Docs - use 1 page for all logging integrations on proxy + add logging features at top level (#6805 )

* use 1 page for bucket logging

* docs logging proxy

* remove dup doc

* docs fix emoji

* docs team logging

2024-11-18 18:35:52 -08:00

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Logging

Log Proxy input, output, and exceptions using:

Langfuse
OpenTelemetry
GCS and s3 Buckets
Custom Callbacks
Langsmith
DataDog
DynamoDB
etc.

import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

Getting the LiteLLM Call ID

LiteLLM generates a unique call_id for each request. This call_id can be used to track the request across the system. This can be very useful for finding the info for a particular request in a logging system like one of the systems mentioned in this page.

curl -i -sSL --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
      "model": "gpt-3.5-turbo",
      "messages": [{"role": "user", "content": "what llm are you"}]
    }' | grep 'x-litellm'

The output of this is:

x-litellm-call-id: b980db26-9512-45cc-b1da-c511a363b83f
x-litellm-model-id: cb41bc03f4c33d310019bae8c5afdb1af0a8f97b36a234405a9807614988457c
x-litellm-model-api-base: https://x-example-1234.openai.azure.com
x-litellm-version: 1.40.21
x-litellm-response-cost: 2.85e-05
x-litellm-key-tpm-limit: None
x-litellm-key-rpm-limit: None

A number of these headers could be useful for troubleshooting, but the x-litellm-call-id is the one that is most useful for tracking a request across components in your system, including in logging tools.

Logging Features

Conditional Logging by Virtual Keys, Teams

Use this to:

Conditionally enable logging for some virtual keys/teams
Set different logging providers for different virtual keys/teams

👉 Get Started - Team/Key Based Logging

Redacting UserAPIKeyInfo

Redact information about the user api key (hashed token, user_id, team id, etc.), from logs.

Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging.

litellm_settings: 
  callbacks: ["langfuse"]
  redact_user_api_key_info: true

Redact Messages, Response Content

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

Example config.yaml

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["langfuse"]
  turn_off_message_logging: True # 👈 Key Change

If you have this feature turned on, you can override it for specific requests by setting a request header LiteLLM-Disable-Message-Redaction: true.

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --header 'LiteLLM-Disable-Message-Redaction: true' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
}'

Removes any field with user_api_key_* from metadata.

What gets logged?

Found under kwargs["standard_logging_object"]. This is a standard payload, logged for every response.


class StandardLoggingPayload(TypedDict):
    id: str
    trace_id: str  # Trace multiple LLM calls belonging to same overall request (e.g. fallbacks/retries)
    call_type: str
    response_cost: float
    response_cost_failure_debug_info: Optional[
        StandardLoggingModelCostFailureDebugInformation
    ]
    status: StandardLoggingPayloadStatus
    total_tokens: int
    prompt_tokens: int
    completion_tokens: int
    startTime: float
    endTime: float
    completionStartTime: float
    model_map_information: StandardLoggingModelInformation
    model: str
    model_id: Optional[str]
    model_group: Optional[str]
    api_base: str
    metadata: StandardLoggingMetadata
    cache_hit: Optional[bool]
    cache_key: Optional[str]
    saved_cache_cost: float
    request_tags: list
    end_user: Optional[str]
    requester_ip_address: Optional[str]
    messages: Optional[Union[str, list, dict]]
    response: Optional[Union[str, list, dict]]
    error_str: Optional[str]
    model_parameters: dict
    hidden_params: StandardLoggingHiddenParams

class StandardLoggingHiddenParams(TypedDict):
    model_id: Optional[str]
    cache_key: Optional[str]
    api_base: Optional[str]
    response_cost: Optional[str]
    additional_headers: Optional[StandardLoggingAdditionalHeaders]

class StandardLoggingAdditionalHeaders(TypedDict, total=False):
    x_ratelimit_limit_requests: int
    x_ratelimit_limit_tokens: int
    x_ratelimit_remaining_requests: int
    x_ratelimit_remaining_tokens: int

class StandardLoggingMetadata(StandardLoggingUserAPIKeyMetadata):
    """
    Specific metadata k,v pairs logged to integration for easier cost tracking
    """

    spend_logs_metadata: Optional[
        dict
    ]  # special param to log k,v pairs to spendlogs for a call
    requester_ip_address: Optional[str]
    requester_metadata: Optional[dict]

class StandardLoggingModelInformation(TypedDict):
    model_map_key: str
    model_map_value: Optional[ModelInfo]
  

StandardLoggingPayloadStatus = Literal["success", "failure"]

class StandardLoggingModelCostFailureDebugInformation(TypedDict, total=False):
    """
    Debug information, if cost tracking fails.

    Avoid logging sensitive information like response or optional params
    """

    error_str: Required[str]
    traceback_str: Required[str]
    model: str
    cache_hit: Optional[bool]
    custom_llm_provider: Optional[str]
    base_model: Optional[str]
    call_type: str
    custom_pricing: Optional[bool]

Langfuse

We will use the --config to set litellm.success_callback = ["langfuse"] this will log all successfull LLM calls to langfuse. Make sure to set LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY in your environment

Step 1 Install langfuse

pip install langfuse>=2.0.0

Step 2: Create a config.yaml file and set litellm_settings: success_callback

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["langfuse"]

Step 3: Set required env variables for logging to langfuse

export LANGFUSE_PUBLIC_KEY="pk_kk"
export LANGFUSE_SECRET_KEY="sk_ss"
# Optional, defaults to https://cloud.langfuse.com
export LANGFUSE_HOST="https://xxx.langfuse.com"

Step 4: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

litellm --test

Expected output on Langfuse

Logging Metadata to Langfuse

Pass metadata as part of the request body

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
    "metadata": {
        "generation_name": "ishaan-test-generation",
        "generation_id": "gen-id22",
        "trace_id": "trace-id22",
        "trace_user_id": "user-id2"
    }
}'

Set extra_body={"metadata": { }} to metadata you want to pass

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={
        "metadata": {
            "generation_name": "ishaan-generation-openai-client",
            "generation_id": "openai-client-gen-id22",
            "trace_id": "openai-client-trace-id22",
            "trace_user_id": "openai-client-user-id2"
        }
    }
)

print(response)

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000",
    model = "gpt-3.5-turbo",
    temperature=0.1,
    extra_body={
        "metadata": {
            "generation_name": "ishaan-generation-langchain-client",
            "generation_id": "langchain-client-gen-id22",
            "trace_id": "langchain-client-trace-id22",
            "trace_user_id": "langchain-client-user-id2"
        }
    }
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

LiteLLM Tags - `cache_hit`, `cache_key`

Use this if you want to control which LiteLLM-specific fields are logged as tags by the LiteLLM proxy. By default LiteLLM Proxy logs no LiteLLM-specific fields

LiteLLM specific field	Description	Example Value
`cache_hit`	Indicates whether a cache hit occured (True) or not (False)	`true`, `false`
`cache_key`	The Cache key used for this request	`d2b758c****`
`proxy_base_url`	The base URL for the proxy server, the value of env var `PROXY_BASE_URL` on your server	`https://proxy.example.com`
`user_api_key_alias`	An alias for the LiteLLM Virtual Key.	`prod-app1`
`user_api_key_user_id`	The unique ID associated with a user's API key.	`user_123`, `user_456`
`user_api_key_user_email`	The email associated with a user's API key.	`user@example.com`, `admin@example.com`
`user_api_key_team_alias`	An alias for a team associated with an API key.	`team_alpha`, `dev_team`

Usage

Specify langfuse_default_tags to control what litellm fields get logged on Langfuse

Example config.yaml

model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

litellm_settings:
  success_callback: ["langfuse"]

  # 👇 Key Change
  langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"]

View POST sent from LiteLLM to provider

Use this when you want to view the RAW curl request sent from LiteLLM to the LLM API

Pass metadata as part of the request body

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
    "metadata": {
        "log_raw_request": true
    }
}'

Set extra_body={"metadata": {"log_raw_request": True }} to metadata you want to pass

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={
        "metadata": {
            "log_raw_request": True
        }
    }
)

print(response)

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000",
    model = "gpt-3.5-turbo",
    temperature=0.1,
    extra_body={
        "metadata": {
            "log_raw_request": True
        }
    }
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

Expected Output on Langfuse

You will see raw_request in your Langfuse Metadata. This is the RAW CURL command sent from LiteLLM to your LLM API provider

OpenTelemetry

:::info

[Optional] Customize OTEL Service Name and OTEL TRACER NAME by setting the following variables in your environment

OTEL_TRACER_NAME=<your-trace-name>     # default="litellm"
OTEL_SERVICE_NAME=<your-service-name>` # default="litellm"

:::

Step 1: Set callbacks and env vars

Add the following to your env

OTEL_EXPORTER="console"

Add otel as a callback on your litellm_config.yaml

litellm_settings:
  callbacks: ["otel"]

Step 2: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --detailed_debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

Step 3: Expect to see the following logged on your server logs / console

This is the Span from OTEL Logging

{
    "name": "litellm-acompletion",
    "context": {
        "trace_id": "0x8d354e2346060032703637a0843b20a3",
        "span_id": "0xd8d3476a2eb12724",
        "trace_state": "[]"
    },
    "kind": "SpanKind.INTERNAL",
    "parent_id": null,
    "start_time": "2024-06-04T19:46:56.415888Z",
    "end_time": "2024-06-04T19:46:56.790278Z",
    "status": {
        "status_code": "OK"
    },
    "attributes": {
        "model": "llama3-8b-8192"
    },
    "events": [],
    "links": [],
    "resource": {
        "attributes": {
            "service.name": "litellm"
        },
        "schema_url": ""
    }
}

Quick Start - Log to Honeycomb

Step 1: Set callbacks and env vars

Add the following to your env

OTEL_EXPORTER="otlp_http"
OTEL_ENDPOINT="https://api.honeycomb.io/v1/traces"
OTEL_HEADERS="x-honeycomb-team=<your-api-key>"

Add otel as a callback on your litellm_config.yaml

litellm_settings:
  callbacks: ["otel"]

Step 2: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --detailed_debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

Quick Start - Log to Traceloop

Step 1: Add the following to your env

OTEL_EXPORTER="otlp_http"
OTEL_ENDPOINT="https://api.traceloop.com"
OTEL_HEADERS="Authorization=Bearer%20<your-api-key>"

Step 2: Add otel as a callbacks

litellm_settings:
  callbacks: ["otel"]

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --detailed_debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

Quick Start - Log to OTEL Collector

Step 1: Set callbacks and env vars

Add the following to your env

OTEL_EXPORTER="otlp_http"
OTEL_ENDPOINT="http:/0.0.0.0:4317"
OTEL_HEADERS="x-honeycomb-team=<your-api-key>" # Optional

Add otel as a callback on your litellm_config.yaml

litellm_settings:
  callbacks: ["otel"]

Step 2: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --detailed_debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

Quick Start - Log to OTEL GRPC Collector

Step 1: Set callbacks and env vars

Add the following to your env

OTEL_EXPORTER="otlp_grpc"
OTEL_ENDPOINT="http:/0.0.0.0:4317"
OTEL_HEADERS="x-honeycomb-team=<your-api-key>" # Optional

Add otel as a callback on your litellm_config.yaml

litellm_settings:
  callbacks: ["otel"]

Step 2: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --detailed_debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

** 🎉 Expect to see this trace logged in your OTEL collector**

Redacting Messages, Response Content

Set message_logging=False for otel, no messages / response will be logged

litellm_settings:
  callbacks: ["otel"]

## 👇 Key Change
callback_settings:
  otel:
    message_logging: False

Traceparent Header

Context propagation across Services `Traceparent HTTP Header`

❓ Use this when you want to pass information about the incoming request in a distributed tracing system

✅ Key change: Pass the traceparent header in your requests. Read more about traceparent headers here

traceparent: 00-80e1afed08e019fc1110464cfa66635c-7a085853722dc6d2-01

Example Usage

Make Request to LiteLLM Proxy with traceparent header

import openai
import uuid

client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
example_traceparent = f"00-80e1afed08e019fc1110464cfa66635c-02e80198930058d4-01"
extra_headers = {
    "traceparent": example_traceparent
}
_trace_id = example_traceparent.split("-")[1]

print("EXTRA HEADERS: ", extra_headers)
print("Trace ID: ", _trace_id)

response = client.chat.completions.create(
    model="llama3",
    messages=[
        {"role": "user", "content": "this is a test request, write a short poem"}
    ],
    extra_headers=extra_headers,
)

print(response)

# EXTRA HEADERS:  {'traceparent': '00-80e1afed08e019fc1110464cfa66635c-02e80198930058d4-01'}
# Trace ID:  80e1afed08e019fc1110464cfa66635c

Lookup Trace ID on OTEL Logger

Search for Trace=80e1afed08e019fc1110464cfa66635c on your OTEL Collector

Forwarding `Traceparent HTTP Header` to LLM APIs

Use this if you want to forward the traceparent headers to your self hosted LLMs like vLLM

Set forward_traceparent_to_llm_provider: True in your config.yaml. This will forward the traceparent header to your LLM API

:::warning

Only use this for self hosted LLMs, this can cause Bedrock, VertexAI calls to fail

:::

litellm_settings:
  forward_traceparent_to_llm_provider: True

Google Cloud Storage Buckets

Log LLM Logs to Google Cloud Storage Buckets

:::info

✨ This is an Enterprise only feature Get Started with Enterprise here

:::

Property	Details
Description	Log LLM Input/Output to cloud storage buckets
Load Test Benchmarks	Benchmarks
Google Docs on Cloud Storage	Google Cloud Storage

Usage

Add gcs_bucket to LiteLLM Config.yaml

model_list:
- litellm_params:
    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
    api_key: my-fake-key
    model: openai/my-fake-model
  model_name: fake-openai-endpoint

litellm_settings:
  callbacks: ["gcs_bucket"] # 👈 KEY CHANGE # 👈 KEY CHANGE

Set required env variables

GCS_BUCKET_NAME="<your-gcs-bucket-name>"
GCS_PATH_SERVICE_ACCOUNT="/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json

Start Proxy

litellm --config /path/to/config.yaml

Test it!

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
    }
'

Expected Logs on GCS Buckets

Fields Logged on GCS Buckets

The standard logging object is logged on GCS Bucket

Getting `service_account.json` from Google Cloud Console

Go to Google Cloud Console
Search for IAM & Admin
Click on Service Accounts
Select a Service Account
Click on 'Keys' -> Add Key -> Create New Key -> JSON
Save the JSON file and add the path to GCS_PATH_SERVICE_ACCOUNT

s3 Buckets

We will use the --config to set

litellm.success_callback = ["s3"]

This will log all successfull LLM calls to s3 Bucket

Step 1 Set AWS Credentials in .env

AWS_ACCESS_KEY_ID = ""
AWS_SECRET_ACCESS_KEY = ""
AWS_REGION_NAME = ""

Step 2: Create a config.yaml file and set litellm_settings: success_callback

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["s3"]
  s3_callback_params:
    s3_bucket_name: logs-bucket-litellm   # AWS Bucket Name for S3
    s3_region_name: us-west-2              # AWS Region Name for S3
    s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID  # us os.environ/<variable name> to pass environment variables. This is AWS Access Key ID for S3
    s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY  # AWS Secret Access Key for S3
    s3_path: my-test-path # [OPTIONAL] set path in bucket you want to write logs to
    s3_endpoint_url: https://s3.amazonaws.com  # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 buckets

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "Azure OpenAI GPT-4 East",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

Your logs should be available on the specified s3 Bucket

Custom Callback Class [Async]

Use this when you want to run custom callbacks in python

Step 1 - Create your custom `litellm` callback class

We use litellm.integrations.custom_logger for this, more details about litellm custom callbacks here

Define your custom callback class in a python file.

Here's an example custom logger for tracking key, user, model, prompt, response, tokens, cost. We create a file called custom_callbacks.py and initialize proxy_handler_instance

from litellm.integrations.custom_logger import CustomLogger
import litellm

# This file includes the custom callbacks for LiteLLM Proxy
# Once defined, these can be passed in proxy_config.yaml
class MyCustomHandler(CustomLogger):
    def log_pre_api_call(self, model, messages, kwargs): 
        print(f"Pre-API Call")
    
    def log_post_api_call(self, kwargs, response_obj, start_time, end_time): 
        print(f"Post-API Call")

    def log_stream_event(self, kwargs, response_obj, start_time, end_time):
        print(f"On Stream")
        
    def log_success_event(self, kwargs, response_obj, start_time, end_time): 
        print("On Success")

    def log_failure_event(self, kwargs, response_obj, start_time, end_time): 
        print(f"On Failure")

    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        print(f"On Async Success!")
        # log: key, user, model, prompt, response, tokens, cost
        # Access kwargs passed to litellm.completion()
        model = kwargs.get("model", None)
        messages = kwargs.get("messages", None)
        user = kwargs.get("user", None)

        # Access litellm_params passed to litellm.completion(), example access `metadata`
        litellm_params = kwargs.get("litellm_params", {})
        metadata = litellm_params.get("metadata", {})   # headers passed to LiteLLM proxy, can be found here

        # Calculate cost using  litellm.completion_cost()
        cost = litellm.completion_cost(completion_response=response_obj)
        response = response_obj
        # tokens used in response 
        usage = response_obj["usage"]

        print(
            f"""
                Model: {model},
                Messages: {messages},
                User: {user},
                Usage: {usage},
                Cost: {cost},
                Response: {response}
                Proxy Metadata: {metadata}
            """
        )
        return

    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): 
        try:
            print(f"On Async Failure !")
            print("\nkwargs", kwargs)
            # Access kwargs passed to litellm.completion()
            model = kwargs.get("model", None)
            messages = kwargs.get("messages", None)
            user = kwargs.get("user", None)

            # Access litellm_params passed to litellm.completion(), example access `metadata`
            litellm_params = kwargs.get("litellm_params", {})
            metadata = litellm_params.get("metadata", {})   # headers passed to LiteLLM proxy, can be found here

            # Acess Exceptions & Traceback
            exception_event = kwargs.get("exception", None)
            traceback_event = kwargs.get("traceback_exception", None)

            # Calculate cost using  litellm.completion_cost()
            cost = litellm.completion_cost(completion_response=response_obj)
            print("now checking response obj")
            
            print(
                f"""
                    Model: {model},
                    Messages: {messages},
                    User: {user},
                    Cost: {cost},
                    Response: {response_obj}
                    Proxy Metadata: {metadata}
                    Exception: {exception_event}
                    Traceback: {traceback_event}
                """
            )
        except Exception as e:
            print(f"Exception: {e}")

proxy_handler_instance = MyCustomHandler()

# Set litellm.callbacks = [proxy_handler_instance] on the proxy
# need to set litellm.callbacks = [proxy_handler_instance] # on the proxy

Step 2 - Pass your custom callback class in `config.yaml`

We pass the custom callback class defined in Step1 to the config.yaml. Set callbacks to python_filename.logger_instance_name

In the config below, we pass

python_filename: custom_callbacks.py
logger_instance_name: proxy_handler_instance. This is defined in Step 1

callbacks: custom_callbacks.proxy_handler_instance

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo

litellm_settings:
  callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance]

Step 3 - Start proxy + test request

litellm --config proxy_config.yaml

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "good morning good sir"
        }
    ],
    "user": "ishaan-app",
    "temperature": 0.2
    }'

Resulting Log on Proxy

On Success
    Model: gpt-3.5-turbo,
    Messages: [{'role': 'user', 'content': 'good morning good sir'}],
    User: ishaan-app,
    Usage: {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21},
    Cost: 3.65e-05,
    Response: {'id': 'chatcmpl-8S8avKJ1aVBg941y5xzGMSKrYCMvN', 'choices': [{'finish_reason': 'stop', 'index': 0, 'message': {'content': 'Good morning! How can I assist you today?', 'role': 'assistant'}}], 'created': 1701716913, 'model': 'gpt-3.5-turbo-0613', 'object': 'chat.completion', 'system_fingerprint': None, 'usage': {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21}}
    Proxy Metadata: {'user_api_key': None, 'headers': Headers({'host': '0.0.0.0:4000', 'user-agent': 'curl/7.88.1', 'accept': '*/*', 'authorization': 'Bearer sk-1234', 'content-length': '199', 'content-type': 'application/x-www-form-urlencoded'}), 'model_group': 'gpt-3.5-turbo', 'deployment': 'gpt-3.5-turbo-ModelID-gpt-3.5-turbo'}

Logging Proxy Request Object, Header, Url

Here's how you can access the url, headers, request body sent to the proxy for each request

class MyCustomHandler(CustomLogger):
    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        print(f"On Async Success!")

        litellm_params = kwargs.get("litellm_params", None)
        proxy_server_request = litellm_params.get("proxy_server_request")
        print(proxy_server_request)

Expected Output

{
  "url": "http://testserver/chat/completions",
  "method": "POST",
  "headers": {
    "host": "testserver",
    "accept": "*/*",
    "accept-encoding": "gzip, deflate",
    "connection": "keep-alive",
    "user-agent": "testclient",
    "authorization": "Bearer None",
    "content-length": "105",
    "content-type": "application/json"
  },
  "body": {
    "model": "Azure OpenAI GPT-4 Canada",
    "messages": [
      {
        "role": "user",
        "content": "hi"
      }
    ],
    "max_tokens": 10
  }
}

Logging `model_info` set in config.yaml

Here is how to log the model_info set in your proxy config.yaml. Information on setting model_info on config.yaml

class MyCustomHandler(CustomLogger):
    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        print(f"On Async Success!")

        litellm_params = kwargs.get("litellm_params", None)
        model_info = litellm_params.get("model_info")
        print(model_info)

Expected Output

{'mode': 'embedding', 'input_cost_per_token': 0.002}

Logging responses from proxy

Both /chat/completions and /embeddings responses are available as response_obj

Note: for /chat/completions, both stream=True and non stream responses are available as response_obj

class MyCustomHandler(CustomLogger):
    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        print(f"On Async Success!")
        print(response_obj)

Expected Output /chat/completion [for both stream and non-stream responses]

ModelResponse(
    id='chatcmpl-8Tfu8GoMElwOZuj2JlHBhNHG01PPo',
    choices=[
        Choices(
            finish_reason='stop',
            index=0,
            message=Message(
                content='As an AI language model, I do not have a physical body and therefore do not possess any degree or educational qualifications. My knowledge and abilities come from the programming and algorithms that have been developed by my creators.',
                role='assistant'
            )
        )
    ],
    created=1702083284,
    model='chatgpt-v-2',
    object='chat.completion',
    system_fingerprint=None,
    usage=Usage(
        completion_tokens=42,
        prompt_tokens=5,
        total_tokens=47
    )
)

Expected Output /embeddings

{
    'model': 'ada',
    'data': [
        {
            'embedding': [
                -0.035126980394124985, -0.020624293014407158, -0.015343423001468182,
                -0.03980357199907303, -0.02750781551003456, 0.02111034281551838,
                -0.022069307044148445, -0.019442008808255196, -0.00955679826438427,
                -0.013143060728907585, 0.029583381488919258, -0.004725852981209755,
                -0.015198921784758568, -0.014069183729588985, 0.00897879246622324,
                0.01521205808967352,
                # ... (truncated for brevity)
            ]
        }
    ]
}

Custom Callback APIs [Async]

:::info

This is an Enterprise only feature Get Started with Enterprise here

:::

Use this if you:

Want to use custom callbacks written in a non Python programming language
Want your callbacks to run on a different microservice

Step 1. Create your generic logging API endpoint

Set up a generic API endpoint that can receive data in JSON format. The data will be included within a "data" field.

Your server should support the following Request format:

curl --location https://your-domain.com/log-event \
     --request POST \
     --header "Content-Type: application/json" \
     --data '{
       "data": {
         "id": "chatcmpl-8sgE89cEQ4q9biRtxMvDfQU1O82PT",
         "call_type": "acompletion",
         "cache_hit": "None",
         "startTime": "2024-02-15 16:18:44.336280",
         "endTime": "2024-02-15 16:18:45.045539",
         "model": "gpt-3.5-turbo",
         "user": "ishaan-2",
         "modelParameters": "{'temperature': 0.7, 'max_tokens': 10, 'user': 'ishaan-2', 'extra_body': {}}",
         "messages": "[{'role': 'user', 'content': 'This is a test'}]",
         "response": "ModelResponse(id='chatcmpl-8sgE89cEQ4q9biRtxMvDfQU1O82PT', choices=[Choices(finish_reason='length', index=0, message=Message(content='Great! How can I assist you with this test', role='assistant'))], created=1708042724, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=10, prompt_tokens=11, total_tokens=21))",
         "usage": "Usage(completion_tokens=10, prompt_tokens=11, total_tokens=21)",
         "metadata": "{}",
         "cost": "3.65e-05"
       }
     }'

Reference FastAPI Python Server

Here's a reference FastAPI Server that is compatible with LiteLLM Proxy:

# this is an example endpoint to receive data from litellm
from fastapi import FastAPI, HTTPException, Request

app = FastAPI()


@app.post("/log-event")
async def log_event(request: Request):
    try:
        print("Received /log-event request")
        # Assuming the incoming request has JSON data
        data = await request.json()
        print("Received request data:")
        print(data)

        # Your additional logic can go here
        # For now, just printing the received data

        return {"message": "Request received successfully"}
    except Exception as e:
        print(f"Error processing request: {str(e)}")
        import traceback

        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="127.0.0.1", port=4000)

Step 2. Set your `GENERIC_LOGGER_ENDPOINT` to the endpoint + route we should send callback logs to

os.environ["GENERIC_LOGGER_ENDPOINT"] = "http://localhost:4000/log-event"

Step 3. Create a `config.yaml` file and set `litellm_settings`: `success_callback` = ["generic"]

Example litellm proxy config.yaml

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["generic"]

Start the LiteLLM Proxy and make a test request to verify the logs reached your callback API

Langsmith

Set success_callback: ["langsmith"] on litellm config.yaml

If you're using a custom LangSmith instance, you can set the LANGSMITH_BASE_URL environment variable to point to your instance.

litellm_settings:
  success_callback: ["langsmith"]

environment_variables:
  LANGSMITH_API_KEY: "lsv2_pt_xxxxxxxx"
  LANGSMITH_PROJECT: "litellm-proxy"

  LANGSMITH_BASE_URL: "https://api.smith.langchain.com" # (Optional - only needed if you have a custom Langsmith instance)

Start Proxy

litellm --config /path/to/config.yaml

Test it!

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "Hello, Claude gm!"
        }
      ],
    }
'

Expect to see your log on Langfuse <Image img={require('../../img/langsmith_new.png')} />

Arize AI

Set success_callback: ["arize"] on litellm config.yaml

model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

litellm_settings:
  callbacks: ["arize"]

environment_variables:
    ARIZE_SPACE_KEY: "d0*****"
    ARIZE_API_KEY: "141a****"
    ARIZE_ENDPOINT: "https://otlp.arize.com/v1" # OPTIONAL - your custom arize GRPC api endpoint
    ARIZE_HTTP_ENDPOINT: "https://otlp.arize.com/v1" # OPTIONAL - your custom arize HTTP api endpoint. Set either this or ARIZE_ENDPOINT

Start Proxy

litellm --config /path/to/config.yaml

Test it!

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "Hello, Claude gm!"
        }
      ],
    }
'

Expect to see your log on Langfuse <Image img={require('../../img/langsmith_new.png')} />

Langtrace

Set success_callback: ["langtrace"] on litellm config.yaml

model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

litellm_settings:
  callbacks: ["langtrace"]

environment_variables:
    LANGTRACE_API_KEY: "141a****"

Start Proxy

litellm --config /path/to/config.yaml

Test it!

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "Hello, Claude gm!"
        }
      ],
    }
'

Galileo

[BETA]

Log LLM I/O on www.rungalileo.io

:::info

Beta Integration

:::

Required Env Variables

export GALILEO_BASE_URL=""  # For most users, this is the same as their console URL except with the word 'console' replaced by 'api' (e.g. http://www.console.galileo.myenterprise.com -> http://www.api.galileo.myenterprise.com)
export GALILEO_PROJECT_ID=""
export GALILEO_USERNAME=""
export GALILEO_PASSWORD=""

Quick Start

Add to Config.yaml

model_list:
- litellm_params:
    api_base: https://exampleopenaiendpoint-production.up.railway.app/
    api_key: my-fake-key
    model: openai/my-fake-model
  model_name: fake-openai-endpoint

litellm_settings:
  success_callback: ["galileo"] # 👈 KEY CHANGE

Start Proxy

litellm --config /path/to/config.yaml

Test it!

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
    }
'

🎉 That's it - Expect to see your Logs on your Galileo Dashboard

OpenMeter

Bill customers according to their LLM API usage with OpenMeter

Required Env Variables

# from https://openmeter.cloud
export OPENMETER_API_ENDPOINT="" # defaults to https://openmeter.cloud
export OPENMETER_API_KEY=""

Quick Start

Add to Config.yaml

model_list:
- litellm_params:
    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
    api_key: my-fake-key
    model: openai/my-fake-model
  model_name: fake-openai-endpoint

litellm_settings:
  success_callback: ["openmeter"] # 👈 KEY CHANGE

Start Proxy

litellm --config /path/to/config.yaml

Test it!

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
    }
'

DataDog

LiteLLM Supports logging to the following Datdog Integrations:

datadog Datadog Logs
datadog_llm_observability Datadog LLM Observability

We will use the --config to set litellm.success_callback = ["datadog"] this will log all successfull LLM calls to DataDog

Step 1: Create a config.yaml file and set litellm_settings: success_callback

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["datadog"] # logs llm success logs on datadog
  service_callback: ["datadog"] # logs redis, postgres failures on datadog

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  callbacks: ["datadog_llm_observability"] # logs llm success logs on datadog

Step 2: Set Required env variables for datadog

DD_API_KEY="5f2d0f310***********" # your datadog API Key
DD_SITE="us5.datadoghq.com"       # your datadog base url
DD_SOURCE="litellm_dev"       # [OPTIONAL] your datadog source. use to differentiate dev vs. prod deployments

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
    "metadata": {
        "your-custom-metadata": "custom-field",
    }
}'

Expected output on Datadog

DynamoDB

We will use the --config to set

litellm.success_callback = ["dynamodb"]
litellm.dynamodb_table_name = "your-table-name"

This will log all successfull LLM calls to DynamoDB

Step 1 Set AWS Credentials in .env

AWS_ACCESS_KEY_ID = ""
AWS_SECRET_ACCESS_KEY = ""
AWS_REGION_NAME = ""

Step 2: Create a config.yaml file and set litellm_settings: success_callback

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["dynamodb"]
  dynamodb_table_name: your-table-name

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "Azure OpenAI GPT-4 East",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ]
    }'

Your logs should be available on DynamoDB

Data Logged to DynamoDB /chat/completions

{
  "id": {
    "S": "chatcmpl-8W15J4480a3fAQ1yQaMgtsKJAicen"
  },
  "call_type": {
    "S": "acompletion"
  },
  "endTime": {
    "S": "2023-12-15 17:25:58.424118"
  },
  "messages": {
    "S": "[{'role': 'user', 'content': 'This is a test'}]"
  },
  "metadata": {
    "S": "{}"
  },
  "model": {
    "S": "gpt-3.5-turbo"
  },
  "modelParameters": {
    "S": "{'temperature': 0.7, 'max_tokens': 100, 'user': 'ishaan-2'}"
  },
  "response": {
    "S": "ModelResponse(id='chatcmpl-8W15J4480a3fAQ1yQaMgtsKJAicen', choices=[Choices(finish_reason='stop', index=0, message=Message(content='Great! What can I assist you with?', role='assistant'))], created=1702641357, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=9, prompt_tokens=11, total_tokens=20))"
  },
  "startTime": {
    "S": "2023-12-15 17:25:56.047035"
  },
  "usage": {
    "S": "Usage(completion_tokens=9, prompt_tokens=11, total_tokens=20)"
  },
  "user": {
    "S": "ishaan-2"
  }
}

Data logged to DynamoDB /embeddings

{
  "id": {
    "S": "4dec8d4d-4817-472d-9fc6-c7a6153eb2ca"
  },
  "call_type": {
    "S": "aembedding"
  },
  "endTime": {
    "S": "2023-12-15 17:25:59.890261"
  },
  "messages": {
    "S": "['hi']"
  },
  "metadata": {
    "S": "{}"
  },
  "model": {
    "S": "text-embedding-ada-002"
  },
  "modelParameters": {
    "S": "{'user': 'ishaan-2'}"
  },
  "response": {
    "S": "EmbeddingResponse(model='text-embedding-ada-002-v2', data=[{'embedding': [-0.03503197431564331, -0.020601635798811913, -0.015375726856291294,
  }
}

Sentry

If api calls fail (llm/database) you can log those to Sentry:

Step 1 Install Sentry

pip install --upgrade sentry-sdk

Step 2: Save your Sentry_DSN and add litellm_settings: failure_callback

export SENTRY_DSN="your-sentry-dsn"

model_list:
 - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  # other settings
  failure_callback: ["sentry"]
general_settings: 
  database_url: "my-bad-url" # set a fake url to trigger a sentry exception

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

litellm --test

Athina

Athina allows you to log LLM Input/Output for monitoring, analytics, and observability.

We will use the --config to set litellm.success_callback = ["athina"] this will log all successfull LLM calls to athina

Step 1 Set Athina API key

ATHINA_API_KEY = "your-athina-api-key"

Step 2: Create a config.yaml file and set litellm_settings: success_callback

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
litellm_settings:
  success_callback: ["athina"]

Step 3: Start the proxy, make a test request

Start proxy

litellm --config config.yaml --debug

Test Request

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "which llm are you"
        }
    ]
    }'

50 KiB Raw Blame History

Logging

Getting the LiteLLM Call ID

Logging Features

Conditional Logging by Virtual Keys, Teams

Redacting UserAPIKeyInfo

Redact Messages, Response Content

What gets logged?

Langfuse

Logging Metadata to Langfuse

LiteLLM Tags - cache_hit, cache_key

View POST sent from LiteLLM to provider

OpenTelemetry

Quick Start - Log to Honeycomb

Quick Start - Log to Traceloop

Quick Start - Log to OTEL Collector

Quick Start - Log to OTEL GRPC Collector

Redacting Messages, Response Content

Traceparent Header

Context propagation across Services Traceparent HTTP Header

Forwarding Traceparent HTTP Header to LLM APIs

Google Cloud Storage Buckets

Usage

Expected Logs on GCS Buckets

Fields Logged on GCS Buckets

Getting service_account.json from Google Cloud Console

s3 Buckets

Custom Callback Class [Async]

Step 1 - Create your custom litellm callback class

Step 2 - Pass your custom callback class in config.yaml

Step 3 - Start proxy + test request

Resulting Log on Proxy

Logging Proxy Request Object, Header, Url

Logging model_info set in config.yaml

Logging responses from proxy

Custom Callback APIs [Async]

Step 1. Create your generic logging API endpoint

Step 2. Set your GENERIC_LOGGER_ENDPOINT to the endpoint + route we should send callback logs to

Step 3. Create a config.yaml file and set litellm_settings: success_callback = ["generic"]

Langsmith

Arize AI

Langtrace

Galileo

Quick Start

OpenMeter

Quick Start

DataDog

DynamoDB

Data Logged to DynamoDB /chat/completions

Data logged to DynamoDB /embeddings

Sentry

Athina

50 KiB

Raw Blame History

LiteLLM Tags - `cache_hit`, `cache_key`

Context propagation across Services `Traceparent HTTP Header`

Forwarding `Traceparent HTTP Header` to LLM APIs

Getting `service_account.json` from Google Cloud Console

Step 1 - Create your custom `litellm` callback class

Step 2 - Pass your custom callback class in `config.yaml`

Logging `model_info` set in config.yaml

Step 2. Set your `GENERIC_LOGGER_ENDPOINT` to the endpoint + route we should send callback logs to

Step 3. Create a `config.yaml` file and set `litellm_settings`: `success_callback` = ["generic"]