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What is LangSmith?

In software development, unforeseen results frequently arise, and having complete visibility into the entire call sequence allows developers to accurately identify the sources of errors and anomalies in real-time. By leveraging unit testing, software engineering plays a crucial role in delivering efficient solutions that are ready for production. Tailored specifically for large language model (LLM) applications, LangSmith provides similar functionalities, allowing users to swiftly create test datasets, run their applications, and assess the outcomes without leaving the platform. This tool is designed to deliver vital observability for critical applications with minimal coding requirements. LangSmith aims to empower developers by simplifying the complexities associated with LLMs, and our mission extends beyond merely providing tools; we strive to foster dependable best practices for developers. As you build and deploy LLM applications, you can rely on comprehensive usage statistics that encompass feedback collection, trace filtering, performance measurement, dataset curation, chain efficiency comparisons, AI-assisted evaluations, and adherence to industry-leading practices, all aimed at refining your development workflow. This all-encompassing strategy ensures that developers are fully prepared to tackle the challenges presented by LLM integrations while continuously improving their processes. With LangSmith, you can enhance your development experience and achieve greater success in your projects.

What is Arize AI?

Arize provides a machine-learning observability platform that automatically identifies and addresses issues to enhance model performance. While machine learning systems are crucial for businesses and clients alike, they frequently encounter challenges in real-world applications. Arize's comprehensive platform facilitates the monitoring and troubleshooting of your AI models throughout their lifecycle. It allows for observation across any model, platform, or environment with ease. The lightweight SDKs facilitate the transmission of production, validation, or training data effortlessly. Users can associate real-time ground truth with either immediate predictions or delayed outcomes. Once deployed, you can build trust in the effectiveness of your models and swiftly pinpoint and mitigate any performance or prediction drift, as well as quality concerns, before they escalate. Even intricate models benefit from a reduced mean time to resolution (MTTR). Furthermore, Arize offers versatile and user-friendly tools that aid in conducting root cause analyses to ensure optimal model functionality. This proactive approach empowers organizations to maintain high standards and adapt to evolving challenges in machine learning.

Media

Media

Integrations Supported

Azure Marketplace
LangChain
LangGraph
AgentForge
Deep Agents
Disco.dev
Overmind
Prism
Reflexio
dcode
evalship

Integrations Supported

Azure Marketplace
LangChain
LangGraph
Amazon Bedrock
Arize Phoenix
Claude for Legal
Flowise
Gemini Enterprise Agent Platform
LlamaCloud
LlamaIndex
OpenAI
neptune.ai

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

$50/month
Free to use - OSS version
$50/month - Enterprise version
Free Version
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training

Company Facts

Organization Name

LangChain

Company Location

United States

Company Website

www.langchain.com/langsmith

Company Facts

Organization Name

Arize AI

Company Location

United States

Company Website

arize.com

Categories and Features

AI Development

Not specified

Context Engineering

Not specified

Debugging

Not specified

Software Testing

Not specified

Categories and Features

AI Observability

Not specified

Machine Learning

Not specified

Observability

Not specified

Root Cause Analysis

Not specified

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