Merge branch 'main' into key-mask-bug-fix

.env.example

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

.gitignore

@@ -86,3 +86,5 @@ litellm/proxy/db/migrations/0_init/migration.sql
 litellm/proxy/db/migrations/*
 litellm/proxy/migrations/*config.yaml
 litellm/proxy/migrations/*
+config.yaml
+tests/litellm/litellm_core_utils/llm_cost_calc/log.txt

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/pass_through/cohere.md

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

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

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

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

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

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

@@ -1011,8 +1011,7 @@ Expected Response:
 | Supported Operations | `/v1/responses`|
 | Azure OpenAI Responses API | [Azure OpenAI Responses API ↗](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/responses?tabs=python-secure) |
 | Cost Tracking, Logging Support | ✅ LiteLLM will log, track cost for Responses API Requests |
-
-
+| Supported OpenAI Params | ✅ All OpenAI params are supported, [See here](https://github.com/BerriAI/litellm/blob/0717369ae6969882d149933da48eeb8ab0e691bd/litellm/llms/openai/responses/transformation.py#L23) |
 
 ## Usage
 

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

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

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

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

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

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

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

@@ -542,6 +542,154 @@ print(resp)
 ```
 
 
+### **Thinking / `reasoning_content`**
+
+LiteLLM translates OpenAI's `reasoning_effort` to Gemini's `thinking` parameter. [Code](https://github.com/BerriAI/litellm/blob/620664921902d7a9bfb29897a7b27c1a7ef4ddfb/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py#L362)
+
+**Mapping**
+
+| reasoning_effort | thinking |
+| ---------------- | -------- |
+| "low"            | "budget_tokens": 1024 |
+| "medium"         | "budget_tokens": 2048 |
+| "high"           | "budget_tokens": 4096 |
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from litellm import completion
+
+# !gcloud auth application-default login - run this to add vertex credentials to your env
+
+resp = completion(
+    model="vertex_ai/gemini-2.5-flash-preview-04-17",
+    messages=[{"role": "user", "content": "What is the capital of France?"}],
+    reasoning_effort="low",
+    vertex_project="project-id",
+    vertex_location="us-central1"
+)
+
+```
+
+</TabItem>
+
+<TabItem value="proxy" label="PROXY">
+
+1. Setup config.yaml
+
+```yaml
+- model_name: gemini-2.5-flash
+  litellm_params:
+    model: vertex_ai/gemini-2.5-flash-preview-04-17
+    vertex_credentials: {"project_id": "project-id", "location": "us-central1", "project_key": "project-key"}
+    vertex_project: "project-id"
+    vertex_location: "us-central1"
+```
+
+2. Start proxy
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it! 
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
+  -d '{
+    "model": "gemini-2.5-flash",
+    "messages": [{"role": "user", "content": "What is the capital of France?"}],
+    "reasoning_effort": "low"
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+
+**Expected Response**
+
+```python
+ModelResponse(
+    id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
+    created=1740470510,
+    model='claude-3-7-sonnet-20250219',
+    object='chat.completion',
+    system_fingerprint=None,
+    choices=[
+        Choices(
+            finish_reason='stop',
+            index=0,
+            message=Message(
+                content="The capital of France is Paris.",
+                role='assistant',
+                tool_calls=None,
+                function_call=None,
+                reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
+            ),
+        )
+    ],
+    usage=Usage(
+        completion_tokens=68,
+        prompt_tokens=42,
+        total_tokens=110,
+        completion_tokens_details=None,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            audio_tokens=None,
+            cached_tokens=0,
+            text_tokens=None,
+            image_tokens=None
+        ),
+        cache_creation_input_tokens=0,
+        cache_read_input_tokens=0
+    )
+)
+```
+
+#### Pass `thinking` to Gemini models
+
+You can also pass the `thinking` parameter to Gemini models.
+
+This is translated to Gemini's [`thinkingConfig` parameter](https://ai.google.dev/gemini-api/docs/thinking#set-budget).
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+from litellm import completion
+
+# !gcloud auth application-default login - run this to add vertex credentials to your env
+
+response = litellm.completion(
+  model="vertex_ai/gemini-2.5-flash-preview-04-17",
+  messages=[{"role": "user", "content": "What is the capital of France?"}],
+  thinking={"type": "enabled", "budget_tokens": 1024},
+  vertex_project="project-id",
+  vertex_location="us-central1"
+)
+```
+
+</TabItem>
+<TabItem value="proxy" label="PROXY">
+
+```bash
+curl http://0.0.0.0:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_KEY" \
+  -d '{
+    "model": "vertex_ai/gemini-2.5-flash-preview-04-17",
+    "messages": [{"role": "user", "content": "What is the capital of France?"}],
+    "thinking": {"type": "enabled", "budget_tokens": 1024}
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+
 ### **Context Caching**
 
 Use Vertex AI context caching is supported by calling provider api directly. (Unified Endpoint support comin soon.).

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

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

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

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

docs/my-website/docs/reasoning_content.md

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

docs/my-website/docs/response_api.md

@@ -14,22 +14,22 @@ LiteLLM provides a BETA endpoint in the spec of [OpenAI's `/responses` API](http
 | Fallbacks | ✅ | Works between supported models |
 | Loadbalancing | ✅ | Works between supported models |
 | Supported LiteLLM Versions | 1.63.8+ | |
-| Supported LLM providers | `openai` | |
+| Supported LLM providers | **All LiteLLM supported providers** | `openai`, `anthropic`, `bedrock`, `vertex_ai`, `gemini`, `azure`, `azure_ai` etc. |
 
 ## Usage
 
-## Create a model response
+### LiteLLM Python SDK
 
 <Tabs>
-<TabItem value="litellm-sdk" label="LiteLLM SDK">
+<TabItem value="openai" label="OpenAI">
 
 #### Non-streaming
-```python showLineNumbers
+```python showLineNumbers title="OpenAI Non-streaming Response"
 import litellm
 
 # Non-streaming response
 response = litellm.responses(
-    model="o1-pro",
+    model="openai/o1-pro",
     input="Tell me a three sentence bedtime story about a unicorn.",
     max_output_tokens=100
 )
@@ -38,12 +38,12 @@ print(response)
 ```
 
 #### Streaming
-```python showLineNumbers
+```python showLineNumbers title="OpenAI Streaming Response"
 import litellm
 
 # Streaming response
 response = litellm.responses(
-    model="o1-pro",
+    model="openai/o1-pro",
     input="Tell me a three sentence bedtime story about a unicorn.",
     stream=True
 )
@@ -53,58 +53,169 @@ for event in response:
 ```
 
 </TabItem>
-<TabItem value="proxy" label="OpenAI SDK with LiteLLM Proxy">
 
-First, add this to your litellm proxy config.yaml:
-```yaml showLineNumbers
-model_list:
-  - model_name: o1-pro
-    litellm_params:
-      model: openai/o1-pro
-      api_key: os.environ/OPENAI_API_KEY
-```
-
-Start your LiteLLM proxy:
-```bash
-litellm --config /path/to/config.yaml
-
-# RUNNING on http://0.0.0.0:4000
-```
-
-Then use the OpenAI SDK pointed to your proxy:
+<TabItem value="anthropic" label="Anthropic">
 
 #### Non-streaming
-```python showLineNumbers
-from openai import OpenAI
+```python showLineNumbers title="Anthropic Non-streaming Response"
+import litellm
+import os
 
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
+# Set API key
+os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
 
 # Non-streaming response
-response = client.responses.create(
-    model="o1-pro",
-    input="Tell me a three sentence bedtime story about a unicorn."
+response = litellm.responses(
+    model="anthropic/claude-3-5-sonnet-20240620",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    max_output_tokens=100
 )
 
 print(response)
 ```
 
 #### Streaming
-```python showLineNumbers
-from openai import OpenAI
+```python showLineNumbers title="Anthropic Streaming Response"
+import litellm
+import os
 
-# Initialize client with your proxy URL
-client = OpenAI(
-    base_url="http://localhost:4000",  # Your proxy URL
-    api_key="your-api-key"             # Your proxy API key
-)
+# Set API key
+os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
 
 # Streaming response
-response = client.responses.create(
-    model="o1-pro",
+response = litellm.responses(
+    model="anthropic/claude-3-5-sonnet-20240620",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+
+<TabItem value="vertex" label="Vertex AI">
+
+#### Non-streaming
+```python showLineNumbers title="Vertex AI Non-streaming Response"
+import litellm
+import os
+
+# Set credentials - Vertex AI uses application default credentials
+# Run 'gcloud auth application-default login' to authenticate
+os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
+os.environ["VERTEXAI_LOCATION"] = "us-central1"
+
+# Non-streaming response
+response = litellm.responses(
+    model="vertex_ai/gemini-1.5-pro",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    max_output_tokens=100
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="Vertex AI Streaming Response"
+import litellm
+import os
+
+# Set credentials - Vertex AI uses application default credentials
+# Run 'gcloud auth application-default login' to authenticate
+os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
+os.environ["VERTEXAI_LOCATION"] = "us-central1"
+
+# Streaming response
+response = litellm.responses(
+    model="vertex_ai/gemini-1.5-pro",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+
+<TabItem value="bedrock" label="AWS Bedrock">
+
+#### Non-streaming
+```python showLineNumbers title="AWS Bedrock Non-streaming Response"
+import litellm
+import os
+
+# Set AWS credentials
+os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
+os.environ["AWS_REGION_NAME"] = "us-west-2"  # or your AWS region
+
+# Non-streaming response
+response = litellm.responses(
+    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    max_output_tokens=100
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="AWS Bedrock Streaming Response"
+import litellm
+import os
+
+# Set AWS credentials
+os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
+os.environ["AWS_REGION_NAME"] = "us-west-2"  # or your AWS region
+
+# Streaming response
+response = litellm.responses(
+    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+
+<TabItem value="gemini" label="Google AI Studio">
+
+#### Non-streaming
+```python showLineNumbers title="Google AI Studio Non-streaming Response"
+import litellm
+import os
+
+# Set API key for Google AI Studio
+os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
+
+# Non-streaming response
+response = litellm.responses(
+    model="gemini/gemini-1.5-flash",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    max_output_tokens=100
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="Google AI Studio Streaming Response"
+import litellm
+import os
+
+# Set API key for Google AI Studio
+os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
+
+# Streaming response
+response = litellm.responses(
+    model="gemini/gemini-1.5-flash",
     input="Tell me a three sentence bedtime story about a unicorn.",
     stream=True
 )
@@ -116,10 +227,407 @@ for event in response:
 </TabItem>
 </Tabs>
 
+### LiteLLM Proxy with OpenAI SDK
 
-## **Supported Providers**
+First, set up and start your LiteLLM proxy server.
 
-| Provider    | Link to Usage      |
-|-------------|--------------------|
-| OpenAI|   [Usage](#usage)                 |
-| Azure OpenAI|   [Usage](../docs/providers/azure#responses-api)                 |  
+```bash title="Start LiteLLM Proxy Server"
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+<Tabs>
+<TabItem value="openai" label="OpenAI">
+
+First, add this to your litellm proxy config.yaml:
+```yaml showLineNumbers title="OpenAI Proxy Configuration"
+model_list:
+  - model_name: openai/o1-pro
+    litellm_params:
+      model: openai/o1-pro
+      api_key: os.environ/OPENAI_API_KEY
+```
+
+#### Non-streaming
+```python showLineNumbers title="OpenAI Proxy Non-streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Non-streaming response
+response = client.responses.create(
+    model="openai/o1-pro",
+    input="Tell me a three sentence bedtime story about a unicorn."
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="OpenAI Proxy Streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Streaming response
+response = client.responses.create(
+    model="openai/o1-pro",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+
+<TabItem value="anthropic" label="Anthropic">
+
+First, add this to your litellm proxy config.yaml:
+```yaml showLineNumbers title="Anthropic Proxy Configuration"
+model_list:
+  - model_name: anthropic/claude-3-5-sonnet-20240620
+    litellm_params:
+      model: anthropic/claude-3-5-sonnet-20240620
+      api_key: os.environ/ANTHROPIC_API_KEY
+```
+
+#### Non-streaming
+```python showLineNumbers title="Anthropic Proxy Non-streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Non-streaming response
+response = client.responses.create(
+    model="anthropic/claude-3-5-sonnet-20240620",
+    input="Tell me a three sentence bedtime story about a unicorn."
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="Anthropic Proxy Streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Streaming response
+response = client.responses.create(
+    model="anthropic/claude-3-5-sonnet-20240620",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+
+<TabItem value="vertex" label="Vertex AI">
+
+First, add this to your litellm proxy config.yaml:
+```yaml showLineNumbers title="Vertex AI Proxy Configuration"
+model_list:
+  - model_name: vertex_ai/gemini-1.5-pro
+    litellm_params:
+      model: vertex_ai/gemini-1.5-pro
+      vertex_project: your-gcp-project-id
+      vertex_location: us-central1
+```
+
+#### Non-streaming
+```python showLineNumbers title="Vertex AI Proxy Non-streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Non-streaming response
+response = client.responses.create(
+    model="vertex_ai/gemini-1.5-pro",
+    input="Tell me a three sentence bedtime story about a unicorn."
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="Vertex AI Proxy Streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Streaming response
+response = client.responses.create(
+    model="vertex_ai/gemini-1.5-pro",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+
+<TabItem value="bedrock" label="AWS Bedrock">
+
+First, add this to your litellm proxy config.yaml:
+```yaml showLineNumbers title="AWS Bedrock Proxy Configuration"
+model_list:
+  - model_name: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
+    litellm_params:
+      model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
+      aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
+      aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
+      aws_region_name: us-west-2
+```
+
+#### Non-streaming
+```python showLineNumbers title="AWS Bedrock Proxy Non-streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Non-streaming response
+response = client.responses.create(
+    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+    input="Tell me a three sentence bedtime story about a unicorn."
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="AWS Bedrock Proxy Streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Streaming response
+response = client.responses.create(
+    model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+
+<TabItem value="gemini" label="Google AI Studio">
+
+First, add this to your litellm proxy config.yaml:
+```yaml showLineNumbers title="Google AI Studio Proxy Configuration"
+model_list:
+  - model_name: gemini/gemini-1.5-flash
+    litellm_params:
+      model: gemini/gemini-1.5-flash
+      api_key: os.environ/GEMINI_API_KEY
+```
+
+#### Non-streaming
+```python showLineNumbers title="Google AI Studio Proxy Non-streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Non-streaming response
+response = client.responses.create(
+    model="gemini/gemini-1.5-flash",
+    input="Tell me a three sentence bedtime story about a unicorn."
+)
+
+print(response)
+```
+
+#### Streaming
+```python showLineNumbers title="Google AI Studio Proxy Streaming Response"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-api-key"             # Your proxy API key
+)
+
+# Streaming response
+response = client.responses.create(
+    model="gemini/gemini-1.5-flash",
+    input="Tell me a three sentence bedtime story about a unicorn.",
+    stream=True
+)
+
+for event in response:
+    print(event)
+```
+
+</TabItem>
+</Tabs>
+
+## Supported Responses API Parameters
+
+| Provider | Supported Parameters |
+|----------|---------------------|
+| `openai` | [All Responses API parameters are supported](https://github.com/BerriAI/litellm/blob/7c3df984da8e4dff9201e4c5353fdc7a2b441831/litellm/llms/openai/responses/transformation.py#L23) |
+| `azure` | [All Responses API parameters are supported](https://github.com/BerriAI/litellm/blob/7c3df984da8e4dff9201e4c5353fdc7a2b441831/litellm/llms/openai/responses/transformation.py#L23) |
+| `anthropic` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
+| `bedrock` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
+| `gemini` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
+| `vertex_ai` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
+| `azure_ai` | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
+| All other llm api providers | [See supported parameters here](https://github.com/BerriAI/litellm/blob/f39d9178868662746f159d5ef642c7f34f9bfe5f/litellm/responses/litellm_completion_transformation/transformation.py#L57) |
+
+## Load Balancing with Routing Affinity
+
+When using the Responses API with multiple deployments of the same model (e.g., multiple Azure OpenAI endpoints), LiteLLM provides routing affinity for conversations. This ensures that follow-up requests using a `previous_response_id` are routed to the same deployment that generated the original response.
+
+
+#### Example Usage
+
+<Tabs>
+<TabItem value="python-sdk" label="Python SDK">
+
+```python showLineNumbers title="Python SDK with Routing Affinity"
+import litellm
+
+# Set up router with multiple deployments of the same model
+router = litellm.Router(
+    model_list=[
+        {
+            "model_name": "azure-gpt4-turbo",
+            "litellm_params": {
+                "model": "azure/gpt-4-turbo",
+                "api_key": "your-api-key-1",
+                "api_version": "2024-06-01",
+                "api_base": "https://endpoint1.openai.azure.com",
+            },
+        },
+        {
+            "model_name": "azure-gpt4-turbo",
+            "litellm_params": {
+                "model": "azure/gpt-4-turbo",
+                "api_key": "your-api-key-2",
+                "api_version": "2024-06-01",
+                "api_base": "https://endpoint2.openai.azure.com",
+            },
+        },
+    ],
+    optional_pre_call_checks=["responses_api_deployment_check"],
+)
+
+# Initial request
+response = await router.aresponses(
+    model="azure-gpt4-turbo",
+    input="Hello, who are you?",
+    truncation="auto",
+)
+
+# Store the response ID
+response_id = response.id
+
+# Follow-up request - will be automatically routed to the same deployment
+follow_up = await router.aresponses(
+    model="azure-gpt4-turbo",
+    input="Tell me more about yourself",
+    truncation="auto",
+    previous_response_id=response_id  # This ensures routing to the same deployment
+)
+```
+
+</TabItem>
+<TabItem value="proxy-server" label="Proxy Server">
+
+#### 1. Setup routing affinity on proxy config.yaml
+
+To enable routing affinity for Responses API in your LiteLLM proxy, set `optional_pre_call_checks: ["responses_api_deployment_check"]` in your proxy config.yaml.
+
+```yaml showLineNumbers title="config.yaml with Responses API Routing Affinity"
+model_list:
+  - model_name: azure-gpt4-turbo
+    litellm_params:
+      model: azure/gpt-4-turbo
+      api_key: your-api-key-1
+      api_version: 2024-06-01
+      api_base: https://endpoint1.openai.azure.com
+  - model_name: azure-gpt4-turbo
+    litellm_params:
+      model: azure/gpt-4-turbo
+      api_key: your-api-key-2
+      api_version: 2024-06-01
+      api_base: https://endpoint2.openai.azure.com
+
+router_settings:
+  optional_pre_call_checks: ["responses_api_deployment_check"]
+```
+
+#### 2. Use the OpenAI Python SDK to make requests to LiteLLM Proxy
+
+```python showLineNumbers title="OpenAI Client with Proxy Server"
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:4000",
+    api_key="your-api-key"
+)
+
+# Initial request
+response = client.responses.create(
+    model="azure-gpt4-turbo",
+    input="Hello, who are you?"
+)
+
+response_id = response.id
+
+# Follow-up request - will be automatically routed to the same deployment
+follow_up = client.responses.create(
+    model="azure-gpt4-turbo",
+    input="Tell me more about yourself",
+    previous_response_id=response_id  # This ensures routing to the same deployment
+)
+```
+
+</TabItem>
+</Tabs>

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

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

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

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

docs/my-website/sidebars.js

@@ -69,6 +69,7 @@ const sidebars = {
             "proxy/clientside_auth",
             "proxy/request_headers",
             "proxy/response_headers",
+            "proxy/model_discovery",
           ],
         },
         {
@@ -101,6 +102,7 @@ const sidebars = {
             "proxy/admin_ui_sso",
             "proxy/self_serve",
             "proxy/public_teams",
+            "tutorials/scim_litellm",
             "proxy/custom_sso",
             "proxy/ui_credentials",
             "proxy/ui_logs"
@@ -330,6 +332,8 @@ const sidebars = {
             "pass_through/vertex_ai",
             "pass_through/google_ai_studio",
             "pass_through/cohere",
+            "pass_through/vllm",
+            "pass_through/mistral",
             "pass_through/openai_passthrough",
             "pass_through/anthropic_completion",
             "pass_through/bedrock",
@@ -407,6 +411,7 @@ const sidebars = {
       type: "category",
       label: "Logging & Observability",
       items: [
+        "observability/agentops_integration",
         "observability/langfuse_integration",
         "observability/lunary_integration",
         "observability/mlflow",

litellm-proxy-extras/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "litellm-proxy-extras"
-version = "0.1.10"
+version = "0.1.11"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 authors = ["BerriAI"]
 readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.commitizen]
-version = "0.1.10"
+version = "0.1.11"
 version_files = [
     "pyproject.toml:version",
     "../requirements.txt:litellm-proxy-extras==",

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
@@ -415,6 +416,7 @@ deepseek_models: List = []
 azure_ai_models: List = []
 jina_ai_models: List = []
 voyage_models: List = []
+infinity_models: List = []
 databricks_models: List = []
 cloudflare_models: List = []
 codestral_models: List = []
@@ -556,6 +558,8 @@ def add_known_models():
             azure_ai_models.append(key)
         elif value.get("litellm_provider") == "voyage":
             voyage_models.append(key)
+        elif value.get("litellm_provider") == "infinity":
+            infinity_models.append(key)
         elif value.get("litellm_provider") == "databricks":
             databricks_models.append(key)
         elif value.get("litellm_provider") == "cloudflare":
@@ -644,6 +648,7 @@ model_list = (
     + deepseek_models
     + azure_ai_models
     + voyage_models
+    + infinity_models
     + databricks_models
     + cloudflare_models
     + codestral_models
@@ -699,6 +704,7 @@ models_by_provider: dict = {
     "mistral": mistral_chat_models,
     "azure_ai": azure_ai_models,
     "voyage": voyage_models,
+    "infinity": infinity_models,
     "databricks": databricks_models,
     "cloudflare": cloudflare_models,
     "codestral": codestral_models,
@@ -946,6 +952,7 @@ from .llms.topaz.image_variations.transformation import TopazImageVariationConfi
 from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig
 from .llms.groq.chat.transformation import GroqChatConfig
 from .llms.voyage.embedding.transformation import VoyageEmbeddingConfig
+from .llms.infinity.embedding.transformation import InfinityEmbeddingConfig
 from .llms.azure_ai.chat.transformation import AzureAIStudioConfig
 from .llms.mistral.mistral_chat_transformation import MistralConfig
 from .llms.openai.responses.transformation import OpenAIResponsesAPIConfig

litellm/assistants/main.py

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

litellm/constants.py

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

litellm/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/get_supported_openai_params.py

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

litellm/litellm_core_utils/litellm_logging.py

@@ -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/llm_cost_calc/utils.py

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

litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py

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

litellm/litellm_core_utils/model_param_helper.py

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

litellm/litellm_core_utils/prompt_templates/common_utils.py

@@ -6,7 +6,7 @@ import io
 import mimetypes
 import re
 from os import PathLike
-from typing import 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]:
@@ -471,3 +500,59 @@ def unpack_defs(schema, defs):
                 unpack_defs(ref, defs)
                 value["items"] = ref
                 continue
+
+
+def _get_image_mime_type_from_url(url: str) -> Optional[str]:
+    """
+    Get mime type for common image URLs
+    See gemini mime types: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/image-understanding#image-requirements
+
+    Supported by Gemini:
+     application/pdf
+    audio/mpeg
+    audio/mp3
+    audio/wav
+    image/png
+    image/jpeg
+    image/webp
+    text/plain
+    video/mov
+    video/mpeg
+    video/mp4
+    video/mpg
+    video/avi
+    video/wmv
+    video/mpegps
+    video/flv
+    """
+    url = url.lower()
+
+    # Map file extensions to mime types
+    mime_types = {
+        # Images
+        (".jpg", ".jpeg"): "image/jpeg",
+        (".png",): "image/png",
+        (".webp",): "image/webp",
+        # Videos
+        (".mp4",): "video/mp4",
+        (".mov",): "video/mov",
+        (".mpeg", ".mpg"): "video/mpeg",
+        (".avi",): "video/avi",
+        (".wmv",): "video/wmv",
+        (".mpegps",): "video/mpegps",
+        (".flv",): "video/flv",
+        # Audio
+        (".mp3",): "audio/mp3",
+        (".wav",): "audio/wav",
+        (".mpeg",): "audio/mpeg",
+        # Documents
+        (".pdf",): "application/pdf",
+        (".txt",): "text/plain",
+    }
+
+    # Check each extension group against the URL
+    for extensions, mime_type in mime_types.items():
+        if any(url.endswith(ext) for ext in extensions):
+            return mime_type
+
+    return None

litellm/litellm_core_utils/prompt_templates/factory.py

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

litellm/llms/anthropic/chat/handler.py

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

litellm/llms/anthropic/chat/transformation.py

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

litellm/llms/anthropic/experimental_pass_through/messages/handler.py

@@ -1,6 +1,6 @@
 """
 - call /messages on Anthropic API
-- Make streaming + non-streaming request - just pass it through direct to Anthropic. No need to do anything special here 
+- Make streaming + non-streaming request - just pass it through direct to Anthropic. No need to do anything special here
 - Ensure requests are logged in the DB - stream + non-stream
 
 """
@@ -43,7 +43,9 @@ class AnthropicMessagesHandler:
         from litellm.proxy.pass_through_endpoints.success_handler import (
             PassThroughEndpointLogging,
         )
-        from litellm.proxy.pass_through_endpoints.types import EndpointType
+        from litellm.types.passthrough_endpoints.pass_through_endpoints import (
+            EndpointType,
+        )
 
         # Create success handler object
         passthrough_success_handler_obj = PassThroughEndpointLogging()
@@ -98,11 +100,11 @@ async def anthropic_messages(
         api_base=optional_params.api_base,
         api_key=optional_params.api_key,
     )
-    anthropic_messages_provider_config: Optional[
-        BaseAnthropicMessagesConfig
-    ] = ProviderConfigManager.get_provider_anthropic_messages_config(
-        model=model,
-        provider=litellm.LlmProviders(_custom_llm_provider),
+    anthropic_messages_provider_config: Optional[BaseAnthropicMessagesConfig] = (
+        ProviderConfigManager.get_provider_anthropic_messages_config(
+            model=model,
+            provider=litellm.LlmProviders(_custom_llm_provider),
+        )
     )
     if anthropic_messages_provider_config is None:
         raise ValueError(

litellm/llms/azure/assistants.py

@@ -288,6 +288,7 @@ class AzureAssistantsAPI(BaseAzureLLM):
             timeout=timeout,
             max_retries=max_retries,
             client=client,
+            litellm_params=litellm_params,
         )
 
         thread_message: OpenAIMessage = openai_client.beta.threads.messages.create(  # type: ignore

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 +91,48 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
         final_url = httpx.URL(new_url).copy_with(params=query_params)
 
         return str(final_url)
+
+    #########################################################
+    ########## 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
+
+        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).
+        """
+        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
+        delete_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
+            )
+        )
+
+        data: Dict = {}
+        verbose_logger.debug(f"delete response url={delete_url}")
+        return delete_url, data

litellm/llms/azure_ai/chat/transformation.py

@@ -1,3 +1,4 @@
+import enum
 from typing import Any, List, Optional, Tuple, cast
 from urllib.parse import urlparse
 
@@ -19,6 +20,10 @@ from litellm.types.utils import ModelResponse, ProviderField
 from litellm.utils import _add_path_to_api_base, supports_tool_choice
 
 
+class AzureFoundryErrorStrings(str, enum.Enum):
+    SET_EXTRA_PARAMETERS_TO_PASS_THROUGH = "Set extra-parameters to 'pass-through'"
+
+
 class AzureAIStudioConfig(OpenAIConfig):
     def get_supported_openai_params(self, model: str) -> List:
         model_supports_tool_choice = True  # azure ai supports this by default
@@ -240,12 +245,18 @@ class AzureAIStudioConfig(OpenAIConfig):
     ) -> bool:
         should_drop_params = litellm_params.get("drop_params") or litellm.drop_params
         error_text = e.response.text
+
         if should_drop_params and "Extra inputs are not permitted" in error_text:
             return True
         elif (
             "unknown field: parameter index is not a valid field" in error_text
         ):  # remove index from tool calls
             return True
+        elif (
+            AzureFoundryErrorStrings.SET_EXTRA_PARAMETERS_TO_PASS_THROUGH.value
+            in error_text
+        ):  # remove extra-parameters from tool calls
+            return True
         return super().should_retry_llm_api_inside_llm_translation_on_http_error(
             e=e, litellm_params=litellm_params
         )
@@ -265,5 +276,46 @@ class AzureAIStudioConfig(OpenAIConfig):
             litellm.remove_index_from_tool_calls(
                 messages=_messages,
             )
+        elif (
+            AzureFoundryErrorStrings.SET_EXTRA_PARAMETERS_TO_PASS_THROUGH.value
+            in e.response.text
+        ):
+            request_data = self._drop_extra_params_from_request_data(
+                request_data, e.response.text
+            )
         data = drop_params_from_unprocessable_entity_error(e=e, data=request_data)
         return data
+
+    def _drop_extra_params_from_request_data(
+        self, request_data: dict, error_text: str
+    ) -> dict:
+        params_to_drop = self._extract_params_to_drop_from_error_text(error_text)
+        if params_to_drop:
+            for param in params_to_drop:
+                if param in request_data:
+                    request_data.pop(param, None)
+        return request_data
+
+    def _extract_params_to_drop_from_error_text(
+        self, error_text: str
+    ) -> Optional[List[str]]:
+        """
+        Error text looks like this"
+            "Extra parameters ['stream_options', 'extra-parameters'] are not allowed when extra-parameters is not set or set to be 'error'.
+        """
+        import re
+
+        # Extract parameters within square brackets
+        match = re.search(r"\[(.*?)\]", error_text)
+        if not match:
+            return []
+
+        # Parse the extracted string into a list of parameter names
+        params_str = match.group(1)
+        params = []
+        for param in params_str.split(","):
+            # Clean up the parameter name (remove quotes, spaces)
+            clean_param = param.strip().strip("'").strip('"')
+            if clean_param:
+                params.append(clean_param)
+        return params

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 +119,31 @@ 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 ##########
+    #########################################################
+
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
     ) -> BaseLLMException:

litellm/llms/bedrock/chat/converse_transformation.py

@@ -22,6 +22,7 @@ from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMExcepti
 from litellm.types.llms.bedrock import *
 from litellm.types.llms.openai import (
     AllMessageValues,
+    ChatCompletionRedactedThinkingBlock,
     ChatCompletionResponseMessage,
     ChatCompletionSystemMessage,
     ChatCompletionThinkingBlock,
@@ -375,25 +376,27 @@ class AmazonConverseConfig(BaseConfig):
         system_content_blocks: List[SystemContentBlock] = []
         for idx, message in enumerate(messages):
             if message["role"] == "system":
-                _system_content_block: Optional[SystemContentBlock] = None
-                _cache_point_block: Optional[SystemContentBlock] = None
-                if isinstance(message["content"], str) and len(message["content"]) > 0:
-                    _system_content_block = SystemContentBlock(text=message["content"])
-                    _cache_point_block = self._get_cache_point_block(
+                system_prompt_indices.append(idx)
+                if isinstance(message["content"], str) and message["content"]:
+                    system_content_blocks.append(
+                        SystemContentBlock(text=message["content"])
+                    )
+                    cache_block = self._get_cache_point_block(
                         message, block_type="system"
                     )
+                    if cache_block:
+                        system_content_blocks.append(cache_block)
                 elif isinstance(message["content"], list):
                     for m in message["content"]:
-                        if m.get("type", "") == "text" and len(m["text"]) > 0:
-                            _system_content_block = SystemContentBlock(text=m["text"])
-                            _cache_point_block = self._get_cache_point_block(
+                        if m.get("type") == "text" and m.get("text"):
+                            system_content_blocks.append(
+                                SystemContentBlock(text=m["text"])
+                            )
+                            cache_block = self._get_cache_point_block(
                                 m, block_type="system"
                             )
-                if _system_content_block is not None:
-                    system_content_blocks.append(_system_content_block)
-                if _cache_point_block is not None:
-                    system_content_blocks.append(_cache_point_block)
-                system_prompt_indices.append(idx)
+                            if cache_block:
+                                system_content_blocks.append(cache_block)
         if len(system_prompt_indices) > 0:
             for idx in reversed(system_prompt_indices):
                 messages.pop(idx)
@@ -627,9 +630,11 @@ class AmazonConverseConfig(BaseConfig):
 
     def _transform_thinking_blocks(
         self, thinking_blocks: List[BedrockConverseReasoningContentBlock]
-    ) -> List[ChatCompletionThinkingBlock]:
+    ) -> List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]:
         """Return a consistent format for thinking blocks between Anthropic and Bedrock."""
-        thinking_blocks_list: List[ChatCompletionThinkingBlock] = []
+        thinking_blocks_list: List[
+            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
+        ] = []
         for block in thinking_blocks:
             if "reasoningText" in block:
                 _thinking_block = ChatCompletionThinkingBlock(type="thinking")
@@ -640,6 +645,11 @@ class AmazonConverseConfig(BaseConfig):
                 if _signature is not None:
                     _thinking_block["signature"] = _signature
                 thinking_blocks_list.append(_thinking_block)
+            elif "redactedContent" in block:
+                _redacted_block = ChatCompletionRedactedThinkingBlock(
+                    type="redacted_thinking", data=block["redactedContent"]
+                )
+                thinking_blocks_list.append(_redacted_block)
         return thinking_blocks_list
 
     def _transform_usage(self, usage: ConverseTokenUsageBlock) -> Usage:

litellm/llms/bedrock/chat/invoke_handler.py

@@ -50,6 +50,7 @@ from litellm.llms.custom_httpx.http_handler import (
 )
 from litellm.types.llms.bedrock import *
 from litellm.types.llms.openai import (
+    ChatCompletionRedactedThinkingBlock,
     ChatCompletionThinkingBlock,
     ChatCompletionToolCallChunk,
     ChatCompletionToolCallFunctionChunk,
@@ -1255,19 +1256,33 @@ class AWSEventStreamDecoder:
 
     def translate_thinking_blocks(
         self, thinking_block: BedrockConverseReasoningContentBlockDelta
-    ) -> Optional[List[ChatCompletionThinkingBlock]]:
+    ) -> Optional[
+        List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]
+    ]:
         """
         Translate the thinking blocks to a string
         """
 
-        thinking_blocks_list: List[ChatCompletionThinkingBlock] = []
-        _thinking_block = ChatCompletionThinkingBlock(type="thinking")
+        thinking_blocks_list: List[
+            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
+        ] = []
+        _thinking_block: Optional[
+            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
+        ] = None
+
         if "text" in thinking_block:
+            _thinking_block = ChatCompletionThinkingBlock(type="thinking")
             _thinking_block["thinking"] = thinking_block["text"]
         elif "signature" in thinking_block:
+            _thinking_block = ChatCompletionThinkingBlock(type="thinking")
             _thinking_block["signature"] = thinking_block["signature"]
             _thinking_block["thinking"] = ""  # consistent with anthropic response
-        thinking_blocks_list.append(_thinking_block)
+        elif "redactedContent" in thinking_block:
+            _thinking_block = ChatCompletionRedactedThinkingBlock(
+                type="redacted_thinking", data=thinking_block["redactedContent"]
+            )
+        if _thinking_block is not None:
+            thinking_blocks_list.append(_thinking_block)
         return thinking_blocks_list
 
     def converse_chunk_parser(self, chunk_data: dict) -> ModelResponseStream:
@@ -1279,31 +1294,44 @@ class AWSEventStreamDecoder:
             usage: Optional[Usage] = None
             provider_specific_fields: dict = {}
             reasoning_content: Optional[str] = None
-            thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
+            thinking_blocks: Optional[
+                List[
+                    Union[
+                        ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock
+                    ]
+                ]
+            ] = None
 
             index = int(chunk_data.get("contentBlockIndex", 0))
             if "start" in chunk_data:
                 start_obj = ContentBlockStartEvent(**chunk_data["start"])
                 self.content_blocks = []  # reset
-                if (
-                    start_obj is not None
-                    and "toolUse" in start_obj
-                    and start_obj["toolUse"] is not None
-                ):
-                    ## check tool name was formatted by litellm
-                    _response_tool_name = start_obj["toolUse"]["name"]
-                    response_tool_name = get_bedrock_tool_name(
-                        response_tool_name=_response_tool_name
-                    )
-                    tool_use = {
-                        "id": start_obj["toolUse"]["toolUseId"],
-                        "type": "function",
-                        "function": {
-                            "name": response_tool_name,
-                            "arguments": "",
-                        },
-                        "index": index,
-                    }
+                if start_obj is not None:
+                    if "toolUse" in start_obj and start_obj["toolUse"] is not None:
+                        ## check tool name was formatted by litellm
+                        _response_tool_name = start_obj["toolUse"]["name"]
+                        response_tool_name = get_bedrock_tool_name(
+                            response_tool_name=_response_tool_name
+                        )
+                        tool_use = {
+                            "id": start_obj["toolUse"]["toolUseId"],
+                            "type": "function",
+                            "function": {
+                                "name": response_tool_name,
+                                "arguments": "",
+                            },
+                            "index": index,
+                        }
+                    elif (
+                        "reasoningContent" in start_obj
+                        and start_obj["reasoningContent"] is not None
+                    ):  # redacted thinking can be in start object
+                        thinking_blocks = self.translate_thinking_blocks(
+                            start_obj["reasoningContent"]
+                        )
+                        provider_specific_fields = {
+                            "reasoningContent": start_obj["reasoningContent"],
+                        }
             elif "delta" in chunk_data:
                 delta_obj = ContentBlockDeltaEvent(**chunk_data["delta"])
                 self.content_blocks.append(delta_obj)

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
@@ -229,13 +230,17 @@ class BaseLLMHTTPHandler:
         api_key: Optional[str] = None,
         headers: Optional[dict] = {},
         client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        provider_config: Optional[BaseConfig] = None,
     ):
         json_mode: bool = optional_params.pop("json_mode", False)
         extra_body: Optional[dict] = optional_params.pop("extra_body", None)
         fake_stream = fake_stream or optional_params.pop("fake_stream", False)
 
-        provider_config = ProviderConfigManager.get_provider_chat_config(
-            model=model, provider=litellm.LlmProviders(custom_llm_provider)
+        provider_config = (
+            provider_config
+            or ProviderConfigManager.get_provider_chat_config(
+                model=model, provider=litellm.LlmProviders(custom_llm_provider)
+            )
         )
         if provider_config is None:
             raise ValueError(
@@ -1011,6 +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,
@@ -1037,6 +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):
@@ -1060,11 +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(
@@ -1109,6 +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(
@@ -1116,6 +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
@@ -1152,6 +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.
@@ -1179,11 +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(
@@ -1230,6 +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
@@ -1238,6 +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
@@ -1261,6 +1269,163 @@ 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 create_file(
         self,
         create_file_data: CreateFileRequest,

litellm/llms/databricks/chat/transformation.py

@@ -37,6 +37,7 @@ from litellm.types.llms.databricks import (
 )
 from litellm.types.llms.openai import (
     AllMessageValues,
+    ChatCompletionRedactedThinkingBlock,
     ChatCompletionThinkingBlock,
     ChatCompletionToolChoiceFunctionParam,
     ChatCompletionToolChoiceObjectParam,
@@ -314,13 +315,24 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
     @staticmethod
     def extract_reasoning_content(
         content: Optional[AllDatabricksContentValues],
-    ) -> Tuple[Optional[str], Optional[List[ChatCompletionThinkingBlock]]]:
+    ) -> Tuple[
+        Optional[str],
+        Optional[
+            List[
+                Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
+            ]
+        ],
+    ]:
         """
         Extract and return the reasoning content and thinking blocks
         """
         if content is None:
             return None, None
-        thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None
+        thinking_blocks: Optional[
+            List[
+                Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
+            ]
+        ] = None
         reasoning_content: Optional[str] = None
         if isinstance(content, list):
             for item in content:

litellm/llms/fireworks_ai/chat/transformation.py

@@ -1,15 +1,33 @@
-from typing import List, Literal, Optional, Tuple, Union, cast
+import json
+import uuid
+from typing import Any, List, Literal, Optional, Tuple, Union, cast
+
+import httpx
 
 import litellm
+from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.llm_response_utils.get_headers import (
+    get_response_headers,
+)
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionImageObject,
+    ChatCompletionToolParam,
     OpenAIChatCompletionToolParam,
 )
-from litellm.types.utils import ProviderSpecificModelInfo
+from litellm.types.utils import (
+    ChatCompletionMessageToolCall,
+    Choices,
+    Function,
+    Message,
+    ModelResponse,
+    ProviderSpecificModelInfo,
+)
 
 from ...openai.chat.gpt_transformation import OpenAIGPTConfig
+from ..common_utils import FireworksAIException
 
 
 class FireworksAIConfig(OpenAIGPTConfig):
@@ -219,6 +237,94 @@ class FireworksAIConfig(OpenAIGPTConfig):
             headers=headers,
         )
 
+    def _handle_message_content_with_tool_calls(
+        self,
+        message: Message,
+        tool_calls: Optional[List[ChatCompletionToolParam]],
+    ) -> Message:
+        """
+        Fireworks AI sends tool calls in the content field instead of tool_calls
+
+        Relevant Issue: https://github.com/BerriAI/litellm/issues/7209#issuecomment-2813208780
+        """
+        if (
+            tool_calls is not None
+            and message.content is not None
+            and message.tool_calls is None
+        ):
+            try:
+                function = Function(**json.loads(message.content))
+                if function.name != RESPONSE_FORMAT_TOOL_NAME and function.name in [
+                    tool["function"]["name"] for tool in tool_calls
+                ]:
+                    tool_call = ChatCompletionMessageToolCall(
+                        function=function, id=str(uuid.uuid4()), type="function"
+                    )
+                    message.tool_calls = [tool_call]
+
+                    message.content = None
+            except Exception:
+                pass
+
+        return message
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key=api_key,
+            original_response=raw_response.text,
+            additional_args={"complete_input_dict": request_data},
+        )
+
+        ## RESPONSE OBJECT
+        try:
+            completion_response = raw_response.json()
+        except Exception as e:
+            response_headers = getattr(raw_response, "headers", None)
+            raise FireworksAIException(
+                message="Unable to get json response - {}, Original Response: {}".format(
+                    str(e), raw_response.text
+                ),
+                status_code=raw_response.status_code,
+                headers=response_headers,
+            )
+
+        raw_response_headers = dict(raw_response.headers)
+
+        additional_headers = get_response_headers(raw_response_headers)
+
+        response = ModelResponse(**completion_response)
+
+        if response.model is not None:
+            response.model = "fireworks_ai/" + response.model
+
+        ## FIREWORKS AI sends tool calls in the content field instead of tool_calls
+        for choice in response.choices:
+            cast(
+                Choices, choice
+            ).message = self._handle_message_content_with_tool_calls(
+                message=cast(Choices, choice).message,
+                tool_calls=optional_params.get("tools", None),
+            )
+
+        response._hidden_params = {"additional_headers": additional_headers}
+
+        return response
+
     def _get_openai_compatible_provider_info(
         self, api_base: Optional[str], api_key: Optional[str]
     ) -> Tuple[Optional[str], Optional[str]]:

litellm/llms/gemini/chat/transformation.py

@@ -7,6 +7,7 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
 )
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.llms.vertex_ai import ContentType, PartType
+from litellm.utils import supports_reasoning
 
 from ...vertex_ai.gemini.transformation import _gemini_convert_messages_with_history
 from ...vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig
@@ -67,7 +68,7 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
         return super().get_config()
 
     def get_supported_openai_params(self, model: str) -> List[str]:
-        return [
+        supported_params = [
             "temperature",
             "top_p",
             "max_tokens",
@@ -83,6 +84,10 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
             "frequency_penalty",
             "modalities",
         ]
+        if supports_reasoning(model):
+            supported_params.append("reasoning_effort")
+            supported_params.append("thinking")
+        return supported_params
 
     def map_openai_params(
         self,

litellm/llms/hosted_vllm/chat/transformation.py

@@ -2,9 +2,19 @@
 Translate from OpenAI's `/v1/chat/completions` to VLLM's `/v1/chat/completions`
 """
 
-from typing import Optional, Tuple
+from typing import List, Optional, Tuple, cast
 
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    _get_image_mime_type_from_url,
+)
+from litellm.litellm_core_utils.prompt_templates.factory import _parse_mime_type
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionFileObject,
+    ChatCompletionVideoObject,
+    ChatCompletionVideoUrlObject,
+)
 
 from ....utils import _remove_additional_properties, _remove_strict_from_schema
 from ...openai.chat.gpt_transformation import OpenAIGPTConfig
@@ -38,3 +48,71 @@ class HostedVLLMChatConfig(OpenAIGPTConfig):
             api_key or get_secret_str("HOSTED_VLLM_API_KEY") or "fake-api-key"
         )  # vllm does not require an api key
         return api_base, dynamic_api_key
+
+    def _is_video_file(self, content_item: ChatCompletionFileObject) -> bool:
+        """
+        Check if the file is a video
+
+        - format: video/<extension>
+        - file_data: base64 encoded video data
+        - file_id: infer mp4 from extension
+        """
+        file = content_item.get("file", {})
+        format = file.get("format")
+        file_data = file.get("file_data")
+        file_id = file.get("file_id")
+        if content_item.get("type") != "file":
+            return False
+        if format and format.startswith("video/"):
+            return True
+        elif file_data:
+            mime_type = _parse_mime_type(file_data)
+            if mime_type and mime_type.startswith("video/"):
+                return True
+        elif file_id:
+            mime_type = _get_image_mime_type_from_url(file_id)
+            if mime_type and mime_type.startswith("video/"):
+                return True
+        return False
+
+    def _convert_file_to_video_url(
+        self, content_item: ChatCompletionFileObject
+    ) -> ChatCompletionVideoObject:
+        file = content_item.get("file", {})
+        file_id = file.get("file_id")
+        file_data = file.get("file_data")
+
+        if file_id:
+            return ChatCompletionVideoObject(
+                type="video_url", video_url=ChatCompletionVideoUrlObject(url=file_id)
+            )
+        elif file_data:
+            return ChatCompletionVideoObject(
+                type="video_url", video_url=ChatCompletionVideoUrlObject(url=file_data)
+            )
+        raise ValueError("file_id or file_data is required")
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str
+    ) -> List[AllMessageValues]:
+        """
+        Support translating video files from file_id or file_data to video_url
+        """
+        for message in messages:
+            if message["role"] == "user":
+                message_content = message.get("content")
+                if message_content and isinstance(message_content, list):
+                    replaced_content_items: List[
+                        Tuple[int, ChatCompletionFileObject]
+                    ] = []
+                    for idx, content_item in enumerate(message_content):
+                        if content_item.get("type") == "file":
+                            content_item = cast(ChatCompletionFileObject, content_item)
+                            if self._is_video_file(content_item):
+                                replaced_content_items.append((idx, content_item))
+                    for idx, content_item in replaced_content_items:
+                        message_content[idx] = self._convert_file_to_video_url(
+                            content_item
+                        )
+        transformed_messages = super()._transform_messages(messages, model)
+        return transformed_messages

litellm/llms/infinity/common_utils.py

@@ -1,10 +1,16 @@
+from typing import Union
 import httpx
 
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 
 
 class InfinityError(BaseLLMException):
-    def __init__(self, status_code, message):
+    def __init__(
+        self, 
+        status_code: int, 
+        message: str,
+        headers: Union[dict, httpx.Headers] = {}
+        ):
         self.status_code = status_code
         self.message = message
         self.request = httpx.Request(
@@ -16,4 +22,5 @@ class InfinityError(BaseLLMException):
             message=message,
             request=self.request,
             response=self.response,
+            headers=headers,
         )  # Call the base class constructor with the parameters it needs

litellm/llms/infinity/embedding/handler.py

@@ -0,0 +1,5 @@
+"""
+Infinity Embedding - uses `llm_http_handler.py` to make httpx requests
+
+Request/Response transformation is handled in `transformation.py`
+"""

litellm/llms/infinity/embedding/transformation.py

@@ -0,0 +1,141 @@
+from typing import List, Optional, Union
+
+import httpx
+
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
+from litellm.types.utils import EmbeddingResponse, Usage
+
+from ..common_utils import InfinityError
+
+
+class InfinityEmbeddingConfig(BaseEmbeddingConfig):
+    """
+    Reference: https://infinity.modal.michaelfeil.eu/docs
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    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:
+        if api_base is None:
+            raise ValueError("api_base is required for Infinity embeddings")
+        # Remove trailing slashes and ensure clean base URL
+        api_base = api_base.rstrip("/")
+        if not api_base.endswith("/embeddings"):
+            api_base = f"{api_base}/embeddings"
+        return api_base
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key is None:
+            api_key = get_secret_str("INFINITY_API_KEY")
+
+        default_headers = {
+            "Authorization": f"Bearer {api_key}",
+            "accept": "application/json",
+            "Content-Type": "application/json",
+        }
+
+        # If 'Authorization' is provided in headers, it overrides the default.
+        if "Authorization" in headers:
+            default_headers["Authorization"] = headers["Authorization"]
+
+        # Merge other headers, overriding any default ones except Authorization
+        return {**default_headers, **headers}
+
+    def get_supported_openai_params(self, model: str) -> list:
+        return [
+            "encoding_format",
+            "modality",
+            "dimensions",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        Map OpenAI params to Infinity params
+
+        Reference: https://infinity.modal.michaelfeil.eu/docs
+        """
+        if "encoding_format" in non_default_params:
+            optional_params["encoding_format"] = non_default_params["encoding_format"]
+        if "modality" in non_default_params:
+            optional_params["modality"] = non_default_params["modality"]
+        if "dimensions" in non_default_params:
+            optional_params["output_dimension"] = non_default_params["dimensions"]
+        return optional_params
+
+    def transform_embedding_request(
+        self,
+        model: str,
+        input: AllEmbeddingInputValues,
+        optional_params: dict,
+        headers: dict,
+    ) -> dict:
+        return {
+            "input": input,
+            "model": model,
+            **optional_params,
+        }
+
+    def transform_embedding_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: EmbeddingResponse,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str] = None,
+        request_data: dict = {},
+        optional_params: dict = {},
+        litellm_params: dict = {},
+    ) -> EmbeddingResponse:
+        try:
+            raw_response_json = raw_response.json()
+        except Exception:
+            raise InfinityError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+
+        # model_response.usage
+        model_response.model = raw_response_json.get("model")
+        model_response.data = raw_response_json.get("data")
+        model_response.object = raw_response_json.get("object")
+
+        usage = Usage(
+            prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0),
+            total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
+        )
+        model_response.usage = usage
+        return model_response
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return InfinityError(
+            message=error_message, status_code=status_code, headers=headers
+        )

litellm/llms/infinity/rerank/transformation.py

@@ -22,7 +22,7 @@ from litellm.types.rerank import (
     RerankTokens,
 )
 
-from .common_utils import InfinityError
+from ..common_utils import InfinityError
 
 
 class InfinityRerankConfig(CohereRerankConfig):

litellm/llms/litellm_proxy/chat/transformation.py

@@ -13,6 +13,7 @@ class LiteLLMProxyChatConfig(OpenAIGPTConfig):
     def get_supported_openai_params(self, model: str) -> List:
         list = super().get_supported_openai_params(model)
         list.append("thinking")
+        list.append("reasoning_effort")
         return list
 
     def _map_openai_params(

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 +214,39 @@ 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)

litellm/llms/triton/completion/transformation.py

@@ -201,8 +201,6 @@ class TritonGenerateConfig(TritonConfig):
                 "max_tokens": int(
                     optional_params.get("max_tokens", DEFAULT_MAX_TOKENS_FOR_TRITON)
                 ),
-                "bad_words": [""],
-                "stop_words": [""],
             },
             "stream": bool(stream),
         }

litellm/llms/vertex_ai/gemini/transformation.py

@@ -12,6 +12,9 @@ from pydantic import BaseModel
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    _get_image_mime_type_from_url,
+)
 from litellm.litellm_core_utils.prompt_templates.factory import (
     convert_to_anthropic_image_obj,
     convert_to_gemini_tool_call_invoke,
@@ -99,62 +102,6 @@ def _process_gemini_image(image_url: str, format: Optional[str] = None) -> PartT
         raise e
 
 
-def _get_image_mime_type_from_url(url: str) -> Optional[str]:
-    """
-    Get mime type for common image URLs
-    See gemini mime types: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/image-understanding#image-requirements
-
-    Supported by Gemini:
-     application/pdf
-    audio/mpeg
-    audio/mp3
-    audio/wav
-    image/png
-    image/jpeg
-    image/webp
-    text/plain
-    video/mov
-    video/mpeg
-    video/mp4
-    video/mpg
-    video/avi
-    video/wmv
-    video/mpegps
-    video/flv
-    """
-    url = url.lower()
-
-    # Map file extensions to mime types
-    mime_types = {
-        # Images
-        (".jpg", ".jpeg"): "image/jpeg",
-        (".png",): "image/png",
-        (".webp",): "image/webp",
-        # Videos
-        (".mp4",): "video/mp4",
-        (".mov",): "video/mov",
-        (".mpeg", ".mpg"): "video/mpeg",
-        (".avi",): "video/avi",
-        (".wmv",): "video/wmv",
-        (".mpegps",): "video/mpegps",
-        (".flv",): "video/flv",
-        # Audio
-        (".mp3",): "audio/mp3",
-        (".wav",): "audio/wav",
-        (".mpeg",): "audio/mpeg",
-        # Documents
-        (".pdf",): "application/pdf",
-        (".txt",): "text/plain",
-    }
-
-    # Check each extension group against the URL
-    for extensions, mime_type in mime_types.items():
-        if any(url.endswith(ext) for ext in extensions):
-            return mime_type
-
-    return None
-
-
 def _gemini_convert_messages_with_history(  # noqa: PLR0915
     messages: List[AllMessageValues],
 ) -> List[ContentType]:
@@ -269,6 +216,11 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                     msg_dict = messages[msg_i]  # type: ignore
                 assistant_msg = ChatCompletionAssistantMessage(**msg_dict)  # type: ignore
                 _message_content = assistant_msg.get("content", None)
+                reasoning_content = assistant_msg.get("reasoning_content", None)
+                if reasoning_content is not None:
+                    assistant_content.append(
+                        PartType(thought=True, text=reasoning_content)
+                    )
                 if _message_content is not None and isinstance(_message_content, list):
                     _parts = []
                     for element in _message_content:
@@ -276,6 +228,7 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                             if element["type"] == "text":
                                 _part = PartType(text=element["text"])
                                 _parts.append(_part)
+
                     assistant_content.extend(_parts)
                 elif (
                     _message_content is not None

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -24,6 +24,11 @@ import litellm
 import litellm.litellm_core_utils
 import litellm.litellm_core_utils.litellm_logging
 from litellm import verbose_logger
+from litellm.constants import (
+    DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
+    DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
+    DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
+)
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
 from litellm.llms.custom_httpx.http_handler import (
@@ -31,6 +36,7 @@ from litellm.llms.custom_httpx.http_handler import (
     HTTPHandler,
     get_async_httpx_client,
 )
+from litellm.types.llms.anthropic import AnthropicThinkingParam
 from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionResponseMessage,
@@ -45,11 +51,13 @@ from litellm.types.llms.vertex_ai import (
     ContentType,
     FunctionCallingConfig,
     FunctionDeclaration,
+    GeminiThinkingConfig,
     GenerateContentResponseBody,
     HttpxPartType,
     LogprobsResult,
     ToolConfig,
     Tools,
+    UsageMetadata,
 )
 from litellm.types.utils import (
     ChatCompletionTokenLogprob,
@@ -59,7 +67,7 @@ from litellm.types.utils import (
     TopLogprob,
     Usage,
 )
-from litellm.utils import CustomStreamWrapper, ModelResponse
+from litellm.utils import CustomStreamWrapper, ModelResponse, supports_reasoning
 
 from ....utils import _remove_additional_properties, _remove_strict_from_schema
 from ..common_utils import VertexAIError, _build_vertex_schema
@@ -190,7 +198,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         return super().get_config()
 
     def get_supported_openai_params(self, model: str) -> List[str]:
-        return [
+        supported_params = [
             "temperature",
             "top_p",
             "max_tokens",
@@ -210,6 +218,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             "top_logprobs",
             "modalities",
         ]
+        if supports_reasoning(model):
+            supported_params.append("reasoning_effort")
+            supported_params.append("thinking")
+        return supported_params
 
     def map_tool_choice_values(
         self, model: str, tool_choice: Union[str, dict]
@@ -313,10 +325,14 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         if isinstance(old_schema, list):
             for item in old_schema:
                 if isinstance(item, dict):
-                    item = _build_vertex_schema(parameters=item, add_property_ordering=True)
+                    item = _build_vertex_schema(
+                        parameters=item, add_property_ordering=True
+                    )
 
         elif isinstance(old_schema, dict):
-            old_schema = _build_vertex_schema(parameters=old_schema, add_property_ordering=True)
+            old_schema = _build_vertex_schema(
+                parameters=old_schema, add_property_ordering=True
+            )
         return old_schema
 
     def apply_response_schema_transformation(self, value: dict, optional_params: dict):
@@ -343,6 +359,43 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 value=optional_params["response_schema"]
             )
 
+    @staticmethod
+    def _map_reasoning_effort_to_thinking_budget(
+        reasoning_effort: str,
+    ) -> GeminiThinkingConfig:
+        if reasoning_effort == "low":
+            return {
+                "thinkingBudget": DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
+                "includeThoughts": True,
+            }
+        elif reasoning_effort == "medium":
+            return {
+                "thinkingBudget": DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
+                "includeThoughts": True,
+            }
+        elif reasoning_effort == "high":
+            return {
+                "thinkingBudget": DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
+                "includeThoughts": True,
+            }
+        else:
+            raise ValueError(f"Invalid reasoning effort: {reasoning_effort}")
+
+    @staticmethod
+    def _map_thinking_param(
+        thinking_param: AnthropicThinkingParam,
+    ) -> GeminiThinkingConfig:
+        thinking_enabled = thinking_param.get("type") == "enabled"
+        thinking_budget = thinking_param.get("budget_tokens")
+
+        params: GeminiThinkingConfig = {}
+        if thinking_enabled:
+            params["includeThoughts"] = True
+        if thinking_budget is not None and isinstance(thinking_budget, int):
+            params["thinkingBudget"] = thinking_budget
+
+        return params
+
     def map_openai_params(
         self,
         non_default_params: Dict,
@@ -399,6 +452,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     optional_params["tool_choice"] = _tool_choice_value
             elif param == "seed":
                 optional_params["seed"] = value
+            elif param == "reasoning_effort" and isinstance(value, str):
+                optional_params[
+                    "thinkingConfig"
+                ] = VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(value)
+            elif param == "thinking":
+                optional_params[
+                    "thinkingConfig"
+                ] = VertexGeminiConfig._map_thinking_param(
+                    cast(AnthropicThinkingParam, value)
+                )
             elif param == "modalities" and isinstance(value, list):
                 response_modalities = []
                 for modality in value:
@@ -514,19 +577,28 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
 
     def get_assistant_content_message(
         self, parts: List[HttpxPartType]
-    ) -> Optional[str]:
-        _content_str = ""
+    ) -> Tuple[Optional[str], Optional[str]]:
+        content_str: Optional[str] = None
+        reasoning_content_str: Optional[str] = None
         for part in parts:
+            _content_str = ""
             if "text" in part:
                 _content_str += part["text"]
             elif "inlineData" in part:  # base64 encoded image
                 _content_str += "data:{};base64,{}".format(
                     part["inlineData"]["mimeType"], part["inlineData"]["data"]
                 )
+            if len(_content_str) > 0:
+                if part.get("thought") is True:
+                    if reasoning_content_str is None:
+                        reasoning_content_str = ""
+                    reasoning_content_str += _content_str
+                else:
+                    if content_str is None:
+                        content_str = ""
+                    content_str += _content_str
 
-        if _content_str:
-            return _content_str
-        return None
+        return content_str, reasoning_content_str
 
     def _transform_parts(
         self,
@@ -669,6 +741,23 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
 
         return model_response
 
+    def is_candidate_token_count_inclusive(self, usage_metadata: UsageMetadata) -> bool:
+        """
+        Check if the candidate token count is inclusive of the thinking token count
+
+        if prompttokencount + candidatesTokenCount == totalTokenCount, then the candidate token count is inclusive of the thinking token count
+
+        else the candidate token count is exclusive of the thinking token count
+
+        Addresses - https://github.com/BerriAI/litellm/pull/10141#discussion_r2052272035
+        """
+        if usage_metadata.get("promptTokenCount", 0) + usage_metadata.get(
+            "candidatesTokenCount", 0
+        ) == usage_metadata.get("totalTokenCount", 0):
+            return True
+        else:
+            return False
+
     def _calculate_usage(
         self,
         completion_response: GenerateContentResponseBody,
@@ -677,6 +766,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         audio_tokens: Optional[int] = None
         text_tokens: Optional[int] = None
         prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
+        reasoning_tokens: Optional[int] = None
         if "cachedContentTokenCount" in completion_response["usageMetadata"]:
             cached_tokens = completion_response["usageMetadata"][
                 "cachedContentTokenCount"
@@ -687,22 +777,35 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     audio_tokens = detail["tokenCount"]
                 elif detail["modality"] == "TEXT":
                     text_tokens = detail["tokenCount"]
-
+        if "thoughtsTokenCount" in completion_response["usageMetadata"]:
+            reasoning_tokens = completion_response["usageMetadata"][
+                "thoughtsTokenCount"
+            ]
         prompt_tokens_details = PromptTokensDetailsWrapper(
             cached_tokens=cached_tokens,
             audio_tokens=audio_tokens,
             text_tokens=text_tokens,
         )
+
+        completion_tokens = completion_response["usageMetadata"].get(
+            "candidatesTokenCount", 0
+        )
+        if (
+            not self.is_candidate_token_count_inclusive(
+                completion_response["usageMetadata"]
+            )
+            and reasoning_tokens
+        ):
+            completion_tokens = reasoning_tokens + completion_tokens
         ## GET USAGE ##
         usage = Usage(
             prompt_tokens=completion_response["usageMetadata"].get(
                 "promptTokenCount", 0
             ),
-            completion_tokens=completion_response["usageMetadata"].get(
-                "candidatesTokenCount", 0
-            ),
+            completion_tokens=completion_tokens,
             total_tokens=completion_response["usageMetadata"].get("totalTokenCount", 0),
             prompt_tokens_details=prompt_tokens_details,
+            reasoning_tokens=reasoning_tokens,
         )
 
         return usage
@@ -731,11 +834,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 citation_metadata.append(candidate["citationMetadata"])
 
             if "parts" in candidate["content"]:
-                chat_completion_message[
-                    "content"
-                ] = VertexGeminiConfig().get_assistant_content_message(
+                (
+                    content,
+                    reasoning_content,
+                ) = VertexGeminiConfig().get_assistant_content_message(
                     parts=candidate["content"]["parts"]
                 )
+                if content is not None:
+                    chat_completion_message["content"] = content
+                if reasoning_content is not None:
+                    chat_completion_message["reasoning_content"] = reasoning_content
 
                 functions, tools = self._transform_parts(
                     parts=candidate["content"]["parts"],

litellm/llms/watsonx/common_utils.py

@@ -38,7 +38,7 @@ def generate_iam_token(api_key=None, **params) -> str:
         headers = {}
         headers["Content-Type"] = "application/x-www-form-urlencoded"
         if api_key is None:
-            api_key = get_secret_str("WX_API_KEY") or get_secret_str("WATSONX_API_KEY")
+            api_key = get_secret_str("WX_API_KEY") or get_secret_str("WATSONX_API_KEY") or get_secret_str("WATSONX_APIKEY")
         if api_key is None:
             raise ValueError("API key is required")
         headers["Accept"] = "application/json"

litellm/main.py

@@ -1435,6 +1435,7 @@ def completion(  # type: ignore # noqa: PLR0915
                     custom_llm_provider=custom_llm_provider,
                     encoding=encoding,
                     stream=stream,
+                    provider_config=provider_config,
                 )
             except Exception as e:
                 ## LOGGING - log the original exception returned
@@ -1596,6 +1597,37 @@ def completion(  # type: ignore # noqa: PLR0915
                     additional_args={"headers": headers},
                 )
             response = _response
+        elif custom_llm_provider == "fireworks_ai":
+            ## COMPLETION CALL
+            try:
+                response = base_llm_http_handler.completion(
+                    model=model,
+                    messages=messages,
+                    headers=headers,
+                    model_response=model_response,
+                    api_key=api_key,
+                    api_base=api_base,
+                    acompletion=acompletion,
+                    logging_obj=logging,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    timeout=timeout,  # type: ignore
+                    client=client,
+                    custom_llm_provider=custom_llm_provider,
+                    encoding=encoding,
+                    stream=stream,
+                    provider_config=provider_config,
+                )
+            except Exception as e:
+                ## LOGGING - log the original exception returned
+                logging.post_call(
+                    input=messages,
+                    api_key=api_key,
+                    original_response=str(e),
+                    additional_args={"headers": headers},
+                )
+                raise e
+
         elif custom_llm_provider == "groq":
             api_base = (
                 api_base  # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
@@ -3852,6 +3884,21 @@ def embedding(  # noqa: PLR0915
                 aembedding=aembedding,
                 litellm_params={},
             )
+        elif custom_llm_provider == "infinity":
+            response = base_llm_http_handler.embedding(
+                model=model,
+                input=input,
+                custom_llm_provider=custom_llm_provider,
+                api_base=api_base,
+                api_key=api_key,
+                logging_obj=logging,
+                timeout=timeout,
+                model_response=EmbeddingResponse(),
+                optional_params=optional_params,
+                client=client,
+                aembedding=aembedding,
+                litellm_params={},
+            )
         elif custom_llm_provider == "watsonx":
             credentials = IBMWatsonXMixin.get_watsonx_credentials(
                 optional_params=optional_params, api_key=api_key, api_base=api_base

litellm/model_prices_and_context_window_backup.json

@@ -5,6 +5,7 @@
         "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", 
         "input_cost_per_token": 0.0000,
         "output_cost_per_token": 0.000,
+        "output_cost_per_reasoning_token": 0.000,
         "litellm_provider": "one of https://docs.litellm.ai/docs/providers",
         "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank",
         "supports_function_calling": true,
@@ -1471,6 +1472,72 @@
         "litellm_provider": "openai",
         "supported_endpoints": ["/v1/audio/speech"]
     },
+    "azure/computer-use-preview": {
+        "max_tokens": 1024,
+        "max_input_tokens": 8192,
+        "max_output_tokens": 1024,
+        "input_cost_per_token": 0.000003,
+        "output_cost_per_token": 0.000012,
+        "litellm_provider": "azure",
+        "mode": "chat",
+        "supported_endpoints": ["/v1/responses"],
+        "supported_modalities": ["text", "image"],
+        "supported_output_modalities": ["text"],
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_response_schema": true,
+        "supports_vision": true,
+        "supports_prompt_caching": false,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_reasoning": true
+    },
+    "azure/gpt-4o-audio-preview-2024-12-17": {
+        "max_tokens": 16384,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 16384,
+        "input_cost_per_token": 0.0000025,
+        "input_cost_per_audio_token": 0.00004,
+        "output_cost_per_token": 0.00001,
+        "output_cost_per_audio_token": 0.00008,
+        "litellm_provider": "azure",
+        "mode": "chat",
+        "supported_endpoints": ["/v1/chat/completions"],
+        "supported_modalities": ["text", "audio"],
+        "supported_output_modalities": ["text", "audio"],
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_response_schema": false,
+        "supports_vision": false,
+        "supports_prompt_caching": false,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_native_streaming": true,
+        "supports_reasoning": false
+    },
+    "azure/gpt-4o-mini-audio-preview-2024-12-17": {
+        "max_tokens": 16384,
+        "max_input_tokens": 128000,
+        "max_output_tokens": 16384,
+        "input_cost_per_token": 0.0000025,
+        "input_cost_per_audio_token": 0.00004,
+        "output_cost_per_token": 0.00001,
+        "output_cost_per_audio_token": 0.00008,
+        "litellm_provider": "azure",
+        "mode": "chat",
+        "supported_endpoints": ["/v1/chat/completions"],
+        "supported_modalities": ["text", "audio"],
+        "supported_output_modalities": ["text", "audio"],
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_response_schema": false,
+        "supports_vision": false,
+        "supports_prompt_caching": false,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_native_streaming": true,
+        "supports_reasoning": false
+    },
     "azure/gpt-4.1": {
         "max_tokens": 32768,
         "max_input_tokens": 1047576,
@@ -1529,6 +1596,170 @@
             "search_context_size_high": 50e-3
         }
     },
+    "azure/gpt-4.1-mini": {
+        "max_tokens": 32768,
+        "max_input_tokens": 1047576,
+        "max_output_tokens": 32768,
+        "input_cost_per_token": 0.4e-6,
+        "output_cost_per_token": 1.6e-6,
+        "input_cost_per_token_batches": 0.2e-6,
+        "output_cost_per_token_batches": 0.8e-6,
+        "cache_read_input_token_cost": 0.1e-6,
+        "litellm_provider": "azure",
+        "mode": "chat",
+        "supported_endpoints": ["/v1/chat/completions", "/v1/batch", "/v1/responses"],
+        "supported_modalities": ["text", "image"],
+        "supported_output_modalities": ["text"],
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_response_schema": true,
+        "supports_vision": true,
+        "supports_prompt_caching": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_native_streaming": true,
+        "supports_web_search": true,
+        "search_context_cost_per_query": {
+            "search_context_size_low": 25e-3,
+            "search_context_size_medium": 27.5e-3,
+            "search_context_size_high": 30e-3
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litellm/openai-responses-starter-app

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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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 1:null

litellm/proxy/_new_secret_config.yaml

@@ -26,12 +26,14 @@ model_list:
       model: azure/gpt-4.1
       api_key: os.environ/AZURE_API_KEY_REALTIME
       api_base: https://krris-m2f9a9i7-eastus2.openai.azure.com/
-
-
+  - model_name: "xai/*"
+    litellm_params:
+      model: xai/*
+      api_key: os.environ/XAI_API_KEY
 
 litellm_settings:
   num_retries: 0
-  callbacks: ["prometheus"]
+  callbacks: ["datadog_llm_observability"]
   check_provider_endpoint: true
 
 files_settings:

litellm/proxy/_types.py

@@ -287,6 +287,7 @@ class LiteLLMRoutes(enum.Enum):
         "/v1/models",
         # token counter
         "/utils/token_counter",
+        "/utils/transform_request",
         # rerank
         "/rerank",
         "/v1/rerank",
@@ -462,6 +463,7 @@ class LiteLLMRoutes(enum.Enum):
         "/team/member_delete",
         "/team/permissions_list",
         "/team/permissions_update",
+        "/team/daily/activity",
         "/model/new",
         "/model/update",
         "/model/delete",
@@ -650,9 +652,9 @@ class GenerateRequestBase(LiteLLMPydanticObjectBase):
     allowed_cache_controls: Optional[list] = []
     config: Optional[dict] = {}
     permissions: Optional[dict] = {}
-    model_max_budget: Optional[dict] = (
-        {}
-    )  # {"gpt-4": 5.0, "gpt-3.5-turbo": 5.0}, defaults to {}
+    model_max_budget: Optional[
+        dict
+    ] = {}  # {"gpt-4": 5.0, "gpt-3.5-turbo": 5.0}, defaults to {}
 
     model_config = ConfigDict(protected_namespaces=())
     model_rpm_limit: Optional[dict] = None
@@ -685,6 +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
@@ -911,12 +915,12 @@ class NewCustomerRequest(BudgetNewRequest):
     alias: Optional[str] = None  # human-friendly alias
     blocked: bool = False  # allow/disallow requests for this end-user
     budget_id: Optional[str] = None  # give either a budget_id or max_budget
-    allowed_model_region: Optional[AllowedModelRegion] = (
-        None  # require all user requests to use models in this specific region
-    )
-    default_model: Optional[str] = (
-        None  # if no equivalent model in allowed region - default all requests to this model
-    )
+    allowed_model_region: Optional[
+        AllowedModelRegion
+    ] = None  # require all user requests to use models in this specific region
+    default_model: Optional[
+        str
+    ] = None  # if no equivalent model in allowed region - default all requests to this model
 
     @model_validator(mode="before")
     @classmethod
@@ -938,12 +942,12 @@ class UpdateCustomerRequest(LiteLLMPydanticObjectBase):
     blocked: bool = False  # allow/disallow requests for this end-user
     max_budget: Optional[float] = None
     budget_id: Optional[str] = None  # give either a budget_id or max_budget
-    allowed_model_region: Optional[AllowedModelRegion] = (
-        None  # require all user requests to use models in this specific region
-    )
-    default_model: Optional[str] = (
-        None  # if no equivalent model in allowed region - default all requests to this model
-    )
+    allowed_model_region: Optional[
+        AllowedModelRegion
+    ] = None  # require all user requests to use models in this specific region
+    default_model: Optional[
+        str
+    ] = None  # if no equivalent model in allowed region - default all requests to this model
 
 
 class DeleteCustomerRequest(LiteLLMPydanticObjectBase):
@@ -1079,9 +1083,9 @@ class BlockKeyRequest(LiteLLMPydanticObjectBase):
 
 class AddTeamCallback(LiteLLMPydanticObjectBase):
     callback_name: str
-    callback_type: Optional[Literal["success", "failure", "success_and_failure"]] = (
-        "success_and_failure"
-    )
+    callback_type: Optional[
+        Literal["success", "failure", "success_and_failure"]
+    ] = "success_and_failure"
     callback_vars: Dict[str, str]
 
     @model_validator(mode="before")
@@ -1339,9 +1343,9 @@ class ConfigList(LiteLLMPydanticObjectBase):
     stored_in_db: Optional[bool]
     field_default_value: Any
     premium_field: bool = False
-    nested_fields: Optional[List[FieldDetail]] = (
-        None  # For nested dictionary or Pydantic fields
-    )
+    nested_fields: Optional[
+        List[FieldDetail]
+    ] = None  # For nested dictionary or Pydantic fields
 
 
 class ConfigGeneralSettings(LiteLLMPydanticObjectBase):
@@ -1609,9 +1613,9 @@ class LiteLLM_OrganizationMembershipTable(LiteLLMPydanticObjectBase):
     budget_id: Optional[str] = None
     created_at: datetime
     updated_at: datetime
-    user: Optional[Any] = (
-        None  # You might want to replace 'Any' with a more specific type if available
-    )
+    user: Optional[
+        Any
+    ] = None  # You might want to replace 'Any' with a more specific type if available
     litellm_budget_table: Optional[LiteLLM_BudgetTable] = None
 
     model_config = ConfigDict(protected_namespaces=())
@@ -2359,9 +2363,9 @@ class TeamModelDeleteRequest(BaseModel):
 # Organization Member Requests
 class OrganizationMemberAddRequest(OrgMemberAddRequest):
     organization_id: str
-    max_budget_in_organization: Optional[float] = (
-        None  # Users max budget within the organization
-    )
+    max_budget_in_organization: Optional[
+        float
+    ] = None  # Users max budget within the organization
 
 
 class OrganizationMemberDeleteRequest(MemberDeleteRequest):
@@ -2550,9 +2554,9 @@ class ProviderBudgetResponse(LiteLLMPydanticObjectBase):
     Maps provider names to their budget configs.
     """
 
-    providers: Dict[str, ProviderBudgetResponseObject] = (
-        {}
-    )  # Dictionary mapping provider names to their budget configurations
+    providers: Dict[
+        str, ProviderBudgetResponseObject
+    ] = {}  # Dictionary mapping provider names to their budget configurations
 
 
 class ProxyStateVariables(TypedDict):
@@ -2680,9 +2684,9 @@ class LiteLLM_JWTAuth(LiteLLMPydanticObjectBase):
     enforce_rbac: bool = False
     roles_jwt_field: Optional[str] = None  # v2 on role mappings
     role_mappings: Optional[List[RoleMapping]] = None
-    object_id_jwt_field: Optional[str] = (
-        None  # can be either user / team, inferred from the role mapping
-    )
+    object_id_jwt_field: Optional[
+        str
+    ] = None  # can be either user / team, inferred from the role mapping
     scope_mappings: Optional[List[ScopeMapping]] = None
     enforce_scope_based_access: bool = False
     enforce_team_based_model_access: bool = False

litellm/proxy/auth/auth_checks.py

@@ -88,7 +88,7 @@ async def common_checks(
     9. Check if request body is safe
     10. [OPTIONAL] Organization checks - is user_object.organization_id is set, run these checks
     """
-    _model = request_body.get("model", None)
+    _model: Optional[str] = cast(Optional[str], request_body.get("model", None))
 
     # 1. If team is blocked
     if team_object is not None and team_object.blocked is True:
@@ -112,7 +112,7 @@ async def common_checks(
             )
 
     ## 2.1 If user can call model (if personal key)
-    if team_object is None and user_object is not None:
+    if _model and team_object is None and user_object is not None:
         await can_user_call_model(
             model=_model,
             llm_router=llm_router,
@@ -644,6 +644,7 @@ async def get_user_object(
     proxy_logging_obj: Optional[ProxyLogging] = None,
     sso_user_id: Optional[str] = None,
     user_email: Optional[str] = None,
+    check_db_only: Optional[bool] = None,
 ) -> Optional[LiteLLM_UserTable]:
     """
     - Check if user id in proxy User Table
@@ -655,12 +656,13 @@ async def get_user_object(
         return None
 
     # check if in cache
-    cached_user_obj = await user_api_key_cache.async_get_cache(key=user_id)
-    if cached_user_obj is not None:
-        if isinstance(cached_user_obj, dict):
-            return LiteLLM_UserTable(**cached_user_obj)
-        elif isinstance(cached_user_obj, LiteLLM_UserTable):
-            return cached_user_obj
+    if not check_db_only:
+        cached_user_obj = await user_api_key_cache.async_get_cache(key=user_id)
+        if cached_user_obj is not None:
+            if isinstance(cached_user_obj, dict):
+                return LiteLLM_UserTable(**cached_user_obj)
+            elif isinstance(cached_user_obj, LiteLLM_UserTable):
+                return cached_user_obj
     # else, check db
     if prisma_client is None:
         raise Exception("No db connected")

litellm/proxy/hooks/proxy_track_cost_callback.py

@@ -199,9 +199,13 @@ class _ProxyDBLogger(CustomLogger):
         except Exception as e:
             error_msg = f"Error in tracking cost callback - {str(e)}\n Traceback:{traceback.format_exc()}"
             model = kwargs.get("model", "")
-            metadata = kwargs.get("litellm_params", {}).get("metadata", {})
+            metadata = get_litellm_metadata_from_kwargs(kwargs=kwargs)
+            litellm_metadata = kwargs.get("litellm_params", {}).get(
+                "litellm_metadata", {}
+            )
+            old_metadata = kwargs.get("litellm_params", {}).get("metadata", {})
             call_type = kwargs.get("call_type", "")
-            error_msg += f"\n Args to _PROXY_track_cost_callback\n model: {model}\n metadata: {metadata}\n call_type: {call_type}\n"
+            error_msg += f"\n Args to _PROXY_track_cost_callback\n model: {model}\n chosen_metadata: {metadata}\n litellm_metadata: {litellm_metadata}\n old_metadata: {old_metadata}\n call_type: {call_type}\n"
             asyncio.create_task(
                 proxy_logging_obj.failed_tracking_alert(
                     error_message=error_msg,

litellm/proxy/litellm_pre_call_utils.py

@@ -433,14 +433,13 @@ class LiteLLMProxyRequestSetup:
     ) -> Optional[List[str]]:
         tags = None
 
-        if llm_router and llm_router.enable_tag_filtering is True:
-            # Check request headers for tags
-            if "x-litellm-tags" in headers:
-                if isinstance(headers["x-litellm-tags"], str):
-                    _tags = headers["x-litellm-tags"].split(",")
-                    tags = [tag.strip() for tag in _tags]
-                elif isinstance(headers["x-litellm-tags"], list):
-                    tags = headers["x-litellm-tags"]
+        # Check request headers for tags
+        if "x-litellm-tags" in headers:
+            if isinstance(headers["x-litellm-tags"], str):
+                _tags = headers["x-litellm-tags"].split(",")
+                tags = [tag.strip() for tag in _tags]
+            elif isinstance(headers["x-litellm-tags"], list):
+                tags = headers["x-litellm-tags"]
         # Check request body for tags
         if "tags" in data and isinstance(data["tags"], list):
             tags = data["tags"]

litellm/proxy/management_endpoints/common_daily_activity.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Set, Union
 
 from fastapi import HTTPException, status
 
@@ -39,6 +39,7 @@ def update_breakdown_metrics(
     provider_metadata: Dict[str, Dict[str, Any]],
     api_key_metadata: Dict[str, Dict[str, Any]],
     entity_id_field: Optional[str] = None,
+    entity_metadata_field: Optional[Dict[str, dict]] = None,
 ) -> BreakdownMetrics:
     """Updates breakdown metrics for a single record using the existing update_metrics function"""
 
@@ -74,7 +75,8 @@ def update_breakdown_metrics(
             metadata=KeyMetadata(
                 key_alias=api_key_metadata.get(record.api_key, {}).get(
                     "key_alias", None
-                )
+                ),
+                team_id=api_key_metadata.get(record.api_key, {}).get("team_id", None),
             ),  # Add any api_key-specific metadata here
         )
     breakdown.api_keys[record.api_key].metrics = update_metrics(
@@ -87,7 +89,10 @@ def update_breakdown_metrics(
         if entity_value:
             if entity_value not in breakdown.entities:
                 breakdown.entities[entity_value] = MetricWithMetadata(
-                    metrics=SpendMetrics(), metadata={}
+                    metrics=SpendMetrics(),
+                    metadata=entity_metadata_field.get(entity_value, {})
+                    if entity_metadata_field
+                    else {},
                 )
             breakdown.entities[entity_value].metrics = update_metrics(
                 breakdown.entities[entity_value].metrics, record
@@ -96,17 +101,32 @@ def update_breakdown_metrics(
     return breakdown
 
 
+async def get_api_key_metadata(
+    prisma_client: PrismaClient,
+    api_keys: Set[str],
+) -> Dict[str, Dict[str, Any]]:
+    """Update api key metadata for a single record."""
+    key_records = await prisma_client.db.litellm_verificationtoken.find_many(
+        where={"token": {"in": list(api_keys)}}
+    )
+    return {
+        k.token: {"key_alias": k.key_alias, "team_id": k.team_id} for k in key_records
+    }
+
+
 async def get_daily_activity(
     prisma_client: Optional[PrismaClient],
     table_name: str,
     entity_id_field: str,
     entity_id: Optional[Union[str, List[str]]],
+    entity_metadata_field: Optional[Dict[str, dict]],
     start_date: Optional[str],
     end_date: Optional[str],
     model: Optional[str],
     api_key: Optional[str],
     page: int,
     page_size: int,
+    exclude_entity_ids: Optional[List[str]] = None,
 ) -> SpendAnalyticsPaginatedResponse:
     """Common function to get daily activity for any entity type."""
     if prisma_client is None:
@@ -134,11 +154,15 @@ async def get_daily_activity(
             where_conditions["model"] = model
         if api_key:
             where_conditions["api_key"] = api_key
-        if entity_id:
+        if entity_id is not None:
             if isinstance(entity_id, list):
                 where_conditions[entity_id_field] = {"in": entity_id}
             else:
                 where_conditions[entity_id_field] = entity_id
+        if exclude_entity_ids:
+            where_conditions.setdefault(entity_id_field, {})["not"] = {
+                "in": exclude_entity_ids
+            }
 
         # Get total count for pagination
         total_count = await getattr(prisma_client.db, table_name).count(
@@ -166,12 +190,7 @@ async def get_daily_activity(
         model_metadata: Dict[str, Dict[str, Any]] = {}
         provider_metadata: Dict[str, Dict[str, Any]] = {}
         if api_keys:
-            key_records = await prisma_client.db.litellm_verificationtoken.find_many(
-                where={"token": {"in": list(api_keys)}}
-            )
-            api_key_metadata.update(
-                {k.token: {"key_alias": k.key_alias} for k in key_records}
-            )
+            api_key_metadata = await get_api_key_metadata(prisma_client, api_keys)
 
         # Process results
         results = []
@@ -198,6 +217,7 @@ async def get_daily_activity(
                 provider_metadata,
                 api_key_metadata,
                 entity_id_field=entity_id_field,
+                entity_metadata_field=entity_metadata_field,
             )
 
             # Update total metrics

litellm/proxy/management_endpoints/common_utils.py

@@ -4,11 +4,19 @@ from litellm.proxy._types import (
     GenerateKeyRequest,
     LiteLLM_ManagementEndpoint_MetadataFields_Premium,
     LiteLLM_TeamTable,
+    LitellmUserRoles,
     UserAPIKeyAuth,
 )
 from litellm.proxy.utils import _premium_user_check
 
 
+def _user_has_admin_view(user_api_key_dict: UserAPIKeyAuth) -> bool:
+    return (
+        user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN
+        or user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY
+    )
+
+
 def _is_user_team_admin(
     user_api_key_dict: UserAPIKeyAuth, team_obj: LiteLLM_TeamTable
 ) -> bool:

litellm/proxy/management_endpoints/internal_user_endpoints.py

@@ -25,6 +25,8 @@ from litellm._logging import verbose_proxy_logger
 from litellm.litellm_core_utils.duration_parser import duration_in_seconds
 from litellm.proxy._types import *
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.proxy.management_endpoints.common_daily_activity import get_daily_activity
+from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view
 from litellm.proxy.management_endpoints.key_management_endpoints import (
     generate_key_helper_fn,
     prepare_metadata_fields,
@@ -34,8 +36,6 @@ from litellm.proxy.management_helpers.utils import management_endpoint_wrapper
 from litellm.proxy.utils import handle_exception_on_proxy
 from litellm.types.proxy.management_endpoints.common_daily_activity import (
     BreakdownMetrics,
-    DailySpendData,
-    DailySpendMetadata,
     KeyMetadata,
     KeyMetricWithMetadata,
     LiteLLM_DailyUserSpend,
@@ -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 = []
 
@@ -1382,136 +1456,22 @@ async def get_user_daily_activity(
         )
 
     try:
-        # Build filter conditions
-        where_conditions: Dict[str, Any] = {
-            "date": {
-                "gte": start_date,
-                "lte": end_date,
-            }
-        }
+        entity_id: Optional[str] = None
+        if not _user_has_admin_view(user_api_key_dict):
+            entity_id = user_api_key_dict.user_id
 
-        if model:
-            where_conditions["model"] = model
-        if api_key:
-            where_conditions["api_key"] = api_key
-
-        if (
-            user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN
-            and user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY
-        ):
-            where_conditions[
-                "user_id"
-            ] = user_api_key_dict.user_id  # only allow access to own data
-
-        # Get total count for pagination
-        total_count = await prisma_client.db.litellm_dailyuserspend.count(
-            where=where_conditions
-        )
-
-        # Fetch paginated results
-        daily_spend_data = await prisma_client.db.litellm_dailyuserspend.find_many(
-            where=where_conditions,
-            order=[
-                {"date": "desc"},
-            ],
-            skip=(page - 1) * page_size,
-            take=page_size,
-        )
-
-        daily_spend_data_pydantic_list = [
-            LiteLLM_DailyUserSpend(**record.model_dump()) for record in daily_spend_data
-        ]
-
-        # Get all unique API keys from the spend data
-        api_keys = set()
-        for record in daily_spend_data_pydantic_list:
-            if record.api_key:
-                api_keys.add(record.api_key)
-
-        # Fetch key aliases in bulk
-
-        api_key_metadata: Dict[str, Dict[str, Any]] = {}
-        model_metadata: Dict[str, Dict[str, Any]] = {}
-        provider_metadata: Dict[str, Dict[str, Any]] = {}
-        if api_keys:
-            key_records = await prisma_client.db.litellm_verificationtoken.find_many(
-                where={"token": {"in": list(api_keys)}}
-            )
-            api_key_metadata.update(
-                {k.token: {"key_alias": k.key_alias} for k in key_records}
-            )
-        # Process results
-        results = []
-        total_metrics = SpendMetrics()
-
-        # Group data by date and other dimensions
-
-        grouped_data: Dict[str, Dict[str, Any]] = {}
-        for record in daily_spend_data_pydantic_list:
-            date_str = record.date
-            if date_str not in grouped_data:
-                grouped_data[date_str] = {
-                    "metrics": SpendMetrics(),
-                    "breakdown": BreakdownMetrics(),
-                }
-
-            # Update metrics
-            grouped_data[date_str]["metrics"] = update_metrics(
-                grouped_data[date_str]["metrics"], record
-            )
-            # Update breakdowns
-            grouped_data[date_str]["breakdown"] = update_breakdown_metrics(
-                grouped_data[date_str]["breakdown"],
-                record,
-                model_metadata,
-                provider_metadata,
-                api_key_metadata,
-            )
-
-            # Update total metrics
-            total_metrics.spend += record.spend
-            total_metrics.prompt_tokens += record.prompt_tokens
-            total_metrics.completion_tokens += record.completion_tokens
-            total_metrics.total_tokens += (
-                record.prompt_tokens + record.completion_tokens
-            )
-            total_metrics.cache_read_input_tokens += record.cache_read_input_tokens
-            total_metrics.cache_creation_input_tokens += (
-                record.cache_creation_input_tokens
-            )
-            total_metrics.api_requests += record.api_requests
-            total_metrics.successful_requests += record.successful_requests
-            total_metrics.failed_requests += record.failed_requests
-
-        # Convert grouped data to response format
-        for date_str, data in grouped_data.items():
-            results.append(
-                DailySpendData(
-                    date=datetime.strptime(date_str, "%Y-%m-%d").date(),
-                    metrics=data["metrics"],
-                    breakdown=data["breakdown"],
-                )
-            )
-
-        # Sort results by date
-        results.sort(key=lambda x: x.date, reverse=True)
-
-        return SpendAnalyticsPaginatedResponse(
-            results=results,
-            metadata=DailySpendMetadata(
-                total_spend=total_metrics.spend,
-                total_prompt_tokens=total_metrics.prompt_tokens,
-                total_completion_tokens=total_metrics.completion_tokens,
-                total_tokens=total_metrics.total_tokens,
-                total_api_requests=total_metrics.api_requests,
-                total_successful_requests=total_metrics.successful_requests,
-                total_failed_requests=total_metrics.failed_requests,
-                total_cache_read_input_tokens=total_metrics.cache_read_input_tokens,
-                total_cache_creation_input_tokens=total_metrics.cache_creation_input_tokens,
-                page=page,
-                total_pages=-(-total_count // page_size),  # Ceiling division
-                has_more=(page * page_size) < total_count,
-            ),
+        return await get_daily_activity(
+            prisma_client=prisma_client,
+            table_name="litellm_dailyuserspend",
+            entity_id_field="user_id",
+            entity_id=entity_id,
+            entity_metadata_field=None,
+            start_date=start_date,
+            end_date=end_date,
+            model=model,
+            api_key=api_key,
+            page=page,
+            page_size=page_size,
         )
 
     except Exception as e:

litellm/proxy/management_endpoints/key_management_endpoints.py

@@ -577,12 +577,16 @@ async def generate_key_fn(  # noqa: PLR0915
             request_type="key", **data_json, table_name="key"
         )
 
-        response["soft_budget"] = (
-            data.soft_budget
-        )  # include the user-input soft budget in the response
+        response[
+            "soft_budget"
+        ] = data.soft_budget  # include the user-input soft budget in the response
 
         response = GenerateKeyResponse(**response)
 
+        response.token = (
+            response.token_id
+        )  # remap token to use the hash, and leave the key in the `key` field [TODO]: clean up generate_key_helper_fn to do this
+
         asyncio.create_task(
             KeyManagementEventHooks.async_key_generated_hook(
                 data=data,
@@ -1343,10 +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(
@@ -1470,10 +1477,10 @@ async def delete_verification_tokens(
     try:
         if prisma_client:
             tokens = [_hash_token_if_needed(token=key) for key in tokens]
-            _keys_being_deleted: List[LiteLLM_VerificationToken] = (
-                await prisma_client.db.litellm_verificationtoken.find_many(
-                    where={"token": {"in": tokens}}
-                )
+            _keys_being_deleted: List[
+                LiteLLM_VerificationToken
+            ] = await prisma_client.db.litellm_verificationtoken.find_many(
+                where={"token": {"in": tokens}}
             )
 
             # Assuming 'db' is your Prisma Client instance
@@ -1575,9 +1582,9 @@ async def _rotate_master_key(
     from litellm.proxy.proxy_server import proxy_config
 
     try:
-        models: Optional[List] = (
-            await prisma_client.db.litellm_proxymodeltable.find_many()
-        )
+        models: Optional[
+            List
+        ] = await prisma_client.db.litellm_proxymodeltable.find_many()
     except Exception:
         models = None
     # 2. process model table
@@ -1864,11 +1871,11 @@ async def validate_key_list_check(
             param="user_id",
             code=status.HTTP_403_FORBIDDEN,
         )
-    complete_user_info_db_obj: Optional[BaseModel] = (
-        await prisma_client.db.litellm_usertable.find_unique(
-            where={"user_id": user_api_key_dict.user_id},
-            include={"organization_memberships": True},
-        )
+    complete_user_info_db_obj: Optional[
+        BaseModel
+    ] = await prisma_client.db.litellm_usertable.find_unique(
+        where={"user_id": user_api_key_dict.user_id},
+        include={"organization_memberships": True},
     )
 
     if complete_user_info_db_obj is None:
@@ -1929,10 +1936,10 @@ async def get_admin_team_ids(
     if complete_user_info is None:
         return []
     # Get all teams that user is an admin of
-    teams: Optional[List[BaseModel]] = (
-        await prisma_client.db.litellm_teamtable.find_many(
-            where={"team_id": {"in": complete_user_info.teams}}
-        )
+    teams: Optional[
+        List[BaseModel]
+    ] = await prisma_client.db.litellm_teamtable.find_many(
+        where={"team_id": {"in": complete_user_info.teams}}
     )
     if teams is None:
         return []

litellm/proxy/management_endpoints/tag_management_endpoints.py

@@ -12,7 +12,7 @@ All /tag management endpoints
 
 import datetime
 import json
-from typing import Dict, Optional
+from typing import Dict, List, Optional
 
 from fastapi import APIRouter, Depends, HTTPException
 
@@ -25,6 +25,7 @@ from litellm.proxy.management_endpoints.common_daily_activity import (
     get_daily_activity,
 )
 from litellm.types.tag_management import (
+    LiteLLM_DailyTagSpendTable,
     TagConfig,
     TagDeleteRequest,
     TagInfoRequest,
@@ -301,6 +302,7 @@ async def info_tag(
     "/tag/list",
     tags=["tag management"],
     dependencies=[Depends(user_api_key_auth)],
+    response_model=List[TagConfig],
 )
 async def list_tags(
     user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
@@ -314,9 +316,33 @@ async def list_tags(
         raise HTTPException(status_code=500, detail="Database not connected")
 
     try:
+        ## QUERY STORED TAGS ##
         tags_config = await _get_tags_config(prisma_client)
         list_of_tags = list(tags_config.values())
-        return list_of_tags
+
+        ## QUERY DYNAMIC TAGS ##
+        dynamic_tags = await prisma_client.db.litellm_dailytagspend.find_many(
+            distinct=["tag"],
+        )
+
+        dynamic_tags_list = [
+            LiteLLM_DailyTagSpendTable(**dynamic_tag.model_dump())
+            for dynamic_tag in dynamic_tags
+        ]
+
+        dynamic_tag_config = [
+            TagConfig(
+                name=tag.tag,
+                description="This is just a spend tag that was passed dynamically in a request. It does not control any LLM models.",
+                models=None,
+                created_at=tag.created_at.isoformat(),
+                updated_at=tag.updated_at.isoformat(),
+            )
+            for tag in dynamic_tags_list
+            if tag.tag not in tags_config
+        ]
+
+        return list_of_tags + dynamic_tag_config
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -400,6 +426,7 @@ async def get_tag_daily_activity(
         table_name="litellm_dailytagspend",
         entity_id_field="tag",
         entity_id=tag_list,
+        entity_metadata_field=None,
         start_date=start_date,
         end_date=end_date,
         model=model,

litellm/proxy/management_endpoints/team_endpoints.py

@@ -56,11 +56,13 @@ from litellm.proxy._types import (
 from litellm.proxy.auth.auth_checks import (
     allowed_route_check_inside_route,
     get_team_object,
+    get_user_object,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.management_endpoints.common_utils import (
     _is_user_team_admin,
     _set_object_metadata_field,
+    _user_has_admin_view,
 )
 from litellm.proxy.management_endpoints.tag_management_endpoints import (
     get_daily_activity,
@@ -2091,7 +2093,6 @@ async def update_team_member_permissions(
     "/team/daily/activity",
     response_model=SpendAnalyticsPaginatedResponse,
     tags=["team management"],
-    dependencies=[Depends(user_api_key_auth)],
 )
 async def get_team_daily_activity(
     team_ids: Optional[str] = None,
@@ -2101,6 +2102,8 @@ async def get_team_daily_activity(
     api_key: Optional[str] = None,
     page: int = 1,
     page_size: int = 10,
+    exclude_team_ids: Optional[str] = None,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
 ):
     """
     Get daily activity for specific teams or all teams.
@@ -2113,20 +2116,82 @@ async def get_team_daily_activity(
         api_key (Optional[str]): Filter by API key.
         page (int): Page number for pagination.
         page_size (int): Number of items per page.
-
+        exclude_team_ids (Optional[str]): Comma-separated list of team IDs to exclude.
     Returns:
         SpendAnalyticsPaginatedResponse: Paginated response containing daily activity data.
     """
-    from litellm.proxy.proxy_server import prisma_client
+    from litellm.proxy.proxy_server import (
+        prisma_client,
+        proxy_logging_obj,
+        user_api_key_cache,
+    )
+
+    if prisma_client is None:
+        raise HTTPException(
+            status_code=500,
+            detail={"error": CommonProxyErrors.db_not_connected_error.value},
+        )
 
     # Convert comma-separated tags string to list if provided
     team_ids_list = team_ids.split(",") if team_ids else None
+    exclude_team_ids_list: Optional[List[str]] = None
+
+    if exclude_team_ids:
+        exclude_team_ids_list = (
+            exclude_team_ids.split(",") if exclude_team_ids else None
+        )
+
+    if not _user_has_admin_view(user_api_key_dict):
+        user_info = await get_user_object(
+            user_id=user_api_key_dict.user_id,
+            prisma_client=prisma_client,
+            user_id_upsert=False,
+            user_api_key_cache=user_api_key_cache,
+            parent_otel_span=user_api_key_dict.parent_otel_span,
+            proxy_logging_obj=proxy_logging_obj,
+            check_db_only=True,
+        )
+        if user_info is None:
+            raise HTTPException(
+                status_code=404,
+                detail={
+                    "error": "User= {} not found".format(user_api_key_dict.user_id)
+                },
+            )
+
+        if team_ids_list is None:
+            team_ids_list = user_info.teams
+        else:
+            # check if all team_ids are in user_info.teams
+            for team_id in team_ids_list:
+                if team_id not in user_info.teams:
+                    raise HTTPException(
+                        status_code=404,
+                        detail={
+                            "error": "User does not belong to Team= {}. Call `/user/info` to see user's teams".format(
+                                team_id
+                            )
+                        },
+                    )
+
+    ## Fetch team aliases
+    where_condition = {}
+    if team_ids_list:
+        where_condition["team_id"] = {"in": list(team_ids_list)}
+    team_aliases = await prisma_client.db.litellm_teamtable.find_many(
+        where=where_condition
+    )
+    team_alias_metadata = {
+        t.team_id: {"team_alias": t.team_alias} for t in team_aliases
+    }
 
     return await get_daily_activity(
         prisma_client=prisma_client,
         table_name="litellm_dailyteamspend",
         entity_id_field="team_id",
         entity_id=team_ids_list,
+        entity_metadata_field=team_alias_metadata,
+        exclude_entity_ids=exclude_team_ids_list,
         start_date=start_date,
         end_date=end_date,
         model=model,

litellm/proxy/management_endpoints/ui_sso.py

@@ -553,7 +553,7 @@ async def auth_callback(request: Request):  # noqa: PLR0915
         algorithm="HS256",
     )
     if user_id is not None and isinstance(user_id, str):
-        litellm_dashboard_ui += "?userID=" + user_id
+        litellm_dashboard_ui += "?login=success"
     redirect_response = RedirectResponse(url=litellm_dashboard_ui, status_code=303)
     redirect_response.set_cookie(key="token", value=jwt_token, secure=True)
     return redirect_response
@@ -592,9 +592,9 @@ async def insert_sso_user(
         if user_defined_values.get("max_budget") is None:
             user_defined_values["max_budget"] = litellm.max_internal_user_budget
         if user_defined_values.get("budget_duration") is None:
-            user_defined_values["budget_duration"] = (
-                litellm.internal_user_budget_duration
-            )
+            user_defined_values[
+                "budget_duration"
+            ] = litellm.internal_user_budget_duration
 
     if user_defined_values["user_role"] is None:
         user_defined_values["user_role"] = LitellmUserRoles.INTERNAL_USER_VIEW_ONLY
@@ -787,9 +787,9 @@ class SSOAuthenticationHandler:
                 if state:
                     redirect_params["state"] = state
                 elif "okta" in generic_authorization_endpoint:
-                    redirect_params["state"] = (
-                        uuid.uuid4().hex
-                    )  # set state param for okta - required
+                    redirect_params[
+                        "state"
+                    ] = uuid.uuid4().hex  # set state param for okta - required
                 return await generic_sso.get_login_redirect(**redirect_params)  # type: ignore
         raise ValueError(
             "Unknown SSO provider. Please setup SSO with client IDs https://docs.litellm.ai/docs/proxy/admin_ui_sso"
@@ -1023,7 +1023,7 @@ class MicrosoftSSOHandler:
         original_msft_result = (
             await microsoft_sso.verify_and_process(
                 request=request,
-                convert_response=False,
+                convert_response=False,  # type: ignore
             )
             or {}
         )
@@ -1034,9 +1034,9 @@ class MicrosoftSSOHandler:
 
         # if user is trying to get the raw sso response for debugging, return the raw sso response
         if return_raw_sso_response:
-            original_msft_result[MicrosoftSSOHandler.GRAPH_API_RESPONSE_KEY] = (
-                user_team_ids
-            )
+            original_msft_result[
+                MicrosoftSSOHandler.GRAPH_API_RESPONSE_KEY
+            ] = user_team_ids
             return original_msft_result or {}
 
         result = MicrosoftSSOHandler.openid_from_response(
@@ -1086,12 +1086,13 @@ class MicrosoftSSOHandler:
             service_principal_group_ids: Optional[List[str]] = []
             service_principal_teams: Optional[List[MicrosoftServicePrincipalTeam]] = []
             if service_principal_id:
-                service_principal_group_ids, service_principal_teams = (
-                    await MicrosoftSSOHandler.get_group_ids_from_service_principal(
-                        service_principal_id=service_principal_id,
-                        async_client=async_client,
-                        access_token=access_token,
-                    )
+                (
+                    service_principal_group_ids,
+                    service_principal_teams,
+                ) = await MicrosoftSSOHandler.get_group_ids_from_service_principal(
+                    service_principal_id=service_principal_id,
+                    async_client=async_client,
+                    access_token=access_token,
                 )
                 verbose_proxy_logger.debug(
                     f"Service principal group IDs: {service_principal_group_ids}"
@@ -1103,9 +1104,9 @@ class MicrosoftSSOHandler:
 
             # Fetch user membership from Microsoft Graph API
             all_group_ids = []
-            next_link: Optional[str] = (
-                MicrosoftSSOHandler.graph_api_user_groups_endpoint
-            )
+            next_link: Optional[
+                str
+            ] = MicrosoftSSOHandler.graph_api_user_groups_endpoint
             auth_headers = {"Authorization": f"Bearer {access_token}"}
             page_count = 0
 
@@ -1304,7 +1305,7 @@ class GoogleSSOHandler:
             return (
                 await google_sso.verify_and_process(
                     request=request,
-                    convert_response=False,
+                    convert_response=False,  # type: ignore
                 )
                 or {}
             )