LangChain is a popular framework for developing applications powered by language models. It provides a standard interface for chains, integrations with other tools, and end-to-end chains for common applications. This guide demonstrates how to integrate Maxim’s observability capabilities with LangChain applications, allowing you to:
  1. Track LLM interactions - Monitor all calls to language models
  2. Analyze performance - Measure latency, token usage, and costs
  3. Debug chains - Visualize the flow of information through your LangChain applications
  4. Evaluate outputs - Assess the quality of responses from your LLM chains
The integration is simple and requires minimal changes to your existing LangChain code.

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Requirements

maxim-py
langchain-openai>=0.0.1
langchain
python-dotenv

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Env variables

MAXIM_LOG_REPO_ID=
OPENAI_API_KEY=

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Initialize logger

from maxim import Maxim, Config, LoggerConfig

# Instantiate Maxim and create a logger
logger = Maxim(Config()).logger(
    LoggerConfig(id=MAXIM_LOG_REPO_ID)
)

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Initialize MaximLangchainTracer

from maxim.logger.langchain import MaximLangchainTracer

# Create the LangChain-specific tracer
langchain_tracer = MaximLangchainTracer(logger)

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Make LLM calls using MaximLangchainTracer

from langchain_openai import ChatOpenAI

MODEL_NAME = "gpt-4o"
llm = ChatOpenAI(model=MODEL_NAME, api_key=OPENAI_API_KEY)

messages = [
  ("system", "You are a helpful assistant."),
  ("human", "Describe big bang theory")
]

response = llm.invoke(
  messages,
  config={"callbacks": [langchain_tracer]}
)
print(response.content)
Langchain Gif

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Streaming example

# Enable streaming
llm_stream = ChatOpenAI(
  model=MODEL_NAME,
  api_key=OPENAI_API_KEY,
  streaming=True
)

response_text = ""
for chunk in llm_stream.stream(
  messages,
  config={"callbacks": [langchain_tracer]}
):
  response_text += chunk.content

print("\nFull response:", response_text)