A brand new course from the makers of LangSmith themselves, promises to teach you how to observe, evaluate, and deploy your agents. And all that in less than 30 minutes.
But first of all, what is Langsmith? It is easy to get confused between LangChain, LangGraph and LangSmith.
LangChain is the foundational toolkit for creating linear workflows and straightforward retrieval-augmented generation (RAG) systems where the software follows a predefined path. In contrast, LangGraph is designed for more sophisticated, agentic AI that requires reasoning, multiple steps and autonomous decision-making through a stateful, graph-based structure.
While the first two tools focus on development, LangSmith acts as a dedicated observability platform used to monitor performance and debug issues. I…
A brand new course from the makers of LangSmith themselves, promises to teach you how to observe, evaluate, and deploy your agents. And all that in less than 30 minutes.
But first of all, what is Langsmith? It is easy to get confused between LangChain, LangGraph and LangSmith.
LangChain is the foundational toolkit for creating linear workflows and straightforward retrieval-augmented generation (RAG) systems where the software follows a predefined path. In contrast, LangGraph is designed for more sophisticated, agentic AI that requires reasoning, multiple steps and autonomous decision-making through a stateful, graph-based structure.
While the first two tools focus on development, LangSmith acts as a dedicated observability platform used to monitor performance and debug issues. It allows developers to track token usage, evaluate latency, and manage prompts within a production environment. Together, these three components form a comprehensive ecosystem for managing everything from simple chatbots to complex, multi-step agents.
This new course on LangSmith Essentials teaches you quickly what you need to know in order to use Langsmith to evaluate your agents before they hit production. It briefly explains how Langsmith addresses the elephant in the room, that is it shows how to test an LLM workflow when the LLM is in essence non-deterministic, something that renders traditional software testing insufficient. The course goes through the facilities that LangSmith provides to do just that:
• Trace step-by-step behavior: You can view the full execution path of an agent to understand exactly how it arrived at a specific output.
• Analyze inputs and outputs: Developers can review the exact input sent to a model and the resulting output, allowing them to pinpoint where logic failed or hallucinations occurred.
• Fix performance bottlenecks: By tracing requests, teams can identify specific steps that are slowing down the application or hurting response quality.
And it does that in 4 modules :
Tracing Tracing acts as a recording system for your application, capturing the complete sequence of steps taken from the moment an input is received to when the final output is produced. By just decorating any of your Pythonic code functions with the "traceable" decorator, you get full visibility into their doings.
Evaluation While Tracing records what happened, Evaluation interprets that data to tell you how well it happened. It allows you to assess response quality, safety, and correctness throughout the application lifecycle. The course explains the difference between the two modes of Evaluation; oflline and online.
Prompt Engineering In the context of our previous discussions, Prompt Engineering is the active process of improving the system, whereas Tracing observes the results and Evaluation scores them. The course goes through the platform’s Playground where you can test your prompts in order discover the ones most efficient.
Deployment Deployment in LangSmith is the process of transforming your local application logic into a production-ready Agent Server. The course showcase how you can deploy your Github repo on the Langchaing platform which will handle the complexities of scaling, concurrency, and API management, allowing you to focus on the agent’s cognitive architecture rather than server maintenance. You can also deploy it locally.
All fine, however LangSmith is not entirely free— it offers a free tier with limits, then paid plans starting at the Developer level. The free Personal organization includes 5,000 traces/month and basic features.
That said if you are already living inside the LangChain ecosystem, are a user of LangSmith or looking to evaluate it, then this free course will answer all your questions; in a snap.
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