What is LangWatch?
LangWatch is a powerful platform for testing AI agents, evaluating large language models (LLMs), and monitoring their performance. It helps teams catch issues before they reach users, debug problems, and optimize AI agents for better results. LangWatch is easy to use, works with any LLM or agent framework, and can be self-hosted for full control over your data.
What are the features of LangWatch?
- Agent Simulation: Test AI agents with simulated users to spot problems early.
- LLM Evaluation: Run daily evaluations to prevent hallucinations and ensure quality.
- Observability: Get complete visibility into your AI’s performance in production.
- Flexible Integration: Works with Python, Typescript, OpenTelemetry, and all major LLM frameworks.
- Self-Hosted Option: Deploy locally or on your own servers—no data lock-in.
- Analytics Dashboard: Track responses, failures, and improvements with intuitive analytics.
- Collaboration Tools: Both technical and non-technical users can run experiments and manage prompts.
- Enterprise-Grade Security: GDPR & ISO27001 certified, role-based access controls, and custom model support.
What are the use cases of LangWatch?
- Evaluating RAG Quality: Make sure your retrieval-augmented generation is accurate.
- Testing Multimodal and Voice Agents: Check how your agents handle different types of input.
- Multi-Turn Conversation Testing: Ensure agents perform well in longer chats.
- Tool Usage Simulation: Confirm agents use the right tools during simulations.
- Regression Prevention: Catch failures before they impact real users.
- Team Collaboration: Let engineers, data scientists, and product managers work together.
How to use LangWatch?
- Sign up or book a demo on the LangWatch website.
- Integrate LangWatch with your existing LLM app or agent framework using the Python or JS/TS SDK.
- Run agent simulations and evaluations from the UI or programmatically.
- Monitor results in the analytics dashboard and optimize your agents.
- Export data anytime and collaborate with your team to improve performance.
