How do I enable tracing in my existing code?

You can simply use the @traceable decorator on any Python function or use the LangSmith wrappers to automatically trace clients like the OpenAI library.

Does it support frameworks other than LangChain?

While built by the LangChain team, LangSmith works seamlessly with OpenAI, Anthropic, LlamaIndex, and custom Python applications via its standalone SDK.

How does LangSmith improve LLM debugging?

LangSmith captures detailed traces of every input, output, and metadata for each step in your AI application, allowing you to pinpoint exactly where a chain or prompt failed.

Can I use this for production monitoring?

Yes, LangSmith is designed to track production metrics like latency, error rates, and costs, with support for sampling to manage high-volume traffic efficiently.

LangSmith LLM Observability

Name: LangSmith LLM Observability
Author: Orchestra-Research

byOrchestra-Research

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3,983

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Analytics & Monitoring

Monitors, debugs, and evaluates large language model applications with comprehensive tracing and systematic testing tools.

The LangSmith skill provides a robust platform for the entire LLM application lifecycle, from initial development to production monitoring. It enables developers to capture detailed execution traces of prompts, chains, and agents, making it easy to identify bottlenecks and debug complex workflows. With support for creating datasets from production logs, running automated evaluations, and tracking production metrics like latency and token costs, this skill ensures that AI applications remain reliable, performant, and cost-effective as they scale.

Key Features

013,983 GitHub stars

02Deep integration with OpenAI, Anthropic, LangChain, and LlamaIndex

03Full execution tracing for LLM calls, chains, and agentic workflows

04Real-time production monitoring for latency, errors, and token usage

05Systematic evaluation against custom datasets and production traces

06Automated regression testing pipelines for AI features

Use Cases

01Debugging nested LLM chains to identify failure points in complex prompts

02Collecting and analyzing user feedback on production AI responses

03Comparing model performance across different versions using custom evaluators

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills langsmith

For use in Claude.ai and ChatGPT

Key Features

013,983 GitHub stars

02Deep integration with OpenAI, Anthropic, LangChain, and LlamaIndex

03Full execution tracing for LLM calls, chains, and agentic workflows

04Real-time production monitoring for latency, errors, and token usage

05Systematic evaluation against custom datasets and production traces

06Automated regression testing pipelines for AI features

Use Cases

01Debugging nested LLM chains to identify failure points in complex prompts

02Collecting and analyzing user feedback on production AI responses

03Comparing model performance across different versions using custom evaluators

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills langsmith

For use in Claude.ai and ChatGPT