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Litellm dev 03 22 2025 release note
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Krish Dholakia 2025-03-22 20:12:43 -07:00 • committed by GitHub
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89
docs/my-website/docs/completion/document_understanding.md
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@ -200,3 +200,92 @@ Expected Response
</TabItem>
</Tabs>
## OpenAI 'file' message type
This is currently only supported for OpenAI models.
This will be supported for all providers soon.
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import base64
from litellm import completion
with open("draconomicon.pdf", "rb") as f:
data = f.read()
base64_string = base64.b64encode(data).decode("utf-8")
completion = completion(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{
"type": "file",
"file": {
"filename": "draconomicon.pdf",
"file_data": f"data:application/pdf;base64,{base64_string}",
}
},
{
"type": "text",
"text": "What is the first dragon in the book?",
}
],
},
],
)
print(completion.choices[0].message.content)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: openai-model
litellm_params:
model: gpt-4o
api_key: os.environ/OPENAI_API_KEY
```
2. Start the proxy
```bash
litellm --config config.yaml
```
3. Test it!
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
"model": "openai-model",
"messages": [
{"role": "user", "content": [
{
"type": "file",
"file": {
"filename": "draconomicon.pdf",
"file_data": f"data:application/pdf;base64,{base64_string}",
}
}
]}
]
}'
```
</TabItem>
</Tabs>

66
docs/my-website/docs/guides/security_settings.md Normal file
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@ -0,0 +1,66 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# SSL Security Settings
If you're in an environment using an older TTS bundle, with an older encryption, follow this guide.
LiteLLM uses HTTPX for network requests, unless otherwise specified.
1. Disable SSL verification
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
litellm.ssl_verify = False
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
litellm_settings:
ssl_verify: false
```
</TabItem>
<TabItem value="env_var" label="Environment Variables">
```bash
export SSL_VERIFY="False"
```
</TabItem>
</Tabs>
2. Lower security settings
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
litellm.ssl_security_level = 1
litellm.ssl_certificate = "/path/to/certificate.pem"
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
litellm_settings:
ssl_security_level: 1
ssl_certificate: "/path/to/certificate.pem"
```
</TabItem>
<TabItem value="env_var" label="Environment Variables">
```bash
export SSL_SECURITY_LEVEL="1"
export SSL_CERTIFICATE="/path/to/certificate.pem"
```
</TabItem>
</Tabs>

132
docs/my-website/docs/observability/arize_integration.md
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@ -1,4 +1,7 @@
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Arize AI
@ -11,6 +14,8 @@ https://github.com/BerriAI/litellm
:::
<Image img={require('../../img/arize.png')} />
## Pre-Requisites
@ -24,7 +29,9 @@ You can also use the instrumentor option instead of the callback, which you can
```python
litellm.callbacks = ["arize"]
```
```python
import litellm
import os
@ -48,7 +55,7 @@ response = litellm.completion(
### Using with LiteLLM Proxy
1. Setup config.yaml
```yaml
model_list:
- model_name: gpt-4
@ -60,13 +67,134 @@ model_list:
litellm_settings:
callbacks: ["arize"]
general_settings:
master_key: "sk-1234" # can also be set as an environment variable
environment_variables:
ARIZE_SPACE_KEY: "d0*****"
ARIZE_API_KEY: "141a****"
ARIZE_ENDPOINT: "https://otlp.arize.com/v1" # OPTIONAL - your custom arize GRPC api endpoint
ARIZE_HTTP_ENDPOINT: "https://otlp.arize.com/v1" # OPTIONAL - your custom arize HTTP api endpoint. Set either this or ARIZE_ENDPOINT
ARIZE_HTTP_ENDPOINT: "https://otlp.arize.com/v1" # OPTIONAL - your custom arize HTTP api endpoint. Set either this or ARIZE_ENDPOINT or Neither (defaults to https://otlp.arize.com/v1 on grpc)
```
2. Start the proxy
```bash
litellm --config config.yaml
```
3. Test it!
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{ "model": "gpt-4", "messages": [{"role": "user", "content": "Hi 👋 - i'm openai"}]}'
```
## Pass Arize Space/Key per-request
Supported parameters:
- `arize_api_key`
- `arize_space_key`
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
import os
# LLM API Keys
os.environ['OPENAI_API_KEY']=""
# set arize as a callback, litellm will send the data to arize
litellm.callbacks = ["arize"]
# openai call
response = litellm.completion(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": "Hi 👋 - i'm openai"}
],
arize_api_key=os.getenv("ARIZE_SPACE_2_API_KEY"),
arize_space_key=os.getenv("ARIZE_SPACE_2_KEY"),
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: gpt-4
litellm_params:
model: openai/fake
api_key: fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
litellm_settings:
callbacks: ["arize"]
general_settings:
master_key: "sk-1234" # can also be set as an environment variable
```
2. Start the proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
<Tabs>
<TabItem value="curl" label="CURL">
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
"model": "gpt-4",
"messages": [{"role": "user", "content": "Hi 👋 - i'm openai"}],
"arize_api_key": "ARIZE_SPACE_2_API_KEY",
"arize_space_key": "ARIZE_SPACE_2_KEY"
}'
```
</TabItem>
<TabItem value="openai_python" label="OpenAI Python">
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_body={
"arize_api_key": "ARIZE_SPACE_2_API_KEY",
"arize_space_key": "ARIZE_SPACE_2_KEY"
}
)
print(response)
```
</TabItem>
</Tabs>
</TabItem>
</Tabs>
## Support & Talk to Founders
- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)

194
docs/my-website/docs/providers/azure.md
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@ -291,14 +291,15 @@ response = completion(
)
```
## Azure O1 Models
## O-Series Models
| Model Name | Function Call |
|---------------------|----------------------------------------------------|
| o1-mini | `response = completion(model="azure/<your deployment name>", messages=messages)` |
| o1-preview | `response = completion(model="azure/<your deployment name>", messages=messages)` |
Azure OpenAI O-Series models are supported on LiteLLM.
Set `litellm.enable_preview_features = True` to use Azure O1 Models with streaming support.
LiteLLM routes any deployment name with `o1` or `o3` in the model name, to the O-Series [transformation](https://github.com/BerriAI/litellm/blob/91ed05df2962b8eee8492374b048d27cc144d08c/litellm/llms/azure/chat/o1_transformation.py#L4) logic.
To set this explicitly, set `model` to `azure/o_series/<your-deployment-name>`.
**Automatic Routing**
<Tabs>
<TabItem value="sdk" label="SDK">
@ -306,60 +307,112 @@ Set `litellm.enable_preview_features = True` to use Azure O1 Models with streami
```python
import litellm
litellm.enable_preview_features = True # 👈 KEY CHANGE
response = litellm.completion(
model="azure/<your deployment name>",
messages=[{"role": "user", "content": "What is the weather like in Boston?"}],
stream=True
)
for chunk in response:
print(chunk)
litellm.completion(model="azure/my-o3-deployment", messages=[{"role": "user", "content": "Hello, world!"}]) # 👈 Note: 'o3' in the deployment name
```
</TabItem>
<TabItem value="proxy" label="Proxy">
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: o1-mini
- model_name: o3-mini
litellm_params:
model: azure/o1-mini
api_base: "os.environ/AZURE_API_BASE"
api_key: "os.environ/AZURE_API_KEY"
api_version: "os.environ/AZURE_API_VERSION"
litellm_settings:
enable_preview_features: true # 👈 KEY CHANGE
model: azure/o3-model
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
```
2. Start proxy
</TabItem>
</Tabs>
**Explicit Routing**
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
litellm.completion(model="azure/o_series/my-random-deployment-name", messages=[{"role": "user", "content": "Hello, world!"}]) # 👈 Note: 'o_series/' in the deployment name
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
model_list:
- model_name: o3-mini
litellm_params:
model: azure/o_series/my-random-deployment-name
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
```
</TabItem>
</Tabs>
## Azure Audio Model
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
response = completion(
model="azure/azure-openai-4o-audio",
messages=[
{
"role": "user",
"content": "I want to try out speech to speech"
}
],
modalities=["text","audio"],
audio={"voice": "alloy", "format": "wav"}
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: azure-openai-4o-audio
litellm_params:
model: azure/azure-openai-4o-audio
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: os.environ/AZURE_API_VERSION
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it
3. Test it!
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(model="o1-mini", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
stream=True)
for chunk in response:
print(chunk)
```bash
curl http://localhost:4000/v1/chat/completions \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "azure-openai-4o-audio",
"messages": [{"role": "user", "content": "I want to try out speech to speech"}],
"modalities": ["text","audio"],
"audio": {"voice": "alloy", "format": "wav"}
}'
```
</TabItem>
</Tabs>
@ -948,62 +1001,9 @@ Expected Response:
{"data":[{"id":"batch_R3V...}
```
## O-Series Models
Azure OpenAI O-Series models are supported on LiteLLM.
LiteLLM routes any deployment name with `o1` or `o3` in the model name, to the O-Series [transformation](https://github.com/BerriAI/litellm/blob/91ed05df2962b8eee8492374b048d27cc144d08c/litellm/llms/azure/chat/o1_transformation.py#L4) logic.
To set this explicitly, set `model` to `azure/o_series/<your-deployment-name>`.
**Automatic Routing**
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
litellm.completion(model="azure/my-o3-deployment", messages=[{"role": "user", "content": "Hello, world!"}]) # 👈 Note: 'o3' in the deployment name
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
model_list:
- model_name: o3-mini
litellm_params:
model: azure/o3-model
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
```
</TabItem>
</Tabs>
**Explicit Routing**
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
litellm.completion(model="azure/o_series/my-random-deployment-name", messages=[{"role": "user", "content": "Hello, world!"}]) # 👈 Note: 'o_series/' in the deployment name
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
model_list:
- model_name: o3-mini
litellm_params:
model: azure/o_series/my-random-deployment-name
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
```
</TabItem>
</Tabs>

4
docs/my-website/docs/providers/bedrock.md
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@ -1428,10 +1428,14 @@ response = litellm.embedding(
## Supported AWS Bedrock Models
LiteLLM supports ALL Bedrock models.
Here's an example of using a bedrock model with LiteLLM. For a complete list, refer to the [model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json)
| Model Name | Command |
|----------------------------|------------------------------------------------------------------|
| Deepseek R1 | `completion(model='bedrock/us.deepseek.r1-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']` |
| Anthropic Claude-V3.5 Sonnet | `completion(model='bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']` |
| Anthropic Claude-V3 sonnet | `completion(model='bedrock/anthropic.claude-3-sonnet-20240229-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']` |
| Anthropic Claude-V3 Haiku | `completion(model='bedrock/anthropic.claude-3-haiku-20240307-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']` |

61
docs/my-website/docs/providers/ollama.md
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@ -202,6 +202,67 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
</TabItem>
</Tabs>
## Using Ollama FIM on `/v1/completions`
LiteLLM supports calling Ollama's `/api/generate` endpoint on `/v1/completions` requests.
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
litellm._turn_on_debug() # turn on debug to see the request
from litellm import completion
response = completion(
model="ollama/llama3.1",
prompt="Hello, world!",
api_base="http://localhost:11434"
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: "llama3.1"
litellm_params:
model: "ollama/llama3.1"
api_base: "http://localhost:11434"
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml --detailed_debug
# RUNNING ON http://0.0.0.0:4000
```
3. Test it!
```python
from openai import OpenAI
client = OpenAI(
api_key="anything", # 👈 PROXY KEY (can be anything, if master_key not set)
base_url="http://0.0.0.0:4000" # 👈 PROXY BASE URL
)
response = client.completions.create(
model="ollama/llama3.1",
prompt="Hello, world!",
api_base="http://localhost:11434"
)
print(response)
```
</TabItem>
</Tabs>
## Using ollama `api/chat`
In order to send ollama requests to `POST /api/chat` on your ollama server, set the model prefix to `ollama_chat`

106
docs/my-website/docs/providers/openai.md
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@ -228,6 +228,92 @@ response = completion(
```
## PDF File Parsing
OpenAI has a new `file` message type that allows you to pass in a PDF file and have it parsed into a structured output. [Read more](https://platform.openai.com/docs/guides/pdf-files?api-mode=chat&lang=python)
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import base64
from litellm import completion
with open("draconomicon.pdf", "rb") as f:
data = f.read()
base64_string = base64.b64encode(data).decode("utf-8")
completion = completion(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{
"type": "file",
"file": {
"filename": "draconomicon.pdf",
"file_data": f"data:application/pdf;base64,{base64_string}",
}
},
{
"type": "text",
"text": "What is the first dragon in the book?",
}
],
},
],
)
print(completion.choices[0].message.content)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: openai-model
litellm_params:
model: gpt-4o
api_key: os.environ/OPENAI_API_KEY
```
2. Start the proxy
```bash
litellm --config config.yaml
```
3. Test it!
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
"model": "openai-model",
"messages": [
{"role": "user", "content": [
{
"type": "file",
"file": {
"filename": "draconomicon.pdf",
"file_data": f"data:application/pdf;base64,{base64_string}",
}
}
]}
]
}'
```
</TabItem>
</Tabs>
## OpenAI Fine Tuned Models
| Model Name | Function Call |
@ -449,26 +535,6 @@ response = litellm.acompletion(
)
```
### Using Helicone Proxy with LiteLLM
```python
import os
import litellm
from litellm import completion
os.environ["OPENAI_API_KEY"] = ""
# os.environ["OPENAI_API_BASE"] = ""
litellm.api_base = "https://oai.hconeai.com/v1"
litellm.headers = {
"Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}",
"Helicone-Cache-Enabled": "true",
}
messages = [{ "content": "Hello, how are you?","role": "user"}]
# openai call
response = completion("gpt-3.5-turbo", messages)
```
### Using OpenAI Proxy with LiteLLM
```python

6
docs/my-website/docs/providers/openrouter.md
View file

@ -10,9 +10,11 @@ LiteLLM supports all the text / chat / vision models from [OpenRouter](https://o
import os
from litellm import completion
os.environ["OPENROUTER_API_KEY"] = ""
os.environ["OPENROUTER_API_BASE"] = "" # [OPTIONAL] defaults to https://openrouter.ai/api/v1
os.environ["OR_SITE_URL"] = "" # optional
os.environ["OR_APP_NAME"] = "" # optional
os.environ["OR_SITE_URL"] = "" # [OPTIONAL]
os.environ["OR_APP_NAME"] = "" # [OPTIONAL]
response = completion(
model="openrouter/google/palm-2-chat-bison",

1
docs/my-website/docs/proxy/config_settings.md
View file

@ -147,6 +147,7 @@ general_settings:
|------|------|-------------|
| completion_model | string | The default model to use for completions when `model` is not specified in the request |
| disable_spend_logs | boolean | If true, turns off writing each transaction to the database |
| disable_spend_updates | boolean | If true, turns off all spend updates to the DB. Including key/user/team spend updates. |
| disable_master_key_return | boolean | If true, turns off returning master key on UI. (checked on '/user/info' endpoint) |
| disable_retry_on_max_parallel_request_limit_error | boolean | If true, turns off retries when max parallel request limit is reached |
| disable_reset_budget | boolean | If true, turns off reset budget scheduled task |

1
docs/my-website/docs/proxy/logging_spec.md
View file

@ -79,6 +79,7 @@ Inherits from `StandardLoggingUserAPIKeyMetadata` and adds:
| `response_cost` | `Optional[str]` | Optional response cost |
| `additional_headers` | `Optional[StandardLoggingAdditionalHeaders]` | Additional headers |
| `batch_models` | `Optional[List[str]]` | Only set for Batches API. Lists the models used for cost calculation |
| `litellm_model_name` | `Optional[str]` | Model name sent in request |
## StandardLoggingModelInformation

22
docs/my-website/docs/proxy/response_headers.md
View file

@ -43,19 +43,19 @@ These headers are useful for clients to understand the current rate limit status
| `x-litellm-max-fallbacks` | int | Maximum number of fallback attempts allowed |
## Cost Tracking Headers
| Header | Type | Description |
|--------|------|-------------|
| `x-litellm-response-cost` | float | Cost of the API call |
| `x-litellm-key-spend` | float | Total spend for the API key |
| Header | Type | Description | Available on Pass-Through Endpoints |
|--------|------|-------------|-------------|
| `x-litellm-response-cost` | float | Cost of the API call | |
| `x-litellm-key-spend` | float | Total spend for the API key | ✅ |
## LiteLLM Specific Headers
| Header | Type | Description |
|--------|------|-------------|
| `x-litellm-call-id` | string | Unique identifier for the API call |
| `x-litellm-model-id` | string | Unique identifier for the model used |
| `x-litellm-model-api-base` | string | Base URL of the API endpoint |
| `x-litellm-version` | string | Version of LiteLLM being used |
| `x-litellm-model-group` | string | Model group identifier |
| Header | Type | Description | Available on Pass-Through Endpoints |
|--------|------|-------------|-------------|
| `x-litellm-call-id` | string | Unique identifier for the API call | ✅ |
| `x-litellm-model-id` | string | Unique identifier for the model used | |
| `x-litellm-model-api-base` | string | Base URL of the API endpoint | ✅ |
| `x-litellm-version` | string | Version of LiteLLM being used | |
| `x-litellm-model-group` | string | Model group identifier | |
## Response headers from LLM providers

BIN
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docs/my-website/release_notes/v1.63.11-stable/index.md
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@ -26,14 +26,6 @@ This release is primarily focused on:
- UI - Credential Management, re-use credentials when adding new models
- UI - Test Connection to LLM Provider before adding a model
:::info
This release will be live on 03/16/2025
:::
<!-- <Image img={require('../../img/release_notes/v16311_release.jpg')} /> -->
## Known Issues
- 🚨 Known issue on Azure OpenAI - We don't recommend upgrading if you use Azure OpenAI. This version failed our Azure OpenAI load test

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---
title: v1.63.14-stable
slug: v1.63.14-stable
date: 2025-03-22T10:00:00
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=1743638400&v=beta&t=39KOXMUFedvukiWWVPHf3qI45fuQD7lNglICwN31DrI
- 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: [credential management, thinking content, responses api, snowflake]
hide_table_of_contents: false
---
import Image from '@theme/IdealImage';
These are the changes since `v1.63.11-stable`.
This release brings:
- LLM Translation Improvements (MCP Support and Bedrock Application Profiles)
- Perf improvements for Usage-based Routing
- Streaming guardrail support via websockets
## Docker Run LiteLLM Proxy
```
docker run
-e STORE_MODEL_IN_DB=True
-p 4000:4000
ghcr.io/berriai/litellm:main-v1.63.14-stable
```
## Demo Instance
Here's a Demo Instance to test changes:
- Instance: https://demo.litellm.ai/
- Login Credentials:
- Username: admin
- Password: sk-1234
## New Models / Updated Models
- Azure gpt-4o - fixed pricing to latest global pricing - [PR](https://github.com/BerriAI/litellm/pull/9361)
- O1-Pro - add pricing + model information - [PR](https://github.com/BerriAI/litellm/pull/9397)
- Azure AI - mistral 3.1 small pricing added - [PR](https://github.com/BerriAI/litellm/pull/9453)
- Azure - gpt-4.5-preview pricing added - [PR](https://github.com/BerriAI/litellm/pull/9453)
## LLM Translation
1. **New LLM Features**
- Bedrock: Support bedrock application inference profiles [Docs](https://docs.litellm.ai/docs/providers/bedrock#bedrock-application-inference-profile)
- Infer aws region from bedrock application profile id - (`arn:aws:bedrock:us-east-1:...`)
- Ollama - support calling via `/v1/completions` [Get Started](../../docs/providers/ollama#using-ollama-fim-on-v1completions)
- Bedrock - support `us.deepseek.r1-v1:0` model name [Docs](../../docs/providers/bedrock#supported-aws-bedrock-models)
https://github.com/BerriAI/litellm/pull/9363
- OpenRouter - `OPENROUTER_API_BASE` env var support [Docs](../../docs/providers/openrouter.md)
- Azure - add audio model parameter support - [Docs](../../docs/providers/azure#azure-audio-model)
- OpenAI - PDF File support [Docs](../../docs/completion/document_understanding#openai-file-message-type)
- OpenAI - o1-pro Responses API streaming support [Docs](../../docs/response_api.md#streaming)
- [BETA] MCP - Use MCP Tools with LiteLLM SDK [Docs](../../docs/mcp)
2. **Bug Fixes**
- Voyage: prompt token on embedding tracking fix - [PR](https://github.com/BerriAI/litellm/commit/56d3e75b330c3c3862dc6e1c51c1210e48f1068e)
- Streaming - Prevents final chunk w/ usage from being ignored (impacted bedrock streaming + cost tracking) - [PR](https://github.com/BerriAI/litellm/commit/dd2c980d5bb9e1a3b125e364c5d841751e67c96d)
- Sagemaker - Fix ‘Too little data for declared Content-Length’ error - [PR](https://github.com/BerriAI/litellm/pull/9326)
- OpenAI-compatible models - fix issue when calling openai-compatible models w/ custom_llm_provider set - [PR](https://github.com/BerriAI/litellm/pull/9355)
- VertexAI - Embedding ‘outputDimensionality’ support - [PR](https://github.com/BerriAI/litellm/commit/437dbe724620675295f298164a076cbd8019d304)
- Anthropic - return consistent json response format on streaming/non-streaming - [PR](https://github.com/BerriAI/litellm/pull/9437)
## Spend Tracking Improvements
- `litellm_proxy/` - support reading litellm response cost header from proxy, when using client sdk
- Reset Budget Job - fix budget reset error on keys/teams/users - [PR](https://github.com/BerriAI/litellm/pull/9329)
## UI
1. Users Page
- Feature: Control default internal user settings [PR](https://github.com/BerriAI/litellm/pull/9374)
2. Icons:
- Feature: Replace external "artificialanalysis.ai" icons by local svg [PR](https://github.com/BerriAI/litellm/pull/9374)
3. Sign In/Sign Out
- Fix: Default login when `default_user_id` user does not exist in DB [PR](https://github.com/BerriAI/litellm/pull/9395)
## Logging Integrations
- Support post-call guardrails for streaming responses - https://github.com/BerriAI/litellm/commit/4a31b32a88b7729a032e58ab046079d17000087f [NEEDS DOCS]
- Arize [Get Started](../../docs/observability/arize_integration)
- fix invalid package import [PR](https://github.com/BerriAI/litellm/pull/9338)
- migrate to using standardloggingpayload for metadata, ensures spans land successfully [PR](https://github.com/BerriAI/litellm/pull/9338)
- fix logging to just log the LLM I/O [PR](https://github.com/BerriAI/litellm/pull/9353)
- Dynamic API Key/Space param support [Get Started](../../docs/observability/arize_integration#pass-arize-spacekey-per-request)
- StandardLoggingPayload - Log litellm_model_name in payload. Allows knowing what the model sent to API provider was [Get Started](../../docs/proxy/logging_spec#standardlogginghiddenparams)
- Prompt Management - Allow building custom prompt management integration [Get Started](../../docs/proxy/custom_prompt_management.md)
## Performance / Reliability improvements
- Redis Caching - add 5s default timeout, prevents hanging redis connection from impacting llm calls [PR](https://github.com/BerriAI/litellm/commit/db92956ae33ed4c4e3233d7e1b0c7229817159bf)
- Allow disabling all spend updates / writes to DB - patch to allow disabling all spend updates to DB with a flag [PR](https://github.com/BerriAI/litellm/pull/9331)
- Azure OpenAI - correctly re-use azure openai client, fixes perf issue from previous Stable release [PR](https://github.com/BerriAI/litellm/commit/f2026ef907c06d94440930917add71314b901413)
- Azure OpenAI - uses litellm.ssl_verify on Azure/OpenAI clients [PR](https://github.com/BerriAI/litellm/commit/f2026ef907c06d94440930917add71314b901413)
- Usage-based routing - Wildcard model support [Get Started](../../docs/proxy/usage_based_routing#wildcard-model-support)
- Usage-based routing - Support batch writing increments to redis - reduces latency to same as ‘simple-shuffle’ [PR](https://github.com/BerriAI/litellm/pull/9357)
- Router - show reason for model cooldown on ‘no healthy deployments available error’ [PR](https://github.com/BerriAI/litellm/pull/9438)
- Caching - add max value limit to an item in in-memory cache (1MB) - prevents OOM errors on large image url’s being sent through proxy [PR](https://github.com/BerriAI/litellm/pull/9448)
## General Improvements
- Passthrough Endpoints - support returning api-base on pass-through endpoints Response Headers [Docs](../../docs/proxy/response_headers#litellm-specific-headers)
- SSL - support reading ssl security level from env var - Allows user to specify lower security settings [Get Started](../../docs/guides/security_settings)
- Credentials - only poll Credentials table when `STORE_MODEL_IN_DB` is True [PR](https://github.com/BerriAI/litellm/pull/9376)
- Image URL Handling - new architecture doc on image url handling [Docs](../../docs/proxy/image_handling)
- OpenAI - bump to pip install "openai==1.68.2" [PR](https://github.com/BerriAI/litellm/commit/e85e3bc52a9de86ad85c3dbb12d87664ee567a5a)
- Gunicorn - security fix - bump gunicorn==23.0.0 [PR](https://github.com/BerriAI/litellm/commit/7e9fc92f5c7fea1e7294171cd3859d55384166eb)
## Complete Git Diff
[Here's the complete git diff](https://github.com/BerriAI/litellm/compare/v1.63.11-stable...v1.63.14.rc)

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@ -243,6 +243,7 @@ const sidebars = {
"exception_mapping",
"completion/provider_specific_params",
"guides/finetuned_models",
"guides/security_settings",
"completion/audio",
"completion/web_search",
"completion/document_understanding",