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How to evaluate your LLM when the task at hand is very subjective

LangWatch is the best Humanloop alternative with full LLM evaluations, tracing, agent testing, and free migration support.

Learn why simulation is key to AI agent testing and how LangWatch Scenario brings scenario-based tests to your CI/CD.

Discover how Scenario enables bulletproof testing of multilingual LLM agents through, ensuring your AI is production-ready

Watch simulated conversations play out in real-time with parallel execution, versioned runs, and interactive debugging.

Scripted simulations let you orchestrate how conversations unfold, when evaluations occur, and what custom logic runs.

Scenario is framework-agnostic, supporting any agent architecture through the AgentAdapter interface.

Test your agents with scenarios directly in your codebase using Python and TypeScript.

Agent simulations are the new unit tests. You shouldn’t ship agents without simulations. 

Explore a detailed, developer-tested comparison of top AI agent frameworks in 2025, including LangGraph, DSPy, Agno and more.

Using LangWatch Scenario, the Rojoom product team built a daily automation way to ship new AI features with confidence.

LLMs make it easy to build agent demos. But building reliable and policy-aware agents takes more than a good prompt.

A Pragmatic Framework for Agent Evaluation

A practical playbook that frames evals as a CI/CD pipeline.

AI agents: The real challenge is making sure it works: reliably, accurately, and at scale.

Why reinforcement learning in LLMs isn't broken our eval methods are. Learn how we can unlock RL's potential in LLMs

How I stopped evaluating AI Agents like robots and started testing them like humans (with simulations)

This post breaks down the core components of LLM evaluation, from datasets to evaluators, explores best practices

We have just dropped the the next major version of the LangWatch Python SDK. standards-driven OpenTelemetry compatible SDK

LangWatch Selente - Atla, LLM Evaluations, prompt versioning, structured output, OpenTelemetry SDK, LLMops ISO certified

Key challenges teams face when put LLM-powered apps in production, and why continuous monitoring and evaluation is essential

Improving RAG agents: Build a basic system, Create evaluation data, run experiments

Learn how to effectively evaluate and test LLMs with LangWatch's new Evaluations Wizard. Improve your AI model performance

What is MCP? What does MCP stand for? And what is Function Calling?

The start of LLMOps: DevOps for Generative AI

Compare LangWatch, LangSmith, Braintrust, and Langfuse in this 2025 guide to LLM evaluation and monitoring tools

Scenario is an automated testing library for LLM agents that simulates real user interactions end-to-end.

How do we objectively know if the AI output is good? LLM evaluation reports & feedback loops

LangWatch partners with Adesso to support Enterprise companies with LLMops

The best GenAI teams are now introducing a critical new role: the AI PM (Quality) Lead.

LLMs can do amazing things, but only if they understand context. That context lives in the heads of domain experts.

Evals are the thing of 2025 and building high-quality Evals is one of the most impact investments you can make.

What are LLM Hallucinations? What causes LLM hallucinations? How to monitor and evaluate LLM-apps

The Model Context Protocol is a new standard that lets AI agents easily connect to external tools and data sources.

Improve your LLM performance with real-time observability and optimization

LangWatch: €1M pre-seed funding round led by Passion Capital, with great support from Volta Ventures and Antler.

OpenAI, Anthropic, Deepseek and other LLM Providers keep dropping prices: Should you host your own model?

AI is evolving at speed, and the landscape in 2025 will be shaped across agents, multimodal data, and model efficiency.

LangWatch has been a part of HolidayHero's LLM production environment for over two months, overseeing thousands of guestchats

LangWatch Optimization Studio – Built for AI Engineers, by AI Engineers

Leverage the power of DSPy’s MIPROv2 without diving into complex code? Enter LangWatch’s Optimization Studio

LangWatch’s Optimization Studio, a more precise, scientific and better approach to prompt optimization

This tutorial guides you through building chatbots using Retrieval Augmented Generation with OpenAI in Python using FastAPI

How can we test in a probabilistic environment? Test Driven Development for LLM's

Data Flywheel: using your production data to build better LLM products

How Algomo increased the quality of their AI app with LangWatch

Understand what is The AI Team and what are Their Roles

Understand how to leverage datasets for LLM unit testing

What is DSPy? DSPy Visualizer, allows you to log your DSPy training sessions, track the performance

LangWatch, a new innovative Amsterdam-based startup: Meet the Team

Driving to help AI leaders create RAG chatbots with minimal hallucinations

Journey of launching your first LLM-powered product is filled with potential and challenges.

Retrieval Augmented Generation. Its popularity continues to surge, offering various methods for its successful implementation

Discover how User Analytics for LLMs can transform AI interactions, revealing user behavior

Successfully integrating LLMs into your business requires careful monitoring and evaluation of options

Delve into the challenges of LLM hallucinations, explore their types, causes, and effective mitigation strategies

Learn valuable insights from the frontlines of GenAI product development

Prevent AI chatbots from handling out-of-scope questions, being manipulated, and addressing sensitive topics

Explore how to minimize AI hallucinations in LLMs

Explore the race of generative AI

Learn how AI start-ups can succeed by creating targeted generative AI solutions and effectively monitoring LLMs.

Struck.build + LangWatch = Improved AI Perfomance

Dive into a customer's journey with LangWatch, revealing how to successfully integrate AI into your organization.