LangChain

AgentOps works seamlessly with applications built using LangChain.

Adding AgentOps to LangChain applications

Install the AgentOps SDK and the additional LangChain dependency

pip install agentops
pip install agentops[langchain]

poetry add agentops
poetry add agentops[langchain]

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Set up your import statements

Import the following LangChain and AgentOps dependencies

import os
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from agentops.integration.callbacks.langchain import LangchainCallbackHandler

Set up your LangChain handler to make the calls

Note that you don’t need to set up a separate agentops.init() call, as the LangChain callback handler will automatically initialize the AgentOps client for you.

Set up your LangChain agent with the AgentOps callback handler, and AgentOps will automatically record your LangChain sessions.

handler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=['LangChain Example'])



llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,
	callbacks=[handler],
	model='gpt-3.5-turbo')

agent = initialize_agent(tools,
	llm,
	agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
	verbose=True,
	callbacks=[handler], # You must pass in a callback handler to record your agent
	handle_parsing_errors=True)

Set your API key as an .env variable for easy access.

AGENTOPS_API_KEY=<YOUR API KEY>

Read more about environment variables in Advanced Configuration

Run your agent

Execute your program and visit app.agentops.ai/drilldown to observe your LangChain Agent! 🕵️

After your run, AgentOps prints a clickable URL to the console linking directly to your session in the Dashboard

Full Examples

import os
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from agentops.integration.callbacks.langchain import LangchainCallbackHandler

handler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=['LangChain Example'])

llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,
	callbacks=[handler],
	model='gpt-3.5-turbo')

agent = initialize_agent(tools,
	llm,
	agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
	verbose=True,
	callbacks=[handler], # You must pass in a callback handler to record your agent
	handle_parsing_errors=True)

Getting Started

Concepts

Integrations

Usage

Other Info

Adding AgentOps to LangChain applications

Full Examples

​Adding AgentOps to LangChain applications

​Full Examples

Adding AgentOps to LangChain applications

Full Examples