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AI infrastructure · Tool
LangSmith
ResearchedLangSmith by LangChain is an AI agent and LLM observability platform offering tracing, real-time monitoring, evaluation, and production deployment. It supports Python, TypeScript, Go, and Java SDKs, plus tiered pricing from a free Developer plan to custom Enterprise.
Official site snapshots
Visit live site See the official site at a glance
Read-only public captures of LangSmith’s homepage. Screenshots are dated, never live embeds, and open full-screen.
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In one minute
Start here for the decision-making essentials: what LangSmith does, who it is for, how it is accessed, and the first-party sources behind this profile.
Pricing8 options
Developer plan: $0 / seat per month then pay as you go
up to 5k base traces / mo included
Plus plan: $39 / seat per month then pay as you go
up to 10k base traces / mo included
u
Enterprise plan: Custom pricing then pay as you go
self-hosted and hybrid deployment opti
Usage metered via LCU (LangChain Compute Units) at $1.50 / LCU for work done & compute and
Platforms
LangSmith Platform
API accessNot public
FoundedLangChain the company started in early 2023
AvailabilityWeb / remote
LicenseOffers open source frameworks deepagents, langgraph, and langchain
Best suited to
Source-backed fit Teams building and deploying AI agents Engineers debugging LLM and agent failures Organizations needing self-hosted agent observability Companies scaling agents across teams Developers using LangChain, LangGraph, or DeepAgents JavaScript/TypeScript developers were a growing audience, driven by…
Decision support
6 sourced answersCommon questions and adoption checks
Short answers to the questions buyers and builders commonly ask about LangSmith. Each answer cites the shared ledger below, where every source is listed once.
01What does LangSmith say it can do?
Complete AI agent and LLM observability platform with tracing and real-time monitoring · Debug agents, find failures fast, and track costs and latency · Message threading for multi-turn chat interactions · Cost tracking across agent operations
Complete AI agent and LLM observability platform with tracing and real-time monitoring.
02Who is LangSmith intended for?
JavaScript/TypeScript developers were a growing audience, driven by mainstream adoption of · Startups (LangSmith for Startups program) · Startups
the idea of using LLMs has gone mainstream. As such, we saw a massive increase in interest in LangChain from folks across the stack, many of whom prefer to using javascript.
03What use cases does LangSmith describe?
Speed up I/O-bound tasks such as LLM API calls and data store interactions · ChatLangChain documentation chatbot was recreated in TypeScript as a showcase. · Accelerating agent and agentic workflow development · Deferred nodes are ideal for map-reduce, consensus, and agent collaboration workflows.
speed up I/O-bound tasks
04What should teams verify before adopting LangSmith?
The TypeScript version has fewer implementations than the Python version because it is new · ML-centric functionality such as tokenizers and LLMs have worse TypeScript support than th
Since the Typescript version is much newer, there are fewer of these implementations in there.
Ledger citation[2] langchain.com/blog/typescript-support
05What pricing information is available for LangSmith?
Developer plan: $0 / seat per month then pay as you go; up to 5k base traces / mo included · Plus plan: $39 / seat per month then pay as you go; up to 10k base traces / mo included; u · Enterprise plan: Custom pricing then pay as you go; self-hosted and hybrid deployment opti · Usage metered via LCU (LangChain Compute Units) at $1.50 / LCU for work done & compute and
Developer For solo users getting started. $0 / seat per month then pay as you go Start for free Up to 5k base traces / mo, then pay-as-you-go Community support 1 seat
Ledger citation[4] langchain.com/pricing
06Does LangSmith document API access?
Built-in APIs
built-in APIs, and autoscaling to handle enterprise-scale traffic
Ledger citation[3] langchain.com/customers
Decision guide
AI infrastructureCapabilities and operating fit
This profile connects the jobs LangSmith is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Common use cases
- Tracing and monitoring LLM applications in real time
- Debugging agent failures and tracking costs and latency
- Running online LLM-as-judge and code evals
- Monitoring tool and agent trajectories
- Scoring and improving agent performance
- Shipping and scaling agents in production
Topics mapped
Verified capabilities
- Complete AI agent and LLM observability platform with tracing and real-time monitoring
- Debug agents, find failures fast, and track costs and latency
- Message threading for multi-turn chat interactions
- Cost tracking across agent operations
- Online LLM-as-judge and code evals
- Tool and agent trajectory monitoring
- SmithDB purpose-built for agent observability with random access, full-text search, JSON k
- Agent Improvement Engine improves agents autonomously
Recorded integrations
Integrates with any agent stack via Python, TypeScript, Go, and Java SDKsNative tracing for popular agent frameworks and OpenTelemetry SDKs for Python, TypeScript,Webhook and PagerDuty alertsFastAPIIntegrates with Amazon EKS clustersLangGraph integrates with Remote MCP tools via the prebuilt ReAct agent.Enables seamless integration across LLM providers via chat model abstractionsSlack
Intended audiences
Access signals
- Pricing model
- Developer plan: $0 / seat per month then pay as you go; up to 5k base traces / mo included · Plus plan: $39 / seat per month then pay as you go; up to 10k base traces / mo included; u · Enterprise plan: Custom pricing then pay as you go; self-hosted and hybrid deployment opti · Usage metered via LCU (LangChain Compute Units) at $1.50 / LCU for work done & compute and
- API
- Not publicly listed
- Source links
- 17 recorded
Source-backed
Updated July 22, 2026Verified facts
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
LangSmith: Agent & LLM Observability Platform
Complete AI agent and LLM observability platform with tracing and real-time monitoring · Debug agents, find failures fast, and track costs and latency · Integrates with any agent stack via Python, TypeScript, Go, and Java SDKs
Complete AI agent and LLM observability platform with tracing and real-time monitoring
Debug agents, find failures fast, and track costs and latency
Integrates with any agent stack via Python, TypeScript, Go, and Java SDKs
Native tracing for popular agent frameworks and OpenTelemetry SDKs for Python, TypeScript, Go, and Java
Message threading for multi-turn chat interactions
View 32 more verified facts
Cost tracking across agent operations
Online LLM-as-judge and code evals
Tool and agent trajectory monitoring
Webhook and PagerDuty alerts
Self-host SmithDB inside your VPC so sensitive traces never leave the environment
SmithDB purpose-built for agent observability with random access, full-text search, JSON key-path filtering, and trajectory queries
Offers open source frameworks deepagents, langgraph, and langchain
Agent Improvement Engine improves agents autonomously
Observability service shows exactly what agents are doing
Evaluation service scores and improves agent performance
Agent Infrastructure Deployment ships and scales agents in production
Sandboxes run agent-generated code safely
LangSmith Platform encompasses Agent Improvement Engine, Observability, Evaluation, Agent Infrastructure Deployment, Sandboxes, and No-Code Agents
Open Source Frameworks include deepagents (long-running agents), langgraph (low-level agent control), and langchain (quick start with any model provider)
Developer plan: $0 / seat per month then pay as you go; up to 5k base traces / mo included; 1 seat; community support
Plus plan: $39 / seat per month then pay as you go; up to 10k base traces / mo included; unlimited seats; access to Deployment, Engine, and more; 1 free Serverless (Small) deployment included
Enterprise plan: Custom pricing then pay as you go; self-hosted and hybrid deployment options; custom SSO and RBAC; Support SLA; custom seats and workspaces
Usage metered via LCU (LangChain Compute Units) at $1.50 / LCU for work done & compute and LSU (LangChain Storage Units) at $1.00 / LSU for traces & storage
Additional Serverless and Dedicated deployments available by size (Small, Medium, Large) and charged based on resources consumed
Supports async operations with asyncio
Run LLMs, chains, and agents concurrently
Speed up I/O-bound tasks such as LLM API calls and data store interactions
FastAPI
LangSmith Platform
Observability
Evaluation
Agent Infrastructure Deployment
deepagents, langgraph, and langchain are open source frameworks
LangChain
LangChain supports TypeScript with native prompts, chains, and agents.
Python and TypeScript LangChain versions share a serializable format, enabling seamless artifact sharing between languages.
The TypeScript LangChain package includes abstractions for Prompts, LLMs, Text Splitters, Embeddings, Vectorstores, Chains, Agents, and Memory.
Practical capabilities
8 documented areasWhat it helps with
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Tracing and monitoring LLM applications in real time
Debugging agent failures and tracking costs and latency
Running online LLM-as-judge and code evals
Monitoring tool and agent trajectories
Scoring and improving agent performance
Shipping and scaling agents in production
Running agent-generated code safely in sandboxes
Alerting via Webhooks and PagerDuty
Availability
Source checkedWhere it runs and where to get it
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
Cost / license
Developer plan: $0 / seat per month then pay as you go; up to 5k base traces / mo included · Plus plan: $39 / seat per month then pay as you go; up to 10k base traces / mo included; u · Enterprise plan: Custom pricing then pay as you go; self-hosted and hybrid deployment opti · Usage metered via LCU (LangChain Compute Units) at $1.50 / LCU for work done & compute andOffers open source frameworks deepagents, langgraph, and langchainOpen Source Frameworks include deepagents (long-running agents), langgraph (low-level agendeepagents, langgraph, and langchain are open source frameworksOffers open source frameworks deepagents, langgraph, and langchain · Open Source Frameworks include deepagents (long-running agents), langgraph (low-level agen · deepagents, langgraph, and langchain are open source frameworks
Application types
ObservabilityEvaluationAgent Infrastructure DeploymentNo-code agent fleet for whole company
Origin
Began as Harrison Chase's side project in late 2022
Platforms
LangSmith Platform encompasses Agent Improvement Engine, Observability, Evaluation, Agent LangSmith PlatformLangSmith Platform offers products including Observability, Evaluation, Agent InfrastructuLangGraph is available for both JavaScript and Python.LangSmith — independent commercial platform for AI observability
Adoption notes
DeploymentSelf-host SmithDB inside your VPC so sensitive traces never leave the environment · Additional Serverless and Dedicated deployments available by size (Small, Medium, Large) a · Self-hosted deployments can run entirely within AWS VPCs via Helm charts · 1-click deployments with autoscaling for enterprise-scale traffic
LicenseOffers open source frameworks deepagents, langgraph, and langchain · Open Source Frameworks include deepagents (long-running agents), langgraph (low-level agen · deepagents, langgraph, and langchain are open source frameworks
Model supportNot disclosed by source
Data controlProvides full visibility into LLM workflows without compromising on data privacy or contro
Learning curveIntermediate
Primary use casesTracing and monitoring LLM applications in real time, Debugging agent failures and tracking costs and latency, Running online LLM-as-judge and code evals, Monitoring tool and agent trajectories, Scoring and improving agent performance, Shipping and scaling agents in production, Running agent-generated code safely in sandboxes, Alerting via Webhooks and PagerDuty, Speed up I/O-bound tasks such as LLM API calls and data store interactions, ChatLangChain documentation chatbot was recreated in TypeScript as a showcase., Accelerating agent and agentic workflow development, Deferred nodes are ideal for map-reduce, consensus, and agent collaboration workflows., Pre model hooks are great for summarizing message history (controlling context bloat) and, Building needle-moving GenAI applications, Build gen AI apps from prototype through production, LangSmith Fleet provides no-code agents for the whole company
What to verify before adopting
- The TypeScript version has fewer implementations than the Python version because it is new
- ML-centric functionality such as tokenizers and LLMs have worse TypeScript support than th
Evolution and major updates
Research in progressLangSmith timeline
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Scheduled for research
Building a reliable release history.
This profile is being checked for dated releases and material product changes. Nothing appears here until the exact date and event can be verified from a recorded source.
Dated event Short explanation Original source
Citation ledger
17 unique pagesRecorded sources
Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
- 1langchain.com/langsmith/observability 17 facts · 1 answer · Official site
- 2langchain.com/blog/typescript-support 11 facts · 4 answers · Official site
- 3langchain.com/customers 12 facts · 2 answers · Official site
- 4langchain.com/pricing 12 facts · 2 answers · Pricing
- 5langchain.com/about 12 facts · Official site
- 6langchain.com/blog/async-api 10 facts · 2 answers · Documentation
- 7langchain.com/blog/aws-marketplace-july-2025-announce 11 facts · 1 answer · Official site
- 8langchain.com/blog/langgraph-release-week-recap 11 facts · 1 answer · Official site
- 9langchain.com/blog/chat-models 8 facts · Official site
- 10langchain.com/blog 1 answer · Official site
- 11langchain.com/privacy-policy 1 fact · Security
- 12langchain.com/blog/code-interpreter-api Documentation
- 13langchain.com/blog/langchain-documentation-refresh Documentation
- 14langchain.com/blog/llms-to-improve-documentation Documentation
- 15langchain.com/blog/our-docs-test-themselves Documentation
- 16langchain.com/blog/plan-and-execute-agents Official site
- 17langchain.com/support-plans Pricing
Research status120 substantive facts · 17 source pages · quality score 95/100
Connected directory
Semantic matchesExplore related solutions
These tools share a workflow, capability or audience with LangSmith. They may complement it rather than replace it, and are not presented as integrations or endorsements.
