.env.example

@@ -20,8 +20,6 @@ REPLICATE_API_TOKEN = ""
 ANTHROPIC_API_KEY = ""
 # Infisical
 INFISICAL_TOKEN = ""
-# INFINITY
-INFINITY_API_KEY = ""
 
 # Development Configs
 LITELLM_MASTER_KEY = "sk-1234"

.github/workflows/ghcr_deploy.yml

@@ -118,50 +118,7 @@ jobs:
             ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm:main-stable', env.REGISTRY) || '' }}
           labels: ${{ steps.meta.outputs.labels }}
           platforms: local,linux/amd64,linux/arm64,linux/arm64/v8
-  
-  build-and-push-image-ee:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.commit_hash }}
-
-      - name: Log in to the Container registry
-        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for EE Dockerfile
-        id: meta-ee
-        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-ee
-      # Configure multi platform Docker builds
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@e0e4588fad221d38ee467c0bffd91115366dc0c5
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@edfb0fe6204400c56fbfd3feba3fe9ad1adfa345
-
-      - name: Build and push EE Docker image
-        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
-        with:
-          context: .
-          file: Dockerfile
-          push: true
-          tags: |
-            ${{ steps.meta-ee.outputs.tags }}-${{ github.event.inputs.tag || 'latest' }},
-            ${{ steps.meta-ee.outputs.tags }}-${{ github.event.inputs.release_type }}
-            ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-ee:main-{1}', env.REGISTRY, github.event.inputs.tag) || '' }},
-            ${{ github.event.inputs.release_type == 'stable' && format('{0}/berriai/litellm-ee:main-stable', env.REGISTRY) || '' }}
-          labels: ${{ steps.meta-ee.outputs.labels }}
-          platforms: local,linux/amd64,linux/arm64,linux/arm64/v8
-   
+          
   build-and-push-image-database:
     runs-on: ubuntu-latest
     permissions:

.gitignore

@@ -86,6 +86,3 @@ litellm/proxy/db/migrations/0_init/migration.sql
 litellm/proxy/db/migrations/*
 litellm/proxy/migrations/*config.yaml
 litellm/proxy/migrations/*
-config.yaml
-tests/litellm/litellm_core_utils/llm_cost_calc/log.txt
-tests/test_custom_dir/*

cookbook/litellm_proxy_server/grafana_dashboard/dashboard_1/grafana_dashboard.json

@@ -32,7 +32,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "rMzWaBvIk"
         },
         "fieldConfig": {
           "defaults": {
@@ -110,7 +110,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "rMzWaBvIk"
             },
             "editorMode": "code",
             "expr": "histogram_quantile(0.99, sum(rate(litellm_self_latency_bucket{self=\"self\"}[1m])) by (le))",
@@ -125,7 +125,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "rMzWaBvIk"
         },
         "fieldConfig": {
           "defaults": {
@@ -216,7 +216,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "rMzWaBvIk"
             },
             "editorMode": "code",
             "expr": "sum(increase(litellm_spend_metric_total[30d])) by (hashed_api_key)",
@@ -232,7 +232,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "rMzWaBvIk"
         },
         "fieldConfig": {
           "defaults": {
@@ -309,7 +309,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "rMzWaBvIk"
             },
             "editorMode": "code",
             "expr": "sum by (model) (increase(litellm_requests_metric_total[5m]))",
@@ -324,7 +324,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "rMzWaBvIk"
         },
         "fieldConfig": {
           "defaults": {
@@ -375,7 +375,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "rMzWaBvIk"
             },
             "editorMode": "code",
             "expr": "sum(increase(litellm_llm_api_failed_requests_metric_total[1h]))",
@@ -390,7 +390,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "rMzWaBvIk"
         },
         "fieldConfig": {
           "defaults": {
@@ -468,7 +468,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "rMzWaBvIk"
             },
             "editorMode": "code",
             "expr": "sum(increase(litellm_spend_metric_total[30d])) by (model)",
@@ -483,7 +483,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "rMzWaBvIk"
         },
         "fieldConfig": {
           "defaults": {
@@ -560,7 +560,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "rMzWaBvIk"
             },
             "editorMode": "code",
             "expr": "sum(increase(litellm_total_tokens_total[5m])) by (model)",
@@ -579,27 +579,7 @@
     "style": "dark",
     "tags": [],
     "templating": {
-      "list": [
-        {
-          "current": {
-            "selected": false,
-            "text": "prometheus",
-            "value": "edx8memhpd9tsa"
-          },
-          "hide": 0,
-          "includeAll": false,
-          "label": "datasource",
-          "multi": false,
-          "name": "DS_PROMETHEUS",
-          "options": [],
-          "query": "prometheus",
-          "queryValue": "",
-          "refresh": 1,
-          "regex": "",
-          "skipUrlSync": false,
-          "type": "datasource"
-        }
-      ]
+      "list": []
     },
     "time": {
       "from": "now-1h",

cookbook/litellm_proxy_server/grafana_dashboard/dashboard_v2/grafana_dashboard.json

@@ -37,7 +37,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "bdiyc60dco54we"
         },
         "description": "Total requests per second made to proxy - success + failure ",
         "fieldConfig": {
@@ -119,7 +119,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "disableTextWrap": false,
             "editorMode": "code",
@@ -138,7 +138,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "bdiyc60dco54we"
         },
         "description": "Failures per second by Exception Class",
         "fieldConfig": {
@@ -220,7 +220,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "disableTextWrap": false,
             "editorMode": "code",
@@ -239,7 +239,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "bdiyc60dco54we"
         },
         "description": "Average Response latency (seconds)",
         "fieldConfig": {
@@ -346,7 +346,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "disableTextWrap": false,
             "editorMode": "code",
@@ -361,7 +361,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "editorMode": "code",
             "expr": "histogram_quantile(0.5, sum(rate(litellm_request_total_latency_metric_bucket[2m])) by (le))",
@@ -391,7 +391,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "bdiyc60dco54we"
         },
         "description": "x-ratelimit-remaining-requests returning from LLM APIs",
         "fieldConfig": {
@@ -473,7 +473,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "editorMode": "code",
             "expr": "topk(5, sort(litellm_remaining_requests))",
@@ -488,7 +488,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "bdiyc60dco54we"
         },
         "description": "x-ratelimit-remaining-tokens from LLM API ",
         "fieldConfig": {
@@ -570,7 +570,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "editorMode": "code",
             "expr": "topk(5, sort(litellm_remaining_tokens))",
@@ -598,7 +598,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "bdiyc60dco54we"
         },
         "description": "Requests per second by Key Alias (keys are LiteLLM Virtual Keys). If key is None - means no Alias Set ",
         "fieldConfig": {
@@ -679,7 +679,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "editorMode": "code",
             "expr": "sum(rate(litellm_proxy_total_requests_metric_total[2m])) by (api_key_alias)\n",
@@ -694,7 +694,7 @@
       {
         "datasource": {
           "type": "prometheus",
-          "uid": "${DS_PROMETHEUS}"
+          "uid": "bdiyc60dco54we"
         },
         "description": "Requests per second by Team Alias. If team is None - means no team alias Set ",
         "fieldConfig": {
@@ -775,7 +775,7 @@
           {
             "datasource": {
               "type": "prometheus",
-              "uid": "${DS_PROMETHEUS}"
+              "uid": "bdiyc60dco54we"
             },
             "editorMode": "code",
             "expr": "sum(rate(litellm_proxy_total_requests_metric_total[2m])) by (team_alias)\n",
@@ -792,27 +792,7 @@
     "schemaVersion": 40,
     "tags": [],
     "templating": {
-      "list": [
-        {
-          "current": {
-            "selected": false,
-            "text": "prometheus",
-            "value": "edx8memhpd9tsb"
-          },
-          "hide": 0,
-          "includeAll": false,
-          "label": "datasource",
-          "multi": false,
-          "name": "DS_PROMETHEUS",
-          "options": [],
-          "query": "prometheus",
-          "queryValue": "",
-          "refresh": 1,
-          "regex": "",
-          "skipUrlSync": false,
-          "type": "datasource"
-        }
-      ]
+      "list": []
     },
     "time": {
       "from": "now-6h",

deploy/charts/litellm-helm/templates/migrations-job.yaml

@@ -16,7 +16,6 @@ spec:
         {{- toYaml . | nindent 8 }}
         {{- end }}
     spec:
-      serviceAccountName: {{ include "litellm.serviceAccountName" . }}
       containers:
         - name: prisma-migrations
           image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default (printf "main-%s" .Chart.AppVersion) }}"

docker/build_from_pip/requirements.txt

@@ -1,4 +1,4 @@
-litellm[proxy]==1.67.4.dev1 # Specify the litellm version you want to use
+litellm[proxy] # Specify the litellm version you want to use
 prometheus_client
 langfuse
 prisma

docs/my-website/docs/audio_transcription.md

@@ -1,15 +1,13 @@
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-# /audio/transcriptions
+# Speech to Text
 
 Use this to loadbalance across Azure + OpenAI. 
 
 ## Quick Start
 
-### LiteLLM Python SDK
-
-```python showLineNumbers
+```python
 from litellm import transcription
 import os 
 
@@ -22,7 +20,7 @@ response = transcription(model="whisper", file=audio_file)
 print(f"response: {response}")
 ```
 
-### LiteLLM Proxy
+## Proxy Usage
 
 ### Add model to config 
 
@@ -30,7 +28,7 @@ print(f"response: {response}")
 <Tabs>
 <TabItem value="openai" label="OpenAI">
 
-```yaml showLineNumbers
+```yaml
 model_list:
 - model_name: whisper
   litellm_params:
@@ -45,7 +43,7 @@ general_settings:
 </TabItem>
 <TabItem value="openai+azure" label="OpenAI + Azure">
 
-```yaml showLineNumbers
+```yaml
 model_list:
 - model_name: whisper
   litellm_params:
@@ -90,9 +88,9 @@ curl --location 'http://0.0.0.0:8000/v1/audio/transcriptions' \
 ```
 
 </TabItem>
-<TabItem value="openai" label="OpenAI Python SDK">
+<TabItem value="openai" label="OpenAI">
 
-```python showLineNumbers
+```python
 from openai import OpenAI
 client = openai.OpenAI(
     api_key="sk-1234",

docs/my-website/docs/completion/audio.md

@@ -3,7 +3,7 @@ import TabItem from '@theme/TabItem';
 
 # Using Audio Models
 
-How to send / receive audio to a `/chat/completions` endpoint
+How to send / receieve audio to a `/chat/completions` endpoint
 
 
 ## Audio Output from a model

docs/my-website/docs/completion/document_understanding.md

@@ -3,7 +3,7 @@ import TabItem from '@theme/TabItem';
 
 # Using PDF Input
 
-How to send / receive pdf's (other document types) to a `/chat/completions` endpoint
+How to send / receieve pdf's (other document types) to a `/chat/completions` endpoint
 
 Works for:
 - Vertex AI models (Gemini + Anthropic)

docs/my-website/docs/completion/vision.md

@@ -194,7 +194,7 @@ Expected Response
 
 ## Explicitly specify image type 
 
-If you have images without a mime-type, or if litellm is incorrectly inferring the mime type of your image (e.g. calling `gs://` url's with vertex ai), you can set this explicitly via the `format` param. 
+If you have images without a mime-type, or if litellm is incorrectly inferring the mime type of your image (e.g. calling `gs://` url's with vertex ai), you can set this explicity via the `format` param. 
 
 ```python
 "image_url": {

docs/my-website/docs/image_generation.md

@@ -1,15 +1,8 @@
-
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# Image Generations
+# Images
 
 ## Quick Start
 
-### LiteLLM Python SDK
-
-```python showLineNumbers
+```python
 from litellm import image_generation
 import os 
 
@@ -21,23 +14,24 @@ response = image_generation(prompt="A cute baby sea otter", model="dall-e-3")
 print(f"response: {response}")
 ```
 
-### LiteLLM Proxy
+## Proxy Usage
 
 ### Setup config.yaml 
 
-```yaml showLineNumbers
+```yaml
 model_list:
-  - model_name: gpt-image-1 ### RECEIVED MODEL NAME ###
+  - model_name: dall-e-2 ### RECEIVED MODEL NAME ###
     litellm_params: # all params accepted by litellm.image_generation()
-      model: azure/gpt-image-1 ### MODEL NAME sent to `litellm.image_generation()` ###
+      model: azure/dall-e-2 ### MODEL NAME sent to `litellm.image_generation()` ###
       api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
       api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")
+      rpm: 6      # [OPTIONAL] Rate limit for this deployment: in requests per minute (rpm)
 
 ```
 
 ### Start proxy 
 
-```bash showLineNumbers
+```bash
 litellm --config /path/to/config.yaml 
 
 # RUNNING on http://0.0.0.0:4000
@@ -53,7 +47,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/images/generations' \
 -H 'Content-Type: application/json' \
 -H 'Authorization: Bearer sk-1234' \
 -D '{
-    "model": "gpt-image-1",
+    "model": "dall-e-2",
     "prompt": "A cute baby sea otter",
     "n": 1,
     "size": "1024x1024"
@@ -63,7 +57,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/images/generations' \
 </TabItem>
 <TabItem value="openai" label="OpenAI">
 
-```python showLineNumbers
+```python
 from openai import OpenAI
 client = openai.OpenAI(
     api_key="sk-1234",
@@ -110,19 +104,15 @@ Any non-openai params, will be treated as provider-specific params, and sent in
     litellm_logging_obj=None,
     custom_llm_provider=None,
 
-- `model`: *string (optional)* The model to use for image generation. Defaults to openai/gpt-image-1
+- `model`: *string (optional)* The model to use for image generation. Defaults to openai/dall-e-2
 
 - `n`: *int (optional)* The number of images to generate. Must be between 1 and 10. For dall-e-3, only n=1 is supported.
 
-- `quality`: *string (optional)* The quality of the image that will be generated.
-  *   `auto` (default value) will automatically select the best quality for the given model.
-  *   `high`, `medium` and `low` are supported for `gpt-image-1`.
-  *   `hd` and `standard` are supported for `dall-e-3`.
-  *   `standard` is the only option for `dall-e-2`.
-  
+- `quality`: *string (optional)* The quality of the image that will be generated. hd creates images with finer details and greater consistency across the image. This param is only supported for dall-e-3.
+
 - `response_format`: *string (optional)* The format in which the generated images are returned. Must be one of url or b64_json.
 
-- `size`: *string (optional)* The size of the generated images. Must be one of `1024x1024`, `1536x1024` (landscape), `1024x1536` (portrait), or `auto` (default value) for `gpt-image-1`, one of `256x256`, `512x512`, or `1024x1024` for `dall-e-2`, and one of `1024x1024`, `1792x1024`, or `1024x1792` for `dall-e-3`.
+- `size`: *string (optional)* The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024 for dall-e-2. Must be one of 1024x1024, 1792x1024, or 1024x1792 for dall-e-3 models.
 
 - `timeout`: *integer* - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes).
 
@@ -158,14 +148,13 @@ Any non-openai params, will be treated as provider-specific params, and sent in
 from litellm import image_generation
 import os
 os.environ['OPENAI_API_KEY'] = ""
-response = image_generation(model='gpt-image-1', prompt="cute baby otter")
+response = image_generation(model='dall-e-2', prompt="cute baby otter")
 ```
 
 | Model Name           | Function Call                               | Required OS Variables                |
 |----------------------|---------------------------------------------|--------------------------------------|
-| gpt-image-1 | `image_generation(model='gpt-image-1', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |
-| dall-e-3 | `image_generation(model='dall-e-3', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |
 | dall-e-2 | `image_generation(model='dall-e-2', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |
+| dall-e-3 | `image_generation(model='dall-e-3', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |
 
 ## Azure OpenAI Image Generation Models
 
@@ -193,9 +182,8 @@ print(response)
 
 | Model Name           | Function Call                               |
 |----------------------|---------------------------------------------|
-| gpt-image-1 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
-| dall-e-3 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
 | dall-e-2 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
+| dall-e-3 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
 
 
 ## OpenAI Compatible Image Generation Models

docs/my-website/docs/observability/agentops_integration.md

@@ -1,83 +0,0 @@
-# 🖇️ AgentOps - LLM Observability Platform
-
-:::tip
-
-This is community maintained. Please make an issue if you run into a bug:
-https://github.com/BerriAI/litellm
-
-:::
-
-[AgentOps](https://docs.agentops.ai) is an observability platform that enables tracing and monitoring of LLM calls, providing detailed insights into your AI operations.
-
-## Using AgentOps with LiteLLM
-
-LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily integrate AgentOps for comprehensive tracing and monitoring of your LLM operations.
-
-### Integration
-
-Use just a few lines of code to instantly trace your responses **across all providers** with AgentOps:
-Get your AgentOps API Keys from https://app.agentops.ai/
-```python
-import litellm
-
-# Configure LiteLLM to use AgentOps
-litellm.success_callback = ["agentops"]
-
-# Make your LLM calls as usual
-response = litellm.completion(
-    model="gpt-3.5-turbo",
-    messages=[{"role": "user", "content": "Hello, how are you?"}],
-)
-```
-
-Complete Code:
-
-```python
-import os
-from litellm import completion
-
-# Set env variables
-os.environ["OPENAI_API_KEY"] = "your-openai-key"
-os.environ["AGENTOPS_API_KEY"] = "your-agentops-api-key"
-
-# Configure LiteLLM to use AgentOps
-litellm.success_callback = ["agentops"]
-
-# OpenAI call
-response = completion(
-    model="gpt-4",
-    messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}],
-)
-
-print(response)
-```
-
-### Configuration Options
-
-The AgentOps integration can be configured through environment variables:
-
-- `AGENTOPS_API_KEY` (str, optional): Your AgentOps API key
-- `AGENTOPS_ENVIRONMENT` (str, optional): Deployment environment (defaults to "production")
-- `AGENTOPS_SERVICE_NAME` (str, optional): Service name for tracing (defaults to "agentops")
-
-### Advanced Usage
-
-You can configure additional settings through environment variables:
-
-```python
-import os
-
-# Configure AgentOps settings
-os.environ["AGENTOPS_API_KEY"] = "your-agentops-api-key"
-os.environ["AGENTOPS_ENVIRONMENT"] = "staging"
-os.environ["AGENTOPS_SERVICE_NAME"] = "my-service"
-
-# Enable AgentOps tracing
-litellm.success_callback = ["agentops"]
-```
-
-### Support
-
-For issues or questions, please refer to:
-- [AgentOps Documentation](https://docs.agentops.ai)
-- [LiteLLM Documentation](https://docs.litellm.ai) 

docs/my-website/docs/observability/greenscale_integration.md

@@ -53,7 +53,7 @@ response = completion(
 
 ## Additional information in metadata
 
-You can send any additional information to Greenscale by using the `metadata` field in completion and `greenscale_` prefix. This can be useful for sending metadata about the request, such as the project and application name, customer_id, environment, or any other information you want to track usage. `greenscale_project` and `greenscale_application` are required fields.
+You can send any additional information to Greenscale by using the `metadata` field in completion and `greenscale_` prefix. This can be useful for sending metadata about the request, such as the project and application name, customer_id, enviornment, or any other information you want to track usage. `greenscale_project` and `greenscale_application` are required fields.
 
 ```python
 #openai call with additional metadata

docs/my-website/docs/observability/langfuse_integration.md

@@ -185,7 +185,7 @@ curl --location --request POST 'http://0.0.0.0:4000/chat/completions' \
 * `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, defaults to `None`
+* `tags`           - Tags for the trace, defeaults to `None`
 
 ##### Updatable Parameters on Continuation
 

docs/my-website/docs/pass_through/cohere.md

@@ -4,7 +4,7 @@ Pass-through endpoints for Cohere - call provider-specific endpoint, in native f
 
 | Feature | Supported | Notes | 
 |-------|-------|-------|
-| Cost Tracking | ✅ | Supported for `/v1/chat`, and `/v2/chat` |
+| Cost Tracking | ✅ | works across all integrations |
 | Logging | ✅ | works across all integrations |
 | End-user Tracking | ❌ | [Tell us if you need this](https://github.com/BerriAI/litellm/issues/new) |
 | Streaming | ✅ | |

docs/my-website/docs/pass_through/mistral.md

@@ -1,217 +0,0 @@
-# Mistral
-
-Pass-through endpoints for Mistral - call provider-specific endpoint, in native format (no translation).
-
-| Feature | Supported | Notes | 
-|-------|-------|-------|
-| Cost Tracking | ❌ | Not supported |
-| Logging | ✅ | works across all integrations |
-| End-user Tracking | ❌ | [Tell us if you need this](https://github.com/BerriAI/litellm/issues/new) |
-| Streaming | ✅ | |
-
-Just replace `https://api.mistral.ai/v1` with `LITELLM_PROXY_BASE_URL/mistral` 🚀
-
-#### **Example Usage**
-
-```bash
-curl -L -X POST 'http://0.0.0.0:4000/mistral/v1/ocr' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer sk-1234' \
--d '{
-    "model": "mistral-ocr-latest",
-    "document": {
-        "type": "image_url",
-        "image_url": "https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/ocr/receipt.png"
-    }
-
-}'
-```
-
-Supports **ALL** Mistral Endpoints (including streaming).
-
-## Quick Start
-
-Let's call the Mistral [`/chat/completions` endpoint](https://docs.mistral.ai/api/#tag/chat/operation/chat_completion_v1_chat_completions_post)
-
-1. Add MISTRAL_API_KEY to your environment 
-
-```bash
-export MISTRAL_API_KEY="sk-1234"
-```
-
-2. Start LiteLLM Proxy 
-
-```bash
-litellm
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-3. Test it! 
-
-Let's call the Mistral `/ocr` endpoint
-
-```bash
-curl -L -X POST 'http://0.0.0.0:4000/mistral/v1/ocr' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer sk-1234' \
--d '{
-    "model": "mistral-ocr-latest",
-    "document": {
-        "type": "image_url",
-        "image_url": "https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/ocr/receipt.png"
-    }
-
-}'
-```
-
-
-## Examples
-
-Anything after `http://0.0.0.0:4000/mistral` is treated as a provider-specific route, and handled accordingly.
-
-Key Changes: 
-
-| **Original Endpoint**                                | **Replace With**                  |
-|------------------------------------------------------|-----------------------------------|
-| `https://api.mistral.ai/v1`          | `http://0.0.0.0:4000/mistral` (LITELLM_PROXY_BASE_URL="http://0.0.0.0:4000")      |
-| `bearer $MISTRAL_API_KEY`                                 | `bearer anything` (use `bearer LITELLM_VIRTUAL_KEY` if Virtual Keys are setup on proxy)                    |
-
-
-### **Example 1: OCR endpoint**
-
-#### LiteLLM Proxy Call 
-
-```bash
-curl -L -X POST 'http://0.0.0.0:4000/mistral/v1/ocr' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer $LITELLM_API_KEY' \
--d '{
-    "model": "mistral-ocr-latest",
-    "document": {
-        "type": "image_url",
-        "image_url": "https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/ocr/receipt.png"
-    }
-}'
-```
-
-
-#### Direct Mistral API Call 
-
-```bash
-curl https://api.mistral.ai/v1/ocr \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer ${MISTRAL_API_KEY}" \
-  -d '{
-    "model": "mistral-ocr-latest",
-    "document": {
-        "type": "document_url",
-        "document_url": "https://arxiv.org/pdf/2201.04234"
-    },
-    "include_image_base64": true
-  }'
-```
-
-### **Example 2: Chat API**
-
-#### LiteLLM Proxy Call 
-
-```bash
-curl -L -X POST 'http://0.0.0.0:4000/mistral/v1/chat/completions' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer $LITELLM_VIRTUAL_KEY' \
--d '{
-    "messages": [
-        {
-            "role": "user",
-            "content": "I am going to Paris, what should I see?"
-        }
-    ],
-    "max_tokens": 2048,
-    "temperature": 0.8,
-    "top_p": 0.1,
-    "model": "mistral-large-latest",
-}'
-```
-
-#### Direct Mistral API Call 
-
-```bash
-curl -L -X POST 'https://api.mistral.ai/v1/chat/completions' \
--H 'Content-Type: application/json' \
--d '{
-    "messages": [
-        {
-            "role": "user",
-            "content": "I am going to Paris, what should I see?"
-        }
-    ],
-    "max_tokens": 2048,
-    "temperature": 0.8,
-    "top_p": 0.1,
-    "model": "mistral-large-latest",
-}'
-```
-
-
-## Advanced - Use with Virtual Keys 
-
-Pre-requisites
-- [Setup proxy with DB](../proxy/virtual_keys.md#setup)
-
-Use this, to avoid giving developers the raw Mistral API key, but still letting them use Mistral endpoints.
-
-### Usage
-
-1. Setup environment
-
-```bash
-export DATABASE_URL=""
-export LITELLM_MASTER_KEY=""
-export MISTRAL_API_BASE=""
-```
-
-```bash
-litellm
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-2. Generate virtual key 
-
-```bash
-curl -X POST 'http://0.0.0.0:4000/key/generate' \
--H 'Authorization: Bearer sk-1234' \
--H 'Content-Type: application/json' \
--d '{}'
-```
-
-Expected Response 
-
-```bash
-{
-    ...
-    "key": "sk-1234ewknldferwedojwojw"
-}
-```
-
-3. Test it! 
-
-
-```bash
-curl -L -X POST 'http://0.0.0.0:4000/mistral/v1/chat/completions' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer sk-1234ewknldferwedojwojw' \
-  --data '{
-    "messages": [
-        {
-            "role": "user",
-            "content": "I am going to Paris, what should I see?"
-        }
-    ],
-    "max_tokens": 2048,
-    "temperature": 0.8,
-    "top_p": 0.1,
-    "model": "qwen2.5-7b-instruct",
-}'
-```

docs/my-website/docs/pass_through/vertex_ai.md

@@ -13,15 +13,6 @@ Pass-through endpoints for Vertex AI - call provider-specific endpoint, in nativ
 | End-user Tracking | ❌ | [Tell us if you need this](https://github.com/BerriAI/litellm/issues/new) |
 | Streaming | ✅ | |
 
-## Supported Endpoints
-
-LiteLLM supports 2 vertex ai passthrough routes:
-
-1. `/vertex_ai` → routes to `https://{vertex_location}-aiplatform.googleapis.com/`
-2. `/vertex_ai/discovery` → routes to [`https://discoveryengine.googleapis.com`](https://discoveryengine.googleapis.com/)
-
-## How to use
-
 Just replace `https://REGION-aiplatform.googleapis.com` with `LITELLM_PROXY_BASE_URL/vertex_ai`
 
 LiteLLM supports 3 flows for calling Vertex AI endpoints via pass-through:
@@ -222,7 +213,7 @@ curl http://localhost:4000/vertex-ai/v1/projects/${PROJECT_ID}/locations/us-cent
 
 LiteLLM Proxy Server supports two methods of authentication to Vertex AI:
 
-1. Pass Vertex Credentials client side to proxy server
+1. Pass Vertex Credetials client side to proxy server
 
 2. Set Vertex AI credentials on proxy server
 

docs/my-website/docs/pass_through/vllm.md

@@ -1,185 +0,0 @@
-# VLLM
-
-Pass-through endpoints for VLLM - call provider-specific endpoint, in native format (no translation).
-
-| Feature | Supported | Notes | 
-|-------|-------|-------|
-| Cost Tracking | ❌ | Not supported |
-| Logging | ✅ | works across all integrations |
-| End-user Tracking | ❌ | [Tell us if you need this](https://github.com/BerriAI/litellm/issues/new) |
-| Streaming | ✅ | |
-
-Just replace `https://my-vllm-server.com` with `LITELLM_PROXY_BASE_URL/vllm` 🚀
-
-#### **Example Usage**
-
-```bash
-curl -L -X GET 'http://0.0.0.0:4000/vllm/metrics' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer sk-1234' \
-```
-
-Supports **ALL** VLLM Endpoints (including streaming).
-
-## Quick Start
-
-Let's call the VLLM [`/metrics` endpoint](https://vllm.readthedocs.io/en/latest/api_reference/api_reference.html)
-
-1. Add HOSTED VLLM API BASE to your environment 
-
-```bash
-export HOSTED_VLLM_API_BASE="https://my-vllm-server.com"
-```
-
-2. Start LiteLLM Proxy 
-
-```bash
-litellm
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-3. Test it! 
-
-Let's call the VLLM `/metrics` endpoint
-
-```bash
-curl -L -X GET 'http://0.0.0.0:4000/vllm/metrics' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer sk-1234' \
-```
-
-
-## Examples
-
-Anything after `http://0.0.0.0:4000/vllm` is treated as a provider-specific route, and handled accordingly.
-
-Key Changes: 
-
-| **Original Endpoint**                                | **Replace With**                  |
-|------------------------------------------------------|-----------------------------------|
-| `https://my-vllm-server.com`          | `http://0.0.0.0:4000/vllm` (LITELLM_PROXY_BASE_URL="http://0.0.0.0:4000")      |
-| `bearer $VLLM_API_KEY`                                 | `bearer anything` (use `bearer LITELLM_VIRTUAL_KEY` if Virtual Keys are setup on proxy)                    |
-
-
-### **Example 1: Metrics endpoint**
-
-#### LiteLLM Proxy Call 
-
-```bash
-curl -L -X GET 'http://0.0.0.0:4000/vllm/metrics' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer $LITELLM_VIRTUAL_KEY' \
-```
-
-
-#### Direct VLLM API Call 
-
-```bash
-curl -L -X GET 'https://my-vllm-server.com/metrics' \
--H 'Content-Type: application/json' \
-```
-
-### **Example 2: Chat API**
-
-#### LiteLLM Proxy Call 
-
-```bash
-curl -L -X POST 'http://0.0.0.0:4000/vllm/chat/completions' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer $LITELLM_VIRTUAL_KEY' \
--d '{
-    "messages": [
-        {
-            "role": "user",
-            "content": "I am going to Paris, what should I see?"
-        }
-    ],
-    "max_tokens": 2048,
-    "temperature": 0.8,
-    "top_p": 0.1,
-    "model": "qwen2.5-7b-instruct",
-}'
-```
-
-#### Direct VLLM API Call 
-
-```bash
-curl -L -X POST 'https://my-vllm-server.com/chat/completions' \
--H 'Content-Type: application/json' \
--d '{
-    "messages": [
-        {
-            "role": "user",
-            "content": "I am going to Paris, what should I see?"
-        }
-    ],
-    "max_tokens": 2048,
-    "temperature": 0.8,
-    "top_p": 0.1,
-    "model": "qwen2.5-7b-instruct",
-}'
-```
-
-
-## Advanced - Use with Virtual Keys 
-
-Pre-requisites
-- [Setup proxy with DB](../proxy/virtual_keys.md#setup)
-
-Use this, to avoid giving developers the raw Cohere API key, but still letting them use Cohere endpoints.
-
-### Usage
-
-1. Setup environment
-
-```bash
-export DATABASE_URL=""
-export LITELLM_MASTER_KEY=""
-export HOSTED_VLLM_API_BASE=""
-```
-
-```bash
-litellm
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-2. Generate virtual key 
-
-```bash
-curl -X POST 'http://0.0.0.0:4000/key/generate' \
--H 'Authorization: Bearer sk-1234' \
--H 'Content-Type: application/json' \
--d '{}'
-```
-
-Expected Response 
-
-```bash
-{
-    ...
-    "key": "sk-1234ewknldferwedojwojw"
-}
-```
-
-3. Test it! 
-
-
-```bash
-curl -L -X POST 'http://0.0.0.0:4000/vllm/chat/completions' \
--H 'Content-Type: application/json' \
--H 'Authorization: Bearer sk-1234ewknldferwedojwojw' \
-  --data '{
-    "messages": [
-        {
-            "role": "user",
-            "content": "I am going to Paris, what should I see?"
-        }
-    ],
-    "max_tokens": 2048,
-    "temperature": 0.8,
-    "top_p": 0.1,
-    "model": "qwen2.5-7b-instruct",
-}'
-```

docs/my-website/docs/providers/anthropic.md

@@ -1095,7 +1095,7 @@ response = completion(
 print(response.choices[0])
 ```
 </TabItem>
-<TabItem value="proxy" label="PROXY">
+<TabItem value="proxy" lable="PROXY">
 
 1. Add model to config 
 

docs/my-website/docs/providers/azure.md

@@ -483,7 +483,7 @@ response.stream_to_file(speech_file_path)
 This is a walkthrough on how to use Azure Active Directory Tokens - Microsoft Entra ID to make `litellm.completion()` calls 
 
 Step 1 - Download Azure CLI 
-Installation instructions: https://learn.microsoft.com/en-us/cli/azure/install-azure-cli
+Installation instructons: https://learn.microsoft.com/en-us/cli/azure/install-azure-cli
 ```shell
 brew update && brew install azure-cli
 ```
@@ -1002,125 +1002,8 @@ Expected Response:
 ```
 
 
-## **Azure Responses API**
 
-| Property | Details |
-|-------|-------|
-| Description | Azure OpenAI Responses API |
-| `custom_llm_provider` on LiteLLM | `azure/` |
-| Supported Operations | `/v1/responses`|
-| Azure OpenAI Responses API | [Azure OpenAI Responses API ↗](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/responses?tabs=python-secure) |
-| Cost Tracking, Logging Support | ✅ LiteLLM will log, track cost for Responses API Requests |
-| Supported OpenAI Params | ✅ All OpenAI params are supported, [See here](https://github.com/BerriAI/litellm/blob/0717369ae6969882d149933da48eeb8ab0e691bd/litellm/llms/openai/responses/transformation.py#L23) |
 
-## Usage
-
-## Create a model response
-
-<Tabs>
-<TabItem value="litellm-sdk" label="LiteLLM SDK">
-
-#### Non-streaming
-
-```python showLineNumbers title="Azure Responses API"
-import litellm
-
-# Non-streaming response
-response = litellm.responses(
-    model="azure/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    max_output_tokens=100,
-    api_key=os.getenv("AZURE_RESPONSES_OPENAI_API_KEY"),
-    api_base="https://litellm8397336933.openai.azure.com/",
-    api_version="2023-03-15-preview",
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="Azure Responses API"
-import litellm
-
-# Streaming response
-response = litellm.responses(
-    model="azure/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True,
-    api_key=os.getenv("AZURE_RESPONSES_OPENAI_API_KEY"),
-    api_base="https://litellm8397336933.openai.azure.com/",
-    api_version="2023-03-15-preview",
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-<TabItem value="proxy" label="OpenAI SDK with LiteLLM Proxy">
-
-First, add this to your litellm proxy config.yaml:
-```yaml showLineNumbers title="Azure Responses API"
-model_list:
-  - model_name: o1-pro
-    litellm_params:
-      model: azure/o1-pro
-      api_key: os.environ/AZURE_RESPONSES_OPENAI_API_KEY
-      api_base: https://litellm8397336933.openai.azure.com/
-      api_version: 2023-03-15-preview
-```
-
-Start your LiteLLM proxy:
-```bash
-litellm --config /path/to/config.yaml
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-Then use the OpenAI SDK pointed to your proxy:
-
-#### Non-streaming
-```python showLineNumbers
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Non-streaming response
-response = client.responses.create(
-    model="o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Streaming response
-response = client.responses.create(
-    model="o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-</Tabs>
 
 
 

docs/my-website/docs/providers/cohere.md

@@ -13,9 +13,7 @@ os.environ["COHERE_API_KEY"] = ""
 
 ## Usage
 
-### LiteLLM Python SDK
-
-```python showLineNumbers
+```python
 from litellm import completion
 
 ## set ENV variables
@@ -28,9 +26,9 @@ response = completion(
 )
 ```
 
-#### Streaming
+## Usage - Streaming
 
-```python showLineNumbers
+```python
 from litellm import completion
 
 ## set ENV variables
@@ -48,90 +46,15 @@ for chunk in response:
 ```
 
 
-
-## Usage with LiteLLM Proxy 
-
-Here's how to call Cohere with the LiteLLM Proxy Server
-
-### 1. Save key in your environment
-
-```bash
-export COHERE_API_KEY="your-api-key"
-```
-
-### 2. Start the proxy 
-
-Define the cohere models you want to use in the config.yaml
-
-```yaml showLineNumbers
-model_list:
-  - model_name: command-a-03-2025 
-    litellm_params:
-      model: command-a-03-2025
-      api_key: "os.environ/COHERE_API_KEY"
-```
-
-```bash
-litellm --config /path/to/config.yaml
-```
-
-
-### 3. Test it
-
-
-<Tabs>
-<TabItem value="Curl" label="Curl Request">
-
-```shell showLineNumbers
-curl --location 'http://0.0.0.0:4000/chat/completions' \
---header 'Content-Type: application/json' \
---header 'Authorization: Bearer <your-litellm-api-key>' \
---data ' {
-      "model": "command-a-03-2025",
-      "messages": [
-        {
-          "role": "user",
-          "content": "what llm are you"
-        }
-      ]
-    }
-'
-```
-</TabItem>
-<TabItem value="openai" label="OpenAI v1.0.0+">
-
-```python showLineNumbers
-import openai
-client = openai.OpenAI(
-    api_key="anything",
-    base_url="http://0.0.0.0:4000"
-)
-
-# request sent to model set on litellm proxy
-response = client.chat.completions.create(model="command-a-03-2025", messages = [
-    {
-        "role": "user",
-        "content": "this is a test request, write a short poem"
-    }
-])
-
-print(response)
-
-```
-</TabItem>
-</Tabs>
-
-
 ## Supported Models
 | Model Name | Function Call |
 |------------|----------------|
-| command-a-03-2025 | `litellm.completion('command-a-03-2025', messages)` |
-| command-r-plus-08-2024 | `litellm.completion('command-r-plus-08-2024', messages)` |  
-| command-r-08-2024 | `litellm.completion('command-r-08-2024', messages)` |
-| command-r-plus | `litellm.completion('command-r-plus', messages)` |  
-| command-r | `litellm.completion('command-r', messages)` |
-| command-light | `litellm.completion('command-light', messages)` |  
-| command-nightly | `litellm.completion('command-nightly', messages)` |
+| command-r-plus-08-2024 | `completion('command-r-plus-08-2024', messages)` |  
+| command-r-08-2024 | `completion('command-r-08-2024', messages)` |
+| command-r-plus | `completion('command-r-plus', messages)` |  
+| command-r | `completion('command-r', messages)` |
+| command-light | `completion('command-light', messages)` |  
+| command-nightly | `completion('command-nightly', messages)` |
 
 
 ## Embedding

docs/my-website/docs/providers/gemini.md

@@ -39,164 +39,14 @@ response = completion(
 - temperature
 - top_p
 - max_tokens
-- max_completion_tokens
 - stream
 - tools
 - tool_choice
-- functions
 - response_format
 - n
 - stop
-- logprobs
-- frequency_penalty
-- modalities
-- reasoning_content
-
-**Anthropic Params**
-- thinking (used to set max budget tokens across anthropic/gemini models)
-
-[**See Updated List**](https://github.com/BerriAI/litellm/blob/main/litellm/llms/gemini/chat/transformation.py#L70)
-
-
-
-## Usage - Thinking / `reasoning_content`
-
-LiteLLM translates OpenAI's `reasoning_effort` to Gemini's `thinking` parameter. [Code](https://github.com/BerriAI/litellm/blob/620664921902d7a9bfb29897a7b27c1a7ef4ddfb/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py#L362)
-
-**Mapping**
-
-| reasoning_effort | thinking |
-| ---------------- | -------- |
-| "low"            | "budget_tokens": 1024 |
-| "medium"         | "budget_tokens": 2048 |
-| "high"           | "budget_tokens": 4096 |
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-from litellm import completion
-
-resp = completion(
-    model="gemini/gemini-2.5-flash-preview-04-17",
-    messages=[{"role": "user", "content": "What is the capital of France?"}],
-    reasoning_effort="low",
-)
-
-```
-
-</TabItem>
-
-<TabItem value="proxy" label="PROXY">
-
-1. Setup config.yaml
-
-```yaml
-- model_name: gemini-2.5-flash
-  litellm_params:
-    model: gemini/gemini-2.5-flash-preview-04-17
-    api_key: os.environ/GEMINI_API_KEY
-```
-
-2. Start proxy
-
-```bash
-litellm --config /path/to/config.yaml
-```
-
-3. Test it! 
-
-```bash
-curl http://0.0.0.0:4000/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
-  -d '{
-    "model": "gemini-2.5-flash",
-    "messages": [{"role": "user", "content": "What is the capital of France?"}],
-    "reasoning_effort": "low"
-  }'
-```
-
-</TabItem>
-</Tabs>
-
-
-**Expected Response**
-
-```python
-ModelResponse(
-    id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
-    created=1740470510,
-    model='claude-3-7-sonnet-20250219',
-    object='chat.completion',
-    system_fingerprint=None,
-    choices=[
-        Choices(
-            finish_reason='stop',
-            index=0,
-            message=Message(
-                content="The capital of France is Paris.",
-                role='assistant',
-                tool_calls=None,
-                function_call=None,
-                reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
-            ),
-        )
-    ],
-    usage=Usage(
-        completion_tokens=68,
-        prompt_tokens=42,
-        total_tokens=110,
-        completion_tokens_details=None,
-        prompt_tokens_details=PromptTokensDetailsWrapper(
-            audio_tokens=None,
-            cached_tokens=0,
-            text_tokens=None,
-            image_tokens=None
-        ),
-        cache_creation_input_tokens=0,
-        cache_read_input_tokens=0
-    )
-)
-```
-
-### Pass `thinking` to Gemini models
-
-You can also pass the `thinking` parameter to Gemini models.
-
-This is translated to Gemini's [`thinkingConfig` parameter](https://ai.google.dev/gemini-api/docs/thinking#set-budget).
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-response = litellm.completion(
-  model="gemini/gemini-2.5-flash-preview-04-17",
-  messages=[{"role": "user", "content": "What is the capital of France?"}],
-  thinking={"type": "enabled", "budget_tokens": 1024},
-)
-```
-
-</TabItem>
-<TabItem value="proxy" label="PROXY">
-
-```bash
-curl http://0.0.0.0:4000/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer $LITELLM_KEY" \
-  -d '{
-    "model": "gemini/gemini-2.5-flash-preview-04-17",
-    "messages": [{"role": "user", "content": "What is the capital of France?"}],
-    "thinking": {"type": "enabled", "budget_tokens": 1024}
-  }'
-```
-
-</TabItem>
-</Tabs>
-
-
-
 
+[**See Updated List**](https://github.com/BerriAI/litellm/blob/1c747f3ad372399c5b95cc5696b06a5fbe53186b/litellm/llms/vertex_httpx.py#L122)
 
 ## Passing Gemini Specific Params
 ### Response schema 
@@ -655,7 +505,7 @@ import os
 
 os.environ["GEMINI_API_KEY"] = ".."
 
-tools = [{"googleSearch": {}}] # 👈 ADD GOOGLE SEARCH
+tools = [{"googleSearchRetrieval": {}}] # 👈 ADD GOOGLE SEARCH
 
 response = completion(
     model="gemini/gemini-2.0-flash",
@@ -691,7 +541,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 -d '{
   "model": "gemini-2.0-flash",
   "messages": [{"role": "user", "content": "What is the weather in San Francisco?"}],
-  "tools": [{"googleSearch": {}}]
+  "tools": [{"googleSearchRetrieval": {}}]
 }
 '
 ```

docs/my-website/docs/providers/infinity.md

@@ -3,17 +3,18 @@ import TabItem from '@theme/TabItem';
 
 # Infinity
 
-| Property                  | Details                                                                                                    |
-| ------------------------- | ---------------------------------------------------------------------------------------------------------- |
-| Description               | Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models and clip |
-| Provider Route on LiteLLM | `infinity/`                                                                                                |
-| Supported Operations      | `/rerank`, `/embeddings`                                                                                   |
-| Link to Provider Doc      | [Infinity ↗](https://github.com/michaelfeil/infinity)                                                      |
+| Property | Details |
+|-------|-------|
+| Description | Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models and clip|
+| Provider Route on LiteLLM | `infinity/` |
+| Supported Operations | `/rerank` |
+| Link to Provider Doc | [Infinity ↗](https://github.com/michaelfeil/infinity) |
+
 
 ## **Usage - LiteLLM Python SDK**
 
 ```python
-from litellm import rerank, embedding
+from litellm import rerank
 import os
 
 os.environ["INFINITY_API_BASE"] = "http://localhost:8080"
@@ -38,8 +39,8 @@ model_list:
   - model_name: custom-infinity-rerank
     litellm_params:
       model: infinity/rerank
-      api_base: https://localhost:8080
       api_key: os.environ/INFINITY_API_KEY
+      api_base: https://localhost:8080
 ```
 
 Start litellm
@@ -50,9 +51,7 @@ litellm --config /path/to/config.yaml
 # RUNNING on http://0.0.0.0:4000
 ```
 
-## Test request:
-
-### Rerank
+Test request
 
 ```bash
 curl http://0.0.0.0:4000/rerank \
@@ -71,14 +70,15 @@ curl http://0.0.0.0:4000/rerank \
   }'
 ```
 
-#### Supported Cohere Rerank API Params
 
-| Param              | Type        | Description                                     |
-| ------------------ | ----------- | ----------------------------------------------- |
-| `query`            | `str`       | The query to rerank the documents against       |
-| `documents`        | `list[str]` | The documents to rerank                         |
-| `top_n`            | `int`       | The number of documents to return               |
-| `return_documents` | `bool`      | Whether to return the documents in the response |
+## Supported Cohere Rerank API Params
+
+| Param | Type | Description |
+|-------|-------|-------|
+| `query` | `str` | The query to rerank the documents against |
+| `documents` | `list[str]` | The documents to rerank |
+| `top_n` | `int` | The number of documents to return |
+| `return_documents` | `bool` | Whether to return the documents in the response |
 
 ### Usage - Return Documents
 
@@ -138,7 +138,6 @@ response = rerank(
     raw_scores=True, # 👈 PROVIDER-SPECIFIC PARAM
 )
 ```
-
 </TabItem>
 
 <TabItem value="proxy" label="PROXY">
@@ -162,7 +161,7 @@ litellm --config /path/to/config.yaml
 # RUNNING on http://0.0.0.0:4000
 ```
 
-3. Test it!
+3. Test it!  
 
 ```bash
 curl http://0.0.0.0:4000/rerank \
@@ -180,121 +179,6 @@ curl http://0.0.0.0:4000/rerank \
     "raw_scores": True # 👈 PROVIDER-SPECIFIC PARAM
   }'
 ```
-
 </TabItem>
 
 </Tabs>
-
-## Embeddings
-
-LiteLLM provides an OpenAI api compatible `/embeddings` endpoint for embedding calls.
-
-**Setup**
-
-Add this to your litellm proxy config.yaml
-
-```yaml
-model_list:
-  - model_name: custom-infinity-embedding
-    litellm_params:
-      model: infinity/provider/custom-embedding-v1
-      api_base: http://localhost:8080
-      api_key: os.environ/INFINITY_API_KEY
-```
-
-### Test request:
-
-```bash
-curl http://0.0.0.0:4000/embeddings \
-  -H "Authorization: Bearer sk-1234" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "custom-infinity-embedding",
-    "input": ["hello"]
-  }'
-```
-
-#### Supported Embedding API Params
-
-| Param             | Type        | Description                                                 |
-| ----------------- | ----------- | ----------------------------------------------------------- |
-| `model`           | `str`       | The embedding model to use                                  |
-| `input`           | `list[str]` | The text inputs to generate embeddings for                  |
-| `encoding_format` | `str`       | The format to return embeddings in (e.g. "float", "base64") |
-| `modality`        | `str`       | The type of input (e.g. "text", "image", "audio")           |
-
-### Usage - Basic Examples
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-from litellm import embedding
-import os
-
-os.environ["INFINITY_API_BASE"] = "http://localhost:8080"
-
-response = embedding(
-    model="infinity/bge-small",
-    input=["good morning from litellm"]
-)
-
-print(response.data[0]['embedding'])
-```
-
-</TabItem>
-
-<TabItem value="proxy" label="PROXY">
-
-```bash
-curl http://0.0.0.0:4000/embeddings \
-  -H "Authorization: Bearer sk-1234" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "custom-infinity-embedding",
-    "input": ["hello"]
-  }'
-```
-
-</TabItem>
-</Tabs>
-
-### Usage - OpenAI Client
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-from openai import OpenAI
-
-client = OpenAI(
-  api_key="<LITELLM_MASTER_KEY>",
-  base_url="<LITELLM_URL>"
-)
-
-response = client.embeddings.create(
-  model="bge-small",
-  input=["The food was delicious and the waiter..."],
-  encoding_format="float"
-)
-
-print(response.data[0].embedding)
-```
-
-</TabItem>
-
-<TabItem value="proxy" label="PROXY">
-
-```bash
-curl http://0.0.0.0:4000/embeddings \
-  -H "Authorization: Bearer sk-1234" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "bge-small",
-    "input": ["The food was delicious and the waiter..."],
-    "encoding_format": "float"
-  }'
-```
-
-</TabItem>
-</Tabs>

docs/my-website/docs/providers/openai.md

@@ -163,12 +163,6 @@ os.environ["OPENAI_API_BASE"] = "openaiai-api-base"     # OPTIONAL
 
 | Model Name            | Function Call                                                   |
 |-----------------------|-----------------------------------------------------------------|
-| gpt-4.1 | `response = completion(model="gpt-4.1", messages=messages)` |
-| gpt-4.1-mini | `response = completion(model="gpt-4.1-mini", messages=messages)` |
-| gpt-4.1-nano | `response = completion(model="gpt-4.1-nano", messages=messages)` |
-| o4-mini | `response = completion(model="o4-mini", messages=messages)` |
-| o3-mini | `response = completion(model="o3-mini", messages=messages)` |
-| o3 | `response = completion(model="o3", messages=messages)` |
 | o1-mini | `response = completion(model="o1-mini", messages=messages)` |
 | o1-preview | `response = completion(model="o1-preview", messages=messages)` |
 | gpt-4o-mini  | `response = completion(model="gpt-4o-mini", messages=messages)` |

docs/my-website/docs/providers/vertex.md

@@ -364,7 +364,7 @@ from litellm import completion
 ## SETUP ENVIRONMENT
 # !gcloud auth application-default login - run this to add vertex credentials to your env
 
-tools = [{"googleSearch": {}}] # 👈 ADD GOOGLE SEARCH
+tools = [{"googleSearchRetrieval": {}}] # 👈 ADD GOOGLE SEARCH
 
 resp = litellm.completion(
                     model="vertex_ai/gemini-1.0-pro-001",
@@ -391,7 +391,7 @@ client = OpenAI(
 response = client.chat.completions.create(
     model="gemini-pro",
     messages=[{"role": "user", "content": "Who won the world cup?"}],
-    tools=[{"googleSearch": {}}],
+    tools=[{"googleSearchRetrieval": {}}],
 )
 
 print(response)
@@ -410,7 +410,7 @@ curl http://localhost:4000/v1/chat/completions \
     ],
    "tools": [
         {
-            "googleSearch": {} 
+            "googleSearchRetrieval": {} 
         }
     ]
   }'
@@ -529,7 +529,7 @@ from litellm import completion
 
 # !gcloud auth application-default login - run this to add vertex credentials to your env
 
-tools = [{"googleSearch": {"disable_attributon": False}}] # 👈 ADD GOOGLE SEARCH
+tools = [{"googleSearchRetrieval": {"disable_attributon": False}}] # 👈 ADD GOOGLE SEARCH
 
 resp = litellm.completion(
                     model="vertex_ai/gemini-1.0-pro-001",
@@ -542,157 +542,9 @@ print(resp)
 ```
 
 
-### **Thinking / `reasoning_content`**
-
-LiteLLM translates OpenAI's `reasoning_effort` to Gemini's `thinking` parameter. [Code](https://github.com/BerriAI/litellm/blob/620664921902d7a9bfb29897a7b27c1a7ef4ddfb/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py#L362)
-
-**Mapping**
-
-| reasoning_effort | thinking |
-| ---------------- | -------- |
-| "low"            | "budget_tokens": 1024 |
-| "medium"         | "budget_tokens": 2048 |
-| "high"           | "budget_tokens": 4096 |
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-from litellm import completion
-
-# !gcloud auth application-default login - run this to add vertex credentials to your env
-
-resp = completion(
-    model="vertex_ai/gemini-2.5-flash-preview-04-17",
-    messages=[{"role": "user", "content": "What is the capital of France?"}],
-    reasoning_effort="low",
-    vertex_project="project-id",
-    vertex_location="us-central1"
-)
-
-```
-
-</TabItem>
-
-<TabItem value="proxy" label="PROXY">
-
-1. Setup config.yaml
-
-```yaml
-- model_name: gemini-2.5-flash
-  litellm_params:
-    model: vertex_ai/gemini-2.5-flash-preview-04-17
-    vertex_credentials: {"project_id": "project-id", "location": "us-central1", "project_key": "project-key"}
-    vertex_project: "project-id"
-    vertex_location: "us-central1"
-```
-
-2. Start proxy
-
-```bash
-litellm --config /path/to/config.yaml
-```
-
-3. Test it! 
-
-```bash
-curl http://0.0.0.0:4000/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
-  -d '{
-    "model": "gemini-2.5-flash",
-    "messages": [{"role": "user", "content": "What is the capital of France?"}],
-    "reasoning_effort": "low"
-  }'
-```
-
-</TabItem>
-</Tabs>
-
-
-**Expected Response**
-
-```python
-ModelResponse(
-    id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
-    created=1740470510,
-    model='claude-3-7-sonnet-20250219',
-    object='chat.completion',
-    system_fingerprint=None,
-    choices=[
-        Choices(
-            finish_reason='stop',
-            index=0,
-            message=Message(
-                content="The capital of France is Paris.",
-                role='assistant',
-                tool_calls=None,
-                function_call=None,
-                reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
-            ),
-        )
-    ],
-    usage=Usage(
-        completion_tokens=68,
-        prompt_tokens=42,
-        total_tokens=110,
-        completion_tokens_details=None,
-        prompt_tokens_details=PromptTokensDetailsWrapper(
-            audio_tokens=None,
-            cached_tokens=0,
-            text_tokens=None,
-            image_tokens=None
-        ),
-        cache_creation_input_tokens=0,
-        cache_read_input_tokens=0
-    )
-)
-```
-
-#### Pass `thinking` to Gemini models
-
-You can also pass the `thinking` parameter to Gemini models.
-
-This is translated to Gemini's [`thinkingConfig` parameter](https://ai.google.dev/gemini-api/docs/thinking#set-budget).
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-from litellm import completion
-
-# !gcloud auth application-default login - run this to add vertex credentials to your env
-
-response = litellm.completion(
-  model="vertex_ai/gemini-2.5-flash-preview-04-17",
-  messages=[{"role": "user", "content": "What is the capital of France?"}],
-  thinking={"type": "enabled", "budget_tokens": 1024},
-  vertex_project="project-id",
-  vertex_location="us-central1"
-)
-```
-
-</TabItem>
-<TabItem value="proxy" label="PROXY">
-
-```bash
-curl http://0.0.0.0:4000/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer $LITELLM_KEY" \
-  -d '{
-    "model": "vertex_ai/gemini-2.5-flash-preview-04-17",
-    "messages": [{"role": "user", "content": "What is the capital of France?"}],
-    "thinking": {"type": "enabled", "budget_tokens": 1024}
-  }'
-```
-
-</TabItem>
-</Tabs>
-
-
 ### **Context Caching**
 
-Use Vertex AI context caching is supported by calling provider api directly. (Unified Endpoint support coming soon.).
+Use Vertex AI context caching is supported by calling provider api directly. (Unified Endpoint support comin soon.).
 
 [**Go straight to provider**](../pass_through/vertex_ai.md#context-caching)
 
@@ -910,7 +762,7 @@ export VERTEXAI_PROJECT="my-test-project" # ONLY use if model project is differe
 
 
 ## Specifying Safety Settings 
-In certain use-cases you may need to make calls to the models and pass [safety settings](https://ai.google.dev/docs/safety_setting_gemini) different from the defaults. To do so, simple pass the `safety_settings` argument to `completion` or `acompletion`. For example:
+In certain use-cases you may need to make calls to the models and pass [safety settigns](https://ai.google.dev/docs/safety_setting_gemini) different from the defaults. To do so, simple pass the `safety_settings` argument to `completion` or `acompletion`. For example:
 
 ### Set per model/request
 
@@ -2050,7 +1902,7 @@ response = completion(
 print(response.choices[0])
 ```
 </TabItem>
-<TabItem value="proxy" label="PROXY">
+<TabItem value="proxy" lable="PROXY">
 
 1. Add model to config 
 

docs/my-website/docs/providers/vllm.md

@@ -161,120 +161,6 @@ curl -L -X POST 'http://0.0.0.0:4000/embeddings' \
 
 Example Implementation from VLLM [here](https://github.com/vllm-project/vllm/pull/10020)
 
-<Tabs>
-<TabItem value="files_message" label="(Unified) Files Message">
-
-Use this to send a video url to VLLM + Gemini in the same format, using OpenAI's `files` message type.
-
-There are two ways to send a video url to VLLM:
-
-1. Pass the video url directly
-
-```
-{"type": "file", "file": {"file_id": video_url}},
-```
-
-2. Pass the video data as base64
-
-```
-{"type": "file", "file": {"file_data": f"data:video/mp4;base64,{video_data_base64}"}}
-```
-
-<Tabs>
-<TabItem value="sdk" label="SDK">
-
-```python
-from litellm import completion
-
-messages=[
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "text",
-                "text": "Summarize the following video"
-            },
-            {
-                "type": "file",
-                "file": {
-                    "file_id": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
-                }
-            }
-        ]
-    }
-]
-
-# call vllm 
-os.environ["HOSTED_VLLM_API_BASE"] = "https://hosted-vllm-api.co"
-os.environ["HOSTED_VLLM_API_KEY"] = "" # [optional], if your VLLM server requires an API key
-response = completion(
-    model="hosted_vllm/qwen", # pass the vllm model name
-    messages=messages,
-)
-
-# call gemini 
-os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
-response = completion(
-    model="gemini/gemini-1.5-flash", # pass the gemini model name
-    messages=messages,
-)
-
-print(response)
-```
-
-</TabItem>
-<TabItem value="proxy" label="PROXY">
-
-1. Setup config.yaml
-
-```yaml
-model_list:
-    - model_name: my-model
-      litellm_params:
-        model: hosted_vllm/qwen  # add hosted_vllm/ prefix to route as OpenAI provider
-        api_base: https://hosted-vllm-api.co      # add api base for OpenAI compatible provider
-    - model_name: my-gemini-model
-      litellm_params:
-        model: gemini/gemini-1.5-flash  # add gemini/ prefix to route as Google AI Studio provider
-        api_key: os.environ/GEMINI_API_KEY
-```
-
-2. Start the proxy 
-
-```bash
-$ litellm --config /path/to/config.yaml
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-3. Test it! 
-
-```bash
-curl -X POST http://0.0.0.0:4000/chat/completions \
--H "Authorization: Bearer sk-1234" \
--H "Content-Type: application/json" \
--d '{
-    "model": "my-model",
-    "messages": [
-        {"role": "user", "content": 
-            [
-                {"type": "text", "text": "Summarize the following video"},
-                {"type": "file", "file": {"file_id": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"}}
-            ]
-        }
-    ]
-}'
-```
-
-</TabItem>
-</Tabs>
-
-
-</TabItem>
-<TabItem value="video_url" label="(VLLM-specific) Video Message">
-
-Use this to send a video url to VLLM in it's native message format (`video_url`).
-
 There are two ways to send a video url to VLLM:
 
 1. Pass the video url directly
@@ -363,10 +249,6 @@ curl -X POST http://0.0.0.0:4000/chat/completions \
 </Tabs>
 
 
-</TabItem>
-</Tabs>
-
-
 ## (Deprecated) for `vllm pip package` 
 ### Using - `litellm.completion`
 

docs/my-website/docs/proxy/admin_ui_sso.md

@@ -243,12 +243,12 @@ We allow you to pass a local image or a an http/https url of your image
 
 Set `UI_LOGO_PATH` on your env. We recommend using a hosted image, it's a lot easier to set up and configure / debug
 
-Example setting Hosted image
+Exaple setting Hosted image
 ```shell
 UI_LOGO_PATH="https://litellm-logo-aws-marketplace.s3.us-west-2.amazonaws.com/berriai-logo-github.png"
 ```
 
-Example setting a local image (on your container)
+Exaple setting a local image (on your container)
 ```shell
 UI_LOGO_PATH="ui_images/logo.jpg"
 ```

docs/my-website/docs/proxy/alerting.md

@@ -213,7 +213,7 @@ model_list:
 general_settings: 
   master_key: sk-1234
   alerting: ["slack"]
-  alerting_threshold: 0.0001 # (Seconds) set an artificially low threshold for testing alerting
+  alerting_threshold: 0.0001 # (Seconds) set an artifically low threshold for testing alerting
   alert_to_webhook_url: {
     "llm_exceptions": "https://hooks.slack.com/services/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",
     "llm_too_slow": "https://hooks.slack.com/services/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",
@@ -247,7 +247,7 @@ model_list:
 general_settings: 
   master_key: sk-1234
   alerting: ["slack"]
-  alerting_threshold: 0.0001 # (Seconds) set an artificially low threshold for testing alerting
+  alerting_threshold: 0.0001 # (Seconds) set an artifically low threshold for testing alerting
   alert_to_webhook_url: {
     "llm_exceptions": ["os.environ/SLACK_WEBHOOK_URL", "os.environ/SLACK_WEBHOOK_URL_2"],
     "llm_too_slow": ["https://webhook.site/7843a980-a494-4967-80fb-d502dbc16886", "https://webhook.site/28cfb179-f4fb-4408-8129-729ff55cf213"],
@@ -425,7 +425,7 @@ curl -X GET --location 'http://0.0.0.0:4000/health/services?service=webhook' \
 - `projected_exceeded_date` *str or null*: The date when the budget is projected to be exceeded, returned when 'soft_budget' is set for key (optional).
 - `projected_spend` *float or null*: The projected spend amount, returned when 'soft_budget' is set for key (optional).
 - `event` *Literal["budget_crossed", "threshold_crossed", "projected_limit_exceeded"]*: The type of event that triggered the webhook. Possible values are:
-    * "spend_tracked": Emitted whenever spend is tracked for a customer id. 
+    * "spend_tracked": Emitted whenver spend is tracked for a customer id. 
     * "budget_crossed": Indicates that the spend has exceeded the max budget.
     * "threshold_crossed": Indicates that spend has crossed a threshold (currently sent when 85% and 95% of budget is reached).
     * "projected_limit_exceeded": For "key" only - Indicates that the projected spend is expected to exceed the soft budget threshold.
@@ -480,7 +480,7 @@ LLM-related Alerts
 | `cooldown_deployment` | Alerts when a deployment is put into cooldown | ✅ |
 | `new_model_added` | Notifications when a new model is added to litellm proxy through /model/new| ✅ |
 | `outage_alerts` | Alerts when a specific LLM deployment is facing an outage | ✅ |
-| `region_outage_alerts` | Alerts when a specific LLM region is facing an outage. Example us-east-1 | ✅ |
+| `region_outage_alerts` | Alerts when a specfic LLM region is facing an outage. Example us-east-1 | ✅ |
 
 Budget and Spend Alerts
 

docs/my-website/docs/proxy/config_settings.md

@@ -299,9 +299,6 @@ router_settings:
 |------|-------------|
 | ACTIONS_ID_TOKEN_REQUEST_TOKEN | Token for requesting ID in GitHub Actions
 | ACTIONS_ID_TOKEN_REQUEST_URL | URL for requesting ID token in GitHub Actions
-| AGENTOPS_ENVIRONMENT | Environment for AgentOps logging integration
-| AGENTOPS_API_KEY | API Key for AgentOps logging integration
-| AGENTOPS_SERVICE_NAME | Service Name for AgentOps logging integration
 | AISPEND_ACCOUNT_ID | Account ID for AI Spend
 | AISPEND_API_KEY | API Key for AI Spend
 | ALLOWED_EMAIL_DOMAINS | List of email domains allowed for access
@@ -442,7 +439,6 @@ router_settings:
 | LITELLM_EMAIL | Email associated with LiteLLM account
 | LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRIES | Maximum retries for parallel requests in LiteLLM
 | LITELLM_GLOBAL_MAX_PARALLEL_REQUEST_RETRY_TIMEOUT | Timeout for retries of parallel requests in LiteLLM
-| LITELLM_MIGRATION_DIR | Custom migrations directory for prisma migrations, used for baselining db in read-only file systems.
 | LITELLM_HOSTED_UI | URL of the hosted UI for LiteLLM
 | LITELLM_LICENSE | License key for LiteLLM usage
 | LITELLM_LOCAL_MODEL_COST_MAP | Local configuration for model cost mapping in LiteLLM

docs/my-website/docs/proxy/custom_pricing.md

@@ -56,7 +56,7 @@ model_list:
       model: azure/<your_deployment_name>
       api_key: os.environ/AZURE_API_KEY
       api_base: os.environ/AZURE_API_BASE
-      api_version: os.environ/AZURE_API_VERSION
+      api_version: os.envrion/AZURE_API_VERSION
     model_info:
       input_cost_per_token: 0.000421 # 👈 ONLY to track cost per token
       output_cost_per_token: 0.000520 # 👈 ONLY to track cost per token
@@ -133,4 +133,4 @@ acompletion(
 
 If these keys are not present, LiteLLM will not use your custom pricing. 
 
-If the problem persists, please file an issue on [GitHub](https://github.com/BerriAI/litellm/issues). 
+If the problem persists, please file an issue on [GitHub](https://github.com/BerriAI/litellm/issues). 

docs/my-website/docs/proxy/db_deadlocks.md

@@ -19,7 +19,7 @@ LiteLLM writes `UPDATE` and `UPSERT` queries to the DB. When using 10+ instances
 
 ### Stage 1. Each instance writes updates to redis
 
-Each instance will accumulate the spend updates for a key, user, team, etc and write the updates to a redis queue. 
+Each instance will accumlate the spend updates for a key, user, team, etc and write the updates to a redis queue. 
 
 <Image img={require('../../img/deadlock_fix_1.png')}  style={{ width: '900px', height: 'auto' }} />
 <p style={{textAlign: 'left', color: '#666'}}>

docs/my-website/docs/proxy/deploy.md

@@ -22,7 +22,7 @@ echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
 
 # Add the litellm salt key - you cannot change this after adding a model
 # It is used to encrypt / decrypt your LLM API Key credentials
-# We recommend - https://1password.com/password-generator/ 
+# We recommned - https://1password.com/password-generator/ 
 # password generator to get a random hash for litellm salt key
 echo 'LITELLM_SALT_KEY="sk-1234"' >> .env
 
@@ -125,7 +125,7 @@ CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"]
 
 ### Build from litellm `pip` package
 
-Follow these instructions to build a docker container from the litellm pip package. If your company has a strict requirement around security / building images you can follow these steps.
+Follow these instructons to build a docker container from the litellm pip package. If your company has a strict requirement around security / building images you can follow these steps.
 
 Dockerfile 
 
@@ -999,7 +999,7 @@ services:
       - "4000:4000" # Map the container port to the host, change the host port if necessary
     volumes:
       - ./litellm-config.yaml:/app/config.yaml # Mount the local configuration file
-    # You can change the port or number of workers as per your requirements or pass any new supported CLI argument. Make sure the port passed here matches with the container port defined above in `ports` value
+    # You can change the port or number of workers as per your requirements or pass any new supported CLI augument. Make sure the port passed here matches with the container port defined above in `ports` value
     command: [ "--config", "/app/config.yaml", "--port", "4000", "--num_workers", "8" ]
 
 # ...rest of your docker-compose config if any

docs/my-website/docs/proxy/enterprise.md

@@ -691,7 +691,7 @@ curl --request POST \
 <TabItem value="admin_only_routes" label="Test `admin_only_routes`">
 
 
-**Successful Request**
+**Successfull Request**
 
 ```shell
 curl --location 'http://0.0.0.0:4000/key/generate' \
@@ -729,7 +729,7 @@ curl --location 'http://0.0.0.0:4000/key/generate' \
 <TabItem value="allowed_routes" label="Test `allowed_routes`">
 
 
-**Successful Request**
+**Successfull Request**
 
 ```shell
 curl http://localhost:4000/chat/completions \

docs/my-website/docs/proxy/guardrails/quick_start.md

@@ -164,7 +164,7 @@ curl -i http://localhost:4000/v1/chat/completions \
 
 **Expected response**
 
-Your response headers will include `x-litellm-applied-guardrails` with the guardrail applied 
+Your response headers will incude `x-litellm-applied-guardrails` with the guardrail applied 
 
 ```
 x-litellm-applied-guardrails: aporia-pre-guard

docs/my-website/docs/proxy/logging.md

@@ -277,7 +277,7 @@ Found under `kwargs["standard_logging_object"]`. This is a standard payload, log
 
 ## Langfuse
 
-We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successful LLM calls to langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your environment
+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
 
@@ -534,15 +534,15 @@ print(response)
 
 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 occurred (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`                       |
+| 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**
@@ -1190,7 +1190,7 @@ We will use the `--config` to set
 
 - `litellm.success_callback = ["s3"]` 
 
-This will log all successful LLM calls to s3 Bucket
+This will log all successfull LLM calls to s3 Bucket
 
 **Step 1** Set AWS Credentials in .env
 
@@ -1279,7 +1279,7 @@ Log LLM Logs to [Azure Data Lake Storage](https://learn.microsoft.com/en-us/azur
 
 | Property | Details |
 |----------|---------|
-| Description | Log LLM Input/Output to Azure Blob Storage (Bucket) |
+| Description | Log LLM Input/Output to Azure Blob Storag (Bucket) |
 | Azure Docs on Data Lake Storage | [Azure Data Lake Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction) |
 
 
@@ -1360,7 +1360,7 @@ LiteLLM Supports logging to the following Datdog Integrations:
 <Tabs>
 <TabItem value="datadog" label="Datadog Logs">
 
-We will use the `--config` to set `litellm.callbacks = ["datadog"]` this will log all successful LLM calls to DataDog
+We will use the `--config` to set `litellm.callbacks = ["datadog"]` this will log all successfull LLM calls to DataDog
 
 **Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
 
@@ -1636,7 +1636,7 @@ class MyCustomHandler(CustomLogger):
             litellm_params = kwargs.get("litellm_params", {})
             metadata = litellm_params.get("metadata", {})   # headers passed to LiteLLM proxy, can be found here
 
-            # Access Exceptions & Traceback
+            # Acess Exceptions & Traceback
             exception_event = kwargs.get("exception", None)
             traceback_event = kwargs.get("traceback_exception", None)
 
@@ -2205,7 +2205,7 @@ We will use the `--config` to set
 - `litellm.success_callback = ["dynamodb"]` 
 - `litellm.dynamodb_table_name = "your-table-name"`
 
-This will log all successful LLM calls to DynamoDB
+This will log all successfull LLM calls to DynamoDB
 
 **Step 1** Set AWS Credentials in .env
 
@@ -2370,7 +2370,7 @@ litellm --test
 
 [Athina](https://athina.ai/) 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 successful LLM calls to athina
+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
 

docs/my-website/docs/proxy/model_discovery.md

@@ -1,108 +0,0 @@
-# Model Discovery
-
-Use this to give users an accurate list of models available behind provider endpoint, when calling `/v1/models` for wildcard models.
-
-## Supported Models
-
-- Fireworks AI
-- OpenAI
-- Gemini
-- LiteLLM Proxy
-- Topaz
-- Anthropic
-- XAI
-- VLLM
-- Vertex AI
-
-### Usage
-
-**1. Setup config.yaml**
-
-```yaml
-model_list:
-    - model_name: xai/*
-      litellm_params:
-        model: xai/*
-        api_key: os.environ/XAI_API_KEY
-
-litellm_settings:
-    check_provider_endpoint: true # 👈 Enable checking provider endpoint for wildcard models
-```
-
-**2. Start proxy**
-
-```bash
-litellm --config /path/to/config.yaml
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-**3. Call `/v1/models`**
-
-```bash
-curl -X GET "http://localhost:4000/v1/models" -H "Authorization: Bearer $LITELLM_KEY"
-```
-
-Expected response
-
-```json
-{
-    "data": [
-        {
-            "id": "xai/grok-2-1212",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-2-vision-1212",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-3-beta",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-3-fast-beta",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-3-mini-beta",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-3-mini-fast-beta",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-beta",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-vision-beta",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        },
-        {
-            "id": "xai/grok-2-image-1212",
-            "object": "model",
-            "created": 1677610602,
-            "owned_by": "openai"
-        }
-    ],
-    "object": "list"
-}
-```

docs/my-website/docs/proxy/prod.md

@@ -61,7 +61,7 @@ CMD ["--port", "4000", "--config", "./proxy_server_config.yaml"]
 
 ## 3. Use Redis 'port','host', 'password'. NOT 'redis_url'
 
-If you decide to use Redis, DO NOT use 'redis_url'. We recommend using redis port, host, and password params. 
+If you decide to use Redis, DO NOT use 'redis_url'. We recommend usig redis port, host, and password params. 
 
 `redis_url`is 80 RPS slower
 
@@ -169,7 +169,7 @@ If you plan on using the DB, set a salt key for encrypting/decrypting variables
 
 Do not change this after adding a model. It is used to encrypt / decrypt your LLM API Key credentials
 
-We recommend - https://1password.com/password-generator/ password generator to get a random hash for litellm salt key.
+We recommned - https://1password.com/password-generator/ password generator to get a random hash for litellm salt key.
 
 ```bash
 export LITELLM_SALT_KEY="sk-1234"

docs/my-website/docs/proxy/temporary_budget_increase.md

@@ -3,7 +3,7 @@
 Set temporary budget increase for a LiteLLM Virtual Key. Use this if you get asked to increase the budget for a key temporarily.
 
 
-| Hierarchy | Supported | 
+| Heirarchy | Supported | 
 |-----------|-----------|
 | LiteLLM Virtual Key | ✅ |
 | User | ❌ |

docs/my-website/docs/proxy/ui_credentials.md

@@ -4,7 +4,7 @@ import TabItem from '@theme/TabItem';
 
 # Adding LLM Credentials
 
-You can add LLM provider credentials on the UI. Once you add credentials you can reuse them when adding new models
+You can add LLM provider credentials on the UI. Once you add credentials you can re-use them when adding new models
 
 ## Add a credential + model
 

docs/my-website/docs/proxy/ui_logs.md

@@ -3,7 +3,7 @@ import Image from '@theme/IdealImage';
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-# Getting Started with UI Logs
+# UI Logs Page
 
 View Spend, Token Usage, Key, Team Name for Each Request to LiteLLM
 
@@ -52,3 +52,4 @@ If you do not want to store spend logs in DB, you can opt out with this setting
 general_settings:
   disable_spend_logs: True   # Disable writing spend logs to DB
 ```
+

docs/my-website/docs/proxy/ui_logs_sessions.md

@@ -1,320 +0,0 @@
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# Session Logs
-
-Group requests into sessions. This allows you to group related requests together.
-
-
-<Image img={require('../../img/ui_session_logs.png')}/>
-
-## Usage 
-
-### `/chat/completions`
-
-To group multiple requests into a single session, pass the same `litellm_session_id` in the metadata for each request. Here's how to do it:
-
-<Tabs>
-<TabItem value="openai" label="OpenAI Python v1.0.0+">
-
-**Request 1**
-Create a new session with a unique ID and make the first request. The session ID will be used to track all related requests.
-
-```python showLineNumbers
-import openai
-import uuid
-
-# Create a session ID
-session_id = str(uuid.uuid4())
-
-client = openai.OpenAI(
-    api_key="<your litellm api key>",
-    base_url="http://0.0.0.0:4000"
-)
-
-# First request in session
-response1 = client.chat.completions.create(
-    model="gpt-4o",
-    messages=[
-        {
-            "role": "user",
-            "content": "Write a short story about a robot"
-        }
-    ],
-    extra_body={
-        "metadata": {
-            "litellm_session_id": session_id  # Pass the session ID
-        }
-    }
-)
-```
-
-**Request 2**
-Make another request using the same session ID to link it with the previous request. This allows tracking related requests together.
-
-```python showLineNumbers
-# Second request using same session ID
-response2 = client.chat.completions.create(
-    model="gpt-4o", 
-    messages=[
-        {
-            "role": "user",
-            "content": "Now write a poem about that robot"
-        }
-    ],
-    extra_body={
-        "metadata": {
-            "litellm_session_id": session_id  # Reuse the same session ID
-        }
-    }
-)
-```
-
-</TabItem>
-<TabItem value="langchain" label="Langchain">
-
-**Request 1**
-Initialize a new session with a unique ID and create a chat model instance for making requests. The session ID is embedded in the model's configuration.
-
-```python showLineNumbers
-from langchain.chat_models import ChatOpenAI
-import uuid
-
-# Create a session ID
-session_id = str(uuid.uuid4())
-
-chat = ChatOpenAI(
-    openai_api_base="http://0.0.0.0:4000",
-    api_key="<your litellm api key>",
-    model="gpt-4o",
-    extra_body={
-        "metadata": {
-            "litellm_session_id": session_id  # Pass the session ID
-        }
-    }
-)
-
-# First request in session
-response1 = chat.invoke("Write a short story about a robot")
-```
-
-**Request 2**
-Use the same chat model instance to make another request, automatically maintaining the session context through the previously configured session ID.
-
-```python showLineNumbers
-# Second request using same chat object and session ID
-response2 = chat.invoke("Now write a poem about that robot")
-```
-
-</TabItem>
-<TabItem value="curl" label="Curl">
-
-**Request 1**
-Generate a new session ID and make the initial API call. The session ID in the metadata will be used to track this conversation.
-
-```bash showLineNumbers
-# Create a session ID
-SESSION_ID=$(uuidgen)
-
-# Store your API key
-API_KEY="<your litellm api key>"
-
-# First request in session
-curl --location 'http://0.0.0.0:4000/chat/completions' \
-    --header 'Content-Type: application/json' \
-    --header "Authorization: Bearer $API_KEY" \
-    --data '{
-    "model": "gpt-4o",
-    "messages": [
-        {
-        "role": "user",
-        "content": "Write a short story about a robot"
-        }
-    ],
-    "metadata": {
-        "litellm_session_id": "'$SESSION_ID'"
-    }
-}'
-```
-
-**Request 2**
-Make a follow-up request using the same session ID to maintain conversation context and tracking.
-
-```bash showLineNumbers
-# Second request using same session ID
-curl --location 'http://0.0.0.0:4000/chat/completions' \
-    --header 'Content-Type: application/json' \
-    --header "Authorization: Bearer $API_KEY" \
-    --data '{
-    "model": "gpt-4o",
-    "messages": [
-        {
-        "role": "user",
-        "content": "Now write a poem about that robot"
-        }
-    ],
-    "metadata": {
-        "litellm_session_id": "'$SESSION_ID'"
-    }
-}'
-```
-
-</TabItem>
-<TabItem value="litellm" label="LiteLLM Python SDK">
-
-**Request 1**
-Start a new session by creating a unique ID and making the initial request. This session ID will be used to group related requests together.
-
-```python showLineNumbers
-import litellm
-import uuid
-
-# Create a session ID
-session_id = str(uuid.uuid4())
-
-# First request in session
-response1 = litellm.completion(
-    model="gpt-4o",
-    messages=[{"role": "user", "content": "Write a short story about a robot"}],
-    api_base="http://0.0.0.0:4000",
-    api_key="<your litellm api key>",
-    metadata={
-        "litellm_session_id": session_id  # Pass the session ID
-    }
-)
-```
-
-**Request 2**
-Continue the conversation by making another request with the same session ID, linking it to the previous interaction.
-
-```python showLineNumbers
-# Second request using same session ID
-response2 = litellm.completion(
-    model="gpt-4o",
-    messages=[{"role": "user", "content": "Now write a poem about that robot"}],
-    api_base="http://0.0.0.0:4000",
-    api_key="<your litellm api key>",
-    metadata={
-        "litellm_session_id": session_id  # Reuse the same session ID
-    }
-)
-```
-
-</TabItem>
-</Tabs>
-
-### `/responses`
-
-For the `/responses` endpoint, use `previous_response_id` to group requests into a session. The `previous_response_id` is returned in the response of each request.
-
-<Tabs>
-<TabItem value="openai" label="OpenAI Python v1.0.0+">
-
-**Request 1**
-Make the initial request and store the response ID for linking follow-up requests.
-
-```python showLineNumbers
-from openai import OpenAI
-
-client = OpenAI(
-    api_key="<your litellm api key>",
-    base_url="http://0.0.0.0:4000"
-)
-
-# First request in session
-response1 = client.responses.create(
-    model="anthropic/claude-3-sonnet-20240229-v1:0",
-    input="Write a short story about a robot"
-)
-
-# Store the response ID for the next request
-response_id = response1.id
-```
-
-**Request 2**
-Make a follow-up request using the previous response ID to maintain the conversation context.
-
-```python showLineNumbers
-# Second request using previous response ID
-response2 = client.responses.create(
-    model="anthropic/claude-3-sonnet-20240229-v1:0",
-    input="Now write a poem about that robot",
-    previous_response_id=response_id  # Link to previous request
-)
-```
-
-</TabItem>
-<TabItem value="curl" label="Curl">
-
-**Request 1**
-Make the initial request. The response will include an ID that can be used to link follow-up requests.
-
-```bash showLineNumbers
-# Store your API key
-API_KEY="<your litellm api key>"
-
-# First request in session
-curl http://localhost:4000/v1/responses \
-    --header 'Content-Type: application/json' \
-    --header "Authorization: Bearer $API_KEY" \
-    --data '{
-        "model": "anthropic/claude-3-sonnet-20240229-v1:0",
-        "input": "Write a short story about a robot"
-    }'
-
-# Response will include an 'id' field that you'll use in the next request
-```
-
-**Request 2**
-Make a follow-up request using the previous response ID to maintain the conversation context.
-
-```bash showLineNumbers
-# Second request using previous response ID
-curl http://localhost:4000/v1/responses \
-    --header 'Content-Type: application/json' \
-    --header "Authorization: Bearer $API_KEY" \
-    --data '{
-        "model": "anthropic/claude-3-sonnet-20240229-v1:0",
-        "input": "Now write a poem about that robot",
-        "previous_response_id": "resp_abc123..."  # Replace with actual response ID from previous request
-    }'
-```
-
-</TabItem>
-<TabItem value="litellm" label="LiteLLM Python SDK">
-
-**Request 1**
-Make the initial request and store the response ID for linking follow-up requests.
-
-```python showLineNumbers
-import litellm
-
-# First request in session
-response1 = litellm.responses(
-    model="anthropic/claude-3-sonnet-20240229-v1:0",
-    input="Write a short story about a robot",
-    api_base="http://0.0.0.0:4000",
-    api_key="<your litellm api key>"
-)
-
-# Store the response ID for the next request
-response_id = response1.id
-```
-
-**Request 2**
-Make a follow-up request using the previous response ID to maintain the conversation context.
-
-```python showLineNumbers
-# Second request using previous response ID
-response2 = litellm.responses(
-    model="anthropic/claude-3-sonnet-20240229-v1:0",
-    input="Now write a poem about that robot",
-    api_base="http://0.0.0.0:4000",
-    api_key="<your litellm api key>",
-    previous_response_id=response_id  # Link to previous request
-)
-```
-
-</TabItem>
-</Tabs>

docs/my-website/docs/proxy/virtual_keys.md

@@ -23,7 +23,7 @@ Requirements:
   - ** Set on config.yaml** set your master key under `general_settings:master_key`, example below
   - ** Set env variable** set `LITELLM_MASTER_KEY`
 
-(the proxy Dockerfile checks if the `DATABASE_URL` is set and then initializes the DB connection)
+(the proxy Dockerfile checks if the `DATABASE_URL` is set and then intializes the DB connection)
 
 ```shell
 export DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname>
@@ -333,7 +333,7 @@ curl http://localhost:4000/v1/chat/completions \
 
 **Expected Response**
 
-Expect to see a successful response from the litellm proxy since the key passed in `X-Litellm-Key` is valid
+Expect to see a successfull response from the litellm proxy since the key passed in `X-Litellm-Key` is valid
 ```shell
 {"id":"chatcmpl-f9b2b79a7c30477ab93cd0e717d1773e","choices":[{"finish_reason":"stop","index":0,"message":{"content":"\n\nHello there, how may I assist you today?","role":"assistant","tool_calls":null,"function_call":null}}],"created":1677652288,"model":"gpt-3.5-turbo-0125","object":"chat.completion","system_fingerprint":"fp_44709d6fcb","usage":{"completion_tokens":12,"prompt_tokens":9,"total_tokens":21}
 ```

docs/my-website/docs/reasoning_content.md

@@ -16,8 +16,6 @@ Supported Providers:
 - Vertex AI (Anthropic) (`vertexai/`)
 - OpenRouter (`openrouter/`)
 - XAI (`xai/`)
-- Google AI Studio (`google/`)
-- Vertex AI (`vertex_ai/`)
 
 LiteLLM will standardize the `reasoning_content` in the response and `thinking_blocks` in the assistant message.
 
@@ -25,7 +23,7 @@ LiteLLM will standardize the `reasoning_content` in the response and `thinking_b
 "message": {
     ...
     "reasoning_content": "The capital of France is Paris.",
-    "thinking_blocks": [ # only returned for Anthropic models
+    "thinking_blocks": [
         {
             "type": "thinking",
             "thinking": "The capital of France is Paris.",

docs/my-website/docs/response_api.md

@@ -13,24 +13,23 @@ LiteLLM provides a BETA endpoint in the spec of [OpenAI's `/responses` API](http
 | Streaming | ✅ | |
 | Fallbacks | ✅ | Works between supported models |
 | Loadbalancing | ✅ | Works between supported models |
-| Supported operations | Create a response, Get a response, Delete a response | |
 | Supported LiteLLM Versions | 1.63.8+ | |
-| Supported LLM providers | **All LiteLLM supported providers** | `openai`, `anthropic`, `bedrock`, `vertex_ai`, `gemini`, `azure`, `azure_ai` etc. |
+| Supported LLM providers | `openai` | |
 
 ## Usage
 
-### LiteLLM Python SDK
+## Create a model response
 
 <Tabs>
-<TabItem value="openai" label="OpenAI">
+<TabItem value="litellm-sdk" label="LiteLLM SDK">
 
 #### Non-streaming
-```python showLineNumbers title="OpenAI Non-streaming Response"
+```python
 import litellm
 
 # Non-streaming response
 response = litellm.responses(
-    model="openai/o1-pro",
+    model="o1-pro",
     input="Tell me a three sentence bedtime story about a unicorn.",
     max_output_tokens=100
 )
@@ -39,103 +38,12 @@ print(response)
 ```
 
 #### Streaming
-```python showLineNumbers title="OpenAI Streaming Response"
+```python
 import litellm
 
 # Streaming response
 response = litellm.responses(
-    model="openai/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-#### GET a Response
-```python showLineNumbers title="Get Response by ID"
-import litellm
-
-# First, create a response
-response = litellm.responses(
-    model="openai/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    max_output_tokens=100
-)
-
-# Get the response ID
-response_id = response.id
-
-# Retrieve the response by ID
-retrieved_response = litellm.get_responses(
-    response_id=response_id
-)
-
-print(retrieved_response)
-
-# For async usage
-# retrieved_response = await litellm.aget_responses(response_id=response_id)
-```
-
-#### DELETE a Response
-```python showLineNumbers title="Delete Response by ID"
-import litellm
-
-# First, create a response
-response = litellm.responses(
-    model="openai/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    max_output_tokens=100
-)
-
-# Get the response ID
-response_id = response.id
-
-# Delete the response by ID
-delete_response = litellm.delete_responses(
-    response_id=response_id
-)
-
-print(delete_response)
-
-# For async usage
-# delete_response = await litellm.adelete_responses(response_id=response_id)
-```
-
-</TabItem>
-
-<TabItem value="anthropic" label="Anthropic">
-
-#### Non-streaming
-```python showLineNumbers title="Anthropic Non-streaming Response"
-import litellm
-import os
-
-# Set API key
-os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
-
-# Non-streaming response
-response = litellm.responses(
-    model="anthropic/claude-3-5-sonnet-20240620",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    max_output_tokens=100
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="Anthropic Streaming Response"
-import litellm
-import os
-
-# Set API key
-os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
-
-# Streaming response
-response = litellm.responses(
-    model="anthropic/claude-3-5-sonnet-20240620",
+    model="o1-pro",
     input="Tell me a three sentence bedtime story about a unicorn.",
     stream=True
 )
@@ -145,793 +53,65 @@ for event in response:
 ```
 
 </TabItem>
-
-<TabItem value="vertex" label="Vertex AI">
-
-#### Non-streaming
-```python showLineNumbers title="Vertex AI Non-streaming Response"
-import litellm
-import os
-
-# Set credentials - Vertex AI uses application default credentials
-# Run 'gcloud auth application-default login' to authenticate
-os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
-os.environ["VERTEXAI_LOCATION"] = "us-central1"
-
-# Non-streaming response
-response = litellm.responses(
-    model="vertex_ai/gemini-1.5-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    max_output_tokens=100
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="Vertex AI Streaming Response"
-import litellm
-import os
-
-# Set credentials - Vertex AI uses application default credentials
-# Run 'gcloud auth application-default login' to authenticate
-os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
-os.environ["VERTEXAI_LOCATION"] = "us-central1"
-
-# Streaming response
-response = litellm.responses(
-    model="vertex_ai/gemini-1.5-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-
-<TabItem value="bedrock" label="AWS Bedrock">
-
-#### Non-streaming
-```python showLineNumbers title="AWS Bedrock Non-streaming Response"
-import litellm
-import os
-
-# Set AWS credentials
-os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
-os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
-os.environ["AWS_REGION_NAME"] = "us-west-2"  # or your AWS region
-
-# Non-streaming response
-response = litellm.responses(
-    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    max_output_tokens=100
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="AWS Bedrock Streaming Response"
-import litellm
-import os
-
-# Set AWS credentials
-os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
-os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
-os.environ["AWS_REGION_NAME"] = "us-west-2"  # or your AWS region
-
-# Streaming response
-response = litellm.responses(
-    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-
-<TabItem value="gemini" label="Google AI Studio">
-
-#### Non-streaming
-```python showLineNumbers title="Google AI Studio Non-streaming Response"
-import litellm
-import os
-
-# Set API key for Google AI Studio
-os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
-
-# Non-streaming response
-response = litellm.responses(
-    model="gemini/gemini-1.5-flash",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    max_output_tokens=100
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="Google AI Studio Streaming Response"
-import litellm
-import os
-
-# Set API key for Google AI Studio
-os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
-
-# Streaming response
-response = litellm.responses(
-    model="gemini/gemini-1.5-flash",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-</Tabs>
-
-### LiteLLM Proxy with OpenAI SDK
-
-First, set up and start your LiteLLM proxy server.
-
-```bash title="Start LiteLLM Proxy Server"
-litellm --config /path/to/config.yaml
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-<Tabs>
-<TabItem value="openai" label="OpenAI">
+<TabItem value="proxy" label="OpenAI SDK with LiteLLM Proxy">
 
 First, add this to your litellm proxy config.yaml:
-```yaml showLineNumbers title="OpenAI Proxy Configuration"
+```yaml
 model_list:
-  - model_name: openai/o1-pro
+  - model_name: o1-pro
     litellm_params:
       model: openai/o1-pro
       api_key: os.environ/OPENAI_API_KEY
 ```
 
-#### Non-streaming
-```python showLineNumbers title="OpenAI Proxy Non-streaming Response"
-from openai import OpenAI
+Start your LiteLLM proxy:
+```bash
+litellm --config /path/to/config.yaml
 
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Non-streaming response
-response = client.responses.create(
-    model="openai/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-print(response)
+# RUNNING on http://0.0.0.0:4000
 ```
 
-#### Streaming
-```python showLineNumbers title="OpenAI Proxy Streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Streaming response
-response = client.responses.create(
-    model="openai/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-#### GET a Response
-```python showLineNumbers title="Get Response by ID with OpenAI SDK"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# First, create a response
-response = client.responses.create(
-    model="openai/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-# Get the response ID
-response_id = response.id
-
-# Retrieve the response by ID
-retrieved_response = client.responses.retrieve(response_id)
-
-print(retrieved_response)
-```
-
-#### DELETE a Response
-```python showLineNumbers title="Delete Response by ID with OpenAI SDK"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# First, create a response
-response = client.responses.create(
-    model="openai/o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-# Get the response ID
-response_id = response.id
-
-# Delete the response by ID
-delete_response = client.responses.delete(response_id)
-
-print(delete_response)
-```
-
-</TabItem>
-
-<TabItem value="anthropic" label="Anthropic">
-
-First, add this to your litellm proxy config.yaml:
-```yaml showLineNumbers title="Anthropic Proxy Configuration"
-model_list:
-  - model_name: anthropic/claude-3-5-sonnet-20240620
-    litellm_params:
-      model: anthropic/claude-3-5-sonnet-20240620
-      api_key: os.environ/ANTHROPIC_API_KEY
-```
+Then use the OpenAI SDK pointed to your proxy:
 
 #### Non-streaming
-```python showLineNumbers title="Anthropic Proxy Non-streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Non-streaming response
-response = client.responses.create(
-    model="anthropic/claude-3-5-sonnet-20240620",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="Anthropic Proxy Streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Streaming response
-response = client.responses.create(
-    model="anthropic/claude-3-5-sonnet-20240620",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-
-<TabItem value="vertex" label="Vertex AI">
-
-First, add this to your litellm proxy config.yaml:
-```yaml showLineNumbers title="Vertex AI Proxy Configuration"
-model_list:
-  - model_name: vertex_ai/gemini-1.5-pro
-    litellm_params:
-      model: vertex_ai/gemini-1.5-pro
-      vertex_project: your-gcp-project-id
-      vertex_location: us-central1
-```
-
-#### Non-streaming
-```python showLineNumbers title="Vertex AI Proxy Non-streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Non-streaming response
-response = client.responses.create(
-    model="vertex_ai/gemini-1.5-pro",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="Vertex AI Proxy Streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Streaming response
-response = client.responses.create(
-    model="vertex_ai/gemini-1.5-pro",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-
-<TabItem value="bedrock" label="AWS Bedrock">
-
-First, add this to your litellm proxy config.yaml:
-```yaml showLineNumbers title="AWS Bedrock Proxy Configuration"
-model_list:
-  - model_name: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
-    litellm_params:
-      model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
-      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
-      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
-      aws_region_name: us-west-2
-```
-
-#### Non-streaming
-```python showLineNumbers title="AWS Bedrock Proxy Non-streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Non-streaming response
-response = client.responses.create(
-    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="AWS Bedrock Proxy Streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Streaming response
-response = client.responses.create(
-    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-
-<TabItem value="gemini" label="Google AI Studio">
-
-First, add this to your litellm proxy config.yaml:
-```yaml showLineNumbers title="Google AI Studio Proxy Configuration"
-model_list:
-  - model_name: gemini/gemini-1.5-flash
-    litellm_params:
-      model: gemini/gemini-1.5-flash
-      api_key: os.environ/GEMINI_API_KEY
-```
-
-#### Non-streaming
-```python showLineNumbers title="Google AI Studio Proxy Non-streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Non-streaming response
-response = client.responses.create(
-    model="gemini/gemini-1.5-flash",
-    input="Tell me a three sentence bedtime story about a unicorn."
-)
-
-print(response)
-```
-
-#### Streaming
-```python showLineNumbers title="Google AI Studio Proxy Streaming Response"
-from openai import OpenAI
-
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
-
-# Streaming response
-response = client.responses.create(
-    model="gemini/gemini-1.5-flash",
-    input="Tell me a three sentence bedtime story about a unicorn.",
-    stream=True
-)
-
-for event in response:
-    print(event)
-```
-
-</TabItem>
-</Tabs>
-
-## Supported Responses API Parameters
-
-| Provider | Supported Parameters |
-|----------|---------------------|
-| `openai` | [All Responses API parameters are supported](https://github.com/BerriAI/litellm/blob/7c3df984da8e4dff9201e4c5353fdc7a2b441831/litellm/llms/openai/responses/transformation.py#L23) |
-| `azure` | [All Responses API parameters are supported](https://github.com/BerriAI/litellm/blob/7c3df984da8e4dff9201e4c5353fdc7a2b441831/litellm/llms/openai/responses/transformation.py#L23) |
-| `anthropic` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
-| `bedrock` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
-| `gemini` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
-| `vertex_ai` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
-| `azure_ai` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
-| All other llm api providers | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
-
-## Load Balancing with Session Continuity.
-
-When using the Responses API with multiple deployments of the same model (e.g., multiple Azure OpenAI endpoints), LiteLLM provides session continuity. This ensures that follow-up requests using a `previous_response_id` are routed to the same deployment that generated the original response.
-
-
-#### Example Usage
-
-<Tabs>
-<TabItem value="python-sdk" label="Python SDK">
-
-```python showLineNumbers title="Python SDK with Session Continuity"
-import litellm
-
-# Set up router with multiple deployments of the same model
-router = litellm.Router(
-    model_list=[
-        {
-            "model_name": "azure-gpt4-turbo",
-            "litellm_params": {
-                "model": "azure/gpt-4-turbo",
-                "api_key": "your-api-key-1",
-                "api_version": "2024-06-01",
-                "api_base": "https://endpoint1.openai.azure.com",
-            },
-        },
-        {
-            "model_name": "azure-gpt4-turbo",
-            "litellm_params": {
-                "model": "azure/gpt-4-turbo",
-                "api_key": "your-api-key-2",
-                "api_version": "2024-06-01",
-                "api_base": "https://endpoint2.openai.azure.com",
-            },
-        },
-    ],
-    optional_pre_call_checks=["responses_api_deployment_check"],
-)
-
-# Initial request
-response = await router.aresponses(
-    model="azure-gpt4-turbo",
-    input="Hello, who are you?",
-    truncation="auto",
-)
-
-# Store the response ID
-response_id = response.id
-
-# Follow-up request - will be automatically routed to the same deployment
-follow_up = await router.aresponses(
-    model="azure-gpt4-turbo",
-    input="Tell me more about yourself",
-    truncation="auto",
-    previous_response_id=response_id  # This ensures routing to the same deployment
-)
-```
-
-</TabItem>
-<TabItem value="proxy-server" label="Proxy Server">
-
-#### 1. Setup session continuity on proxy config.yaml
-
-To enable session continuity for Responses API in your LiteLLM proxy, set `optional_pre_call_checks: ["responses_api_deployment_check"]` in your proxy config.yaml.
-
-```yaml showLineNumbers title="config.yaml with Session Continuity"
-model_list:
-  - model_name: azure-gpt4-turbo
-    litellm_params:
-      model: azure/gpt-4-turbo
-      api_key: your-api-key-1
-      api_version: 2024-06-01
-      api_base: https://endpoint1.openai.azure.com
-  - model_name: azure-gpt4-turbo
-    litellm_params:
-      model: azure/gpt-4-turbo
-      api_key: your-api-key-2
-      api_version: 2024-06-01
-      api_base: https://endpoint2.openai.azure.com
-
-router_settings:
-  optional_pre_call_checks: ["responses_api_deployment_check"]
-```
-
-#### 2. Use the OpenAI Python SDK to make requests to LiteLLM Proxy
-
-```python showLineNumbers title="OpenAI Client with Proxy Server"
-from openai import OpenAI
-
-client = OpenAI(
-    base_url="http://localhost:4000",
-    api_key="your-api-key"
-)
-
-# Initial request
-response = client.responses.create(
-    model="azure-gpt4-turbo",
-    input="Hello, who are you?"
-)
-
-response_id = response.id
-
-# Follow-up request - will be automatically routed to the same deployment
-follow_up = client.responses.create(
-    model="azure-gpt4-turbo",
-    input="Tell me more about yourself",
-    previous_response_id=response_id  # This ensures routing to the same deployment
-)
-```
-
-</TabItem>
-</Tabs>
-
-## Session Management - Non-OpenAI Models
-
-LiteLLM Proxy supports session management for non-OpenAI models. This allows you to store and fetch conversation history (state) in LiteLLM Proxy. 
-
-#### Usage
-
-1. Enable storing request / response content in the database
-
-Set `store_prompts_in_spend_logs: true` in your proxy config.yaml. When this is enabled, LiteLLM will store the request and response content in the database.
-
-```yaml
-general_settings:
-  store_prompts_in_spend_logs: true
-```
-
-2. Make request 1 with no `previous_response_id` (new session)
-
-Start a new conversation by making a request without specifying a previous response ID.
-
-<Tabs>
-<TabItem value="curl" label="Curl">
-
-```curl
-curl http://localhost:4000/v1/responses \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer sk-1234" \
-  -d '{
-    "model": "anthropic/claude-3-5-sonnet-latest",
-    "input": "who is Michael Jordan"
-  }'
-```
-
-</TabItem>
-<TabItem value="openai-sdk" label="OpenAI Python SDK">
-
 ```python
 from openai import OpenAI
 
-# Initialize the client with your LiteLLM proxy URL
+# Initialize client with your proxy URL
 client = OpenAI(
-    base_url="http://localhost:4000",
-    api_key="sk-1234"
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
 )
 
-# Make initial request to start a new conversation
+# Non-streaming response
 response = client.responses.create(
-    model="anthropic/claude-3-5-sonnet-latest",
-    input="who is Michael Jordan"
+    model="o1-pro",
+    input="Tell me a three sentence bedtime story about a unicorn."
 )
 
-print(response.id)  # Store this ID for future requests in same session
-print(response.output[0].content[0].text)
+print(response)
 ```
 
-</TabItem>
-</Tabs>
-
-Response:
-
-```json
-{
-  "id":"resp_123abc",
-  "model":"claude-3-5-sonnet-20241022",
-  "output":[{
-    "type":"message",
-    "content":[{
-      "type":"output_text",
-      "text":"Michael Jordan is widely considered one of the greatest basketball players of all time. He played for the Chicago Bulls (1984-1993, 1995-1998) and Washington Wizards (2001-2003), winning 6 NBA Championships with the Bulls."
-    }]
-  }]
-}
-```
-
-3. Make request 2 with `previous_response_id` (same session)
-
-Continue the conversation by referencing the previous response ID to maintain conversation context.
-
-<Tabs>
-<TabItem value="curl" label="Curl">
-
-```curl
-curl http://localhost:4000/v1/responses \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer sk-1234" \
-  -d '{
-    "model": "anthropic/claude-3-5-sonnet-latest",
-    "input": "can you tell me more about him",
-    "previous_response_id": "resp_123abc"
-  }'
-```
-
-</TabItem>
-<TabItem value="openai-sdk" label="OpenAI Python SDK">
-
+#### Streaming
 ```python
 from openai import OpenAI
 
-# Initialize the client with your LiteLLM proxy URL
+# Initialize client with your proxy URL
 client = OpenAI(
-    base_url="http://localhost:4000",
-    api_key="sk-1234"
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
 )
 
-# Make follow-up request in the same conversation session
-follow_up_response = client.responses.create(
-    model="anthropic/claude-3-5-sonnet-latest",
-    input="can you tell me more about him",
-    previous_response_id="resp_123abc"  # ID from the previous response
+# Streaming response
+response = client.responses.create(
+    model="o1-pro",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
 )
 
-print(follow_up_response.output[0].content[0].text)
+for event in response:
+    print(event)
 ```
 
 </TabItem>
 </Tabs>
-
-Response:
-
-```json
-{
-  "id":"resp_456def",
-  "model":"claude-3-5-sonnet-20241022",
-  "output":[{
-    "type":"message",
-    "content":[{
-      "type":"output_text",
-      "text":"Michael Jordan was born February 17, 1963. He attended University of North Carolina before being drafted 3rd overall by the Bulls in 1984. Beyond basketball, he built the Air Jordan brand with Nike and later became owner of the Charlotte Hornets."
-    }]
-  }]
-}
-```
-
-4. Make request 3 with no `previous_response_id` (new session)
-
-Start a brand new conversation without referencing previous context to demonstrate how context is not maintained between sessions.
-
-<Tabs>
-<TabItem value="curl" label="Curl">
-
-```curl
-curl http://localhost:4000/v1/responses \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer sk-1234" \
-  -d '{
-    "model": "anthropic/claude-3-5-sonnet-latest",
-    "input": "can you tell me more about him"
-  }'
-```
-
-</TabItem>
-<TabItem value="openai-sdk" label="OpenAI Python SDK">
-
-```python
-from openai import OpenAI
-
-# Initialize the client with your LiteLLM proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",
-    api_key="sk-1234"
-)
-
-# Make a new request without previous context
-new_session_response = client.responses.create(
-    model="anthropic/claude-3-5-sonnet-latest",
-    input="can you tell me more about him"
-    # No previous_response_id means this starts a new conversation
-)
-
-print(new_session_response.output[0].content[0].text)
-```
-
-</TabItem>
-</Tabs>
-
-Response:
-
-```json
-{
-  "id":"resp_789ghi",
-  "model":"claude-3-5-sonnet-20241022",
-  "output":[{
-    "type":"message",
-    "content":[{
-      "type":"output_text",
-      "text":"I don't see who you're referring to in our conversation. Could you let me know which person you'd like to learn more about?"
-    }]
-  }]
-}
-```
-
-
-
-
-
-
-
-
-
-
-
-
-

docs/my-website/docs/simple_proxy_old_doc.md

@@ -994,16 +994,16 @@ litellm --health
 
 ## Logging Proxy Input/Output - OpenTelemetry
 
-### Step 1 Start OpenTelemetry Collector Docker Container
+### Step 1 Start OpenTelemetry Collecter Docker Container
 This container sends logs to your selected destination 
 
-#### Install OpenTelemetry Collector Docker Image
+#### Install OpenTelemetry Collecter Docker Image
 ```shell
 docker pull otel/opentelemetry-collector:0.90.0
 docker run -p 127.0.0.1:4317:4317 -p 127.0.0.1:55679:55679 otel/opentelemetry-collector:0.90.0
 ```
 
-#### Set Destination paths on OpenTelemetry Collector
+#### Set Destination paths on OpenTelemetry Collecter
 
 Here's the OpenTelemetry yaml config to use with Elastic Search
 ```yaml
@@ -1077,7 +1077,7 @@ general_settings:
 LiteLLM will read the `OTEL_ENDPOINT` environment variable to send data to your OTEL collector 
 
 ```python
-os.environ['OTEL_ENDPOINT'] # defaults to 127.0.0.1:4317 if not provided
+os.environ['OTEL_ENDPOINT'] # defauls to 127.0.0.1:4317 if not provided
 ```
 
 #### Start LiteLLM Proxy
@@ -1101,8 +1101,8 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 ```
 
 
-#### Test & View Logs on OpenTelemetry Collector
-On successful logging you should be able to see this log on your `OpenTelemetry Collector` Docker Container
+#### Test & View Logs on OpenTelemetry Collecter
+On successfull logging you should be able to see this log on your `OpenTelemetry Collecter` Docker Container
 ```shell
 Events:
 SpanEvent #0
@@ -1149,7 +1149,7 @@ Here's the log view on Elastic Search. You can see the request `input`, `output`
 <Image img={require('../img/elastic_otel.png')} />
 
 ## Logging Proxy Input/Output - Langfuse
-We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successful LLM calls to langfuse
+We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse
 
 **Step 1** Install langfuse
 

docs/my-website/docs/text_to_speech.md

@@ -1,8 +1,4 @@
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# /audio/speech
+# Text to Speech
 
 ## **LiteLLM Python SDK Usage**
 ### Quick Start 

docs/my-website/docs/tutorials/compare_llms.md

@@ -117,7 +117,7 @@ response = completion("command-nightly", messages)
 """
 
 
-# questions/logs you want to run the LLM on
+# qustions/logs you want to run the LLM on
 questions = [
     "what is litellm?",
     "why should I use LiteLLM",

docs/my-website/docs/tutorials/gradio_integration.md

@@ -30,7 +30,7 @@ def inference(message, history):
             yield partial_message
     except Exception as e:
         print("Exception encountered:", str(e))
-        yield f"An Error occurred please 'Clear' the error and try your question again"
+        yield f"An Error occured please 'Clear' the error and try your question again"
 ```
 
 ### Define Chat Interface

docs/my-website/docs/tutorials/openai_codex.md

@@ -1,146 +0,0 @@
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-# Using LiteLLM with OpenAI Codex
-
-This guide walks you through connecting OpenAI Codex to LiteLLM. Using LiteLLM with Codex allows teams to:
-- Access 100+ LLMs through the Codex interface
-- Use powerful models like Gemini through a familiar interface
-- Track spend and usage with LiteLLM's built-in analytics
-- Control model access with virtual keys
-
-<Image img={require('../../img/litellm_codex.gif')} />
-
-## Quickstart
-
-:::info
-
-Requires LiteLLM v1.66.3.dev5 and higher
-
-:::
-
-
-Make sure to set up LiteLLM with the [LiteLLM Getting Started Guide](../proxy/docker_quick_start.md).
-
-## 1. Install OpenAI Codex
-
-Install the OpenAI Codex CLI tool globally using npm:
-
-<Tabs>
-<TabItem value="npm" label="npm">
-
-```bash showLineNumbers
-npm i -g @openai/codex
-```
-
-</TabItem>
-<TabItem value="yarn" label="yarn">
-
-```bash showLineNumbers
-yarn global add @openai/codex
-```
-
-</TabItem>
-</Tabs>
-
-## 2. Start LiteLLM Proxy
-
-<Tabs>
-<TabItem value="docker" label="Docker">
-
-```bash showLineNumbers
-docker run \
-    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
-    -p 4000:4000 \
-    ghcr.io/berriai/litellm:main-latest \
-    --config /app/config.yaml
-```
-
-</TabItem>
-<TabItem value="pip" label="LiteLLM CLI">
-
-```bash showLineNumbers
-litellm --config /path/to/config.yaml
-```
-
-</TabItem>
-</Tabs>
-
-LiteLLM should now be running on [http://localhost:4000](http://localhost:4000)
-
-## 3. Configure LiteLLM for Model Routing
-
-Ensure your LiteLLM Proxy is properly configured to route to your desired models. Create a `litellm_config.yaml` file with the following content:
-
-```yaml showLineNumbers
-model_list:
-  - model_name: o3-mini
-    litellm_params:
-      model: openai/o3-mini
-      api_key: os.environ/OPENAI_API_KEY
-  - model_name: claude-3-7-sonnet-latest
-    litellm_params:
-      model: anthropic/claude-3-7-sonnet-latest
-      api_key: os.environ/ANTHROPIC_API_KEY
-  - model_name: gemini-2.0-flash
-    litellm_params:
-      model: gemini/gemini-2.0-flash
-      api_key: os.environ/GEMINI_API_KEY
-
-litellm_settings:
-  drop_params: true
-```
-
-This configuration enables routing to specific OpenAI, Anthropic, and Gemini models with explicit names.
-
-## 4. Configure Codex to Use LiteLLM Proxy
-
-Set the required environment variables to point Codex to your LiteLLM Proxy:
-
-```bash
-# Point to your LiteLLM Proxy server
-export OPENAI_BASE_URL=http://0.0.0.0:4000 
-
-# Use your LiteLLM API key (if you've set up authentication)
-export OPENAI_API_KEY="sk-1234"
-```
-
-## 5. Run Codex with Gemini
-
-With everything configured, you can now run Codex with Gemini:
-
-```bash showLineNumbers
-codex --model gemini-2.0-flash --full-auto
-```
-
-<Image img={require('../../img/litellm_codex.gif')} />
-
-The `--full-auto` flag allows Codex to automatically generate code without additional prompting.
-
-## 6. Advanced Options
-
-### Using Different Models
-
-You can use any model configured in your LiteLLM proxy:
-
-```bash
-# Use Claude models
-codex --model claude-3-7-sonnet-latest
-
-# Use Google AI Studio Gemini models
-codex --model gemini/gemini-2.0-flash
-```
-
-## Troubleshooting
-
-- If you encounter connection issues, ensure your LiteLLM Proxy is running and accessible at the specified URL
-- Verify your LiteLLM API key is valid if you're using authentication
-- Check that your model routing configuration is correct
-- For model-specific errors, ensure the model is properly configured in your LiteLLM setup
-
-## Additional Resources
-
-- [LiteLLM Docker Quick Start Guide](../proxy/docker_quick_start.md)
-- [OpenAI Codex GitHub Repository](https://github.com/openai/codex)
-- [LiteLLM Virtual Keys and Authentication](../proxy/virtual_keys.md)

docs/my-website/docs/tutorials/scim_litellm.md

@@ -1,74 +0,0 @@
-
-import Image from '@theme/IdealImage';
-
-# SCIM with LiteLLM
-
-Enables identity providers (Okta, Azure AD, OneLogin, etc.) to automate user and team (group) provisioning, updates, and deprovisioning on LiteLLM.
-
-
-This tutorial will walk you through the steps to connect your IDP to LiteLLM SCIM Endpoints.
-
-### Supported SSO Providers for SCIM
-Below is a list of supported SSO providers for connecting to LiteLLM SCIM Endpoints.
-- Microsoft Entra ID (Azure AD)
-- Okta
-- Google Workspace
-- OneLogin
-- Keycloak
-- Auth0
-
-
-## 1. Get your SCIM Tenant URL and Bearer Token
-
-On LiteLLM, navigate to the Settings > Admin Settings > SCIM. On this page you will create a SCIM Token, this allows your IDP to authenticate to litellm `/scim` endpoints.
-
-<Image img={require('../../img/scim_2.png')}  style={{ width: '800px', height: 'auto' }} />
-
-## 2. Connect your IDP to LiteLLM SCIM Endpoints
-
-On your IDP provider, navigate to your SSO application and select `Provisioning` > `New provisioning configuration`.
-
-On this page, paste in your litellm scim tenant url and bearer token.
-
-Once this is pasted in, click on `Test Connection` to ensure your IDP can authenticate to the LiteLLM SCIM endpoints.
-
-<Image img={require('../../img/scim_4.png')}  style={{ width: '800px', height: 'auto' }} />
-
-
-## 3. Test SCIM Connection
-
-### 3.1 Assign the group to your LiteLLM Enterprise App
-
-On your IDP Portal, navigate to `Enterprise Applications` > Select your litellm app 
-
-<Image img={require('../../img/msft_enterprise_app.png')}  style={{ width: '800px', height: 'auto' }} />
-
-<br />
-<br />
-
-Once you've selected your litellm app, click on `Users and Groups` > `Add user/group` 
-
-<Image img={require('../../img/msft_enterprise_assign_group.png')}  style={{ width: '800px', height: 'auto' }} />
-
-<br />
-
-Now select the group you created in step 1.1. And add it to the LiteLLM Enterprise App. At this point we have added `Production LLM Evals Group` to the LiteLLM Enterprise App. The next step is having LiteLLM automatically create the `Production LLM Evals Group` on the LiteLLM DB when a new user signs in.
-
-<Image img={require('../../img/msft_enterprise_select_group.png')}  style={{ width: '800px', height: 'auto' }} />
-
-
-### 3.2 Sign in to LiteLLM UI via SSO
-
-Sign into the LiteLLM UI via SSO. You should be redirected to the Entra ID SSO page. This SSO sign in flow will trigger LiteLLM to fetch the latest Groups and Members from Azure Entra ID.
-
-<Image img={require('../../img/msft_sso_sign_in.png')}  style={{ width: '800px', height: 'auto' }} />
-
-### 3.3 Check the new team on LiteLLM UI
-
-On the LiteLLM UI, Navigate to `Teams`, You should see the new team `Production LLM Evals Group` auto-created on LiteLLM. 
-
-<Image img={require('../../img/msft_auto_team.png')}  style={{ width: '900px', height: 'auto' }} />
-
-
-
-

docs/my-website/release_notes/v1.67.0-stable/index.md

@@ -1,153 +0,0 @@
----
-title: v1.67.0-stable - SCIM Integration
-slug: v1.67.0-stable
-date: 2025-04-19T10:00:00
-authors:
-  - name: Krrish Dholakia
-    title: CEO, LiteLLM
-    url: https://www.linkedin.com/in/krish-d/
-    image_url: https://media.licdn.com/dms/image/v2/D4D03AQGrlsJ3aqpHmQ/profile-displayphoto-shrink_400_400/B4DZSAzgP7HYAg-/0/1737327772964?e=1749686400&v=beta&t=Hkl3U8Ps0VtvNxX0BNNq24b4dtX5wQaPFp6oiKCIHD8
-  - name: Ishaan Jaffer
-    title: CTO, LiteLLM
-    url: https://www.linkedin.com/in/reffajnaahsi/
-    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
-
-tags: ["sso", "unified_file_id", "cost_tracking", "security"]
-hide_table_of_contents: false
----
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-## Key Highlights
-
-- **SCIM Integration**: Enables identity providers (Okta, Azure AD, OneLogin, etc.) to automate user and team (group) provisioning, updates, and deprovisioning
-- **Team and Tag based usage tracking**: You can now see usage and spend by team and tag at 1M+ spend logs.
-- **Unified Responses API**: Support for calling Anthropic, Gemini, Groq, etc. via OpenAI's new Responses API.
-
-Let's dive in.
-
-## SCIM Integration
-
-<Image img={require('../../img/scim_integration.png')}/>
-
-This release adds SCIM support to LiteLLM. This allows your SSO provider (Okta, Azure AD, etc) to automatically create/delete users, teams, and memberships on LiteLLM. This means that when you remove a team on your SSO provider, your SSO provider will automatically delete the corresponding team on LiteLLM. 
-
-[Read more](../../docs/tutorials/scim_litellm)
-## Team and Tag based usage tracking
-
-<Image img={require('../../img/release_notes/new_team_usage_highlight.jpg')}/>
-
-
-This release improves team and tag based usage tracking at 1m+ spend logs, making it easy to monitor your LLM API Spend in production. This covers:
-
-- View **daily spend** by teams + tags
-- View **usage / spend by key**, within teams
-- View **spend by multiple tags**
-- Allow **internal users** to view spend of teams they're a member of
-
-[Read more](#management-endpoints--ui)
-
-## Unified Responses API
-
-This release allows you to call Azure OpenAI, Anthropic, AWS Bedrock, and Google Vertex AI models via the POST /v1/responses endpoint on LiteLLM. This means you can now use popular tools like [OpenAI Codex](https://docs.litellm.ai/docs/tutorials/openai_codex) with your own models. 
-
-<Image img={require('../../img/release_notes/unified_responses_api_rn.png')}/>
-
-
-[Read more](https://docs.litellm.ai/docs/response_api)
-
-
-## New Models / Updated Models
-
-- **OpenAI**
-    1. gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, o3, o3-mini, o4-mini pricing - [Get Started](../../docs/providers/openai#usage), [PR](https://github.com/BerriAI/litellm/pull/9990)
-    2. o4 - correctly map o4 to openai o_series model
-- **Azure AI**
-    1. Phi-4 output cost per token fix - [PR](https://github.com/BerriAI/litellm/pull/9880)
-    2. Responses API support [Get Started](../../docs/providers/azure#azure-responses-api),[PR](https://github.com/BerriAI/litellm/pull/10116)
-- **Anthropic**
-    1. redacted message thinking support - [Get Started](../../docs/providers/anthropic#usage---thinking--reasoning_content),[PR](https://github.com/BerriAI/litellm/pull/10129)
-- **Cohere**
-    1. `/v2/chat` Passthrough endpoint support w/ cost tracking - [Get Started](../../docs/pass_through/cohere), [PR](https://github.com/BerriAI/litellm/pull/9997)
-- **Azure**
-    1. Support azure tenant_id/client_id env vars - [Get Started](../../docs/providers/azure#entra-id---use-tenant_id-client_id-client_secret), [PR](https://github.com/BerriAI/litellm/pull/9993)
-    2. Fix response_format check for 2025+ api versions - [PR](https://github.com/BerriAI/litellm/pull/9993)
-    3. Add gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, o3, o3-mini, o4-mini pricing
-- **VLLM**
-    1. Files - Support 'file' message type for VLLM video url's - [Get Started](../../docs/providers/vllm#send-video-url-to-vllm), [PR](https://github.com/BerriAI/litellm/pull/10129)
-    2. Passthrough - new `/vllm/` passthrough endpoint support [Get Started](../../docs/pass_through/vllm), [PR](https://github.com/BerriAI/litellm/pull/10002)
-- **Mistral**
-    1. new `/mistral` passthrough endpoint support [Get Started](../../docs/pass_through/mistral), [PR](https://github.com/BerriAI/litellm/pull/10002)
-- **AWS**
-    1. New mapped bedrock regions - [PR](https://github.com/BerriAI/litellm/pull/9430)
-- **VertexAI / Google AI Studio**
-    1. Gemini - Response format - Retain schema field ordering for google gemini and vertex by specifying propertyOrdering - [Get Started](../../docs/providers/vertex#json-schema), [PR](https://github.com/BerriAI/litellm/pull/9828)
-    2. Gemini-2.5-flash - return reasoning content [Google AI Studio](../../docs/providers/gemini#usage---thinking--reasoning_content), [Vertex AI](../../docs/providers/vertex#thinking--reasoning_content)
-    3. Gemini-2.5-flash - pricing + model information [PR](https://github.com/BerriAI/litellm/pull/10125)
-    4. Passthrough - new `/vertex_ai/discovery` route - enables calling AgentBuilder API routes [Get Started](../../docs/pass_through/vertex_ai#supported-api-endpoints), [PR](https://github.com/BerriAI/litellm/pull/10084)
-- **Fireworks AI**
-    1. return tool calling responses in `tool_calls` field (fireworks incorrectly returns this as a json str in content) [PR](https://github.com/BerriAI/litellm/pull/10130)
-- **Triton**
-    1. Remove fixed remove bad_words / stop words from `/generate` call - [Get Started](../../docs/providers/triton-inference-server#triton-generate---chat-completion), [PR](https://github.com/BerriAI/litellm/pull/10163)
-- **Other**
-    1. Support for all litellm providers on Responses API (works with Codex) - [Get Started](../../docs/tutorials/openai_codex), [PR](https://github.com/BerriAI/litellm/pull/10132)
-    2. Fix combining multiple tool calls in streaming response - [Get Started](../../docs/completion/stream#helper-function), [PR](https://github.com/BerriAI/litellm/pull/10040)
-
-
-## Spend Tracking Improvements
-
-- **Cost Control** - inject cache control points in prompt for cost reduction [Get Started](../../docs/tutorials/prompt_caching), [PR](https://github.com/BerriAI/litellm/pull/10000)
-- **Spend Tags** - spend tags in headers - support x-litellm-tags even if tag based routing not enabled [Get Started](../../docs/proxy/request_headers#litellm-headers), [PR](https://github.com/BerriAI/litellm/pull/10000)
-- **Gemini-2.5-flash** - support cost calculation for reasoning tokens [PR](https://github.com/BerriAI/litellm/pull/10141)
-
-## Management Endpoints / UI
-- **Users**
-    1. Show created_at and updated_at on users page - [PR](https://github.com/BerriAI/litellm/pull/10033)
-- **Virtual Keys**
-    1. Filter by key alias - https://github.com/BerriAI/litellm/pull/10085
-- **Usage Tab**
-
-    1. Team based usage
-        
-        - New `LiteLLM_DailyTeamSpend` Table for aggregate team based usage logging - [PR](https://github.com/BerriAI/litellm/pull/10039)
-        
-        - New Team based usage dashboard + new `/team/daily/activity` API - [PR](https://github.com/BerriAI/litellm/pull/10081)
-        - Return team alias on /team/daily/activity API - [PR](https://github.com/BerriAI/litellm/pull/10157)
-        - allow internal user view spend for teams they belong to - [PR](https://github.com/BerriAI/litellm/pull/10157)
-        - allow viewing top keys by team - [PR](https://github.com/BerriAI/litellm/pull/10157)
-
-        <Image img={require('../../img/release_notes/new_team_usage.png')}/>
-
-    2. Tag Based Usage
-        - New `LiteLLM_DailyTagSpend` Table for aggregate tag based usage logging - [PR](https://github.com/BerriAI/litellm/pull/10071)
-        - Restrict to only Proxy Admins - [PR](https://github.com/BerriAI/litellm/pull/10157)
-        - allow viewing top keys by tag
-        - Return tags passed in request (i.e. dynamic tags) on `/tag/list` API - [PR](https://github.com/BerriAI/litellm/pull/10157)
-        <Image img={require('../../img/release_notes/new_tag_usage.png')}/>
-    3. Track prompt caching metrics in daily user, team, tag tables - [PR](https://github.com/BerriAI/litellm/pull/10029)
-    4. Show usage by key (on all up, team, and tag usage dashboards) - [PR](https://github.com/BerriAI/litellm/pull/10157)
-    5. swap old usage with new usage tab
-- **Models**
-    1. Make columns resizable/hideable - [PR](https://github.com/BerriAI/litellm/pull/10119)
-- **API Playground**
-    1. Allow internal user to call api playground - [PR](https://github.com/BerriAI/litellm/pull/10157)
-- **SCIM**
-    1. Add LiteLLM SCIM Integration for Team and User management - [Get Started](../../docs/tutorials/scim_litellm), [PR](https://github.com/BerriAI/litellm/pull/10072)
-
-
-## Logging / Guardrail Integrations
-- **GCS**
-    1. Fix gcs pub sub logging with env var GCS_PROJECT_ID - [Get Started](../../docs/observability/gcs_bucket_integration#usage), [PR](https://github.com/BerriAI/litellm/pull/10042)
-- **AIM**
-    1. Add litellm call id passing to Aim guardrails on pre and post-hooks calls - [Get Started](../../docs/proxy/guardrails/aim_security), [PR](https://github.com/BerriAI/litellm/pull/10021)
-- **Azure blob storage**
-    1. Ensure logging works in high throughput scenarios - [Get Started](../../docs/proxy/logging#azure-blob-storage), [PR](https://github.com/BerriAI/litellm/pull/9962)
-
-## General Proxy Improvements
-
-- **Support setting `litellm.modify_params` via env var** [PR](https://github.com/BerriAI/litellm/pull/9964)
-- **Model Discovery** - Check provider’s `/models` endpoints when calling proxy’s `/v1/models` endpoint - [Get Started](../../docs/proxy/model_discovery), [PR](https://github.com/BerriAI/litellm/pull/9958)
-- **`/utils/token_counter`** - fix retrieving custom tokenizer for db models - [Get Started](../../docs/proxy/configs#set-custom-tokenizer), [PR](https://github.com/BerriAI/litellm/pull/10047)
-- **Prisma migrate** - handle existing columns in db table - [PR](https://github.com/BerriAI/litellm/pull/10138)
-

docs/my-website/release_notes/v1.67.4-stable/index.md

@@ -1,197 +0,0 @@
----
-title: v1.67.4-stable - Improved User Management
-slug: v1.67.4-stable
-date: 2025-04-26T10:00:00
-authors:
-  - name: Krrish Dholakia
-    title: CEO, LiteLLM
-    url: https://www.linkedin.com/in/krish-d/
-    image_url: https://media.licdn.com/dms/image/v2/D4D03AQGrlsJ3aqpHmQ/profile-displayphoto-shrink_400_400/B4DZSAzgP7HYAg-/0/1737327772964?e=1749686400&v=beta&t=Hkl3U8Ps0VtvNxX0BNNq24b4dtX5wQaPFp6oiKCIHD8
-  - name: Ishaan Jaffer
-    title: CTO, LiteLLM
-    url: https://www.linkedin.com/in/reffajnaahsi/
-    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
-
-tags: ["responses_api", "ui_improvements", "security", "session_management"]
-hide_table_of_contents: false
----
-import Image from '@theme/IdealImage';
-import Tabs from '@theme/Tabs';
-import TabItem from '@theme/TabItem';
-
-
-
-## Deploy this version
-
-<Tabs>
-<TabItem value="docker" label="Docker">
-
-``` showLineNumbers title="docker run litellm"
-docker run
--e STORE_MODEL_IN_DB=True
--p 4000:4000
-ghcr.io/berriai/litellm:main-v1.67.4-stable
-```
-</TabItem>
-
-<TabItem value="pip" label="Pip">
-
-``` showLineNumbers title="pip install litellm"
-pip install litellm==1.67.4.post1
-```
-</TabItem>
-</Tabs>
-
-## Key Highlights
-
-- **Improved User Management**: This release enables search and filtering across users, keys, teams, and models.
-- **Responses API Load Balancing**: Route requests across provider regions and ensure session continuity. 
-- **UI Session Logs**: Group several requests to LiteLLM into a session. 
-
-## Improved User Management
-
-<Image img={require('../../img/release_notes/ui_search_users.png')}/>
-<br/>
-
-This release makes it easier to manage users and keys on LiteLLM. You can now search and filter across users, keys, teams, and models, and control user settings more easily.
-
-New features include:
-
-- Search for users by email, ID, role, or team.
-- See all of a user's models, teams, and keys in one place.
-- Change user roles and model access right from the Users Tab.
-
-These changes help you spend less time on user setup and management on LiteLLM.
-
-## Responses API Load Balancing
-
-<Image img={require('../../img/release_notes/ui_responses_lb.png')}/>
-<br/>
-
-This release introduces load balancing for the Responses API, allowing you to route requests across provider regions and ensure session continuity. It works as follows:
-
-- If a `previous_response_id` is provided, LiteLLM will route the request to the original deployment that generated the prior response — ensuring session continuity.
-- If no `previous_response_id` is provided, LiteLLM will load-balance requests across your available deployments.
-
-[Read more](https://docs.litellm.ai/docs/response_api#load-balancing-with-session-continuity)
-
-## UI Session Logs
-
-<Image img={require('../../img/ui_session_logs.png')}/>
-<br/>
-
-This release allow you to group requests to LiteLLM proxy into a session. If you specify a litellm_session_id in your request LiteLLM will automatically group all logs in the same session. This allows you to easily track usage and request content per session. 
-
-[Read more](https://docs.litellm.ai/docs/proxy/ui_logs_sessions)
-
-## New Models / Updated Models
-
-- **OpenAI**
-    1. Added `gpt-image-1` cost tracking [Get Started](https://docs.litellm.ai/docs/image_generation)
-    2. Bug fix: added cost tracking for gpt-image-1 when quality is unspecified [PR](https://github.com/BerriAI/litellm/pull/10247)
-- **Azure**
-    1. Fixed timestamp granularities passing to whisper in Azure [Get Started](https://docs.litellm.ai/docs/audio_transcription)
-    2. Added azure/gpt-image-1 pricing [Get Started](https://docs.litellm.ai/docs/image_generation), [PR](https://github.com/BerriAI/litellm/pull/10327)
-    3. Added cost tracking for `azure/computer-use-preview`, `azure/gpt-4o-audio-preview-2024-12-17`, `azure/gpt-4o-mini-audio-preview-2024-12-17` [PR](https://github.com/BerriAI/litellm/pull/10178)
-- **Bedrock**
-    1. Added support for all compatible Bedrock parameters when model="arn:.." (Bedrock application inference profile models) [Get started](https://docs.litellm.ai/docs/providers/bedrock#bedrock-application-inference-profile), [PR](https://github.com/BerriAI/litellm/pull/10256)
-    2. Fixed wrong system prompt transformation [PR](https://github.com/BerriAI/litellm/pull/10120)
-- **VertexAI / Google AI Studio**
-    1. Allow setting `budget_tokens=0` for `gemini-2.5-flash` [Get Started](https://docs.litellm.ai/docs/providers/gemini#usage---thinking--reasoning_content),[PR](https://github.com/BerriAI/litellm/pull/10198)
-    2. Ensure returned `usage` includes thinking token usage [PR](https://github.com/BerriAI/litellm/pull/10198)
-    3. Added cost tracking for `gemini-2.5-pro-preview-03-25` [PR](https://github.com/BerriAI/litellm/pull/10178)
-- **Cohere**
-    1. Added support for cohere command-a-03-2025 [Get Started](https://docs.litellm.ai/docs/providers/cohere), [PR](https://github.com/BerriAI/litellm/pull/10295)
-- **SageMaker**
-    1. Added support for max_completion_tokens parameter [Get Started](https://docs.litellm.ai/docs/providers/sagemaker), [PR](https://github.com/BerriAI/litellm/pull/10300)
-- **Responses API**
-    1. Added support for GET and DELETE operations - `/v1/responses/{response_id}` [Get Started](../../docs/response_api)
-    2. Added session management support for non-OpenAI models [PR](https://github.com/BerriAI/litellm/pull/10321)
-    3. Added routing affinity to maintain model consistency within sessions [Get Started](https://docs.litellm.ai/docs/response_api#load-balancing-with-routing-affinity), [PR](https://github.com/BerriAI/litellm/pull/10193)
-
-
-## Spend Tracking Improvements
-
-- **Bug Fix**: Fixed spend tracking bug, ensuring default litellm params aren't modified in memory [PR](https://github.com/BerriAI/litellm/pull/10167)
-- **Deprecation Dates**: Added deprecation dates for Azure, VertexAI models [PR](https://github.com/BerriAI/litellm/pull/10308)
-
-## Management Endpoints / UI
-
-#### Users
-- **Filtering and Searching**: 
-  - Filter users by user_id, role, team, sso_id 
-  - Search users by email
-
-  <br/>
-
-  <Image img={require('../../img/release_notes/user_filters.png')}/>
-
-- **User Info Panel**: Added a new user information pane [PR](https://github.com/BerriAI/litellm/pull/10213)
-  - View teams, keys, models associated with User 
-  - Edit user role, model permissions 
-
-
-
-#### Teams
-- **Filtering and Searching**: 
-    - Filter teams by Organization, Team ID [PR](https://github.com/BerriAI/litellm/pull/10324)
-    - Search teams by Team Name [PR](https://github.com/BerriAI/litellm/pull/10324)
-
-  <br/>
-
-  <Image img={require('../../img/release_notes/team_filters.png')}/>
-
-
-
-#### Keys
-- **Key Management**: 
-  - Support for cross-filtering and filtering by key hash [PR](https://github.com/BerriAI/litellm/pull/10322)
-  - Fixed key alias reset when resetting filters [PR](https://github.com/BerriAI/litellm/pull/10099)
-  - Fixed table rendering on key creation [PR](https://github.com/BerriAI/litellm/pull/10224)
-
-#### UI Logs Page
-
-- **Session Logs**: Added UI Session Logs [Get Started](https://docs.litellm.ai/docs/proxy/ui_logs_sessions)
-
-
-#### UI Authentication & Security
-- **Required Authentication**: Authentication now required for all dashboard pages [PR](https://github.com/BerriAI/litellm/pull/10229)
-- **SSO Fixes**: Fixed SSO user login invalid token error [PR](https://github.com/BerriAI/litellm/pull/10298)
-- [BETA] **Encrypted Tokens**: Moved UI to encrypted token usage [PR](https://github.com/BerriAI/litellm/pull/10302)
-- **Token Expiry**: Support token refresh by re-routing to login page (fixes issue where expired token would show a blank page) [PR](https://github.com/BerriAI/litellm/pull/10250)
-
-#### UI General fixes
-- **Fixed UI Flicker**: Addressed UI flickering issues in Dashboard [PR](https://github.com/BerriAI/litellm/pull/10261)
-- **Improved Terminology**: Better loading and no-data states on Keys and Tools pages [PR](https://github.com/BerriAI/litellm/pull/10253)
-- **Azure Model Support**: Fixed editing Azure public model names and changing model names after creation [PR](https://github.com/BerriAI/litellm/pull/10249)
-- **Team Model Selector**: Bug fix for team model selection [PR](https://github.com/BerriAI/litellm/pull/10171)
-
-
-## Logging / Guardrail Integrations
-
-- **Datadog**:
-    1. Fixed Datadog LLM observability logging [Get Started](https://docs.litellm.ai/docs/proxy/logging#datadog), [PR](https://github.com/BerriAI/litellm/pull/10206)
-- **Prometheus / Grafana**: 
-    1. Enable datasource selection on LiteLLM Grafana Template [Get Started](https://docs.litellm.ai/docs/proxy/prometheus#-litellm-maintained-grafana-dashboards-), [PR](https://github.com/BerriAI/litellm/pull/10257)
-- **AgentOps**: 
-    1. Added AgentOps Integration [Get Started](https://docs.litellm.ai/docs/observability/agentops_integration), [PR](https://github.com/BerriAI/litellm/pull/9685)
-- **Arize**: 
-    1. Added missing attributes for Arize & Phoenix Integration [Get Started](https://docs.litellm.ai/docs/observability/arize_integration), [PR](https://github.com/BerriAI/litellm/pull/10215)
-
-
-## General Proxy Improvements
-
-- **Caching**: Fixed caching to account for `thinking` or `reasoning_effort` when calculating cache key [PR](https://github.com/BerriAI/litellm/pull/10140)
-- **Model Groups**: Fixed handling for cases where user sets model_group inside model_info [PR](https://github.com/BerriAI/litellm/pull/10191)
-- **Passthrough Endpoints**: Ensured `PassthroughStandardLoggingPayload` is logged with method, URL, request/response body [PR](https://github.com/BerriAI/litellm/pull/10194)
-- **Fix SQL Injection**: Fixed potential SQL injection vulnerability in spend_management_endpoints.py [PR](https://github.com/BerriAI/litellm/pull/9878)
-
-
-
-## Helm
-
-- Fixed serviceAccountName on migration job [PR](https://github.com/BerriAI/litellm/pull/10258)
-
-## Full Changelog
-
-The complete list of changes can be found in the [GitHub release notes](https://github.com/BerriAI/litellm/compare/v1.67.0-stable...v1.67.4-stable).

docs/my-website/sidebars.js

@@ -69,7 +69,6 @@ const sidebars = {
             "proxy/clientside_auth",
             "proxy/request_headers",
             "proxy/response_headers",
-            "proxy/model_discovery",
           ],
         },
         {
@@ -102,17 +101,9 @@ const sidebars = {
             "proxy/admin_ui_sso",
             "proxy/self_serve",
             "proxy/public_teams",
-            "tutorials/scim_litellm",
             "proxy/custom_sso",
             "proxy/ui_credentials",
-            {
-              type: "category",
-              label: "UI Logs",
-              items: [
-                "proxy/ui_logs",
-                "proxy/ui_logs_sessions"
-              ]
-            }
+            "proxy/ui_logs"
           ],
         },
         {
@@ -339,8 +330,6 @@ const sidebars = {
             "pass_through/vertex_ai",
             "pass_through/google_ai_studio",
             "pass_through/cohere",
-            "pass_through/vllm",
-            "pass_through/mistral",
             "pass_through/openai_passthrough",
             "pass_through/anthropic_completion",
             "pass_through/bedrock",
@@ -418,7 +407,6 @@ const sidebars = {
       type: "category",
       label: "Logging & Observability",
       items: [
-        "observability/agentops_integration",
         "observability/langfuse_integration",
         "observability/lunary_integration",
         "observability/mlflow",
@@ -455,7 +443,6 @@ const sidebars = {
       label: "Tutorials",
       items: [
         "tutorials/openweb_ui",
-        "tutorials/openai_codex",
         "tutorials/msft_sso",
         "tutorials/prompt_caching",
         "tutorials/tag_management",

enterprise/enterprise_hooks/session_handler.py

@@ -1,132 +0,0 @@
-from litellm.proxy._types import SpendLogsPayload
-from litellm.integrations.custom_logger import CustomLogger
-from litellm._logging import verbose_proxy_logger
-from typing import Optional, List, Union
-import json
-from litellm.types.utils import ModelResponse, Message
-from litellm.types.llms.openai import (
-    AllMessageValues,
-    ChatCompletionResponseMessage,
-    GenericChatCompletionMessage,
-    ResponseInputParam,
-)
-from litellm.types.utils import ChatCompletionMessageToolCall
-from litellm.responses.utils import ResponsesAPIRequestUtils
-from litellm.responses.litellm_completion_transformation.transformation import ChatCompletionSession
-
-
-class _ENTERPRISE_ResponsesSessionHandler:
-    @staticmethod
-    async def get_chat_completion_message_history_for_previous_response_id(
-        previous_response_id: str,
-    ) -> ChatCompletionSession:
-        """
-        Return the chat completion message history for a previous response id
-        """
-        from litellm.responses.litellm_completion_transformation.transformation import LiteLLMCompletionResponsesConfig
-        all_spend_logs: List[SpendLogsPayload] = await _ENTERPRISE_ResponsesSessionHandler.get_all_spend_logs_for_previous_response_id(previous_response_id)
-        
-        litellm_session_id: Optional[str] = None
-        if len(all_spend_logs) > 0:
-            litellm_session_id = all_spend_logs[0].get("session_id")
-
-        chat_completion_message_history: List[
-            Union[
-                AllMessageValues,
-                GenericChatCompletionMessage,
-                ChatCompletionMessageToolCall,
-                ChatCompletionResponseMessage,
-                Message,
-            ]
-        ] = []
-        for spend_log in all_spend_logs:
-            proxy_server_request: Union[str, dict] = spend_log.get("proxy_server_request") or "{}"
-            proxy_server_request_dict: Optional[dict] = None
-            response_input_param: Optional[Union[str, ResponseInputParam]] = None
-            if isinstance(proxy_server_request, dict):
-                proxy_server_request_dict = proxy_server_request
-            else:
-                proxy_server_request_dict = json.loads(proxy_server_request)
-            
-            ############################################################
-            # Add Input messages for this Spend Log
-            ############################################################
-            if proxy_server_request_dict:
-                _response_input_param = proxy_server_request_dict.get("input", None)
-                if isinstance(_response_input_param, str):
-                    response_input_param = _response_input_param
-                elif isinstance(_response_input_param, dict):
-                    response_input_param = ResponseInputParam(**_response_input_param)
-            
-            if response_input_param:
-                chat_completion_messages = LiteLLMCompletionResponsesConfig.transform_responses_api_input_to_messages(
-                    input=response_input_param,
-                    responses_api_request=proxy_server_request_dict or {}
-                )
-                chat_completion_message_history.extend(chat_completion_messages)
-            
-            ############################################################
-            # Add Output messages for this Spend Log
-            ############################################################
-            _response_output = spend_log.get("response", "{}")
-            if isinstance(_response_output, dict):
-                # transform `ChatCompletion Response` to `ResponsesAPIResponse`
-                model_response = ModelResponse(**_response_output)
-                for choice in model_response.choices:
-                    if hasattr(choice, "message"):
-                        chat_completion_message_history.append(choice.message)
-        
-        verbose_proxy_logger.debug("chat_completion_message_history %s", json.dumps(chat_completion_message_history, indent=4, default=str))
-        return ChatCompletionSession(
-            messages=chat_completion_message_history,
-            litellm_session_id=litellm_session_id
-        )
-
-    @staticmethod
-    async def get_all_spend_logs_for_previous_response_id(
-        previous_response_id: str
-    ) -> List[SpendLogsPayload]:
-        """
-        Get all spend logs for a previous response id
-
-
-        SQL query
-
-        SELECT session_id FROM spend_logs WHERE response_id = previous_response_id, SELECT * FROM spend_logs WHERE session_id = session_id
-        """
-        from litellm.proxy.proxy_server import prisma_client
-        decoded_response_id = ResponsesAPIRequestUtils._decode_responses_api_response_id(previous_response_id)
-        previous_response_id = decoded_response_id.get("response_id", previous_response_id)
-        if prisma_client is None:
-            return []
-
-        query = """
-            WITH matching_session AS (
-                SELECT session_id
-                FROM "LiteLLM_SpendLogs"
-                WHERE request_id = $1
-            )
-            SELECT *
-            FROM "LiteLLM_SpendLogs"
-            WHERE session_id IN (SELECT session_id FROM matching_session)
-            ORDER BY "endTime" ASC;
-        """
-
-        spend_logs = await prisma_client.db.query_raw(
-            query,
-            previous_response_id
-        )
-
-        verbose_proxy_logger.debug(
-            "Found the following spend logs for previous response id %s: %s",
-            previous_response_id,
-            json.dumps(spend_logs, indent=4, default=str)
-        )
-
-
-        return spend_logs
-        
-
-    
-    
-    

litellm-proxy-extras/litellm_proxy_extras/migrations/20250425182129_add_session_id/migration.sql

@@ -1,4 +0,0 @@
--- AlterTable
-ALTER TABLE "LiteLLM_SpendLogs" ADD COLUMN     "proxy_server_request" JSONB DEFAULT '{}',
-ADD COLUMN     "session_id" TEXT;
-

litellm-proxy-extras/litellm_proxy_extras/schema.prisma

@@ -226,8 +226,6 @@ model LiteLLM_SpendLogs {
   requester_ip_address String?
   messages            Json?     @default("{}")
   response            Json?     @default("{}")
-  session_id          String?
-  proxy_server_request Json?     @default("{}")
   @@index([startTime])
   @@index([end_user])
 }

litellm-proxy-extras/litellm_proxy_extras/utils.py

@@ -1,11 +1,8 @@
 import glob
 import os
 import random
-import re
-import shutil
 import subprocess
 import time
-from datetime import datetime
 from pathlib import Path
 from typing import Optional
 
@@ -21,30 +18,9 @@ def str_to_bool(value: Optional[str]) -> bool:
 class ProxyExtrasDBManager:
     @staticmethod
     def _get_prisma_dir() -> str:
-        """
-        Get the path to the migrations directory
-
-        Set os.environ["LITELLM_MIGRATION_DIR"] to a custom migrations directory, to support baselining db in read-only fs.
-        """
-        custom_migrations_dir = os.getenv("LITELLM_MIGRATION_DIR")
-        pkg_migrations_dir = os.path.dirname(__file__)
-        if custom_migrations_dir:
-            # If migrations_dir exists, copy contents
-            if os.path.exists(custom_migrations_dir):
-                # Copy contents instead of directory itself
-                for item in os.listdir(pkg_migrations_dir):
-                    src_path = os.path.join(pkg_migrations_dir, item)
-                    dst_path = os.path.join(custom_migrations_dir, item)
-                    if os.path.isdir(src_path):
-                        shutil.copytree(src_path, dst_path, dirs_exist_ok=True)
-                    else:
-                        shutil.copy2(src_path, dst_path)
-            else:
-                # If directory doesn't exist, create it and copy everything
-                shutil.copytree(pkg_migrations_dir, custom_migrations_dir)
-            return custom_migrations_dir
-
-        return pkg_migrations_dir
+        """Get the path to the migrations directory"""
+        migrations_dir = os.path.dirname(__file__)
+        return migrations_dir
 
     @staticmethod
     def _create_baseline_migration(schema_path: str) -> bool:
@@ -56,29 +32,27 @@ class ProxyExtrasDBManager:
         # Create migrations/0_init directory
         init_dir.mkdir(parents=True, exist_ok=True)
 
-        database_url = os.getenv("DATABASE_URL")
+        # Generate migration SQL file
+        migration_file = init_dir / "migration.sql"
 
         try:
-            # 1. Generate migration SQL file by comparing empty state to current db state
-            logger.info("Generating baseline migration...")
-            migration_file = init_dir / "migration.sql"
+            # Generate migration diff with increased timeout
             subprocess.run(
                 [
                     "prisma",
                     "migrate",
                     "diff",
                     "--from-empty",
-                    "--to-url",
-                    database_url,
+                    "--to-schema-datamodel",
+                    str(schema_path),
                     "--script",
                 ],
                 stdout=open(migration_file, "w"),
                 check=True,
                 timeout=30,
-            )
+            )  # 30 second timeout
 
-            # 3. Mark the migration as applied since it represents current state
-            logger.info("Marking baseline migration as applied...")
+            # Mark migration as applied with increased timeout
             subprocess.run(
                 [
                     "prisma",
@@ -98,10 +72,8 @@ class ProxyExtrasDBManager:
             )
             return False
         except subprocess.CalledProcessError as e:
-            logger.warning(
-                f"Error creating baseline migration: {e}, {e.stderr}, {e.stdout}"
-            )
-            raise e
+            logger.warning(f"Error creating baseline migration: {e}")
+            return False
 
     @staticmethod
     def _get_migration_names(migrations_dir: str) -> list:
@@ -111,105 +83,8 @@ class ProxyExtrasDBManager:
         return [Path(p).parent.name for p in migration_paths]
 
     @staticmethod
-    def _roll_back_migration(migration_name: str):
-        """Mark a specific migration as rolled back"""
-        subprocess.run(
-            ["prisma", "migrate", "resolve", "--rolled-back", migration_name],
-            timeout=60,
-            check=True,
-            capture_output=True,
-        )
-
-    @staticmethod
-    def _resolve_specific_migration(migration_name: str):
-        """Mark a specific migration as applied"""
-        subprocess.run(
-            ["prisma", "migrate", "resolve", "--applied", migration_name],
-            timeout=60,
-            check=True,
-            capture_output=True,
-        )
-
-    @staticmethod
-    def _resolve_all_migrations(migrations_dir: str, schema_path: str):
-        """
-        1. Compare the current database state to schema.prisma and generate a migration for the diff.
-        2. Run prisma migrate deploy to apply any pending migrations.
-        3. Mark all existing migrations as applied.
-        """
-        database_url = os.getenv("DATABASE_URL")
-        diff_dir = (
-            Path(migrations_dir)
-            / "migrations"
-            / f"{datetime.now().strftime('%Y%m%d%H%M%S')}_baseline_diff"
-        )
-        try:
-            diff_dir.mkdir(parents=True, exist_ok=True)
-        except Exception as e:
-            if "Permission denied" in str(e):
-                logger.warning(
-                    f"Permission denied - {e}\nunable to baseline db. Set LITELLM_MIGRATION_DIR environment variable to a writable directory to enable migrations."
-                )
-                return
-            raise e
-        diff_sql_path = diff_dir / "migration.sql"
-
-        # 1. Generate migration SQL for the diff between DB and schema
-        try:
-            logger.info("Generating migration diff between DB and schema.prisma...")
-            with open(diff_sql_path, "w") as f:
-                subprocess.run(
-                    [
-                        "prisma",
-                        "migrate",
-                        "diff",
-                        "--from-url",
-                        database_url,
-                        "--to-schema-datamodel",
-                        schema_path,
-                        "--script",
-                    ],
-                    check=True,
-                    timeout=60,
-                    stdout=f,
-                )
-        except subprocess.CalledProcessError as e:
-            logger.warning(f"Failed to generate migration diff: {e.stderr}")
-        except subprocess.TimeoutExpired:
-            logger.warning("Migration diff generation timed out.")
-
-        # check if the migration was created
-        if not diff_sql_path.exists():
-            logger.warning("Migration diff was not created")
-            return
-        logger.info(f"Migration diff created at {diff_sql_path}")
-
-        # 2. Run prisma db execute to apply the migration
-        try:
-            logger.info("Running prisma db execute to apply the migration diff...")
-            result = subprocess.run(
-                [
-                    "prisma",
-                    "db",
-                    "execute",
-                    "--file",
-                    str(diff_sql_path),
-                    "--schema",
-                    schema_path,
-                ],
-                timeout=60,
-                check=True,
-                capture_output=True,
-                text=True,
-            )
-            logger.info(f"prisma db execute stdout: {result.stdout}")
-            logger.info("✅ Migration diff applied successfully")
-        except subprocess.CalledProcessError as e:
-            logger.warning(f"Failed to apply migration diff: {e.stderr}")
-        except subprocess.TimeoutExpired:
-            logger.warning("Migration diff application timed out.")
-
-        # 3. Mark all migrations as applied
+    def _resolve_all_migrations(migrations_dir: str):
+        """Mark all existing migrations as applied"""
         migration_names = ProxyExtrasDBManager._get_migration_names(migrations_dir)
         logger.info(f"Resolving {len(migration_names)} migrations")
         for migration_name in migration_names:
@@ -230,7 +105,7 @@ class ProxyExtrasDBManager:
                     )
 
     @staticmethod
-    def setup_database(use_migrate: bool = False) -> bool:
+    def setup_database(schema_path: str, use_migrate: bool = False) -> bool:
         """
         Set up the database using either prisma migrate or prisma db push
         Uses migrations from litellm-proxy-extras package
@@ -242,7 +117,6 @@ class ProxyExtrasDBManager:
         Returns:
             bool: True if setup was successful, False otherwise
         """
-        schema_path = ProxyExtrasDBManager._get_prisma_dir() + "/schema.prisma"
         use_migrate = str_to_bool(os.getenv("USE_PRISMA_MIGRATE")) or use_migrate
         for attempt in range(4):
             original_dir = os.getcwd()
@@ -267,34 +141,7 @@ class ProxyExtrasDBManager:
                         return True
                     except subprocess.CalledProcessError as e:
                         logger.info(f"prisma db error: {e.stderr}, e: {e.stdout}")
-                        if "P3009" in e.stderr:
-                            # Extract the failed migration name from the error message
-                            migration_match = re.search(
-                                r"`(\d+_.*)` migration", e.stderr
-                            )
-                            if migration_match:
-                                failed_migration = migration_match.group(1)
-                                logger.info(
-                                    f"Found failed migration: {failed_migration}, marking as rolled back"
-                                )
-                                # Mark the failed migration as rolled back
-                                subprocess.run(
-                                    [
-                                        "prisma",
-                                        "migrate",
-                                        "resolve",
-                                        "--rolled-back",
-                                        failed_migration,
-                                    ],
-                                    timeout=60,
-                                    check=True,
-                                    capture_output=True,
-                                    text=True,
-                                )
-                                logger.info(
-                                    f"✅ Migration {failed_migration} marked as rolled back... retrying"
-                                )
-                        elif (
+                        if (
                             "P3005" in e.stderr
                             and "database schema is not empty" in e.stderr
                         ):
@@ -305,34 +152,9 @@ class ProxyExtrasDBManager:
                             logger.info(
                                 "Baseline migration created, resolving all migrations"
                             )
-                            ProxyExtrasDBManager._resolve_all_migrations(
-                                migrations_dir, schema_path
-                            )
+                            ProxyExtrasDBManager._resolve_all_migrations(migrations_dir)
                             logger.info("✅ All migrations resolved.")
                             return True
-                        elif (
-                            "P3018" in e.stderr
-                        ):  # PostgreSQL error code for duplicate column
-                            logger.info(
-                                "Migration already exists, resolving specific migration"
-                            )
-                            # Extract the migration name from the error message
-                            migration_match = re.search(
-                                r"Migration name: (\d+_.*)", e.stderr
-                            )
-                            if migration_match:
-                                migration_name = migration_match.group(1)
-                                logger.info(f"Rolling back migration {migration_name}")
-                                ProxyExtrasDBManager._roll_back_migration(
-                                    migration_name
-                                )
-                                logger.info(
-                                    f"Resolving migration {migration_name} that failed due to existing columns"
-                                )
-                                ProxyExtrasDBManager._resolve_specific_migration(
-                                    migration_name
-                                )
-                                logger.info("✅ Migration resolved.")
                 else:
                     # Use prisma db push with increased timeout
                     subprocess.run(

litellm-proxy-extras/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm-proxy-extras"
-version = "0.1.13"
+version = "0.1.10"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 authors = ["BerriAI"]
 readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "0.1.13"
+version = "0.1.10"
 version_files = [
     "pyproject.toml:version",
     "../requirements.txt:litellm-proxy-extras==",

litellm/__init__.py

@@ -113,7 +113,6 @@ _custom_logger_compatible_callbacks_literal = Literal[
     "pagerduty",
     "humanloop",
     "gcs_pubsub",
-    "agentops",
     "anthropic_cache_control_hook",
 ]
 logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
@@ -129,19 +128,19 @@ prometheus_initialize_budget_metrics: Optional[bool] = False
 require_auth_for_metrics_endpoint: Optional[bool] = False
 argilla_batch_size: Optional[int] = None
 datadog_use_v1: Optional[bool] = False  # if you want to use v1 datadog logged payload
-gcs_pub_sub_use_v1: Optional[bool] = (
-    False  # if you want to use v1 gcs pubsub logged payload
-)
+gcs_pub_sub_use_v1: Optional[
+    bool
+] = False  # if you want to use v1 gcs pubsub logged payload
 argilla_transformation_object: Optional[Dict[str, Any]] = None
-_async_input_callback: List[Union[str, Callable, CustomLogger]] = (
-    []
-)  # internal variable - async custom callbacks are routed here.
-_async_success_callback: List[Union[str, Callable, CustomLogger]] = (
-    []
-)  # internal variable - async custom callbacks are routed here.
-_async_failure_callback: List[Union[str, Callable, CustomLogger]] = (
-    []
-)  # internal variable - async custom callbacks are routed here.
+_async_input_callback: List[
+    Union[str, Callable, CustomLogger]
+] = []  # internal variable - async custom callbacks are routed here.
+_async_success_callback: List[
+    Union[str, Callable, CustomLogger]
+] = []  # internal variable - async custom callbacks are routed here.
+_async_failure_callback: List[
+    Union[str, Callable, CustomLogger]
+] = []  # internal variable - async custom callbacks are routed here.
 pre_call_rules: List[Callable] = []
 post_call_rules: List[Callable] = []
 turn_off_message_logging: Optional[bool] = False
@@ -149,18 +148,18 @@ log_raw_request_response: bool = False
 redact_messages_in_exceptions: Optional[bool] = False
 redact_user_api_key_info: Optional[bool] = False
 filter_invalid_headers: Optional[bool] = False
-add_user_information_to_llm_headers: Optional[bool] = (
-    None  # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
-)
+add_user_information_to_llm_headers: Optional[
+    bool
+] = None  # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
 store_audit_logs = False  # Enterprise feature, allow users to see audit logs
 ### end of callbacks #############
 
-email: Optional[str] = (
-    None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
-token: Optional[str] = (
-    None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
+email: Optional[
+    str
+] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+token: Optional[
+    str
+] = None  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
 telemetry = True
 max_tokens: int = DEFAULT_MAX_TOKENS  # OpenAI Defaults
 drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
@@ -236,24 +235,20 @@ enable_loadbalancing_on_batch_endpoints: Optional[bool] = None
 enable_caching_on_provider_specific_optional_params: bool = (
     False  # feature-flag for caching on optional params - e.g. 'top_k'
 )
-caching: bool = (
-    False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
-caching_with_models: bool = (
-    False  # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
-)
-cache: Optional[Cache] = (
-    None  # cache object <- use this - https://docs.litellm.ai/docs/caching
-)
+caching: bool = False  # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+caching_with_models: bool = False  # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
+cache: Optional[
+    Cache
+] = None  # cache object <- use this - https://docs.litellm.ai/docs/caching
 default_in_memory_ttl: Optional[float] = None
 default_redis_ttl: Optional[float] = None
 default_redis_batch_cache_expiry: Optional[float] = None
 model_alias_map: Dict[str, str] = {}
 model_group_alias_map: Dict[str, str] = {}
 max_budget: float = 0.0  # set the max budget across all providers
-budget_duration: Optional[str] = (
-    None  # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
-)
+budget_duration: Optional[
+    str
+] = None  # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
 default_soft_budget: float = (
     DEFAULT_SOFT_BUDGET  # by default all litellm proxy keys have a soft budget of 50.0
 )
@@ -262,15 +257,11 @@ forward_traceparent_to_llm_provider: bool = False
 
 _current_cost = 0.0  # private variable, used if max budget is set
 error_logs: Dict = {}
-add_function_to_prompt: bool = (
-    False  # if function calling not supported by api, append function call details to system prompt
-)
+add_function_to_prompt: bool = False  # if function calling not supported by api, append function call details to system prompt
 client_session: Optional[httpx.Client] = None
 aclient_session: Optional[httpx.AsyncClient] = None
 model_fallbacks: Optional[List] = None  # Deprecated for 'litellm.fallbacks'
-model_cost_map_url: str = (
-    "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
-)
+model_cost_map_url: str = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 suppress_debug_info = False
 dynamodb_table_name: Optional[str] = None
 s3_callback_params: Optional[Dict] = None
@@ -293,9 +284,7 @@ disable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
 custom_prometheus_metadata_labels: List[str] = []
 #### REQUEST PRIORITIZATION ####
 priority_reservation: Optional[Dict[str, float]] = None
-force_ipv4: bool = (
-    False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
-)
+force_ipv4: bool = False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
 module_level_aclient = AsyncHTTPHandler(
     timeout=request_timeout, client_alias="module level aclient"
 )
@@ -309,13 +298,13 @@ fallbacks: Optional[List] = None
 context_window_fallbacks: Optional[List] = None
 content_policy_fallbacks: Optional[List] = None
 allowed_fails: int = 3
-num_retries_per_request: Optional[int] = (
-    None  # for the request overall (incl. fallbacks + model retries)
-)
+num_retries_per_request: Optional[
+    int
+] = None  # for the request overall (incl. fallbacks + model retries)
 ####### SECRET MANAGERS #####################
-secret_manager_client: Optional[Any] = (
-    None  # list of instantiated key management clients - e.g. azure kv, infisical, etc.
-)
+secret_manager_client: Optional[
+    Any
+] = None  # list of instantiated key management clients - e.g. azure kv, infisical, etc.
 _google_kms_resource_name: Optional[str] = None
 _key_management_system: Optional[KeyManagementSystem] = None
 _key_management_settings: KeyManagementSettings = KeyManagementSettings()
@@ -416,7 +405,6 @@ deepseek_models: List = []
 azure_ai_models: List = []
 jina_ai_models: List = []
 voyage_models: List = []
-infinity_models: List = []
 databricks_models: List = []
 cloudflare_models: List = []
 codestral_models: List = []
@@ -558,8 +546,6 @@ def add_known_models():
             azure_ai_models.append(key)
         elif value.get("litellm_provider") == "voyage":
             voyage_models.append(key)
-        elif value.get("litellm_provider") == "infinity":
-            infinity_models.append(key)
         elif value.get("litellm_provider") == "databricks":
             databricks_models.append(key)
         elif value.get("litellm_provider") == "cloudflare":
@@ -648,7 +634,6 @@ model_list = (
     + deepseek_models
     + azure_ai_models
     + voyage_models
-    + infinity_models
     + databricks_models
     + cloudflare_models
     + codestral_models
@@ -704,7 +689,6 @@ models_by_provider: dict = {
     "mistral": mistral_chat_models,
     "azure_ai": azure_ai_models,
     "voyage": voyage_models,
-    "infinity": infinity_models,
     "databricks": databricks_models,
     "cloudflare": cloudflare_models,
     "codestral": codestral_models,
@@ -952,11 +936,9 @@ from .llms.topaz.image_variations.transformation import TopazImageVariationConfi
 from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig
 from .llms.groq.chat.transformation import GroqChatConfig
 from .llms.voyage.embedding.transformation import VoyageEmbeddingConfig
-from .llms.infinity.embedding.transformation import InfinityEmbeddingConfig
 from .llms.azure_ai.chat.transformation import AzureAIStudioConfig
 from .llms.mistral.mistral_chat_transformation import MistralConfig
 from .llms.openai.responses.transformation import OpenAIResponsesAPIConfig
-from .llms.azure.responses.transformation import AzureOpenAIResponsesAPIConfig
 from .llms.openai.chat.o_series_transformation import (
     OpenAIOSeriesConfig as OpenAIO1Config,  # maintain backwards compatibility
     OpenAIOSeriesConfig,
@@ -1073,10 +1055,10 @@ from .types.llms.custom_llm import CustomLLMItem
 from .types.utils import GenericStreamingChunk
 
 custom_provider_map: List[CustomLLMItem] = []
-_custom_providers: List[str] = (
-    []
-)  # internal helper util, used to track names of custom providers
-disable_hf_tokenizer_download: Optional[bool] = (
-    None  # disable huggingface tokenizer download. Defaults to openai clk100
-)
+_custom_providers: List[
+    str
+] = []  # internal helper util, used to track names of custom providers
+disable_hf_tokenizer_download: Optional[
+    bool
+] = None  # disable huggingface tokenizer download. Defaults to openai clk100
 global_disable_no_log_param: bool = False

litellm/assistants/main.py

@@ -304,11 +304,6 @@ def create_assistants(
         "response_format": response_format,
     }
 
-    # only send params that are not None
-    create_assistant_data = {
-        k: v for k, v in create_assistant_data.items() if v is not None
-    }
-
     response: Optional[Union[Coroutine[Any, Any, Assistant], Assistant]] = None
     if custom_llm_provider == "openai":
         api_base = (

litellm/constants.py

@@ -21,10 +21,6 @@ DEFAULT_MAX_TOKENS = 256  # used when providers need a default
 MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = 1024  # 1MB = 1024KB
 SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = 1000  # Minimum number of requests to consider "reasonable traffic". Used for single-deployment cooldown logic.
 
-DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = 1024
-DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET = 2048
-DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET = 4096
-
 ########## Networking constants ##############################################################
 _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600  # 1 hour, re-use the same httpx client for 1 hour
 

litellm/cost_calculator.py

@@ -57,7 +57,6 @@ from litellm.llms.vertex_ai.image_generation.cost_calculator import (
 from litellm.responses.utils import ResponseAPILoggingUtils
 from litellm.types.llms.openai import (
     HttpxBinaryResponseContent,
-    ImageGenerationRequestQuality,
     OpenAIRealtimeStreamList,
     OpenAIRealtimeStreamResponseBaseObject,
     OpenAIRealtimeStreamSessionEvents,
@@ -914,7 +913,7 @@ def completion_cost(  # noqa: PLR0915
 
 
 def get_response_cost_from_hidden_params(
-    hidden_params: Union[dict, BaseModel],
+    hidden_params: Union[dict, BaseModel]
 ) -> Optional[float]:
     if isinstance(hidden_params, BaseModel):
         _hidden_params_dict = hidden_params.model_dump()
@@ -1102,39 +1101,31 @@ def default_image_cost_calculator(
         f"{quality}/{base_model_name}" if quality else base_model_name
     )
 
-    # gpt-image-1 models use low, medium, high quality. If user did not specify quality, use medium fot gpt-image-1 model family
-    model_name_with_v2_quality = (
-        f"{ImageGenerationRequestQuality.MEDIUM.value}/{base_model_name}"
-    )
-
     verbose_logger.debug(
         f"Looking up cost for models: {model_name_with_quality}, {base_model_name}"
     )
 
-    model_without_provider = f"{size_str}/{model.split('/')[-1]}"
-    model_with_quality_without_provider = (
-        f"{quality}/{model_without_provider}" if quality else model_without_provider
-    )
-
     # Try model with quality first, fall back to base model name
-    cost_info: Optional[dict] = None
-    models_to_check = [
-        model_name_with_quality,
-        base_model_name,
-        model_name_with_v2_quality,
-        model_with_quality_without_provider,
-        model_without_provider,
-        model,
-    ]
-    for model in models_to_check:
-        if model in litellm.model_cost:
-            cost_info = litellm.model_cost[model]
-            break
-    if cost_info is None:
-        raise Exception(
-            f"Model not found in cost map. Tried checking {models_to_check}"
+    if model_name_with_quality in litellm.model_cost:
+        cost_info = litellm.model_cost[model_name_with_quality]
+    elif base_model_name in litellm.model_cost:
+        cost_info = litellm.model_cost[base_model_name]
+    else:
+        # Try without provider prefix
+        model_without_provider = f"{size_str}/{model.split('/')[-1]}"
+        model_with_quality_without_provider = (
+            f"{quality}/{model_without_provider}" if quality else model_without_provider
         )
 
+        if model_with_quality_without_provider in litellm.model_cost:
+            cost_info = litellm.model_cost[model_with_quality_without_provider]
+        elif model_without_provider in litellm.model_cost:
+            cost_info = litellm.model_cost[model_without_provider]
+        else:
+            raise Exception(
+                f"Model not found in cost map. Tried {model_name_with_quality}, {base_model_name}, {model_with_quality_without_provider}, and {model_without_provider}"
+            )
+
     return cost_info["input_cost_per_pixel"] * height * width * n
 
 

litellm/integrations/_types/open_inference.py

@@ -45,14 +45,6 @@ class SpanAttributes:
     """
     The name of the model being used.
     """
-    LLM_PROVIDER = "llm.provider"
-    """
-    The provider of the model, such as OpenAI, Azure, Google, etc.
-    """
-    LLM_SYSTEM = "llm.system"
-    """
-    The AI product as identified by the client or server
-    """
     LLM_PROMPTS = "llm.prompts"
     """
     Prompts provided to a completions API.
@@ -73,40 +65,15 @@ class SpanAttributes:
     """
     Number of tokens in the prompt.
     """
-    LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_WRITE = "llm.token_count.prompt_details.cache_write"
-    """
-    Number of tokens in the prompt that were written to cache.
-    """
-    LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_READ = "llm.token_count.prompt_details.cache_read"
-    """
-    Number of tokens in the prompt that were read from cache.
-    """
-    LLM_TOKEN_COUNT_PROMPT_DETAILS_AUDIO = "llm.token_count.prompt_details.audio"
-    """
-    The number of audio input tokens presented in the prompt
-    """
     LLM_TOKEN_COUNT_COMPLETION = "llm.token_count.completion"
     """
     Number of tokens in the completion.
     """
-    LLM_TOKEN_COUNT_COMPLETION_DETAILS_REASONING = "llm.token_count.completion_details.reasoning"
-    """
-    Number of tokens used for reasoning steps in the completion.
-    """
-    LLM_TOKEN_COUNT_COMPLETION_DETAILS_AUDIO = "llm.token_count.completion_details.audio"
-    """
-    The number of audio input tokens generated by the model
-    """
     LLM_TOKEN_COUNT_TOTAL = "llm.token_count.total"
     """
     Total number of tokens, including both prompt and completion.
     """
 
-    LLM_TOOLS = "llm.tools"
-    """
-    List of tools that are advertised to the LLM to be able to call
-    """
-
     TOOL_NAME = "tool.name"
     """
     Name of the tool being used.
@@ -145,19 +112,6 @@ class SpanAttributes:
     The id of the user
     """
 
-    PROMPT_VENDOR = "prompt.vendor"
-    """
-    The vendor or origin of the prompt, e.g. a prompt library, a specialized service, etc.
-    """
-    PROMPT_ID = "prompt.id"
-    """
-    A vendor-specific id used to locate the prompt.
-    """
-    PROMPT_URL = "prompt.url"
-    """
-    A vendor-specific url used to locate the prompt.
-    """
-
 
 class MessageAttributes:
     """
@@ -197,10 +151,6 @@ class MessageAttributes:
     The JSON string representing the arguments passed to the function
     during a function call.
     """
-    MESSAGE_TOOL_CALL_ID = "message.tool_call_id"
-    """
-    The id of the tool call.
-    """
 
 
 class MessageContentAttributes:
@@ -236,25 +186,6 @@ class ImageAttributes:
     """
 
 
-class AudioAttributes:
-    """
-    Attributes for audio
-    """
-
-    AUDIO_URL = "audio.url"
-    """
-    The url to an audio file
-    """
-    AUDIO_MIME_TYPE = "audio.mime_type"
-    """
-    The mime type of the audio file
-    """
-    AUDIO_TRANSCRIPT = "audio.transcript"
-    """
-    The transcript of the audio file
-    """
-
-
 class DocumentAttributes:
     """
     Attributes for a document.
@@ -326,10 +257,6 @@ class ToolCallAttributes:
     Attributes for a tool call
     """
 
-    TOOL_CALL_ID = "tool_call.id"
-    """
-    The id of the tool call.
-    """
     TOOL_CALL_FUNCTION_NAME = "tool_call.function.name"
     """
     The name of function that is being called during a tool call.
@@ -341,18 +268,6 @@ class ToolCallAttributes:
     """
 
 
-class ToolAttributes:
-    """
-    Attributes for a tools
-    """
-
-    TOOL_JSON_SCHEMA = "tool.json_schema"
-    """
-    The json schema of a tool input, It is RECOMMENDED that this be in the
-    OpenAI tool calling format: https://platform.openai.com/docs/assistants/tools
-    """
-
-
 class OpenInferenceSpanKindValues(Enum):
     TOOL = "TOOL"
     CHAIN = "CHAIN"
@@ -369,21 +284,3 @@ class OpenInferenceSpanKindValues(Enum):
 class OpenInferenceMimeTypeValues(Enum):
     TEXT = "text/plain"
     JSON = "application/json"
-
-
-class OpenInferenceLLMSystemValues(Enum):
-    OPENAI = "openai"
-    ANTHROPIC = "anthropic"
-    COHERE = "cohere"
-    MISTRALAI = "mistralai"
-    VERTEXAI = "vertexai"
-
-
-class OpenInferenceLLMProviderValues(Enum):
-    OPENAI = "openai"
-    ANTHROPIC = "anthropic"
-    COHERE = "cohere"
-    MISTRALAI = "mistralai"
-    GOOGLE = "google"
-    AZURE = "azure"
-    AWS = "aws"

litellm/integrations/agentops/__init__.py

@@ -1,3 +0,0 @@
-from .agentops import AgentOps
-
-__all__ = ["AgentOps"] 

litellm/integrations/agentops/agentops.py

@@ -1,118 +0,0 @@
-"""
-AgentOps integration for LiteLLM - Provides OpenTelemetry tracing for LLM calls
-"""
-import os
-from dataclasses import dataclass
-from typing import Optional, Dict, Any
-from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig
-from litellm.llms.custom_httpx.http_handler import _get_httpx_client
-
-@dataclass
-class AgentOpsConfig:
-    endpoint: str = "https://otlp.agentops.cloud/v1/traces"
-    api_key: Optional[str] = None
-    service_name: Optional[str] = None
-    deployment_environment: Optional[str] = None
-    auth_endpoint: str = "https://api.agentops.ai/v3/auth/token"
-
-    @classmethod
-    def from_env(cls):
-        return cls(
-            endpoint="https://otlp.agentops.cloud/v1/traces",
-            api_key=os.getenv("AGENTOPS_API_KEY"),
-            service_name=os.getenv("AGENTOPS_SERVICE_NAME", "agentops"),
-            deployment_environment=os.getenv("AGENTOPS_ENVIRONMENT", "production"),
-            auth_endpoint="https://api.agentops.ai/v3/auth/token"
-        )
-
-class AgentOps(OpenTelemetry):
-    """
-    AgentOps integration - built on top of OpenTelemetry
-
-    Example usage:
-        ```python
-        import litellm
-        
-        litellm.success_callback = ["agentops"]
-
-        response = litellm.completion(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": "Hello, how are you?"}],
-        )
-        ```
-    """
-    def __init__(
-        self,
-        config: Optional[AgentOpsConfig] = None,
-    ):
-        if config is None:
-            config = AgentOpsConfig.from_env()
-
-        # Prefetch JWT token for authentication
-        jwt_token = None
-        project_id = None
-        if config.api_key:
-            try:
-                response = self._fetch_auth_token(config.api_key, config.auth_endpoint)
-                jwt_token = response.get("token")
-                project_id = response.get("project_id")
-            except Exception:
-                pass
-
-        headers = f"Authorization=Bearer {jwt_token}" if jwt_token else None
-        
-        otel_config = OpenTelemetryConfig(
-            exporter="otlp_http",
-            endpoint=config.endpoint,
-            headers=headers
-        )
-
-        # Initialize OpenTelemetry with our config
-        super().__init__(
-            config=otel_config,
-            callback_name="agentops"
-        )
-
-        # Set AgentOps-specific resource attributes
-        resource_attrs = {
-            "service.name": config.service_name or "litellm",
-            "deployment.environment": config.deployment_environment or "production",
-            "telemetry.sdk.name": "agentops",
-        }
-        
-        if project_id:
-            resource_attrs["project.id"] = project_id
-            
-        self.resource_attributes = resource_attrs
-
-    def _fetch_auth_token(self, api_key: str, auth_endpoint: str) -> Dict[str, Any]:
-        """
-        Fetch JWT authentication token from AgentOps API
-        
-        Args:
-            api_key: AgentOps API key
-            auth_endpoint: Authentication endpoint
-            
-        Returns:
-            Dict containing JWT token and project ID
-        """
-        headers = {
-            "Content-Type": "application/json",
-            "Connection": "keep-alive",
-        }
-        
-        client = _get_httpx_client()
-        try:
-            response = client.post(
-                url=auth_endpoint,
-                headers=headers,
-                json={"api_key": api_key},
-                timeout=10
-            )
-            
-            if response.status_code != 200:
-                raise Exception(f"Failed to fetch auth token: {response.text}")
-            
-            return response.json()
-        finally:
-            client.close() 

litellm/integrations/arize/_utils.py

@@ -1,4 +1,3 @@
-import json
 from typing import TYPE_CHECKING, Any, Optional, Union
 
 from litellm._logging import verbose_logger
@@ -13,141 +12,36 @@ else:
     Span = Any
 
 
-def cast_as_primitive_value_type(value) -> Union[str, bool, int, float]:
-    """
-    Converts a value to an OTEL-supported primitive for Arize/Phoenix observability.
-    """
-    if value is None:
-        return ""
-    if isinstance(value, (str, bool, int, float)):
-        return value
-    try:
-        return str(value)
-    except Exception:
-        return ""
-
-
-def safe_set_attribute(span: Span, key: str, value: Any):
-    """
-    Sets a span attribute safely with OTEL-compliant primitive typing for Arize/Phoenix.
-    """
-    primitive_value = cast_as_primitive_value_type(value)
-    span.set_attribute(key, primitive_value)
-
-
-def set_attributes(span: Span, kwargs, response_obj):  # noqa: PLR0915
-    """
-    Populates span with OpenInference-compliant LLM attributes for Arize and Phoenix tracing.
-    """
+def set_attributes(span: Span, kwargs, response_obj):
     from litellm.integrations._types.open_inference import (
         MessageAttributes,
         OpenInferenceSpanKindValues,
         SpanAttributes,
-        ToolCallAttributes,
     )
 
     try:
-        optional_params = kwargs.get("optional_params", {})
-        litellm_params = kwargs.get("litellm_params", {})
         standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
             "standard_logging_object"
         )
-        if standard_logging_payload is None:
-            raise ValueError("standard_logging_object not found in kwargs")
 
         #############################################
         ############ LLM CALL METADATA ##############
         #############################################
 
-        # Set custom metadata for observability and trace enrichment.
-        metadata = (
-            standard_logging_payload.get("metadata")
-            if standard_logging_payload
-            else None
-        )
-        if metadata is not None:
-            safe_set_attribute(span, SpanAttributes.METADATA, safe_dumps(metadata))
+        if standard_logging_payload and (
+            metadata := standard_logging_payload["metadata"]
+        ):
+            span.set_attribute(SpanAttributes.METADATA, safe_dumps(metadata))
 
         #############################################
         ########## LLM Request Attributes ###########
         #############################################
 
-        # The name of the LLM a request is being made to.
+        # The name of the LLM a request is being made to
         if kwargs.get("model"):
-            safe_set_attribute(
-                span,
-                SpanAttributes.LLM_MODEL_NAME,
-                kwargs.get("model"),
-            )
+            span.set_attribute(SpanAttributes.LLM_MODEL_NAME, kwargs.get("model"))
 
-        # The LLM request type.
-        safe_set_attribute(
-            span,
-            "llm.request.type",
-            standard_logging_payload["call_type"],
-        )
-
-        # The Generative AI Provider: Azure, OpenAI, etc.
-        safe_set_attribute(
-            span,
-            SpanAttributes.LLM_PROVIDER,
-            litellm_params.get("custom_llm_provider", "Unknown"),
-        )
-
-        # The maximum number of tokens the LLM generates for a request.
-        if optional_params.get("max_tokens"):
-            safe_set_attribute(
-                span,
-                "llm.request.max_tokens",
-                optional_params.get("max_tokens"),
-            )
-
-        # The temperature setting for the LLM request.
-        if optional_params.get("temperature"):
-            safe_set_attribute(
-                span,
-                "llm.request.temperature",
-                optional_params.get("temperature"),
-            )
-
-        # The top_p sampling setting for the LLM request.
-        if optional_params.get("top_p"):
-            safe_set_attribute(
-                span,
-                "llm.request.top_p",
-                optional_params.get("top_p"),
-            )
-
-        # Indicates whether response is streamed.
-        safe_set_attribute(
-            span,
-            "llm.is_streaming",
-            str(optional_params.get("stream", False)),
-        )
-
-        # Logs the user ID if present.
-        if optional_params.get("user"):
-            safe_set_attribute(
-                span,
-                "llm.user",
-                optional_params.get("user"),
-            )
-
-        # The unique identifier for the completion.
-        if response_obj and response_obj.get("id"):
-            safe_set_attribute(span, "llm.response.id", response_obj.get("id"))
-
-        # The model used to generate the response.
-        if response_obj and response_obj.get("model"):
-            safe_set_attribute(
-                span,
-                "llm.response.model",
-                response_obj.get("model"),
-            )
-
-        # Required by OpenInference to mark span as LLM kind.
-        safe_set_attribute(
-            span,
+        span.set_attribute(
             SpanAttributes.OPENINFERENCE_SPAN_KIND,
             OpenInferenceSpanKindValues.LLM.value,
         )
@@ -156,132 +50,77 @@ def set_attributes(span: Span, kwargs, response_obj):  # noqa: PLR0915
         # for /chat/completions
         # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
         if messages:
-            last_message = messages[-1]
-            safe_set_attribute(
-                span,
+            span.set_attribute(
                 SpanAttributes.INPUT_VALUE,
-                last_message.get("content", ""),
+                messages[-1].get("content", ""),  # get the last message for input
             )
 
-            # LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page.
+            # LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page
             for idx, msg in enumerate(messages):
-                prefix = f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}"
-                # Set the role per message.
-                safe_set_attribute(
-                    span, f"{prefix}.{MessageAttributes.MESSAGE_ROLE}", msg.get("role")
+                # Set the role per message
+                span.set_attribute(
+                    f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_ROLE}",
+                    msg["role"],
                 )
-                # Set the content per message.
-                safe_set_attribute(
-                    span,
-                    f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
+                # Set the content per message
+                span.set_attribute(
+                    f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_CONTENT}",
                     msg.get("content", ""),
                 )
 
-        # Capture tools (function definitions) used in the LLM call.
-        tools = optional_params.get("tools")
-        if tools:
-            for idx, tool in enumerate(tools):
-                function = tool.get("function")
-                if not function:
-                    continue
-                prefix = f"{SpanAttributes.LLM_TOOLS}.{idx}"
-                safe_set_attribute(
-                    span, f"{prefix}.{SpanAttributes.TOOL_NAME}", function.get("name")
-                )
-                safe_set_attribute(
-                    span,
-                    f"{prefix}.{SpanAttributes.TOOL_DESCRIPTION}",
-                    function.get("description"),
-                )
-                safe_set_attribute(
-                    span,
-                    f"{prefix}.{SpanAttributes.TOOL_PARAMETERS}",
-                    json.dumps(function.get("parameters")),
-                )
-
-        # Capture tool calls made during function-calling LLM flows.
-        functions = optional_params.get("functions")
-        if functions:
-            for idx, function in enumerate(functions):
-                prefix = f"{MessageAttributes.MESSAGE_TOOL_CALLS}.{idx}"
-                safe_set_attribute(
-                    span,
-                    f"{prefix}.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
-                    function.get("name"),
-                )
-
-        # Capture invocation parameters and user ID if available.
-        model_params = (
-            standard_logging_payload.get("model_parameters")
-            if standard_logging_payload
-            else None
-        )
-        if model_params:
+        if standard_logging_payload and (
+            model_params := standard_logging_payload["model_parameters"]
+        ):
             # The Generative AI Provider: Azure, OpenAI, etc.
-            safe_set_attribute(
-                span,
-                SpanAttributes.LLM_INVOCATION_PARAMETERS,
-                safe_dumps(model_params),
+            span.set_attribute(
+                SpanAttributes.LLM_INVOCATION_PARAMETERS, safe_dumps(model_params)
             )
 
             if model_params.get("user"):
                 user_id = model_params.get("user")
                 if user_id is not None:
-                    safe_set_attribute(span, SpanAttributes.USER_ID, user_id)
+                    span.set_attribute(SpanAttributes.USER_ID, user_id)
 
         #############################################
         ########## LLM Response Attributes ##########
+        # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
         #############################################
-
-        # Captures response tokens, message, and content.
         if hasattr(response_obj, "get"):
-            for idx, choice in enumerate(response_obj.get("choices", [])):
+            for choice in response_obj.get("choices", []):
                 response_message = choice.get("message", {})
-                safe_set_attribute(
-                    span,
-                    SpanAttributes.OUTPUT_VALUE,
-                    response_message.get("content", ""),
+                span.set_attribute(
+                    SpanAttributes.OUTPUT_VALUE, response_message.get("content", "")
                 )
 
-                # This shows up under `output_messages` tab on the span page.
-                prefix = f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.{idx}"
-                safe_set_attribute(
-                    span,
-                    f"{prefix}.{MessageAttributes.MESSAGE_ROLE}",
+                # This shows up under `output_messages` tab on the span page
+                # This code assumes a single response
+                span.set_attribute(
+                    f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
                     response_message.get("role"),
                 )
-                safe_set_attribute(
-                    span,
-                    f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
+                span.set_attribute(
+                    f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
                     response_message.get("content", ""),
                 )
 
-            # Token usage info.
-            usage = response_obj and response_obj.get("usage")
+            usage = response_obj.get("usage")
             if usage:
-                safe_set_attribute(
-                    span,
+                span.set_attribute(
                     SpanAttributes.LLM_TOKEN_COUNT_TOTAL,
                     usage.get("total_tokens"),
                 )
 
                 # The number of tokens used in the LLM response (completion).
-                safe_set_attribute(
-                    span,
+                span.set_attribute(
                     SpanAttributes.LLM_TOKEN_COUNT_COMPLETION,
                     usage.get("completion_tokens"),
                 )
 
                 # The number of tokens used in the LLM prompt.
-                safe_set_attribute(
-                    span,
+                span.set_attribute(
                     SpanAttributes.LLM_TOKEN_COUNT_PROMPT,
                     usage.get("prompt_tokens"),
                 )
-
+        pass
     except Exception as e:
-        verbose_logger.error(
-            f"[Arize/Phoenix] Failed to set OpenInference span attributes: {e}"
-        )
-        if hasattr(span, "record_exception"):
-            span.record_exception(e)
+        verbose_logger.error(f"Error setting arize attributes: {e}")

litellm/integrations/datadog/datadog_llm_obs.py

@@ -13,15 +13,10 @@ import uuid
 from datetime import datetime
 from typing import Any, Dict, List, Optional, Union
 
-import httpx
-
 import litellm
 from litellm._logging import verbose_logger
 from litellm.integrations.custom_batch_logger import CustomBatchLogger
 from litellm.integrations.datadog.datadog import DataDogLogger
-from litellm.litellm_core_utils.prompt_templates.common_utils import (
-    handle_any_messages_to_chat_completion_str_messages_conversion,
-)
 from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
     httpxSpecialProvider,
@@ -111,6 +106,7 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
                 },
             )
 
+            response.raise_for_status()
             if response.status_code != 202:
                 raise Exception(
                     f"DataDogLLMObs: Unexpected response - status_code: {response.status_code}, text: {response.text}"
@@ -120,10 +116,6 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
                 f"DataDogLLMObs: Successfully sent batch - status_code: {response.status_code}"
             )
             self.log_queue.clear()
-        except httpx.HTTPStatusError as e:
-            verbose_logger.exception(
-                f"DataDogLLMObs: Error sending batch - {e.response.text}"
-            )
         except Exception as e:
             verbose_logger.exception(f"DataDogLLMObs: Error sending batch - {str(e)}")
 
@@ -141,11 +133,7 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
 
         metadata = kwargs.get("litellm_params", {}).get("metadata", {})
 
-        input_meta = InputMeta(
-            messages=handle_any_messages_to_chat_completion_str_messages_conversion(
-                messages
-            )
-        )
+        input_meta = InputMeta(messages=messages)  # type: ignore
         output_meta = OutputMeta(messages=self._get_response_messages(response_obj))
 
         meta = Meta(

litellm/integrations/prometheus.py

@@ -1000,9 +1000,9 @@ class PrometheusLogger(CustomLogger):
     ):
         try:
             verbose_logger.debug("setting remaining tokens requests metric")
-            standard_logging_payload: Optional[
-                StandardLoggingPayload
-            ] = request_kwargs.get("standard_logging_object")
+            standard_logging_payload: Optional[StandardLoggingPayload] = (
+                request_kwargs.get("standard_logging_object")
+            )
 
             if standard_logging_payload is None:
                 return
@@ -1453,7 +1453,6 @@ class PrometheusLogger(CustomLogger):
                 user_id=None,
                 team_id=None,
                 key_alias=None,
-                key_hash=None,
                 exclude_team_id=UI_SESSION_TOKEN_TEAM_ID,
                 return_full_object=True,
                 organization_id=None,
@@ -1772,10 +1771,10 @@ class PrometheusLogger(CustomLogger):
         from litellm.integrations.custom_logger import CustomLogger
         from litellm.integrations.prometheus import PrometheusLogger
 
-        prometheus_loggers: List[
-            CustomLogger
-        ] = litellm.logging_callback_manager.get_custom_loggers_for_type(
-            callback_type=PrometheusLogger
+        prometheus_loggers: List[CustomLogger] = (
+            litellm.logging_callback_manager.get_custom_loggers_for_type(
+                callback_type=PrometheusLogger
+            )
         )
         # we need to get the initialized prometheus logger instance(s) and call logger.initialize_remaining_budget_metrics() on them
         verbose_logger.debug("found %s prometheus loggers", len(prometheus_loggers))

litellm/litellm_core_utils/exception_mapping_utils.py

@@ -311,9 +311,6 @@ def exception_type(  # type: ignore  # noqa: PLR0915
                 elif (
                     "invalid_request_error" in error_str
                     and "content_policy_violation" in error_str
-                ) or (
-                    "Invalid prompt" in error_str
-                    and "violating our usage policy" in error_str
                 ):
                     exception_mapping_worked = True
                     raise ContentPolicyViolationError(

litellm/litellm_core_utils/fallback_utils.py

@@ -1,6 +1,5 @@
 import uuid
 from copy import deepcopy
-from typing import Optional
 
 import litellm
 from litellm._logging import verbose_logger
@@ -32,16 +31,13 @@ async def async_completion_with_fallbacks(**kwargs):
     kwargs.pop("acompletion", None)  # Remove to prevent keyword conflicts
     litellm_call_id = str(uuid.uuid4())
     base_kwargs = {**kwargs, **nested_kwargs, "litellm_call_id": litellm_call_id}
-
-    # fields to remove
     base_kwargs.pop("model", None)  # Remove model as it will be set per fallback
-    litellm_logging_obj = base_kwargs.pop("litellm_logging_obj", None)
 
     # Try each fallback model
-    most_recent_exception_str: Optional[str] = None
     for fallback in fallbacks:
         try:
             completion_kwargs = deepcopy(base_kwargs)
+
             # Handle dictionary fallback configurations
             if isinstance(fallback, dict):
                 model = fallback.pop("model", original_model)
@@ -49,11 +45,7 @@ async def async_completion_with_fallbacks(**kwargs):
             else:
                 model = fallback
 
-            response = await litellm.acompletion(
-                **completion_kwargs,
-                model=model,
-                litellm_logging_obj=litellm_logging_obj,
-            )
+            response = await litellm.acompletion(**completion_kwargs, model=model)
 
             if response is not None:
                 return response
@@ -62,11 +54,10 @@ async def async_completion_with_fallbacks(**kwargs):
             verbose_logger.exception(
                 f"Fallback attempt failed for model {model}: {str(e)}"
             )
-            most_recent_exception_str = str(e)
             continue
 
     raise Exception(
-        f"{most_recent_exception_str}. All fallback attempts failed. Enable verbose logging with `litellm.set_verbose=True` for details."
+        "All fallback attempts failed. Enable verbose logging with `litellm.set_verbose=True` for details."
     )
 
 

litellm/litellm_core_utils/get_litellm_params.py

@@ -48,7 +48,6 @@ def get_litellm_params(
     user_continue_message=None,
     base_model: Optional[str] = None,
     litellm_trace_id: Optional[str] = None,
-    litellm_session_id: Optional[str] = None,
     hf_model_name: Optional[str] = None,
     custom_prompt_dict: Optional[dict] = None,
     litellm_metadata: Optional[dict] = None,
@@ -92,7 +91,6 @@ def get_litellm_params(
         "base_model": base_model
         or _get_base_model_from_litellm_call_metadata(metadata=metadata),
         "litellm_trace_id": litellm_trace_id,
-        "litellm_session_id": litellm_session_id,
         "hf_model_name": hf_model_name,
         "custom_prompt_dict": custom_prompt_dict,
         "litellm_metadata": litellm_metadata,

litellm/litellm_core_utils/get_supported_openai_params.py

@@ -221,8 +221,6 @@ def get_supported_openai_params(  # noqa: PLR0915
         return litellm.PredibaseConfig().get_supported_openai_params(model=model)
     elif custom_llm_provider == "voyage":
         return litellm.VoyageEmbeddingConfig().get_supported_openai_params(model=model)
-    elif custom_llm_provider == "infinity":
-        return litellm.InfinityEmbeddingConfig().get_supported_openai_params(model=model)
     elif custom_llm_provider == "triton":
         if request_type == "embeddings":
             return litellm.TritonEmbeddingConfig().get_supported_openai_params(

litellm/litellm_core_utils/litellm_logging.py

@@ -36,7 +36,6 @@ from litellm.cost_calculator import (
     RealtimeAPITokenUsageProcessor,
     _select_model_name_for_cost_calc,
 )
-from litellm.integrations.agentops import AgentOps
 from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook
 from litellm.integrations.arize.arize import ArizeLogger
 from litellm.integrations.custom_guardrail import CustomGuardrail
@@ -63,7 +62,7 @@ from litellm.types.llms.openai import (
     ResponsesAPIResponse,
 )
 from litellm.types.rerank import RerankResponse
-from litellm.types.router import CustomPricingLiteLLMParams
+from litellm.types.router import SPECIAL_MODEL_INFO_PARAMS
 from litellm.types.utils import (
     CallTypes,
     EmbeddingResponse,
@@ -248,7 +247,7 @@ class Logging(LiteLLMLoggingBaseClass):
         self.start_time = start_time  # log the call start time
         self.call_type = call_type
         self.litellm_call_id = litellm_call_id
-        self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4())
+        self.litellm_trace_id = litellm_trace_id
         self.function_id = function_id
         self.streaming_chunks: List[Any] = []  # for generating complete stream response
         self.sync_streaming_chunks: List[Any] = (
@@ -446,10 +445,13 @@ class Logging(LiteLLMLoggingBaseClass):
         if "stream_options" in additional_params:
             self.stream_options = additional_params["stream_options"]
         ## check if custom pricing set ##
-        custom_pricing_keys = CustomPricingLiteLLMParams.model_fields.keys()
-        for key in custom_pricing_keys:
-            if litellm_params.get(key) is not None:
-                self.custom_pricing = True
+        if (
+            litellm_params.get("input_cost_per_token") is not None
+            or litellm_params.get("input_cost_per_second") is not None
+            or litellm_params.get("output_cost_per_token") is not None
+            or litellm_params.get("output_cost_per_second") is not None
+        ):
+            self.custom_pricing = True
 
         if "custom_llm_provider" in self.model_call_details:
             self.custom_llm_provider = self.model_call_details["custom_llm_provider"]
@@ -2683,15 +2685,7 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
     """
     try:
         custom_logger_init_args = custom_logger_init_args or {}
-        if logging_integration == "agentops":  # Add AgentOps initialization
-            for callback in _in_memory_loggers:
-                if isinstance(callback, AgentOps):
-                    return callback  # type: ignore
-
-            agentops_logger = AgentOps()
-            _in_memory_loggers.append(agentops_logger)
-            return agentops_logger  # type: ignore
-        elif logging_integration == "lago":
+        if logging_integration == "lago":
             for callback in _in_memory_loggers:
                 if isinstance(callback, LagoLogger):
                     return callback  # type: ignore
@@ -3145,11 +3139,10 @@ def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool:
     metadata: dict = litellm_params.get("metadata", {}) or {}
     model_info: dict = metadata.get("model_info", {}) or {}
 
-    custom_pricing_keys = CustomPricingLiteLLMParams.model_fields.keys()
-    for key in custom_pricing_keys:
-        if litellm_params.get(key, None) is not None:
+    for _custom_cost_param in SPECIAL_MODEL_INFO_PARAMS:
+        if litellm_params.get(_custom_cost_param, None) is not None:
             return True
-        elif model_info.get(key, None) is not None:
+        elif model_info.get(_custom_cost_param, None) is not None:
             return True
 
     return False
@@ -3497,28 +3490,6 @@ class StandardLoggingPayloadSetup:
         else:
             return end_time_float - start_time_float
 
-    @staticmethod
-    def _get_standard_logging_payload_trace_id(
-        logging_obj: Logging,
-        litellm_params: dict,
-    ) -> str:
-        """
-        Returns the `litellm_trace_id` for this request
-
-        This helps link sessions when multiple requests are made in a single session
-        """
-        dynamic_litellm_session_id = litellm_params.get("litellm_session_id")
-        dynamic_litellm_trace_id = litellm_params.get("litellm_trace_id")
-
-        # Note: we recommend using `litellm_session_id` for session tracking
-        # `litellm_trace_id` is an internal litellm param
-        if dynamic_litellm_session_id:
-            return str(dynamic_litellm_session_id)
-        elif dynamic_litellm_trace_id:
-            return str(dynamic_litellm_trace_id)
-        else:
-            return logging_obj.litellm_trace_id
-
 
 def get_standard_logging_object_payload(
     kwargs: Optional[dict],
@@ -3671,10 +3642,7 @@ def get_standard_logging_object_payload(
 
         payload: StandardLoggingPayload = StandardLoggingPayload(
             id=str(id),
-            trace_id=StandardLoggingPayloadSetup._get_standard_logging_payload_trace_id(
-                logging_obj=logging_obj,
-                litellm_params=litellm_params,
-            ),
+            trace_id=kwargs.get("litellm_trace_id"),  # type: ignore
             call_type=call_type or "",
             cache_hit=cache_hit,
             stream=stream,

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -265,10 +265,8 @@ def generic_cost_per_token(
     )
 
     ## CALCULATE OUTPUT COST
-    text_tokens = 0
+    text_tokens = usage.completion_tokens
     audio_tokens = 0
-    reasoning_tokens = 0
-    is_text_tokens_total = False
     if usage.completion_tokens_details is not None:
         audio_tokens = (
             cast(
@@ -282,20 +280,9 @@ def generic_cost_per_token(
                 Optional[int],
                 getattr(usage.completion_tokens_details, "text_tokens", None),
             )
-            or 0  # default to completion tokens, if this field is not set
-        )
-        reasoning_tokens = (
-            cast(
-                Optional[int],
-                getattr(usage.completion_tokens_details, "reasoning_tokens", 0),
-            )
-            or 0
+            or usage.completion_tokens  # default to completion tokens, if this field is not set
         )
 
-    if text_tokens == 0:
-        text_tokens = usage.completion_tokens
-    if text_tokens == usage.completion_tokens:
-        is_text_tokens_total = True
     ## TEXT COST
     completion_cost = float(text_tokens) * completion_base_cost
 
@@ -303,26 +290,12 @@ def generic_cost_per_token(
         "output_cost_per_audio_token"
     )
 
-    _output_cost_per_reasoning_token: Optional[float] = model_info.get(
-        "output_cost_per_reasoning_token"
-    )
-
     ## AUDIO COST
-    if not is_text_tokens_total and audio_tokens is not None and audio_tokens > 0:
-        _output_cost_per_audio_token = (
-            _output_cost_per_audio_token
-            if _output_cost_per_audio_token is not None
-            else completion_base_cost
-        )
+    if (
+        _output_cost_per_audio_token is not None
+        and audio_tokens is not None
+        and audio_tokens > 0
+    ):
         completion_cost += float(audio_tokens) * _output_cost_per_audio_token
 
-    ## REASONING COST
-    if not is_text_tokens_total and reasoning_tokens and reasoning_tokens > 0:
-        _output_cost_per_reasoning_token = (
-            _output_cost_per_reasoning_token
-            if _output_cost_per_reasoning_token is not None
-            else completion_base_cost
-        )
-        completion_cost += float(reasoning_tokens) * _output_cost_per_reasoning_token
-
     return prompt_cost, completion_cost

litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py

@@ -14,7 +14,6 @@ from litellm.types.llms.openai import ChatCompletionThinkingBlock
 from litellm.types.utils import (
     ChatCompletionDeltaToolCall,
     ChatCompletionMessageToolCall,
-    ChatCompletionRedactedThinkingBlock,
     Choices,
     Delta,
     EmbeddingResponse,
@@ -487,14 +486,7 @@ def convert_to_model_response_object(  # noqa: PLR0915
                     )
 
                     # Handle thinking models that display `thinking_blocks` within `content`
-                    thinking_blocks: Optional[
-                        List[
-                            Union[
-                                ChatCompletionThinkingBlock,
-                                ChatCompletionRedactedThinkingBlock,
-                            ]
-                        ]
-                    ] = None
+                    thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
                     if "thinking_blocks" in choice["message"]:
                         thinking_blocks = choice["message"]["thinking_blocks"]
                         provider_specific_fields["thinking_blocks"] = thinking_blocks

litellm/litellm_core_utils/model_param_helper.py

@@ -75,10 +75,6 @@ class ModelParamHelper:
         combined_kwargs = combined_kwargs.difference(exclude_kwargs)
         return combined_kwargs
 
-    @staticmethod
-    def get_litellm_provider_specific_params_for_chat_params() -> Set[str]:
-        return set(["thinking"])
-
     @staticmethod
     def _get_litellm_supported_chat_completion_kwargs() -> Set[str]:
         """
@@ -86,18 +82,11 @@ class ModelParamHelper:
 
         This follows the OpenAI API Spec
         """
-        non_streaming_params: Set[str] = set(
+        all_chat_completion_kwargs = set(
             getattr(CompletionCreateParamsNonStreaming, "__annotations__", {}).keys()
+        ).union(
+            set(getattr(CompletionCreateParamsStreaming, "__annotations__", {}).keys())
         )
-        streaming_params: Set[str] = set(
-            getattr(CompletionCreateParamsStreaming, "__annotations__", {}).keys()
-        )
-        litellm_provider_specific_params: Set[str] = (
-            ModelParamHelper.get_litellm_provider_specific_params_for_chat_params()
-        )
-        all_chat_completion_kwargs: Set[str] = non_streaming_params.union(
-            streaming_params
-        ).union(litellm_provider_specific_params)
         return all_chat_completion_kwargs
 
     @staticmethod

litellm/litellm_core_utils/prompt_templates/common_utils.py

@@ -6,7 +6,7 @@ import io
 import mimetypes
 import re
 from os import PathLike
-from typing import Any, Dict, List, Literal, Mapping, Optional, Union, cast
+from typing import Dict, List, Literal, Mapping, Optional, Union, cast
 
 from litellm.types.llms.openai import (
     AllMessageValues,
@@ -32,35 +32,6 @@ DEFAULT_ASSISTANT_CONTINUE_MESSAGE = ChatCompletionAssistantMessage(
 )
 
 
-def handle_any_messages_to_chat_completion_str_messages_conversion(
-    messages: Any,
-) -> List[Dict[str, str]]:
-    """
-    Handles any messages to chat completion str messages conversion
-
-    Relevant Issue: https://github.com/BerriAI/litellm/issues/9494
-    """
-    import json
-
-    if isinstance(messages, list):
-        try:
-            return cast(
-                List[Dict[str, str]],
-                handle_messages_with_content_list_to_str_conversion(messages),
-            )
-        except Exception:
-            return [{"input": json.dumps(message, default=str)} for message in messages]
-    elif isinstance(messages, dict):
-        try:
-            return [{"input": json.dumps(messages, default=str)}]
-        except Exception:
-            return [{"input": str(messages)}]
-    elif isinstance(messages, str):
-        return [{"input": messages}]
-    else:
-        return [{"input": str(messages)}]
-
-
 def handle_messages_with_content_list_to_str_conversion(
     messages: List[AllMessageValues],
 ) -> List[AllMessageValues]:
@@ -500,59 +471,3 @@ def unpack_defs(schema, defs):
                 unpack_defs(ref, defs)
                 value["items"] = ref
                 continue
-
-
-def _get_image_mime_type_from_url(url: str) -> Optional[str]:
-    """
-    Get mime type for common image URLs
-    See gemini mime types: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/image-understanding#image-requirements
-
-    Supported by Gemini:
-     application/pdf
-    audio/mpeg
-    audio/mp3
-    audio/wav
-    image/png
-    image/jpeg
-    image/webp
-    text/plain
-    video/mov
-    video/mpeg
-    video/mp4
-    video/mpg
-    video/avi
-    video/wmv
-    video/mpegps
-    video/flv
-    """
-    url = url.lower()
-
-    # Map file extensions to mime types
-    mime_types = {
-        # Images
-        (".jpg", ".jpeg"): "image/jpeg",
-        (".png",): "image/png",
-        (".webp",): "image/webp",
-        # Videos
-        (".mp4",): "video/mp4",
-        (".mov",): "video/mov",
-        (".mpeg", ".mpg"): "video/mpeg",
-        (".avi",): "video/avi",
-        (".wmv",): "video/wmv",
-        (".mpegps",): "video/mpegps",
-        (".flv",): "video/flv",
-        # Audio
-        (".mp3",): "audio/mp3",
-        (".wav",): "audio/wav",
-        (".mpeg",): "audio/mpeg",
-        # Documents
-        (".pdf",): "application/pdf",
-        (".txt",): "text/plain",
-    }
-
-    # Check each extension group against the URL
-    for extensions, mime_type in mime_types.items():
-        if any(url.endswith(ext) for ext in extensions):
-            return mime_type
-
-    return None

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -2258,14 +2258,6 @@ def _parse_content_type(content_type: str) -> str:
     return m.get_content_type()
 
 
-def _parse_mime_type(base64_data: str) -> Optional[str]:
-    mime_type_match = re.match(r"data:(.*?);base64", base64_data)
-    if mime_type_match:
-        return mime_type_match.group(1)
-    else:
-        return None
-
-
 class BedrockImageProcessor:
     """Handles both sync and async image processing for Bedrock conversations."""
 

litellm/litellm_core_utils/streaming_chunk_builder_utils.py

@@ -348,17 +348,11 @@ class ChunkProcessor:
                     and usage_chunk_dict["completion_tokens"] > 0
                 ):
                     completion_tokens = usage_chunk_dict["completion_tokens"]
-                if usage_chunk_dict["cache_creation_input_tokens"] is not None and (
-                    usage_chunk_dict["cache_creation_input_tokens"] > 0
-                    or cache_creation_input_tokens is None
-                ):
+                if usage_chunk_dict["cache_creation_input_tokens"] is not None:
                     cache_creation_input_tokens = usage_chunk_dict[
                         "cache_creation_input_tokens"
                     ]
-                if usage_chunk_dict["cache_read_input_tokens"] is not None and (
-                    usage_chunk_dict["cache_read_input_tokens"] > 0
-                    or cache_read_input_tokens is None
-                ):
+                if usage_chunk_dict["cache_read_input_tokens"] is not None:
                     cache_read_input_tokens = usage_chunk_dict[
                         "cache_read_input_tokens"
                     ]

litellm/llms/anthropic/chat/handler.py

@@ -29,7 +29,6 @@ from litellm.types.llms.anthropic import (
     UsageDelta,
 )
 from litellm.types.llms.openai import (
-    ChatCompletionRedactedThinkingBlock,
     ChatCompletionThinkingBlock,
     ChatCompletionToolCallChunk,
 )
@@ -502,19 +501,18 @@ class ModelResponseIterator:
     ) -> Tuple[
         str,
         Optional[ChatCompletionToolCallChunk],
-        List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]],
+        List[ChatCompletionThinkingBlock],
         Dict[str, Any],
     ]:
         """
         Helper function to handle the content block delta
         """
+
         text = ""
         tool_use: Optional[ChatCompletionToolCallChunk] = None
         provider_specific_fields = {}
         content_block = ContentBlockDelta(**chunk)  # type: ignore
-        thinking_blocks: List[
-            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
-        ] = []
+        thinking_blocks: List[ChatCompletionThinkingBlock] = []
 
         self.content_blocks.append(content_block)
         if "text" in content_block["delta"]:
@@ -543,25 +541,20 @@ class ModelResponseIterator:
                 )
             ]
             provider_specific_fields["thinking_blocks"] = thinking_blocks
-
         return text, tool_use, thinking_blocks, provider_specific_fields
 
     def _handle_reasoning_content(
-        self,
-        thinking_blocks: List[
-            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
-        ],
+        self, thinking_blocks: List[ChatCompletionThinkingBlock]
     ) -> Optional[str]:
         """
         Handle the reasoning content
         """
         reasoning_content = None
         for block in thinking_blocks:
-            thinking_content = cast(Optional[str], block.get("thinking"))
             if reasoning_content is None:
                 reasoning_content = ""
-            if thinking_content is not None:
-                reasoning_content += thinking_content
+            if "thinking" in block:
+                reasoning_content += block["thinking"]
         return reasoning_content
 
     def chunk_parser(self, chunk: dict) -> ModelResponseStream:
@@ -574,13 +567,7 @@ class ModelResponseIterator:
             usage: Optional[Usage] = None
             provider_specific_fields: Dict[str, Any] = {}
             reasoning_content: Optional[str] = None
-            thinking_blocks: Optional[
-                List[
-                    Union[
-                        ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock
-                    ]
-                ]
-            ] = None
+            thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
 
             index = int(chunk.get("index", 0))
             if type_chunk == "content_block_delta":
@@ -618,15 +605,6 @@ class ModelResponseIterator:
                         },
                         "index": self.tool_index,
                     }
-                elif (
-                    content_block_start["content_block"]["type"] == "redacted_thinking"
-                ):
-                    thinking_blocks = [
-                        ChatCompletionRedactedThinkingBlock(
-                            type="redacted_thinking",
-                            data=content_block_start["content_block"]["data"],
-                        )
-                    ]
             elif type_chunk == "content_block_stop":
                 ContentBlockStop(**chunk)  # type: ignore
                 # check if tool call content block

litellm/llms/anthropic/chat/transformation.py

@@ -7,9 +7,6 @@ import httpx
 import litellm
 from litellm.constants import (
     DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS,
-    DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
-    DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
-    DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
     RESPONSE_FORMAT_TOOL_NAME,
 )
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
@@ -30,7 +27,6 @@ from litellm.types.llms.openai import (
     REASONING_EFFORT,
     AllMessageValues,
     ChatCompletionCachedContent,
-    ChatCompletionRedactedThinkingBlock,
     ChatCompletionSystemMessage,
     ChatCompletionThinkingBlock,
     ChatCompletionToolCallChunk,
@@ -280,20 +276,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         if reasoning_effort is None:
             return None
         elif reasoning_effort == "low":
-            return AnthropicThinkingParam(
-                type="enabled",
-                budget_tokens=DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
-            )
+            return AnthropicThinkingParam(type="enabled", budget_tokens=1024)
         elif reasoning_effort == "medium":
-            return AnthropicThinkingParam(
-                type="enabled",
-                budget_tokens=DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
-            )
+            return AnthropicThinkingParam(type="enabled", budget_tokens=2048)
         elif reasoning_effort == "high":
-            return AnthropicThinkingParam(
-                type="enabled",
-                budget_tokens=DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
-            )
+            return AnthropicThinkingParam(type="enabled", budget_tokens=4096)
         else:
             raise ValueError(f"Unmapped reasoning effort: {reasoning_effort}")
 
@@ -576,21 +563,13 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
     ) -> Tuple[
         str,
         Optional[List[Any]],
-        Optional[
-            List[
-                Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
-            ]
-        ],
+        Optional[List[ChatCompletionThinkingBlock]],
         Optional[str],
         List[ChatCompletionToolCallChunk],
     ]:
         text_content = ""
         citations: Optional[List[Any]] = None
-        thinking_blocks: Optional[
-            List[
-                Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
-            ]
-        ] = None
+        thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
         reasoning_content: Optional[str] = None
         tool_calls: List[ChatCompletionToolCallChunk] = []
         for idx, content in enumerate(completion_response["content"]):
@@ -609,30 +588,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                         index=idx,
                     )
                 )
-
-            elif content.get("thinking", None) is not None:
-                if thinking_blocks is None:
-                    thinking_blocks = []
-                thinking_blocks.append(cast(ChatCompletionThinkingBlock, content))
-            elif content["type"] == "redacted_thinking":
-                if thinking_blocks is None:
-                    thinking_blocks = []
-                thinking_blocks.append(
-                    cast(ChatCompletionRedactedThinkingBlock, content)
-                )
-
             ## CITATIONS
-            if content.get("citations") is not None:
+            if content.get("citations", None) is not None:
                 if citations is None:
                     citations = []
                 citations.append(content["citations"])
+            if content.get("thinking", None) is not None:
+                if thinking_blocks is None:
+                    thinking_blocks = []
+                thinking_blocks.append(cast(ChatCompletionThinkingBlock, content))
         if thinking_blocks is not None:
             reasoning_content = ""
             for block in thinking_blocks:
-                thinking_content = cast(Optional[str], block.get("thinking"))
-                if thinking_content is not None:
-                    reasoning_content += thinking_content
-
+                if "thinking" in block:
+                    reasoning_content += block["thinking"]
         return text_content, citations, thinking_blocks, reasoning_content, tool_calls
 
     def calculate_usage(
@@ -722,13 +691,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         else:
             text_content = ""
             citations: Optional[List[Any]] = None
-            thinking_blocks: Optional[
-                List[
-                    Union[
-                        ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock
-                    ]
-                ]
-            ] = None
+            thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
             reasoning_content: Optional[str] = None
             tool_calls: List[ChatCompletionToolCallChunk] = []