LangSmith

LangSmith is an AI engineering platform from LangChain designed to help developers and teams build, test, evaluate, deploy, monitor, and continuously improve large language model (LLM) applications and AI agents. Unlike a general-purpose AI assistant, LangSmith is primarily a development and operations platform for teams creating AI-powered software.

LangSmith was introduced in July 2023 as a platform for debugging, testing, evaluating, and monitoring LLM applications. It initially addressed a major problem in generative AI development: moving from an experimental prototype to an application that performs reliably in production. LangSmith reached general availability in February 2024.

One of LangSmith’s central capabilities is observability and tracing. Developers can capture detailed traces of what an AI application does, including model calls, prompts, tool calls, intermediate steps, retrieval operations, and outputs. This makes it possible to investigate why an agent produced a particular response and identify problems affecting quality, latency, or cost. LangSmith is framework-agnostic, so it can be used with LangChain and LangGraph as well as custom Python applications and other frameworks.

LangSmith is particularly focused on AI-agent development. Teams can observe individual agent steps, monitor agent behavior, evaluate results, and use the information collected from production runs to improve their applications. The platform brings testing, deployment, and monitoring into a continuous development cycle rather than treating them as separate processes.

The platform includes online and offline evaluation. Developers can evaluate AI applications before deployment using datasets and test cases, while production traces can be evaluated after deployment. Evaluation methods can include LLM-as-a-judge, custom evaluators, automated checks, and human annotation. This allows teams to measure factors such as correctness, relevance, quality, and application-specific criteria.

LangSmith also provides datasets for systematic testing. Developers can collect examples from production, create evaluation datasets, run experiments, and compare application versions. This creates a feedback loop where problematic production examples can become test cases used to prevent the same issues from returning in later versions.

Another component is Prompt Hub and Playground. Teams can experiment with prompts, compare configurations, and manage prompt versions. This can help developers separate prompt development from application code and test changes before deploying them.

LangSmith provides monitoring and analytics for production applications. Teams can track metrics such as latency, costs, errors, usage, and response quality. Monitoring dashboards and alerts help teams identify problems and investigate individual traces when something goes wrong.

The platform also includes human feedback and annotation workflows. Teams can send selected AI outputs for human review and use those judgments as evaluation data. This is useful when automated evaluation cannot fully capture whether an output meets a company’s requirements.

LangSmith has expanded beyond observability and evaluation into agent deployment. LangSmith Deployment provides infrastructure for deploying long-running agents, with features including serverless and dedicated deployments, real-time streaming, authentication and authorization, and APIs for agent state and memory.

The platform also includes LangSmith Fleet, which allows users to create agents for daily tasks using natural-language instructions. Fleet includes prebuilt templates and supports connecting tools through remote MCP servers.

Another newer capability is LangSmith Engine, which analyzes agent traces to identify recurring failures and potential fixes. It can cluster agent behavior into issues, diagnose code failures, recommend improvements to prompts and code, and generate datasets and evaluations for problematic behavior.

LangSmith also offers Sandboxes, which provide isolated environments intended for agent-generated code. These environments can be configured with custom images, storage, time-to-live settings, port tunneling, and other controls.

The platform includes an LLM Gateway for managing model access and governance. It provides capabilities such as cost controls, rate limiting, model fallbacks, PII and secrets redaction, and bring-your-own model API keys. The Gateway can also connect model activity to LangSmith traces.

LangSmith is designed to work across different model providers and application architectures. It is not restricted to LangChain applications; developers can send traces from custom code and other frameworks through its SDK and API. This framework-agnostic approach allows organizations to use LangSmith even when their AI stack contains multiple technologies.

LangSmith is also designed for enterprise deployments. Enterprise customers can choose cloud, hybrid, or self-hosted deployment options. Enterprise capabilities include custom SSO, attribute-based and role-based access control, support SLAs, custom seats and workspaces, and self-hosted or hybrid infrastructure.

For organizations concerned about data privacy, LangSmith states that it does not use customer data to train models and that customer traces, prompts, and outputs remain private to the organization. Enterprise deployments can also be hosted within customer-controlled infrastructure.

LangSmith’s pricing combines subscription fees with usage-based charges. The current Developer plan is free at $0 per seat per month and includes up to 5,000 base traces per month. The Plus plan costs $39 per seat per month and includes up to 10,000 base traces per month. Both plans then use pay-as-you-go pricing for additional usage. Enterprise pricing is customized.

LangSmith also uses LangChain Compute Units (LCUs) and LangChain Storage Units (LSUs) for certain services. One LCU costs $1.50, while one LSU costs $1.00. Different LangSmith products consume these units according to their compute and storage requirements.

The Developer plan is aimed at individual developers and small projects, while Plus is designed for teams building and deploying agents. Enterprise is intended for organizations requiring advanced security, hosting, administration, support, and deployment capabilities.

LangSmith is therefore best suited to developers, AI engineering teams, ML teams, startups, and enterprises building production LLM applications and AI agents. It is especially useful when a team needs visibility into agent behavior, systematic evaluations, production monitoring, and a repeatable process for improving AI applications.

Its biggest distinction is that LangSmith goes beyond simple LLM logging. It connects observability → evaluation → debugging → deployment → monitoring → improvement within one AI engineering platform.

Pricing

LangSmith currently uses a combination of subscription pricing and usage-based billing. The Developer plan is free, Plus costs $39 per seat per month, and Enterprise uses custom pricing. Additional usage is billed according to trace volume and other metered services.

Pricing Model

Paid

Pricing Note

LangSmith has a free Developer plan with up to 5,000 base traces per month. Plus costs $39/seat/month with 10,000 base traces per month, followed by pay-as-you-go usage. Enterprise pricing is customized. Additional services may use LCUs and LSUs.

Review

LangSmith is a strong platform for teams building production-grade LLM applications and AI agents. Its biggest advantage is the depth of its observability and evaluation capabilities. Developers can inspect individual agent steps, monitor production behavior, create evaluations, and use real-world failures to improve future versions.

Its framework-agnostic approach is also valuable because teams are not required to build their entire AI stack around LangChain. LangSmith can work with LangChain, LangGraph, custom Python applications, and other frameworks.

The addition of deployment, Fleet, Engine, Sandboxes, and LLM Gateway makes LangSmith more than a traditional observability tool. It increasingly functions as an end-to-end platform for building and operating AI agents.

The main drawback is complexity. LangSmith is primarily aimed at developers and AI engineering teams, so casual users may find its tracing, evaluations, datasets, deployments, and usage metrics more involved than necessary.

Pricing can also become more complicated at higher usage because the platform combines seat charges with usage-based billing. Teams running large numbers of traces or resource-intensive agent workloads need to monitor their consumption carefully.