LangSmith
Platform for debugging, testing, and monitoring LangChain applications
⭐ 4.5
freemium closed-source development
#ai-monitoring
#debugging
#testing
#langchain
#observability
Overview
LangSmith is a comprehensive platform designed specifically for debugging, testing, and monitoring applications built with LangChain. It provides developers with deep insights into their LLM applications’ behavior, performance, and reliability, making it easier to build production-ready AI systems.
Key Features
Debugging and Tracing
- Complete trace visualization of LangChain runs
- Step-by-step execution breakdown
- Error tracking and root cause analysis
- Real-time debugging capabilities
Performance Monitoring
- Latency and cost tracking
- Token usage analytics
- Success rate monitoring
- Historical performance trends
Testing and Evaluation
- Dataset creation and management
- Automated testing pipelines
- Custom evaluation metrics
- Regression testing for model updates
Prompt Engineering
- A/B testing for different prompts
- Prompt version management
- Performance comparison across prompts
- Collaborative prompt development
Use Cases
- Production Monitoring: Track LLM application performance in real-time
- Debugging: Identify and fix issues in complex LangChain applications
- Testing: Ensure reliability before deploying to production
- Optimization: Improve performance and reduce costs
- Team Collaboration: Share insights and collaborate on AI development
- Compliance: Track and audit AI system behavior
Pricing
- Free Tier: Limited traces and basic monitoring features
- Pro Plan: Unlimited traces, advanced analytics, and team collaboration
- Enterprise: Custom solutions with dedicated support and advanced security
Getting Started
- Create a LangSmith account at smith.langchain.com
- Install the LangSmith SDK in your project
- Configure tracing in your LangChain application
- Start monitoring your application’s performance
- Set up evaluations and testing pipelines
LangSmith bridges the gap between development and production for LLM applications, providing the observability and testing tools necessary to build reliable, performant AI systems at scale.