Sold by: LangChain
Deployed on AWS
LangSmith is an agent engineering platform to build, test, deploy and observe your agents. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place. LangSmith is framework agnostic, so you can use it with or without LangChain open-source libraries langchain and langgraph. Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems. LangSmith provides: - Observability to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics. - Evaluation to test and score agent behavior on production data and offline datasets for continuous improvement. - Deployment to ship your agent in one click, using scalable infrastructure built for long-running tasks.
Overview
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LangSmith Observability
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LangSmith Deployments
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LangSmith Evaluations
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LangSmith Fleet
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Agent Development Lifecycle
LangSmith Observability and Evals is a unified observability & evals platform where teams can debug, test, and monitor AI app performance - whether building with LangChain or not. Find failures fast with agent observability. Quickly debug and understand non-deterministic LLM app behavior with tracing. See what your agent is doing step by step, then fix issues to improve latency and response quality. Evaluate your agent's performance. Evaluate your app by saving production traces to datasets, then score performance with LLM-as-Judge evaluators. Gather human feedback from subject-matter experts to assess response relevance, correctness, harmfulness, and other criteria. Experiment with models and prompts in the Playground, and compare outputs across different prompt versions. Any teammate can use the Prompt Canvas UI to directly recommend and improve prompts. Track business-critical metrics like costs, latency, and response quality with live dashboards, then get alerted when problems arise and drill into root cause. LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents -- offering: - 1-click deployment to go live in minutes, - 30 API endpoints for designing custom user experiences that fit any interaction pattern - Horizontal scaling to handle bursty, long-running traffic - A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows - Native LangSmith Studio, the agent IDE, for easy debugging, visibility, and iteration
Highlights
- LangSmith is the framework agnostic agent engineering platform for observing, evaluating, and deploying agents. It's hard to build agents because you can't plan for every input, and LLMs decide every output on the fly at runtime.
- LangSmith Fleet is a no-code platform for creating and managing AI agents. It allows you to create agents from templates, connect your accounts, and let the agent handle routine work while you stay in control.
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Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Usage Fee | Units of metered usage as calculated based on platform activity | $0.01 |
Dimensions summary
You pay through a single usage-based dimension: metered Units tied to platform activity. There are no fixed tiers or seat charges on this listing. Instead, your bill scales with how much work your agents perform and how much data you store. Activity across services like observability tracing, deployments, engine runs, and sandboxes converts into normalized compute and storage units. The more you run and store, the more Units you consume. This keeps costs aligned with actual use, so you pay only for the platform activity you generate each billing period.
Top-of-mind questions for buyers
Your bill uses two normalized units. A Compute Unit measures work done — compute, memory, and model usage across services like engine runs, deployments, and sandboxes. A Storage Unit measures data stored or managed, including traces. Each service meters at its own rate, then rolls up into these units.
Charges accrue only while resources run. Serverless deployments scale to zero when idle, so you pay only during active runtime. Sandboxes are billed per second and shut down automatically using configurable time-to-live settings. Stopping or deleting these resources ends the metered charges tied to them.
Both compute and storage charges apply at the same time. Trace volume and storage consume Storage Units for observability workloads. Engine runs, deployments, fleet activity, and sandboxes consume Compute Units. One engine run can consume roughly 5–30 Compute Units, depending on trace volume and application complexity.
www.langchain.com
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LangSmith provides tools for developing, debugging, and deploying LLM applications. It helps you trace requests, evaluate outputs, test prompts, and manage deployments in one place.
LangSmith is framework agnostic, so you can use it with or without LangChain open-source libraries langchain and langgraph.
Prototype locally, then move to production with integrated monitoring and evaluation to build more reliable AI systems.
LangSmith provides:
Observability to see exactly how your agent thinks and acts with detailed tracing and aggregate trend metrics.
Evaluation to test and score agent behavior on production data and offline datasets for continuous improvement.
Deployment to ship your agent in one click, using scalable infrastructure built for long-running tasks.
Observability, prompt management and evaluation for LLM applications.
We help customers design and implement Agentic applications on AWS using our modular GenAI Building Block approach. Our experts support rapid PoCs and production deployments, guiding you in selecting and integrating the right LLM for your use case. This ensures flexibility, scalability, and cost-efficiency in your AWS environment.
This product has charges associated with it for hardening, security configuration, and support.
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