Description
LangWatch provides a comprehensive platform for AI agent testing, LLM evaluation, and LLM observability, designed to help developers ship reliable AI. The platform addresses the common challenges of AI agents behaving unpredictably in production, model swaps degrading quality, or prompt changes introducing regressions.
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It offers a developer-first, yet collaborative environment for defining evaluations, running experiments, and simulating multi-step agent behavior. This ensures that changes to prompts, models, or agents can be rigorously tested and validated before they are deployed. LangWatch enables teams to move beyond manual checks and production feedback by providing structured evaluations and simulations.
Key capabilities include prompt and model management with versioning, comparison, and full traceability. It facilitates safe rollout of experiments using feature-flag-style controls and provides clear audit trails. Real-time evaluations can be created and tuned to measure product-specific quality, while LLM observability allows instant searching and inspection of LLM interactions across environments for debugging and auditing.
The platform supports agent simulations for complex agentic AI, running thousands of synthetic conversations across various scenarios, languages, and edge cases. Batch tests and experiments can be run directly from the platform or code, tracking the impact of every change. Auto-evals automatically execute test suites for pre-release testing and production monitoring.
LangWatch also emphasizes collaboration, with workflows for data review and labeling, enabling teams to inspect, annotate, and analyze data together. Dataset management converts production traces into reusable test cases and benchmarks. Performance optimization is supported through structured experimentation and optimization techniques, including DSPy integration.
The platform is designed for seamless integration, working with any LLM or agent framework, and is OpenTelemetry native. It can be run locally or self-hosted, ensuring no data lock-in. LangWatch is built for enterprise-grade controls, offering on-prem, VPC, or hybrid deployment options, with ISO27001 and SOC2 certification, GDPR control, and role-based access controls.
- CategoryMLOps & Model Deployment
- Pricing ModelOpen Source
- DeploymentSelf-Hosted, Cloud
- PlatformsWeb
LangWatch's Core Features
AI Agent Testing
LLM Evaluation
LLM Observability
Simulate real-world scenarios
Turn production traces into evaluations
Prevent regressions
Debug issues
Prompt Management
Model Management
Agent Simulations
Batch Tests & Experiments
Auto-Evals
Data review & labeling
Dataset management
Performance optimization with DSPy
How to use LangWatch?
Prototype: Build and iterate on AI features.
Evaluate: Define and run evaluations for quality assurance.
Simulate: Run agent simulations for complex scenarios.
Deploy: Safely roll out changes with feature-flag controls.
Monitor: Inspect LLM interactions and production signals.
Optimize: Improve AI agents based on feedback and data.
LangWatch's Use Cases
- AI Agent Testing
- LLM Evaluation
- LLM Observability
- Prompt Engineering
- Model Comparison
- Regression Prevention
- Data Annotation
- RAG Quality Testing
- Multimodal Agent Testing
- Multi-turn Conversation Testing
