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There are two recommended ways to trace your LangChains:
  1. Setting the LANGCHAIN_WANDB_TRACING environment variable to “true”.
  2. Using a context manager with wandb_tracing_enabled() to trace a particular block of code.
Note if the environment variable is set, all code will be traced, regardless of whether or not it’s within the context manager. 
import os

from langchain_community.callbacks import wandb_tracing_enabled

os.environ["LANGCHAIN_WANDB_TRACING"] = "true"

# wandb documentation to configure wandb using env variables
# https://docs.wandb.ai/guides/track/advanced/environment-variables
os.environ["WANDB_PROJECT"] = "langchain-tracing"

from langchain.agents import create_agent, load_tools
from langchain_openai import OpenAI

# Agent run with tracing. Ensure OPENAI_API_KEY is set appropriately to run this example.

llm = OpenAI(temperature=0)
tools = load_tools(["llm-math"], llm=llm)

agent = create_agent(
    model=llm,
    tools=tools,
    verbose=True,
)

agent.invoke("What is 2 raised to .123243 power?")

# Now, we unset the environment variable and use a context manager.
if "LANGCHAIN_WANDB_TRACING" in os.environ:
    del os.environ["LANGCHAIN_WANDB_TRACING"]

# enable tracing using a context manager
with wandb_tracing_enabled():
    agent.invoke("What is 5 raised to .123243 power?")

agent.invoke("What is 2 raised to .123243 power?")

> Entering new AgentExecutor chain...
 I need to use a calculator to solve this.
Action: Calculator
Action Input: 5^.123243
Observation: Answer: 1.2193914912400514
Thought: I now know the final answer.
Final Answer: 1.2193914912400514

> Finished chain.


> Entering new AgentExecutor chain...
 I need to use a calculator to solve this.
Action: Calculator
Action Input: 2^.123243
Observation: Answer: 1.0891804557407723
Thought: I now know the final answer.
Final Answer: 1.0891804557407723

> Finished chain.

'1.0891804557407723'
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