From cf4efec48528aa9a92f04af929722f9e15c99660 Mon Sep 17 00:00:00 2001
From: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Date: Tue, 15 Jul 2025 15:48:15 -0700
Subject: [PATCH] Step 2b - Loading Custom Callbacks

---
 docs/my-website/docs/proxy/logging.md | 66 +++++++++++++++++++++++++++
 litellm/proxy/proxy_config.yaml       |  8 +---
 2 files changed, 67 insertions(+), 7 deletions(-)

diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md
index b5b2b8bf50f..3467cf300f5 100644
--- a/docs/my-website/docs/proxy/logging.md
+++ b/docs/my-website/docs/proxy/logging.md
@@ -1768,6 +1768,72 @@ litellm_settings:
 
 ```
 
+#### Step 2b - Loading Custom Callbacks from S3/GCS (Alternative)
+
+Instead of using local Python files, you can load custom callbacks directly from S3 or GCS buckets. This is useful for centralized callback management or when deploying in containerized environments.
+
+**URL Format:**
+- **S3**: `s3://bucket-name/module_name.instance_name`
+- **GCS**: `gcs://bucket-name/module_name.instance_name`
+
+**Example - Loading from S3:**
+
+Let's say you have a file `custom_ui_sso_hook.py` stored in your S3 bucket `litellm-proxy` with the following content:
+
+```python
+# custom_ui_sso_hook.py (stored in S3)
+from litellm.integrations.custom_logger import CustomLogger
+import litellm
+
+class MyCustomHandler(CustomLogger):
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"Custom UI SSO callback executed!")
+        # Your custom logic here
+  
+    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): 
+        print(f"Custom UI SSO failure callback!")
+        # Your failure handling logic
+
+# Instance that will be loaded by LiteLLM
+custom_handler = MyCustomHandler()
+```
+
+**Configuration:**
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+
+litellm_settings:
+  callbacks: ["s3://litellm-proxy/custom_ui_sso_hook.custom_ui_sso_sign_in_handler"]
+```
+
+**Example - Loading from GCS:**
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: gpt-3.5-turbo
+
+litellm_settings:
+  callbacks: ["gcs://my-gcs-bucket/custom_callbacks.proxy_handler_instance"]
+```
+
+**How it works:**
+1. LiteLLM detects the S3/GCS URL prefix
+2. Downloads the Python file to a temporary location
+3. Loads the module and extracts the specified instance
+4. Cleans up the temporary file
+5. Uses the callback instance for logging
+
+This approach allows you to:
+- Centrally manage callback files across multiple proxy instances
+- Share callbacks across different environments
+- Version control callback files in cloud storage
+
 #### Step 3 - Start proxy + test request
 
 ```shell
diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml
index bbccf93abdd..2f931e57b62 100644
--- a/litellm/proxy/proxy_config.yaml
+++ b/litellm/proxy/proxy_config.yaml
@@ -6,13 +6,7 @@ model_list:
     litellm_params:
       model: openai/*
 
-guardrails:
-  - guardrail_name: "bedrock-pre-guard"
-    litellm_params:
-      guardrail: bedrock  # supported values: "aporia", "bedrock", "lakera"
-      mode: "during_call"
-      guardrailIdentifier: ff6ujrregl1q
-      guardrailVersion: "DRAFT"
+
 
 litellm_settings:
   callbacks: ["s3://litellm-proxy/custom_ui_sso_hook.custom_ui_sso_sign_in_handler"]
\ No newline at end of file