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Langsmith - Logging LLM Input/Output

An all-in-one developer platform for every step of the application lifecycle https://smith.langchain.com/

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Pre-Requisites

pip install litellm

Quick Start

Use just 2 lines of code, to instantly log your responses across all providers with Langsmith

litellm.success_callback = ["langsmith"]
import litellm
import os

os.environ["LANGSMITH_API_KEY"] = ""
os.environ["LANGSMITH_PROJECT"] = "" # defaults to litellm-completion
os.environ["LANGSMITH_DEFAULT_RUN_NAME"] = "" # defaults to LLMRun
# LLM API Keys
os.environ['OPENAI_API_KEY']=""

# set langsmith as a callback, litellm will send the data to langsmith
litellm.success_callback = ["langsmith"]

# openai call
response = litellm.completion(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": "Hi 👋 - i'm openai"}
]
)

Advanced

Set Custom Project & Run names

import litellm
import os

os.environ["LANGSMITH_API_KEY"] = ""
# LLM API Keys
os.environ['OPENAI_API_KEY']=""

# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langsmith"]

response = litellm.completion(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": "Hi 👋 - i'm openai"}
],
metadata={
"run_name": "litellmRUN", # langsmith run name
"project_name": "litellm-completion", # langsmith project name
}
)
print(response)

Make LiteLLM Proxy use Custom LANGSMITH_BASE_URL

If you're using a custom LangSmith instance, you can set the LANGSMITH_BASE_URL environment variable to point to your instance. For example, you can make LiteLLM Proxy log to a local LangSmith instance with this config:

litellm_settings:
success_callback: ["langsmith"]

environment_variables:
LANGSMITH_BASE_URL: "http://localhost:1984"
LANGSMITH_PROJECT: "litellm-proxy"

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