langwatch

langwatch

LangWatch is an open-source platform designed to help teams build, evaluate, and monitor AI agents with complete visibility and control. It supports collaboration between technical and non-technical users to catch edge cases, reduce rework, and optimize AI quality before production deployment.

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Added 0 year ago
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Quick Overview

Best for: Creative & Design

What it does

AI Agents software for decision-makers comparing workflow fit and alternatives.

Best fit

Creative & Design

Pricing snapshot

Free

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langwatch

LangWatch is a comprehensive AI agent testing and evaluation platform that enables teams to simulate AI agents, track responses, and catch failures before production. It provides built-in tools for data selection, evaluation, and testing, allowing teams to move quickly while maintaining control over AI quality. The platform supports collaboration across roles including AI engineers, data scientists, product managers, and domain experts, empowering both technical and non-technical users to contribute to AI quality assurance. LangWatch is fully open-source and can be run locally or self-hosted, ensuring no data lock-in and seamless integration with existing AI stacks.

LLM observability and evaluation platform for monitoring, evaluating, and optimizing LLM applications.

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Key Features

Agent Simulation

Simulate AI agents to test their behavior and catch edge cases before deployment.

Evaluations and Testing

Built-in tools for running evaluations, managing datasets, and testing AI models.

Collaborative Platform

Enables both technical and non-technical team members to collaborate on experiments, prompt management, and annotation.

OpenTelemetry Integration

Native support for OpenTelemetry to integrate with all LLMs and AI agent frameworks.

Self-Hosted and On-Premise Options

Supports deployment on-premises, in VPCs, air-gapped, or hybrid environments.

Role-Based Access Controls

Enterprise-grade security with role-based access and compliance with GDPR and ISO27001.

Optimization Studio with DSPy

Advanced tools for optimizing AI agents and workflows.

Data Export and Interoperability

No data lock-in with the ability to export data and integrate with other tools.

Pricing

Free Tier Available

LangWatch offers a fully open-source platform that can be self-hosted, allowing free access to core features without vendor lock-in.

Use Cases

Design Smarter AI Agents

Use simulations and evaluations to design AI agents based on evidence rather than guesswork.

Evaluate Retrieval-Augmented Generation (RAG) Quality

Test and measure the quality of RAG models to ensure accurate and reliable outputs.

Test Multimodal Voice Agents

Simulate and evaluate voice-based AI agents across multiple interaction turns.

Manage AI Quality Control

Reduce rework and manage regressions by continuously monitoring and optimizing AI performance.

Integrations

OpenTelemetry

Enables native integration with all LLMs and AI agent frameworks for telemetry and monitoring.

Python SDK

Allows programmatic access to LangWatch features for AI engineers.

JavaScript/TypeScript SDK

Enables integration with web and agent frameworks using JS/TS.

AI Frameworks

Supports integration with LangChain, LangGraph, Pydantic AI, AWS BedRock, AgnoCrew AI, and others.

Benefits

Complete visibility into AI agent performance and failures before production.
Collaboration between technical and non-technical users to improve AI quality.
Flexible deployment options including self-hosted and on-premise for data control.
Integration with existing AI frameworks and tools via OpenTelemetry and APIs.
Enterprise-grade security and compliance with GDPR and ISO27001 standards.

Limitations

Pricing details for hosted or enterprise plans are not publicly available.
Requires technical knowledge to fully leverage SDKs and integrations.
No explicit mention of cloud-hosted SaaS offering; primarily self-hosted.

Frequently Asked Questions

How does LangWatch work?
LangWatch works by simulating AI agents, tracking their responses, and running evaluations to catch failures before production. It provides tools for data selection, testing, and monitoring to ensure AI quality.
What is LLM observability?
LLM observability refers to the ability to monitor and understand the behavior and performance of large language models in production environments.
Is LangWatch self-hosted available?
Yes, LangWatch is fully open-source and can be self-hosted on-premises, in VPCs, air-gapped, or hybrid environments.
Can I try LangWatch for free?
Yes, since LangWatch is open-source, you can run it locally or self-host it without cost.
How does LangWatch handle security and compliance?
LangWatch supports enterprise-grade controls including role-based access, GDPR compliance, ISO27001 certification, and flexible deployment options to meet security requirements.

Getting Started

  1. 1 Step 1: Sign up and book a demo or access the platform.
  2. 2 Step 2: Integrate LangWatch with your AI agents using the Python or TypeScript SDKs.
  3. 3 Step 3: Start running simulations, evaluations, and monitoring AI agent responses.
  4. 4 Step 4: Collaborate with your team using the UI to manage prompts, datasets, and annotations.
  5. 5 Step 5: Optimize AI agents using the built-in Optimization Studio.

Support

Documentation

Comprehensive docs available at https://langwatch.com/docs

Demo Booking

Schedule a demo via the website to get personalized support.

Community & GitHub

Open-source project with community support via GitHub repository.

API

Available: Yes
Documentation:

API and SDK documentation available at https://langwatch.com/docs/integrations

Rate Limits:

Not publicly specified

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