Langsmith - Logging LLM Input/Output
An all-in-one developer platform for every step of the application lifecycle https://smith.langchain.com/
info
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"
Support & Talk to Founders
- Schedule Demo 👋
 - Community Discord 💭
 - Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai