Sold by: LangChain
Deployed on AWS
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.
4.6
Overview
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
LangSmith Agent Builder: Give every team the ability to build, use, and improve AI agents with the security your org requires.
Highlights
- 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. 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.
- LangSmith Deployments is a purpose-built infrastructure and management layer for deploying and scaling long-running, stateful agents offering 1/1-click deployment to go live in minutes, 2/Horizontal scaling to handle bursty, long-running traffic 3/A persistence layer to support memory, conversational history, and async collaboration with human-in-the-loop or multi-agent workflows.
- Please note: there is a $150k annual Platform License plus a minimum $150k annual usage commitment to access this package. To discuss enterprise pricing or to activate your commitment and obtain your license key after signup, please contact us at https://www.langchain.com/contact-sales - alternatively, our self-serve cloud-based products are available at https://www.langchain.com
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LangSmith Helm Chart
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Linux
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 | Cost/unit |
|---|---|
Per Trace | $0.01 |
Per Agent Run | $0.01 |
Metered Usage Amount | $0.01 |
Minimum annual usage commitment, billed in advance | $150,000.00 |
Per Agent Builder Run | $0.10 |
Vendor refund policy
https://www.langchain.com/terms-of-service#:~:text=Customer%20will%20pay%20LangChain%20all ,Fees%20paid%20are%20non%2Drefundable.
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Delivery details
LangSmith Helm Chart
Supported services: Learn more
- Amazon EKS
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Version release notes
LangSmith 0.13.14 release
Additional details
Usage instructions
See https://docs.smith.langchain.com/self_hosting for full installation and configuration instructions.
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Observability, prompt management and evaluation for LLM applications.
This product has charges associated with it for deployment, configuration, and ongoing support. Self-hosted LangChain application server. Deploy chains, agents, and RAG pipelines as production REST APIs with playground and monitoring.
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Customer reviews
Atharva S.
LangChain Makes Building AI Apps Fast with Great Integrations and a Clean Developer Experience
Reviewed on Jul 27, 2026
Review provided by G2
What do you like best about the product?
What I like best about LangChain is how it simplifies building AI-powered applications by providing a well-structured framework for working with large language models. It offers a clean developer experience, extensive integrations with vector databases, APIs, and cloud services, making it easy to create end-to-end AI workflows. The modular architecture allows for rapid prototyping while still supporting complex production use cases. Performance is solid, the documentation and community resources make onboarding easier, and features like agents, memory, and retrieval-augmented generation (RAG) enable the development of intelligent applications with far less boilerplate code.
What do you dislike about the product?
While LangChain is a powerful framework, its rapid pace of development can make it challenging to keep up with API changes and breaking updates. As projects become more complex, debugging multi-step chains and agents can also be difficult, especially when tracing errors across different integrations. The documentation has improved, but some advanced features still require digging through examples or community discussions. I’d also like to see better built-in observability, more streamlined onboarding for beginners, and further optimisation for production deployments to reduce complexity and improve the overall developer experience.
What problems is the product solving and how is that benefiting you?
LangChain solves the challenge of building production-ready AI applications by providing a unified framework for orchestrating LLMs, prompts, tools, vector databases, and external APIs. Instead of writing custom logic for every integration, I can use its modular components to build features like RAG pipelines, AI agents, and conversational workflows much more efficiently. This has reduced development time, improved maintainability, and made it easier to experiment with new AI capabilities. Its broad ecosystem, strong integration support, and open-source model also provide excellent ROI, allowing me to prototype quickly and scale applications without being locked into a single AI provider.
Information Technology and Services
Langchain Makes Building Large Applications Efficient
Reviewed on Jul 26, 2026
Review provided by G2
What do you like best about the product?
The best about Langchain is its ability to build large applications efficiently
What do you dislike about the product?
The dislike about Langchain is it involves code level changes for any existing API level changes
What problems is the product solving and how is that benefiting you?
The problems that Langchain solving for me is creation of application with prebuilt frameworks instead of creating everything from scratch
Krishnakant R.
Perfect cloud notebook for running Python code and AI scripts without heavy laptop setup
Reviewed on Jul 26, 2026
Review provided by G2
What do you like best about the product?
The best part about Google Colab is that it gives free access to Python environments directly in the browser. I don't have to worry about configuring complex dependencies or heavy libraries locally on my laptop. It allows me to test Python code, run AI libraries, and share notebooks with simple link sharing for college assignments.
What do you dislike about the product?
The active session disconnects if left idle for a while, which wipes out temporary files unless you save them directly to Google Drive. Also, free runtime limits can occasionally get exhausted during long coding sessions, but for daily project testing, it works fine.
What problems is the product solving and how is that benefiting you?
It solves the problem of hardware limitations and complex environment setup on personal laptops. It lets me quickly write, execute, and debug Python code from any device without installing local tools.
Parth c.
LangChain’s Modular Integrations Make Building AI Workflows Fast and Flexible
Reviewed on Jul 24, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is how easily it connects language models with tools, APIs, memory, and external data sources. Its modular structure makes it straightforward for me to build, iterate on, and test AI workflows quickly, without having to create every integration from scratch.
What do you dislike about the product?
LangChain can get complicated as workflows expand, and frequent updates sometimes leave older examples or documentation out of date. Debugging multi-step chains can be challenging too, since errors might originate from the model, the tool integrations, or the framework itself.
What problems is the product solving and how is that benefiting you?
I use LangChain in my AI agent projects to connect LLMs with tools, APIs, memory, and structured workflows. In projects like Wyre and Agent Census, it cuts down on the orchestration code I have to write and makes it simpler to manage multi-step tasks, tool calls, and overall agent state.
Surita S.
LangChain Speeds Up AI App Development with Flexible, Well-Structured Integrations
Reviewed on Jul 24, 2026
Review provided by G2
What do you like best about the product?
What I like most about LangChain is how it simplifies building AI applications by connecting language models with tools, APIs, databases, and memory. Rather than having to wire up all of those integrations from scratch, it offers a structured framework that helps speed up development. I also appreciate its flexibility: it supports multiple LLM providers and lets developers build more complex workflows, agents, and retrieval pipelines, while still keeping the codebase organized and maintainable.
What do you dislike about the product?
One thing I dislike about LangChain is that it can feel unnecessarily complex for straightforward projects. It adds a lot of abstractions, and that can make it harder to debug issues or clearly understand what’s happening behind the scenes. The framework also changes quickly, so staying on top of API updates and the documentation can be frustrating at times.
What problems is the product solving and how is that benefiting you?
LangChain solves the challenge of connecting AI models with external data, tools, APIs, and workflows in a structured way. Instead of building these integrations from scratch, I can use its components to create applications like chatbots, document Q&A systems, and AI agents much more quickly. This saves development time, keeps projects organized, and makes it easier to scale and maintain AI applications as they grow.