Documentation Index
Fetch the complete documentation index at: https://www.getmaxim.ai/docs/llms.txt
Use this file to discover all available pages before exploring further.
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:
- Track LLM interactions - Monitor all calls to language models
- Analyze performance - Measure latency, token usage, and costs
- Debug chains - Visualize the flow of information through your LangChain applications
- Evaluate outputs - Assess the quality of responses from your LLM chains
The integration is simple and requires minimal changes to your existing LangChain code.
Requirements
maxim-py
langchain-openai>=0.0.1
langchain
python-dotenv
Env variables
MAXIM_LOG_REPO_ID=
OPENAI_API_KEY=
Initialize logger
from maxim import Maxim, Config, LoggerConfig
# Instantiate Maxim and create a logger
logger = Maxim(Config()).logger(
LoggerConfig(id=MAXIM_LOG_REPO_ID)
)
Initialize MaximLangchainTracer
from maxim.logger.langchain import MaximLangchainTracer
# Create the LangChain-specific tracer
langchain_tracer = MaximLangchainTracer(logger)
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)
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)
