Fix UI Flicker in Dashboard (#10261)

* fix(create_user_button.tsx): do not set 'no-default-models' when user is a proxy admin

* fix(user_info_view.tsx): show all user personal models

* feat(user_info_view.tsx): allow giving users more personal models

* feat(user_edit_view.tsx): allow proxy admin to edit user role, available models, etc.

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

@@ -16,6 +16,7 @@ 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) }}"

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

@@ -3,7 +3,7 @@ import TabItem from '@theme/TabItem';
 
 # Using Audio Models
 
-How to send / receieve audio to a `/chat/completions` endpoint
+How to send / receive 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 / receieve pdf's (other document types) to a `/chat/completions` endpoint
+How to send / receive 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 explicity 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 explicitly via the `format` param. 
 
 ```python
 "image_url": {

docs/my-website/docs/image_generation.md

@@ -20,9 +20,9 @@ print(f"response: {response}")
 
 ```yaml
 model_list:
-  - model_name: dall-e-2 ### RECEIVED MODEL NAME ###
+  - model_name: gpt-image-1 ### RECEIVED MODEL NAME ###
     litellm_params: # all params accepted by litellm.image_generation()
-      model: azure/dall-e-2 ### MODEL NAME sent to `litellm.image_generation()` ###
+      model: azure/gpt-image-1 ### 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)
@@ -47,7 +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": "dall-e-2",
+    "model": "gpt-image-1",
     "prompt": "A cute baby sea otter",
     "n": 1,
     "size": "1024x1024"
@@ -104,7 +104,7 @@ 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/dall-e-2
+- `model`: *string (optional)* The model to use for image generation. Defaults to openai/gpt-image-1
 
 - `n`: *int (optional)* The number of images to generate. Must be between 1 and 10. For dall-e-3, only n=1 is supported.
 
@@ -112,7 +112,7 @@ Any non-openai params, will be treated as provider-specific params, and sent in
 
 - `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 256x256, 512x512, or 1024x1024 for dall-e-2. Must be one of 1024x1024, 1792x1024, or 1024x1792 for dall-e-3 models.
+- `size`: *string (optional)* The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024 for gpt-image-1. 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).
 
@@ -148,13 +148,14 @@ 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='dall-e-2', prompt="cute baby otter")
+response = image_generation(model='gpt-image-1', prompt="cute baby otter")
 ```
 
 | Model Name           | Function Call                               | Required OS Variables                |
 |----------------------|---------------------------------------------|--------------------------------------|
-| dall-e-2 | `image_generation(model='dall-e-2', prompt="cute baby otter")` | `os.environ['OPENAI_API_KEY']`       |
+| 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']`       |
 
 ## Azure OpenAI Image Generation Models
 
@@ -182,8 +183,9 @@ print(response)
 
 | Model Name           | Function Call                               |
 |----------------------|---------------------------------------------|
-| dall-e-2 | `image_generation(model="azure/<your deployment name>", prompt="cute baby otter")` |
+| 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")` |
 
 
 ## OpenAI Compatible Image Generation Models

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

@@ -0,0 +1,83 @@
+# 🖇️ 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, enviornment, 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, environment, 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, defeaults to `None`
+* `tags`           - Tags for the trace, defaults to `None`
 
 ##### Updatable Parameters on Continuation
 

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

@@ -222,7 +222,7 @@ 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 Credetials client side to proxy server
+1. Pass Vertex Credentials client side to proxy server
 
 2. Set Vertex AI credentials on proxy server
 

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

@@ -1095,7 +1095,7 @@ response = completion(
 print(response.choices[0])
 ```
 </TabItem>
-<TabItem value="proxy" lable="PROXY">
+<TabItem value="proxy" label="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 instructons: https://learn.microsoft.com/en-us/cli/azure/install-azure-cli
+Installation instructions: https://learn.microsoft.com/en-us/cli/azure/install-azure-cli
 ```shell
 brew update && brew install azure-cli
 ```

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

@@ -655,7 +655,7 @@ import os
 
 os.environ["GEMINI_API_KEY"] = ".."
 
-tools = [{"googleSearchRetrieval": {}}] # 👈 ADD GOOGLE SEARCH
+tools = [{"googleSearch": {}}] # 👈 ADD GOOGLE SEARCH
 
 response = completion(
     model="gemini/gemini-2.0-flash",
@@ -691,7 +691,7 @@ 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": [{"googleSearchRetrieval": {}}]
+  "tools": [{"googleSearch": {}}]
 }
 '
 ```

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 = [{"googleSearchRetrieval": {}}] # 👈 ADD GOOGLE SEARCH
+tools = [{"googleSearch": {}}] # 👈 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=[{"googleSearchRetrieval": {}}],
+    tools=[{"googleSearch": {}}],
 )
 
 print(response)
@@ -410,7 +410,7 @@ curl http://localhost:4000/v1/chat/completions \
     ],
    "tools": [
         {
-            "googleSearchRetrieval": {} 
+            "googleSearch": {} 
         }
     ]
   }'
@@ -529,7 +529,7 @@ from litellm import completion
 
 # !gcloud auth application-default login - run this to add vertex credentials to your env
 
-tools = [{"googleSearchRetrieval": {"disable_attributon": False}}] # 👈 ADD GOOGLE SEARCH
+tools = [{"googleSearch": {"disable_attributon": False}}] # 👈 ADD GOOGLE SEARCH
 
 resp = litellm.completion(
                     model="vertex_ai/gemini-1.0-pro-001",
@@ -692,7 +692,7 @@ curl http://0.0.0.0:4000/v1/chat/completions \
 
 ### **Context Caching**
 
-Use Vertex AI context caching is supported by calling provider api directly. (Unified Endpoint support comin soon.).
+Use Vertex AI context caching is supported by calling provider api directly. (Unified Endpoint support coming soon.).
 
 [**Go straight to provider**](../pass_through/vertex_ai.md#context-caching)
 
@@ -910,7 +910,7 @@ 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 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:
+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:
 
 ### Set per model/request
 
@@ -2050,7 +2050,7 @@ response = completion(
 print(response.choices[0])
 ```
 </TabItem>
-<TabItem value="proxy" lable="PROXY">
+<TabItem value="proxy" label="PROXY">
 
 1. Add model to config 
 

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
 
-Exaple setting Hosted image
+Example setting Hosted image
 ```shell
 UI_LOGO_PATH="https://litellm-logo-aws-marketplace.s3.us-west-2.amazonaws.com/berriai-logo-github.png"
 ```
 
-Exaple setting a local image (on your container)
+Example 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 artifically low threshold for testing alerting
+  alerting_threshold: 0.0001 # (Seconds) set an artificially 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 artifically low threshold for testing alerting
+  alerting_threshold: 0.0001 # (Seconds) set an artificially 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 whenver spend is tracked for a customer id. 
+    * "spend_tracked": Emitted whenever 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 specfic LLM region is facing an outage. Example us-east-1 | ✅ |
+| `region_outage_alerts` | Alerts when a specific LLM region is facing an outage. Example us-east-1 | ✅ |
 
 Budget and Spend Alerts
 

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

@@ -299,6 +299,9 @@ 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

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.envrion/AZURE_API_VERSION
+      api_version: os.environ/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

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 accumlate the spend updates for a key, user, team, etc and write the updates to a redis queue. 
+Each instance will accumulate 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 recommned - https://1password.com/password-generator/ 
+# We recommend - 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 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.
+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.
 
 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 augument. 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 argument. 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`">
 
 
-**Successfull Request**
+**Successful 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`">
 
 
-**Successfull Request**
+**Successful 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 incude `x-litellm-applied-guardrails` with the guardrail applied 
+Your response headers will include `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 successfull 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 successful LLM calls to langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your environment
 
 **Step 1** Install langfuse
 
@@ -535,11 +535,11 @@ 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 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`        |
+|---------------------------|-----------------------------------------------------------------------------------------|------------------------------------------------|
+| `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`                       |
@@ -1190,7 +1190,7 @@ We will use the `--config` to set
 
 - `litellm.success_callback = ["s3"]` 
 
-This will log all successfull LLM calls to s3 Bucket
+This will log all successful 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 Storag (Bucket) |
+| Description | Log LLM Input/Output to Azure Blob Storage (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 successfull LLM calls to DataDog
+We will use the `--config` to set `litellm.callbacks = ["datadog"]` this will log all successful 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
 
-            # Acess Exceptions & Traceback
+            # Access 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 successfull LLM calls to DynamoDB
+This will log all successful 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 successfull LLM calls to athina
+We will use the `--config` to set `litellm.success_callback = ["athina"]` this will log all successful LLM calls to athina
 
 **Step 1** Set Athina API key
 

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 usig redis port, host, and password params. 
+If you decide to use Redis, DO NOT use 'redis_url'. We recommend using 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 recommned - https://1password.com/password-generator/ password generator to get a random hash for litellm salt key.
+We recommend - 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.
 
 
-| Heirarchy | Supported | 
+| Hierarchy | 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 re-use them when adding new models
+You can add LLM provider credentials on the UI. Once you add credentials you can reuse them when adding new models
 
 ## Add a credential + model
 

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 intializes the DB connection)
+(the proxy Dockerfile checks if the `DATABASE_URL` is set and then initializes 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 successfull response from the litellm proxy since the key passed in `X-Litellm-Key` is valid
+Expect to see a successful 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/response_api.md

@@ -631,10 +631,3 @@ follow_up = client.responses.create(
 
 </TabItem>
 </Tabs>
-
-#### How It Works
-
-1. When a user makes an initial request to the Responses API, LiteLLM caches which model deployment that returned the specific response. (Stored in Redis if you connected LiteLLM to Redis)
-2. When a subsequent request includes `previous_response_id`, LiteLLM automatically routes it to the same deployment
-3. If the original deployment is unavailable, or if the `previous_response_id` isn't found in the cache, LiteLLM falls back to normal routing
-

docs/my-website/docs/simple_proxy_old_doc.md

@@ -994,16 +994,16 @@ litellm --health
 
 ## Logging Proxy Input/Output - OpenTelemetry
 
-### Step 1 Start OpenTelemetry Collecter Docker Container
+### Step 1 Start OpenTelemetry Collector Docker Container
 This container sends logs to your selected destination 
 
-#### Install OpenTelemetry Collecter Docker Image
+#### Install OpenTelemetry Collector 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 Collecter
+#### Set Destination paths on OpenTelemetry Collector
 
 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'] # defauls to 127.0.0.1:4317 if not provided
+os.environ['OTEL_ENDPOINT'] # defaults 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 Collecter
-On successfull logging you should be able to see this log on your `OpenTelemetry Collecter` Docker Container
+#### Test & View Logs on OpenTelemetry Collector
+On successful logging you should be able to see this log on your `OpenTelemetry Collector` 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 successfull LLM calls to langfuse
+We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successful LLM calls to langfuse
 
 **Step 1** Install langfuse
 

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

@@ -117,7 +117,7 @@ response = completion("command-nightly", messages)
 """
 
 
-# qustions/logs you want to run the LLM on
+# questions/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 occured please 'Clear' the error and try your question again"
+        yield f"An Error occurred please 'Clear' the error and try your question again"
 ```
 
 ### Define Chat Interface

docs/my-website/sidebars.js

@@ -411,6 +411,7 @@ const sidebars = {
       type: "category",
       label: "Logging & Observability",
       items: [
+        "observability/agentops_integration",
         "observability/langfuse_integration",
         "observability/lunary_integration",
         "observability/mlflow",

litellm/__init__.py

@@ -113,6 +113,7 @@ _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

litellm/cost_calculator.py

@@ -57,6 +57,7 @@ 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,
@@ -642,9 +643,9 @@ def completion_cost(  # noqa: PLR0915
                     or isinstance(completion_response, dict)
                 ):  # tts returns a custom class
                     if isinstance(completion_response, dict):
-                        usage_obj: Optional[
-                            Union[dict, Usage]
-                        ] = completion_response.get("usage", {})
+                        usage_obj: Optional[Union[dict, Usage]] = (
+                            completion_response.get("usage", {})
+                        )
                     else:
                         usage_obj = getattr(completion_response, "usage", {})
                     if isinstance(usage_obj, BaseModel) and not _is_known_usage_objects(
@@ -913,7 +914,7 @@ 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()
@@ -1101,29 +1102,36 @@ 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}"
     )
 
-    # Try model with quality first, fall back to base model name
-    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:
+    # 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,
+    ]
+    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 {model_name_with_quality}, {base_model_name}, {model_with_quality_without_provider}, and {model_without_provider}"
+            f"Model not found in cost map. Tried checking {models_to_check}"
         )
 
     return cost_info["input_cost_per_pixel"] * height * width * n

litellm/integrations/_types/open_inference.py

@@ -45,6 +45,14 @@ 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.
@@ -65,15 +73,40 @@ 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.
@@ -112,6 +145,19 @@ 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:
     """
@@ -151,6 +197,10 @@ 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:
@@ -186,6 +236,25 @@ 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.
@@ -257,6 +326,10 @@ 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.
@@ -268,6 +341,18 @@ 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"
@@ -284,3 +369,21 @@ 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

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

litellm/integrations/agentops/agentops.py

@@ -0,0 +1,118 @@
+"""
+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

@@ -1,3 +1,4 @@
+import json
 from typing import TYPE_CHECKING, Any, Optional, Union
 
 from litellm._logging import verbose_logger
@@ -12,36 +13,141 @@ else:
     Span = Any
 
 
-def set_attributes(span: Span, kwargs, response_obj):
+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.
+    """
     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 ##############
         #############################################
 
-        if standard_logging_payload and (
-            metadata := standard_logging_payload["metadata"]
-        ):
-            span.set_attribute(SpanAttributes.METADATA, safe_dumps(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))
 
         #############################################
         ########## 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"):
-            span.set_attribute(SpanAttributes.LLM_MODEL_NAME, kwargs.get("model"))
+            safe_set_attribute(
+                span,
+                SpanAttributes.LLM_MODEL_NAME,
+                kwargs.get("model"),
+            )
 
-        span.set_attribute(
+        # 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,
             SpanAttributes.OPENINFERENCE_SPAN_KIND,
             OpenInferenceSpanKindValues.LLM.value,
         )
@@ -50,77 +156,132 @@ def set_attributes(span: Span, kwargs, response_obj):
         # for /chat/completions
         # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
         if messages:
-            span.set_attribute(
+            last_message = messages[-1]
+            safe_set_attribute(
+                span,
                 SpanAttributes.INPUT_VALUE,
-                messages[-1].get("content", ""),  # get the last message for input
+                last_message.get("content", ""),
             )
 
-            # 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):
-                # Set the role per message
-                span.set_attribute(
-                    f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_ROLE}",
-                    msg["role"],
+                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 content per message
-                span.set_attribute(
-                    f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_CONTENT}",
+                # Set the content per message.
+                safe_set_attribute(
+                    span,
+                    f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
                     msg.get("content", ""),
                 )
 
-        if standard_logging_payload and (
-            model_params := standard_logging_payload["model_parameters"]
-        ):
+        # 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:
             # The Generative AI Provider: Azure, OpenAI, etc.
-            span.set_attribute(
-                SpanAttributes.LLM_INVOCATION_PARAMETERS, safe_dumps(model_params)
+            safe_set_attribute(
+                span,
+                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:
-                    span.set_attribute(SpanAttributes.USER_ID, user_id)
+                    safe_set_attribute(span, SpanAttributes.USER_ID, user_id)
 
         #############################################
         ########## LLM Response Attributes ##########
-        # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
         #############################################
-        if hasattr(response_obj, "get"):
-            for choice in response_obj.get("choices", []):
-                response_message = choice.get("message", {})
-                span.set_attribute(
-                    SpanAttributes.OUTPUT_VALUE, response_message.get("content", "")
-                )
 
-                # 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"),
-                )
-                span.set_attribute(
-                    f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
+        # Captures response tokens, message, and content.
+        if hasattr(response_obj, "get"):
+            for idx, choice in enumerate(response_obj.get("choices", [])):
+                response_message = choice.get("message", {})
+                safe_set_attribute(
+                    span,
+                    SpanAttributes.OUTPUT_VALUE,
                     response_message.get("content", ""),
                 )
 
-            usage = response_obj.get("usage")
+                # 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}",
+                    response_message.get("role"),
+                )
+                safe_set_attribute(
+                    span,
+                    f"{prefix}.{MessageAttributes.MESSAGE_CONTENT}",
+                    response_message.get("content", ""),
+                )
+
+            # Token usage info.
+            usage = response_obj and response_obj.get("usage")
             if usage:
-                span.set_attribute(
+                safe_set_attribute(
+                    span,
                     SpanAttributes.LLM_TOKEN_COUNT_TOTAL,
                     usage.get("total_tokens"),
                 )
 
                 # The number of tokens used in the LLM response (completion).
-                span.set_attribute(
+                safe_set_attribute(
+                    span,
                     SpanAttributes.LLM_TOKEN_COUNT_COMPLETION,
                     usage.get("completion_tokens"),
                 )
 
                 # The number of tokens used in the LLM prompt.
-                span.set_attribute(
+                safe_set_attribute(
+                    span,
                     SpanAttributes.LLM_TOKEN_COUNT_PROMPT,
                     usage.get("prompt_tokens"),
                 )
-        pass
+
     except Exception as e:
-        verbose_logger.error(f"Error setting arize attributes: {e}")
+        verbose_logger.error(
+            f"[Arize/Phoenix] Failed to set OpenInference span attributes: {e}"
+        )
+        if hasattr(span, "record_exception"):
+            span.record_exception(e)

litellm/integrations/datadog/datadog_llm_obs.py

@@ -13,10 +13,15 @@ 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,
@@ -106,7 +111,6 @@ 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}"
@@ -116,6 +120,10 @@ 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)}")
 
@@ -133,7 +141,11 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
 
         metadata = kwargs.get("litellm_params", {}).get("metadata", {})
 
-        input_meta = InputMeta(messages=messages)  # type: ignore
+        input_meta = InputMeta(
+            messages=handle_any_messages_to_chat_completion_str_messages_conversion(
+                messages
+            )
+        )
         output_meta = OutputMeta(messages=self._get_response_messages(response_obj))
 
         meta = Meta(

litellm/litellm_core_utils/exception_mapping_utils.py

@@ -311,6 +311,9 @@ 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/litellm_logging.py

@@ -28,6 +28,7 @@ from litellm._logging import _is_debugging_on, verbose_logger
 from litellm.batches.batch_utils import _handle_completed_batch
 from litellm.caching.caching import DualCache, InMemoryCache
 from litellm.caching.caching_handler import LLMCachingHandler
+
 from litellm.constants import (
     DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT,
     DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT,
@@ -36,6 +37,7 @@ 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
@@ -2685,7 +2687,15 @@ def _init_custom_logger_compatible_class(  # noqa: PLR0915
     """
     try:
         custom_logger_init_args = custom_logger_init_args or {}
-        if logging_integration == "lago":
+        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":
             for callback in _in_memory_loggers:
                 if isinstance(callback, LagoLogger):
                     return callback  # type: ignore

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 Dict, List, Literal, Mapping, Optional, Union, cast
+from typing import Any, Dict, List, Literal, Mapping, Optional, Union, cast
 
 from litellm.types.llms.openai import (
     AllMessageValues,
@@ -32,6 +32,35 @@ 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]:

litellm/llms/azure/responses/transformation.py

@@ -1,11 +1,14 @@
-from typing import TYPE_CHECKING, Any, Optional, cast
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, cast
 
 import httpx
 
 import litellm
+from litellm._logging import verbose_logger
 from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import *
+from litellm.types.responses.main import *
+from litellm.types.router import GenericLiteLLMParams
 from litellm.utils import _add_path_to_api_base
 
 if TYPE_CHECKING:
@@ -41,11 +44,7 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
     def get_complete_url(
         self,
         api_base: Optional[str],
-        api_key: Optional[str],
-        model: str,
-        optional_params: dict,
         litellm_params: dict,
-        stream: Optional[bool] = None,
     ) -> str:
         """
         Constructs a complete URL for the API request.
@@ -92,3 +91,82 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
         final_url = httpx.URL(new_url).copy_with(params=query_params)
 
         return str(final_url)
+
+    #########################################################
+    ########## DELETE RESPONSE API TRANSFORMATION ##############
+    #########################################################
+    def _construct_url_for_response_id_in_path(
+        self, api_base: str, response_id: str
+    ) -> str:
+        """
+        Constructs a URL for the API request with the response_id in the path.
+        """
+        from urllib.parse import urlparse, urlunparse
+
+        # Parse the URL to separate its components
+        parsed_url = urlparse(api_base)
+
+        # Insert the response_id at the end of the path component
+        # Remove trailing slash if present to avoid double slashes
+        path = parsed_url.path.rstrip("/")
+        new_path = f"{path}/{response_id}"
+
+        # Reconstruct the URL with all original components but with the modified path
+        constructed_url = urlunparse(
+            (
+                parsed_url.scheme,  # http, https
+                parsed_url.netloc,  # domain name, port
+                new_path,  # path with response_id added
+                parsed_url.params,  # parameters
+                parsed_url.query,  # query string
+                parsed_url.fragment,  # fragment
+            )
+        )
+        return constructed_url
+
+    def transform_delete_response_api_request(
+        self,
+        response_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the delete response API request into a URL and data
+
+        Azure OpenAI API expects the following request:
+        - DELETE /openai/responses/{response_id}?api-version=xxx
+
+        This function handles URLs with query parameters by inserting the response_id
+        at the correct location (before any query parameters).
+        """
+        delete_url = self._construct_url_for_response_id_in_path(
+            api_base=api_base, response_id=response_id
+        )
+
+        data: Dict = {}
+        verbose_logger.debug(f"delete response url={delete_url}")
+        return delete_url, data
+
+    #########################################################
+    ########## GET RESPONSE API TRANSFORMATION ###############
+    #########################################################
+    def transform_get_response_api_request(
+        self,
+        response_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the get response API request into a URL and data
+
+        OpenAI API expects the following request
+        - GET /v1/responses/{response_id}
+        """
+        get_url = self._construct_url_for_response_id_in_path(
+            api_base=api_base, response_id=response_id
+        )
+        data: Dict = {}
+        verbose_logger.debug(f"get response url={get_url}")
+        return get_url, data

litellm/llms/base_llm/responses/transformation.py

@@ -1,6 +1,6 @@
 import types
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
 
 import httpx
 
@@ -10,6 +10,7 @@ from litellm.types.llms.openai import (
     ResponsesAPIResponse,
     ResponsesAPIStreamingResponse,
 )
+from litellm.types.responses.main import *
 from litellm.types.router import GenericLiteLLMParams
 
 if TYPE_CHECKING:
@@ -73,11 +74,7 @@ class BaseResponsesAPIConfig(ABC):
     def get_complete_url(
         self,
         api_base: Optional[str],
-        api_key: Optional[str],
-        model: str,
-        optional_params: dict,
         litellm_params: dict,
-        stream: Optional[bool] = None,
     ) -> str:
         """
         OPTIONAL
@@ -122,6 +119,56 @@ class BaseResponsesAPIConfig(ABC):
         """
         pass
 
+    #########################################################
+    ########## DELETE RESPONSE API TRANSFORMATION ##############
+    #########################################################
+    @abstractmethod
+    def transform_delete_response_api_request(
+        self,
+        response_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        pass
+
+    @abstractmethod
+    def transform_delete_response_api_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> DeleteResponseResult:
+        pass
+
+    #########################################################
+    ########## END DELETE RESPONSE API TRANSFORMATION #######
+    #########################################################
+
+    #########################################################
+    ########## GET RESPONSE API TRANSFORMATION ###############
+    #########################################################
+    @abstractmethod
+    def transform_get_response_api_request(
+        self,
+        response_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        pass
+    
+    @abstractmethod
+    def transform_get_response_api_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> ResponsesAPIResponse:
+        pass
+
+    #########################################################
+    ########## END GET RESPONSE API TRANSFORMATION ##########
+    #########################################################
+    
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
     ) -> BaseLLMException:

litellm/llms/bedrock/chat/converse_transformation.py

@@ -107,6 +107,15 @@ class AmazonConverseConfig(BaseConfig):
             "response_format",
         ]
 
+        if (
+            "arn" in model
+        ):  # we can't infer the model from the arn, so just add all params
+            supported_params.append("tools")
+            supported_params.append("tool_choice")
+            supported_params.append("thinking")
+            supported_params.append("reasoning_effort")
+            return supported_params
+
         ## Filter out 'cross-region' from model name
         base_model = BedrockModelInfo.get_base_model(model)
 

litellm/llms/custom_httpx/http_handler.py

@@ -650,6 +650,49 @@ class HTTPHandler:
         except Exception as e:
             raise e
 
+    def delete(
+        self,
+        url: str,
+        data: Optional[Union[dict, str]] = None,  # type: ignore
+        json: Optional[dict] = None,
+        params: Optional[dict] = None,
+        headers: Optional[dict] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        stream: bool = False,
+    ):
+        try:
+            if timeout is not None:
+                req = self.client.build_request(
+                    "DELETE", url, data=data, json=json, params=params, headers=headers, timeout=timeout  # type: ignore
+                )
+            else:
+                req = self.client.build_request(
+                    "DELETE", url, data=data, json=json, params=params, headers=headers  # type: ignore
+                )
+            response = self.client.send(req, stream=stream)
+            response.raise_for_status()
+            return response
+        except httpx.TimeoutException:
+            raise litellm.Timeout(
+                message=f"Connection timed out after {timeout} seconds.",
+                model="default-model-name",
+                llm_provider="litellm-httpx-handler",
+            )
+        except httpx.HTTPStatusError as e:
+            if stream is True:
+                setattr(e, "message", mask_sensitive_info(e.response.read()))
+                setattr(e, "text", mask_sensitive_info(e.response.read()))
+            else:
+                error_text = mask_sensitive_info(e.response.text)
+                setattr(e, "message", error_text)
+                setattr(e, "text", error_text)
+
+            setattr(e, "status_code", e.response.status_code)
+
+            raise e
+        except Exception as e:
+            raise e
+
     def __del__(self) -> None:
         try:
             self.close()

litellm/llms/custom_httpx/llm_http_handler.py

@@ -36,6 +36,7 @@ from litellm.types.llms.openai import (
     ResponsesAPIResponse,
 )
 from litellm.types.rerank import OptionalRerankParams, RerankResponse
+from litellm.types.responses.main import DeleteResponseResult
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.utils import EmbeddingResponse, FileTypes, TranscriptionResponse
 from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager
@@ -1015,6 +1016,7 @@ class BaseLLMHTTPHandler:
         client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
         _is_async: bool = False,
         fake_stream: bool = False,
+        litellm_metadata: Optional[Dict[str, Any]] = None,
     ) -> Union[
         ResponsesAPIResponse,
         BaseResponsesAPIStreamingIterator,
@@ -1041,6 +1043,7 @@ class BaseLLMHTTPHandler:
                 timeout=timeout,
                 client=client if isinstance(client, AsyncHTTPHandler) else None,
                 fake_stream=fake_stream,
+                litellm_metadata=litellm_metadata,
             )
 
         if client is None or not isinstance(client, HTTPHandler):
@@ -1064,11 +1067,7 @@ class BaseLLMHTTPHandler:
 
         api_base = responses_api_provider_config.get_complete_url(
             api_base=litellm_params.api_base,
-            api_key=litellm_params.api_key,
-            model=model,
-            optional_params=response_api_optional_request_params,
             litellm_params=dict(litellm_params),
-            stream=stream,
         )
 
         data = responses_api_provider_config.transform_responses_api_request(
@@ -1113,6 +1112,8 @@ class BaseLLMHTTPHandler:
                         model=model,
                         logging_obj=logging_obj,
                         responses_api_provider_config=responses_api_provider_config,
+                        litellm_metadata=litellm_metadata,
+                        custom_llm_provider=custom_llm_provider,
                     )
 
                 return SyncResponsesAPIStreamingIterator(
@@ -1120,6 +1121,8 @@ class BaseLLMHTTPHandler:
                     model=model,
                     logging_obj=logging_obj,
                     responses_api_provider_config=responses_api_provider_config,
+                    litellm_metadata=litellm_metadata,
+                    custom_llm_provider=custom_llm_provider,
                 )
             else:
                 # For non-streaming requests
@@ -1156,6 +1159,7 @@ class BaseLLMHTTPHandler:
         timeout: Optional[Union[float, httpx.Timeout]] = None,
         client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
         fake_stream: bool = False,
+        litellm_metadata: Optional[Dict[str, Any]] = None,
     ) -> Union[ResponsesAPIResponse, BaseResponsesAPIStreamingIterator]:
         """
         Async version of the responses API handler.
@@ -1183,11 +1187,7 @@ class BaseLLMHTTPHandler:
 
         api_base = responses_api_provider_config.get_complete_url(
             api_base=litellm_params.api_base,
-            api_key=litellm_params.api_key,
-            model=model,
-            optional_params=response_api_optional_request_params,
             litellm_params=dict(litellm_params),
-            stream=stream,
         )
 
         data = responses_api_provider_config.transform_responses_api_request(
@@ -1234,6 +1234,8 @@ class BaseLLMHTTPHandler:
                         model=model,
                         logging_obj=logging_obj,
                         responses_api_provider_config=responses_api_provider_config,
+                        litellm_metadata=litellm_metadata,
+                        custom_llm_provider=custom_llm_provider,
                     )
 
                 # Return the streaming iterator
@@ -1242,6 +1244,8 @@ class BaseLLMHTTPHandler:
                     model=model,
                     logging_obj=logging_obj,
                     responses_api_provider_config=responses_api_provider_config,
+                    litellm_metadata=litellm_metadata,
+                    custom_llm_provider=custom_llm_provider,
                 )
             else:
                 # For non-streaming, proceed as before
@@ -1265,6 +1269,319 @@ class BaseLLMHTTPHandler:
             logging_obj=logging_obj,
         )
 
+    async def async_delete_response_api_handler(
+        self,
+        response_id: str,
+        responses_api_provider_config: BaseResponsesAPIConfig,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str],
+        extra_headers: Optional[Dict[str, Any]] = None,
+        extra_body: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        _is_async: bool = False,
+    ) -> DeleteResponseResult:
+        """
+        Async version of the delete response API handler.
+        Uses async HTTP client to make requests.
+        """
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            async_httpx_client = get_async_httpx_client(
+                llm_provider=litellm.LlmProviders(custom_llm_provider),
+                params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+            )
+        else:
+            async_httpx_client = client
+
+        headers = responses_api_provider_config.validate_environment(
+            api_key=litellm_params.api_key,
+            headers=extra_headers or {},
+            model="None",
+        )
+
+        if extra_headers:
+            headers.update(extra_headers)
+
+        api_base = responses_api_provider_config.get_complete_url(
+            api_base=litellm_params.api_base,
+            litellm_params=dict(litellm_params),
+        )
+
+        url, data = responses_api_provider_config.transform_delete_response_api_request(
+            response_id=response_id,
+            api_base=api_base,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=input,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": api_base,
+                "headers": headers,
+            },
+        )
+
+        try:
+            response = await async_httpx_client.delete(
+                url=url, headers=headers, data=json.dumps(data), timeout=timeout
+            )
+
+        except Exception as e:
+            raise self._handle_error(
+                e=e,
+                provider_config=responses_api_provider_config,
+            )
+
+        return responses_api_provider_config.transform_delete_response_api_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+        )
+
+    def delete_response_api_handler(
+        self,
+        response_id: str,
+        responses_api_provider_config: BaseResponsesAPIConfig,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str],
+        extra_headers: Optional[Dict[str, Any]] = None,
+        extra_body: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        _is_async: bool = False,
+    ) -> Union[DeleteResponseResult, Coroutine[Any, Any, DeleteResponseResult]]:
+        """
+        Async version of the responses API handler.
+        Uses async HTTP client to make requests.
+        """
+        if _is_async:
+            return self.async_delete_response_api_handler(
+                response_id=response_id,
+                responses_api_provider_config=responses_api_provider_config,
+                litellm_params=litellm_params,
+                logging_obj=logging_obj,
+                custom_llm_provider=custom_llm_provider,
+                extra_headers=extra_headers,
+                extra_body=extra_body,
+                timeout=timeout,
+                client=client,
+            )
+        if client is None or not isinstance(client, HTTPHandler):
+            sync_httpx_client = _get_httpx_client(
+                params={"ssl_verify": litellm_params.get("ssl_verify", None)}
+            )
+        else:
+            sync_httpx_client = client
+
+        headers = responses_api_provider_config.validate_environment(
+            api_key=litellm_params.api_key,
+            headers=extra_headers or {},
+            model="None",
+        )
+
+        if extra_headers:
+            headers.update(extra_headers)
+
+        api_base = responses_api_provider_config.get_complete_url(
+            api_base=litellm_params.api_base,
+            litellm_params=dict(litellm_params),
+        )
+
+        url, data = responses_api_provider_config.transform_delete_response_api_request(
+            response_id=response_id,
+            api_base=api_base,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=input,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": api_base,
+                "headers": headers,
+            },
+        )
+
+        try:
+            response = sync_httpx_client.delete(
+                url=url, headers=headers, data=json.dumps(data), timeout=timeout
+            )
+
+        except Exception as e:
+            raise self._handle_error(
+                e=e,
+                provider_config=responses_api_provider_config,
+            )
+
+        return responses_api_provider_config.transform_delete_response_api_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+        )
+
+    def get_responses(
+        self,
+        response_id: str,
+        responses_api_provider_config: BaseResponsesAPIConfig,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        extra_body: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        _is_async: bool = False,
+    ) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]:
+        """
+        Get a response by ID
+        Uses GET /v1/responses/{response_id} endpoint in the responses API
+        """
+        if _is_async:
+            return self.async_get_responses(
+                response_id=response_id,
+                responses_api_provider_config=responses_api_provider_config,
+                litellm_params=litellm_params,
+                logging_obj=logging_obj,
+                custom_llm_provider=custom_llm_provider,
+                extra_headers=extra_headers,
+                extra_body=extra_body,
+                timeout=timeout,
+                client=client,
+            )
+        
+        if client is None or not isinstance(client, HTTPHandler):
+            sync_httpx_client = _get_httpx_client(
+                params={"ssl_verify": litellm_params.get("ssl_verify", None)}
+            )
+        else:
+            sync_httpx_client = client
+
+        headers = responses_api_provider_config.validate_environment(
+            api_key=litellm_params.api_key,
+            headers=extra_headers or {},
+            model="None",
+        )
+
+        if extra_headers:
+            headers.update(extra_headers)
+
+        api_base = responses_api_provider_config.get_complete_url(
+            api_base=litellm_params.api_base,
+            litellm_params=dict(litellm_params),
+        )
+
+        url, data = responses_api_provider_config.transform_get_response_api_request(
+            response_id=response_id,
+            api_base=api_base,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": api_base,
+                "headers": headers,
+            },
+        )
+
+        try:
+            response = sync_httpx_client.get(
+                url=url, headers=headers, params=data
+            )
+        except Exception as e:
+            raise self._handle_error(
+                e=e,
+                provider_config=responses_api_provider_config,
+            )
+
+        return responses_api_provider_config.transform_get_response_api_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+        )
+
+    async def async_get_responses(
+        self,
+        response_id: str,
+        responses_api_provider_config: BaseResponsesAPIConfig,
+        litellm_params: GenericLiteLLMParams,
+        logging_obj: LiteLLMLoggingObj,
+        custom_llm_provider: Optional[str] = None,
+        extra_headers: Optional[Dict[str, Any]] = None,
+        extra_body: Optional[Dict[str, Any]] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+    ) -> ResponsesAPIResponse:
+        """
+        Async version of get_responses
+        """
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            async_httpx_client = get_async_httpx_client(
+                llm_provider=litellm.LlmProviders(custom_llm_provider),
+                params={"ssl_verify": litellm_params.get("ssl_verify", None)},
+            )
+        else:
+            async_httpx_client = client
+
+        headers = responses_api_provider_config.validate_environment(
+            api_key=litellm_params.api_key,
+            headers=extra_headers or {},
+            model="None",
+        )
+
+        if extra_headers:
+            headers.update(extra_headers)
+
+        api_base = responses_api_provider_config.get_complete_url(
+            api_base=litellm_params.api_base,
+            litellm_params=dict(litellm_params),
+        )
+
+        url, data = responses_api_provider_config.transform_get_response_api_request(
+            response_id=response_id,
+            api_base=api_base,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input="",
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": api_base,
+                "headers": headers,
+            },
+        )
+
+        try:
+            response = await async_httpx_client.get(
+                url=url, headers=headers, params=data
+            )
+
+        except Exception as e:
+            verbose_logger.exception(f"Error retrieving response: {e}")
+            raise self._handle_error(
+                e=e,
+                provider_config=responses_api_provider_config,
+            )
+
+        return responses_api_provider_config.transform_get_response_api_response(
+            raw_response=response,
+            logging_obj=logging_obj,
+        )
+
     def create_file(
         self,
         create_file_data: CreateFileRequest,

litellm/llms/openai/responses/transformation.py

@@ -7,6 +7,7 @@ from litellm._logging import verbose_logger
 from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import *
+from litellm.types.responses.main import *
 from litellm.types.router import GenericLiteLLMParams
 
 from ..common_utils import OpenAIError
@@ -110,11 +111,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
     def get_complete_url(
         self,
         api_base: Optional[str],
-        api_key: Optional[str],
-        model: str,
-        optional_params: dict,
         litellm_params: dict,
-        stream: Optional[bool] = None,
     ) -> str:
         """
         Get the endpoint for OpenAI responses API
@@ -190,7 +187,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
 
         model_class = event_models.get(cast(ResponsesAPIStreamEvents, event_type))
         if not model_class:
-            raise ValueError(f"Unknown event type: {event_type}")
+            return GenericEvent
 
         return model_class
 
@@ -217,3 +214,75 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
                     f"Error getting model info in OpenAIResponsesAPIConfig: {e}"
                 )
         return False
+
+    #########################################################
+    ########## DELETE RESPONSE API TRANSFORMATION ##############
+    #########################################################
+    def transform_delete_response_api_request(
+        self,
+        response_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the delete response API request into a URL and data
+
+        OpenAI API expects the following request
+        - DELETE /v1/responses/{response_id}
+        """
+        url = f"{api_base}/{response_id}"
+        data: Dict = {}
+        return url, data
+
+    def transform_delete_response_api_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> DeleteResponseResult:
+        """
+        Transform the delete response API response into a DeleteResponseResult
+        """
+        try:
+            raw_response_json = raw_response.json()
+        except Exception:
+            raise OpenAIError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+        return DeleteResponseResult(**raw_response_json)
+    
+    #########################################################
+    ########## GET RESPONSE API TRANSFORMATION ###############
+    #########################################################
+    def transform_get_response_api_request(
+        self,
+        response_id: str,
+        api_base: str,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Tuple[str, Dict]:
+        """
+        Transform the get response API request into a URL and data
+
+        OpenAI API expects the following request
+        - GET /v1/responses/{response_id}
+        """
+        url = f"{api_base}/{response_id}"
+        data: Dict = {}
+        return url, data
+    
+    def transform_get_response_api_response(
+        self,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> ResponsesAPIResponse:
+        """
+        Transform the get response API response into a ResponsesAPIResponse
+        """
+        try:
+            raw_response_json = raw_response.json()
+        except Exception:
+            raise OpenAIError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+        return ResponsesAPIResponse(**raw_response_json)

litellm/main.py

@@ -182,6 +182,7 @@ from .types.llms.openai import (
     ChatCompletionPredictionContentParam,
     ChatCompletionUserMessage,
     HttpxBinaryResponseContent,
+    ImageGenerationRequestQuality,
 )
 from .types.utils import (
     LITELLM_IMAGE_VARIATION_PROVIDERS,
@@ -2688,9 +2689,9 @@ def completion(  # type: ignore # noqa: PLR0915
                     "aws_region_name" not in optional_params
                     or optional_params["aws_region_name"] is None
                 ):
-                    optional_params[
-                        "aws_region_name"
-                    ] = aws_bedrock_client.meta.region_name
+                    optional_params["aws_region_name"] = (
+                        aws_bedrock_client.meta.region_name
+                    )
 
             bedrock_route = BedrockModelInfo.get_bedrock_route(model)
             if bedrock_route == "converse":
@@ -4412,9 +4413,9 @@ def adapter_completion(
     new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
 
     response: Union[ModelResponse, CustomStreamWrapper] = completion(**new_kwargs)  # type: ignore
-    translated_response: Optional[
-        Union[BaseModel, AdapterCompletionStreamWrapper]
-    ] = None
+    translated_response: Optional[Union[BaseModel, AdapterCompletionStreamWrapper]] = (
+        None
+    )
     if isinstance(response, ModelResponse):
         translated_response = translation_obj.translate_completion_output_params(
             response=response
@@ -4567,7 +4568,7 @@ def image_generation(  # noqa: PLR0915
     prompt: str,
     model: Optional[str] = None,
     n: Optional[int] = None,
-    quality: Optional[str] = None,
+    quality: Optional[Union[str, ImageGenerationRequestQuality]] = None,
     response_format: Optional[str] = None,
     size: Optional[str] = None,
     style: Optional[str] = None,
@@ -5834,9 +5835,9 @@ def stream_chunk_builder(  # noqa: PLR0915
         ]
 
         if len(content_chunks) > 0:
-            response["choices"][0]["message"][
-                "content"
-            ] = processor.get_combined_content(content_chunks)
+            response["choices"][0]["message"]["content"] = (
+                processor.get_combined_content(content_chunks)
+            )
 
         reasoning_chunks = [
             chunk
@@ -5847,9 +5848,9 @@ def stream_chunk_builder(  # noqa: PLR0915
         ]
 
         if len(reasoning_chunks) > 0:
-            response["choices"][0]["message"][
-                "reasoning_content"
-            ] = processor.get_combined_reasoning_content(reasoning_chunks)
+            response["choices"][0]["message"]["reasoning_content"] = (
+                processor.get_combined_reasoning_content(reasoning_chunks)
+            )
 
         audio_chunks = [
             chunk

litellm/model_prices_and_context_window_backup.json

@@ -1437,6 +1437,76 @@
         "output_cost_per_pixel": 0.0,
         "litellm_provider": "openai"
     },
+    "gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "low/1024-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.0490417e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "medium/1024-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "high/1024-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.59263611e-7,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "low/1024-x-1536/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.0172526e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "medium/1024-x-1536/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "high/1024-x-1536/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.58945719e-7,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "low/1536-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.0172526e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "medium/1536-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "high/1536-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.58945719e-7,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
     "gpt-4o-transcribe": {
         "mode": "audio_transcription",
         "input_cost_per_token": 0.0000025,
@@ -1490,7 +1560,6 @@
         "supports_prompt_caching": false,
         "supports_system_messages": true,
         "supports_tool_choice": true,
-        "supports_native_streaming": false,
         "supports_reasoning": true
     },
     "azure/gpt-4o-audio-preview-2024-12-17": {

litellm/openai-responses-starter-app

@@ -1 +0,0 @@
-Subproject commit bf0485467c343957ba5c217db777f407b2e65453

litellm/proxy/_experimental/out/index.html

@@ -1 +1 @@
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litellm/proxy/_experimental/out/index.txt

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litellm/proxy/_experimental/out/model_hub.txt

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litellm/proxy/_experimental/out/onboarding.txt

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 6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
 1:null

litellm/proxy/_new_secret_config.yaml

@@ -33,7 +33,7 @@ model_list:
 
 litellm_settings:
   num_retries: 0
-  callbacks: ["prometheus"]
+  callbacks: ["datadog_llm_observability"]
   check_provider_endpoint: true
 
 files_settings:

litellm/proxy/_types.py

@@ -687,6 +687,8 @@ class GenerateKeyResponse(KeyRequestBase):
     token: Optional[str] = None
     created_by: Optional[str] = None
     updated_by: Optional[str] = None
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
 
     @model_validator(mode="before")
     @classmethod

litellm/proxy/management_endpoints/internal_user_endpoints.py

@@ -43,6 +43,9 @@ from litellm.types.proxy.management_endpoints.common_daily_activity import (
     SpendAnalyticsPaginatedResponse,
     SpendMetrics,
 )
+from litellm.types.proxy.management_endpoints.internal_user_endpoints import (
+    UserListResponse,
+)
 
 router = APIRouter()
 
@@ -899,15 +902,47 @@ async def get_user_key_counts(
     return result
 
 
-@router.get(
-    "/user/get_users",
-    tags=["Internal User management"],
-    dependencies=[Depends(user_api_key_auth)],
-)
+def _validate_sort_params(
+    sort_by: Optional[str], sort_order: str
+) -> Optional[Dict[str, str]]:
+    order_by: Dict[str, str] = {}
+
+    if sort_by is None:
+        return None
+    # Validate sort_by is a valid column
+    valid_columns = [
+        "user_id",
+        "user_email",
+        "created_at",
+        "spend",
+        "user_alias",
+        "user_role",
+    ]
+    if sort_by not in valid_columns:
+        raise HTTPException(
+            status_code=400,
+            detail={
+                "error": f"Invalid sort column. Must be one of: {', '.join(valid_columns)}"
+            },
+        )
+
+    # Validate sort_order
+    if sort_order.lower() not in ["asc", "desc"]:
+        raise HTTPException(
+            status_code=400,
+            detail={"error": "Invalid sort order. Must be 'asc' or 'desc'"},
+        )
+
+    order_by[sort_by] = sort_order.lower()
+
+    return order_by
+
+
 @router.get(
     "/user/list",
     tags=["Internal User management"],
     dependencies=[Depends(user_api_key_auth)],
+    response_model=UserListResponse,
 )
 async def get_users(
     role: Optional[str] = fastapi.Query(
@@ -916,15 +951,29 @@ async def get_users(
     user_ids: Optional[str] = fastapi.Query(
         default=None, description="Get list of users by user_ids"
     ),
+    sso_user_ids: Optional[str] = fastapi.Query(
+        default=None, description="Get list of users by sso_user_id"
+    ),
+    user_email: Optional[str] = fastapi.Query(
+        default=None, description="Filter users by partial email match"
+    ),
+    team: Optional[str] = fastapi.Query(
+        default=None, description="Filter users by team id"
+    ),
     page: int = fastapi.Query(default=1, ge=1, description="Page number"),
     page_size: int = fastapi.Query(
         default=25, ge=1, le=100, description="Number of items per page"
     ),
+    sort_by: Optional[str] = fastapi.Query(
+        default=None,
+        description="Column to sort by (e.g. 'user_id', 'user_email', 'created_at', 'spend')",
+    ),
+    sort_order: str = fastapi.Query(
+        default="asc", description="Sort order ('asc' or 'desc')"
+    ),
 ):
     """
-    Get a paginated list of users, optionally filtered by role.
-
-    Used by the UI to populate the user lists.
+    Get a paginated list of users with filtering and sorting options.
 
     Parameters:
         role: Optional[str]
@@ -935,17 +984,20 @@ async def get_users(
             - internal_user_viewer
         user_ids: Optional[str]
             Get list of users by user_ids. Comma separated list of user_ids.
+        sso_ids: Optional[str]
+            Get list of users by sso_ids. Comma separated list of sso_ids.
+        user_email: Optional[str]
+            Filter users by partial email match
+        team: Optional[str]
+            Filter users by team id. Will match if user has this team in their teams array.
         page: int
             The page number to return
         page_size: int
             The number of items per page
-
-    Currently - admin-only endpoint.
-
-    Example curl:
-    ```
-    http://0.0.0.0:4000/user/list?user_ids=default_user_id,693c1a4a-1cc0-4c7c-afe8-b5d2c8d52e17
-    ```
+        sort_by: Optional[str]
+            Column to sort by (e.g. 'user_id', 'user_email', 'created_at', 'spend')
+        sort_order: Optional[str]
+            Sort order ('asc' or 'desc')
     """
     from litellm.proxy.proxy_server import prisma_client
 
@@ -958,35 +1010,57 @@ async def get_users(
     # Calculate skip and take for pagination
     skip = (page - 1) * page_size
 
-    # Prepare the query conditions
     # Build where conditions based on provided parameters
     where_conditions: Dict[str, Any] = {}
 
     if role:
-        where_conditions["user_role"] = {
-            "contains": role,
-            "mode": "insensitive",  # Case-insensitive search
-        }
+        where_conditions["user_role"] = role  # Exact match instead of contains
 
     if user_ids and isinstance(user_ids, str):
         user_id_list = [uid.strip() for uid in user_ids.split(",") if uid.strip()]
         where_conditions["user_id"] = {
-            "in": user_id_list,  # Now passing a list of strings as required by Prisma
+            "in": user_id_list,
         }
 
-    users: Optional[
-        List[LiteLLM_UserTable]
-    ] = await prisma_client.db.litellm_usertable.find_many(
+    if user_email is not None and isinstance(user_email, str):
+        where_conditions["user_email"] = {
+            "contains": user_email,
+            "mode": "insensitive",  # Case-insensitive search
+        }
+
+    if team is not None and isinstance(team, str):
+        where_conditions["teams"] = {
+            "has": team  # Array contains for string arrays in Prisma
+        }
+
+    if sso_user_ids is not None and isinstance(sso_user_ids, str):
+        sso_id_list = [sid.strip() for sid in sso_user_ids.split(",") if sid.strip()]
+        where_conditions["sso_user_id"] = {
+            "in": sso_id_list,
+        }
+
+    ## Filter any none fastapi.Query params - e.g. where_conditions: {'user_email': {'contains': Query(None), 'mode': 'insensitive'}, 'teams': {'has': Query(None)}}
+    where_conditions = {k: v for k, v in where_conditions.items() if v is not None}
+
+    # Build order_by conditions
+
+    order_by: Optional[Dict[str, str]] = (
+        _validate_sort_params(sort_by, sort_order)
+        if sort_by is not None and isinstance(sort_by, str)
+        else None
+    )
+
+    users = await prisma_client.db.litellm_usertable.find_many(
         where=where_conditions,
         skip=skip,
         take=page_size,
-        order={"created_at": "desc"},
+        order=order_by
+        if order_by
+        else {"created_at": "desc"},  # Default to created_at desc if no sort specified
     )
 
     # Get total count of user rows
-    total_count = await prisma_client.db.litellm_usertable.count(
-        where=where_conditions  # type: ignore
-    )
+    total_count = await prisma_client.db.litellm_usertable.count(where=where_conditions)
 
     # Get key count for each user
     if users is not None:
@@ -1009,7 +1083,7 @@ async def get_users(
                 LiteLLM_UserTableWithKeyCount(
                     **user.model_dump(), key_count=user_key_counts.get(user.user_id, 0)
                 )
-            )  # Return full key object
+            )
     else:
         user_list = []
 

litellm/proxy/management_endpoints/key_management_endpoints.py

@@ -1347,10 +1347,13 @@ async def generate_key_helper_fn(  # noqa: PLR0915
             create_key_response = await prisma_client.insert_data(
                 data=key_data, table_name="key"
             )
+
             key_data["token_id"] = getattr(create_key_response, "token", None)
             key_data["litellm_budget_table"] = getattr(
                 create_key_response, "litellm_budget_table", None
             )
+            key_data["created_at"] = getattr(create_key_response, "created_at", None)
+            key_data["updated_at"] = getattr(create_key_response, "updated_at", None)
     except Exception as e:
         verbose_proxy_logger.error(
             "litellm.proxy.proxy_server.generate_key_helper_fn(): Exception occured - {}".format(

litellm/proxy/proxy_server.py

@@ -1296,7 +1296,7 @@ class ProxyConfig:
             config=config, base_dir=os.path.dirname(os.path.abspath(file_path or ""))
         )
 
-        verbose_proxy_logger.debug(f"loaded config={json.dumps(config, indent=4)}")
+        # verbose_proxy_logger.debug(f"loaded config={json.dumps(config, indent=4)}")
         return config
 
     def _process_includes(self, config: dict, base_dir: str) -> dict:

litellm/responses/main.py

@@ -1,7 +1,7 @@
 import asyncio
 import contextvars
 from functools import partial
-from typing import Any, Dict, Iterable, List, Literal, Optional, Union
+from typing import Any, Coroutine, Dict, Iterable, List, Literal, Optional, Union
 
 import httpx
 
@@ -24,6 +24,7 @@ from litellm.types.llms.openai import (
     ToolChoice,
     ToolParam,
 )
+from litellm.types.responses.main import *
 from litellm.types.router import GenericLiteLLMParams
 from litellm.utils import ProviderConfigManager, client
 
@@ -121,7 +122,8 @@ async def aresponses(
         if isinstance(response, ResponsesAPIResponse):
             response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                 responses_api_response=response,
-                kwargs=kwargs,
+                litellm_metadata=kwargs.get("litellm_metadata", {}),
+                custom_llm_provider=custom_llm_provider,
             )
         return response
     except Exception as e:
@@ -253,13 +255,15 @@ def responses(
             fake_stream=responses_api_provider_config.should_fake_stream(
                 model=model, stream=stream, custom_llm_provider=custom_llm_provider
             ),
+            litellm_metadata=kwargs.get("litellm_metadata", {}),
         )
 
         # Update the responses_api_response_id with the model_id
         if isinstance(response, ResponsesAPIResponse):
             response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                 responses_api_response=response,
-                kwargs=kwargs,
+                litellm_metadata=kwargs.get("litellm_metadata", {}),
+                custom_llm_provider=custom_llm_provider,
             )
 
         return response
@@ -271,3 +275,347 @@ def responses(
             completion_kwargs=local_vars,
             extra_kwargs=kwargs,
         )
+
+
+@client
+async def adelete_responses(
+    response_id: str,
+    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+    # The extra values given here take precedence over values defined on the client or passed to this method.
+    extra_headers: Optional[Dict[str, Any]] = None,
+    extra_query: Optional[Dict[str, Any]] = None,
+    extra_body: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    # LiteLLM specific params,
+    custom_llm_provider: Optional[str] = None,
+    **kwargs,
+) -> DeleteResponseResult:
+    """
+    Async version of the DELETE Responses API
+
+    DELETE /v1/responses/{response_id} endpoint in the responses API
+
+    """
+    local_vars = locals()
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["adelete_responses"] = True
+
+        # get custom llm provider from response_id
+        decoded_response_id: DecodedResponseId = (
+            ResponsesAPIRequestUtils._decode_responses_api_response_id(
+                response_id=response_id,
+            )
+        )
+        response_id = decoded_response_id.get("response_id") or response_id
+        custom_llm_provider = (
+            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
+        )
+
+        func = partial(
+            delete_responses,
+            response_id=response_id,
+            custom_llm_provider=custom_llm_provider,
+            extra_headers=extra_headers,
+            extra_query=extra_query,
+            extra_body=extra_body,
+            timeout=timeout,
+            **kwargs,
+        )
+
+        ctx = contextvars.copy_context()
+        func_with_context = partial(ctx.run, func)
+        init_response = await loop.run_in_executor(None, func_with_context)
+
+        if asyncio.iscoroutine(init_response):
+            response = await init_response
+        else:
+            response = init_response
+        return response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=None,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+
+@client
+def delete_responses(
+    response_id: str,
+    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+    # The extra values given here take precedence over values defined on the client or passed to this method.
+    extra_headers: Optional[Dict[str, Any]] = None,
+    extra_query: Optional[Dict[str, Any]] = None,
+    extra_body: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    # LiteLLM specific params,
+    custom_llm_provider: Optional[str] = None,
+    **kwargs,
+) -> Union[DeleteResponseResult, Coroutine[Any, Any, DeleteResponseResult]]:
+    """
+    Synchronous version of the DELETE Responses API
+
+    DELETE /v1/responses/{response_id} endpoint in the responses API
+
+    """
+    local_vars = locals()
+    try:
+        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
+        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+        _is_async = kwargs.pop("adelete_responses", False) is True
+
+        # get llm provider logic
+        litellm_params = GenericLiteLLMParams(**kwargs)
+
+        # get custom llm provider from response_id
+        decoded_response_id: DecodedResponseId = (
+            ResponsesAPIRequestUtils._decode_responses_api_response_id(
+                response_id=response_id,
+            )
+        )
+        response_id = decoded_response_id.get("response_id") or response_id
+        custom_llm_provider = (
+            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
+        )
+
+        if custom_llm_provider is None:
+            raise ValueError("custom_llm_provider is required but passed as None")
+
+        # get provider config
+        responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
+            ProviderConfigManager.get_provider_responses_api_config(
+                model=None,
+                provider=litellm.LlmProviders(custom_llm_provider),
+            )
+        )
+
+        if responses_api_provider_config is None:
+            raise ValueError(
+                f"DELETE responses is not supported for {custom_llm_provider}"
+            )
+
+        local_vars.update(kwargs)
+
+        # Pre Call logging
+        litellm_logging_obj.update_environment_variables(
+            model=None,
+            optional_params={
+                "response_id": response_id,
+            },
+            litellm_params={
+                "litellm_call_id": litellm_call_id,
+            },
+            custom_llm_provider=custom_llm_provider,
+        )
+
+        # Call the handler with _is_async flag instead of directly calling the async handler
+        response = base_llm_http_handler.delete_response_api_handler(
+            response_id=response_id,
+            custom_llm_provider=custom_llm_provider,
+            responses_api_provider_config=responses_api_provider_config,
+            litellm_params=litellm_params,
+            logging_obj=litellm_logging_obj,
+            extra_headers=extra_headers,
+            extra_body=extra_body,
+            timeout=timeout or request_timeout,
+            _is_async=_is_async,
+            client=kwargs.get("client"),
+        )
+
+        return response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=None,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+@client
+async def aget_responses(
+    response_id: str,
+    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+    # The extra values given here take precedence over values defined on the client or passed to this method.
+    extra_headers: Optional[Dict[str, Any]] = None,
+    extra_query: Optional[Dict[str, Any]] = None,
+    extra_body: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    # LiteLLM specific params,
+    custom_llm_provider: Optional[str] = None,
+    **kwargs,
+) -> ResponsesAPIResponse:
+    """
+    Async: Fetch a response by its ID.
+    
+    GET /v1/responses/{response_id} endpoint in the responses API
+    
+    Args:
+        response_id: The ID of the response to fetch.
+        custom_llm_provider: Optional provider name. If not specified, will be decoded from response_id.
+        
+    Returns:
+        The response object with complete information about the stored response.
+    """
+    local_vars = locals()
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["aget_responses"] = True
+
+        # get custom llm provider from response_id
+        decoded_response_id: DecodedResponseId = (
+            ResponsesAPIRequestUtils._decode_responses_api_response_id(
+                response_id=response_id,
+            )
+        )
+        response_id = decoded_response_id.get("response_id") or response_id
+        custom_llm_provider = (
+            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
+        )
+
+        func = partial(
+            get_responses,
+            response_id=response_id,
+            custom_llm_provider=custom_llm_provider,
+            extra_headers=extra_headers,
+            extra_query=extra_query,
+            extra_body=extra_body,
+            timeout=timeout,
+            **kwargs,
+        )
+
+        ctx = contextvars.copy_context()
+        func_with_context = partial(ctx.run, func)
+        init_response = await loop.run_in_executor(None, func_with_context)
+
+        if asyncio.iscoroutine(init_response):
+            response = await init_response
+        else:
+            response = init_response
+
+         # Update the responses_api_response_id with the model_id
+        if isinstance(response, ResponsesAPIResponse):
+            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
+                responses_api_response=response,
+                litellm_metadata=kwargs.get("litellm_metadata", {}),
+                custom_llm_provider=custom_llm_provider,
+            )
+        return response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=None,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )
+
+@client
+def get_responses(
+    response_id: str,
+    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+    # The extra values given here take precedence over values defined on the client or passed to this method.
+    extra_headers: Optional[Dict[str, Any]] = None,
+    extra_query: Optional[Dict[str, Any]] = None,
+    extra_body: Optional[Dict[str, Any]] = None,
+    timeout: Optional[Union[float, httpx.Timeout]] = None,
+    # LiteLLM specific params,
+    custom_llm_provider: Optional[str] = None,
+    **kwargs,
+) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]:
+    """
+    Fetch a response by its ID.
+    
+    GET /v1/responses/{response_id} endpoint in the responses API
+    
+    Args:
+        response_id: The ID of the response to fetch.
+        custom_llm_provider: Optional provider name. If not specified, will be decoded from response_id.
+        
+    Returns:
+        The response object with complete information about the stored response.
+    """
+    local_vars = locals()
+    try:
+        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
+        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
+        _is_async = kwargs.pop("aget_responses", False) is True
+
+        # get llm provider logic
+        litellm_params = GenericLiteLLMParams(**kwargs)
+
+        # get custom llm provider from response_id
+        decoded_response_id: DecodedResponseId = (
+            ResponsesAPIRequestUtils._decode_responses_api_response_id(
+                response_id=response_id,
+            )
+        )
+        response_id = decoded_response_id.get("response_id") or response_id
+        custom_llm_provider = (
+            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
+        )
+
+        if custom_llm_provider is None:
+            raise ValueError("custom_llm_provider is required but passed as None")
+
+        # get provider config
+        responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
+            ProviderConfigManager.get_provider_responses_api_config(
+                model=None,
+                provider=litellm.LlmProviders(custom_llm_provider),
+            )
+        )
+
+        if responses_api_provider_config is None:
+            raise ValueError(
+                f"GET responses is not supported for {custom_llm_provider}"
+            )
+
+        local_vars.update(kwargs)
+
+        # Pre Call logging
+        litellm_logging_obj.update_environment_variables(
+            model=None,
+            optional_params={
+                "response_id": response_id,
+            },
+            litellm_params={
+                "litellm_call_id": litellm_call_id,
+            },
+            custom_llm_provider=custom_llm_provider,
+        )
+
+        # Call the handler with _is_async flag instead of directly calling the async handler
+        response = base_llm_http_handler.get_responses(
+            response_id=response_id,
+            custom_llm_provider=custom_llm_provider,
+            responses_api_provider_config=responses_api_provider_config,
+            litellm_params=litellm_params,
+            logging_obj=litellm_logging_obj,
+            extra_headers=extra_headers,
+            extra_body=extra_body,
+            timeout=timeout or request_timeout,
+            _is_async=_is_async,
+            client=kwargs.get("client"),
+        )
+
+        # Update the responses_api_response_id with the model_id
+        if isinstance(response, ResponsesAPIResponse):
+            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
+                responses_api_response=response,
+                litellm_metadata=kwargs.get("litellm_metadata", {}),
+                custom_llm_provider=custom_llm_provider,
+            )
+
+        return response
+    except Exception as e:
+        raise litellm.exception_type(
+            model=None,
+            custom_llm_provider=custom_llm_provider,
+            original_exception=e,
+            completion_kwargs=local_vars,
+            extra_kwargs=kwargs,
+        )

litellm/responses/streaming_iterator.py

@@ -1,7 +1,7 @@
 import asyncio
 import json
 from datetime import datetime
-from typing import Optional
+from typing import Any, Dict, Optional
 
 import httpx
 
@@ -10,6 +10,7 @@ from litellm.litellm_core_utils.asyncify import run_async_function
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.litellm_core_utils.thread_pool_executor import executor
 from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
+from litellm.responses.utils import ResponsesAPIRequestUtils
 from litellm.types.llms.openai import (
     OutputTextDeltaEvent,
     ResponseCompletedEvent,
@@ -33,6 +34,8 @@ class BaseResponsesAPIStreamingIterator:
         model: str,
         responses_api_provider_config: BaseResponsesAPIConfig,
         logging_obj: LiteLLMLoggingObj,
+        litellm_metadata: Optional[Dict[str, Any]] = None,
+        custom_llm_provider: Optional[str] = None,
     ):
         self.response = response
         self.model = model
@@ -42,7 +45,11 @@ class BaseResponsesAPIStreamingIterator:
         self.completed_response: Optional[ResponsesAPIStreamingResponse] = None
         self.start_time = datetime.now()
         
-    def _process_chunk(self, chunk):
+        # set request kwargs
+        self.litellm_metadata = litellm_metadata
+        self.custom_llm_provider = custom_llm_provider
+
+    def _process_chunk(self, chunk) -> Optional[ResponsesAPIStreamingResponse]:
         """Process a single chunk of data from the stream"""
         if not chunk:
             return None
@@ -70,6 +77,17 @@ class BaseResponsesAPIStreamingIterator:
                         logging_obj=self.logging_obj,
                     )
                 )
+
+                # if "response" in parsed_chunk, then encode litellm specific information like custom_llm_provider
+                response_object = getattr(openai_responses_api_chunk, "response", None)
+                if response_object:
+                    response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
+                        responses_api_response=response_object,
+                        litellm_metadata=self.litellm_metadata,
+                        custom_llm_provider=self.custom_llm_provider,
+                    )
+                    setattr(openai_responses_api_chunk, "response", response)
+
                 # Store the completed response
                 if (
                     openai_responses_api_chunk
@@ -102,8 +120,17 @@ class ResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
         model: str,
         responses_api_provider_config: BaseResponsesAPIConfig,
         logging_obj: LiteLLMLoggingObj,
+        litellm_metadata: Optional[Dict[str, Any]] = None,
+        custom_llm_provider: Optional[str] = None,
     ):
-        super().__init__(response, model, responses_api_provider_config, logging_obj)
+        super().__init__(
+            response,
+            model,
+            responses_api_provider_config,
+            logging_obj,
+            litellm_metadata,
+            custom_llm_provider,
+        )
         self.stream_iterator = response.aiter_lines()
 
     def __aiter__(self):
@@ -163,8 +190,17 @@ class SyncResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
         model: str,
         responses_api_provider_config: BaseResponsesAPIConfig,
         logging_obj: LiteLLMLoggingObj,
+        litellm_metadata: Optional[Dict[str, Any]] = None,
+        custom_llm_provider: Optional[str] = None,
     ):
-        super().__init__(response, model, responses_api_provider_config, logging_obj)
+        super().__init__(
+            response,
+            model,
+            responses_api_provider_config,
+            logging_obj,
+            litellm_metadata,
+            custom_llm_provider,
+        )
         self.stream_iterator = response.iter_lines()
 
     def __iter__(self):
@@ -228,12 +264,16 @@ class MockResponsesAPIStreamingIterator(BaseResponsesAPIStreamingIterator):
         model: str,
         responses_api_provider_config: BaseResponsesAPIConfig,
         logging_obj: LiteLLMLoggingObj,
+        litellm_metadata: Optional[Dict[str, Any]] = None,
+        custom_llm_provider: Optional[str] = None,
     ):
         super().__init__(
             response=response,
             model=model,
             responses_api_provider_config=responses_api_provider_config,
             logging_obj=logging_obj,
+            litellm_metadata=litellm_metadata,
+            custom_llm_provider=custom_llm_provider,
         )
 
         # one-time transform

litellm/responses/utils.py

@@ -1,5 +1,5 @@
 import base64
-from typing import Any, Dict, Optional, Tuple, Union, cast, get_type_hints
+from typing import Any, Dict, Optional, Union, cast, get_type_hints
 
 import litellm
 from litellm._logging import verbose_logger
@@ -9,6 +9,7 @@ from litellm.types.llms.openai import (
     ResponsesAPIOptionalRequestParams,
     ResponsesAPIResponse,
 )
+from litellm.types.responses.main import DecodedResponseId
 from litellm.types.utils import SpecialEnums, Usage
 
 
@@ -83,30 +84,36 @@ class ResponsesAPIRequestUtils:
     @staticmethod
     def _update_responses_api_response_id_with_model_id(
         responses_api_response: ResponsesAPIResponse,
-        kwargs: Dict[str, Any],
+        custom_llm_provider: Optional[str],
+        litellm_metadata: Optional[Dict[str, Any]] = None,
     ) -> ResponsesAPIResponse:
-        """Update the responses_api_response_id with the model_id"""
-        litellm_metadata: Dict[str, Any] = kwargs.get("litellm_metadata", {}) or {}
+        """
+        Update the responses_api_response_id with model_id and custom_llm_provider
+
+        This builds a composite ID containing the custom LLM provider, model ID, and original response ID
+        """
+        litellm_metadata = litellm_metadata or {}
         model_info: Dict[str, Any] = litellm_metadata.get("model_info", {}) or {}
         model_id = model_info.get("id")
         updated_id = ResponsesAPIRequestUtils._build_responses_api_response_id(
             model_id=model_id,
+            custom_llm_provider=custom_llm_provider,
             response_id=responses_api_response.id,
         )
+
         responses_api_response.id = updated_id
         return responses_api_response
 
     @staticmethod
     def _build_responses_api_response_id(
+        custom_llm_provider: Optional[str],
         model_id: Optional[str],
         response_id: str,
     ) -> str:
         """Build the responses_api_response_id"""
-        if model_id is None:
-            return response_id
         assembled_id: str = str(
             SpecialEnums.LITELLM_MANAGED_RESPONSE_COMPLETE_STR.value
-        ).format(model_id, response_id)
+        ).format(custom_llm_provider, model_id, response_id)
         base64_encoded_id: str = base64.b64encode(assembled_id.encode("utf-8")).decode(
             "utf-8"
         )
@@ -115,12 +122,12 @@ class ResponsesAPIRequestUtils:
     @staticmethod
     def _decode_responses_api_response_id(
         response_id: str,
-    ) -> Tuple[Optional[str], str]:
+    ) -> DecodedResponseId:
         """
         Decode the responses_api_response_id
 
         Returns:
-            Tuple of model_id, response_id (from upstream provider)
+            DecodedResponseId: Structured tuple with custom_llm_provider, model_id, and response_id
         """
         try:
             # Remove prefix and decode
@@ -129,16 +136,45 @@ class ResponsesAPIRequestUtils:
 
             # Parse components using known prefixes
             if ";" not in decoded_id:
-                return None, response_id
+                return DecodedResponseId(
+                    custom_llm_provider=None,
+                    model_id=None,
+                    response_id=response_id,
+                )
 
-            model_part, response_part = decoded_id.split(";", 1)
-            model_id = model_part.replace("litellm:model_id:", "")
+            parts = decoded_id.split(";")
+
+            # Format: litellm:custom_llm_provider:{};model_id:{};response_id:{}
+            custom_llm_provider = None
+            model_id = None
+
+            if (
+                len(parts) >= 3
+            ):  # Full format with custom_llm_provider, model_id, and response_id
+                custom_llm_provider_part = parts[0]
+                model_id_part = parts[1]
+                response_part = parts[2]
+
+                custom_llm_provider = custom_llm_provider_part.replace(
+                    "litellm:custom_llm_provider:", ""
+                )
+                model_id = model_id_part.replace("model_id:", "")
                 decoded_response_id = response_part.replace("response_id:", "")
+            else:
+                decoded_response_id = response_id
 
-            return model_id, decoded_response_id
+            return DecodedResponseId(
+                custom_llm_provider=custom_llm_provider,
+                model_id=model_id,
+                response_id=decoded_response_id,
+            )
         except Exception as e:
             verbose_logger.debug(f"Error decoding response_id '{response_id}': {e}")
-            return None, response_id
+            return DecodedResponseId(
+                custom_llm_provider=None,
+                model_id=None,
+                response_id=response_id,
+            )
 
 
 class ResponseAPILoggingUtils:

litellm/router_utils/pre_call_checks/responses_api_deployment_check.py

@@ -31,11 +31,10 @@ class ResponsesApiDeploymentCheck(CustomLogger):
         if previous_response_id is None:
             return healthy_deployments
 
-        model_id, response_id = (
-            ResponsesAPIRequestUtils._decode_responses_api_response_id(
+        decoded_response = ResponsesAPIRequestUtils._decode_responses_api_response_id(
             response_id=previous_response_id,
         )
-        )
+        model_id = decoded_response.get("model_id")
         if model_id is None:
             return healthy_deployments
 

litellm/types/integrations/datadog_llm_obs.py

@@ -8,7 +8,9 @@ from typing import Any, Dict, List, Literal, Optional, TypedDict
 
 
 class InputMeta(TypedDict):
-    messages: List[Any]
+    messages: List[
+        Dict[str, str]
+    ]  # Relevant Issue: https://github.com/BerriAI/litellm/issues/9494
 
 
 class OutputMeta(TypedDict):

litellm/types/llms/openai.py

@@ -50,7 +50,7 @@ from openai.types.responses.response_create_params import (
     ToolParam,
 )
 from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
-from pydantic import BaseModel, Discriminator, Field, PrivateAttr
+from pydantic import BaseModel, ConfigDict, Discriminator, Field, PrivateAttr
 from typing_extensions import Annotated, Dict, Required, TypedDict, override
 
 from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
@@ -824,12 +824,12 @@ class OpenAIChatCompletionChunk(ChatCompletionChunk):
 
 class Hyperparameters(BaseModel):
     batch_size: Optional[Union[str, int]] = None  # "Number of examples in each batch."
-    learning_rate_multiplier: Optional[
-        Union[str, float]
-    ] = None  # Scaling factor for the learning rate
-    n_epochs: Optional[
-        Union[str, int]
-    ] = None  # "The number of epochs to train the model for"
+    learning_rate_multiplier: Optional[Union[str, float]] = (
+        None  # Scaling factor for the learning rate
+    )
+    n_epochs: Optional[Union[str, int]] = (
+        None  # "The number of epochs to train the model for"
+    )
 
 
 class FineTuningJobCreate(BaseModel):
@@ -856,18 +856,18 @@ class FineTuningJobCreate(BaseModel):
 
     model: str  # "The name of the model to fine-tune."
     training_file: str  # "The ID of an uploaded file that contains training data."
-    hyperparameters: Optional[
-        Hyperparameters
-    ] = None  # "The hyperparameters used for the fine-tuning job."
-    suffix: Optional[
-        str
-    ] = None  # "A string of up to 18 characters that will be added to your fine-tuned model name."
-    validation_file: Optional[
-        str
-    ] = None  # "The ID of an uploaded file that contains validation data."
-    integrations: Optional[
-        List[str]
-    ] = None  # "A list of integrations to enable for your fine-tuning job."
+    hyperparameters: Optional[Hyperparameters] = (
+        None  # "The hyperparameters used for the fine-tuning job."
+    )
+    suffix: Optional[str] = (
+        None  # "A string of up to 18 characters that will be added to your fine-tuned model name."
+    )
+    validation_file: Optional[str] = (
+        None  # "The ID of an uploaded file that contains validation data."
+    )
+    integrations: Optional[List[str]] = (
+        None  # "A list of integrations to enable for your fine-tuning job."
+    )
     seed: Optional[int] = None  # "The seed controls the reproducibility of the job."
 
 
@@ -1013,6 +1013,9 @@ class ResponsesAPIStreamEvents(str, Enum):
     RESPONSE_FAILED = "response.failed"
     RESPONSE_INCOMPLETE = "response.incomplete"
 
+    # Part added
+    RESPONSE_PART_ADDED = "response.reasoning_summary_part.added"
+
     # Output item events
     OUTPUT_ITEM_ADDED = "response.output_item.added"
     OUTPUT_ITEM_DONE = "response.output_item.done"
@@ -1200,6 +1203,12 @@ class ErrorEvent(BaseLiteLLMOpenAIResponseObject):
     param: Optional[str]
 
 
+class GenericEvent(BaseLiteLLMOpenAIResponseObject):
+    type: str
+
+    model_config = ConfigDict(extra="allow", protected_namespaces=())
+
+
 # Union type for all possible streaming responses
 ResponsesAPIStreamingResponse = Annotated[
     Union[
@@ -1226,6 +1235,7 @@ ResponsesAPIStreamingResponse = Annotated[
         WebSearchCallSearchingEvent,
         WebSearchCallCompletedEvent,
         ErrorEvent,
+        GenericEvent,
     ],
     Discriminator("type"),
 ]
@@ -1249,3 +1259,12 @@ class OpenAIRealtimeStreamResponseBaseObject(TypedDict):
 OpenAIRealtimeStreamList = List[
     Union[OpenAIRealtimeStreamResponseBaseObject, OpenAIRealtimeStreamSessionEvents]
 ]
+
+
+class ImageGenerationRequestQuality(str, Enum):
+    LOW = "low"
+    MEDIUM = "medium"
+    HIGH = "high"
+    AUTO = "auto"
+    STANDARD = "standard"
+    HD = "hd"

litellm/types/proxy/management_endpoints/internal_user_endpoints.py

@@ -0,0 +1,18 @@
+from typing import Any, Dict, List, Literal, Optional, Union
+
+from fastapi import HTTPException
+from pydantic import BaseModel, EmailStr
+
+from litellm.proxy._types import LiteLLM_UserTableWithKeyCount
+
+
+class UserListResponse(BaseModel):
+    """
+    Response model for the user list endpoint
+    """
+
+    users: List[LiteLLM_UserTableWithKeyCount]
+    total: int
+    page: int
+    page_size: int
+    total_pages: int

litellm/types/responses/main.py

@@ -1,5 +1,6 @@
 from typing import Literal
 
+from pydantic import PrivateAttr
 from typing_extensions import Any, List, Optional, TypedDict
 
 from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
@@ -46,3 +47,30 @@ class GenericResponseOutputItem(BaseLiteLLMOpenAIResponseObject):
     status: str  # "completed", "in_progress", etc.
     role: str  # "assistant", "user", etc.
     content: List[OutputText]
+
+
+class DeleteResponseResult(BaseLiteLLMOpenAIResponseObject):
+    """
+    Result of a delete response request
+
+    {
+        "id": "resp_6786a1bec27481909a17d673315b29f6",
+        "object": "response",
+        "deleted": true
+    }
+    """
+
+    id: Optional[str]
+    object: Optional[str]
+    deleted: Optional[bool]
+
+    # Define private attributes using PrivateAttr
+    _hidden_params: dict = PrivateAttr(default_factory=dict)
+
+
+class DecodedResponseId(TypedDict, total=False):
+    """Structure representing a decoded response ID"""
+
+    custom_llm_provider: Optional[str]
+    model_id: Optional[str]
+    response_id: str

litellm/types/utils.py

@@ -2254,7 +2254,9 @@ class SpecialEnums(Enum):
     LITELM_MANAGED_FILE_ID_PREFIX = "litellm_proxy"
     LITELLM_MANAGED_FILE_COMPLETE_STR = "litellm_proxy:{};unified_id,{}"
 
-    LITELLM_MANAGED_RESPONSE_COMPLETE_STR = "litellm:model_id:{};response_id:{}"
+    LITELLM_MANAGED_RESPONSE_COMPLETE_STR = (
+        "litellm:custom_llm_provider:{};model_id:{};response_id:{}"
+    )
 
 
 LLMResponseTypes = Union[

litellm/utils.py

@@ -180,10 +180,18 @@ from litellm.types.utils import (
     all_litellm_params,
 )
 
-with resources.open_text(
-    "litellm.litellm_core_utils.tokenizers", "anthropic_tokenizer.json"
-) as f:
+try:
+    # Python 3.9+
+    with resources.files("litellm.litellm_core_utils.tokenizers").joinpath(
+        "anthropic_tokenizer.json"
+    ).open("r") as f:
         json_data = json.load(f)
+except (ImportError, AttributeError, TypeError):
+    with resources.open_text(
+        "litellm.litellm_core_utils.tokenizers", "anthropic_tokenizer.json"
+    ) as f:
+        json_data = json.load(f)
+
 # Convert to str (if necessary)
 claude_json_str = json.dumps(json_data)
 import importlib.metadata
@@ -516,9 +524,9 @@ def function_setup(  # noqa: PLR0915
         function_id: Optional[str] = kwargs["id"] if "id" in kwargs else None
 
         ## DYNAMIC CALLBACKS ##
-        dynamic_callbacks: Optional[
-            List[Union[str, Callable, CustomLogger]]
-        ] = kwargs.pop("callbacks", None)
+        dynamic_callbacks: Optional[List[Union[str, Callable, CustomLogger]]] = (
+            kwargs.pop("callbacks", None)
+        )
         all_callbacks = get_dynamic_callbacks(dynamic_callbacks=dynamic_callbacks)
 
         if len(all_callbacks) > 0:
@@ -1202,9 +1210,9 @@ def client(original_function):  # noqa: PLR0915
                         exception=e,
                         retry_policy=kwargs.get("retry_policy"),
                     )
-                    kwargs[
-                        "retry_policy"
-                    ] = reset_retry_policy()  # prevent infinite loops
+                    kwargs["retry_policy"] = (
+                        reset_retry_policy()
+                    )  # prevent infinite loops
                 litellm.num_retries = (
                     None  # set retries to None to prevent infinite loops
                 )
@@ -3028,16 +3036,16 @@ def get_optional_params(  # noqa: PLR0915
                     True  # so that main.py adds the function call to the prompt
                 )
                 if "tools" in non_default_params:
-                    optional_params[
-                        "functions_unsupported_model"
-                    ] = non_default_params.pop("tools")
+                    optional_params["functions_unsupported_model"] = (
+                        non_default_params.pop("tools")
+                    )
                     non_default_params.pop(
                         "tool_choice", None
                     )  # causes ollama requests to hang
                 elif "functions" in non_default_params:
-                    optional_params[
-                        "functions_unsupported_model"
-                    ] = non_default_params.pop("functions")
+                    optional_params["functions_unsupported_model"] = (
+                        non_default_params.pop("functions")
+                    )
             elif (
                 litellm.add_function_to_prompt
             ):  # if user opts to add it to prompt instead
@@ -3060,11 +3068,11 @@ def get_optional_params(  # noqa: PLR0915
 
     if "response_format" in non_default_params:
         if provider_config is not None:
-            non_default_params[
-                "response_format"
-            ] = provider_config.get_json_schema_from_pydantic_object(
+            non_default_params["response_format"] = (
+                provider_config.get_json_schema_from_pydantic_object(
                     response_format=non_default_params["response_format"]
                 )
+            )
         else:
             non_default_params["response_format"] = type_to_response_format_param(
                 response_format=non_default_params["response_format"]
@@ -4079,9 +4087,9 @@ def _count_characters(text: str) -> int:
 
 
 def get_response_string(response_obj: Union[ModelResponse, ModelResponseStream]) -> str:
-    _choices: Union[
-        List[Union[Choices, StreamingChoices]], List[StreamingChoices]
-    ] = response_obj.choices
+    _choices: Union[List[Union[Choices, StreamingChoices]], List[StreamingChoices]] = (
+        response_obj.choices
+    )
 
     response_str = ""
     for choice in _choices:
@@ -6625,8 +6633,8 @@ class ProviderConfigManager:
 
     @staticmethod
     def get_provider_responses_api_config(
-        model: str,
         provider: LlmProviders,
+        model: Optional[str] = None,
     ) -> Optional[BaseResponsesAPIConfig]:
         if litellm.LlmProviders.OPENAI == provider:
             return litellm.OpenAIResponsesAPIConfig()

model_prices_and_context_window.json

@@ -1437,6 +1437,76 @@
         "output_cost_per_pixel": 0.0,
         "litellm_provider": "openai"
     },
+    "gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "low/1024-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.0490417e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "medium/1024-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "high/1024-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.59263611e-7,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "low/1024-x-1536/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.0172526e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "medium/1024-x-1536/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "high/1024-x-1536/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.58945719e-7,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "low/1536-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.0172526e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "medium/1536-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 4.0054321e-8,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
+    "high/1536-x-1024/gpt-image-1": {
+        "mode": "image_generation",
+        "input_cost_per_pixel": 1.58945719e-7,
+        "output_cost_per_pixel": 0.0,
+        "litellm_provider": "openai",
+        "supported_endpoints": ["/v1/images/generations"]
+    },
     "gpt-4o-transcribe": {
         "mode": "audio_transcription",
         "input_cost_per_token": 0.0000025,
@@ -1490,7 +1560,6 @@
         "supports_prompt_caching": false,
         "supports_system_messages": true,
         "supports_tool_choice": true,
-        "supports_native_streaming": false,
         "supports_reasoning": true
     },
     "azure/gpt-4o-audio-preview-2024-12-17": {

poetry.lock

@@ -548,7 +548,7 @@ version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
+groups = ["main", "dev", "proxy-dev"]
 files = [
     {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"},
     {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"},
@@ -742,6 +742,24 @@ ssh = ["bcrypt (>=3.1.5)"]
 test = ["certifi", "cryptography-vectors (==43.0.3)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
+[[package]]
+name = "deprecated"
+version = "1.2.18"
+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec"},
+    {file = "deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d"},
+]
+
+[package.dependencies]
+wrapt = ">=1.10,<2"
+
+[package.extras]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools ; python_version >= \"3.12\"", "tox"]
+
 [[package]]
 name = "distro"
 version = "1.9.0"
@@ -1116,14 +1134,14 @@ protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4
 name = "googleapis-common-protos"
 version = "1.70.0"
 description = "Common protobufs used in Google APIs"
-optional = true
+optional = false
 python-versions = ">=3.7"
-groups = ["main"]
-markers = "extra == \"extra-proxy\""
+groups = ["main", "dev", "proxy-dev"]
 files = [
     {file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"},
     {file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"},
 ]
+markers = {main = "extra == \"extra-proxy\""}
 
 [package.dependencies]
 grpcio = {version = ">=1.44.0,<2.0.0", optional = true, markers = "extra == \"grpc\""}
@@ -1154,10 +1172,9 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
 name = "grpcio"
 version = "1.70.0"
 description = "HTTP/2-based RPC framework"
-optional = true
+optional = false
 python-versions = ">=3.8"
-groups = ["main"]
-markers = "python_version < \"3.10\" and extra == \"extra-proxy\""
+groups = ["main", "dev", "proxy-dev"]
 files = [
     {file = "grpcio-1.70.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:95469d1977429f45fe7df441f586521361e235982a0b39e33841549143ae2851"},
     {file = "grpcio-1.70.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:ed9718f17fbdb472e33b869c77a16d0b55e166b100ec57b016dc7de9c8d236bf"},
@@ -1215,6 +1232,7 @@ files = [
     {file = "grpcio-1.70.0-cp39-cp39-win_amd64.whl", hash = "sha256:a31d7e3b529c94e930a117b2175b2efd179d96eb3c7a21ccb0289a8ab05b645c"},
     {file = "grpcio-1.70.0.tar.gz", hash = "sha256:8d1584a68d5922330025881e63a6c1b54cc8117291d382e4fa69339b6d914c56"},
 ]
+markers = {main = "python_version < \"3.10\" and extra == \"extra-proxy\"", dev = "python_version < \"3.10\"", proxy-dev = "python_version < \"3.10\""}
 
 [package.extras]
 protobuf = ["grpcio-tools (>=1.70.0)"]
@@ -1223,10 +1241,9 @@ protobuf = ["grpcio-tools (>=1.70.0)"]
 name = "grpcio"
 version = "1.71.0"
 description = "HTTP/2-based RPC framework"
-optional = true
+optional = false
 python-versions = ">=3.9"
-groups = ["main"]
-markers = "python_version >= \"3.10\" and extra == \"extra-proxy\""
+groups = ["main", "dev", "proxy-dev"]
 files = [
     {file = "grpcio-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:c200cb6f2393468142eb50ab19613229dcc7829b5ccee8b658a36005f6669fdd"},
     {file = "grpcio-1.71.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:b2266862c5ad664a380fbbcdbdb8289d71464c42a8c29053820ee78ba0119e5d"},
@@ -1280,45 +1297,28 @@ files = [
     {file = "grpcio-1.71.0-cp39-cp39-win_amd64.whl", hash = "sha256:63e41b91032f298b3e973b3fa4093cbbc620c875e2da7b93e249d4728b54559a"},
     {file = "grpcio-1.71.0.tar.gz", hash = "sha256:2b85f7820475ad3edec209d3d89a7909ada16caab05d3f2e08a7e8ae3200a55c"},
 ]
+markers = {main = "python_version >= \"3.10\" and extra == \"extra-proxy\"", dev = "python_version >= \"3.10\"", proxy-dev = "python_version >= \"3.10\""}
 
 [package.extras]
 protobuf = ["grpcio-tools (>=1.71.0)"]
 
 [[package]]
 name = "grpcio-status"
-version = "1.70.0"
+version = "1.62.3"
 description = "Status proto mapping for gRPC"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.6"
 groups = ["main"]
-markers = "python_version < \"3.10\" and extra == \"extra-proxy\""
+markers = "extra == \"extra-proxy\""
 files = [
-    {file = "grpcio_status-1.70.0-py3-none-any.whl", hash = "sha256:fc5a2ae2b9b1c1969cc49f3262676e6854aa2398ec69cb5bd6c47cd501904a85"},
-    {file = "grpcio_status-1.70.0.tar.gz", hash = "sha256:0e7b42816512433b18b9d764285ff029bde059e9d41f8fe10a60631bd8348101"},
+    {file = "grpcio-status-1.62.3.tar.gz", hash = "sha256:289bdd7b2459794a12cf95dc0cb727bd4a1742c37bd823f760236c937e53a485"},
+    {file = "grpcio_status-1.62.3-py3-none-any.whl", hash = "sha256:f9049b762ba8de6b1086789d8315846e094edac2c50beaf462338b301a8fd4b8"},
 ]
 
 [package.dependencies]
 googleapis-common-protos = ">=1.5.5"
-grpcio = ">=1.70.0"
-protobuf = ">=5.26.1,<6.0dev"
-
-[[package]]
-name = "grpcio-status"
-version = "1.71.0"
-description = "Status proto mapping for gRPC"
-optional = true
-python-versions = ">=3.9"
-groups = ["main"]
-markers = "extra == \"extra-proxy\" and python_version >= \"3.10\""
-files = [
-    {file = "grpcio_status-1.71.0-py3-none-any.whl", hash = "sha256:843934ef8c09e3e858952887467f8256aac3910c55f077a359a65b2b3cde3e68"},
-    {file = "grpcio_status-1.71.0.tar.gz", hash = "sha256:11405fed67b68f406b3f3c7c5ae5104a79d2d309666d10d61b152e91d28fb968"},
-]
-
-[package.dependencies]
-googleapis-common-protos = ">=1.5.5"
-grpcio = ">=1.71.0"
-protobuf = ">=5.26.1,<6.0dev"
+grpcio = ">=1.62.3"
+protobuf = ">=4.21.6"
 
 [[package]]
 name = "gunicorn"
@@ -1550,27 +1550,23 @@ all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2
 
 [[package]]
 name = "importlib-metadata"
-version = "8.5.0"
+version = "7.1.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "dev", "proxy-dev"]
 files = [
-    {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
-    {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
+    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
+    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
 ]
 
 [package.dependencies]
-zipp = ">=3.20"
+zipp = ">=0.5"
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
-type = ["pytest-mypy"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy ; platform_python_implementation != \"PyPy\"", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "importlib-resources"
@@ -2326,6 +2322,142 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 realtime = ["websockets (>=13,<16)"]
 voice-helpers = ["numpy (>=2.0.2)", "sounddevice (>=0.5.1)"]
 
+[[package]]
+name = "opentelemetry-api"
+version = "1.25.0"
+description = "OpenTelemetry Python API"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "opentelemetry_api-1.25.0-py3-none-any.whl", hash = "sha256:757fa1aa020a0f8fa139f8959e53dec2051cc26b832e76fa839a6d76ecefd737"},
+    {file = "opentelemetry_api-1.25.0.tar.gz", hash = "sha256:77c4985f62f2614e42ce77ee4c9da5fa5f0bc1e1821085e9a47533a9323ae869"},
+]
+
+[package.dependencies]
+deprecated = ">=1.2.6"
+importlib-metadata = ">=6.0,<=7.1"
+
+[[package]]
+name = "opentelemetry-exporter-otlp"
+version = "1.25.0"
+description = "OpenTelemetry Collector Exporters"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "opentelemetry_exporter_otlp-1.25.0-py3-none-any.whl", hash = "sha256:d67a831757014a3bc3174e4cd629ae1493b7ba8d189e8a007003cacb9f1a6b60"},
+    {file = "opentelemetry_exporter_otlp-1.25.0.tar.gz", hash = "sha256:ce03199c1680a845f82e12c0a6a8f61036048c07ec7a0bd943142aca8fa6ced0"},
+]
+
+[package.dependencies]
+opentelemetry-exporter-otlp-proto-grpc = "1.25.0"
+opentelemetry-exporter-otlp-proto-http = "1.25.0"
+
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-common"
+version = "1.25.0"
+description = "OpenTelemetry Protobuf encoding"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "opentelemetry_exporter_otlp_proto_common-1.25.0-py3-none-any.whl", hash = "sha256:15637b7d580c2675f70246563363775b4e6de947871e01d0f4e3881d1848d693"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.25.0.tar.gz", hash = "sha256:c93f4e30da4eee02bacd1e004eb82ce4da143a2f8e15b987a9f603e0a85407d3"},
+]
+
+[package.dependencies]
+opentelemetry-proto = "1.25.0"
+
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-grpc"
+version = "1.25.0"
+description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0-py3-none-any.whl", hash = "sha256:3131028f0c0a155a64c430ca600fd658e8e37043cb13209f0109db5c1a3e4eb4"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.25.0.tar.gz", hash = "sha256:c0b1661415acec5af87625587efa1ccab68b873745ca0ee96b69bb1042087eac"},
+]
+
+[package.dependencies]
+deprecated = ">=1.2.6"
+googleapis-common-protos = ">=1.52,<2.0"
+grpcio = ">=1.0.0,<2.0.0"
+opentelemetry-api = ">=1.15,<2.0"
+opentelemetry-exporter-otlp-proto-common = "1.25.0"
+opentelemetry-proto = "1.25.0"
+opentelemetry-sdk = ">=1.25.0,<1.26.0"
+
+[[package]]
+name = "opentelemetry-exporter-otlp-proto-http"
+version = "1.25.0"
+description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "opentelemetry_exporter_otlp_proto_http-1.25.0-py3-none-any.whl", hash = "sha256:2eca686ee11b27acd28198b3ea5e5863a53d1266b91cda47c839d95d5e0541a6"},
+    {file = "opentelemetry_exporter_otlp_proto_http-1.25.0.tar.gz", hash = "sha256:9f8723859e37c75183ea7afa73a3542f01d0fd274a5b97487ea24cb683d7d684"},
+]
+
+[package.dependencies]
+deprecated = ">=1.2.6"
+googleapis-common-protos = ">=1.52,<2.0"
+opentelemetry-api = ">=1.15,<2.0"
+opentelemetry-exporter-otlp-proto-common = "1.25.0"
+opentelemetry-proto = "1.25.0"
+opentelemetry-sdk = ">=1.25.0,<1.26.0"
+requests = ">=2.7,<3.0"
+
+[[package]]
+name = "opentelemetry-proto"
+version = "1.25.0"
+description = "OpenTelemetry Python Proto"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "opentelemetry_proto-1.25.0-py3-none-any.whl", hash = "sha256:f07e3341c78d835d9b86665903b199893befa5e98866f63d22b00d0b7ca4972f"},
+    {file = "opentelemetry_proto-1.25.0.tar.gz", hash = "sha256:35b6ef9dc4a9f7853ecc5006738ad40443701e52c26099e197895cbda8b815a3"},
+]
+
+[package.dependencies]
+protobuf = ">=3.19,<5.0"
+
+[[package]]
+name = "opentelemetry-sdk"
+version = "1.25.0"
+description = "OpenTelemetry Python SDK"
+optional = false
+python-versions = ">=3.8"
+groups = ["dev", "proxy-dev"]
+files = [
+    {file = "opentelemetry_sdk-1.25.0-py3-none-any.whl", hash = "sha256:d97ff7ec4b351692e9d5a15af570c693b8715ad78b8aafbec5c7100fe966b4c9"},
+    {file = "opentelemetry_sdk-1.25.0.tar.gz", hash = "sha256:ce7fc319c57707ef5bf8b74fb9f8ebdb8bfafbe11898410e0d2a761d08a98ec7"},
+]
+
+[package.dependencies]
+opentelemetry-api = "1.25.0"
+opentelemetry-semantic-conventions = "0.46b0"
+typing-extensions = ">=3.7.4"
+
+[[package]]
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+python-versions = ">=3.8"
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+
 [[package]]
 name = "orjson"
 version = "3.10.15"
@@ -2668,25 +2800,25 @@ testing = ["google-api-core (>=1.31.5)"]
 
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 name = "protobuf"
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 name = "pyasn1"
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 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.7"
-groups = ["main"]
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 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 optional = false
 python-versions = ">=3.8"
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+    {file = "wrapt-1.17.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58455b79ec2661c3600e65c0a716955adc2410f7383755d537584b0de41b1d8a"},
+    {file = "wrapt-1.17.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b4e42a40a5e164cbfdb7b386c966a588b1047558a990981ace551ed7e12ca9c2"},
+    {file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:91bd7d1773e64019f9288b7a5101f3ae50d3d8e6b1de7edee9c2ccc1d32f0c0a"},
+    {file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:bb90fb8bda722a1b9d48ac1e6c38f923ea757b3baf8ebd0c82e09c5c1a0e7a04"},
+    {file = "wrapt-1.17.2-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:08e7ce672e35efa54c5024936e559469436f8b8096253404faeb54d2a878416f"},
+    {file = "wrapt-1.17.2-cp38-cp38-win32.whl", hash = "sha256:410a92fefd2e0e10d26210e1dfb4a876ddaf8439ef60d6434f21ef8d87efc5b7"},
+    {file = "wrapt-1.17.2-cp38-cp38-win_amd64.whl", hash = "sha256:95c658736ec15602da0ed73f312d410117723914a5c91a14ee4cdd72f1d790b3"},
+    {file = "wrapt-1.17.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:99039fa9e6306880572915728d7f6c24a86ec57b0a83f6b2491e1d8ab0235b9a"},
+    {file = "wrapt-1.17.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2696993ee1eebd20b8e4ee4356483c4cb696066ddc24bd70bcbb80fa56ff9061"},
+    {file = "wrapt-1.17.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:612dff5db80beef9e649c6d803a8d50c409082f1fedc9dbcdfde2983b2025b82"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:62c2caa1585c82b3f7a7ab56afef7b3602021d6da34fbc1cf234ff139fed3cd9"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c958bcfd59bacc2d0249dcfe575e71da54f9dcf4a8bdf89c4cb9a68a1170d73f"},
+    {file = "wrapt-1.17.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc78a84e2dfbc27afe4b2bd7c80c8db9bca75cc5b85df52bfe634596a1da846b"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:ba0f0eb61ef00ea10e00eb53a9129501f52385c44853dbd6c4ad3f403603083f"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:1e1fe0e6ab7775fd842bc39e86f6dcfc4507ab0ffe206093e76d61cde37225c8"},
+    {file = "wrapt-1.17.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:c86563182421896d73858e08e1db93afdd2b947a70064b813d515d66549e15f9"},
+    {file = "wrapt-1.17.2-cp39-cp39-win32.whl", hash = "sha256:f393cda562f79828f38a819f4788641ac7c4085f30f1ce1a68672baa686482bb"},
+    {file = "wrapt-1.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:36ccae62f64235cf8ddb682073a60519426fdd4725524ae38874adf72b5f2aeb"},
+    {file = "wrapt-1.17.2-py3-none-any.whl", hash = "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8"},
+    {file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
+]
+
 [[package]]
 name = "wsproto"
 version = "1.2.0"
@@ -4363,7 +4584,7 @@ version = "3.20.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
-groups = ["main"]
+groups = ["main", "dev", "proxy-dev"]
 files = [
     {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
     {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
@@ -4384,4 +4605,4 @@ proxy = ["PyJWT", "apscheduler", "backoff", "boto3", "cryptography", "fastapi",
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.8.1,<4.0, !=3.9.7"
-content-hash = "40074b2e47aae8ece058be9a42eda3ca0618e27e4fc9d6529793816df7adb6c8"
+content-hash = "adefc5c35b625ab156ff674c880256643a22880012451d4ade7fa2ef11f5885d"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm"
-version = "1.67.1"
+version = "1.67.3"
 description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
@@ -107,18 +107,24 @@ types-requests = "*"
 types-setuptools = "*"
 types-redis = "*"
 types-PyYAML = "*"
+opentelemetry-api = "1.25.0"
+opentelemetry-sdk = "1.25.0"
+opentelemetry-exporter-otlp = "1.25.0"
 
 [tool.poetry.group.proxy-dev.dependencies]
 prisma = "0.11.0"
 hypercorn = "^0.15.0"
 prometheus-client = "0.20.0"
+opentelemetry-api = "1.25.0"
+opentelemetry-sdk = "1.25.0"
+opentelemetry-exporter-otlp = "1.25.0"
 
 [build-system]
 requires = ["poetry-core", "wheel"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "1.67.1"
+version = "1.67.3"
 version_files = [
     "pyproject.toml:^version"
 ]

security.md

@@ -22,9 +22,6 @@
 - Audit Logs with retention policy
 - Control Allowed IP Addresses that can access your Cloud LiteLLM Instance
 
-For security inquiries, please contact us at support@berri.ai
-
-
 For security inquiries, please contact us at support@berri.ai
 
 #### Supported data regions for LiteLLM Cloud

test-results/.last-run.json

@@ -0,0 +1,4 @@
+{
+  "status": "failed",
+  "failedTests": []
+}

tests/image_gen_tests/base_image_generation_test.py

@@ -66,8 +66,8 @@ class BaseImageGenTest(ABC):
             logged_standard_logging_payload = custom_logger.standard_logging_payload
             print("logged_standard_logging_payload", logged_standard_logging_payload)
             assert logged_standard_logging_payload is not None
-            # assert logged_standard_logging_payload["response_cost"] is not None
-            # assert logged_standard_logging_payload["response_cost"] > 0
+            assert logged_standard_logging_payload["response_cost"] is not None
+            assert logged_standard_logging_payload["response_cost"] > 0
 
             from openai.types.images_response import ImagesResponse
 

tests/image_gen_tests/test_image_generation.py

@@ -161,6 +161,9 @@ class TestOpenAIDalle3(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:
         return {"model": "dall-e-3"}
 
+class TestOpenAIGPTImage1(BaseImageGenTest):
+    def get_base_image_generation_call_args(self) -> dict:
+        return {"model": "gpt-image-1"}
 
 class TestAzureOpenAIDalle3(BaseImageGenTest):
     def get_base_image_generation_call_args(self) -> dict:

tests/litellm/integrations/arize/test_arize_utils.py

@@ -0,0 +1,231 @@
+import json
+import os
+import sys
+from typing import Optional
+
+# Adds the grandparent directory to sys.path to allow importing project modules
+sys.path.insert(0, os.path.abspath("../.."))
+
+import asyncio
+import litellm
+import pytest
+from litellm.integrations.arize.arize import ArizeLogger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.integrations._types.open_inference import (
+    SpanAttributes,
+    MessageAttributes,
+    ToolCallAttributes,
+)
+from litellm.types.utils import Choices, StandardCallbackDynamicParams
+
+
+def test_arize_set_attributes():
+    """
+    Test setting attributes for Arize, including all custom LLM attributes.
+    Ensures that the correct span attributes are being added during a request.
+    """
+    from unittest.mock import MagicMock
+    from litellm.types.utils import ModelResponse
+
+    span = MagicMock()  # Mocked tracing span to test attribute setting
+
+    # Construct kwargs to simulate a real LLM request scenario
+    kwargs = {
+        "model": "gpt-4o",
+        "messages": [{"role": "user", "content": "Basic Request Content"}],
+        "standard_logging_object": {
+            "model_parameters": {"user": "test_user"},
+            "metadata": {"key_1": "value_1", "key_2": None},
+            "call_type": "completion",
+        },
+        "optional_params": {
+            "max_tokens": "100",
+            "temperature": "1",
+            "top_p": "5",
+            "stream": False,
+            "user": "test_user",
+            "tools": [
+                {
+                    "function": {
+                        "name": "get_weather",
+                        "description": "Fetches weather details.",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "location": {
+                                    "type": "string",
+                                    "description": "City name",
+                                }
+                            },
+                            "required": ["location"],
+                        },
+                    }
+                }
+            ],
+            "functions": [{"name": "get_weather"}, {"name": "get_stock_price"}],
+        },
+        "litellm_params": {"custom_llm_provider": "openai"},
+    }
+
+    # Simulated LLM response object
+    response_obj = ModelResponse(
+        usage={"total_tokens": 100, "completion_tokens": 60, "prompt_tokens": 40},
+        choices=[
+            Choices(message={"role": "assistant", "content": "Basic Response Content"})
+        ],
+        model="gpt-4o",
+        id="chatcmpl-ID",
+    )
+
+    # Apply attribute setting via ArizeLogger
+    ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
+
+    # Validate that the expected number of attributes were set
+    assert span.set_attribute.call_count == 28
+
+    # Metadata attached to the span
+    span.set_attribute.assert_any_call(
+        SpanAttributes.METADATA, json.dumps({"key_1": "value_1", "key_2": None})
+    )
+
+    # Basic LLM information
+    span.set_attribute.assert_any_call(SpanAttributes.LLM_MODEL_NAME, "gpt-4o")
+    span.set_attribute.assert_any_call("llm.request.type", "completion")
+    span.set_attribute.assert_any_call(SpanAttributes.LLM_PROVIDER, "openai")
+
+    # LLM generation parameters
+    span.set_attribute.assert_any_call("llm.request.max_tokens", "100")
+    span.set_attribute.assert_any_call("llm.request.temperature", "1")
+    span.set_attribute.assert_any_call("llm.request.top_p", "5")
+
+    # Streaming and user info
+    span.set_attribute.assert_any_call("llm.is_streaming", "False")
+    span.set_attribute.assert_any_call("llm.user", "test_user")
+
+    # Response metadata
+    span.set_attribute.assert_any_call("llm.response.id", "chatcmpl-ID")
+    span.set_attribute.assert_any_call("llm.response.model", "gpt-4o")
+    span.set_attribute.assert_any_call(SpanAttributes.OPENINFERENCE_SPAN_KIND, "LLM")
+
+    # Request message content and metadata
+    span.set_attribute.assert_any_call(
+        SpanAttributes.INPUT_VALUE, "Basic Request Content"
+    )
+    span.set_attribute.assert_any_call(
+        f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
+        "user",
+    )
+    span.set_attribute.assert_any_call(
+        f"{SpanAttributes.LLM_INPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
+        "Basic Request Content",
+    )
+
+    # Tool call definitions and function names
+    span.set_attribute.assert_any_call(
+        f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_NAME}", "get_weather"
+    )
+    span.set_attribute.assert_any_call(
+        f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_DESCRIPTION}",
+        "Fetches weather details.",
+    )
+    span.set_attribute.assert_any_call(
+        f"{SpanAttributes.LLM_TOOLS}.0.{SpanAttributes.TOOL_PARAMETERS}",
+        json.dumps(
+            {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string", "description": "City name"}
+                },
+                "required": ["location"],
+            }
+        ),
+    )
+
+    # Tool calls captured from optional_params
+    span.set_attribute.assert_any_call(
+        f"{MessageAttributes.MESSAGE_TOOL_CALLS}.0.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
+        "get_weather",
+    )
+    span.set_attribute.assert_any_call(
+        f"{MessageAttributes.MESSAGE_TOOL_CALLS}.1.{ToolCallAttributes.TOOL_CALL_FUNCTION_NAME}",
+        "get_stock_price",
+    )
+
+    # Invocation parameters
+    span.set_attribute.assert_any_call(
+        SpanAttributes.LLM_INVOCATION_PARAMETERS, '{"user": "test_user"}'
+    )
+
+    # User ID
+    span.set_attribute.assert_any_call(SpanAttributes.USER_ID, "test_user")
+
+    # Output message content
+    span.set_attribute.assert_any_call(
+        SpanAttributes.OUTPUT_VALUE, "Basic Response Content"
+    )
+    span.set_attribute.assert_any_call(
+        f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}",
+        "assistant",
+    )
+    span.set_attribute.assert_any_call(
+        f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}",
+        "Basic Response Content",
+    )
+
+    # Token counts
+    span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_TOTAL, 100)
+    span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, 60)
+    span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_PROMPT, 40)
+
+
+class TestArizeLogger(CustomLogger):
+    """
+    Custom logger implementation to capture standard_callback_dynamic_params.
+    Used to verify that dynamic config keys are being passed to callbacks.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.standard_callback_dynamic_params: Optional[
+            StandardCallbackDynamicParams
+        ] = None
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        # Capture dynamic params and print them for verification
+        print("logged kwargs", json.dumps(kwargs, indent=4, default=str))
+        self.standard_callback_dynamic_params = kwargs.get(
+            "standard_callback_dynamic_params"
+        )
+
+
+@pytest.mark.asyncio
+async def test_arize_dynamic_params():
+    """
+    Test to ensure that dynamic Arize keys (API key and space key)
+    are received inside the callback logger at runtime.
+    """
+    test_arize_logger = TestArizeLogger()
+    litellm.callbacks = [test_arize_logger]
+
+    # Perform a mocked async completion call to trigger logging
+    await litellm.acompletion(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": "Basic Request Content"}],
+        mock_response="test",
+        arize_api_key="test_api_key_dynamic",
+        arize_space_key="test_space_key_dynamic",
+    )
+
+    # Allow for async propagation
+    await asyncio.sleep(2)
+
+    # Assert dynamic parameters were received in the callback
+    assert test_arize_logger.standard_callback_dynamic_params is not None
+    assert (
+        test_arize_logger.standard_callback_dynamic_params.get("arize_api_key")
+        == "test_api_key_dynamic"
+    )
+    assert (
+        test_arize_logger.standard_callback_dynamic_params.get("arize_space_key")
+        == "test_space_key_dynamic"
+    )

tests/litellm/integrations/test_agentops.py

@@ -0,0 +1,98 @@
+import os
+import sys
+import pytest
+from unittest.mock import patch, MagicMock
+
+sys.path.insert(0, os.path.abspath("../.."))  # Adds the parent directory to the system-path
+
+from litellm.integrations.agentops.agentops import AgentOps, AgentOpsConfig
+
+@pytest.fixture
+def mock_auth_response():
+    return {
+        "token": "test_jwt_token",
+        "project_id": "test_project_id"
+    }
+
+@pytest.fixture
+def agentops_config():
+    return AgentOpsConfig(
+        endpoint="https://otlp.agentops.cloud/v1/traces",
+        api_key="test_api_key",
+        service_name="test_service",
+        deployment_environment="test_env",
+        auth_endpoint="https://api.agentops.ai/v3/auth/token"
+    )
+
+def test_agentops_config_from_env():
+    """Test that AgentOpsConfig correctly reads from environment variables"""
+    with patch.dict(os.environ, {
+        "AGENTOPS_API_KEY": "test_key",
+        "AGENTOPS_SERVICE_NAME": "test_service",
+        "AGENTOPS_ENVIRONMENT": "test_env"
+    }):
+        config = AgentOpsConfig.from_env()
+        assert config.api_key == "test_key"
+        assert config.service_name == "test_service"
+        assert config.deployment_environment == "test_env"
+        assert config.endpoint == "https://otlp.agentops.cloud/v1/traces"
+        assert config.auth_endpoint == "https://api.agentops.ai/v3/auth/token"
+
+def test_agentops_config_defaults():
+    """Test that AgentOpsConfig uses correct default values"""
+    config = AgentOpsConfig()
+    assert config.service_name is None
+    assert config.deployment_environment is None
+    assert config.api_key is None
+    assert config.endpoint == "https://otlp.agentops.cloud/v1/traces"
+    assert config.auth_endpoint == "https://api.agentops.ai/v3/auth/token"
+
+@patch('litellm.integrations.agentops.agentops.AgentOps._fetch_auth_token')
+def test_fetch_auth_token_success(mock_fetch_auth_token, mock_auth_response):
+    """Test successful JWT token fetch"""
+    mock_fetch_auth_token.return_value = mock_auth_response
+    
+    config = AgentOpsConfig(api_key="test_key")
+    agentops = AgentOps(config=config)
+    
+    mock_fetch_auth_token.assert_called_once_with("test_key", "https://api.agentops.ai/v3/auth/token")
+    assert agentops.resource_attributes.get("project.id") == mock_auth_response.get("project_id")
+
+@patch('litellm.integrations.agentops.agentops.AgentOps._fetch_auth_token')
+def test_fetch_auth_token_failure(mock_fetch_auth_token):
+    """Test failed JWT token fetch"""
+    mock_fetch_auth_token.side_effect = Exception("Failed to fetch auth token: Unauthorized")
+    
+    config = AgentOpsConfig(api_key="test_key")
+    agentops = AgentOps(config=config)
+    
+    mock_fetch_auth_token.assert_called_once()
+    assert "project.id" not in agentops.resource_attributes
+
+@patch('litellm.integrations.agentops.agentops.AgentOps._fetch_auth_token')
+def test_agentops_initialization(mock_fetch_auth_token, agentops_config, mock_auth_response):
+    """Test AgentOps initialization with config"""
+    mock_fetch_auth_token.return_value = mock_auth_response
+    
+    agentops = AgentOps(config=agentops_config)
+    
+    assert agentops.resource_attributes["service.name"] == "test_service"
+    assert agentops.resource_attributes["deployment.environment"] == "test_env"
+    assert agentops.resource_attributes["telemetry.sdk.name"] == "agentops"
+    assert agentops.resource_attributes["project.id"] == "test_project_id"
+
+def test_agentops_initialization_no_auth():
+    """Test AgentOps initialization without authentication"""
+    test_config = AgentOpsConfig(
+        endpoint="https://otlp.agentops.cloud/v1/traces",
+        api_key=None,  # No API key
+        service_name="test_service",
+        deployment_environment="test_env"
+    )
+    
+    agentops = AgentOps(config=test_config)
+    
+    assert agentops.resource_attributes["service.name"] == "test_service"
+    assert agentops.resource_attributes["deployment.environment"] == "test_env"
+    assert agentops.resource_attributes["telemetry.sdk.name"] == "agentops"
+    assert "project.id" not in agentops.resource_attributes