Tracing Walkthrough

There are two recommended ways to trace your LangChains:

Setting the LANGCHAIN_TRACING environment variable to "true".
Using a context manager with 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

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

## Uncomment below if using hosted setup.
# os.environ["LANGCHAIN_ENDPOINT"] = "https://langchain-api-gateway-57eoxz8z.uc.gateway.dev"

## Uncomment below if you want traces to be recorded to "my_session" instead of "default".
# os.environ["LANGCHAIN_SESSION"] = "my_session"

## Better to set this environment variable in the terminal
## Uncomment below if using hosted version. Replace "my_api_key" with your actual API Key.
# os.environ["LANGCHAIN_API_KEY"] = "my_api_key"

import langchain
from langchain.agents import Tool, initialize_agent, load_tools
from langchain.agents import AgentType
from langchain.callbacks import tracing_enabled
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI

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

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

agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

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

[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m I need to use a calculator to solve this.
Action: Calculator
Action Input: 2^.123243[0m
Observation: [36;1m[1;3mAnswer: 1.0891804557407723[0m
Thought:[32;1m[1;3m I now know the final answer.
Final Answer: 1.0891804557407723[0m

[1m> Finished chain.[0m





'1.0891804557407723'

# Agent run with tracing using a chat model
agent = initialize_agent(
    tools,
    ChatOpenAI(temperature=0),
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
)

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

[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mI need to use a calculator to solve this.
Action: Calculator
Action Input: 2 ^ .123243[0m
Observation: [36;1m[1;3mAnswer: 1.0891804557407723[0m
Thought:[32;1m[1;3mI now know the answer to the question. 
Final Answer: 1.0891804557407723[0m

[1m> Finished chain.[0m





'1.0891804557407723'

# Both of the agent runs will be traced because the environment variable is set
agent.run("What is 2 raised to .123243 power?")
with tracing_enabled() as session:
    agent.run("What is 5 raised to .123243 power?")

[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mI need to use a calculator to solve this.
Action: Calculator
Action Input: 2 ^ .123243[0m
Observation: [36;1m[1;3mAnswer: 1.0891804557407723[0m
Thought:[32;1m[1;3mI now know the answer to the question. 
Final Answer: 1.0891804557407723[0m

[1m> Finished chain.[0m


[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mI need to use a calculator to solve this.
Action: Calculator
Action Input: 5 ^ .123243[0m
Observation: [36;1m[1;3mAnswer: 1.2193914912400514[0m
Thought:[32;1m[1;3mI now know the answer to the question. 
Final Answer: 1.2193914912400514[0m

[1m> Finished chain.[0m

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

# here, we are writing traces to "my_test_session"
with tracing_enabled("my_session") as session:
    assert session
    agent.run("What is 5 raised to .123243 power?")  # this should be traced

agent.run("What is 2 raised to .123243 power?")  # this should not be traced

[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mI need to use a calculator to solve this.
Action: Calculator
Action Input: 5 ^ .123243[0m
Observation: [36;1m[1;3mAnswer: 1.2193914912400514[0m
Thought:[32;1m[1;3mI now know the answer to the question. 
Final Answer: 1.2193914912400514[0m

[1m> Finished chain.[0m


[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mI need to use a calculator to solve this.
Action: Calculator
Action Input: 2 ^ .123243[0m
Observation: [36;1m[1;3mAnswer: 1.0891804557407723[0m
Thought:[32;1m[1;3mI now know the answer to the question. 
Final Answer: 1.0891804557407723[0m

[1m> Finished chain.[0m





'1.0891804557407723'

# The context manager is concurrency safe:
import asyncio

if "LANGCHAIN_TRACING" in os.environ:
    del os.environ["LANGCHAIN_TRACING"]

questions = [f"What is {i} raised to .123 power?" for i in range(1, 4)]

# start a background task
task = asyncio.create_task(agent.arun(questions[0]))  # this should not be traced
with tracing_enabled() as session:
    assert session
    tasks = [agent.arun(q) for q in questions[1:3]]  # these should be traced
    await asyncio.gather(*tasks)

await task

[1m> Entering new AgentExecutor chain...[0m

[1m> Entering new AgentExecutor chain...[0m


[1m> Entering new AgentExecutor chain...[0m

[32;1m[1;3mI need to use a calculator to solve this.
Action: Calculator
Action Input: 3^0.123[0m[32;1m[1;3mI need to use a calculator to solve this.
Action: Calculator
Action Input: 2^0.123[0m[32;1m[1;3mAny number raised to the power of 0 is 1, but I'm not sure about a decimal power.
Action: Calculator
Action Input: 1^.123[0m
Observation: [36;1m[1;3mAnswer: 1.1446847956963533[0m
Thought:
Observation: [36;1m[1;3mAnswer: 1.0889970153361064[0m
Thought:
Observation: [36;1m[1;3mAnswer: 1.0[0m
Thought:
[1m> Finished chain.[0m

[1m> Finished chain.[0m

[1m> Finished chain.[0m





'1.0'

[Beta] Tracing V2

We are rolling out a newer version of our tracing service with more features coming soon. Here are the instructions on how to use it to trace your runs.

To use, you can use the tracing_v2_enabled context manager or set LANGCHAIN_TRACING_V2 = 'true'

Option 1 (Local):

Run the local LangChainPlus Server

pip install --upgrade langchain
langchain plus start

Option 2 (Hosted):

After making an account an grabbing a LangChainPlus API Key, set the LANGCHAIN_ENDPOINT and LANGCHAIN_API_KEY environment variables

import os

os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_ENDPOINT"] = "https://api.langchain.plus"  # Uncomment this line if you want to use the hosted version
# os.environ["LANGCHAIN_API_KEY"] = "<YOUR-LANGCHAINPLUS-API-KEY>"  # Uncomment this line if you want to use the hosted version.

import langchain
from langchain.agents import Tool, initialize_agent, load_tools
from langchain.agents import AgentType
from langchain.callbacks import tracing_enabled
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI

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

llm = OpenAI(temperature=0)
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

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

[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m I need to use a calculator to solve this.
Action: Calculator
Action Input: 2^.123243[0m
Observation: [36;1m[1;3mAnswer: 1.0891804557407723[0m
Thought:[32;1m[1;3m I now know the final answer.
Final Answer: 1.0891804557407723[0m

[1m> Finished chain.[0m





'1.0891804557407723'

Tracing Walkthrough

[Beta] Tracing V2​

[Beta] Tracing V2