemium model suits solo developers with 5k free traces, but scaling teams quickly hit overage charges. Plus at $39/seat is competitive for mid-sized teams, but Enterprise (custom) is needed for SSO and self-hosting. Compared to Datadog or Grafana, LangSmith is purpose-built for agents but may be pricier per trace. Startups can get discounted rates through the LangSmith for Startups program.
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.
For individual developers, tracing is set up in minutes by setting the LANGSMITH_TRACING=true environment variable and installing the SDK. For teams, deploying the cloud platform takes under an hour; self-hosting may take 2–3 days. The new LangSmith Engine requires no configuration beyond enabling it in the dashboard.
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 Datadog: export existing logs/traces and re-ingest into LangSmith via OTel or SDK integration.
- →From custom logging: replace your logging calls with LangSmith SDK callbacks; traces are captured automatically.
- →From other LLM observability tools: use the data export feature to migrate historical traces to LangSmith's SmithDB.
- ↗To Datadog: use LangSmith's OpenTelemetry exporter to send traces to Datadog.
- ↗To self-hosted alternative: export trace data via the LangSmith API (bulk data export is available on all plans).
- ↗To open-source tooling: download traces as JSON and import into your own database.
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
Official links
Tools that pair well with LangSmith
Common stack mates teams adopt alongside LangSmith, with the specific reason each pairing earns its keep.
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Frequently Asked Questions
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