LangWatch is an open-source LLMOps platform designed to monitor, evaluate, debug, and optimize large language model (LLM) applications and AI agents. It provides observability into AI workflows, capturing detailed traces, conversations, token usage, and costs, enabling teams to collaborate and improve AI performance efficiently.

What are the main features of LangWatch?

LangWatch offers several main features including Experimentation & Optimization, allowing users to test and improve custom LLM pipelines with ease. It provides Monitoring to track AI behavior and performance metrics in real-time with detailed traces and spans of each AI interaction. Evaluation tools are available to assess LLM outputs for accuracy and correctness via datasets and versioned prompt testing. Agent Testing & Simulation enables running scripted simulations and scenario tests to catch failures before production. For Collaboration, it supports both technical and non-technical users through UI and API for prompt management, dataset annotation, and feedback. Lastly, Enterprise Controls offer role-based access, data privacy options like on-prem, VPC, or hybrid, and certifications such as GDPR and ISO27001.

How do I install and use LangWatch?

LangWatch can be installed as a Python package using pip (pip install langwatch). After installation, you sign up on their platform, create projects, and set an API key in your environment. You then instrument your code by importing LangWatch and wrapping your LLM logic with trace decorators to automatically capture inputs, outputs, latency, and cost data.

How does LangWatch help with prompt management and evaluation?

You can create and version prompts, run evaluations on datasets to identify failing examples, iterate on prompts, and compare prompt versions using LangWatch’s evaluation tools. This allows validation before deploying prompts into production applications, preventing regressions and improving response quality.

What kinds of integrations does LangWatch support?

It integrates smoothly with Python-based chatbots and AI frameworks like Chainlit and OpenAI, supports any LLM app or agent framework, and provides APIs and a web UI for monitoring and managing workflows.

Can LangWatch track cost and token usage for LLMs?

Yes, LangWatch automatically tracks token counts and costs associated with each LLM call, providing analytics to monitor and optimize usage expenses.

Does LangWatch support collaborative workflows?

Yes, it is designed for collaboration across teams including AI engineers, data scientists, product managers, and domain experts. Both technical and non-technical users can participate via programmatic APIs or user-friendly interfaces.

What support resources are available?

LangWatch provides documentation, a Discord community for discussions and support, and a troubleshooting section to help resolve issues during usage.

LangWatch

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LangWatch is an open-source platform that helps you build better AI. It lets you test and monitor your AI agents and large language models, catching problems like wrong answers before users do. This tool provides clear visibility into your AI's performance, ensuring it's reliable and works as expected.

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Overview Features Use Cases FAQ Integrations Pricing

About LangWatch

Who It's For

LangWatch is for teams building AI agents and applications, from engineers to non-technical experts. It allows everyone to collaborate on testing and improving AI quality. This ensures reliable AI systems where all team members contribute to better performance.

What You Get

You get clear insight into your AI's performance. The platform provides agent simulations to catch problems early and tools to evaluate AI responses for accuracy. It also tracks key metrics like token usage and costs. This helps prevent issues and build more trustworthy AI applications.

How It Works

This open-source tool integrates with any AI setup. Simply install it and connect it to your AI code. LangWatch automatically collects data on your AI's interactions, letting you run simulations and evaluations. This helps you quickly optimize and confidently deploy your AI.

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Features & Capabilities

🧪 AI Agent Testing & Evaluation

Agent Simulations

Catch edge cases before users do by simulating complex AI agent behaviors and interactions.

LLM Evaluation

Evaluate model quality, track responses, and prevent failures like hallucinations in production.

LLM Observability

Gain complete visibility into your production AI's performance, traces, and execution.

Prompt Optimization Studio

Tools for managing prompts and flows, enabling iterative optimization and experimentation with DSPy.

⚙️ Flexible Deployment & Integrations

Framework Agnostic

Works with any LLM application, agent framework, or model, offering broad compatibility.

Open-Source & Self-Hostable

A fully open-source platform, deployable locally or self-hosted for complete control.

OpenTelemetry Native

Integrates seamlessly with existing testing infrastructure and observability stacks.

Data Portability

Ensures no data lock-in, allowing easy export and interoperation with your existing stack.

🤝 Collaborative AI Development

Cross-Team Collaboration

Enables technical and non-technical teams to collaborate on experiments, evaluations, and prompt management.

Empower Domain Experts

Allows non-technical users to contribute to AI quality by easily building evaluations and annotating model outputs.

Unified Workflow

Manages prompts, flows, and datasets within a single platform to streamline AI development.

🔒 Enterprise Security & Control

Flexible Deployment Models

Supports on-premise, VPC, air-gapped, or hybrid environments for diverse infrastructure needs.

Compliance Certifications

Meets stringent security and privacy standards, including GDPR and ISO27001 certification.

Role-Based Access Control

Implements granular access permissions to secure sensitive data and control operations.

Custom Model & API Support

Allows integration and use of custom AI models and external APIs for tailored solutions.

Use Cases

Ensuring Robust AI Agent Performance and Preventing Regressions

Developers and product teams building AI agents face the challenge of unforeseen edge cases and performance degradations in production. LangWatch provides comprehensive agent simulations, evaluation tools, and continuous monitoring to proactively identify and fix failures, ensuring agents perform reliably and preventing regressions before they impact users.

AI DevelopmentFor: AI Engineer

Streamlining Collaborative LLM Application Development and Optimization

Developing effective LLM applications requires continuous iteration and feedback from various stakeholders, including non-technical domain experts. LangWatch fosters collaboration by enabling teams to jointly manage prompts, run experiments, annotate datasets, and evaluate model outputs, accelerating the development cycle and improving AI quality across the board.

Enterprise SoftwareFor: Product Manager

Gaining Visibility and Optimizing LLM Costs and Performance

Managing the operational costs and ensuring optimal performance of LLM applications can be complex. LangWatch offers detailed observability, automatically tracking token usage and costs, alongside tools for real-time monitoring and evaluation, allowing teams to analyze performance metrics and make data-driven decisions to reduce expenses and enhance efficiency.

Cloud ServicesFor: Data Scientist

Deploying Secure and Compliant Enterprise-Grade LLM Systems

Enterprises require robust security and compliance measures for their AI initiatives, often necessitating on-premise solutions and adherence to strict data governance standards. LangWatch addresses these needs with enterprise-grade controls, including self-hosting options, role-based access, and certifications like GDPR and ISO27001, allowing organizations to deploy AI with confidence.

Financial ServicesFor: Enterprise AI Architect

Frequently asked questions

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