Agentic AI Testing
Enterprise-grade testing for production AI agents
Test your AI agents with the same confidence you test your code. Integrated with your existing workflow.
Ship agents faster with developer-first testing
Deploy autonomous agents with testing discipline that satisfies both technical teams and executive stakeholders.
Execute agent simulations directly from your local machine or CI/CD pipeline
Combine LangWatch with any LLM eval framework or custom evals
Designed for collaboration
Define specific flows and expected outcomes to test critical agent behaviors
Integrate your Agent by implementing just one call() method
Visualized agent conversations
Identify failure points and understand interaction patterns during testing
Pause, evaluate, and annotate agent responses during simulated conversations
Multiple response format support
Handle agent responses in any format without additional parsing or conversion
Instead of manually testing conversations or writing rigid input-output tests, simulated users interact naturally with your agents, testing edge cases and scenarios you might not consider.
Simulated users behave like real customers with natural language
Automatically tests complex multi-turn conversations
Catches edge cases manual testing misses
LangWatch integrates with all major LLM providers and agent frameworks through standardized APIs and a framework agnostic protocol. Test agents regardless of your underlying infrastructure.
Works with OpenAI, Anthropic, Google, and local models
Supports LangGraph, CrewAI, AutoGen, and custom frameworks
Single API for testing across different providers
Visual debugging makes agent simulations actionable
Watch simulated conversations unfold in real-time to identify exactly where your agent fails and understand the complete interaction flow.
See conversation flows as they happen
Debug failed interactions step-by-step
Share results with non-technical stakeholders
Export conversation logs for analysis
Add domain expert input to your workflows to generate high-quality annotations, catch edge cases, and fine-tune datasets for more accurate, robust AI models..
Share findings with team members
Collaborate on prompt improvements
Document changes and their effects
Automatically build datasets from annotations
