diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md
index 8a5a06a8e..27f1789e0 100644
--- a/docs/my-website/docs/proxy/logging.md
+++ b/docs/my-website/docs/proxy/logging.md
@@ -14,6 +14,39 @@ 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.
+
+```shell
+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:
+
+```output
+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 Proxy Input/Output - 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
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 0e074e990..eaa99aee5 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -446,6 +446,7 @@ def _get_pydantic_json_dict(pydantic_obj: BaseModel) -> dict:
 def get_custom_headers(
     *,
     user_api_key_dict: UserAPIKeyAuth,
+    call_id: Optional[str] = None,
     model_id: Optional[str] = None,
     cache_key: Optional[str] = None,
     api_base: Optional[str] = None,
@@ -457,6 +458,7 @@ def get_custom_headers(
 ) -> dict:
     exclude_values = {"", None}
     headers = {
+        "x-litellm-call-id": call_id,
         "x-litellm-model-id": model_id,
         "x-litellm-cache-key": cache_key,
         "x-litellm-model-api-base": api_base,
@@ -2902,6 +2904,7 @@ async def chat_completion(
         ):  # use generate_responses to stream responses
             custom_headers = get_custom_headers(
                 user_api_key_dict=user_api_key_dict,
+                call_id=logging_obj.litellm_call_id,
                 model_id=model_id,
                 cache_key=cache_key,
                 api_base=api_base,
@@ -2932,6 +2935,7 @@ async def chat_completion(
         fastapi_response.headers.update(
             get_custom_headers(
                 user_api_key_dict=user_api_key_dict,
+                call_id=logging_obj.litellm_call_id,
                 model_id=model_id,
                 cache_key=cache_key,
                 api_base=api_base,
@@ -3145,6 +3149,7 @@ async def completion(
         ):  # use generate_responses to stream responses
             custom_headers = get_custom_headers(
                 user_api_key_dict=user_api_key_dict,
+                call_id=logging_obj.litellm_call_id,
                 model_id=model_id,
                 cache_key=cache_key,
                 api_base=api_base,
@@ -3165,6 +3170,7 @@ async def completion(
         fastapi_response.headers.update(
             get_custom_headers(
                 user_api_key_dict=user_api_key_dict,
+                call_id=logging_obj.litellm_call_id,
                 model_id=model_id,
                 cache_key=cache_key,
                 api_base=api_base,