LangSmith by LangChain
LangSmith is an online tool that helps developers get their Large Language Model (LLM) app from prototype to production. It is an all-in-one DevOps platform for every step of the LLM-powered application lifecycle. In other words, LangSmith is made to help with developing, collaborating, testing, deploying, and monitoring LLM applications.
The problem is that while LLM-apps are powerful, they have peculiar characteristics. The non-determinism, coupled with unpredictable, natural language inputs, make for countless ways the system can fall short. Traditional engineering best practices need to be re-imagined for working with LLMs, and that’s where LangSmith kicks in to support all phases of the development lifecycle.
It offers full visibility into the entire sequence of calls, so that developers can spot the source of errors and performance bottlenecks in real-time with surgical precision. They can debug, experiment, observe and repeat — until they’re happy with the results.
LangSmith also lets developers collaborate with their teammates to get app behavior just right. And finally, the platform supports testing and AI-assisted evaluations, with off-the-shelf and custom evaluators that can check for relevance, correctness, harmfulness, insensitivity, and more.
As of May 2024, LangSmith has more than 100K users signed up, 200M+ traces logged, and 20K+ monthly active teams.
Video Overview ▶️
What are the key features? ⭐
- Traces: Easily share a chain trace with colleagues, clients, or end users, bringing explainability to anyone with the shared link.
- Hub: LangSmith Hub lets you craft, version, and comment on prompts. No engineering experience required.
- Annotation Queues: LangSmith Annotation Queues is used to add human labels and feedback on traces.
- Datasets: Easily collect examples and construct datasets from production data or existing sources. Datasets can be used for evaluations, few-shot prompting, and even fine-tuning.
- Test & evaluate: Measure quality over large test suites. Layer in human feedback on runs or use AI-assisted evaluation with off-the-shelf and custom evaluators that can check for relevance, correctness, harmfulness, insensitivity, and more.
Who is it for? 🤔
Examples of what you can use it for 💭
- Collaborate with teammates to get app behavior just right
- Quickly save debugging and production traces to datasets, which are collections of either exemplary or problematic inputs and outputs
- Use an LLM and prompt to score your application output, or write your own functional evaluation tests
- See how the performance of the evaluation criteria that you've defined is affected by changes to your application
- Track qualitative characteristics of any live application and spot issues in real-time with LangSmith monitoring.
Pros & Cons ⚖️
- A unified DevOps platform for your LLM applications
- Helps developers deliver LLM-based software fast and easy
- Makes it easier to manage the complexity of LLM software
- It's not a magic wand, someone still has to do the work
