LangSmith

Overview

LangSmith is a developer platform designed to assist in the lifecycle management of Large Language Model (LLM) applications, from initial prototyping to production deployment and ongoing maintenance. It offers a suite of tools that address common challenges in building with LLMs, such as understanding complex chain executions, ensuring model reliability, and managing evaluation datasets. The platform was launched in 2023 by the creators of the LangChain framework, aiming to provide a dedicated environment for observing and improving LLM-powered systems LangSmith homepage.

Developers utilize LangSmith to gain visibility into the internal workings of their LLM applications. This includes tracing the inputs, outputs, and intermediate steps of LLM calls, agents, and chains. Such detailed tracing is critical for debugging unexpected behaviors, identifying latency issues, and understanding how different components interact within a larger application flow. For instance, in an agent-based application that uses multiple tool calls and LLM prompts, LangSmith can visualize each step, the prompt used, the response received, and any errors encountered, simplifying the diagnostic process.

Beyond debugging, LangSmith supports the evaluation of LLM models and applications. It enables the creation and management of datasets, which are collections of inputs and reference outputs used to test model performance. Users can run various evaluation metrics, both automated (e.g., semantic similarity, correctness checks) and human-in-the-loop, to quantitatively assess how their application responds to different scenarios. This capability is particularly useful for tracking improvements across model versions or prompt changes. For example, a developer might create a dataset of customer support queries and evaluate how well different iterations of a RAG system retrieve relevant information and generate accurate answers.

The platform is suitable for individual developers and teams building applications that integrate with LLMs. Its collaborative features allow multiple team members to share traces, datasets, and evaluation results, fostering a shared understanding of application performance and facilitating iterative development. LangSmith is often used in conjunction with the LangChain framework, offering seamless integration, but also supports tracing and evaluation for applications built with other LLM orchestration libraries LangSmith documentation. The comprehensive approach to LLM application development, covering tracing, debugging, evaluation, and dataset management, positions LangSmith as a central tool for maintaining and improving the quality of AI applications.

Key features

• Traces: Visualizes the execution flow of LLM chains and agents, showing inputs, outputs, intermediate steps, and latency for each component. This helps in understanding the runtime behavior and debugging complex interactions within an LLM application.
• Evaluations: Provides tools for defining and running automated or human-assisted evaluations against datasets. This allows quantitative measurement of model performance, tracking changes over time, and comparing different model versions or prompt strategies.
• Datasets: Manages collections of inputs and optional reference outputs for testing and evaluation purposes. Datasets can be imported, created, and curated within the platform, serving as ground truth for assessing application quality.
• Monitoring: Offers dashboards and metrics to observe the performance of LLM applications in production. This includes tracking token usage, latency, error rates, and user feedback, enabling proactive identification of operational issues.
• Prompt Hub: Facilitates the versioning and management of prompts, allowing developers to iterate on prompt engineering strategies and track their impact on application performance.
• Annotation Queue: Supports human feedback loops by allowing users to annotate traces, correct model outputs, or provide additional data for fine-tuning or evaluation datasets.
• Collaboration Tools: Enables teams to share traces, datasets, and evaluation runs, promoting collaborative debugging and development workflows across multiple contributors.
• API Access: Provides a programmatic interface for interacting with LangSmith, allowing for automation of tasks such as trace ingestion, dataset management, and evaluation execution LangSmith API reference.

Pricing

LangSmith offers a free plan and a usage-based developer plan, with enterprise options available for larger organizations. Pricing is primarily based on the number of traces recorded.

For the most current pricing details and specific feature breakdowns for each tier, refer to the official LangSmith pricing page.

Common integrations

• LangChain: Deep integration with the LangChain framework for Python and JavaScript/TypeScript, allowing automatic tracing of chains, agents, and LLM calls LangChain integration guide.
• OpenAI API: Direct integration for tracing calls made to OpenAI models, including GPT-3.5 and GPT-4 Tracing OpenAI calls.
• Anthropic API: Support for tracing interactions with Anthropic's Claude models Tracing Anthropic calls.
• Hugging Face Transformers: Compatibility for tracing models hosted on Hugging Face or run locally using the Transformers library Tracing Hugging Face models.
• Custom LLM Providers: Ability to integrate and trace calls to other custom or proprietary LLM providers through custom instrumentation.

Alternatives

• Arize AI: An ML observability platform offering monitoring, root cause analysis, and explainability for various machine learning models, including LLMs.
• Weights & Biases: A MLOps platform providing experiment tracking, model versioning, and dataset management, with features applicable to LLM development and evaluation.
• Helicone: An open-source and hosted platform for LLM observability, offering caching, rate limiting, and cost monitoring alongside tracing and logging capabilities.

Getting started

To begin using LangSmith, you typically start by installing the client library and configuring your environment with an API key. The following Python example demonstrates how to set up tracing for a simple LangChain LLM application. This setup allows LangSmith to automatically capture the execution details of your LLM calls and chains.

First, ensure you have the necessary libraries installed:

pip install langchain langchain-openai langsmith

Next, configure your environment variables with your LangSmith API key and project name, along with your OpenAI API key:

export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_API_KEY="<your_langsmith_api_key>"
export LANGCHAIN_PROJECT="MyFirstLLMApp"
export OPENAI_API_KEY="<your_openai_api_key>"

Now, you can write a simple Python script to use LangChain with an OpenAI LLM. LangSmith will automatically trace the execution when LANGCHAIN_TRACING_V2 is set to true.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Initialize the LLM
llm = ChatOpenAI(model="gpt-4o", temperature=0.7)

# Define a simple prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful AI assistant."),
    ("user", "{question}")
])

# Create a simple chain
chain = prompt | llm | StrOutputParser()

# Invoke the chain
response = chain.invoke({"question": "What is the capital of France?"})
print(f"LLM Response: {response}")

response_two = chain.invoke({"question": "Tell me a short story about a brave knight."})
print(f"LLM Response: {response_two}")

After running this script, you can visit your LangSmith project dashboard to view the traces generated by these LLM calls. Each invocation of the chain will appear as a separate run, detailing the prompt, LLM response, and any intermediate steps. This initial setup provides immediate visibility into your LLM application's behavior, which is a foundational step for debugging and evaluation. For more advanced configurations and integration patterns, consult the LangSmith official documentation.