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.

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Evaluating LangChain applications

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

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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 “Settings” > “Workspace secrets,” and add the credentials as secrets. Adding secrets
If you do not see a field for the API credential your application needs, click the “Add secret” button on the top to add additional secrets.
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: Missing LangChain secret

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Monitoring

To use the monitoring mode, you must set up a way to publish the requests your AI system receives to the Openlayer platform. This process is streamlined for LangChain applications with the Openlayer Callback Handler. 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?")

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Advanced Callback Handler Features

The Openlayer LangChain callback handler supports several advanced features for enhanced observability and control:

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Metadata Transformer

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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Streaming Response Support

The callback handler automatically captures usage information from streaming responses when stream_usage=True:
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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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?")

See full Python example
The code snippet above uses LangChain’s ChatOpenAI. However, the Openlayer Callback Handler works for all LangChain chat models and LLMs.
Once the code is set up, all your invocations are automatically published to Openlayer, along with metadata, such as latency, number of tokens, cost estimate, and more. If you navigate to the “Requests” page of your Openlayer inference pipeline, you can see the traces for each request. LangChain trace
If the LangChain LLM/chain invocation is just one of the steps of your AI system, you can use the code snippets above together with tracing. In this case, your LLM/chain invocations get added as a step of a larger trace. You can enrich traces with metadata using update_current_trace(user_id="123", inferenceId="custom_id") and update_current_step(chain_type="retrieval", tokens=200) for enhanced observability and request correlation. 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.