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🪢 Langfuse - Logging LLM Input/Output

What is Langfuse?​

Langfuse (GitHub) is an open-source LLM engineering platform for model tracing, prompt management, and application evaluation. Langfuse helps teams to collaboratively debug, analyze, and iterate on their LLM applications.

Example trace in Langfuse using multiple models via LiteLLM:

Usage with LiteLLM Proxy (LLM Gateway)​

👉 Follow this link to start sending logs to langfuse with LiteLLM Proxy server

Usage with LiteLLM Python SDK​

Pre-Requisites​

Ensure you have run pip install langfuse for this integration

pip install langfuse>=2.0.0 litellm

Quick Start​

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

Open In Colab

Get your Langfuse API Keys from https://cloud.langfuse.com/

litellm.success_callback = ["langfuse"]
litellm.failure_callback = ["langfuse"] # logs errors to langfuse
# pip install langfuse 
import litellm
import os

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
# Optional, defaults to https://cloud.langfuse.com
os.environ["LANGFUSE_HOST"] # optional

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

# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langfuse"] 
 
# openai call
response = litellm.completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ]
)

Advanced​

Set Custom Generation Names, pass Metadata​

Pass generation_name in metadata

import litellm
from litellm import completion
import os

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-..."


# OpenAI and Cohere keys 
# You can use any of the litellm supported providers: https://docs.litellm.ai/docs/providers
os.environ['OPENAI_API_KEY']="sk-..."

# set langfuse as a callback, litellm will send the data to langfuse
litellm.success_callback = ["langfuse"] 
 
# openai call
response = completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ],
  metadata = {
    "generation_name": "litellm-ishaan-gen", # set langfuse generation name
    # custom metadata fields
    "project": "litellm-proxy" 
  }
)
 
print(response)

Set Custom Trace ID, Trace User ID, Trace Metadata, Trace Version, Trace Release and Tags​

Pass trace_id, trace_user_id, trace_metadata, trace_version, trace_release, tags in metadata

import litellm
from litellm import completion
import os

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-..."

os.environ['OPENAI_API_KEY']="sk-..."

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

# set custom langfuse trace params and generation params
response = completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ],
  metadata={
      "generation_name": "ishaan-test-generation",  # set langfuse Generation Name
      "generation_id": "gen-id22",                  # set langfuse Generation ID 
      "parent_observation_id": "obs-id9"            # set langfuse Parent Observation ID
      "version":  "test-generation-version"         # set langfuse Generation Version
      "trace_user_id": "user-id2",                  # set langfuse Trace User ID
      "session_id": "session-1",                    # set langfuse Session ID
      "tags": ["tag1", "tag2"],                     # set langfuse Tags
      "trace_name": "new-trace-name"                # set langfuse Trace Name
      "trace_id": "trace-id22",                     # set langfuse Trace ID
      "trace_metadata": {"key": "value"},           # set langfuse Trace Metadata
      "trace_version": "test-trace-version",        # set langfuse Trace Version (if not set, defaults to Generation Version)
      "trace_release": "test-trace-release",        # set langfuse Trace Release
      ### OR ### 
      "existing_trace_id": "trace-id22",            # if generation is continuation of past trace. This prevents default behaviour of setting a trace name
      ### OR enforce that certain fields are trace overwritten in the trace during the continuation ###
      "existing_trace_id": "trace-id22",
      "trace_metadata": {"key": "updated_trace_value"},            # The new value to use for the langfuse Trace Metadata
      "update_trace_keys": ["input", "output", "trace_metadata"],  # Updates the trace input & output to be this generations input & output also updates the Trace Metadata to match the passed in value
      "debug_langfuse": True,                                      # Will log the exact metadata sent to litellm for the trace/generation as `metadata_passed_to_litellm` 
  },
)

print(response)

You can also pass metadata as part of the request header with a langfuse_* prefix:

curl --location --request POST 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'langfuse_trace_id: trace-id2' \
    --header 'langfuse_trace_user_id: user-id2' \
    --header 'langfuse_trace_metadata: {"key":"value"}' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
}'

Trace & Generation Parameters​

Trace Specific Parameters​
  • trace_id - Identifier for the trace, must use existing_trace_id instead of trace_id if this is an existing trace, auto-generated by default
  • trace_name - Name of the trace, auto-generated by default
  • session_id - Session identifier for the trace, defaults to None
  • trace_version - Version for the trace, defaults to value for version
  • trace_release - Release for the trace, defaults to None
  • trace_metadata - Metadata for the trace, defaults to None
  • trace_user_id - User identifier for the trace, defaults to completion argument user
  • tags - Tags for the trace, defeaults to None
Updatable Parameters on Continuation​

The following parameters can be updated on a continuation of a trace by passing in the following values into the update_trace_keys in the metadata of the completion.

  • input - Will set the traces input to be the input of this latest generation
  • output - Will set the traces output to be the output of this generation
  • trace_version - Will set the trace version to be the provided value (To use the latest generations version instead, use version)
  • trace_release - Will set the trace release to be the provided value
  • trace_metadata - Will set the trace metadata to the provided value
  • trace_user_id - Will set the trace user id to the provided value

Generation Specific Parameters​

  • generation_id - Identifier for the generation, auto-generated by default
  • generation_name - Identifier for the generation, auto-generated by default
  • parent_observation_id - Identifier for the parent observation, defaults to None
  • prompt - Langfuse prompt object used for the generation, defaults to None

Any other key value pairs passed into the metadata not listed in the above spec for a litellm completion will be added as a metadata key value pair for the generation.

Disable Logging - Specific Calls​

To disable logging for specific calls use the no-log flag.

completion(messages = ..., model = ..., **{"no-log": True})

Use LangChain ChatLiteLLM + Langfuse​

Pass trace_user_id, session_id in model_kwargs

import os
from langchain.chat_models import ChatLiteLLM
from langchain.schema import HumanMessage
import litellm

# from https://cloud.langfuse.com/
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-..."

os.environ['OPENAI_API_KEY']="sk-..."

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

chat = ChatLiteLLM(
  model="gpt-3.5-turbo"
  model_kwargs={
      "metadata": {
        "trace_user_id": "user-id2", # set langfuse Trace User ID
        "session_id": "session-1" ,  # set langfuse Session ID
        "tags": ["tag1", "tag2"] 
      }
    }
  )
messages = [
    HumanMessage(
        content="what model are you"
    )
]
chat(messages)

Redacting Messages, Response Content from Langfuse Logging​

Redact Messages and Responses from all Langfuse Logging​

Set litellm.turn_off_message_logging=True This will prevent the messages and responses from being logged to langfuse, but request metadata will still be logged.

Redact Messages and Responses from specific Langfuse Logging​

In the metadata typically passed for text completion or embedding calls you can set specific keys to mask the messages and responses for this call.

Setting mask_input to True will mask the input from being logged for this call

Setting mask_output to True will make the output from being logged for this call.

Be aware that if you are continuing an existing trace, and you set update_trace_keys to include either input or output and you set the corresponding mask_input or mask_output, then that trace will have its existing input and/or output replaced with a redacted message.

Troubleshooting & Errors​

Data not getting logged to Langfuse ?​

  • Ensure you're on the latest version of langfuse pip install langfuse -U. The latest version allows litellm to log JSON input/outputs to langfuse
  • Follow this checklist if you don't see any traces in langfuse.

Support & Talk to Founders​