LangChain hero Openlayer integrates with Langchain using Langchain Callbacks. Therfore, Openlayer automatically traces every run of your Langchain applications. This allows you to set up tests, log, and analyze your LangChain application with minimal integration efforts.
Want to integrate with LangGraph? Check out the LangGraph integration page.

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Evaluating LangChain applications

You can set up Openlayer tests to evaluate your LangChain applications in monitoring and development.

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Monitoring

To use the monitoring mode, you must instrument your code to publish the requests your AI system receives to the Openlayer platform. To set it up, you must follow the steps in the code snippet below: 
# 1. Set the environment variables
import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY_HERE"
os.environ["OPENLAYER_API_KEY"] = "YOUR_OPENLAYER_API_KEY_HERE"
os.environ["OPENLAYER_INFERENCE_PIPELINE_ID"] = "YOUR_OPENLAYER_INFERENCE_PIPELINE_ID_HERE"

# 2. Instantiate the `OpenlayerHandler`
from openlayer.lib.integrations import langchain_callback

openlayer_handler = langchain_callback.OpenlayerHandler()

# 3. Pass the handler to your LLM/chain invocations
from langchain_openai import ChatOpenAI

chat = ChatOpenAI(max_tokens=25, callbacks=[openlayer_handler])
chat.invoke("What's the meaning of life?")

See full Python example

 Once the code is instrumented, all your LangChain LLM/chain invocations are automatically published to Openlayer, along with metadata, such as latency, number of tokens, cost estimate, and more. If you navigate to the “Data” page of your Openlayer data source, you can see the traces for each request. LangChain trace
If the LangChain LLM/chain invocations are just one of the steps of your AI system, you can use the code snippets above together with tracing. In this case, your LangChain LLM/chain invocations get added as a step of a larger trace. Refer to the Tracing guide for details.
After your AI system requests are continuously published and logged by Openlayer, you can create tests that run at a regular cadence on top of them. Refer to the Monitoring overview, for details on Openlayer’s monitoring mode, to the Publishing data guide, for more information on setting it up, or to the Tracing guide, to understand how to trace more complex systems.

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Development

You can use the LangChain template to check out how a sample app fully set up with Openlayer looks like.
In development mode, Openlayer becomes a step in your CI/CD pipeline, and your tests get automatically evaluated after being triggered by some events. Openlayer tests often rely on your AI system’s outputs on a validation dataset. As discussed in the Configuring output generation guide, you have two options:
  1. either provide a way for Openlayer to run your AI system on your datasets, or
  2. before pushing, generate the model outputs yourself and push them alongside your artifacts.
For LangChain applications, if you are not computing your system’s outputs yourself, you must provide the required API credentials. For example, if you application uses LangChain’s ChatOpenAI, you provide an OPENAI_API_KEY, if it uses ChatMistralAI, you must provide a MISTRAL_API_KEY, and so on. To provide the required API credentials, navigate to “Workspace settings” -> “Environment variables,” and add the credentials as variables. If fail to add the required credentials, you’ll likely encounter a “Missing API key” error when Openlayer tries to run your AI system to get its outputs.

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Advanced callback handler features

The Openlayer LangChain callback handler supports several advanced features for enhanced observability, including support for:

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Asynchronous usage

When using asynchronous usage, make sure you use the AsyncOpenlayerHandler instead of the OpenlayerHandler.
Python
from openlayer.lib.integrations import langchain_callback

openlayer_handler = langchain_callback.AsyncOpenlayerHandler()

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Streaming responses

When using streaming, make sure you set stream_usage=True when calling the streaming method. This way, the Openlayer callback handler is able to capture usage information from the streaming responses.
Python
from langchain_openai import ChatOpenAI

chat = ChatOpenAI(callbacks=[openlayer_handler])

# Streaming with usage tracking
for chunk in chat.stream("Explain quantum computing", stream_usage=True):
    print(chunk.content, end="")
# Usage information is automatically logged at the end

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Metadata transformation

You can use a metadata_transformer function to filter, modify, or enrich metadata before it’s logged to Openlayer:
Python
from typing import Dict, Any

def custom_metadata_transformer(metadata: Dict[str, Any]) -> Dict[str, Any]:
    # Filter out sensitive fields
    filtered = {k: v for k, v in metadata.items() if not k.startswith("_private")}

    # Add custom context
    filtered["environment"] = "production"
    filtered["user_session"] = get_current_session_id()

    return filtered

openlayer_handler = langchain_callback.OpenlayerHandler(
    metadata_transformer=custom_metadata_transformer
)

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Context logging for RAG systems

The handler automatically logs context from retrieval steps and chains containing source_documents, enabling context-dependent metrics:
Python
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

# Set up a retrieval chain
vectorstore = FAISS.from_texts(texts, OpenAIEmbeddings())
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(callbacks=[openlayer_handler]),
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

# Context from retrieved documents is automatically logged
response = qa_chain.run("What is machine learning?")