What is LangSmith used for?

LangSmith is an agent engineering platform for observing, evaluating, and deploying AI agents. It helps debug agent behavior, run evaluations, ship agents to production, and create agents for routine tasks using Fleet.

How is LangSmith priced?

LangSmith offers a Developer plan ($0/seat, up to 5k traces), Plus plan ($39/seat, up to 10k traces), and Enterprise plan (custom pricing). Additional usage for traces, deployments, Engine, Fleet, and Sandboxes is metered via LCUs and LSUs.

Does LangSmith train on my data?

No. LangSmith does not use your data to train models. Your traces, prompts, and outputs remain private to your organization.

What is an LCU and an LSU?

LCU (LangChain Compute Unit) measures compute work like Engine analysis and deployments ($1.50/LCU). LSU (LangChain Storage Unit) measures data storage like traces and databases ($1.00/LSU).

Can I self-host LangSmith?

Yes, self-hosted and hybrid deployment options are available with the Enterprise plan, allowing you to run the platform in your own VPC.

LangChain

Updated Jul 25, 2026

Build, test, deploy, and monitor AI agents with LangSmith. Trusted by top teams. Improve every step of the agent development lifecycle.

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Editor's Verdict

Rating: 4.3/5.0•Reviewed by RAGWiki

At $0 / seat per month, LangChain stands out as a powerful solution in the developer tools,chatbots,research landscape. It is especially well-suited for professionals like Software Engineer and Machine Learning Engineer. However, potential buyers should note that it might not be perfect if you are strictly trying to avoid pay-as-you-go complexity. Overall, it offers a robust toolset that significantly accelerates workflows.

Key Takeaways

Observability
Evaluation
Deployment
Fleet

In-Depth Review: What is LangChain?

LangSmith is the ultimate agent engineering platform that powers the entire agent development lifecycle. With observability, evaluation, deployment, and fleet capabilities, teams can build, test, and deploy reliable AI agents at scale. LangSmith offers native tracing for popular frameworks, LLM-as-judge evals, scalable agent servers with memory and human-in-the-loop, and autonomous agents for routine tasks. Trusted by 5 of the Fortune 10 and with over 100M monthly open source downloads, LangSmith helps you iterate faster and productionize agents with confidence.

Core Features

Observability

Understand exactly what your agent is doing with structured tracing, analytics, and AI-driven insights.

Evaluation

Use real-world usage for iterative improvement with reusable LLM-as-judge evals, human feedback, and offline/online scoring.

Deployment

Ship and scale agents in production with memory, conversational threads, durable checkpointing, and human-in-the-loop interactions.

Fleet

Create agents for daily tasks using plain language, with prebuilt templates, MCP server integration, and autonomous improvement.

Engine

Surface and diagnose undetected issues autonomously by clustering production failures, finding root causes, and proposing fixes.

Open Source Frameworks

Build agents fast with deepagents, LangChain, and LangGraph, offering varying levels of control and autonomy.

Pricing

Developer

$0 / seat per month

Up to 5k base traces / mo, then pay-as-you-go
Community support
1 seat

Plus

$39 / seat per month

Up to 10k base traces / mo, then pay-as-you-go
Access to Deployment, Engine, and more
Add unlimited seats

Enterprise

Custom pricing

Self-hosted and hybrid deployment options
Custom SSO and RBAC
Support SLA
Custom seats and workspaces

Pros and Cons

Pros

Comprehensive Agent Lifecycle PlatformCovers build, test, deploy, and monitor with integrated observability, evaluation, deployment, and fleet management.
Framework-AgnosticSupports Python, TypeScript, Go, Java SDKs and integrates with OpenTelemetry and popular agent frameworks.
Enterprise-Grade SecurityOffers self-hosted options, custom SSO, RBAC, and SOC 2 compliance for large organizations.
Autonomous Issue DetectionLangSmith Engine autonomously clusters failures, finds root causes, and suggests fixes, reducing debugging time.
Generous Free TierDeveloper plan includes 5k base traces/mo for free, suitable for personal projects and small teams.

Cons

Pay-as-You-Go ComplexityPricing includes multiple metered units (LCU, LSU) that can make cost estimation challenging for heavy usage.
Limited Free TracesFree tier only includes 5k base traces; larger projects may incur costs quickly.
Learning Curve for Advanced FeaturesFeatures like Engine, Fleet, and custom deployments may require deep understanding of agent architectures.
No Self-Hosted in Developer PlanSelf-hosted and hybrid options are only available in Enterprise, limiting deployment control for smaller teams.
Dependency on LangChain EcosystemWhile framework-agnostic, tight integration with LangChain libraries may encourage vendor lock-in.

Use Cases & Recommended Professions

Software Engineer→ View Toolkit

Need to debug and monitor agent behavior, iterate on prompts, and deploy reliable AI applications.

Machine Learning Engineer→ View Toolkit

Require robust evaluation pipelines, trace analysis, and model fine-tuning using real-world data.

AI Product Manager→ View Toolkit

Need to oversee agent performance, manage fleet of agents, and ensure alignment with business goals.

Data Scientist→ View Toolkit

Use tracing and evaluation to understand agent decisions and improve model accuracy with human feedback.

QA Engineer→ View Toolkit

Require automated evals, regression testing, and anomaly detection for agent systems.

DevOps Engineer→ View Toolkit

Need to manage deployment infrastructure, scaling, and monitoring for agent services in production.

Frequently Asked Questions

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ℹ️ Curation Disclosure: The overview and features of LangChain were synthesized using AI and fact-checked by our curation team to ensure accuracy.