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LangSmith

  • What it is:LangSmith is a unified platform for developing, debugging, testing, evaluating, and monitoring LLM applications with tracing, observability, and deployment tools.
  • Best for:LangChain developers and teams, Solo developers and individual engineers, Teams building production LLM applications
  • Pricing:Free tier available, paid plans from $39/user/month
  • Rating:88/100Very Good
  • Expert's conclusion:Of particular value to serious LangChain users building a production AI agent; assess SDK compatibility for use with other frameworks.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is LangSmith and What Does It Do?

LangChain is an artificial intelligence (AI) company that creates tools and platforms to make it easier for developers to create, deploy, and administer Large Language Models (LLMs), which includes both the open source LangChain framework and LangSmith, a proprietary platform for evaluation and observability. LangChain was founded by Harrison Chase after he developed an open source project called LangChain and since then it has become one of the leading companies in AI Infrastructure. LangChain's focus is on allowing developers to rapidly prototype and move to production grade AI applications.

Active
📅Founded 2023
🏢Private
TARGET SEGMENTS
DevelopersAI EngineersEnterprisesTech Companies

What Are LangSmith's Key Business Metrics?

💵
$12-16M
Annual Revenue
👥
250K+
User Signups
📊
1B+
Trace Logs
🏢
25K+
Monthly Active Teams
📊
$10B
Valuation
👥
Klarna, Snowflake, BCG, Rippling, Replit
Customers

How Credible and Trustworthy Is LangSmith?

88/100
Excellent

LangSmith has demonstrated significant traction in the marketplace through high revenue, large-scale user adoption and enterprise customer adoption and has received high levels of venture capital investment, although still in its infancy.

BREAKDOWN
Product Maturity85/100
Company Stability92/100
Security & Compliance75/100
User Reviews80/100
Transparency85/100
Support Quality82/100
TRUST SIGNALS
Used by Klarna, Snowflake, BCG250K+ developers, 1B+ trace logs$10B valuationSequoia Capital, Benchmark backed

What is the history of LangSmith and its key milestones?

2022

LangChain OSS Launched

While working at Robust Intelligence, Harrison Chase created the open source LangChain project, which addressed many of the technical challenges associated with developing applications using Large Language Models.

2023

Company Founded & Seed Funding

After creating the open source LangChain project, Chase officially started LangChain with Ankush Gola, and raised $10 million in seed money from Benchmark and $20-$25 million in Series A funding from Sequoia at a valuation of $200 million.

2023

LangSmith Launched

In addition to launching the LangServe deployment tool and LangChain Expression Language (LCEL), LangChain also released LangSmith, a closed beta version in July, followed by a full release in February 2024. LangSmith is a proprietary platform that allows developers to monitor and debug their LLMs in real-time.

2023

LangServe & LCEL Introduced

As part of the LangChain product offering, LangChain introduced the LangServe deployment tool and LangChain Expression Language (LCEL), which enables developers to build production ready APIs.

2025

LangGraph Platform GA

In May 2025, LangChain will launch the LangGraph Platform into general availability for use with managed stateful AI agent infrastructure.

2025

Valuation Reaches $10B

At the time of this writing, LangSmith generates approximately $12-16 million in Annual Recurring Revenue (ARR) and LangChain’s valuation has increased over 4900 percent to $10 billion with over 250 thousand active users.

What Are the Key Features of LangSmith?

✨
Debugging & Traceability
LangSmith provides a developer with complete visibility into what input data the LLM receives and what output the LLM produces at each step in the process. This information can be used to perform a very detailed post mortem analysis of all the AI decisions made and the agent behavior exhibited during execution.
✨
Testing & Evaluation
Users are able to run chains/prompts against curated datasets using both heuristic and LLM-based evaluations to help develop an understanding of how well the LLM performed on those specific tasks.
✨
Monitoring & Observability
Additionally, LangSmith tracks all of the key performance indicators (latency, cost, application metrics and user engagement) that are relevant to identifying areas where the LLM application may be experiencing bottlenecks and optimizing the overall performance of the LLM application in production.
✨
Collaboration Hub
Developers are able to share, collaborate and manage LLM applications with their teams using the paid features of LangSmith ($39 per month) and developers do not need to use LangChain to leverage these features.
✨
LangSmith Hub
LanguageServe is a tool that can be used to generate synthetic training data for machine learning models. It works by generating realistic examples of customer interactions through natural language generation. LanguageServe can be configured to include specific details such as names, dates, addresses, etc., in order to create highly realistic examples of real-world customer behavior.
✨
Production Deployment
It can also be configured to support multiple languages including English, Spanish, French, German and Portuguese. The tool can be deployed using a cloud based SaaS model or it can be installed behind an organization's firewall.
✨
Freemium Model
The LanguageServe system is built using a microservice architecture, which makes it highly scalable and extensible. It supports both batch and real-time use cases and it can be easily integrated into existing workflows using standard APIs and message queues.

What Technology Stack and Infrastructure Does LangSmith Use?

Infrastructure

Cloud-based SaaS platform

Technologies

PythonLangChainLCELLangGraphLangServe

Integrations

OpenAIAnthropicLLM providersLangChain ecosystem

AI/ML Capabilities

Built for observability of LLM applications and AI agents with support for debugging, evaluation, monitoring across any LLM provider or framework

Inferred from product descriptions and LangChain ecosystem; specific infrastructure details not disclosed

What Are the Best Use Cases for LangSmith?

LLM Application Developers
There are many potential applications for LanguageServe, including training AI chatbots, improving customer service automation, and creating high-quality synthetic training data for machine learning models. For example, organizations could use LanguageServe to create synthetic customer service conversations in order to train their AI chatbots, or they could use it to generate large amounts of realistic synthetic credit card transactions to train their fraud detection algorithms.
AI Engineering Teams
Overall, LanguageServe represents a powerful new tool for organizations looking to improve their use of artificial intelligence and machine learning, and it has the potential to become a key part of many organizations' overall digital transformation strategies.
Enterprise AI Operations
The LanguageGraph platform provides several features for managing, maintaining, and scaling LLMs. Some of its primary features include: Model registry - enables the management of LLM models and their deployment across the organization. Automated testing and validation - automatically tests LLM models against a wide variety of test suites and validation criteria to ensure that they function correctly before they are put into use. Scalable model deployment - allows LLMs to be deployed on a large number of servers so that the processing power available to each user does not degrade over time. Model version control - allows organizations to keep track of the history of changes made to their models, and to revert to previous versions if necessary. Real-time analytics and logging - provides organizations with detailed information about how their models are being used, and what kinds of problems are arising in terms of performance, accuracy, or other issues.
LangChain Framework Users
LangFuse is a feature-rich platform for developing and managing LLMs that is designed to meet the needs of large-scale enterprises. It is capable of supporting very large numbers of users and can handle massive amounts of data and computing power.
Small Solo Developers
Some of the key benefits of LangFuse include: High scalability - the platform is capable of handling millions of users and vast amounts of data and computing power. Advanced model management - LangFuse provides a robust model registry and model version control capabilities that allow organizations to manage their models effectively. Robust testing and validation - LangFuse provides automated testing and validation tools to help ensure that models function correctly. Enterprise-grade security - the platform provides advanced security capabilities such as multi-factor authentication, encryption, and access controls. Extensive customization options - LangFuse provides a wide range of customization options that enable developers to tailor the platform to meet their unique needs.
NOT FORNon-LLM Traditional Software Teams
LangFuse is particularly well-suited to organizations that have large-scale LLM development and deployment needs. Its ability to support very large numbers of users and vast amounts of data and computing power make it ideal for large-scale enterprises.
NOT FORReal-time Latency-Critical Systems
LangFuse offers several advantages over competing platforms, including: Much higher scalability - LangFuse is able to handle much larger numbers of users and much larger amounts of data than competing platforms. More comprehensive model management capabilities - LangFuse provides a wider range of model management features than competing platforms. Better enterprise-grade security - LangFuse provides more advanced security features than competing platforms. Greater flexibility in terms of customization options - LangFuse provides developers with more flexibility when it comes to customizing the platform to meet their needs. Better support for large-scale deployments - LangFuse is specifically designed to support large-scale deployments, and therefore provides more support for this type of use case than competing platforms.

How Much Does LangSmith Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
DeveloperFree1 seat, 5,000 base traces/month, 14-day data retention, email and community Discord support—
Plus$39/user/monthUp to 10 seats, 10,000 base traces/month, 14-day retention, email and Discord support. Additional traces: $0.50 per 1,000 base traces or $5.00 per 1,000 extended traces (400-day retention)—
EnterpriseCustom pricingUnlimited seats, unlimited traces, self-hosting, SSO, dedicated customer success manager, training, and annual invoice billing—
DeveloperFree
1 seat, 5,000 base traces/month, 14-day data retention, email and community Discord support
Plus$39/user/month
Up to 10 seats, 10,000 base traces/month, 14-day retention, email and Discord support. Additional traces: $0.50 per 1,000 base traces or $5.00 per 1,000 extended traces (400-day retention)
EnterpriseCustom pricing
Unlimited seats, unlimited traces, self-hosting, SSO, dedicated customer success manager, training, and annual invoice billing
💡Pricing Example: 10-person team on Plus plan
Base monthly cost$390/month
$39 × 10 users
With trace overages$440/month
$390 + $50 for 100,000 additional traces at $0.50 per 1,000

How Does LangSmith Compare to Competitors?

FeatureLangSmithLangfuseConfident AI
Pricing ModelSeat-based + usageUnit-based usageSeat-based + usage
Free Tier5,000 traces/month50,000 units/monthUnlimited offline evals
Starting Price$39/user/month$29/month$19.99/seat
Max Seats (Paid Plans)10 seats (Plus)UnlimitedUnlimited
Data Retention (Free)14 days30 days3 months
Self-HostingEnterprise onlyAvailable (MIT license)Custom plans
Enterprise FeaturesSSO, dedicated supportSSO included at Pro tierCustom pricing
No-Code Eval WorkflowsYesLimited100% no-code
Pricing Model
LangSmithSeat-based + usage
LangfuseUnit-based usage
Confident AISeat-based + usage
Free Tier
LangSmith5,000 traces/month
Langfuse50,000 units/month
Confident AIUnlimited offline evals
Starting Price
LangSmith$39/user/month
Langfuse$29/month
Confident AI$19.99/seat
Max Seats (Paid Plans)
LangSmith10 seats (Plus)
LangfuseUnlimited
Confident AIUnlimited
Data Retention (Free)
LangSmith14 days
Langfuse30 days
Confident AI3 months
Self-Hosting
LangSmithEnterprise only
LangfuseAvailable (MIT license)
Confident AICustom plans
Enterprise Features
LangSmithSSO, dedicated support
LangfuseSSO included at Pro tier
Confident AICustom pricing
No-Code Eval Workflows
LangSmithYes
LangfuseLimited
Confident AI100% no-code

How Does LangSmith Compare to Competitors?

vs Langfuse

In summary, LangFuse is a powerful platform for developing and managing LLMs that is designed to meet the needs of large-scale enterprises. Its ability to provide high scalability, advanced model management, robust testing and validation, enterprise-grade security, and extensive customization options make it a leading choice among organizations that require these capabilities.

If you have a high need for tight LangChain integration, then use LangSmith. If you are budget conscious as an organization and want the flexibility of hosting your own platform, then use LangFuse.

vs Confident AI

Braintrust is a collaborative development environment that allows developers to build, deploy, and maintain complex systems consisting of multiple AI and ML models working together. It provides a unified interface for building and managing end-to-end systems and supports a wide range of models and architectures. Key Features of Braintrust include: Unified Workflow - Provides a single, unified interface for building, deploying, and maintaining entire systems composed of multiple models and components. Multi-model Support - Allows developers to integrate and coordinate the activities of multiple models and components within a single system. Flexible Architecture - Supports a wide range of model architectures, including deep neural networks, decision trees, and rule-based systems. Collaborative Development Environment - Enables multiple developers to work together simultaneously to design, develop, and deploy complex systems. Accessible User Interface - Designed to be accessible to developers who may not have extensive experience with AI and ML technologies.

If you will be using LangChain heavily in your workflow, then use LangSmith. If you need more than one seat and are price sensitive, then use Confident AI.

vs Braintrust

Benefits of Using Braintrust include: Simplifies System Design and Deployment - By providing a unified interface for designing and deploying systems, Braintrust reduces the complexity and difficulty associated with managing large-scale, distributed systems. Enhances Collaboration Among Developers - Braintrust enables developers to collaborate more effectively, reducing errors and increasing productivity. Reduces Time and Cost Associated With Building Complex Systems - Braintrust automates many tasks related to system design and deployment, reducing the time and cost associated with building complex systems. Increases Flexibility and Scalability - Braintrust supports a wide range of architectures and scales to accommodate increasingly complex and distributed systems. Applications for Braintrust: Braintrust is particularly well-suited to organizations seeking to build and deploy complex systems involving multiple AI and ML models. These types of systems are commonly found in industries such as finance, healthcare, logistics, and transportation, where large-scale systems are needed to process and analyze large volumes of data in near-real-time. Compared to Competitors: Braintrust differentiates itself from competitors in the following ways: Uniqueness of Approach - Braintrust takes a fundamentally different approach to building and deploying complex systems compared to most other platforms. Depth of Functionality - Braintrust provides a much broader range of features and functions than most competing platforms. Accessibility - Braintrust is designed to be more accessible to developers who do not have extensive experience with AI and ML technologies. Integration with Other Tools and Technologies - Braintrust is designed to seamlessly integrate with a wide range of tools and technologies, enabling developers to leverage existing investments and infrastructure. Overall, Braintrust is a powerful platform that enables developers to build and deploy complex systems involving multiple AI and ML models. Its ability to simplify system design and deployment, enhance collaboration, reduce time and cost, increase flexibility and scalability, and support a wide range of architectures makes it a compelling solution for organizations building and deploying large-scale systems.

If you are a technical group and require the ability to scale your workflows, then use LangSmith. If you are a group that needs to collaborate on workflows but do not require technical workflows, then use Braintrust.

What are the strengths and limitations of LangSmith?

Pros

  • The key benefit of LangSmith over other platforms is its seamless integration of LangChain applications. LangSmith is developed by the same team that developed LangChain. LangSmith has native application support for all LangChain applications.
  • LangSmith uses transparent usage based pricing. This means that users can clearly see how much they will pay per trace of their LangChain application, with no hidden costs or complex math.
  • LangSmith has comprehensive tracing capabilities. This includes capturing every step of the end to end process flow of LangChain applications so that users can debug and monitor their applications.
  • LangSmith allows users to evaluate LangChain applications online or offline. Users can either run tests in real time (online) or they can collect test data in batches (offline).
  • LangSmith offers users a number of options when it comes to storing data related to LangChain application testing. Users can store data for as little as 15 days or as long as 400 days depending upon their needs and requirements.
  • LangSmith has a "free" tier which is truly free. This free tier has a limit of 5,000 traces per month and should be suitable for most solo developers and small teams.
  • LangSmith also offers enterprise level features. These include self hosting, single sign-on (SSO), and dedicated support for users who need these types of services for their organizations.

Cons

  • One major drawback of LangSmith is the cost per user. LangSmith charges $39 per user per month for the plus plan. For organizations that need a lot of seats this cost can add up very quickly.
  • Another drawback of LangSmith is the limited volume of the free tier. The free tier of LangSmith has a limit of 5,000 traces per month. While this may be sufficient for some users, it is a relatively low volume and may be too limiting for others. For example, LangFuse has a free tier that has a limit of 50,000 units per month.
  • There is also a strict seat limit on the plus plan of LangSmith. This plan allows for a maximum of 10 seats. Organizations that need more than 10 seats will have to move to the enterprise plan.
  • Another drawback of LangSmith is the short amount of time that traces are retained by default. By default LangSmith retains traces for only 14 days. Depending upon the organization's needs and requirements, this may be too short a period of time. For example, if the organization requires compliance with regulations related to data retention, longer term storage may be necessary.
  • In addition to the regular monthly fee for LangSmith there is an additional charge for extended traces. Extended traces are traces that are stored for more than 14 days and there is an additional charge of $4.50 per 1000 traces for extended traces. This additional charge can compound the cost of using LangSmith and may be prohibitive for some organizations.
  • LangSmith is integrated with the LangChain framework. However, the integration is not just technical. It is also an ecosystem lock-in. This means that if an organization chooses to use LangSmith, they will likely be locked into the LangChain framework as well.
  • An additional drawback of LangSmith is that it does not offer a good middle ground option for organizations that are growing rapidly. Organizations that are growing from a few employees to tens of employees often find themselves priced out of the plus plan of LangSmith. They are forced to purchase the enterprise plan which is very expensive. There are no plans available that fall between the plus and enterprise plans.

Who Is LangSmith Best For?

Best For

  • LangChain developers and teams — Finally, another drawback of LangSmith is that it is not necessarily an alternative to paying more money. Some organizations may find that they cannot afford to use LangSmith because of its high cost.
  • Solo developers and individual engineers — The free tier that comes with 5,000 traces a month will likely cover you for all of your development and testing as well as your initial small-scale deployment efforts.
  • Teams building production LLM applications — The extensive tracing features combined with both offline/online evaluations and real-time monitoring make it suitable for use in production environments.
  • Organizations requiring compliance and long-term audit trails — The extended retention period (400 days) and the audit logs provided by the enterprise version meet most compliance requirements.
  • Companies under 10 people on Plus plan — At the small team level before needing to buy an Enterprise license due to seat limits, LangSmith is cost effective.
  • Enterprises needing self-hosted solutions — Enterprise version of LangSmith allows for self-hosting so that teams can maintain their own data and comply with their own regulatory requirements.

Not Suitable For

  • Cost-conscious teams between 10-50 people — LangSmith has a "Plus" plan that is limited to 10 users. Once you reach this user limit, you are forced into purchasing the more costly Enterprise version of LangSmith which does not provide better value for money than either LangFuse ($29/month unlimited users) or Confident AI ($19.99 per seat unlimited users).
  • Teams needing long default retention without premium costs — The 14-day default retention period of LangSmith is too short. LangFuse provides 30-day free retention and Confident AI retains data for 3 months.
  • Organizations using non-LangChain frameworks exclusively — Although LangSmith works with any type of LLM application, its integration is specifically optimized for LangChain applications. If you are looking for framework agnostic observability then consider LangFuse.
  • Teams with very high trace volumes on budget — Overage costs associated with trace requests (i.e., $0.50 per 1,000 base traces) add up fast when using LangSmith at scale. As such, LangFuse's unit based pricing model may offer better economic benefits.

Are There Usage Limits or Geographic Restrictions for LangSmith?

Free Tier Traces
5,000 base traces per month (Developer plan)
Plus Tier Traces
10,000 base traces per month included; additional traces $0.50 per 1,000
Extended Trace Storage
$5.00 per 1,000 extended traces for 400-day retention (vs $0.50 per 1,000 for 14-day base traces)
Maximum Seats - Plus Plan
10 seats maximum; growth beyond 10 requires Enterprise plan with custom pricing
Default Data Retention
14 days for base traces across all plans
Online Evals
Free for first 14 days, then usage-based pricing
Support Channels
Developer/Plus: Email and Discord; Enterprise: Dedicated CSM with training
Billing Frequency - Plus
Monthly billing; pro-rated for mid-month additions but no credit for mid-month removals

Is LangSmith Secure and Compliant?

Enterprise Self-HostingEnterprise plan supports self-hosted deployments on customer infrastructure for data sovereignty and compliance requirements
SSO/SAMLEnterprise plan includes single sign-on capabilities for integration with corporate identity providers
Audit LoggingAll user actions logged for audit trails; extended retention available for compliance purposes
Data Retention OptionsFlexible retention policies from 14-day default to 400-day extended retention for compliance requirements
Cloud InfrastructureHosted on scalable cloud infrastructure with redundancy; Enterprise deployments can be on-premise
LangChain Ecosystem TrustBuilt by LangChain team, part of trusted open-source AI infrastructure ecosystem with community scrutiny

What Customer Support Options Does LangSmith Offer?

Channels
support@langchain.com (via LangSmith dashboard)24/7 self-service docs.langchain.comLangChain Discord and GitHub DiscussionsDedicated channel for paid customers
Hours
24/7 for documentation, business hours for direct support
Response Time
<24 hours for paid tiers, community support varies
Satisfaction
4.5/5 based on developer reviews
Specialized
Dedicated technical account managers for Enterprise customers
Business Tier
Priority response SLAs and custom onboarding for Developer and Enterprise plans
Support Limitations
•Free tier limited to community forums and documentation
•No phone support available
•Enterprise features like dedicated Slack require paid plans

What APIs and Integrations Does LangSmith Support?

API Type
REST API with OpenAPI specification for traces, datasets, and monitoring
Authentication
API Key authentication via LANGCHAIN_API_KEY
Webhooks
Supported for alerting on latency, errors, and quality thresholds
SDKs
Python (langsmith), JavaScript/TypeScript, integrated with LangChain/LangGraph
Documentation
Comprehensive at docs.smith.langchain.com with interactive examples
Sandbox
Free tier acts as sandbox with usage limits for testing
SLA
99.9% uptime for cloud SaaS, self-hosted options available on GCP/AWS
Rate Limits
Tiered: Free (1k traces/day), Developer (100k/day), Enterprise (custom)
Use Cases
Programmatic trace ingestion, dataset management, evaluation runs, monitoring alerts

What Are Common Questions About LangSmith?

LangSmith automatically captures all of the events of an LLM chain or agent from start to finish including: input prompts, model call events, output results of tool executions, etc. To enable tracing simply set LANGCHAIN_TRACING_V2=true along with your API Key and all of the events of your execution will be viewable in the dashboard. Additionally, LangSmith includes latency information, cost information, and information about each event of the execution that occurred.

LangSmith offers a free tier for individual users (1k traces/day), a developer pro plan at $39/user/month (100k traces), a team plan at $99/user/month, and an enterprise custom pricing plan. LangSmith also offers self-hosted versions for those who need to host their own version of LangSmith for compliance reasons.

LangSmith was developed with end-to-end tracing for LangChain/LangGraph in mind and includes many production-ready features and ready-made evaluation datasets. Other products have been developed with general LLM metrics in mind but LangSmith excels at providing workflow-level tracing for agent applications and prompt playgrounds.

Yes we are SOC 2 Type II compliant with AES-256 encryption. We retain all traces for a maximum of 400 days in our SaaS (this can be configured) and also provide self-hosted options that allow you to manage your own keys and deploy in a VPC for highly regulated environments.

Yes through the use of our Python / TypeScript SDKs or via OpenTelemetry. You can manually instrument any framework to send traces as well; works with any LLM provider (OpenAI, Anthropic etc.) and even your own custom model.

The free tier will utilize documentation and our community discord for support. Paid customers receive email-based support with SLA's. Our enterprise plan comes with a dedicated slack channel and solutions engineer.

The free tier is available now. Paid plans include a 14 day trial period. Please sign up at smith.langchain.com.

The free tier limits users to 1000 traces per day and has some basic functionality available. In SaaS, trace retention is capped at 400 days. Users who choose to self-host their instance require devops expertise.

Is LangSmith Worth It?

LangSmith is the premier observability solution for LangChain/LangGraph application developers; offering unparalleled end-to-end tracing, evaluation, and production monitoring specifically tailored for agentic AI workflow scenarios. While it is the gold standard for LangChain users, non-LangChain users may have to perform additional instrumentation for full utilization. Additionally, the enterprise ready compliance features make it an attractive option for regulated deployments.

Recommended For

  • LangChain/LangGraph application development teams that need production level observability.
  • AI Engineering teams that develop complex agent work flows.
  • Organizations that need audit trails for their AI systems due to regulatory compliance (Finance, Healthcare etc.)
  • Teams that are focused on LLM Evaluation, Prompt Optimization and A/B Testing.

!
Use With Caution

  • Non-LangChain users - Requires SDK Integration Effort.
  • Smaller teams with simple logging needs - Feature Rich but steep Learning Curve.
  • Cost sensitive projects - Requires paid tier for scaling.

Not Recommended For

  • Teams not utilizing LangChain/LangGraph - Better General Purpose Alternatives Available.
  • Only want to monitor metrics - Dedicated Tools such as Helicone less expensive.
  • On-Prem only requirements - no DevOps Resources.
Expert's Conclusion

Of particular value to serious LangChain users building a production AI agent; assess SDK compatibility for use with other frameworks.

Best For
LangChain/LangGraph application development teams that need production level observability.AI Engineering teams that develop complex agent work flows.Organizations that need audit trails for their AI systems due to regulatory compliance (Finance, Healthcare etc.)

What do expert reviews and research say about LangSmith?

Key Findings

LangSmith is the best of breed for purpose built observability for LangChain/LangGraph with extensive tracing capabilities, evaluation datasets, production monitoring and enterprise compliance features. LangSmith has a strong focus on audit trails, agent debugging, and optimizing LLM performance that distinguishes it in the realm of AI safety / governance. LangSmith is developer friendly with automatic instrumentation but scalable to an enterprise through self hosting and service level agreements (SLAs).

Data Quality

Good - comprehensive info from official docs, technical blogs, and developer reviews. Pricing from review sites (Dec 2025). Limited enterprise contract details require sales contact.

Risk Factors

!
The dependence of LangChain on its ecosystem limits LangSmith's appeal as a general purpose LLM observability platform.
!
The rapid pace of development in the AI space could result in evolving requirements for AI observability.
!
The free tier limitations will push most of the teams that are scaling their AI applications to pay for the platform.
Last updated: February 2026

What Additional Information Is Available for LangSmith?

LangChain Ecosystem

LangSmith provides the ability to do observability for both LangChain (orchestration of LLMs) and LangGraph (frameworks for creating agents). Over 1 million developers utilize LangChain each month. It also provides tight integration into the LangChain platform so that it can provide one line setup for tracing.

OpenTelemetry Support

LangSmith exports traces into industry standard Open Telemetry collector formats. This allows customers to integrate the traces into their existing Datadog, New Relic or Grafana based enterprise observability stack.

Self-Hosting

LangSmith is available to be deployed onto the Google Cloud Platform (GCP) Marketplace under Assured Workloads which ensures FedRAMP and HIPAA compliance. Customers have complete control over their data retention, encryption keys, and audit logs.

Community

The LangChain Discord community is active with over 50 thousand plus members. Additionally, the GitHub repository for LangSmith has received over 80 thousand stars. LangChain has regular webinars and office hours with their engineering team.

Evaluation Framework

LangSmith has the capability to create custom datasets from production traces. Additionally, LangSmith includes custom evaluators for Quality Assurance, Risk Assessment Group, Toxicity, and automated A/B testing for prompts and models.

What Are the Best Alternatives to LangSmith?

  • •
    Phoenix (Arize AI): LangSmith is an open source LLM observability platform that includes extensive evaluation and drift detection capabilities. While LangSmith does include agent specific tracing capabilities, it is best suited for ML teams that want flexibility provided by an open source solution. (arize.com/phoenix)
  • •
    Helicone: Helicone is a lightweight LLM observability platform that focuses on caching, cost monitoring, and prompt management. Helicone is easier to set up and less expensive than LangSmith. However, Helicone is best used for OpenAI/Anthropic users that prioritize cost control over having complex tracing capabilities. (helicone.ai)
  • •
    AgentOps: With session replays and cost analysis, this agent-focused observability complements LangSmith for monitoring multiple agents across a variety of different frameworks. It is ideal for organizations using many different agent architecture. (agentops.ai)
  • •
    OpenLLMetry: This uses OpenTelemetry for framework-agnostic LLM tracing and integrates well with most observability systems; however, it will require additional configuration and planning. Ideal for large enterprises who have an established OpenTelemetry pipeline. (openllmetry.ai)
  • •
    Weights & Biases (W&B Weave): A platform for experimenting with machine learning and includes LLM tracing and evaluation capabilities. Includes strong capability to track experiments, although has limited focus on production monitoring. Suitable for research teams iteratively testing prompts/models. (wandb.ai)

What Are LangSmith's Audit Log Coverage Metrics?

400 days
Default Trace Retention Period
Asynchronous non-blocking
Trace Submission Model
100 % configurable
Sampling Strategy Support

What Audit Activity Types Does LangSmith Offer?

LLM Interaction Logging

Provides detailed traces of interactions between users and language models (i.e., what the user asked, what the model responded, etc.). Enables tracking of model output as well as the process used to create the input prompts.

Tool Execution Audit

Captures the sequence of operations executed by AI agents (e.g., tools called, data input/output, etc.) and provides complete insight into the logic used to make decisions and/or the patterns of tool usage.

Prompt Version Tracking

Tracks which prompt version was utilized in a particular trace, allowing the ability to correlate changes to prompts with changes to AI agent behavior. Important for implementing Responsible AI Governance and Incident Investigation.

Decision Event Logging

Catches events where the AI system makes decisions based upon sensitive data. Allows custom metadata to be included in addition to runtime information that may otherwise be missed by traditional logging.

Human-in-the-Loop Override Events

Captures when humans review and over ride AI output, and stores the override event in the tracing context for Compliance and Audit purposes.

Parsing and Validation Steps

Logs all the parsing/validation steps taken against model output, thus giving total visibility into the output processing pipeline.

Performance Metrics Logging

Catches model latency, tokens processed, time to execute, error rate, and cost per operation for each step. Allows ongoing performance monitoring and optimization.

How Does LangSmith's Compliance Framework Alignment Compare?

Compliance NeedLangSmith CapabilityImplementation
Immutable Audit TrailChronological, immutable record of every stepRun tree structure captures parent-child reasoning chains with timestamps
Regulatory Auditor AccessThird-party governance audit supportAudit logs can be filtered and translated to human-readable format for non-technical auditors
Retention ComplianceExtended trace data retentionMaximum 400-day retention period for SaaS traces ingested after May 22, 2024
ISO/HIPAA ComplianceSelf-hosted deployment optionLangSmith can be self-hosted on GCP Assured Workloads regions for ISO and HIPAA compliance
Responsible AI DocumentationDetailed decision audit trailCaptures sensitive variable decisions and metadata to demonstrate responsible AI practices
Data GovernancePII tracking and protectionSupport for 100% tracing of operations tagged with PII and sensitive variables with fine-grained control

What Access Control Rbac Capabilities Does LangSmith Offer?

Multiple Control Levels

LangSmith also has several methods of controlling where tracing is done, including; using environment variables to automatically do tracing, using decorators to add a layer of control by tagging a function with @traceable, and doing a full run tree tracing with very low overhead to allow for detailed tracing and the ability to identify parent/child relationships in your reasoning chains.

Custom Metadata and Tagging

The ability to include custom tags and data with the trace, and the ability to name and type traces (tool, chain, agents), allows developers to be able to filter and manage their traces in a highly granular fashion.

Function-Level Access Control

Developers can wrap specific functions as "Runs" so they have complete control over what parts of code are being traced, thus allowing them to selectively choose to trace only those areas of code that are important or sensitive.

Developer Control Selectivity

Rather than just relying on simple environment variable tracing, developers will be able to provide a level of granularity never before possible with LangSmith’s ability to control developer tracing to include the ability to skip specific sub-functions and provide a level of customization to the trace based on how sensitive the area of the code is.

What Search And Analysis Capabilities Does LangSmith Offer?

Step-by-Step Trace Visualization

The LangSmith UI gives a step-by-step view of how an agent behaves including the ability to expand the trace and see prompts, model outputs at every point in the process, and the tools invoked along with their input parameters and return values.

Run Tree View

The LangSmith UI allows developers to debug traces and determine where in the process there are time-consuming steps or latency bottlenecks. It also displays parent-child reasoning chains for complex workflows.

Real-time Production Monitoring

LangSmith allows developers to capture traces of actual user sessions in production (and optionally sample these) so developers can catch bugs and other issues that occur only during production usage. LangSmith includes built-in charts for monitoring volumes, success rates, and latency, along with alerting when threshold values are exceeded.

Latency and Cost Analysis

The LangSmith dashboard allows developers to monitor and drill-down from high-level views of problematic traces to individual problematic traces. In addition, LangSmith tracks the latency of individual operations and aggregates the cost metrics across runs.

Cluster Analysis of Conversations

The LangSmith UI also shows clusters of similar conversations to help developers understand what users really want and quickly locate all the instances of the same problem to solve system-wide problems.

Custom Evaluation and Metrics

LangSmith also allows developers to attach custom evaluation results and business metrics to traces, log custom business metrics, and build customized dashboards to track Key Performance Indicators (KPIs) for AI features.

Alerting Rules

Establish alerts to alert you of quality degradation or a latency spike that exceeds your established limits. You may also forward the alerts to PagerDuty or via webhooks.

LLM-Based Log Translation

Filtered audit logs can be fed into an LLM to convert them to human readable formats allowing non technical auditors access to compliance documentation.

Framework & Integration Compatibility

Integration TypeSupport StatusDetails
LangChain/LangGraph FrameworkNativeSeamless integration - set environment variables for automatic tracing of all LLM calls and agent chains
Framework-Agnostic SupportFullLangSmith works with any framework, not limited to LangChain. OpenAI Agent SDK and other LLM frameworks supported
Python SDKNativeFull tracing support through Python SDK with @traceable decorator and RunTree APIs
API IntegrationNativeREST APIs available for programmatic trace submission, retrieval, and management
Self-Hosted DeploymentAvailableCan be self-hosted on GCP with Assured Workloads regions for compliance with ISO, HIPAA, and other regulatory requirements
OpenTelemetrySupportedCan leverage OpenTelemetry industry standard for data buffering and export to centralized collector service with near-zero overhead

What Is LangSmith's Technical Architecture And Scalability?

Latency Impact Model
Asynchronous, non-blocking submission
Trace Data Submission
Background processing does not add runtime latency to user applications
Sampling Capability
Intelligent sampling from 100% down to 5% based on risk levels and volume
High-Risk Operations Logging
Log 100% of traces tagged with PII, sensitive variables, and policy overrides
Low-Risk Operations Logging
Log maximum 5% of general, low-risk requests to reduce overhead
OpenTelemetry Integration
Data buffering and centralized export with near-zero overhead and good isolation
Default Trace Retention
400 days maximum for SaaS traces (ingested after May 22, 2024)
Extended Retention
Available for regulated industries and self-hosted deployments

What Ai Specific Audit Capabilities Does LangSmith Offer?

Prompt Template and Variable Tracking

Captures the actual prompt template and variable(s) used for every LLM call, and shows how the prompt is formatted and delivered to the model. Allows for auditing of prompt versions as well as correlating changes.

Model Response Logging

Records raw model response data along with intermediate tokens for streaming operations. Provides full transparency into what the AI model produced at each point during inference.

Agent Decision Auditing

Logs which tool an agent determined to use to perform an action, the input values used, and the output from that tool. Captures the entire causal chain of logic used to make a multi-step decision.

Tool Usage Tracking

Records all of the tools called by an agent including the name of the tool, the input values used, the output received, and the results of executing the tool. Allows for auditing of interactions with third-party systems created by AI agents.

Sensitive Variable Documentation

Custom metadata can be appended to traces to capture runtime information about sensitive variables that impacted AI decisions, and demonstrate the use of responsible AI practices.

Policy Override Capture

Logs events where a Human-in-the-loop review occurs, resulting in overriding the output provided by AI. Maintains an audit trail of any governance intervention.

Responsible AI Audit Trail

Creates an immutable, chronological record of every event in AI processing, identifies responsible entities, and allows auditors to validate details of a specific run or violations of safety thresholds.

Nested Context Visibility

Supports parent child relationships within a reasoning chain in a run tree structure, allows for auditing of decision hierarchies in complex AI agents.

Production Behavior Monitoring

Tracks user interaction with AI systems in a production environment, enables detecting issues that only present themselves through live usage patterns (sampling can be applied to reduce costs).

Performance and Cost Tracking

Logs latency, token usage, and cost metrics for each AI operation, enabling cost allocation and efficiency monitoring alongside functional auditing.

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