diff --git a/.circleci/config.yml b/.circleci/config.yml
index 7a982d74cbe..e30dc02b2ab 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -178,6 +178,7 @@ jobs:
pip install "Pillow==10.3.0"
pip install "jsonschema==4.22.0"
pip install "pytest-xdist==3.6.1"
+ pip install "pytest-timeout==2.2.0"
pip install "websockets==13.1.0"
pip install semantic_router --no-deps
pip install aurelio_sdk --no-deps
@@ -208,7 +209,10 @@ jobs:
command: |
pwd
ls
- python -m pytest -vv tests/local_testing --cov=litellm --cov-report=xml --junitxml=test-results/junit.xml --durations=5 -k "not test_python_38.py and not test_basic_python_version.py and not router and not assistants and not langfuse and not caching and not cache" -n 4
+ # Add --timeout to kill hanging tests after 300s (5 min)
+ # Add -v to show test names as they run for debugging
+ # Add --tb=short for shorter tracebacks
+ python -m pytest -vv tests/local_testing --cov=litellm --cov-report=xml --junitxml=test-results/junit.xml --durations=20 -k "not test_python_38.py and not test_basic_python_version.py and not router and not assistants and not langfuse and not caching and not cache" -n 4 --timeout=300 --timeout_method=thread
no_output_timeout: 120m
- run:
name: Rename the coverage files
@@ -614,6 +618,12 @@ jobs:
- run:
name: Install Dependencies
command: |
+ export PATH="$HOME/miniconda/bin:$PATH"
+ source $HOME/miniconda/etc/profile.d/conda.sh
+ conda activate myenv
+ python --version
+ which python
+ pip install --upgrade typing-extensions>=4.12.0
pip install "pytest==7.3.1"
pip install "pytest-asyncio==0.21.1"
pip install aiohttp
@@ -677,6 +687,9 @@ jobs:
- run:
name: Run prisma ./docker/entrypoint.sh
command: |
+ export PATH="$HOME/miniconda/bin:$PATH"
+ source $HOME/miniconda/etc/profile.d/conda.sh
+ conda activate myenv
set +e
chmod +x docker/entrypoint.sh
./docker/entrypoint.sh
@@ -685,6 +698,9 @@ jobs:
- run:
name: Run tests
command: |
+ export PATH="$HOME/miniconda/bin:$PATH"
+ source $HOME/miniconda/etc/profile.d/conda.sh
+ conda activate myenv
pwd
ls
python -m pytest tests/proxy_security_tests --cov=litellm --cov-report=xml -vv -x -v --junitxml=test-results/junit.xml --durations=5
@@ -1090,13 +1106,16 @@ jobs:
pip install "pytest-asyncio==0.21.1"
pip install "respx==0.22.0"
pip install "pytest-xdist==3.6.1"
+ pip install "pytest-timeout==2.2.0"
# Run pytest and generate JUnit XML report
- run:
name: Run tests
command: |
pwd
ls
- python -m pytest -vv tests/llm_translation --cov=litellm --cov-report=xml -v --junitxml=test-results/junit.xml --durations=5 -n 4
+ # Add --timeout to kill hanging tests after 120s (2 min)
+ # Add --durations=20 to show 20 slowest tests for debugging
+ python -m pytest -vv tests/llm_translation --cov=litellm --cov-report=xml -v --junitxml=test-results/junit.xml --durations=20 -n 4 --timeout=120 --timeout_method=thread
no_output_timeout: 120m
- run:
name: Rename the coverage files
@@ -3954,4 +3973,4 @@ workflows:
- proxy_pass_through_endpoint_tests
- check_code_and_doc_quality
- publish_proxy_extras
- - guardrails_testing
+ - guardrails_testing
\ No newline at end of file
diff --git a/.gitguardian.yaml b/.gitguardian.yaml
new file mode 100644
index 00000000000..af8f2489eec
--- /dev/null
+++ b/.gitguardian.yaml
@@ -0,0 +1,104 @@
+version: 2
+
+secret:
+ # Exclude files and paths by globbing
+ ignored_paths:
+ - "**/*.whl"
+ - "**/*.pyc"
+ - "**/__pycache__/**"
+ - "**/node_modules/**"
+ - "**/dist/**"
+ - "**/build/**"
+ - "**/.git/**"
+ - "**/venv/**"
+ - "**/.venv/**"
+
+ # Large data/metadata files that don't need scanning
+ - "**/model_prices_and_context_window*.json"
+ - "**/*_metadata/*.txt"
+ - "**/tokenizers/*.json"
+ - "**/tokenizers/*"
+ - "miniconda.sh"
+
+ # Build outputs and static assets
+ - "litellm/proxy/_experimental/out/**"
+ - "ui/litellm-dashboard/public/**"
+ - "**/swagger/*.js"
+ - "**/*.woff"
+ - "**/*.woff2"
+ - "**/*.avif"
+ - "**/*.webp"
+
+ # Test data files
+ - "**/tests/**/data_map.txt"
+ - "tests/**/*.txt"
+
+ # Documentation and other non-code files
+ - "docs/**"
+ - "**/*.md"
+ - "**/*.lock"
+ - "poetry.lock"
+ - "package-lock.json"
+
+ # Ignore security incidents with the SHA256 of the occurrence (false positives)
+ ignored_matches:
+ # === Current detected false positives (SHA-based) ===
+
+ # gcs_pub_sub_body - folder name, not a password
+ - name: GCS pub/sub test folder name
+ match: 75f377c456eede69e5f6e47399ccee6016a2a93cc5dd11db09cc5b1359ae569a
+
+ # os.environ/APORIA_API_KEY_1 - environment variable reference
+ - name: Environment variable reference APORIA_API_KEY_1
+ match: e2ddeb8b88eca97a402559a2be2117764e11c074d86159ef9ad2375dea188094
+
+ # os.environ/APORIA_API_KEY_2 - environment variable reference
+ - name: Environment variable reference APORIA_API_KEY_2
+ match: 09aa39a29e050b86603aa55138af1ff08fb86a4582aa965c1bd0672e1575e052
+
+ # oidc/circleci_v2/ - test authentication path, not a secret
+ - name: OIDC CircleCI test path
+ match: feb3475e1f89a65b7b7815ac4ec597e18a9ec1847742ad445c36ca617b536e15
+
+ # text-davinci-003 - OpenAI model identifier, not a secret
+ - name: OpenAI model identifier text-davinci-003
+ match: c489000cf6c7600cee0eefb80ad0965f82921cfb47ece880930eb7e7635cf1f1
+
+ # Base64 Basic Auth in test_pass_through_endpoints.py - test fixture, not a real secret
+ - name: Test Base64 Basic Auth header in pass_through_endpoints test
+ match: 61bac0491f395040617df7ef6d06029eac4d92a4457ac784978db80d97be1ae0
+
+ # PostgreSQL password "postgres" in CI configs - standard test database password
+ - name: Test PostgreSQL password in CI configurations
+ match: 6e0d657eb1f0fbc40cf0b8f3c3873ef627cc9cb7c4108d1c07d979c04bc8a4bb
+
+ # Bearer token in locustfile.py - test/example API key for load testing
+ - name: Test Bearer token in locustfile load test
+ match: 2a0abc2b0c3c1760a51ffcdf8d6b1d384cef69af740504b1cfa82dd70cdc7ff9
+
+ # Inkeep API key in docusaurus.config.js - public documentation site key
+ - name: Inkeep API key in documentation config
+ match: c366657791bfb5fc69045ec11d49452f09a0aebbc8648f94e2469b4025e29a75
+
+ # Langfuse credentials in test_completion.py - test credentials for integration test
+ - name: Langfuse test credentials in test_completion
+ match: c39310f68cc3d3e22f7b298bb6353c4f45759adcc37080d8b7f4e535d3cfd7f4
+
+ # === Preventive patterns for test keys (pattern-based) ===
+
+ # Test API keys (124 instances across 45 files)
+ - name: Test API keys with sk-test prefix
+ match: sk-test-
+
+ # Mock API keys
+ - name: Mock API keys with sk-mock prefix
+ match: sk-mock-
+
+ # Fake API keys
+ - name: Fake API keys with sk-fake prefix
+ match: sk-fake-
+
+ # Generic test API key patterns
+ - name: Test API key patterns
+ match: test-api-key
+
diff --git a/AGENTS.md b/AGENTS.md
index 2c778dc0d71..61afbd035fe 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -49,6 +49,27 @@ LiteLLM is a unified interface for 100+ LLMs that:
- Test provider-specific functionality thoroughly
- Consider adding load tests for performance-critical changes
+### MAKING CODE CHANGES FOR THE UI (IGNORE FOR BACKEND)
+
+1. **Use Common Components as much as possible**:
+ - These are usually defined in the `common_components` directory
+ - Use these components as much as possible and avoid building new components unless needed
+ - Tremor components are deprecated; prefer using Ant Design (AntD) as much as possible
+
+2. **Testing**:
+ - The codebase uses **Vitest** and **React Testing Library**
+ - **Query Priority Order**: Use query methods in this order: `getByRole`, `getByLabelText`, `getByPlaceholderText`, `getByText`, `getByTestId`
+ - **Always use `screen`** instead of destructuring from `render()` (e.g., use `screen.getByText()` not `getByText`)
+ - **Wrap user interactions in `act()`**: Always wrap `fireEvent` calls with `act()` to ensure React state updates are properly handled
+ - **Use `query` methods for absence checks**: Use `queryBy*` methods (not `getBy*`) when expecting an element to NOT be present
+ - **Test names must start with "should"**: All test names should follow the pattern `it("should ...")`
+ - **Mock external dependencies**: Check `setupTests.ts` for global mocks and mock child components/networking calls as needed
+ - **Structure tests properly**:
+ - First test should verify the component renders successfully
+ - Subsequent tests should focus on functionality and user interactions
+ - Use `waitFor` for async operations that aren't already awaited
+ - **Avoid using `querySelector`**: Prefer React Testing Library queries over direct DOM manipulation
+
### IMPORTANT PATTERNS
1. **Function/Tool Calling**:
diff --git a/README.md b/README.md
index 9fed1c6dbc7..a020bd80898 100644
--- a/README.md
+++ b/README.md
@@ -2,16 +2,16 @@
🚅 LiteLLM
+
Call 100+ LLMs in OpenAI format. [Bedrock, Azure, OpenAI, VertexAI, Anthropic, Groq, etc.]
+
- Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
-
-
+
-LiteLLM manages:
+
-- Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
-- [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']`
-- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
-- Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway)](https://docs.litellm.ai/docs/simple_proxy)
-LiteLLM Performance: **8ms P95 latency** at 1k RPS (See benchmarks [here](https://docs.litellm.ai/docs/benchmarks))
+## Use LiteLLM for
-[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#litellm-proxy-server-llm-gateway---docs)
-[**Jump to Supported LLM Providers**](https://docs.litellm.ai/docs/providers)
+
+LLMs - Call 100+ LLMs (Python SDK + AI Gateway)
-🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. [More information about the release cycle here](https://docs.litellm.ai/docs/proxy/release_cycle)
+[**All Supported Endpoints**](https://docs.litellm.ai/docs/supported_endpoints) - `/chat/completions`, `/responses`, `/embeddings`, `/images`, `/audio`, `/batches`, `/rerank`, `/a2a`, `/messages` and more.
-Support for more providers. Missing a provider or LLM Platform, raise a [feature request](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+).
-
-# Usage ([**Docs**](https://docs.litellm.ai/docs/))
-
-
- 
-
+### Python SDK
```shell
pip install litellm
@@ -60,249 +50,214 @@ pip install litellm
from litellm import completion
import os
-## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"
-messages = [{ "content": "Hello, how are you?","role": "user"}]
+# OpenAI
+response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}])
-# openai call
-response = completion(model="openai/gpt-4o", messages=messages)
-
-# anthropic call
-response = completion(model="anthropic/claude-sonnet-4-20250514", messages=messages)
-print(response)
+# Anthropic
+response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Hello!"}])
```
-### Response (OpenAI Format)
+### AI Gateway (Proxy Server)
-```json
-{
- "id": "chatcmpl-1214900a-6cdd-4148-b663-b5e2f642b4de",
- "created": 1751494488,
- "model": "claude-sonnet-4-20250514",
- "object": "chat.completion",
- "system_fingerprint": null,
- "choices": [
- {
- "finish_reason": "stop",
- "index": 0,
- "message": {
- "content": "Hello! I'm doing well, thank you for asking. I'm here and ready to help with whatever you'd like to discuss or work on. How are you doing today?",
- "role": "assistant",
- "tool_calls": null,
- "function_call": null
- }
- }
- ],
- "usage": {
- "completion_tokens": 39,
- "prompt_tokens": 13,
- "total_tokens": 52,
- "completion_tokens_details": null,
- "prompt_tokens_details": {
- "audio_tokens": null,
- "cached_tokens": 0
- },
- "cache_creation_input_tokens": 0,
- "cache_read_input_tokens": 0
- }
-}
-```
-
-> **Note:** LiteLLM also supports the [Responses API](https://docs.litellm.ai/docs/response_api) (`litellm.responses()`)
-
-Call any model supported by a provider, with `model=/`. There might be provider-specific details here, so refer to [provider docs for more information](https://docs.litellm.ai/docs/providers)
-
-## Async ([Docs](https://docs.litellm.ai/docs/completion/stream#async-completion))
-
-```python
-from litellm import acompletion
-import asyncio
-
-async def test_get_response():
- user_message = "Hello, how are you?"
- messages = [{"content": user_message, "role": "user"}]
- response = await acompletion(model="openai/gpt-4o", messages=messages)
- return response
-
-response = asyncio.run(test_get_response())
-print(response)
-```
-
-## Streaming ([Docs](https://docs.litellm.ai/docs/completion/stream))
-
-LiteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response.
-Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.)
-
-```python
-from litellm import completion
-
-messages = [{"content": "Hello, how are you?", "role": "user"}]
-
-# gpt-4o
-response = completion(model="openai/gpt-4o", messages=messages, stream=True)
-for part in response:
- print(part.choices[0].delta.content or "")
-
-# claude sonnet 4
-response = completion('anthropic/claude-sonnet-4-20250514', messages, stream=True)
-for part in response:
- print(part)
-```
-
-### Response chunk (OpenAI Format)
-
-```json
-{
- "id": "chatcmpl-fe575c37-5004-4926-ae5e-bfbc31f356ca",
- "created": 1751494808,
- "model": "claude-sonnet-4-20250514",
- "object": "chat.completion.chunk",
- "system_fingerprint": null,
- "choices": [
- {
- "finish_reason": null,
- "index": 0,
- "delta": {
- "provider_specific_fields": null,
- "content": "Hello",
- "role": "assistant",
- "function_call": null,
- "tool_calls": null,
- "audio": null
- },
- "logprobs": null
- }
- ],
- "provider_specific_fields": null,
- "stream_options": null,
- "citations": null
-}
-```
-
-## Logging Observability ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
-
-LiteLLM exposes pre defined callbacks to send data to Lunary, MLflow, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack
-
-```python
-from litellm import completion
-
-## set env variables for logging tools (when using MLflow, no API key set up is required)
-os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
-os.environ["HELICONE_API_KEY"] = "your-helicone-auth-key"
-os.environ["LANGFUSE_PUBLIC_KEY"] = ""
-os.environ["LANGFUSE_SECRET_KEY"] = ""
-os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
-
-os.environ["OPENAI_API_KEY"] = "your-openai-key"
-
-# set callbacks
-litellm.success_callback = ["lunary", "mlflow", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc
-
-#openai call
-response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
-```
-
-# LiteLLM Proxy Server (LLM Gateway) - ([Docs](https://docs.litellm.ai/docs/simple_proxy))
-
-Track spend + Load Balance across multiple projects
-
-[Hosted Proxy](https://docs.litellm.ai/docs/enterprise#hosted-litellm-proxy)
-
-The proxy provides:
-
-1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth)
-2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class)
-3. [Cost tracking](https://docs.litellm.ai/docs/proxy/virtual_keys#tracking-spend)
-4. [Rate Limiting](https://docs.litellm.ai/docs/proxy/users#set-rate-limits)
-
-## 📖 Proxy Endpoints - [Swagger Docs](https://litellm-api.up.railway.app/)
-
-
-## Quick Start Proxy - CLI
+[**Getting Started - E2E Tutorial**](https://docs.litellm.ai/docs/proxy/docker_quick_start) - Setup virtual keys, make your first request
```shell
pip install 'litellm[proxy]'
+litellm --model gpt-4o
```
-### Step 1: Start litellm proxy
-
-```shell
-$ litellm --model huggingface/bigcode/starcoder
-
-#INFO: Proxy running on http://0.0.0.0:4000
-```
-
-### Step 2: Make ChatCompletions Request to Proxy
-
-
-> [!IMPORTANT]
-> 💡 [Use LiteLLM Proxy with Langchain (Python, JS), OpenAI SDK (Python, JS) Anthropic SDK, Mistral SDK, LlamaIndex, Instructor, Curl](https://docs.litellm.ai/docs/proxy/user_keys)
-
```python
-import openai # openai v1.0.0+
-client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
-# 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"
- }
-])
+import openai
-print(response)
+client = openai.OpenAI(api_key="anything", base_url="http://0.0.0.0:4000")
+response = client.chat.completions.create(
+ model="gpt-4o",
+ messages=[{"role": "user", "content": "Hello!"}]
+)
```
-## Proxy Key Management ([Docs](https://docs.litellm.ai/docs/proxy/virtual_keys))
+[**Docs: LLM Providers**](https://docs.litellm.ai/docs/providers)
-Connect the proxy with a Postgres DB to create proxy keys
+
+
+
+Agents - Invoke A2A Agents (Python SDK + AI Gateway)
+
+[**Supported Providers**](https://docs.litellm.ai/docs/a2a#add-a2a-agents) - LangGraph, Vertex AI Agent Engine, Azure AI Foundry, Bedrock AgentCore, Pydantic AI
+
+### Python SDK - A2A Protocol
+
+```python
+from litellm.a2a_protocol import A2AClient
+from a2a.types import SendMessageRequest, MessageSendParams
+from uuid import uuid4
+
+client = A2AClient(base_url="http://localhost:10001")
+
+request = SendMessageRequest(
+ id=str(uuid4()),
+ params=MessageSendParams(
+ message={
+ "role": "user",
+ "parts": [{"kind": "text", "text": "Hello!"}],
+ "messageId": uuid4().hex,
+ }
+ )
+)
+response = await client.send_message(request)
+```
+
+### AI Gateway (Proxy Server)
+
+**Step 1.** [Add your Agent to the AI Gateway](https://docs.litellm.ai/docs/a2a#adding-your-agent)
+
+**Step 2.** Call Agent via A2A SDK
+
+```python
+from a2a.client import A2ACardResolver, A2AClient
+from a2a.types import MessageSendParams, SendMessageRequest
+from uuid import uuid4
+import httpx
+
+base_url = "http://localhost:4000/a2a/my-agent" # LiteLLM proxy + agent name
+headers = {"Authorization": "Bearer sk-1234"} # LiteLLM Virtual Key
+
+async with httpx.AsyncClient(headers=headers) as httpx_client:
+ resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url)
+ agent_card = await resolver.get_agent_card()
+ client = A2AClient(httpx_client=httpx_client, agent_card=agent_card)
+
+ request = SendMessageRequest(
+ id=str(uuid4()),
+ params=MessageSendParams(
+ message={
+ "role": "user",
+ "parts": [{"kind": "text", "text": "Hello!"}],
+ "messageId": uuid4().hex,
+ }
+ )
+ )
+ response = await client.send_message(request)
+```
+
+[**Docs: A2A Agent Gateway**](https://docs.litellm.ai/docs/a2a)
+
+
+
+
+MCP Tools - Connect MCP servers to any LLM (Python SDK + AI Gateway)
+
+### Python SDK - MCP Bridge
+
+```python
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+from litellm import experimental_mcp_client
+import litellm
+
+server_params = StdioServerParameters(command="python", args=["mcp_server.py"])
+
+async with stdio_client(server_params) as (read, write):
+ async with ClientSession(read, write) as session:
+ await session.initialize()
+
+ # Load MCP tools in OpenAI format
+ tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")
+
+ # Use with any LiteLLM model
+ response = await litellm.acompletion(
+ model="gpt-4o",
+ messages=[{"role": "user", "content": "What's 3 + 5?"}],
+ tools=tools
+ )
+```
+
+### AI Gateway - MCP Gateway
+
+**Step 1.** [Add your MCP Server to the AI Gateway](https://docs.litellm.ai/docs/mcp#adding-your-mcp)
+
+**Step 2.** Call MCP tools via `/chat/completions`
```bash
-# Get the code
-git clone https://github.com/BerriAI/litellm
-
-# Go to folder
-cd litellm
-
-# Add the master key - you can change this after setup
-echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
-
-# Add the litellm salt key - you cannot change this after adding a model
-# It is used to encrypt / decrypt your LLM API Key credentials
-# We recommend - https://1password.com/password-generator/
-# password generator to get a random hash for litellm salt key
-echo 'LITELLM_SALT_KEY="sk-1234"' >> .env
-
-# Start
-docker compose up
+curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
+ -H 'Authorization: Bearer sk-1234' \
+ -H 'Content-Type: application/json' \
+ -d '{
+ "model": "gpt-4o",
+ "messages": [{"role": "user", "content": "Summarize the latest open PR"}],
+ "tools": [{
+ "type": "mcp",
+ "server_url": "litellm_proxy/mcp/github",
+ "server_label": "github_mcp",
+ "require_approval": "never"
+ }]
+ }'
```
+### Use with Cursor IDE
-UI on `/ui` on your proxy server
-
-
-Set budgets and rate limits across multiple projects
-`POST /key/generate`
-
-### Request
-
-```shell
-curl 'http://0.0.0.0:4000/key/generate' \
---header 'Authorization: Bearer sk-1234' \
---header 'Content-Type: application/json' \
---data-raw '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m","metadata": {"user": "ishaan@berri.ai", "team": "core-infra"}}'
-```
-
-### Expected Response
-
-```shell
+```json
{
- "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
- "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
+ "mcpServers": {
+ "LiteLLM": {
+ "url": "http://localhost:4000/mcp",
+ "headers": {
+ "x-litellm-api-key": "Bearer sk-1234"
+ }
+ }
+ }
}
```
+[**Docs: MCP Gateway**](https://docs.litellm.ai/docs/mcp)
+
+
+
+---
+
+## How to use LiteLLM
+
+You can use LiteLLM through either the Proxy Server or Python SDK. Both gives you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs:
+
+
+
+
+ |
+LiteLLM AI Gateway |
+LiteLLM Python SDK |
+
+
+
+
+| Use Case |
+Central service (LLM Gateway) to access multiple LLMs |
+Use LiteLLM directly in your Python code |
+
+
+| Who Uses It? |
+Gen AI Enablement / ML Platform Teams |
+Developers building LLM projects |
+
+
+| Key Features |
+Centralized API gateway with authentication and authorization, multi-tenant cost tracking and spend management per project/user, per-project customization (logging, guardrails, caching), virtual keys for secure access control, admin dashboard UI for monitoring and management |
+Direct Python library integration in your codebase, Router with retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router, application-level load balancing and cost tracking, exception handling with OpenAI-compatible errors, observability callbacks (Lunary, MLflow, Langfuse, etc.) |
+
+
+
+
+LiteLLM Performance: **8ms P95 latency** at 1k RPS (See benchmarks [here](https://docs.litellm.ai/docs/benchmarks))
+
+[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://docs.litellm.ai/docs/simple_proxy)
+[**Jump to Supported LLM Providers**](https://docs.litellm.ai/docs/providers)
+
+**Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. [More information about the release cycle here](https://docs.litellm.ai/docs/proxy/release_cycle)
+
+Support for more providers. Missing a provider or LLM Platform, raise a [feature request](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+).
+
## Supported Providers ([Website Supported Models](https://models.litellm.ai/) | [Docs](https://docs.litellm.ai/docs/providers))
| Provider | `/chat/completions` | `/messages` | `/responses` | `/embeddings` | `/image/generations` | `/audio/transcriptions` | `/audio/speech` | `/moderations` | `/batches` | `/rerank` |
@@ -311,6 +266,7 @@ curl 'http://0.0.0.0:4000/key/generate' \
| [AI21 (`ai21`)](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | | | | | | | |
| [AI21 Chat (`ai21_chat`)](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | | | | | | | |
| [Aleph Alpha](https://docs.litellm.ai/docs/providers/aleph_alpha) | ✅ | ✅ | ✅ | | | | | | | |
+| [Amazon Nova](https://docs.litellm.ai/docs/providers/amazon_nova) | ✅ | ✅ | ✅ | | | | | | | |
| [Anthropic (`anthropic`)](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | | | | | | ✅ | |
| [Anthropic Text (`anthropic_text`)](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | | | | | | ✅ | |
| [Anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | | | | | | | |
diff --git a/ci_cd/TEST_KEY_PATTERNS.md b/ci_cd/TEST_KEY_PATTERNS.md
new file mode 100644
index 00000000000..bd59f582839
--- /dev/null
+++ b/ci_cd/TEST_KEY_PATTERNS.md
@@ -0,0 +1,40 @@
+# Test Key Patterns Standard
+
+Standard patterns for test/mock keys and credentials in the LiteLLM codebase to avoid triggering secret detection.
+
+## How GitGuardian Works
+
+GitGuardian uses **machine learning and entropy analysis**, not just pattern matching:
+- **Low entropy** values (like `sk-1234`, `postgres`) are automatically ignored
+- **High entropy** values (realistic-looking secrets) trigger detection
+- **Context-aware** detection understands code syntax like `os.environ["KEY"]`
+
+## Recommended Test Key Patterns
+
+### Option 1: Low Entropy Values (Simplest)
+These won't trigger GitGuardian's ML detector:
+
+```python
+api_key = "sk-1234"
+api_key = "sk-12345"
+database_password = "postgres"
+token = "test123"
+```
+
+### Option 2: High Entropy with Test Prefixes
+If you need realistic-looking test keys with high entropy, use these prefixes:
+
+```python
+api_key = "sk-test-abc123def456ghi789..." # OpenAI-style test key
+api_key = "sk-mock-1234567890abcdef1234..." # Mock key
+api_key = "sk-fake-xyz789uvw456rst123..." # Fake key
+token = "test-api-key-with-high-entropy"
+```
+
+## Configured Ignore Patterns
+
+These patterns are in `.gitguardian.yaml` for high-entropy test keys:
+- `sk-test-*` - OpenAI-style test keys
+- `sk-mock-*` - Mock API keys
+- `sk-fake-*` - Fake API keys
+- `test-api-key` - Generic test tokens
diff --git a/ci_cd/security_scans.sh b/ci_cd/security_scans.sh
index 6950880320b..0036a304417 100755
--- a/ci_cd/security_scans.sh
+++ b/ci_cd/security_scans.sh
@@ -26,6 +26,56 @@ install_grype() {
echo "Grype installed successfully"
}
+# Function to install ggshield
+install_ggshield() {
+ echo "Installing ggshield..."
+ pip3 install --upgrade pip
+ pip3 install ggshield
+ echo "ggshield installed successfully"
+}
+
+# Function to run secret detection scans
+run_secret_detection() {
+ echo "Running secret detection scans..."
+
+ if ! command -v ggshield &> /dev/null; then
+ install_ggshield
+ fi
+
+ # Check if GITGUARDIAN_API_KEY is set (required for CI/CD)
+ if [ -z "$GITGUARDIAN_API_KEY" ]; then
+ echo "Warning: GITGUARDIAN_API_KEY environment variable is not set."
+ echo "ggshield requires a GitGuardian API key to scan for secrets."
+ echo "Please set GITGUARDIAN_API_KEY in your CI/CD environment variables."
+ exit 1
+ fi
+
+ echo "Scanning codebase for secrets..."
+ echo "Note: Large codebases may take several minutes due to API rate limits (50 requests/minute on free plan)"
+ echo "ggshield will automatically handle rate limits and retry as needed."
+ echo "Binary files, cache files, and build artifacts are excluded via .gitguardian.yaml"
+
+ # Use --recursive for directory scanning and auto-confirm if prompted
+ # .gitguardian.yaml will automatically exclude binary files, wheel files, etc.
+ # GITGUARDIAN_API_KEY environment variable will be used for authentication
+ echo y | ggshield secret scan path . --recursive || {
+ echo ""
+ echo "=========================================="
+ echo "ERROR: Secret Detection Failed"
+ echo "=========================================="
+ echo "ggshield has detected secrets in the codebase."
+ echo "Please review discovered secrets above, revoke any actively used secrets"
+ echo "from underlying systems and make changes to inject secrets dynamically at runtime."
+ echo ""
+ echo "For more information, see: https://docs.gitguardian.com/secrets-detection/"
+ echo "=========================================="
+ echo ""
+ exit 1
+ }
+
+ echo "Secret detection scans completed successfully"
+}
+
# Function to run Trivy scans
run_trivy_scans() {
echo "Running Trivy scans..."
@@ -158,6 +208,9 @@ main() {
install_trivy
install_grype
+ echo "Running secret detection scans..."
+ run_secret_detection
+
echo "Running filesystem vulnerability scans..."
run_trivy_scans
diff --git a/cookbook/LiteLLM_PromptLayer.ipynb b/cookbook/LiteLLM_PromptLayer.ipynb
index 3552636011a..8fd54941027 100644
--- a/cookbook/LiteLLM_PromptLayer.ipynb
+++ b/cookbook/LiteLLM_PromptLayer.ipynb
@@ -39,7 +39,7 @@
"import os\n",
"os.environ['OPENAI_API_KEY'] = \"\"\n",
"os.environ['REPLICATE_API_TOKEN'] = \"\"\n",
- "os.environ['PROMPTLAYER_API_KEY'] = \"pl_4ea2bb00a4dca1b8a70cebf2e9e11564\"\n",
+ "os.environ['PROMPTLAYER_API_KEY'] = \"test-promptlayer-key-123\"\n",
"\n",
"# Set Promptlayer as a success callback\n",
"litellm.success_callback =['promptlayer']\n",
diff --git a/cookbook/Migrating_to_LiteLLM_Proxy_from_OpenAI_Azure_OpenAI.ipynb b/cookbook/Migrating_to_LiteLLM_Proxy_from_OpenAI_Azure_OpenAI.ipynb
index 39677ed2a8a..740e7c7a4c8 100644
--- a/cookbook/Migrating_to_LiteLLM_Proxy_from_OpenAI_Azure_OpenAI.ipynb
+++ b/cookbook/Migrating_to_LiteLLM_Proxy_from_OpenAI_Azure_OpenAI.ipynb
@@ -1,21 +1,10 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- }
- },
"cells": [
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "kccfk0mHZ4Ad"
+ },
"source": [
"# Migrating to LiteLLM Proxy from OpenAI/Azure OpenAI\n",
"\n",
@@ -32,29 +21,26 @@
"To pass provider-specific args, [go here](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage)\n",
"\n",
"To drop unsupported params (E.g. frequency_penalty for bedrock with librechat), [go here](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage)\n"
- ],
- "metadata": {
- "id": "kccfk0mHZ4Ad"
- }
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "nmSClzCPaGH6"
+ },
"source": [
"## /chat/completion\n",
"\n"
- ],
- "metadata": {
- "id": "nmSClzCPaGH6"
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### OpenAI Python SDK"
- ],
"metadata": {
"id": "_vqcjwOVaKpO"
- }
+ },
+ "source": [
+ "### OpenAI Python SDK"
+ ]
},
{
"cell_type": "code",
@@ -94,15 +80,20 @@
},
{
"cell_type": "markdown",
- "source": [
- "## Function Calling"
- ],
"metadata": {
"id": "AqkyKk9Scxgj"
- }
+ },
+ "source": [
+ "## Function Calling"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wDg10VqLczE1"
+ },
+ "outputs": [],
"source": [
"from openai import OpenAI\n",
"client = OpenAI(\n",
@@ -139,24 +130,24 @@
")\n",
"\n",
"print(completion)\n"
- ],
- "metadata": {
- "id": "wDg10VqLczE1"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Azure OpenAI Python SDK"
- ],
"metadata": {
"id": "YYoxLloSaNWW"
- }
+ },
+ "source": [
+ "### Azure OpenAI Python SDK"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yA1XcgowaSRy"
+ },
+ "outputs": [],
"source": [
"import openai\n",
"client = openai.AzureOpenAI(\n",
@@ -184,24 +175,24 @@
")\n",
"\n",
"print(response)"
- ],
- "metadata": {
- "id": "yA1XcgowaSRy"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Langchain Python"
- ],
"metadata": {
"id": "yl9qhDvnaTpL"
- }
+ },
+ "source": [
+ "### Langchain Python"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5MUZgSquaW5t"
+ },
+ "outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts.chat import (\n",
@@ -239,24 +230,22 @@
"response = chat(messages)\n",
"\n",
"print(response)"
- ],
- "metadata": {
- "id": "5MUZgSquaW5t"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Curl"
- ],
"metadata": {
"id": "B9eMgnULbRaz"
- }
+ },
+ "source": [
+ "### Curl"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "VWCCk5PFcmhS"
+ },
"source": [
"\n",
"\n",
@@ -280,22 +269,24 @@
"}'\n",
"```\n",
"\n"
- ],
- "metadata": {
- "id": "VWCCk5PFcmhS"
- }
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### LlamaIndex"
- ],
"metadata": {
"id": "drBAm2e1b6xe"
- }
+ },
+ "source": [
+ "### LlamaIndex"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "d0bZcv8fb9mL"
+ },
+ "outputs": [],
"source": [
"import os, dotenv\n",
"\n",
@@ -326,24 +317,24 @@
"query_engine = index.as_query_engine()\n",
"response = query_engine.query(\"What did the author do growing up?\")\n",
"print(response)\n"
- ],
- "metadata": {
- "id": "d0bZcv8fb9mL"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Langchain JS"
- ],
"metadata": {
"id": "xypvNdHnb-Yy"
- }
+ },
+ "source": [
+ "### Langchain JS"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "R55mK2vCcBN2"
+ },
+ "outputs": [],
"source": [
"import { ChatOpenAI } from \"@langchain/openai\";\n",
"\n",
@@ -359,24 +350,24 @@
"const message = await model.invoke(\"Hi there!\");\n",
"\n",
"console.log(message);\n"
- ],
- "metadata": {
- "id": "R55mK2vCcBN2"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### OpenAI JS"
- ],
"metadata": {
"id": "nC4bLifCcCiW"
- }
+ },
+ "source": [
+ "### OpenAI JS"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "MICH8kIMcFpg"
+ },
+ "outputs": [],
"source": [
"const { OpenAI } = require('openai');\n",
"\n",
@@ -398,24 +389,24 @@
"}\n",
"\n",
"main();\n"
- ],
- "metadata": {
- "id": "MICH8kIMcFpg"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Anthropic SDK"
- ],
"metadata": {
"id": "D1Q07pEAcGTb"
- }
+ },
+ "source": [
+ "### Anthropic SDK"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "qBjFcAvgcI3t"
+ },
+ "outputs": [],
"source": [
"import os\n",
"\n",
@@ -423,7 +414,7 @@
"\n",
"client = Anthropic(\n",
" base_url=\"http://localhost:4000\", # proxy endpoint\n",
- " api_key=\"sk-s4xN1IiLTCytwtZFJaYQrA\", # litellm proxy virtual key\n",
+ " api_key=\"sk-test-proxy-key-123\", # litellm proxy virtual key (example)\n",
")\n",
"\n",
"message = client.messages.create(\n",
@@ -437,33 +428,33 @@
" model=\"claude-3-opus-20240229\",\n",
")\n",
"print(message.content)"
- ],
- "metadata": {
- "id": "qBjFcAvgcI3t"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "## /embeddings"
- ],
"metadata": {
"id": "dFAR4AJGcONI"
- }
+ },
+ "source": [
+ "## /embeddings"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### OpenAI Python SDK"
- ],
"metadata": {
"id": "lgNoM281cRzR"
- }
+ },
+ "source": [
+ "### OpenAI Python SDK"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NY3DJhPfcQhA"
+ },
+ "outputs": [],
"source": [
"import openai\n",
"from openai import OpenAI\n",
@@ -478,24 +469,24 @@
")\n",
"\n",
"print(response)\n"
- ],
- "metadata": {
- "id": "NY3DJhPfcQhA"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Langchain Embeddings"
- ],
"metadata": {
"id": "hmbg-DW6cUZs"
- }
+ },
+ "source": [
+ "### Langchain Embeddings"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lX2S8Nl1cWVP"
+ },
+ "outputs": [],
"source": [
"from langchain.embeddings import OpenAIEmbeddings\n",
"\n",
@@ -526,24 +517,22 @@
"\n",
"print(f\"TITAN EMBEDDINGS\")\n",
"print(query_result[:5])"
- ],
- "metadata": {
- "id": "lX2S8Nl1cWVP"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "### Curl Request"
- ],
"metadata": {
"id": "oqGbWBCQcYfd"
- }
+ },
+ "source": [
+ "### Curl Request"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "7rkIMV9LcdwQ"
+ },
"source": [
"\n",
"\n",
@@ -556,10 +545,21 @@
" }'\n",
"```\n",
"\n"
- ],
- "metadata": {
- "id": "7rkIMV9LcdwQ"
- }
+ ]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/docker/Dockerfile.alpine b/docker/Dockerfile.alpine
index f036081549a..ce83cfe653c 100644
--- a/docker/Dockerfile.alpine
+++ b/docker/Dockerfile.alpine
@@ -34,8 +34,8 @@ RUN pip wheel --no-cache-dir --wheel-dir=/wheels/ -r requirements.txt
# Runtime stage
FROM $LITELLM_RUNTIME_IMAGE AS runtime
-# Update dependencies and clean up
-RUN apk upgrade --no-cache
+# Update dependencies and clean up, install libsndfile for audio processing
+RUN apk upgrade --no-cache && apk add --no-cache libsndfile
WORKDIR /app
diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root
index d8a362680e4..7e9147a124e 100644
--- a/docker/Dockerfile.non_root
+++ b/docker/Dockerfile.non_root
@@ -79,7 +79,7 @@ ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \
XDG_CACHE_HOME=/app/.cache \
PATH="/usr/lib/python3.13/site-packages/nodejs/bin:${PATH}"
-RUN pip install --no-cache-dir prisma==0.11.0 nodejs-bin==18.4.0a4 \
+RUN pip install --no-cache-dir prisma==0.11.0 nodejs-wheel-binaries==24.12.0 \
&& mkdir -p /app/.cache/npm
RUN NPM_CONFIG_CACHE=/app/.cache/npm \
diff --git a/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md b/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md
index 1e5f968b2ca..7015918e924 100644
--- a/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md
+++ b/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md
@@ -6,7 +6,7 @@ authors:
- name: Sameer Kankute
title: SWE @ LiteLLM (LLM Translation)
url: https://www.linkedin.com/in/sameer-kankute/
- image_url: https://media.licdn.com/dms/image/v2/D4D03AQHB_loQYd5gjg/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1719137160975?e=1765411200&v=beta&t=c8396f--_lH6Fb_pVvx_jGholPfcl0bvwmNynbNdnII
+ image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
- name: Krrish Dholakia
title: "CEO, LiteLLM"
url: https://www.linkedin.com/in/krish-d/
diff --git a/docs/my-website/blog/gemini_3/index.md b/docs/my-website/blog/gemini_3/index.md
index 1b9ff359f3a..26dbc2d02b5 100644
--- a/docs/my-website/blog/gemini_3/index.md
+++ b/docs/my-website/blog/gemini_3/index.md
@@ -6,7 +6,7 @@ authors:
- name: Sameer Kankute
title: SWE @ LiteLLM (LLM Translation)
url: https://www.linkedin.com/in/sameer-kankute/
- image_url: https://media.licdn.com/dms/image/v2/D4D03AQHB_loQYd5gjg/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1719137160975?e=1765411200&v=beta&t=c8396f--_lH6Fb_pVvx_jGholPfcl0bvwmNynbNdnII
+ image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
- name: Krrish Dholakia
title: "CEO, LiteLLM"
url: https://www.linkedin.com/in/krish-d/
diff --git a/docs/my-website/blog/gemini_3_flash/index.md b/docs/my-website/blog/gemini_3_flash/index.md
new file mode 100644
index 00000000000..6cb8ddad992
--- /dev/null
+++ b/docs/my-website/blog/gemini_3_flash/index.md
@@ -0,0 +1,254 @@
+---
+slug: gemini_3_flash
+title: "DAY 0 Support: Gemini 3 Flash on LiteLLM"
+date: 2025-12-17T10:00:00
+authors:
+ - name: Sameer Kankute
+ title: SWE @ LiteLLM (LLM Translation)
+ url: https://www.linkedin.com/in/sameer-kankute/
+ image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
+ - name: Krrish Dholakia
+ title: "CEO, LiteLLM"
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+ - name: Ishaan Jaff
+ title: "CTO, LiteLLM"
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+tags: [gemini, day 0 support, llms]
+hide_table_of_contents: false
+---
+
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Gemini 3 Flash Day 0 Support
+
+LiteLLM now supports `gemini-3-flash-preview` and all the new API changes along with it.
+
+:::note
+If you only want cost tracking, you need no change in your current Litellm version. But if you want the support for new features introduced along with it like thinking levels, you will need to use v1.80.8-stable.1 or above.
+:::
+
+## Deploy this version
+
+
+
+
+``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+ghcr.io/berriai/litellm:main-v1.80.8-stable.1
+```
+
+
+
+
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==1.80.8.post1
+```
+
+
+
+
+## What's New
+
+### 1. New Thinking Levels: `thinkingLevel` with MINIMAL & MEDIUM
+
+Gemini 3 Flash introduces granular thinking control with `thinkingLevel` instead of `thinkingBudget`.
+- **MINIMAL**: Ultra-lightweight thinking for fast responses
+- **MEDIUM**: Balanced thinking for complex reasoning
+- **HIGH**: Maximum reasoning depth
+
+LiteLLM automatically maps the OpenAI `reasoning_effort` parameter to Gemini's `thinkingLevel`, so you can use familiar `reasoning_effort` values (`minimal`, `low`, `medium`, `high`) without changing your code!
+
+### 2. Thought Signatures
+
+Like `gemini-3-pro`, this model also includes thought signatures for tool calls. LiteLLM handles signature extraction and embedding internally. [Learn more about thought signatures](../gemini_3/index.md#thought-signatures).
+
+**Edge Case Handling**: If thought signatures are missing in the request, LiteLLM adds a dummy signature ensuring the API call doesn't break
+
+---
+## Supported Endpoints
+
+LiteLLM provides **full end-to-end support** for Gemini 3 Flash on:
+
+- ✅ `/v1/chat/completions` - OpenAI-compatible chat completions endpoint
+- ✅ `/v1/responses` - OpenAI Responses API endpoint (streaming and non-streaming)
+- ✅ [`/v1/messages`](../../docs/anthropic_unified) - Anthropic-compatible messages endpoint
+- ✅ `/v1/generateContent` – [Google Gemini API](../../docs/generateContent.md) compatible endpoint
+All endpoints support:
+- Streaming and non-streaming responses
+- Function calling with thought signatures
+- Multi-turn conversations
+- All Gemini 3-specific features
+- Converstion of provider specific thinking related param to thinkingLevel
+
+## Quick Start
+
+
+
+
+**Basic Usage with MEDIUM thinking (NEW)**
+
+```python
+from litellm import completion
+
+# No need to make any changes to your code as we map openai reasoning param to thinkingLevel
+response = completion(
+ model="gemini/gemini-3-flash-preview",
+ messages=[{"role": "user", "content": "Solve this complex math problem: 25 * 4 + 10"}],
+ reasoning_effort="medium", # NEW: MEDIUM thinking level
+)
+
+print(response.choices[0].message.content)
+```
+
+
+
+
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+ - model_name: gemini-3-flash
+ litellm_params:
+ model: gemini/gemini-3-flash-preview
+ api_key: os.environ/GEMINI_API_KEY
+```
+
+**2. Start proxy**
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+**3. Call with MEDIUM thinking**
+
+```bash
+curl -X POST http://localhost:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer " \
+ -d '{
+ "model": "gemini-3-flash",
+ "messages": [{"role": "user", "content": "Complex reasoning task"}],
+ "reasoning_effort": "medium"
+ }'
+``'
+
+
+
+
+---
+
+## All `reasoning_effort` Levels
+
+
+
+
+**Ultra-fast, minimal reasoning**
+
+```python
+from litellm import completion
+
+response = completion(
+ model="gemini/gemini-3-flash-preview",
+ messages=[{"role": "user", "content": "What's 2+2?"}],
+ reasoning_effort="minimal",
+)
+```
+
+
+
+
+
+**Simple instruction following**
+
+```python
+response = completion(
+ model="gemini/gemini-3-flash-preview",
+ messages=[{"role": "user", "content": "Write a haiku about coding"}],
+ reasoning_effort="low",
+)
+```
+
+
+
+
+
+**Balanced reasoning for complex tasks** ✨
+
+```python
+response = completion(
+ model="gemini/gemini-3-flash-preview",
+ messages=[{"role": "user", "content": "Analyze this dataset and find patterns"}],
+ reasoning_effort="medium", # NEW!
+)
+```
+
+
+
+
+
+**Maximum reasoning depth**
+
+```python
+response = completion(
+ model="gemini/gemini-3-flash-preview",
+ messages=[{"role": "user", "content": "Prove this mathematical theorem"}],
+ reasoning_effort="high",
+)
+```
+
+
+
+
+---
+
+## Key Features
+
+✅ **Thinking Levels**: MINIMAL, LOW, MEDIUM, HIGH
+✅ **Thought Signatures**: Track reasoning with unique identifiers
+✅ **Seamless Integration**: Works with existing OpenAI-compatible client
+✅ **Backward Compatible**: Gemini 2.5 models continue using `thinkingBudget`
+
+---
+
+## Installation
+
+```bash
+pip install litellm --upgrade
+```
+
+```python
+import litellm
+from litellm import completion
+
+response = completion(
+ model="gemini/gemini-3-flash-preview",
+ messages=[{"role": "user", "content": "Your question here"}],
+ reasoning_effort="medium", # Use MEDIUM thinking
+)
+print(response)
+```
+
+:::note
+If using this model via vertex_ai, keep the location as global as this is the only supported location as of now.
+:::
+
+
+## `reasoning_effort` Mapping for Gemini 3+
+
+| reasoning_effort | thinking_level |
+|------------------|----------------|
+| `minimal` | `minimal` |
+| `low` | `low` |
+| `medium` | `medium` |
+| `high` | `high` |
+| `disable` | `minimal` |
+| `none` | `minimal` |
+
diff --git a/docs/my-website/docs/benchmarks.md b/docs/my-website/docs/benchmarks.md
index 76b61d4c2bd..640212808bd 100644
--- a/docs/my-website/docs/benchmarks.md
+++ b/docs/my-website/docs/benchmarks.md
@@ -60,6 +60,58 @@ Each machine deploying LiteLLM had the following specs:
- Database: PostgreSQL
- Redis: Not used
+## Infrastructure Recommendations
+
+Recommended specifications based on benchmark results and industry standards for API gateway deployments.
+
+### PostgreSQL
+
+Required for authentication, key management, and usage tracking.
+
+| Workload | CPU | RAM | Storage | Connections |
+|----------|-----|-----|---------|-------------|
+| 1-2K RPS | 4-8 cores | 16GB | 200GB SSD (3000+ IOPS) | 100-200 |
+| 2-5K RPS | 8 cores | 16-32GB | 500GB SSD (5000+ IOPS) | 200-500 |
+| 5K+ RPS | 16+ cores | 32-64GB | 1TB+ SSD (10000+ IOPS) | 500+ |
+
+**Configuration:** Set `proxy_batch_write_at: 60` to batch writes and reduce DB load. Total connections = pool limit × instances.
+
+### Redis (Recommended)
+
+Redis was not used in these benchmarks but provides significant production benefits: 60-80% reduced DB load.
+
+| Workload | CPU | RAM |
+|----------|-----|-----|
+| 1-2K RPS | 2-4 cores | 8GB |
+| 2-5K RPS | 4 cores | 16GB |
+| 5K+ RPS | 8+ cores | 32GB+ |
+
+**Requirements:** Redis 7.0+, AOF persistence enabled, `allkeys-lru` eviction policy.
+
+**Configuration:**
+```yaml
+router_settings:
+ redis_host: os.environ/REDIS_HOST
+ redis_port: os.environ/REDIS_PORT
+ redis_password: os.environ/REDIS_PASSWORD
+
+litellm_settings:
+ cache: True
+ cache_params:
+ type: redis
+ host: os.environ/REDIS_HOST
+ port: os.environ/REDIS_PORT
+ password: os.environ/REDIS_PASSWORD
+```
+
+:::tip
+Use `redis_host`, `redis_port`, and `redis_password` instead of `redis_url` for ~80 RPS better performance.
+:::
+
+**Scaling:** DB connections scale linearly with instances. Consider PostgreSQL read replicas beyond 5K RPS.
+
+See [Production Configuration](./proxy/prod) for detailed best practices.
+
## Locust Settings
- 1000 Users
diff --git a/docs/my-website/docs/image_edits.md b/docs/my-website/docs/image_edits.md
index 5a108aabf3a..a8438334542 100644
--- a/docs/my-website/docs/image_edits.md
+++ b/docs/my-website/docs/image_edits.md
@@ -16,7 +16,7 @@ LiteLLM provides image editing functionality that maps to OpenAI's `/images/edit
| Supported operations | Create image edits | Single and multiple images supported |
| Supported LiteLLM SDK Versions | 1.63.8+ | Gemini support requires 1.79.3+ |
| Supported LiteLLM Proxy Versions | 1.71.1+ | Gemini support requires 1.79.3+ |
-| Supported LLM providers | **OpenAI**, **Gemini (Google AI Studio)**, **Vertex AI** | Gemini supports the new `gemini-2.5-flash-image` family. Vertex AI supports both Gemini and Imagen models. |
+| Supported LLM providers | **OpenAI**, **Gemini (Google AI Studio)**, **Vertex AI**, **Stability AI**, **AWS Bedrock (Stability)** | Gemini supports the new `gemini-2.5-flash-image` family. Vertex AI supports both Gemini and Imagen models. Stability AI and Bedrock Stability support various image editing operations. |
#### ⚡️See all supported models and providers at [models.litellm.ai](https://models.litellm.ai/)
diff --git a/docs/my-website/docs/observability/azure_sentinel.md b/docs/my-website/docs/observability/azure_sentinel.md
new file mode 100644
index 00000000000..6e7e0541795
--- /dev/null
+++ b/docs/my-website/docs/observability/azure_sentinel.md
@@ -0,0 +1,238 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Azure Sentinel
+
+
+
+LiteLLM supports logging to Azure Sentinel via the Azure Monitor Logs Ingestion API. Azure Sentinel uses Log Analytics workspaces for data storage, so logs sent to the workspace will be available in Sentinel for security monitoring and analysis.
+
+## Azure Sentinel Integration
+
+| Feature | Details |
+|---------|---------|
+| **What is logged** | [StandardLoggingPayload](../proxy/logging_spec) |
+| **Events** | Success + Failure |
+| **Product Link** | [Azure Sentinel](https://learn.microsoft.com/en-us/azure/sentinel/overview) |
+| **API Reference** | [Logs Ingestion API](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/logs-ingestion-api-overview) |
+
+We will use the `--config` to set `litellm.callbacks = ["azure_sentinel"]` this will log all successful and failed LLM calls to Azure Sentinel.
+
+**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `callbacks`
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: gpt-3.5-turbo
+litellm_settings:
+ callbacks: ["azure_sentinel"] # logs llm success + failure logs to Azure Sentinel
+```
+
+**Step 2**: Set Up Azure Resources
+
+Before using the Logs Ingestion API, you need to set up the following in Azure:
+
+1. **Create a Log Analytics Workspace** (if you don't have one)
+2. **Create a Custom Table** in your Log Analytics workspace (e.g., `LiteLLM_CL`)
+3. **Create a Data Collection Rule (DCR)** with:
+ - Stream declaration matching your data structure
+ - Transformation to map data to your custom table
+ - Access granted to your app registration
+4. **Register an Application** in Microsoft Entra ID (Azure AD) with:
+ - Client ID
+ - Client Secret
+ - Permissions to write to the DCR
+
+For detailed setup instructions, see the [Microsoft documentation on Logs Ingestion API](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/logs-ingestion-api-overview).
+
+**Step 3**: Set Required Environment Variables
+
+Set the following environment variables with your Azure credentials:
+
+```shell showLineNumbers title="Environment Variables"
+# Required: Data Collection Rule (DCR) configuration
+AZURE_SENTINEL_DCR_IMMUTABLE_ID="dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # DCR Immutable ID from Azure portal
+AZURE_SENTINEL_STREAM_NAME="Custom-LiteLLM_CL_CL" # Stream name from your DCR
+AZURE_SENTINEL_ENDPOINT="https://your-dcr-endpoint.eastus-1.ingest.monitor.azure.com" # DCR logs ingestion endpoint (NOT the DCE endpoint)
+
+# Required: OAuth2 Authentication (App Registration)
+AZURE_SENTINEL_TENANT_ID="your-tenant-id" # Azure Tenant ID
+AZURE_SENTINEL_CLIENT_ID="your-client-id" # Application (client) ID
+AZURE_SENTINEL_CLIENT_SECRET="your-client-secret" # Client secret value
+
+```
+
+**Note**: The `AZURE_SENTINEL_ENDPOINT` should be the DCR's logs ingestion endpoint (found in the DCR Overview page), NOT the Data Collection Endpoint (DCE). The DCR endpoint is associated with your specific DCR and looks like: `https://your-dcr-endpoint.{region}-1.ingest.monitor.azure.com`
+
+**Step 4**: Start the proxy and make a test request
+
+Start proxy
+
+```shell showLineNumbers title="Start Proxy"
+litellm --config config.yaml --debug
+```
+
+Test Request
+
+```shell showLineNumbers title="Test Request"
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+ --header 'Content-Type: application/json' \
+ --data '{
+ "model": "gpt-3.5-turbo",
+ "messages": [
+ {
+ "role": "user",
+ "content": "what llm are you"
+ }
+ ],
+ "metadata": {
+ "your-custom-metadata": "custom-field",
+ }
+}'
+```
+
+**Step 5**: View logs in Azure Sentinel
+
+1. Navigate to your Azure Sentinel workspace in the Azure portal
+2. Go to "Logs" and query your custom table (e.g., `LiteLLM_CL`)
+3. Run a query like:
+
+```kusto showLineNumbers title="KQL Query"
+LiteLLM_CL
+| where TimeGenerated > ago(1h)
+| project TimeGenerated, model, status, total_tokens, response_cost
+| order by TimeGenerated desc
+```
+
+You should see following logs in Azure Workspace.
+
+
+
+## Environment Variables
+
+| Environment Variable | Description | Default Value | Required |
+|---------------------|-------------|---------------|----------|
+| `AZURE_SENTINEL_DCR_IMMUTABLE_ID` | Data Collection Rule (DCR) Immutable ID | None | ✅ Yes |
+| `AZURE_SENTINEL_ENDPOINT` | DCR logs ingestion endpoint URL (from DCR Overview page) | None | ✅ Yes |
+| `AZURE_SENTINEL_STREAM_NAME` | Stream name from DCR (e.g., "Custom-LiteLLM_CL_CL") | "Custom-LiteLLM" | ❌ No |
+| `AZURE_SENTINEL_TENANT_ID` | Azure Tenant ID for OAuth2 authentication | None (falls back to `AZURE_TENANT_ID`) | ✅ Yes |
+| `AZURE_SENTINEL_CLIENT_ID` | Application (client) ID for OAuth2 authentication | None (falls back to `AZURE_CLIENT_ID`) | ✅ Yes |
+| `AZURE_SENTINEL_CLIENT_SECRET` | Client secret for OAuth2 authentication | None (falls back to `AZURE_CLIENT_SECRET`) | ✅ Yes |
+
+## How It Works
+
+The Azure Sentinel integration uses the [Azure Monitor Logs Ingestion API](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/logs-ingestion-api-overview) to send logs to your Log Analytics workspace. The integration:
+
+- Authenticates using OAuth2 client credentials flow with your app registration
+- Sends logs to the Data Collection Rule (DCR) endpoint
+- Batches logs for efficient transmission
+- Sends logs in the [StandardLoggingPayload](../proxy/logging_spec) format
+- Automatically handles both success and failure events
+- Caches OAuth2 tokens and refreshes them automatically
+
+Logs sent to the Log Analytics workspace are automatically available in Azure Sentinel for security monitoring, threat detection, and analysis.
+
+## Azure Sentinel Setup Guide
+
+Follow this step-by-step guide to set up Azure Sentinel with LiteLLM.
+
+### Step 1: Create a Log Analytics Workspace
+
+1. Navigate to [https://portal.azure.com/#home](https://portal.azure.com/#home)
+
+
+
+2. Search for "Log Analytics workspaces" and click "Create"
+
+
+
+3. Enter a name for your workspace (e.g., "litellm-sentinel-prod")
+
+
+
+4. Click "Review + Create"
+
+
+
+### Step 2: Create a Custom Table
+
+1. Go to your Log Analytics workspace and click "Tables"
+
+
+
+2. Click "Create" → "New custom log (Direct Ingest)"
+
+
+
+3. Enter a table name (e.g., "LITELLM_PROD_CL")
+
+
+
+### Step 3: Create a Data Collection Rule (DCR)
+
+1. Click "Create a new data collection rule"
+
+
+
+2. Enter a name for the DCR (e.g., "litellm-prod")
+
+
+
+3. Select a Data Collection Endpoint
+
+
+
+4. Upload the sample JSON file for schema (use the [example_standard_logging_payload.json](https://github.com/BerriAI/litellm/blob/main/litellm/integrations/azure_sentinel/example_standard_logging_payload.json) file)
+
+
+
+5. Click "Next" and then "Create"
+
+
+
+### Step 4: Get the DCR Immutable ID and Logs Ingestion Endpoint
+
+1. Go to "Data Collection Rules" and select your DCR
+
+
+
+2. Copy the **DCR Immutable ID** (starts with `dcr-`)
+
+
+
+3. Copy the **Logs Ingestion Endpoint** URL
+
+
+
+### Step 5: Get the Stream Name
+
+1. Click "JSON View" in the DCR
+
+
+
+2. Find the **Stream Name** in the `streamDeclarations` section (e.g., "Custom-LITELLM_PROD_CL_CL")
+
+
+
+### Step 6: Register an App and Grant Permissions
+
+1. Go to **Microsoft Entra ID** → **App registrations** → **New registration**
+2. Create a new app and note the **Client ID** and **Tenant ID**
+3. Go to **Certificates & secrets** → Create a new client secret and copy the **Secret Value**
+4. Go back to your DCR → **Access Control (IAM)** → **Add role assignment**
+5. Assign the **"Monitoring Metrics Publisher"** role to your app registration
+
+### Summary: Where to Find Each Value
+
+| Environment Variable | Where to Find It |
+|---------------------|------------------|
+| `AZURE_SENTINEL_DCR_IMMUTABLE_ID` | DCR Overview page → Immutable ID (starts with `dcr-`) |
+| `AZURE_SENTINEL_ENDPOINT` | DCR Overview page → Logs Ingestion Endpoint |
+| `AZURE_SENTINEL_STREAM_NAME` | DCR JSON View → `streamDeclarations` section |
+| `AZURE_SENTINEL_TENANT_ID` | App Registration → Overview → Directory (tenant) ID |
+| `AZURE_SENTINEL_CLIENT_ID` | App Registration → Overview → Application (client) ID |
+| `AZURE_SENTINEL_CLIENT_SECRET` | App Registration → Certificates & secrets → Secret Value |
+
+For more details, refer to the [Microsoft Logs Ingestion API documentation](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/logs-ingestion-api-overview).
diff --git a/docs/my-website/docs/observability/cloudzero.md b/docs/my-website/docs/observability/cloudzero.md
index f213ef64e13..19f6d80ca8b 100644
--- a/docs/my-website/docs/observability/cloudzero.md
+++ b/docs/my-website/docs/observability/cloudzero.md
@@ -65,6 +65,52 @@ Start your LiteLLM proxy with the configuration:
litellm --config /path/to/config.yaml
```
+## Setup on UI
+
+1\. Click "Settings"
+
+
+
+
+2\. Click "Logging & Alerts"
+
+
+
+
+3\. Click "CloudZero Cost Tracking"
+
+
+
+
+4\. Click "Add CloudZero Integration"
+
+
+
+
+5\. Enter your CloudZero API Key.
+
+
+
+
+6\. Enter your CloudZero Connection ID.
+
+
+
+
+7\. Click "Create"
+
+
+
+
+8\. Test your payload with "Run Dry Run Simulation"
+
+
+
+
+10\. Click "Export Data Now" to export to CLoudZero
+
+
+
## Testing Your Setup
### Dry Run Export
diff --git a/docs/my-website/docs/oidc.md b/docs/my-website/docs/oidc.md
index 3db4b6ecdc5..b541329aa38 100644
--- a/docs/my-website/docs/oidc.md
+++ b/docs/my-website/docs/oidc.md
@@ -106,7 +106,7 @@ model_list:
aws_region_name: us-west-2
aws_session_name: "my-test-session"
aws_role_name: "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci"
- aws_web_identity_token: "oidc/circleci_v2/"
+ aws_web_identity_token: "oidc/example-provider/"
```
#### Amazon IAM Role Configuration for CircleCI v2 -> Bedrock
diff --git a/docs/my-website/docs/providers/aws_polly.md b/docs/my-website/docs/providers/aws_polly.md
new file mode 100644
index 00000000000..21b0fa679bf
--- /dev/null
+++ b/docs/my-website/docs/providers/aws_polly.md
@@ -0,0 +1,364 @@
+# AWS Polly Text to Speech (tts)
+
+## Overview
+
+| Property | Details |
+|-------|-------|
+| Description | Convert text to natural-sounding speech using AWS Polly's neural and standard TTS engines |
+| Provider Route on LiteLLM | `aws_polly/` |
+| Supported Operations | `/audio/speech` |
+| Link to Provider Doc | [AWS Polly SynthesizeSpeech ↗](https://docs.aws.amazon.com/polly/latest/dg/API_SynthesizeSpeech.html) |
+
+## Quick Start
+
+### **LiteLLM SDK**
+
+```python showLineNumbers title="SDK Usage"
+import litellm
+from pathlib import Path
+import os
+
+# Set environment variables
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = "us-east-1"
+
+# AWS Polly call
+speech_file_path = Path(__file__).parent / "speech.mp3"
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="the quick brown fox jumped over the lazy dogs",
+)
+response.stream_to_file(speech_file_path)
+```
+
+### **LiteLLM PROXY**
+
+```yaml showLineNumbers title="proxy_config.yaml"
+model_list:
+ - model_name: polly-neural
+ litellm_params:
+ model: aws_polly/neural
+ aws_access_key_id: "os.environ/AWS_ACCESS_KEY_ID"
+ aws_secret_access_key: "os.environ/AWS_SECRET_ACCESS_KEY"
+ aws_region_name: "us-east-1"
+```
+
+## Polly Engines
+
+AWS Polly supports different speech synthesis engines. Specify the engine in the model name:
+
+| Model | Engine | Cost (per 1M chars) | Description |
+|-------|--------|---------------------|-------------|
+| `aws_polly/standard` | Standard | $4.00 | Original Polly voices, faster and lowest cost |
+| `aws_polly/neural` | Neural | $16.00 | More natural, human-like speech (recommended) |
+| `aws_polly/generative` | Generative | $30.00 | Most expressive, highest quality (limited voices) |
+| `aws_polly/long-form` | Long-form | $100.00 | Optimized for long content like articles |
+
+### **LiteLLM SDK**
+
+```python showLineNumbers title="Using Different Engines"
+import litellm
+
+# Neural engine (recommended)
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello world",
+)
+
+# Standard engine (lower cost)
+response = litellm.speech(
+ model="aws_polly/standard",
+ voice="Joanna",
+ input="Hello world",
+)
+
+# Generative engine (highest quality)
+response = litellm.speech(
+ model="aws_polly/generative",
+ voice="Matthew",
+ input="Hello world",
+)
+```
+
+### **LiteLLM PROXY**
+
+```yaml showLineNumbers title="proxy_config.yaml"
+model_list:
+ - model_name: polly-neural
+ litellm_params:
+ model: aws_polly/neural
+ aws_region_name: "us-east-1"
+ - model_name: polly-standard
+ litellm_params:
+ model: aws_polly/standard
+ aws_region_name: "us-east-1"
+ - model_name: polly-generative
+ litellm_params:
+ model: aws_polly/generative
+ aws_region_name: "us-east-1"
+```
+
+## Available Voices
+
+### Native Polly Voices
+
+AWS Polly has many voices across different languages. Here are popular US English voices:
+
+| Voice | Gender | Engine Support |
+|-------|--------|----------------|
+| `Joanna` | Female | Neural, Standard |
+| `Matthew` | Male | Neural, Standard, Generative |
+| `Ivy` | Female (child) | Neural, Standard |
+| `Kendra` | Female | Neural, Standard |
+| `Amy` | Female (British) | Neural, Standard |
+| `Brian` | Male (British) | Neural, Standard |
+
+### **LiteLLM SDK**
+
+```python showLineNumbers title="Using Native Polly Voices"
+import litellm
+
+# US English female
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello from Joanna",
+)
+
+# US English male
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Matthew",
+ input="Hello from Matthew",
+)
+
+# British English female
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Amy",
+ input="Hello from Amy",
+)
+```
+
+### **LiteLLM PROXY**
+
+```yaml showLineNumbers title="proxy_config.yaml"
+model_list:
+ - model_name: polly-joanna
+ litellm_params:
+ model: aws_polly/neural
+ voice: "Joanna"
+ aws_region_name: "us-east-1"
+ - model_name: polly-matthew
+ litellm_params:
+ model: aws_polly/neural
+ voice: "Matthew"
+ aws_region_name: "us-east-1"
+```
+
+### OpenAI Voice Mappings
+
+LiteLLM also supports OpenAI voice names, which are automatically mapped to Polly voices:
+
+| OpenAI Voice | Maps to Polly Voice |
+|--------------|---------------------|
+| `alloy` | Joanna |
+| `echo` | Matthew |
+| `fable` | Amy |
+| `onyx` | Brian |
+| `nova` | Ivy |
+| `shimmer` | Kendra |
+
+### **LiteLLM SDK**
+
+```python showLineNumbers title="Using OpenAI Voice Names"
+import litellm
+
+# These are equivalent
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="alloy", # Maps to Joanna
+ input="Hello world",
+)
+
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna", # Native Polly voice
+ input="Hello world",
+)
+```
+
+## SSML Support
+
+AWS Polly supports SSML (Speech Synthesis Markup Language) for advanced control over speech output. LiteLLM automatically detects SSML input.
+
+### **LiteLLM SDK**
+
+```python showLineNumbers title="SSML Example"
+import litellm
+
+ssml_input = """
+
+ Hello,
+ this is a test with emphasis
+ and slower speech.
+
+"""
+
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input=ssml_input,
+)
+```
+
+### **LiteLLM PROXY**
+
+```bash showLineNumbers title="cURL Request with SSML"
+curl -X POST http://localhost:4000/v1/audio/speech \
+ -H "Authorization: Bearer sk-1234" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "polly-neural",
+ "voice": "Joanna",
+ "input": "Hello world"
+ }' \
+ --output speech.mp3
+```
+
+## Supported Parameters
+
+```python showLineNumbers title="All Parameters"
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna", # Required: Voice selection
+ input="text to convert", # Required: Input text (or SSML)
+ response_format="mp3", # Optional: mp3, ogg_vorbis, pcm
+
+ # AWS-specific parameters
+ language_code="en-US", # Optional: Language code
+ sample_rate="22050", # Optional: Sample rate in Hz
+)
+```
+
+## Response Formats
+
+| Format | Description |
+|--------|-------------|
+| `mp3` | MP3 audio (default) |
+| `ogg_vorbis` | Ogg Vorbis audio |
+| `pcm` | Raw PCM audio |
+
+### **LiteLLM SDK**
+
+```python showLineNumbers title="Different Response Formats"
+import litellm
+
+# MP3 (default)
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello",
+ response_format="mp3",
+)
+
+# Ogg Vorbis
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello",
+ response_format="ogg_vorbis",
+)
+```
+
+## AWS Authentication
+
+LiteLLM supports multiple AWS authentication methods.
+
+### **LiteLLM SDK**
+
+```python showLineNumbers title="Authentication Options"
+import litellm
+import os
+
+# Option 1: Environment variables (recommended)
+os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-key"
+os.environ["AWS_REGION_NAME"] = "us-east-1"
+
+response = litellm.speech(model="aws_polly/neural", voice="Joanna", input="Hello")
+
+# Option 2: Pass credentials directly
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello",
+ aws_access_key_id="your-access-key",
+ aws_secret_access_key="your-secret-key",
+ aws_region_name="us-east-1",
+)
+
+# Option 3: IAM Role (when running on AWS)
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello",
+ aws_region_name="us-east-1",
+)
+
+# Option 4: AWS Profile
+response = litellm.speech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello",
+ aws_profile_name="my-profile",
+)
+```
+
+### **LiteLLM PROXY**
+
+```yaml showLineNumbers title="proxy_config.yaml"
+model_list:
+ # Using environment variables
+ - model_name: polly-neural
+ litellm_params:
+ model: aws_polly/neural
+ aws_access_key_id: "os.environ/AWS_ACCESS_KEY_ID"
+ aws_secret_access_key: "os.environ/AWS_SECRET_ACCESS_KEY"
+ aws_region_name: "us-east-1"
+
+ # Using IAM Role (when proxy runs on AWS)
+ - model_name: polly-neural-iam
+ litellm_params:
+ model: aws_polly/neural
+ aws_region_name: "us-east-1"
+
+ # Using AWS Profile
+ - model_name: polly-neural-profile
+ litellm_params:
+ model: aws_polly/neural
+ aws_profile_name: "my-profile"
+```
+
+## Async Support
+
+```python showLineNumbers title="Async Usage"
+import litellm
+import asyncio
+
+async def main():
+ response = await litellm.aspeech(
+ model="aws_polly/neural",
+ voice="Joanna",
+ input="Hello from async AWS Polly",
+ aws_region_name="us-east-1",
+ )
+
+ with open("output.mp3", "wb") as f:
+ f.write(response.content)
+
+asyncio.run(main())
+```
diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md
index e2e7c0dcedd..3c618fe0641 100644
--- a/docs/my-website/docs/providers/bedrock_embedding.md
+++ b/docs/my-website/docs/providers/bedrock_embedding.md
@@ -172,6 +172,125 @@ print(f"Results available at: {output_s3_uri}")
**Note:** The actual embedding results are stored in S3. When the job is completed, download the results from the S3 location specified in `status.metadata['output_file_id']`. The results will be in JSON/JSONL format containing the embedding vectors.
+## Amazon Nova Multimodal Embeddings
+
+Amazon Nova supports multimodal embeddings for text, images, video, and audio. It offers flexible embedding dimensions and purposes optimized for different use cases.
+
+### Supported Features
+
+- **Modalities**: Text, Image, Video, Audio
+- **Dimensions**: 256, 384, 1024, 3072 (default: 3072)
+- **Embedding Purposes**:
+ - `GENERIC_INDEX` (default)
+ - `GENERIC_RETRIEVAL`
+ - `TEXT_RETRIEVAL`
+ - `IMAGE_RETRIEVAL`
+ - `VIDEO_RETRIEVAL`
+ - `AUDIO_RETRIEVAL`
+ - `CLASSIFICATION`
+ - `CLUSTERING`
+
+### Text Embedding
+
+```python
+from litellm import embedding
+
+response = embedding(
+ model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0",
+ input=["Hello, world!"],
+ aws_region_name="us-east-1",
+ dimensions=1024, # Optional: 256, 384, 1024, or 3072
+)
+
+print(response.data[0].embedding)
+```
+
+### Image Embedding with Base64
+
+Amazon Nova accepts images in base64 format using the standard data URL format:
+
+```python
+import base64
+from litellm import embedding
+
+# Method 1: Load image from file
+with open("image.jpg", "rb") as image_file:
+ image_data = base64.b64encode(image_file.read()).decode('utf-8')
+ # Create data URL with proper format
+ image_base64 = f"data:image/jpeg;base64,{image_data}"
+
+response = embedding(
+ model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0",
+ input=[image_base64],
+ aws_region_name="us-east-1",
+ dimensions=1024,
+)
+
+print(f"Image embedding: {response.data[0].embedding[:10]}...") # First 10 dimensions
+```
+
+#### Supported Image Formats
+
+Nova supports the following image formats:
+- JPEG: `data:image/jpeg;base64,...`
+- PNG: `data:image/png;base64,...`
+- GIF: `data:image/gif;base64,...`
+- WebP: `data:image/webp;base64,...`
+
+#### Complete Example with Error Handling
+
+```python
+import base64
+from litellm import embedding
+
+def get_image_embedding(image_path, dimensions=1024):
+ """
+ Get embedding for an image file.
+
+ Args:
+ image_path: Path to the image file
+ dimensions: Embedding dimension (256, 384, 1024, or 3072)
+
+ Returns:
+ List of embedding values
+ """
+ try:
+ # Determine image format from file extension
+ if image_path.lower().endswith('.png'):
+ mime_type = "image/png"
+ elif image_path.lower().endswith(('.jpg', '.jpeg')):
+ mime_type = "image/jpeg"
+ elif image_path.lower().endswith('.gif'):
+ mime_type = "image/gif"
+ elif image_path.lower().endswith('.webp'):
+ mime_type = "image/webp"
+ else:
+ raise ValueError(f"Unsupported image format: {image_path}")
+
+ # Read and encode image
+ with open(image_path, "rb") as image_file:
+ image_data = base64.b64encode(image_file.read()).decode('utf-8')
+ image_base64 = f"data:{mime_type};base64,{image_data}"
+
+ # Get embedding
+ response = embedding(
+ model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0",
+ input=[image_base64],
+ aws_region_name="us-east-1",
+ dimensions=dimensions,
+ )
+
+ return response.data[0].embedding
+
+ except Exception as e:
+ print(f"Error getting image embedding: {e}")
+ raise
+
+# Example usage
+image_embedding = get_image_embedding("photo.jpg", dimensions=1024)
+print(f"Got embedding with {len(image_embedding)} dimensions")
+```
+
### Error Handling
#### Common Errors
diff --git a/docs/my-website/docs/providers/databricks.md b/docs/my-website/docs/providers/databricks.md
index 921b06a17b7..2791d55dff1 100644
--- a/docs/my-website/docs/providers/databricks.md
+++ b/docs/my-website/docs/providers/databricks.md
@@ -11,6 +11,99 @@ LiteLLM supports all models on Databricks
:::
+## Authentication
+
+LiteLLM supports multiple authentication methods for Databricks, listed in order of preference:
+
+### OAuth M2M (Recommended for Production)
+
+OAuth Machine-to-Machine authentication using Service Principal credentials is the **recommended method for production** deployments per Databricks Partner requirements.
+
+```python
+import os
+from litellm import completion
+
+# Set OAuth credentials (Service Principal)
+os.environ["DATABRICKS_CLIENT_ID"] = "your-service-principal-application-id"
+os.environ["DATABRICKS_CLIENT_SECRET"] = "your-service-principal-secret"
+os.environ["DATABRICKS_API_BASE"] = "https://adb-xxx.azuredatabricks.net/serving-endpoints"
+
+response = completion(
+ model="databricks/databricks-dbrx-instruct",
+ messages=[{"role": "user", "content": "Hello!"}],
+)
+```
+
+### Personal Access Token (PAT)
+
+PAT authentication is supported for development and testing scenarios.
+
+```python
+import os
+from litellm import completion
+
+os.environ["DATABRICKS_API_KEY"] = "dapi..." # Your Personal Access Token
+os.environ["DATABRICKS_API_BASE"] = "https://adb-xxx.azuredatabricks.net/serving-endpoints"
+
+response = completion(
+ model="databricks/databricks-dbrx-instruct",
+ messages=[{"role": "user", "content": "Hello!"}],
+)
+```
+
+### Databricks SDK Authentication (Automatic)
+
+If no credentials are provided, LiteLLM will use the Databricks SDK for automatic authentication. This supports OAuth, Azure AD, and other unified auth methods configured in your environment.
+
+```python
+from litellm import completion
+
+# No environment variables needed - uses Databricks SDK unified auth
+# Requires: pip install databricks-sdk
+response = completion(
+ model="databricks/databricks-dbrx-instruct",
+ messages=[{"role": "user", "content": "Hello!"}],
+)
+```
+
+## Custom User-Agent for Partner Attribution
+
+If you're building a product on top of LiteLLM that integrates with Databricks, you can pass your own partner identifier for proper attribution in Databricks telemetry.
+
+The partner name will be prefixed to the LiteLLM user agent:
+
+```python
+# Via parameter
+response = completion(
+ model="databricks/databricks-dbrx-instruct",
+ messages=[{"role": "user", "content": "Hello!"}],
+ user_agent="mycompany/1.0.0",
+)
+# Resulting User-Agent: mycompany_litellm/1.79.1
+
+# Via environment variable
+os.environ["DATABRICKS_USER_AGENT"] = "mycompany/1.0.0"
+# Resulting User-Agent: mycompany_litellm/1.79.1
+```
+
+| Input | Resulting User-Agent |
+|-------|---------------------|
+| (none) | `litellm/1.79.1` |
+| `mycompany/1.0.0` | `mycompany_litellm/1.79.1` |
+| `partner_product/2.5.0` | `partner_product_litellm/1.79.1` |
+| `acme` | `acme_litellm/1.79.1` |
+
+**Note:** The version from your custom user agent is ignored; LiteLLM's version is always used.
+
+## Security
+
+LiteLLM automatically redacts sensitive information (tokens, secrets, API keys) from all debug logs to prevent credential leakage. This includes:
+
+- Authorization headers
+- API keys and tokens
+- Client secrets
+- Personal access tokens (PATs)
+
## Usage
@@ -51,6 +144,7 @@ response = completion(
model: databricks/databricks-dbrx-instruct
api_key: os.environ/DATABRICKS_API_KEY
api_base: os.environ/DATABRICKS_API_BASE
+ user_agent: "mycompany/1.0.0" # Optional: for partner attribution
```
diff --git a/docs/my-website/docs/providers/groq.md b/docs/my-website/docs/providers/groq.md
index ebed31f720f..55c222635d2 100644
--- a/docs/my-website/docs/providers/groq.md
+++ b/docs/my-website/docs/providers/groq.md
@@ -150,15 +150,15 @@ We support ALL Groq models, just set `groq/` as a prefix when sending completion
| Model Name | Usage |
|--------------------|---------------------------------------------------------|
-| llama-3.1-8b-instant | `completion(model="groq/llama-3.1-8b-instant", messages)` |
-| llama-3.1-70b-versatile | `completion(model="groq/llama-3.1-70b-versatile", messages)` |
-| llama3-8b-8192 | `completion(model="groq/llama3-8b-8192", messages)` |
-| llama3-70b-8192 | `completion(model="groq/llama3-70b-8192", messages)` |
-| llama2-70b-4096 | `completion(model="groq/llama2-70b-4096", messages)` |
-| mixtral-8x7b-32768 | `completion(model="groq/mixtral-8x7b-32768", messages)` |
-| gemma-7b-it | `completion(model="groq/gemma-7b-it", messages)` |
-| moonshotai/kimi-k2-instruct | `completion(model="groq/moonshotai/kimi-k2-instruct", messages)` |
-| qwen3-32b | `completion(model="groq/qwen/qwen3-32b", messages)` |
+| llama-3.3-70b-versatile | `completion(model="groq/llama-3.3-70b-versatile", messages)` |
+| llama-3.1-8b-instant | `completion(model="groq/llama-3.1-8b-instant", messages)` |
+| meta-llama/llama-4-scout-17b-16e-instruct | `completion(model="groq/meta-llama/llama-4-scout-17b-16e-instruct", messages)` |
+| meta-llama/llama-4-maverick-17b-128e-instruct | `completion(model="groq/meta-llama/llama-4-maverick-17b-128e-instruct", messages)` |
+| meta-llama/llama-guard-4-12b | `completion(model="groq/meta-llama/llama-guard-4-12b", messages)` |
+| qwen/qwen3-32b | `completion(model="groq/qwen/qwen3-32b", messages)` |
+| moonshotai/kimi-k2-instruct-0905 | `completion(model="groq/moonshotai/kimi-k2-instruct-0905", messages)` |
+| openai/gpt-oss-120b | `completion(model="groq/openai/gpt-oss-120b", messages)` |
+| openai/gpt-oss-20b | `completion(model="groq/openai/gpt-oss-20b", messages)` |
## Groq - Tool / Function Calling Example
@@ -261,31 +261,28 @@ if tool_calls:
print("second response\n", second_response)
```
-## Groq - Vision Example
+## Groq - Vision Example
-Select Groq models support vision. Check out their [model list](https://console.groq.com/docs/vision) for more details.
+Groq's Llama 4 models support vision. Check out their [model list](https://console.groq.com/docs/vision) for more details.
```python
-from litellm import completion
-
-import os
+import os
from litellm import completion
os.environ["GROQ_API_KEY"] = "your-api-key"
-# openai call
response = completion(
- model = "groq/llama-3.2-11b-vision-preview",
+ model = "groq/meta-llama/llama-4-scout-17b-16e-instruct",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
- "text": "What’s in this image?"
+ "text": "What's in this image?"
},
{
"type": "image_url",
diff --git a/docs/my-website/docs/providers/openai/responses_api.md b/docs/my-website/docs/providers/openai/responses_api.md
index 8d91ca674b7..75eab1afac5 100644
--- a/docs/my-website/docs/providers/openai/responses_api.md
+++ b/docs/my-website/docs/providers/openai/responses_api.md
@@ -623,6 +623,58 @@ display(styled_df)
+## Function Calling
+
+```python showLineNumbers title="Function Calling with Parallel Tool Calls"
+import litellm
+import json
+
+tools = [
+ {
+ "type": "function",
+ "name": "get_weather",
+ "description": "Get current weather for a location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {"type": "string"}
+ },
+ "required": ["location"]
+ }
+ }
+]
+
+# Step 1: Request with tools (parallel_tool_calls=True allows multiple calls)
+response = litellm.responses(
+ model="openai/gpt-4o",
+ input=[{"role": "user", "content": "What's the weather in Paris and Tokyo?"}],
+ tools=tools,
+ parallel_tool_calls=True, # Defaults = True
+)
+
+# Step 2: Execute tool calls and collect results
+tool_results = []
+for output in response.output:
+ if output.type == "function_call":
+ result = {"temperature": 15, "condition": "sunny"} # Your function logic here
+ tool_results.append({
+ "type": "function_call_output",
+ "call_id": output.call_id,
+ "output": json.dumps(result)
+ })
+
+# Step 3: Send results back
+final_response = litellm.responses(
+ model="openai/gpt-4o",
+ input=tool_results,
+ tools=tools,
+)
+
+print(final_response.output)
+```
+
+Set `parallel_tool_calls=False` to ensure zero or one tool is called per turn. [More details](https://platform.openai.com/docs/guides/function-calling#parallel-function-calling).
+
## Free-form Function Calling
@@ -633,7 +685,6 @@ display(styled_df)
import litellm
response = litellm.responses(
- response = client.responses.create(
model="gpt-5-mini",
input="Please use the code_exec tool to calculate the area of a circle with radius equal to the number of 'r's in strawberry",
text={"format": {"type": "text"}},
diff --git a/docs/my-website/docs/providers/stability.md b/docs/my-website/docs/providers/stability.md
index 49773fffdb3..6b340267e69 100644
--- a/docs/my-website/docs/providers/stability.md
+++ b/docs/my-website/docs/providers/stability.md
@@ -8,7 +8,7 @@ https://stability.ai/
| Description | Stability AI creates open AI models for image, video, audio, and 3D generation. Known for Stable Diffusion. |
| Provider Route on LiteLLM | `stability/` |
| Link to Provider Doc | [Stability AI API ↗](https://platform.stability.ai/docs/api-reference) |
-| Supported Operations | [`/images/generations`](#image-generation) |
+| Supported Operations | [`/images/generations`](#image-generation), [`/images/edits`](#image-editing) |
LiteLLM supports Stability AI Image Generation calls via the Stability AI REST API (not via Bedrock).
@@ -169,13 +169,285 @@ Stability AI returns images in base64 format. The response is OpenAI-compatible:
}
```
-## Comparing with Bedrock
+## Image Editing
+
+Stability AI supports various image editing operations including inpainting, upscaling, outpainting, background removal, and more.
+
+### Usage - LiteLLM Python SDK
+
+#### Inpainting (Edit with Mask)
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Inpainting - edit specific areas using a mask
+response = image_edit(
+ model="stability/stable-image-inpaint-v1:0",
+ image=open("original_image.png", "rb"),
+ mask=open("mask_image.png", "rb"),
+ prompt="Add a beautiful sunset in the masked area",
+ size="1024x1024",
+)
+print(response)
+```
+
+#### Image Upscaling
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Conservative upscaling - preserves details
+response = image_edit(
+ model="stability/stable-conservative-upscale-v1:0",
+ image=open("low_res_image.png", "rb"),
+ prompt="Upscale this image while preserving details",
+)
+
+# Creative upscaling - adds creative details
+response = image_edit(
+ model="stability/stable-creative-upscale-v1:0",
+ image=open("low_res_image.png", "rb"),
+ prompt="Upscale and enhance with creative details",
+ creativity=0.3, # 0-0.35, higher = more creative
+)
+
+# Fast upscaling - quick upscaling
+response = image_edit(
+ model="stability/stable-fast-upscale-v1:0",
+ image=open("low_res_image.png", "rb"),
+ prompt="Quickly upscale this image",
+)
+print(response)
+```
+
+#### Image Outpainting
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Extend image beyond its borders
+response = image_edit(
+ model="stability/stable-outpaint-v1:0",
+ image=open("original_image.png", "rb"),
+ prompt="Extend this landscape with mountains",
+ left=100, # Pixels to extend on the left
+ right=100, # Pixels to extend on the right
+ up=50, # Pixels to extend on top
+ down=50, # Pixels to extend on bottom
+)
+print(response)
+```
+
+#### Background Removal
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Remove background from image
+response = image_edit(
+ model="stability/stable-image-remove-background-v1:0",
+ image=open("portrait.png", "rb"),
+ prompt="Remove the background",
+)
+print(response)
+```
+
+#### Search and Replace
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Search and replace objects in image
+response = image_edit(
+ model="stability/stable-image-search-replace-v1:0",
+ image=open("scene.png", "rb"),
+ prompt="A red sports car",
+ search_prompt="blue sedan", # What to replace
+)
+
+# Search and recolor
+response = image_edit(
+ model="stability/stable-image-search-recolor-v1:0",
+ image=open("scene.png", "rb"),
+ prompt="Make it golden yellow",
+ select_prompt="the car", # What to recolor
+)
+print(response)
+```
+
+#### Image Control (Sketch/Structure)
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Control with sketch
+response = image_edit(
+ model="stability/stable-image-control-sketch-v1:0",
+ image=open("sketch.png", "rb"),
+ prompt="Turn this sketch into a realistic photo",
+ control_strength=0.7, # 0-1, higher = more control
+)
+
+# Control with structure
+response = image_edit(
+ model="stability/stable-image-control-structure-v1:0",
+ image=open("structure_reference.png", "rb"),
+ prompt="Generate image following this structure",
+ control_strength=0.7,
+)
+print(response)
+```
+
+#### Erase Objects
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+os.environ['STABILITY_API_KEY'] = "your-api-key"
+
+# Erase objects from image
+response = image_edit(
+ model="stability/stable-image-erase-object-v1:0",
+ image=open("scene.png", "rb"),
+ mask=open("object_mask.png", "rb"), # Mask the object to erase
+ prompt="Remove the object",
+)
+print(response)
+```
+
+### Supported Image Edit Models
+
+| Model Name | Function Call | Description |
+|------------|---------------|-------------|
+| stable-image-inpaint-v1:0 | `image_edit(model="stability/stable-image-inpaint-v1:0", ...)` | Inpainting with mask |
+| stable-conservative-upscale-v1:0 | `image_edit(model="stability/stable-conservative-upscale-v1:0", ...)` | Conservative upscaling |
+| stable-creative-upscale-v1:0 | `image_edit(model="stability/stable-creative-upscale-v1:0", ...)` | Creative upscaling |
+| stable-fast-upscale-v1:0 | `image_edit(model="stability/stable-fast-upscale-v1:0", ...)` | Fast upscaling |
+| stable-outpaint-v1:0 | `image_edit(model="stability/stable-outpaint-v1:0", ...)` | Extend image borders |
+| stable-image-remove-background-v1:0 | `image_edit(model="stability/stable-image-remove-background-v1:0", ...)` | Remove background |
+| stable-image-search-replace-v1:0 | `image_edit(model="stability/stable-image-search-replace-v1:0", ...)` | Search and replace objects |
+| stable-image-search-recolor-v1:0 | `image_edit(model="stability/stable-image-search-recolor-v1:0", ...)` | Search and recolor |
+| stable-image-control-sketch-v1:0 | `image_edit(model="stability/stable-image-control-sketch-v1:0", ...)` | Control with sketch |
+| stable-image-control-structure-v1:0 | `image_edit(model="stability/stable-image-control-structure-v1:0", ...)` | Control with structure |
+| stable-image-erase-object-v1:0 | `image_edit(model="stability/stable-image-erase-object-v1:0", ...)` | Erase objects |
+| stable-image-style-guide-v1:0 | `image_edit(model="stability/stable-image-style-guide-v1:0", ...)` | Apply style guide |
+| stable-style-transfer-v1:0 | `image_edit(model="stability/stable-style-transfer-v1:0", ...)` | Transfer style |
+
+### Usage - LiteLLM Proxy Server
+
+#### 1. Setup config.yaml
+
+```yaml showLineNumbers
+model_list:
+ - model_name: stability-inpaint
+ litellm_params:
+ model: stability/stable-image-inpaint-v1:0
+ api_key: os.environ/STABILITY_API_KEY
+ model_info:
+ mode: image_edit
+
+ - model_name: stability-upscale
+ litellm_params:
+ model: stability/stable-conservative-upscale-v1:0
+ api_key: os.environ/STABILITY_API_KEY
+ model_info:
+ mode: image_edit
+
+general_settings:
+ master_key: sk-1234
+```
+
+#### 2. Start the proxy
+
+```bash showLineNumbers
+litellm --config config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+#### 3. Test it
+
+```bash showLineNumbers
+curl -X POST "http://0.0.0.0:4000/v1/images/edits" \
+ -H "Authorization: Bearer sk-1234" \
+ -F "model=stability-inpaint" \
+ -F "image=@original_image.png" \
+ -F "mask=@mask_image.png" \
+ -F "prompt=Add a beautiful garden in the masked area"
+```
+
+## AWS Bedrock (Stability)
+
+LiteLLM also supports Stability AI models via AWS Bedrock. This is useful if you're already using AWS infrastructure.
+
+### Usage - Bedrock Stability
+
+```python showLineNumbers
+from litellm import image_edit
+import os
+
+# Set AWS credentials
+os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-key"
+os.environ["AWS_REGION_NAME"] = "us-east-1"
+
+# Bedrock Stability inpainting
+response = image_edit(
+ model="bedrock/us.stability.stable-image-inpaint-v1:0",
+ image=open("original_image.png", "rb"),
+ mask=open("mask_image.png", "rb"),
+ prompt="Add flowers in the masked area",
+ size="1024x1024",
+)
+print(response)
+```
+
+### Supported Bedrock Stability Models
+
+All Stability AI image edit models are available via Bedrock with the `bedrock/` prefix:
+
+| Direct API Model | Bedrock Model | Description |
+|------------------|---------------|-------------|
+| stability/stable-image-inpaint-v1:0 | bedrock/us.stability.stable-image-inpaint-v1:0 | Inpainting |
+| stability/stable-conservative-upscale-v1:0 | bedrock/stability.stable-conservative-upscale-v1:0 | Conservative upscaling |
+| stability/stable-creative-upscale-v1:0 | bedrock/stability.stable-creative-upscale-v1:0 | Creative upscaling |
+| stability/stable-fast-upscale-v1:0 | bedrock/stability.stable-fast-upscale-v1:0 | Fast upscaling |
+| stability/stable-outpaint-v1:0 | bedrock/stability.stable-outpaint-v1:0 | Outpainting |
+| stability/stable-image-remove-background-v1:0 | bedrock/stability.stable-image-remove-background-v1:0 | Remove background |
+| stability/stable-image-search-replace-v1:0 | bedrock/stability.stable-image-search-replace-v1:0 | Search and replace |
+| stability/stable-image-search-recolor-v1:0 | bedrock/stability.stable-image-search-recolor-v1:0 | Search and recolor |
+| stability/stable-image-control-sketch-v1:0 | bedrock/stability.stable-image-control-sketch-v1:0 | Control with sketch |
+| stability/stable-image-control-structure-v1:0 | bedrock/stability.stable-image-control-structure-v1:0 | Control with structure |
+| stability/stable-image-erase-object-v1:0 | bedrock/stability.stable-image-erase-object-v1:0 | Erase objects |
+
+**Note:** Bedrock model IDs may use `us.stability.*` or `stability.*` prefix depending on the region and model.
+
+## Comparing Routes
LiteLLM supports Stability AI models via two routes:
-| Route | Provider | Use Case |
-|-------|----------|----------|
-| `stability/` | Stability AI Direct API | Direct access, all latest models |
-| `bedrock/stability.*` | AWS Bedrock | AWS integration, enterprise features |
+| Route | Provider | Use Case | Image Generation | Image Editing |
+|-------|----------|----------|------------------|---------------|
+| `stability/` | Stability AI Direct API | Direct access, all latest models | ✅ | ✅ |
+| `bedrock/stability.*` | AWS Bedrock | AWS integration, enterprise features | ✅ | ✅ |
Use `stability/` for direct API access. Use `bedrock/stability.*` if you're already using AWS Bedrock.
diff --git a/docs/my-website/docs/providers/vertex_ocr.md b/docs/my-website/docs/providers/vertex_ocr.md
index 4e3d4b0a063..9ff22a03775 100644
--- a/docs/my-website/docs/providers/vertex_ocr.md
+++ b/docs/my-website/docs/providers/vertex_ocr.md
@@ -140,7 +140,7 @@ with open("document.pdf", "rb") as f:
pdf_base64 = base64.b64encode(f.read()).decode()
response = litellm.ocr(
- model="vertex_ai/mistral-ocr-2505",
+ model="vertex_ai/mistral-ocr-2505", # This doesn't work for deepseek
document={
"type": "document_url",
"document_url": f"data:application/pdf;base64,{pdf_base64}"
@@ -219,7 +219,7 @@ print(f"Cost: ${response._hidden_params.get('response_cost', 0)}")
## Important Notes
:::info URL Conversion
-Vertex AI OCR endpoints don't have internet access. LiteLLM automatically converts public URLs to base64 data URIs before sending requests to Vertex AI.
+Vertex AI Mistral OCR endpoints don't have internet access. LiteLLM automatically converts public URLs to base64 data URIs before sending requests to Vertex AI.
:::
:::tip Regional Availability
@@ -227,11 +227,14 @@ Mistral OCR is available in multiple regions. Specify `vertex_location` to use a
- `us-central1` (default)
- `europe-west1`
- `asia-southeast1`
+
+Deepseek OCR is only available in global region.
:::
## Supported Models
- `mistral-ocr-2505` - Latest Mistral OCR model on Vertex AI
+- `deepseek-ocr-maas` - Lates Deepseek OCR model on Vertex AI
Use the Vertex AI provider prefix: `vertex_ai/`
diff --git a/docs/my-website/docs/providers/xiaomi_mimo.md b/docs/my-website/docs/providers/xiaomi_mimo.md
new file mode 100644
index 00000000000..040f5144015
--- /dev/null
+++ b/docs/my-website/docs/providers/xiaomi_mimo.md
@@ -0,0 +1,137 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Xiaomi MiMo
+https://platform.xiaomimimo.com/#/docs
+
+:::tip
+
+**We support ALL Xiaomi MiMo models, just set `model=xiaomi_mimo/` as a prefix when sending litellm requests**
+
+:::
+
+## API Key
+```python
+# env variable
+os.environ['XIAOMI_MIMO_API_KEY']
+```
+
+## Sample Usage
+```python
+from litellm import completion
+import os
+
+os.environ['XIAOMI_MIMO_API_KEY'] = ""
+response = completion(
+ model="xiaomi_mimo/mimo-v2-flash",
+ messages=[
+ {
+ "role": "user",
+ "content": "What's the weather like in Boston today in Fahrenheit?",
+ }
+ ],
+ max_tokens=1024,
+ temperature=0.3,
+ top_p=0.95,
+)
+print(response)
+```
+
+## Sample Usage - Streaming
+```python
+from litellm import completion
+import os
+
+os.environ['XIAOMI_MIMO_API_KEY'] = ""
+response = completion(
+ model="xiaomi_mimo/mimo-v2-flash",
+ messages=[
+ {
+ "role": "user",
+ "content": "What's the weather like in Boston today in Fahrenheit?",
+ }
+ ],
+ stream=True,
+ max_tokens=1024,
+ temperature=0.3,
+ top_p=0.95,
+)
+
+for chunk in response:
+ print(chunk)
+```
+
+
+## Usage with LiteLLM Proxy Server
+
+Here's how to call a Xiaomi MiMo model with the LiteLLM Proxy Server
+
+1. Modify the config.yaml
+
+ ```yaml
+ model_list:
+ - model_name: my-model
+ litellm_params:
+ model: xiaomi_mimo/ # add xiaomi_mimo/ prefix to route as Xiaomi MiMo provider
+ api_key: api-key # api key to send your model
+ ```
+
+
+2. Start the proxy
+
+ ```bash
+ $ litellm --config /path/to/config.yaml
+ ```
+
+3. Send Request to LiteLLM Proxy Server
+
+
+
+
+
+ ```python
+ import openai
+ client = openai.OpenAI(
+ api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys
+ base_url="http://0.0.0.0:4000" # litellm-proxy-base url
+ )
+
+ response = client.chat.completions.create(
+ model="my-model",
+ messages = [
+ {
+ "role": "user",
+ "content": "what llm are you"
+ }
+ ],
+ )
+
+ print(response)
+ ```
+
+
+
+
+ ```shell
+ curl --location 'http://0.0.0.0:4000/chat/completions' \
+ --header 'Authorization: Bearer sk-1234' \
+ --header 'Content-Type: application/json' \
+ --data '{
+ "model": "my-model",
+ "messages": [
+ {
+ "role": "user",
+ "content": "what llm are you"
+ }
+ ],
+ }'
+ ```
+
+
+
+
+## Supported Models
+
+| Model Name | Usage |
+|------------|-------|
+| mimo-v2-flash | `completion(model="xiaomi_mimo/mimo-v2-flash", messages)` |
diff --git a/docs/my-website/docs/proxy/access_control.md b/docs/my-website/docs/proxy/access_control.md
index 678032be9a2..7ada3f8b237 100644
--- a/docs/my-website/docs/proxy/access_control.md
+++ b/docs/my-website/docs/proxy/access_control.md
@@ -51,7 +51,7 @@ LiteLLM has two types of roles:
| Role Name | Permissions |
|-----------|-------------|
| `org_admin` | Admin over a specific organization. Can create teams and users within their organization ✨ **Premium Feature** |
-| `team_admin` | Admin over a specific team. Can manage team members, update team settings, and create keys for their team. ✨ **Premium Feature** |
+| `team_admin` | Admin over a specific team. Can manage team members, update team member permissions, and create keys for their team. ✨ **Premium Feature** |
## What Can Each Role Do?
diff --git a/docs/my-website/docs/proxy/alerting.md b/docs/my-website/docs/proxy/alerting.md
index 4cbcd0cffce..38d6d47be44 100644
--- a/docs/my-website/docs/proxy/alerting.md
+++ b/docs/my-website/docs/proxy/alerting.md
@@ -215,16 +215,16 @@ general_settings:
alerting: ["slack"]
alerting_threshold: 0.0001 # (Seconds) set an artificially low threshold for testing alerting
alert_to_webhook_url: {
- "llm_exceptions": "https://hooks.slack.com/services/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",
- "llm_too_slow": "https://hooks.slack.com/services/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",
- "llm_requests_hanging": "https://hooks.slack.com/services/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",
- "budget_alerts": "https://hooks.slack.com/services/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",
- "db_exceptions": "https://hooks.slack.com/services/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",
- "daily_reports": "https://hooks.slack.com/services/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",
- "spend_reports": "https://hooks.slack.com/services/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",
- "cooldown_deployment": "https://hooks.slack.com/services/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",
- "new_model_added": "https://hooks.slack.com/services/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",
- "outage_alerts": "https://hooks.slack.com/services/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",
+ "llm_exceptions": "example-slack-webhook-url",
+ "llm_too_slow": "example-slack-webhook-url",
+ "llm_requests_hanging": "example-slack-webhook-url",
+ "budget_alerts": "example-slack-webhook-url",
+ "db_exceptions": "example-slack-webhook-url",
+ "daily_reports": "example-slack-webhook-url",
+ "spend_reports": "example-slack-webhook-url",
+ "cooldown_deployment": "example-slack-webhook-url",
+ "new_model_added": "example-slack-webhook-url",
+ "outage_alerts": "example-slack-webhook-url",
}
litellm_settings:
@@ -399,7 +399,7 @@ curl -X GET --location 'http://0.0.0.0:4000/health/services?service=webhook' \
{
"spend": 1, # the spend for the 'event_group'
"max_budget": 0, # the 'max_budget' set for the 'event_group'
- "token": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
+ "token": "example-api-key-123",
"user_id": "default_user_id",
"team_id": null,
"user_email": null,
diff --git a/docs/my-website/docs/proxy/call_hooks.md b/docs/my-website/docs/proxy/call_hooks.md
index fa420009cf1..fe865f67e09 100644
--- a/docs/my-website/docs/proxy/call_hooks.md
+++ b/docs/my-website/docs/proxy/call_hooks.md
@@ -17,6 +17,7 @@ import Image from '@theme/IdealImage';
| `async_pre_call_hook` | Modify incoming request before it's sent to model | Before the LLM API call is made |
| `async_moderation_hook` | Run checks on input in parallel to LLM API call | In parallel with the LLM API call |
| `async_post_call_success_hook` | Modify outgoing response (non-streaming) | After successful LLM API call, for non-streaming responses |
+| `async_post_call_failure_hook` | Transform error responses sent to clients | After failed LLM API call |
| `async_post_call_streaming_hook` | Modify outgoing response (streaming) | After successful LLM API call, for streaming responses |
See a complete example with our [parallel request rate limiter](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/hooks/parallel_request_limiter.py)
@@ -60,7 +61,21 @@ class MyCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observabilit
original_exception: Exception,
user_api_key_dict: UserAPIKeyAuth,
traceback_str: Optional[str] = None,
- ):
+ ) -> Optional[HTTPException]:
+ """
+ Transform error responses sent to clients.
+
+ Return an HTTPException to replace the original error with a user-friendly message.
+ Return None to use the original exception.
+
+ Example:
+ if isinstance(original_exception, litellm.ContextWindowExceededError):
+ return HTTPException(
+ status_code=400,
+ detail="Your prompt is too long. Please reduce the length and try again."
+ )
+ return None # Use original exception
+ """
pass
async def async_post_call_success_hook(
@@ -339,3 +354,38 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
"usage": {}
}
```
+
+## Advanced - Transform Error Responses
+
+Transform technical API errors into user-friendly messages using `async_post_call_failure_hook`. Return an `HTTPException` to replace the original error, or `None` to use the original exception.
+
+```python
+from litellm.integrations.custom_logger import CustomLogger
+from fastapi import HTTPException
+from typing import Optional
+import litellm
+
+class MyErrorTransformer(CustomLogger):
+ async def async_post_call_failure_hook(
+ self,
+ request_data: dict,
+ original_exception: Exception,
+ user_api_key_dict: UserAPIKeyAuth,
+ traceback_str: Optional[str] = None,
+ ) -> Optional[HTTPException]:
+ if isinstance(original_exception, litellm.ContextWindowExceededError):
+ return HTTPException(
+ status_code=400,
+ detail="Your prompt is too long. Please reduce the length and try again."
+ )
+ if isinstance(original_exception, litellm.RateLimitError):
+ return HTTPException(
+ status_code=429,
+ detail="Rate limit exceeded. Please try again in a moment."
+ )
+ return None # Use original exception
+
+proxy_handler_instance = MyErrorTransformer()
+```
+
+**Result:** Clients receive `"Your prompt is too long..."` instead of `"ContextWindowExceededError: Prompt exceeds context window"`.
diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md
index 3bffc141fde..343cbd0e53f 100644
--- a/docs/my-website/docs/proxy/config_settings.md
+++ b/docs/my-website/docs/proxy/config_settings.md
@@ -346,6 +346,7 @@ router_settings:
| optional_pre_call_checks | List[str] | List of pre-call checks to add to the router. Currently supported: 'router_budget_limiting', 'prompt_caching' |
| ignore_invalid_deployments | boolean | If true, ignores invalid deployments. Default for proxy is True - to prevent invalid models from blocking other models from being loaded. |
| search_tools | List[SearchToolTypedDict] | List of search tool configurations for Search API integration. Each tool specifies a search_tool_name and litellm_params with search_provider, api_key, api_base, etc. [Further Docs](../search.md) |
+| guardrail_list | List[GuardrailTypedDict] | List of guardrail configurations for guardrail load balancing. Enables load balancing across multiple guardrail deployments with the same guardrail_name. [Further Docs](./guardrails/guardrail_load_balancing.md) |
### environment variables - Reference
@@ -413,6 +414,12 @@ router_settings:
| AZURE_FEDERATED_TOKEN_FILE | File path to Azure federated token
| AZURE_FILE_SEARCH_COST_PER_GB_PER_DAY | Cost per GB per day for Azure File Search service
| AZURE_SCOPE | For EntraID Auth, Scope for Azure services, defaults to "https://cognitiveservices.azure.com/.default"
+| AZURE_SENTINEL_DCR_IMMUTABLE_ID | Immutable ID of the Data Collection Rule for Azure Sentinel logging
+| AZURE_SENTINEL_STREAM_NAME | Stream name for Azure Sentinel logging
+| AZURE_SENTINEL_CLIENT_SECRET | Client secret for Azure Sentinel authentication
+| AZURE_SENTINEL_ENDPOINT | Endpoint for Azure Sentinel logging
+| AZURE_SENTINEL_TENANT_ID | Tenant ID for Azure Sentinel authentication
+| AZURE_SENTINEL_CLIENT_ID | Client ID for Azure Sentinel authentication
| AZURE_KEY_VAULT_URI | URI for Azure Key Vault
| AZURE_OPERATION_POLLING_TIMEOUT | Timeout in seconds for Azure operation polling
| AZURE_STORAGE_ACCOUNT_KEY | The Azure Storage Account Key to use for Authentication to Azure Blob Storage logging
@@ -541,10 +548,14 @@ router_settings:
| DOCS_TITLE | Title of the documentation pages
| DOCS_URL | The path to the Swagger API documentation. **By default this is "/"**
| EMAIL_LOGO_URL | URL for the logo used in emails
+| EMAIL_BUDGET_ALERT_TTL | Time-to-live for email budget alerts in seconds
+| EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE | Maximum spend percentage for triggering email budget alerts
| EMAIL_SUPPORT_CONTACT | Support contact email address
| EMAIL_SIGNATURE | Custom HTML footer/signature for all emails. Can include HTML tags for formatting and links.
| EMAIL_SUBJECT_INVITATION | Custom subject template for invitation emails.
| EMAIL_SUBJECT_KEY_CREATED | Custom subject template for key creation emails.
+| EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE | Percentage of max budget that triggers alerts (as decimal: 0.8 = 80%). Default is 0.8
+| EMAIL_BUDGET_ALERT_TTL | Time-to-live for budget alert deduplication in seconds. Default is 86400 (24 hours)
| ENKRYPTAI_API_BASE | Base URL for EnkryptAI Guardrails API. **Default is https://api.enkryptai.com**
| ENKRYPTAI_API_KEY | API key for EnkryptAI Guardrails service
| EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING | Flag to enable new multi-instance rate limiting. **Default is False**
@@ -596,6 +607,8 @@ router_settings:
| GREENSCALE_ENDPOINT | Endpoint URL for Greenscale service
| GRAYSWAN_API_BASE | Base URL for GraySwan API. Default is https://api.grayswan.ai
| GRAYSWAN_API_KEY | API key for GraySwan Cygnal service
+| GRAYSWAN_REASONING_MODE | Reasoning mode for GraySwan guardrail
+| GRAYSWAN_VIOLATION_THRESHOLD | Violation threshold for GraySwan guardrail
| GOOGLE_APPLICATION_CREDENTIALS | Path to Google Cloud credentials JSON file
| GOOGLE_CLIENT_ID | Client ID for Google OAuth
| GOOGLE_CLIENT_SECRET | Client secret for Google OAuth
@@ -825,6 +838,7 @@ router_settings:
| SMTP_TLS | Flag to enable or disable TLS for SMTP connections
| SMTP_USERNAME | Username for SMTP authentication (do not set if SMTP does not require auth)
| SENDGRID_API_KEY | API key for SendGrid email service
+| RESEND_API_KEY | API key for Resend email service
| SENDGRID_SENDER_EMAIL | Email address used as the sender in SendGrid email transactions
| SPEND_LOGS_URL | URL for retrieving spend logs
| SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000
diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md
index 019cd62c620..26a4920c093 100644
--- a/docs/my-website/docs/proxy/cost_tracking.md
+++ b/docs/my-website/docs/proxy/cost_tracking.md
@@ -722,7 +722,7 @@ curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end
```shell
[
{
- "api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
+ "api_key": "example-api-key-123",
"total_cost": 0.3201286305151999,
"total_input_tokens": 36.0,
"total_output_tokens": 1593.0,
@@ -766,7 +766,7 @@ curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end
```shell
[
{
- "api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
+ "api_key": "example-api-key-123",
"total_cost": 0.00013132,
"total_input_tokens": 105.0,
"total_output_tokens": 872.0,
@@ -1151,7 +1151,7 @@ curl -X GET "http://0.0.0.0:4000/spend/logs?request_id= UserAPIKeyAuth:
@@ -114,6 +115,29 @@ UserAPIKeyAuth(
)
```
+### Object Permission Example (MCP, agents, etc.)
+
+```python
+from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+ global_mcp_server_manager,
+)
+
+def _server_id(name: str) -> str:
+ server = global_mcp_server_manager.get_mcp_server_by_name(name)
+ if not server:
+ raise ValueError(f"Unknown MCP server '{name}'")
+ return server.server_id
+
+object_permission = LiteLLM_ObjectPermissionTable(
+ mcp_servers=[_server_id("deepwiki"), _server_id("everything")], # MCP servers this key is allowed to use
+ mcp_tool_permissions={"deepwiki": ["search", "read_doc"]}, # optional per-server tool allow-list
+)
+
+UserAPIKeyAuth(
+ object_permission=object_permission,
+)
+```
+
### Advanced Configuration
```python
UserAPIKeyAuth(
@@ -139,6 +163,7 @@ UserAPIKeyAuth(
### Complete Example
```python
+from fastapi import Request
from datetime import datetime, timedelta
from litellm.proxy._types import UserAPIKeyAuth, LitellmUserRoles
@@ -333,4 +358,4 @@ async def user_api_key_auth(
except Exception:
raise Exception("Invalid API key")
-```
\ No newline at end of file
+```
diff --git a/docs/my-website/docs/proxy/customers.md b/docs/my-website/docs/proxy/customers.md
index 66142ca3d84..1101884c36b 100644
--- a/docs/my-website/docs/proxy/customers.md
+++ b/docs/my-website/docs/proxy/customers.md
@@ -103,7 +103,7 @@ Expected Response
{
"spend": 0.0011120000000000001, # 👈 SPEND
"max_budget": null,
- "token": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
+ "token": "example-api-key-123",
"customer_id": "krrish12", # 👈 CUSTOMER ID
"user_id": null,
"team_id": null,
diff --git a/docs/my-website/docs/proxy/email.md b/docs/my-website/docs/proxy/email.md
index e50cc47f5d5..ad158cb3429 100644
--- a/docs/my-website/docs/proxy/email.md
+++ b/docs/my-website/docs/proxy/email.md
@@ -94,6 +94,35 @@ On the LiteLLM Proxy UI, go to users > create a new user.
After creating a new user, they will receive an email invite a the email you specified when creating the user.
+### 3. Configure Budget Alerts (Optional)
+
+Enable budget alert emails by adding "email" to the `alerts` list in your proxy configuration:
+
+```yaml showLineNumbers title="proxy_config.yaml"
+general_settings:
+ alerts: ["email"]
+```
+
+#### Budget Alert Types
+
+**Soft Budget Alerts**: Automatically triggered when a key exceeds its soft budget limit. These alerts help you monitor spending before reaching critical thresholds.
+
+**Max Budget Alerts**: Automatically triggered when a key reaches a specified percentage of its maximum budget (default: 80%). These alerts warn you when you're approaching budget exhaustion.
+
+Both alert types send a maximum of one email per 24-hour period to prevent spam.
+
+#### Configuration Options
+
+Customize budget alert behavior using these environment variables:
+
+```yaml showLineNumbers title=".env"
+# Percentage of max budget that triggers alerts (as decimal: 0.8 = 80%)
+EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE=0.8
+
+# Time-to-live for alert deduplication in seconds (default: 24 hours)
+EMAIL_BUDGET_ALERT_TTL=86400
+```
+
## Email Templates
diff --git a/docs/my-website/docs/proxy/guardrails/guardrail_load_balancing.md b/docs/my-website/docs/proxy/guardrails/guardrail_load_balancing.md
new file mode 100644
index 00000000000..3f89d9bbccd
--- /dev/null
+++ b/docs/my-website/docs/proxy/guardrails/guardrail_load_balancing.md
@@ -0,0 +1,351 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Guardrail Load Balancing
+
+Load balance guardrail requests across multiple guardrail deployments. This is useful when you have rate limits on guardrail providers (e.g., AWS Bedrock Guardrails) and want to distribute requests across multiple accounts or regions.
+
+## How It Works
+
+```mermaid
+flowchart LR
+ subgraph LiteLLM Gateway
+ Router[Router]
+ G1[Guardrail Instance A]
+ G2[Guardrail Instance B]
+ G3[Guardrail Instance N]
+ end
+
+ Client[Client Request] --> Router
+ Router -->|Round Robin / Weighted| G1
+ Router -->|Round Robin / Weighted| G2
+ Router -->|Round Robin / Weighted| G3
+
+ G1 --> AWS1[AWS Account 1]
+ G2 --> AWS2[AWS Account 2]
+ G3 --> AWSN[AWS Account N]
+```
+
+When you define multiple guardrails with the **same `guardrail_name`**, LiteLLM automatically load balances requests across them using the router's load balancing strategy.
+
+## Why Use Guardrail Load Balancing?
+
+| Use Case | Benefit |
+|----------|---------|
+| **AWS Bedrock Rate Limits** | Bedrock Guardrails have per-account rate limits. Distribute across multiple AWS accounts to increase throughput |
+| **Multi-Region Redundancy** | Deploy guardrails across regions for failover and lower latency |
+| **Cost Optimization** | Spread usage across accounts with different pricing tiers or credits |
+| **A/B Testing** | Test different guardrail configurations with weighted distribution |
+
+## Quick Start
+
+### 1. Define Multiple Guardrails with Same Name
+
+Define multiple guardrail entries with the **same `guardrail_name`** but different configurations:
+
+
+
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: openai/gpt-4
+ api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+ # First Bedrock guardrail - AWS Account 1
+ - guardrail_name: "content-filter"
+ litellm_params:
+ guardrail: bedrock/guardrail
+ mode: "pre_call"
+ guardrailIdentifier: "abc123"
+ guardrailVersion: "1"
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID_1
+ aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY_1
+ aws_region_name: "us-east-1"
+
+ # Second Bedrock guardrail - AWS Account 2
+ - guardrail_name: "content-filter"
+ litellm_params:
+ guardrail: bedrock/guardrail
+ mode: "pre_call"
+ guardrailIdentifier: "def456"
+ guardrailVersion: "1"
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID_2
+ aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY_2
+ aws_region_name: "us-west-2"
+```
+
+
+
+
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: openai/gpt-4
+ api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+ # First custom guardrail instance
+ - guardrail_name: "pii-filter"
+ litellm_params:
+ guardrail: custom_guardrail.PIIFilterA
+ mode: "pre_call"
+
+ # Second custom guardrail instance
+ - guardrail_name: "pii-filter"
+ litellm_params:
+ guardrail: custom_guardrail.PIIFilterB
+ mode: "pre_call"
+```
+
+
+
+
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: openai/gpt-4
+ api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+ # First Aporia instance
+ - guardrail_name: "toxicity-filter"
+ litellm_params:
+ guardrail: aporia
+ mode: "pre_call"
+ api_key: os.environ/APORIA_API_KEY_1
+ api_base: os.environ/APORIA_API_BASE_1
+
+ # Second Aporia instance
+ - guardrail_name: "toxicity-filter"
+ litellm_params:
+ guardrail: aporia
+ mode: "pre_call"
+ api_key: os.environ/APORIA_API_KEY_2
+ api_base: os.environ/APORIA_API_BASE_2
+```
+
+
+
+
+### 2. Start LiteLLM Gateway
+
+```bash showLineNumbers title="Start proxy"
+litellm --config config.yaml --detailed_debug
+```
+
+### 3. Make Requests
+
+Requests using the guardrail will be automatically load balanced:
+
+```bash showLineNumbers title="Test request"
+curl -X POST http://localhost:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer sk-1234" \
+ -d '{
+ "model": "gpt-4",
+ "messages": [{"role": "user", "content": "Hello, how are you?"}],
+ "guardrails": ["content-filter"]
+ }'
+```
+
+## Weighted Load Balancing
+
+Assign weights to distribute traffic unevenly across guardrail instances:
+
+```yaml showLineNumbers title="config.yaml - Weighted distribution"
+guardrails:
+ # 80% of traffic
+ - guardrail_name: "content-filter"
+ litellm_params:
+ guardrail: bedrock/guardrail
+ mode: "pre_call"
+ guardrailIdentifier: "primary-guard"
+ guardrailVersion: "1"
+ weight: 8 # Higher weight = more traffic
+
+ # 20% of traffic
+ - guardrail_name: "content-filter"
+ litellm_params:
+ guardrail: bedrock/guardrail
+ mode: "pre_call"
+ guardrailIdentifier: "secondary-guard"
+ guardrailVersion: "1"
+ weight: 2 # Lower weight = less traffic
+```
+
+## Bedrock Guardrails - Multi-Account Setup
+
+AWS Bedrock Guardrails have rate limits per account. Here's how to set up load balancing across multiple AWS accounts:
+
+### Architecture
+
+```mermaid
+flowchart TB
+ subgraph LiteLLM["LiteLLM Gateway"]
+ LB[Load Balancer]
+ end
+
+ subgraph AWS1["AWS Account 1 (us-east-1)"]
+ BG1[Bedrock Guardrail]
+ end
+
+ subgraph AWS2["AWS Account 2 (us-west-2)"]
+ BG2[Bedrock Guardrail]
+ end
+
+ subgraph AWS3["AWS Account 3 (eu-west-1)"]
+ BG3[Bedrock Guardrail]
+ end
+
+ Client[Client] --> LiteLLM
+ LB --> BG1
+ LB --> BG2
+ LB --> BG3
+```
+
+### Configuration
+
+```yaml showLineNumbers title="config.yaml - Multi-account Bedrock"
+model_list:
+ - model_name: claude-3
+ litellm_params:
+ model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
+
+guardrails:
+ # AWS Account 1 - US East
+ - guardrail_name: "bedrock-content-filter"
+ litellm_params:
+ guardrail: bedrock/guardrail
+ mode: "during_call"
+ guardrailIdentifier: "guard-us-east"
+ guardrailVersion: "DRAFT"
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_1
+ aws_secret_access_key: os.environ/AWS_SECRET_KEY_1
+ aws_region_name: "us-east-1"
+
+ # AWS Account 2 - US West
+ - guardrail_name: "bedrock-content-filter"
+ litellm_params:
+ guardrail: bedrock/guardrail
+ mode: "during_call"
+ guardrailIdentifier: "guard-us-west"
+ guardrailVersion: "DRAFT"
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_2
+ aws_secret_access_key: os.environ/AWS_SECRET_KEY_2
+ aws_region_name: "us-west-2"
+
+ # AWS Account 3 - EU West
+ - guardrail_name: "bedrock-content-filter"
+ litellm_params:
+ guardrail: bedrock/guardrail
+ mode: "during_call"
+ guardrailIdentifier: "guard-eu-west"
+ guardrailVersion: "DRAFT"
+ aws_access_key_id: os.environ/AWS_ACCESS_KEY_3
+ aws_secret_access_key: os.environ/AWS_SECRET_KEY_3
+ aws_region_name: "eu-west-1"
+```
+
+### Test Multi-Account Setup
+
+```bash showLineNumbers title="Run multiple requests to verify load balancing"
+# Run 10 requests - they will be distributed across accounts
+for i in {1..10}; do
+ curl -s -X POST http://localhost:4000/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer sk-1234" \
+ -d '{
+ "model": "claude-3",
+ "messages": [{"role": "user", "content": "Hello"}],
+ "guardrails": ["bedrock-content-filter"]
+ }' &
+done
+wait
+```
+
+Check proxy logs to verify requests are distributed across different AWS accounts.
+
+## Custom Guardrails Example
+
+Create two custom guardrail classes for load balancing:
+
+```python showLineNumbers title="custom_guardrail.py"
+from litellm.integrations.custom_guardrail import CustomGuardrail
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.caching.caching import DualCache
+
+
+class PIIFilterA(CustomGuardrail):
+ """PII Filter Instance A"""
+
+ async def async_pre_call_hook(
+ self,
+ user_api_key_dict: UserAPIKeyAuth,
+ cache: DualCache,
+ data: dict,
+ call_type: str,
+ ):
+ print("PIIFilterA processing request")
+ # Your PII filtering logic here
+ return data
+
+
+class PIIFilterB(CustomGuardrail):
+ """PII Filter Instance B"""
+
+ async def async_pre_call_hook(
+ self,
+ user_api_key_dict: UserAPIKeyAuth,
+ cache: DualCache,
+ data: dict,
+ call_type: str,
+ ):
+ print("PIIFilterB processing request")
+ # Your PII filtering logic here
+ return data
+```
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+ - guardrail_name: "pii-filter"
+ litellm_params:
+ guardrail: custom_guardrail.PIIFilterA
+ mode: "pre_call"
+
+ - guardrail_name: "pii-filter"
+ litellm_params:
+ guardrail: custom_guardrail.PIIFilterB
+ mode: "pre_call"
+```
+
+## Verifying Load Balancing
+
+Enable detailed debug logging to verify load balancing is working:
+
+```bash showLineNumbers title="Start with debug logging"
+litellm --config config.yaml --detailed_debug
+```
+
+You should see logs indicating which guardrail instance is selected:
+
+```
+Selected guardrail deployment: bedrock/guardrail (guard-us-east)
+Selected guardrail deployment: bedrock/guardrail (guard-us-west)
+Selected guardrail deployment: bedrock/guardrail (guard-eu-west)
+...
+```
+
+## Related
+
+- [Guardrails Quick Start](./quick_start.md)
+- [Bedrock Guardrails](./bedrock.md)
+- [Custom Guardrails](./custom_guardrail.md)
+- [Load Balancing for LLM Calls](../load_balancing.md)
+
diff --git a/docs/my-website/docs/proxy/guardrails/lakera_ai.md b/docs/my-website/docs/proxy/guardrails/lakera_ai.md
index 81dd3d8a60d..7aacc3fa924 100644
--- a/docs/my-website/docs/proxy/guardrails/lakera_ai.md
+++ b/docs/my-website/docs/proxy/guardrails/lakera_ai.md
@@ -29,6 +29,13 @@ guardrails:
mode: "pre_call"
api_key: os.environ/LAKERA_API_KEY
api_base: os.environ/LAKERA_API_BASE
+ - guardrail_name: "lakera-monitor"
+ litellm_params:
+ guardrail: lakera_v2
+ mode: "pre_call"
+ on_flagged: "monitor" # Log violations but don't block
+ api_key: os.environ/LAKERA_API_KEY
+ api_base: os.environ/LAKERA_API_BASE
```
@@ -144,6 +151,7 @@ guardrails:
# breakdown: Optional[bool] = True,
# metadata: Optional[Dict] = None,
# dev_info: Optional[bool] = True,
+ # on_flagged: Optional[str] = "block", # "block" or "monitor"
```
- `api_base`: (Optional[str]) The base of the Lakera integration. Defaults to `https://api.lakera.ai`
@@ -153,3 +161,6 @@ guardrails:
- `breakdown`: (Optional[bool]) When true the response will return a breakdown list of the detectors that were run, as defined in the policy, and whether each of them detected something or not.
- `metadata`: (Optional[Dict]) Metadata tags can be attached to screening requests as an object that can contain any arbitrary key-value pairs.
- `dev_info`: (Optional[bool]) When true the response will return an object with developer information about the build of Lakera Guard.
+- `on_flagged`: (Optional[str]) Action to take when content is flagged. Defaults to `"block"`.
+ - `"block"`: Raises an HTTP 400 exception when violations are detected (default behavior)
+ - `"monitor"`: Logs violations but allows the request to proceed. Useful for tuning security policies without blocking legitimate requests.
diff --git a/docs/my-website/docs/proxy/guardrails/litellm_content_filter.md b/docs/my-website/docs/proxy/guardrails/litellm_content_filter.md
index 29183c693a4..f247a327cd6 100644
--- a/docs/my-website/docs/proxy/guardrails/litellm_content_filter.md
+++ b/docs/my-website/docs/proxy/guardrails/litellm_content_filter.md
@@ -3,10 +3,12 @@ import TabItem from '@theme/TabItem';
import Image from '@theme/IdealImage';
-# LiteLLM Content Filter
+# LiteLLM Content Filter (Built-in Guardrails)
**Built-in guardrail** for detecting and filtering sensitive information using regex patterns and keyword matching. No external dependencies required.
+**When to use?** Good for cases which do not require an ML model to detect sensitive information.
+
## Overview
| Property | Details |
@@ -56,6 +58,44 @@ Test examples:
### Step 1: Define Guardrails in config.yaml
+
+
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: openai/gpt-3.5-turbo
+ api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+ - guardrail_name: "harmful-content-filter"
+ litellm_params:
+ guardrail: litellm_content_filter
+ mode: "pre_call"
+
+ # Enable harmful content categories
+ categories:
+ - category: "harmful_self_harm"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ - category: "harmful_violence"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ - category: "harmful_illegal_weapons"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+```
+
+
+
+
+
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: gpt-3.5-turbo
@@ -86,6 +126,48 @@ guardrails:
description: "Sensitive internal information"
```
+
+
+
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-3.5-turbo
+ litellm_params:
+ model: openai/gpt-3.5-turbo
+ api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+ - guardrail_name: "comprehensive-filter"
+ litellm_params:
+ guardrail: litellm_content_filter
+ mode: "pre_call"
+
+ # Harmful content categories
+ categories:
+ - category: "harmful_violence"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "high"
+
+ # PII patterns
+ patterns:
+ - pattern_type: "prebuilt"
+ pattern_name: "us_ssn"
+ action: "BLOCK"
+ - pattern_type: "prebuilt"
+ pattern_name: "email"
+ action: "MASK"
+
+ # Custom keywords
+ blocked_words:
+ - keyword: "confidential"
+ action: "BLOCK"
+```
+
+
+
+
### Step 2: Start LiteLLM Gateway
```shell
@@ -175,7 +257,7 @@ Contact me at [EMAIL_REDACTED]
| `amex` | American Express cards | `3782-822463-10005` |
| `aws_access_key` | AWS access keys | `AKIAIOSFODNN7EXAMPLE` |
| `aws_secret_key` | AWS secret keys | `wJalrXUtnFEMI/K7MDENG/bPxRfi...` |
-| `github_token` | GitHub tokens | `ghp_16C7e42F292c6912E7710c838347Ae178B4a` |
+| `github_token` | GitHub tokens | `example-github-token-123` |
### Using Prebuilt Patterns
@@ -310,6 +392,85 @@ for chunk in response:
# Emails automatically masked in real-time
```
+## Image Content Filtering
+
+Content filter can analyze images by generating descriptions and applying filters to the text descriptions.
+
+:::warning
+
+This can introduce significant latency to the request - depending on the speed of the vision-capable model.
+
+This is because, each request containing images will be sent to the vision-capable model to generate a description.
+
+:::
+
+### Configuration
+
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-4-vision
+ litellm_params:
+ model: openai/gpt-4-vision-preview
+ api_key: os.environ/OPENAI_API_KEY
+
+guardrails:
+ - guardrail_name: "image-filter"
+ litellm_params:
+ guardrail: litellm_content_filter
+ mode: "pre_call"
+ image_model: "gpt-4-vision" # value is `model_name` of the vision-capable model
+
+ # Apply same filters to image descriptions
+ categories:
+ - category: "harmful_violence"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ patterns:
+ - pattern_type: "prebuilt"
+ pattern_name: "email"
+ action: "MASK"
+```
+
+### How It Works
+
+1. Image is sent to the vision model to generate a text description
+2. Content filters are applied to the description
+3. If harmful content is detected, request is blocked with context about the image
+
+**Example:**
+
+```python
+import openai
+
+client = openai.OpenAI(
+ api_key="sk-1234",
+ base_url="http://localhost:4000"
+)
+
+response = client.chat.completions.create(
+ model="gpt-4-vision",
+ messages=[{
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "What's in this image?"},
+ {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+ ]
+ }],
+ extra_body={"guardrails": ["image-filter"]}
+)
+```
+
+If the image description contains filtered content, you'll get:
+
+```json
+{
+ "error": "Content blocked: harmful_violence category keyword 'weapon' detected (severity: high) (Image description): The image shows..."
+}
+```
+
## Customizing Redaction Tags
When using the `MASK` action, sensitive content is replaced with redaction tags. You can customize how these tags appear.
@@ -363,9 +524,171 @@ Output: "Email ***EMAIL***, SSN ***US_SSN***, ***REDACTED*** data"
- Pattern names are automatically uppercased (e.g., `email` → `EMAIL`)
- `keyword_redaction_tag` is a fixed string (no placeholders)
+## Content Categories
+
+Prebuilt categories use **keyword matching** to detect harmful content, bias, and inappropriate advice. Keywords are matched with word boundaries (single words) or as substrings (multi-word phrases), case-insensitive.
+
+### Available Categories
+
+| Category | Description |
+|----------|-------------|
+| **Harmful Content** | |
+| `harmful_self_harm` | Self-harm, suicide, eating disorders |
+| `harmful_violence` | Violence, criminal planning, attacks |
+| `harmful_illegal_weapons` | Illegal weapons, explosives, dangerous materials |
+| **Bias Detection** | |
+| `bias_gender` | Gender-based discrimination, stereotypes |
+| `bias_sexual_orientation` | LGBTQ+ discrimination, homophobia, transphobia |
+| `bias_racial` | Racial/ethnic discrimination, stereotypes |
+| `bias_religious` | Religious discrimination, stereotypes |
+| **Denied Advice** | |
+| `denied_financial_advice` | Personalized financial advice, investment recommendations |
+| `denied_medical_advice` | Medical advice, diagnosis, treatment recommendations |
+| `denied_legal_advice` | Legal advice, representation, legal strategy |
+
+:::info Bias Detection Considerations
+
+Bias detection is **complex and context-dependent**. Rule-based systems catch explicit discriminatory language but may generate false positives on legitimate discussions. Start with **high severity thresholds** and test thoroughly. For mission-critical bias detection, consider combining with AI-based guardrails (e.g., HiddenLayer, Lakera).
+
+:::
+
+### Configuration
+
+```yaml showLineNumbers title="config.yaml"
+guardrails:
+ - guardrail_name: "content-filter"
+ litellm_params:
+ guardrail: litellm_content_filter
+ mode: "pre_call"
+
+ categories:
+ - category: "harmful_self_harm"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium" # Blocks medium+ severity
+
+ - category: "bias_gender"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "high" # Only explicit discrimination
+
+ - category: "denied_financial_advice"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+```
+
+**Severity Thresholds:**
+- `"high"` - Only blocks high severity items
+- `"medium"` - Blocks medium and high severity (default)
+- `"low"` - Blocks all severity levels
+
+### Custom Category Files
+
+Override default categories with custom keyword lists:
+
+```yaml showLineNumbers title="config.yaml"
+categories:
+ - category: "harmful_self_harm"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+ category_file: "/path/to/custom.yaml"
+```
+
+```yaml showLineNumbers title="custom.yaml"
+category_name: "harmful_self_harm"
+description: "Custom self-harm detection"
+default_action: "BLOCK"
+
+keywords:
+ - keyword: "suicide"
+ severity: "high"
+ - keyword: "harm myself"
+ severity: "high"
+
+exceptions:
+ - "suicide prevention"
+ - "mental health"
+```
+
## Use Cases
-### 1. PII Protection
+### 1. Harmful Content Detection
+
+Block or detect requests containing harmful, illegal, or dangerous content:
+
+```yaml
+categories:
+ - category: "harmful_self_harm"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+ - category: "harmful_violence"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "high"
+ - category: "harmful_illegal_weapons"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+```
+
+### 2. Bias and Discrimination Detection
+
+Detect and block biased, discriminatory, or hateful content across multiple dimensions:
+
+```yaml
+categories:
+ # Gender-based discrimination
+ - category: "bias_gender"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ # LGBTQ+ discrimination
+ - category: "bias_sexual_orientation"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ # Racial/ethnic discrimination
+ - category: "bias_racial"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "high" # Only explicit to reduce false positives
+
+ # Religious discrimination
+ - category: "bias_religious"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+```
+
+**Sensitivity Tuning:**
+
+For bias detection, severity thresholds are critical to balance safety and legitimate discourse:
+
+```yaml
+# Conservative (low false positives, may miss subtle bias)
+categories:
+ - category: "bias_racial"
+ severity_threshold: "high" # Only blocks explicit discriminatory language
+
+# Balanced (recommended)
+categories:
+ - category: "bias_gender"
+ severity_threshold: "medium" # Blocks stereotypes and explicit discrimination
+
+# Strict (high safety, may have more false positives)
+categories:
+ - category: "bias_sexual_orientation"
+ severity_threshold: "low" # Blocks all potentially problematic content
+```
+
+
+
+### 3. PII Protection
Block or mask personally identifiable information before sending to LLMs:
```yaml
@@ -409,10 +732,64 @@ For large lists of sensitive terms, use a file:
blocked_words_file: "/path/to/sensitive_terms.yaml"
```
-### 4. Compliance
+### 4. Safe AI for Consumer Applications
+
+Combining harmful content and bias detection for consumer-facing AI:
+
+```yaml
+guardrails:
+ - guardrail_name: "safe-consumer-ai"
+ litellm_params:
+ guardrail: litellm_content_filter
+ mode: "pre_call"
+
+ categories:
+ # Harmful content - strict
+ - category: "harmful_self_harm"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ - category: "harmful_violence"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ # Bias detection - balanced
+ - category: "bias_gender"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "high" # Avoid blocking legitimate gender discussions
+
+ - category: "bias_sexual_orientation"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "medium"
+
+ - category: "bias_racial"
+ enabled: true
+ action: "BLOCK"
+ severity_threshold: "high" # Education and news may discuss race
+```
+
+**Perfect for:**
+- Chatbots and virtual assistants
+- Educational AI tools
+- Customer service AI
+- Content generation platforms
+- Public-facing AI applications
+
+### 5. Compliance
Ensure regulatory compliance by filtering sensitive data types:
```yaml
+# Categories checked first (high priority)
+# Category keywords are matched first
+categories:
+ - category: "harmful_self_harm"
+ severity_threshold: "high"
+
+# Then regex patterns
patterns:
- pattern_type: "prebuilt"
pattern_name: "visa"
@@ -422,34 +799,4 @@ patterns:
action: "BLOCK"
```
-## Troubleshooting
-
-### Pattern Not Matching
-
-**Issue:** Regex pattern isn't detecting expected content
-
-**Solution:** Test your regex pattern:
-```python
-import re
-pattern = r'\b[A-Z]{3}-\d{4}\b'
-test_text = "Employee ID: ABC-1234"
-print(re.search(pattern, test_text)) # Should match
-```
-
-### Multiple Pattern Matches
-
-**Issue:** Text contains multiple sensitive patterns
-
-**Solution:** First matching pattern/keyword is processed. Order patterns by priority:
-```yaml
-patterns:
- # Most critical first
- - pattern_type: "prebuilt"
- pattern_name: "us_ssn"
- action: "BLOCK"
- # Less critical
- - pattern_type: "prebuilt"
- pattern_name: "email"
- action: "MASK"
-```
diff --git a/docs/my-website/docs/proxy/guardrails/pillar_security.md b/docs/my-website/docs/proxy/guardrails/pillar_security.md
index 099919dc393..de983d2a5dd 100644
--- a/docs/my-website/docs/proxy/guardrails/pillar_security.md
+++ b/docs/my-website/docs/proxy/guardrails/pillar_security.md
@@ -790,7 +790,7 @@ curl -X POST "http://localhost:4000/v1/chat/completions" \
"messages": [
{
"role": "user",
- "content": "Generate python code that accesses my Github repo using this PAT: ghp_A1b2C3d4E5f6G7h8I9j0K1l2M3n4O5p6Q7r8"
+ "content": "Generate python code that accesses my Github repo using this PAT: example-github-token-123"
}
],
"max_tokens": 50
@@ -815,7 +815,7 @@ curl -X POST "http://localhost:4000/v1/chat/completions" \
"type": "github_token",
"start_idx": 66,
"end_idx": 106,
- "evidence": "ghp_A1b2C3d4E5f6G7h8I9j0K1l2M3n4O5p6Q7r8",
+ "evidence": "example-github-token-123",
}
]
}
diff --git a/docs/my-website/docs/proxy/guardrails/quick_start.md b/docs/my-website/docs/proxy/guardrails/quick_start.md
index 33dda0fa853..3935e109618 100644
--- a/docs/my-website/docs/proxy/guardrails/quick_start.md
+++ b/docs/my-website/docs/proxy/guardrails/quick_start.md
@@ -69,6 +69,13 @@ guardrails:
- `during_call` Run **during** LLM call, on **input** Same as `pre_call` but runs in parallel as LLM call. Response not returned until guardrail check completes
- A list of the above values to run multiple modes, e.g. `mode: [pre_call, post_call]`
+### Load Balancing Guardrails
+
+Need to distribute guardrail requests across multiple accounts or regions? See [Guardrail Load Balancing](./guardrail_load_balancing.md) for details on:
+- Load balancing across multiple AWS Bedrock accounts (useful for rate limit management)
+- Weighted distribution across guardrail instances
+- Multi-region guardrail deployments
+
## 2. Start LiteLLM Gateway
diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md
index cf36963b7e1..30ffa585130 100644
--- a/docs/my-website/docs/proxy/logging.md
+++ b/docs/my-website/docs/proxy/logging.md
@@ -16,6 +16,7 @@ Log Proxy input, output, and exceptions using:
- Custom Callbacks - Custom code and API endpoints
- Langsmith
- DataDog
+- Azure Sentinel
- DynamoDB
- etc.
@@ -1574,6 +1575,10 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
👉 Go here for using [Datadog LLM Observability](../observability/datadog) with LiteLLM Proxy
+## [Azure Sentinel](../observability/azure_sentinel)
+
+👉 Go here for using [Azure Sentinel](../observability/azure_sentinel) with LiteLLM Proxy
+
## Lunary
#### Step1: Install dependencies and set your environment variables
diff --git a/docs/my-website/docs/proxy/multiple_admins.md b/docs/my-website/docs/proxy/multiple_admins.md
index 479b9323ad1..cf122f85b99 100644
--- a/docs/my-website/docs/proxy/multiple_admins.md
+++ b/docs/my-website/docs/proxy/multiple_admins.md
@@ -89,7 +89,7 @@ curl -X POST 'http://0.0.0.0:4000/team/update' \
"id": "bd136c28-edd0-4cb6-b963-f35464cf6f5a",
"updated_at": "2024-06-08 23:41:14.793",
"changed_by": "krrish@berri.ai", # 👈 CHANGED BY
- "changed_by_api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
+ "changed_by_api_key": "example-api-key-123",
"action": "updated",
"table_name": "LiteLLM_TeamTable",
"object_id": "8bf18b11-7f52-4717-8e1f-7c65f9d01e52",
diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md
index 76698071c65..71f0317cedf 100644
--- a/docs/my-website/docs/proxy/prod.md
+++ b/docs/my-website/docs/proxy/prod.md
@@ -33,7 +33,7 @@ litellm_settings:
Set slack webhook url in your env
```shell
-export SLACK_WEBHOOK_URL="https://hooks.slack.com/services/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"
+export SLACK_WEBHOOK_URL="example-slack-webhook-url"
```
Turn off FASTAPI's default info logs
diff --git a/docs/my-website/docs/proxy/quick_start.md b/docs/my-website/docs/proxy/quick_start.md
index a343bb00e9b..cf1ab78b352 100644
--- a/docs/my-website/docs/proxy/quick_start.md
+++ b/docs/my-website/docs/proxy/quick_start.md
@@ -400,7 +400,7 @@ from anthropic import Anthropic
client = Anthropic(
base_url="http://localhost:4000", # proxy endpoint
- api_key="sk-s4xN1IiLTCytwtZFJaYQrA", # litellm proxy virtual key
+ api_key="sk-test-proxy-key-123", # litellm proxy virtual key (example)
)
message = client.messages.create(
diff --git a/docs/my-website/docs/proxy/user_keys.md b/docs/my-website/docs/proxy/user_keys.md
index 21e1d3dbf40..72ec8ccd759 100644
--- a/docs/my-website/docs/proxy/user_keys.md
+++ b/docs/my-website/docs/proxy/user_keys.md
@@ -285,7 +285,7 @@ from anthropic import Anthropic
client = Anthropic(
base_url="http://localhost:4000", # proxy endpoint
- api_key="sk-s4xN1IiLTCytwtZFJaYQrA", # litellm proxy virtual key
+ api_key="sk-test-proxy-key-123", # litellm proxy virtual key (example)
)
message = client.messages.create(
diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md
index 4e828c6c580..140dfd4faf8 100644
--- a/docs/my-website/docs/response_api.md
+++ b/docs/my-website/docs/response_api.md
@@ -4,7 +4,7 @@ import TabItem from '@theme/TabItem';
# /responses
-LiteLLM provides a BETA endpoint in the spec of [OpenAI's `/responses` API](https://platform.openai.com/docs/api-reference/responses)
+LiteLLM provides an endpoint in the spec of [OpenAI's `/responses` API](https://platform.openai.com/docs/api-reference/responses)
Requests to /chat/completions may be bridged here automatically when the provider lacks support for that endpoint. The model’s default `mode` determines how bridging works.(see `model_prices_and_context_window`)
diff --git a/docs/my-website/docs/search/index.md b/docs/my-website/docs/search/index.md
index 1ec3cd5d6b6..037a1b59388 100644
--- a/docs/my-website/docs/search/index.md
+++ b/docs/my-website/docs/search/index.md
@@ -2,7 +2,7 @@
| Feature | Supported |
|---------|-----------|
-| Supported Providers | `perplexity`, `tavily`, `parallel_ai`, `exa_ai`, `google_pse`, `dataforseo`, `firecrawl`, `searxng` |
+| Supported Providers | `perplexity`, `tavily`, `parallel_ai`, `exa_ai`, `google_pse`, `dataforseo`, `firecrawl`, `searxng`, `linkup` |
| Cost Tracking | ✅ |
| Logging | ✅ |
| Load Balancing | ❌ |
@@ -205,7 +205,7 @@ See the [official Perplexity Search documentation](https://docs.perplexity.ai/ap
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `query` | string or array | Yes | Search query. Can be a single string or array of strings |
-| `search_provider` | string | Yes (SDK) | The search provider to use: `"perplexity"`, `"tavily"`, `"parallel_ai"`, `"exa_ai"`, `"google_pse"`, `"dataforseo"`, `"firecrawl"`, or `"searxng"` |
+| `search_provider` | string | Yes (SDK) | The search provider to use: `"perplexity"`, `"tavily"`, `"parallel_ai"`, `"exa_ai"`, `"google_pse"`, `"dataforseo"`, `"firecrawl"`, `"searxng"`, or `"linkup"` |
| `search_tool_name` | string | Yes (Proxy) | Name of the search tool configured in `config.yaml` |
| `max_results` | integer | No | Maximum number of results to return (1-20). Default: 10 |
| `search_domain_filter` | array | No | List of domains to filter results (max 20 domains) |
@@ -269,6 +269,7 @@ The response follows Perplexity's search format with the following structure:
| DataForSEO | `DATAFORSEO_LOGIN`, `DATAFORSEO_PASSWORD` | `dataforseo` |
| Firecrawl | `FIRECRAWL_API_KEY` | `firecrawl` |
| SearXNG | `SEARXNG_API_BASE` (required) | `searxng` |
+| Linkup | `LINKUP_API_KEY` | `linkup` |
See the individual provider documentation for detailed setup instructions and provider-specific parameters.
diff --git a/docs/my-website/docs/search/linkup.md b/docs/my-website/docs/search/linkup.md
new file mode 100644
index 00000000000..3104ffc3c05
--- /dev/null
+++ b/docs/my-website/docs/search/linkup.md
@@ -0,0 +1,152 @@
+# Linkup Search
+
+**Get API Key:** [https://linkup.so](https://linkup.so)
+
+## LiteLLM Python SDK
+
+```python showLineNumbers title="Linkup Search"
+import os
+from litellm import search
+
+os.environ["LINKUP_API_KEY"] = "..."
+
+response = search(
+ query="latest AI developments",
+ search_provider="linkup",
+ max_results=5
+)
+```
+
+## LiteLLM AI Gateway
+
+### 1. Setup config.yaml
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+ - model_name: gpt-4
+ litellm_params:
+ model: gpt-4
+ api_key: os.environ/OPENAI_API_KEY
+
+search_tools:
+ - search_tool_name: linkup-search
+ litellm_params:
+ search_provider: linkup
+ api_key: os.environ/LINKUP_API_KEY
+```
+
+### 2. Start the proxy
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+### 3. Test the search endpoint
+
+```bash showLineNumbers title="Test Request"
+curl http://0.0.0.0:4000/v1/search/linkup-search \
+ -H "Authorization: Bearer sk-1234" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "query": "latest AI developments",
+ "max_results": 5
+ }'
+```
+
+## Provider-specific Parameters
+
+```python showLineNumbers title="Linkup Search with Provider-specific Parameters"
+import os
+from litellm import search
+
+os.environ["LINKUP_API_KEY"] = "..."
+
+response = search(
+ query="machine learning research",
+ search_provider="linkup",
+ max_results=10,
+ # Linkup-specific parameters
+ depth="deep", # "standard" (faster) or "deep" (more comprehensive)
+ outputType="searchResults", # "searchResults", "sourcedAnswer", or "structured"
+ includeSources=True, # Include sources in response
+ includeImages=True, # Include images in results
+ fromDate="2024-01-01", # Start date filter (YYYY-MM-DD)
+ toDate="2024-12-31", # End date filter (YYYY-MM-DD)
+ includeDomains=["arxiv.org", "nature.com"], # Domains to search (max 100)
+ excludeDomains=["wikipedia.com"], # Domains to exclude
+ includeInlineCitations=True, # Include inline citations in sourcedAnswer
+)
+```
+
+## Features
+
+Linkup provides powerful web search with context retrieval capabilities:
+
+### Search Depth
+Control the precision and speed of your search:
+- `standard` - Returns results faster
+- `deep` - Takes longer but yields more comprehensive results
+
+### Output Types
+Choose how results are formatted:
+- `searchResults` - Returns a list of search results with URLs and content
+- `sourcedAnswer` - Returns an AI-generated answer with sources
+- `structured` - Returns results in a custom JSON schema format
+
+### Date Filtering
+Filter results by date range:
+```python
+response = search(
+ query="AI developments",
+ search_provider="linkup",
+ fromDate="2024-06-01",
+ toDate="2024-12-31"
+)
+```
+
+### Domain Filtering
+Include or exclude specific domains:
+```python
+response = search(
+ query="research papers",
+ search_provider="linkup",
+ includeDomains=["arxiv.org", "nature.com", "ieee.org"],
+ excludeDomains=["wikipedia.com"]
+)
+```
+
+### Structured Output
+Get results in a custom JSON schema format:
+```python
+response = search(
+ query="Microsoft 2024 revenue",
+ search_provider="linkup",
+ outputType="structured",
+ structuredOutputSchema='{"type": "object", "properties": {"revenue": {"type": "string"}, "year": {"type": "string"}}}'
+)
+```
+
+## Response Format
+
+Linkup returns results in the following format:
+
+```json
+{
+ "results": [
+ {
+ "type": "text",
+ "name": "Microsoft 2024 Annual Report",
+ "url": "https://www.microsoft.com/investor/reports/ar24/index.html",
+ "content": "Highlights from fiscal year 2024..."
+ }
+ ]
+}
+```
+
+LiteLLM transforms this to the standard `SearchResponse` format:
+- `results[].name` → `SearchResult.title`
+- `results[].url` → `SearchResult.url`
+- `results[].content` → `SearchResult.snippet`
+
diff --git a/docs/my-website/docs/secret_managers/hashicorp_vault.md b/docs/my-website/docs/secret_managers/hashicorp_vault.md
index 09619609cb7..e9e0116f4f3 100644
--- a/docs/my-website/docs/secret_managers/hashicorp_vault.md
+++ b/docs/my-website/docs/secret_managers/hashicorp_vault.md
@@ -197,3 +197,27 @@ When a Virtual Key is Created / Deleted on LiteLLM, LiteLLM will automatically c
LiteLLM stores secret under the `prefix_for_stored_virtual_keys` path (default: `litellm/`)
+
+### Team-specific overrides
+
+When running the LiteLLM proxy you can override the Vault location per team. Use the [Team-Level Secret Manager Settings](./overview.md#team-level-secret-manager-settings) flow in the dashboard and configure the panel shown below:
+
+
+
+Use the following structure for the JSON payload:
+
+```json
+{
+ "namespace": "teams/team-a",
+ "mount": "kv-prod",
+ "path_prefix": "virtual-keys",
+ "data": "password"
+}
+```
+
+- `namespace` – overrides the `X-Vault-Namespace` header.
+- `mount` – which KV engine mount to use (defaults to `secret`).
+- `path_prefix` – additional path segments between the mount and the secret name.
+- `data` – the field name inside the KV payload (defaults to `key`).
+
+Whenever LiteLLM stores or deletes virtual keys for that team, these overrides are applied so you can keep each team’s credentials in its own namespace, mount, or field layout without changing the global Vault configuration.
diff --git a/docs/my-website/docs/secret_managers/overview.md b/docs/my-website/docs/secret_managers/overview.md
index fa1e82b1d09..a987c72d767 100644
--- a/docs/my-website/docs/secret_managers/overview.md
+++ b/docs/my-website/docs/secret_managers/overview.md
@@ -1,3 +1,5 @@
+import Image from '@theme/IdealImage';
+
# Secret Managers Overview
:::info
@@ -45,3 +47,30 @@ general_settings:
primary_secret_name: "litellm_secrets" # OPTIONAL. Read multiple keys from one JSON secret on AWS Secret Manager
```
+## Team-Level Secret Manager Settings
+
+Team-level secret manager settings let every team bring their own key-management configuration. These settings are used when creating virtual keys tied to the team.
+
+Follow these steps to configure it:
+
+1. **Create a team**
+ Open the Teams page and click `Create Team` to launch the modal.
+
+
+
+2. **Expand Additional Settings**
+ Use the `Additional Settings` toggle to reveal the advanced configuration panel.
+
+
+
+3. **Configure the Secret Manager**
+ In the `Secret Manager Settings` panel, paste the provider-specific JSON. Refer to each provider page (AWS, Azure, Google, Hashicorp, etc.) for the supported keys/values. JSON is required today, but we plan to add a more UI-friendly editor.
+
+
+
+4. **Create the team**
+ Review the inputs and click `Create Team` to save.
+
+
+
+Once saved, LiteLLM will use this configuration.
diff --git a/docs/my-website/docs/text_to_speech.md b/docs/my-website/docs/text_to_speech.md
index ea2a9c2eff3..ce298b538df 100644
--- a/docs/my-website/docs/text_to_speech.md
+++ b/docs/my-website/docs/text_to_speech.md
@@ -14,7 +14,7 @@ import TabItem from '@theme/TabItem';
| Fallbacks | ✅ | Works between supported models |
| Loadbalancing | ✅ | Works between supported models |
| Guardrails | ✅ | Applies to input text (non-streaming only) |
-| Supported Providers | OpenAI, Azure OpenAI, Vertex AI | |
+| Supported Providers | OpenAI, Azure OpenAI, Vertex AI, AWS Polly, ElevenLabs | |
## **LiteLLM Python SDK Usage**
### Quick Start
@@ -101,6 +101,7 @@ litellm --config /path/to/config.yaml
| OpenAI | [Usage](#quick-start) |
| Azure OpenAI| [Usage](../docs/providers/azure#azure-text-to-speech-tts) |
| Azure AI Speech Service (AVA)| [Usage](../docs/providers/azure_ai_speech) |
+| AWS Polly | [Usage](#aws-polly-text-to-speech) |
| Vertex AI | [Usage](../docs/providers/vertex#text-to-speech-apis) |
| Gemini | [Usage](#gemini-text-to-speech) |
| ElevenLabs | [Usage](../docs/providers/elevenlabs#text-to-speech-tts) |
@@ -246,6 +247,12 @@ curl http://0.0.0.0:4000/v1/audio/speech \
--output vertex_speech.mp3
```
+### AWS Polly Text-to-Speech
+
+AWS Polly provides neural and standard text-to-speech engines with support for multiple voices and languages.
+
+See the [AWS Polly provider documentation](../docs/providers/aws_polly) for detailed usage examples.
+
## ✨ Enterprise LiteLLM Proxy - Set Max Request File Size
Use this when you want to limit the file size for requests sent to `audio/transcriptions`
diff --git a/docs/my-website/img/secret_manager_hashicorp_vault_settings.png b/docs/my-website/img/secret_manager_hashicorp_vault_settings.png
new file mode 100644
index 00000000000..c471480a3b6
Binary files /dev/null and b/docs/my-website/img/secret_manager_hashicorp_vault_settings.png differ
diff --git a/docs/my-website/img/secret_manager_settings.png b/docs/my-website/img/secret_manager_settings.png
new file mode 100644
index 00000000000..4b01dd43206
Binary files /dev/null and b/docs/my-website/img/secret_manager_settings.png differ
diff --git a/docs/my-website/img/secret_manager_settings_additional_settings.png b/docs/my-website/img/secret_manager_settings_additional_settings.png
new file mode 100644
index 00000000000..713031cb5c5
Binary files /dev/null and b/docs/my-website/img/secret_manager_settings_additional_settings.png differ
diff --git a/docs/my-website/img/secret_manager_settings_create_button.png b/docs/my-website/img/secret_manager_settings_create_button.png
new file mode 100644
index 00000000000..5c08eae8938
Binary files /dev/null and b/docs/my-website/img/secret_manager_settings_create_button.png differ
diff --git a/docs/my-website/img/secret_manager_settings_create_team.png b/docs/my-website/img/secret_manager_settings_create_team.png
new file mode 100644
index 00000000000..b6bd18e4287
Binary files /dev/null and b/docs/my-website/img/secret_manager_settings_create_team.png differ
diff --git a/docs/my-website/img/sentinel.png b/docs/my-website/img/sentinel.png
new file mode 100644
index 00000000000..66c097253c5
Binary files /dev/null and b/docs/my-website/img/sentinel.png differ
diff --git a/docs/my-website/img/ui_cloudzero.png b/docs/my-website/img/ui_cloudzero.png
new file mode 100644
index 00000000000..2ae39ed86d5
Binary files /dev/null and b/docs/my-website/img/ui_cloudzero.png differ
diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json
index a48056491f4..8af06ec1a94 100644
--- a/docs/my-website/package-lock.json
+++ b/docs/my-website/package-lock.json
@@ -180,6 +180,7 @@
"resolved": "https://registry.npmjs.org/@algolia/client-search/-/client-search-5.44.0.tgz",
"integrity": "sha512-/FRKUM1G4xn3vV8+9xH1WJ9XknU8rkBGlefruq9jDhYUAvYozKimhrmC2pRqw/RyHhPivmgZCRuC8jHP8piz4Q==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@algolia/client-common": "5.44.0",
"@algolia/requester-browser-xhr": "5.44.0",
@@ -327,6 +328,7 @@
"resolved": "https://registry.npmjs.org/@babel/core/-/core-7.28.5.tgz",
"integrity": "sha512-e7jT4DxYvIDLk1ZHmU/m/mB19rex9sv0c2ftBtjSBv+kVM/902eh0fINUzD7UwLLNR+jU585GxUJ8/EBfAM5fw==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@babel/code-frame": "^7.27.1",
"@babel/generator": "^7.28.5",
@@ -2161,6 +2163,7 @@
}
],
"license": "MIT",
+ "peer": true,
"engines": {
"node": ">=18"
},
@@ -2183,6 +2186,7 @@
}
],
"license": "MIT",
+ "peer": true,
"engines": {
"node": ">=18"
}
@@ -2292,6 +2296,7 @@
"resolved": "https://registry.npmjs.org/postcss-selector-parser/-/postcss-selector-parser-7.1.0.tgz",
"integrity": "sha512-8sLjZwK0R+JlxlYcTuVnyT2v+htpdrjDOKuMcOVdYjt52Lh8hWRYpxBPoKx/Zg+bcjc3wx6fmQevMmUztS/ccA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"cssesc": "^3.0.0",
"util-deprecate": "^1.0.2"
@@ -2713,6 +2718,7 @@
"resolved": "https://registry.npmjs.org/postcss-selector-parser/-/postcss-selector-parser-7.1.0.tgz",
"integrity": "sha512-8sLjZwK0R+JlxlYcTuVnyT2v+htpdrjDOKuMcOVdYjt52Lh8hWRYpxBPoKx/Zg+bcjc3wx6fmQevMmUztS/ccA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"cssesc": "^3.0.0",
"util-deprecate": "^1.0.2"
@@ -3589,6 +3595,7 @@
"resolved": "https://registry.npmjs.org/@docusaurus/plugin-content-docs/-/plugin-content-docs-3.8.1.tgz",
"integrity": "sha512-oByRkSZzeGNQByCMaX+kif5Nl2vmtj2IHQI2fWjCfCootsdKZDPFLonhIp5s3IGJO7PLUfe0POyw0Xh/RrGXJA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@docusaurus/core": "3.8.1",
"@docusaurus/logger": "3.8.1",
@@ -4627,6 +4634,7 @@
"resolved": "https://registry.npmjs.org/@mdx-js/react/-/react-3.1.1.tgz",
"integrity": "sha512-f++rKLQgUVYDAtECQ6fn/is15GkEH9+nZPM3MS0RcxVqoTfawHvDlSCH7JbMhAM6uJ32v3eXLvLmLvjGu7PTQw==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@types/mdx": "^2.0.0"
},
@@ -7183,6 +7191,7 @@
"resolved": "https://registry.npmjs.org/@svgr/core/-/core-8.1.0.tgz",
"integrity": "sha512-8QqtOQT5ACVlmsvKOJNEaWmRPmcojMOzCz4Hs2BGG/toAp/K38LcsMRyLp349glq5AzJbCEeimEoxaX6v/fLrA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@babel/core": "^7.21.3",
"@svgr/babel-preset": "8.1.0",
@@ -7840,6 +7849,7 @@
"resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.6.tgz",
"integrity": "sha512-p/jUvulfgU7oKtj6Xpk8cA2Y1xKTtICGpJYeJXz2YVO2UcvjQgeRMLDGfDeqeRW2Ta+0QNFwcc8X3GH8SxZz6w==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"csstype": "^3.2.2"
}
@@ -8264,6 +8274,7 @@
"resolved": "https://registry.npmjs.org/acorn/-/acorn-8.15.0.tgz",
"integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
"license": "MIT",
+ "peer": true,
"bin": {
"acorn": "bin/acorn"
},
@@ -8343,6 +8354,7 @@
"resolved": "https://registry.npmjs.org/ajv/-/ajv-8.17.1.tgz",
"integrity": "sha512-B/gBuNg5SiMTrPkC+A2+cW0RszwxYmn6VYxB/inlBStS5nx6xHIt/ehKRhIMhqusl7a8LjQoZnjCs5vhwxOQ1g==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"fast-deep-equal": "^3.1.3",
"fast-uri": "^3.0.1",
@@ -8388,6 +8400,7 @@
"resolved": "https://registry.npmjs.org/algoliasearch/-/algoliasearch-5.44.0.tgz",
"integrity": "sha512-f8IpsbdQjzTjr/4mJ/jv5UplrtyMnnciGax6/B0OnLCs2/GJTK13O4Y7Ff1AvJVAaztanH+m5nzPoUq6EAy+aA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@algolia/abtesting": "1.10.0",
"@algolia/client-abtesting": "5.44.0",
@@ -8421,9 +8434,9 @@
}
},
"node_modules/altcha-lib": {
- "version": "1.3.0",
- "resolved": "https://registry.npmjs.org/altcha-lib/-/altcha-lib-1.3.0.tgz",
- "integrity": "sha512-PpFg/JPuR+Jiud7Vs54XSDqDxvylcp+0oDa/i1ARxBA/iKDqLeNlO8PorQbfuDTMVLYRypAa/2VDK3nbBTAu5A==",
+ "version": "1.4.1",
+ "resolved": "https://registry.npmjs.org/altcha-lib/-/altcha-lib-1.4.1.tgz",
+ "integrity": "sha512-MAXP9tkQOA2SE9Gwoe3LAcZbcDpp3XzYc5GDVej/y3eMNaFG/eVnRY1/7SGFW0RPsViEjPf+hi5eANjuZrH1xA==",
"license": "MIT"
},
"node_modules/ansi-align": {
@@ -9029,6 +9042,7 @@
}
],
"license": "MIT",
+ "peer": true,
"dependencies": {
"baseline-browser-mapping": "^2.8.25",
"caniuse-lite": "^1.0.30001754",
@@ -9364,6 +9378,7 @@
"resolved": "https://registry.npmjs.org/chevrotain/-/chevrotain-11.0.3.tgz",
"integrity": "sha512-ci2iJH6LeIkvP9eJW6gpueU8cnZhv85ELY8w8WiFtNjMHA5ad6pQLaJo9mEly/9qUyCpvqX8/POVUTf18/HFdw==",
"license": "Apache-2.0",
+ "peer": true,
"dependencies": {
"@chevrotain/cst-dts-gen": "11.0.3",
"@chevrotain/gast": "11.0.3",
@@ -10127,6 +10142,7 @@
"resolved": "https://registry.npmjs.org/postcss-selector-parser/-/postcss-selector-parser-7.1.0.tgz",
"integrity": "sha512-8sLjZwK0R+JlxlYcTuVnyT2v+htpdrjDOKuMcOVdYjt52Lh8hWRYpxBPoKx/Zg+bcjc3wx6fmQevMmUztS/ccA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"cssesc": "^3.0.0",
"util-deprecate": "^1.0.2"
@@ -10446,6 +10462,7 @@
"resolved": "https://registry.npmjs.org/cytoscape/-/cytoscape-3.33.1.tgz",
"integrity": "sha512-iJc4TwyANnOGR1OmWhsS9ayRS3s+XQ185FmuHObThD+5AeJCakAAbWv8KimMTt08xCCLNgneQwFp+JRJOr9qGQ==",
"license": "MIT",
+ "peer": true,
"engines": {
"node": ">=0.10"
}
@@ -10855,6 +10872,7 @@
"resolved": "https://registry.npmjs.org/d3-selection/-/d3-selection-3.0.0.tgz",
"integrity": "sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==",
"license": "ISC",
+ "peer": true,
"engines": {
"node": ">=12"
}
@@ -12111,6 +12129,7 @@
"resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz",
"integrity": "sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"fast-deep-equal": "^3.1.1",
"fast-json-stable-stringify": "^2.0.0",
@@ -16990,6 +17009,7 @@
"resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz",
"integrity": "sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"fast-deep-equal": "^3.1.1",
"fast-json-stable-stringify": "^2.0.0",
@@ -17610,6 +17630,7 @@
}
],
"license": "MIT",
+ "peer": true,
"dependencies": {
"nanoid": "^3.3.11",
"picocolors": "^1.1.1",
@@ -18513,6 +18534,7 @@
"resolved": "https://registry.npmjs.org/postcss-selector-parser/-/postcss-selector-parser-7.1.0.tgz",
"integrity": "sha512-8sLjZwK0R+JlxlYcTuVnyT2v+htpdrjDOKuMcOVdYjt52Lh8hWRYpxBPoKx/Zg+bcjc3wx6fmQevMmUztS/ccA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"cssesc": "^3.0.0",
"util-deprecate": "^1.0.2"
@@ -19404,6 +19426,7 @@
"resolved": "https://registry.npmjs.org/react/-/react-19.2.0.tgz",
"integrity": "sha512-tmbWg6W31tQLeB5cdIBOicJDJRR2KzXsV7uSK9iNfLWQ5bIZfxuPEHp7M8wiHyHnn0DD1i7w3Zmin0FtkrwoCQ==",
"license": "MIT",
+ "peer": true,
"engines": {
"node": ">=0.10.0"
}
@@ -19413,6 +19436,7 @@
"resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.2.0.tgz",
"integrity": "sha512-UlbRu4cAiGaIewkPyiRGJk0imDN2T3JjieT6spoL2UeSf5od4n5LB/mQ4ejmxhCFT1tYe8IvaFulzynWovsEFQ==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"scheduler": "^0.27.0"
},
@@ -19496,6 +19520,7 @@
"resolved": "https://registry.npmjs.org/@docusaurus/react-loadable/-/react-loadable-6.0.0.tgz",
"integrity": "sha512-YMMxTUQV/QFSnbgrP3tjDzLHRg7vsbMn8e9HAa8o/1iXoiomo48b7sk/kkmWEuWNDPJVlKSJRB6Y2fHqdJk+SQ==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@types/react": "*"
},
@@ -19597,6 +19622,7 @@
"resolved": "https://registry.npmjs.org/react-router/-/react-router-5.3.4.tgz",
"integrity": "sha512-Ys9K+ppnJah3QuaRiLxk+jDWOR1MekYQrlytiXxC1RyfbdsZkS5pvKAzCCr031xHixZwpnsYNT5xysdFHQaYsA==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@babel/runtime": "^7.12.13",
"history": "^4.9.0",
@@ -21615,7 +21641,8 @@
"version": "2.8.1",
"resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz",
"integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
- "license": "0BSD"
+ "license": "0BSD",
+ "peer": true
},
"node_modules/tunnel-agent": {
"version": "0.6.0",
@@ -22002,6 +22029,7 @@
"resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz",
"integrity": "sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"fast-deep-equal": "^3.1.1",
"fast-json-stable-stringify": "^2.0.0",
@@ -22353,6 +22381,7 @@
"resolved": "https://registry.npmjs.org/webpack/-/webpack-5.103.0.tgz",
"integrity": "sha512-HU1JOuV1OavsZ+mfigY0j8d1TgQgbZ6M+J75zDkpEAwYeXjWSqrGJtgnPblJjd/mAyTNQ7ygw0MiKOn6etz8yw==",
"license": "MIT",
+ "peer": true,
"dependencies": {
"@types/eslint-scope": "^3.7.7",
"@types/estree": "^1.0.8",
diff --git a/docs/my-website/release_notes/v1.80.11-stable/index.md b/docs/my-website/release_notes/v1.80.11-stable/index.md
new file mode 100644
index 00000000000..b671b795602
--- /dev/null
+++ b/docs/my-website/release_notes/v1.80.11-stable/index.md
@@ -0,0 +1,385 @@
+---
+title: "[Preview] v1.80.11 - Google Interactions API"
+slug: "v1-80-11"
+date: 2025-12-20T10:00:00
+authors:
+ - name: Krrish Dholakia
+ title: CEO, LiteLLM
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+ - name: Ishaan Jaff
+ title: CTO, LiteLLM
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Deploy this version
+
+
+
+
+``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+docker.litellm.ai/berriai/litellm:v1.80.11.rc.1
+```
+
+
+
+
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==1.80.11
+```
+
+
+
+
+---
+
+## Key Highlights
+
+- **Gemini 3 Flash Preview** - [Day 0 support for Google's Gemini 3 Flash Preview with reasoning capabilities](../../docs/providers/gemini)
+- **Stability AI Image Generation** - [New provider for Stability AI image generation and editing](../../docs/providers/stability)
+- **LiteLLM Content Filter** - [Built-in guardrails for harmful content, bias, and PII detection with image support](../../docs/proxy/guardrails/litellm_content_filter)
+- **New Provider: Venice.ai** - Support for Venice.ai API via providers.json
+- **Unified Skills API** - [Skills API works across Anthropic, Vertex, Azure, and Bedrock](../../docs/skills)
+- **Azure Sentinel Logging** - [New logging integration for Azure Sentinel](../../docs/observability/azure_sentinel)
+- **Guardrails Load Balancing** - [Load balance between multiple guardrail providers](../../docs/proxy/guardrails)
+- **Email Budget Alerts** - [Send email notifications when budgets are reached](../../docs/proxy/email)
+- **Cloudzero Integration on UI** - Setup your Cloudzero Integration Directly on the UI
+
+---
+
+### Cloudzero Integration on UI
+
+
+
+Users can now configure their Cloudzero Integration directly on the UI.
+
+---
+### Performance: 50% Reduction in Memory Usage and Import Latency for the LiteLLM SDK
+
+We've completely restructured `litellm.__init__.py` to defer heavy imports until they're actually needed, implementing lazy loading for **109 components**.
+
+This refactoring includes **41 provider config classes**, **40 utility functions**, cache implementations (Redis, DualCache, InMemoryCache), HTTP handlers, logging, types, and other heavy dependencies. Heavy libraries like tiktoken and boto3 are now loaded on-demand rather than eagerly at import time.
+
+This makes LiteLLM especially beneficial for serverless functions, Lambda deployments, and containerized environments where cold start times and memory footprint matter.
+
+---
+
+## New Providers and Endpoints
+
+### New Providers (5 new providers)
+
+| Provider | Supported LiteLLM Endpoints | Description |
+| -------- | ------------------- | ----------- |
+| [Stability AI](../../docs/providers/stability) | `/images/generations`, `/images/edits` | Stable Diffusion 3, SD3.5, image editing and generation |
+| Venice.ai | `/chat/completions`, `/messages`, `/responses` | Venice.ai API integration via providers.json |
+| [Pydantic AI Agents](../../docs/providers/pydantic_ai_agent) | `/a2a` | Pydantic AI agents for A2A protocol workflows |
+| [VertexAI Agent Engine](../../docs/providers/vertex_ai_agent_engine) | `/a2a` | Google Vertex AI Agent Engine for agentic workflows |
+| [LinkUp Search](../../docs/search/linkup) | `/search` | LinkUp web search API integration |
+
+### New LLM API Endpoints (2 new endpoints)
+
+| Endpoint | Method | Description | Documentation |
+| -------- | ------ | ----------- | ------------- |
+| `/interactions` | POST | Google Interactions API for conversational AI | [Docs](../../docs/interactions) |
+| `/search` | POST | RAG Search API with rerankers | [Docs](../../docs/search/index) |
+
+---
+
+## New Models / Updated Models
+
+#### New Model Support (55+ new models)
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
+| Gemini | `gemini/gemini-3-flash-preview` | 1M | $0.50 | $3.00 | Reasoning, vision, audio, video, PDF |
+| Vertex AI | `vertex_ai/gemini-3-flash-preview` | 1M | $0.50 | $3.00 | Reasoning, vision, audio, video, PDF |
+| Azure AI | `azure_ai/deepseek-v3.2` | 164K | $0.58 | $1.68 | Reasoning, function calling, caching |
+| Azure AI | `azure_ai/cohere-rerank-v4.0-pro` | 32K | $0.0025/query | - | Rerank |
+| Azure AI | `azure_ai/cohere-rerank-v4.0-fast` | 32K | $0.002/query | - | Rerank |
+| OpenRouter | `openrouter/openai/gpt-5.2` | 400K | $1.75 | $14.00 | Reasoning, vision, caching |
+| OpenRouter | `openrouter/openai/gpt-5.2-pro` | 400K | $21.00 | $168.00 | Reasoning, vision |
+| OpenRouter | `openrouter/mistralai/devstral-2512` | 262K | $0.15 | $0.60 | Function calling |
+| OpenRouter | `openrouter/mistralai/ministral-3b-2512` | 131K | $0.10 | $0.10 | Function calling, vision |
+| OpenRouter | `openrouter/mistralai/ministral-8b-2512` | 262K | $0.15 | $0.15 | Function calling, vision |
+| OpenRouter | `openrouter/mistralai/ministral-14b-2512` | 262K | $0.20 | $0.20 | Function calling, vision |
+| OpenRouter | `openrouter/mistralai/mistral-large-2512` | 262K | $0.50 | $1.50 | Function calling, vision |
+| OpenAI | `gpt-4o-transcribe-diarize` | 16K | $6.00/audio | - | Audio transcription with diarization |
+| OpenAI | `gpt-image-1.5-2025-12-16` | - | Various | Various | Image generation |
+| Stability | `stability/sd3-large` | - | - | $0.065/image | Image generation |
+| Stability | `stability/sd3.5-large` | - | - | $0.065/image | Image generation |
+| Stability | `stability/stable-image-ultra` | - | - | $0.08/image | Image generation |
+| Stability | `stability/inpaint` | - | - | $0.005/image | Image editing |
+| Stability | `stability/outpaint` | - | - | $0.004/image | Image editing |
+| Bedrock | `stability.stable-conservative-upscale-v1:0` | - | - | $0.40/image | Image upscaling |
+| Bedrock | `stability.stable-creative-upscale-v1:0` | - | - | $0.60/image | Image upscaling |
+| Vertex AI | `vertex_ai/deepseek-ai/deepseek-ocr-maas` | - | $0.30 | $1.20 | OCR |
+| LinkUp | `linkup/search` | - | $5.87/1K queries | - | Web search |
+| LinkUp | `linkup/search-deep` | - | $58.67/1K queries | - | Deep web search |
+| GitHub Copilot | 20+ models | Various | - | - | Chat completions |
+
+#### Features
+
+- **[Gemini](../../docs/providers/gemini)**
+ - Add Gemini 3 Flash Preview day 0 support with reasoning - [PR #18135](https://github.com/BerriAI/litellm/pull/18135)
+ - Support extra_headers in batch embeddings - [PR #18004](https://github.com/BerriAI/litellm/pull/18004)
+ - Propagate token usage when generating images - [PR #17987](https://github.com/BerriAI/litellm/pull/17987)
+ - Use JSON instead of form-data for image edit requests - [PR #18012](https://github.com/BerriAI/litellm/pull/18012)
+ - Fix web search requests count - [PR #17921](https://github.com/BerriAI/litellm/pull/17921)
+- **[Anthropic](../../docs/providers/anthropic)**
+ - Use dynamic max_tokens based on model - [PR #17900](https://github.com/BerriAI/litellm/pull/17900)
+ - Fix claude-3-7-sonnet max_tokens to 64K default - [PR #17979](https://github.com/BerriAI/litellm/pull/17979)
+ - Add OpenAI-compatible API with modify_params=True - [PR #17106](https://github.com/BerriAI/litellm/pull/17106)
+- **[Vertex AI](../../docs/providers/vertex)**
+ - Add Gemini 3 Flash Preview support - [PR #18164](https://github.com/BerriAI/litellm/pull/18164)
+ - Add reasoning support for gemini-3-flash-preview - [PR #18175](https://github.com/BerriAI/litellm/pull/18175)
+ - Fix image edit credential source - [PR #18121](https://github.com/BerriAI/litellm/pull/18121)
+ - Pass credentials to PredictionServiceClient for custom endpoints - [PR #17757](https://github.com/BerriAI/litellm/pull/17757)
+ - Fix multimodal embeddings for text + base64 image combinations - [PR #18172](https://github.com/BerriAI/litellm/pull/18172)
+ - Add OCR support for DeepSeek model - [PR #17971](https://github.com/BerriAI/litellm/pull/17971)
+- **[Azure AI](../../docs/providers/azure_ai)**
+ - Add Azure Cohere 4 reranking models - [PR #17961](https://github.com/BerriAI/litellm/pull/17961)
+ - Add Azure DeepSeek V3.2 versions - [PR #18019](https://github.com/BerriAI/litellm/pull/18019)
+ - Return AzureAnthropicConfig for Claude models in get_provider_chat_config - [PR #18086](https://github.com/BerriAI/litellm/pull/18086)
+- **[Fireworks AI](../../docs/providers/fireworks_ai)**
+ - Add reasoning param support for Fireworks AI models - [PR #17967](https://github.com/BerriAI/litellm/pull/17967)
+- **[Bedrock](../../docs/providers/bedrock)**
+ - Add Qwen 2 and Qwen 3 to get_bedrock_model_id - [PR #18100](https://github.com/BerriAI/litellm/pull/18100)
+ - Remove ttl field when routing to bedrock - [PR #18049](https://github.com/BerriAI/litellm/pull/18049)
+ - Add Bedrock Stability image edit models - [PR #18254](https://github.com/BerriAI/litellm/pull/18254)
+- **[Perplexity](../../docs/providers/perplexity)**
+ - Use API-provided cost instead of manual calculation - [PR #17887](https://github.com/BerriAI/litellm/pull/17887)
+- **[OpenAI](../../docs/providers/openai)**
+ - Add diarize model for audio transcription - [PR #18117](https://github.com/BerriAI/litellm/pull/18117)
+ - Add gpt-image-1.5-2025-12-16 in model cost map - [PR #18107](https://github.com/BerriAI/litellm/pull/18107)
+ - Fix cost calculation of gpt-image-1 model - [PR #17966](https://github.com/BerriAI/litellm/pull/17966)
+- **[GitHub Copilot](../../docs/providers/github_copilot)**
+ - Add github_copilot model info - [PR #17858](https://github.com/BerriAI/litellm/pull/17858)
+- **[Custom LLM](../../docs/providers/custom_llm_server)**
+ - Add image_edit and aimage_edit support - [PR #17999](https://github.com/BerriAI/litellm/pull/17999)
+
+### Bug Fixes
+
+- **[Gemini](../../docs/providers/gemini)**
+ - Fix pricing for Gemini 3 Flash on Vertex AI - [PR #18202](https://github.com/BerriAI/litellm/pull/18202)
+ - Add output_cost_per_image_token for gemini-2.5-flash-image models - [PR #18156](https://github.com/BerriAI/litellm/pull/18156)
+ - Fix properties should be non-empty for OBJECT type - [PR #18237](https://github.com/BerriAI/litellm/pull/18237)
+- **[Qwen](../../docs/providers/fireworks_ai)**
+ - Add qwen3-embedding-8b input per token price - [PR #18018](https://github.com/BerriAI/litellm/pull/18018)
+- **General**
+ - Fix image URL handling - [PR #18139](https://github.com/BerriAI/litellm/pull/18139)
+ - Support Signed URLs with Query Parameters in Image Processing - [PR #17976](https://github.com/BerriAI/litellm/pull/17976)
+ - Add none to encoding_format instead of omitting it - [PR #18042](https://github.com/BerriAI/litellm/pull/18042)
+
+---
+
+## LLM API Endpoints
+
+#### Features
+
+- **[Responses API](../../docs/response_api)**
+ - Add provider specific tools support - [PR #17980](https://github.com/BerriAI/litellm/pull/17980)
+ - Add custom headers support - [PR #18036](https://github.com/BerriAI/litellm/pull/18036)
+ - Fix tool calls transformation in completion bridge - [PR #18226](https://github.com/BerriAI/litellm/pull/18226)
+ - Use list format with input_text for tool results - [PR #18257](https://github.com/BerriAI/litellm/pull/18257)
+ - Add cost tracking in background mode - [PR #18236](https://github.com/BerriAI/litellm/pull/18236)
+ - Fix Claude code responses API bridge errors - [PR #18194](https://github.com/BerriAI/litellm/pull/18194)
+- **[Chat Completions API](../../docs/completion/input)**
+ - Add support for agent skills - [PR #18031](https://github.com/BerriAI/litellm/pull/18031)
+- **[Skills API](../../docs/skills)**
+ - Unified Skills API works across Anthropic, Vertex, Azure, Bedrock - [PR #18232](https://github.com/BerriAI/litellm/pull/18232)
+- **[Search API](../../docs/search/index)**
+ - Add new RAG Search API with rerankers - [PR #18217](https://github.com/BerriAI/litellm/pull/18217)
+- **[Interactions API](../../docs/interactions)**
+ - Add Google Interactions API on SDK and AI Gateway - [PR #18079](https://github.com/BerriAI/litellm/pull/18079), [PR #18081](https://github.com/BerriAI/litellm/pull/18081)
+- **[Image Edit API](../../docs/image_edits)**
+ - Add drop_params support and fix Vertex AI config - [PR #18077](https://github.com/BerriAI/litellm/pull/18077)
+- **General**
+ - Skip adding beta headers for Vertex AI as it is not supported - [PR #18037](https://github.com/BerriAI/litellm/pull/18037)
+ - Fix managed files endpoint - [PR #18046](https://github.com/BerriAI/litellm/pull/18046)
+ - Allow base_model for non-Azure providers in proxy - [PR #18038](https://github.com/BerriAI/litellm/pull/18038)
+
+#### Bugs
+
+- **General**
+ - Fix basemodel import in guardrail translation - [PR #17977](https://github.com/BerriAI/litellm/pull/17977)
+ - Fix No module named 'fastapi' error - [PR #18239](https://github.com/BerriAI/litellm/pull/18239)
+
+---
+
+## Management Endpoints / UI
+
+#### Features
+
+- **Virtual Keys**
+ - Add master key rotation for credentials table - [PR #17952](https://github.com/BerriAI/litellm/pull/17952)
+ - Fix tag management to preserve encrypted fields in litellm_params - [PR #17484](https://github.com/BerriAI/litellm/pull/17484)
+ - Fix key delete and regenerate permissions - [PR #18214](https://github.com/BerriAI/litellm/pull/18214)
+- **Models + Endpoints**
+ - Add Models Conditional Rendering in UI - [PR #18071](https://github.com/BerriAI/litellm/pull/18071)
+ - Add Health Check Model for Wildcard Model in UI - [PR #18269](https://github.com/BerriAI/litellm/pull/18269)
+ - Auto Resolve Vector Store Embedding Model Config - [PR #18167](https://github.com/BerriAI/litellm/pull/18167)
+- **Vector Stores**
+ - Add Milvus Vector Store UI support - [PR #18030](https://github.com/BerriAI/litellm/pull/18030)
+ - Persist Vector Store Settings in Team Update - [PR #18274](https://github.com/BerriAI/litellm/pull/18274)
+- **Logs & Spend**
+ - Add LiteLLM Overhead to Logs - [PR #18033](https://github.com/BerriAI/litellm/pull/18033)
+ - Show LiteLLM Overhead in Logs UI - [PR #18034](https://github.com/BerriAI/litellm/pull/18034)
+ - Resolve Team ID to Team Alias in Usage Page - [PR #18275](https://github.com/BerriAI/litellm/pull/18275)
+ - Fix Usage Page Top Key View Button Visibility - [PR #18203](https://github.com/BerriAI/litellm/pull/18203)
+- **SSO & Health**
+ - Add SSO Readiness Health Check - [PR #18078](https://github.com/BerriAI/litellm/pull/18078)
+ - Fix /health/test_connection to resolve env variables like /chat/completions - [PR #17752](https://github.com/BerriAI/litellm/pull/17752)
+- **CloudZero**
+ - Add CloudZero Cost Tracking UI - [PR #18163](https://github.com/BerriAI/litellm/pull/18163)
+ - Add Delete CloudZero Settings Route and UI - [PR #18168](https://github.com/BerriAI/litellm/pull/18168), [PR #18170](https://github.com/BerriAI/litellm/pull/18170)
+- **General**
+ - Update UI path handling for non-root Docker - [PR #17989](https://github.com/BerriAI/litellm/pull/17989)
+
+#### Bugs
+
+- **UI Fixes**
+ - Fix Login Page Failed To Parse JSON Error - [PR #18159](https://github.com/BerriAI/litellm/pull/18159)
+ - Fix new user route user_id collision handling - [PR #17559](https://github.com/BerriAI/litellm/pull/17559)
+ - Fix Callback Environment Variables Casing - [PR #17912](https://github.com/BerriAI/litellm/pull/17912)
+
+---
+
+## AI Integrations
+
+### Logging
+
+- **[Azure Sentinel](../../docs/observability/azure_sentinel)**
+ - Add new Azure Sentinel Logger integration - [PR #18146](https://github.com/BerriAI/litellm/pull/18146)
+- **[Prometheus](../../docs/proxy/logging#prometheus)**
+ - Add extraction of top level metadata for custom labels - [PR #18087](https://github.com/BerriAI/litellm/pull/18087)
+- **[Langfuse](../../docs/proxy/logging#langfuse)**
+ - Fix not working log_failure_event - [PR #18234](https://github.com/BerriAI/litellm/pull/18234)
+- **[Arize Phoenix](../../docs/observability/phoenix_integration)**
+ - Fix nested spans - [PR #18102](https://github.com/BerriAI/litellm/pull/18102)
+- **General**
+ - Change extra_headers to additional_headers - [PR #17950](https://github.com/BerriAI/litellm/pull/17950)
+
+### Guardrails
+
+- **[LiteLLM Content Filter](../../docs/proxy/guardrails/litellm_content_filter)**
+ - Add built-in guardrails for harmful content, bias, etc. - [PR #18029](https://github.com/BerriAI/litellm/pull/18029)
+ - Add support for running content filters on images - [PR #18044](https://github.com/BerriAI/litellm/pull/18044)
+ - Add support for Brazil PII field - [PR #18076](https://github.com/BerriAI/litellm/pull/18076)
+ - Add configurable guardrail options for content filtering - [PR #18007](https://github.com/BerriAI/litellm/pull/18007)
+- **[Guardrails API](../../docs/adding_provider/generic_guardrail_api)**
+ - Support LLM tool call response checks on `/chat/completions`, `/v1/responses`, `/v1/messages` - [PR #17619](https://github.com/BerriAI/litellm/pull/17619)
+ - Add guardrails load balancing - [PR #18181](https://github.com/BerriAI/litellm/pull/18181)
+ - Fix guardrails for passthrough endpoint - [PR #18109](https://github.com/BerriAI/litellm/pull/18109)
+ - Add headers to metadata for guardrails on pass-through endpoints - [PR #17992](https://github.com/BerriAI/litellm/pull/17992)
+ - Various fixes for guardrail on OpenRouter models - [PR #18085](https://github.com/BerriAI/litellm/pull/18085)
+- **[Lakera](../../docs/proxy/guardrails/lakera_ai)**
+ - Add monitor mode for Lakera - [PR #18084](https://github.com/BerriAI/litellm/pull/18084)
+- **[Pillar Security](../../docs/proxy/guardrails/pillar_security)**
+ - Add masking support and MCP call support - [PR #17959](https://github.com/BerriAI/litellm/pull/17959)
+- **[Bedrock Guardrails](../../docs/proxy/guardrails/bedrock)**
+ - Add support for Bedrock image guardrails - [PR #18115](https://github.com/BerriAI/litellm/pull/18115)
+ - Guardrails block action takes precedence over masking - [PR #17968](https://github.com/BerriAI/litellm/pull/17968)
+
+### Secret Managers
+
+- **[HashiCorp Vault](../../docs/secret_managers/hashicorp_vault)**
+ - Add documentation for configurable Vault mount - [PR #18082](https://github.com/BerriAI/litellm/pull/18082)
+ - Add per-team Vault configuration - [PR #18150](https://github.com/BerriAI/litellm/pull/18150)
+- **UI**
+ - Add secret manager settings controls to team management UI - [PR #18149](https://github.com/BerriAI/litellm/pull/18149)
+
+---
+
+## Spend Tracking, Budgets and Rate Limiting
+
+- **Email Budget Alerts** - Send email notifications when budgets are reached - [PR #17995](https://github.com/BerriAI/litellm/pull/17995)
+
+---
+
+## MCP Gateway
+
+- **Auth Header Propagation** - Add MCP auth header propagation - [PR #17963](https://github.com/BerriAI/litellm/pull/17963)
+- **Fix deepcopy error** - Fix MCP tool call deepcopy error when processing requests - [PR #18010](https://github.com/BerriAI/litellm/pull/18010)
+- **Fix list tool** - Fix MCP list_tools not working without database connection - [PR #18161](https://github.com/BerriAI/litellm/pull/18161)
+
+---
+
+## Agent Gateway (A2A)
+
+- **New Provider: Agent Gateway** - Add pydantic ai agents support - [PR #18013](https://github.com/BerriAI/litellm/pull/18013)
+- **VertexAI Agent Engine** - Add Vertex AI Agent Engine provider - [PR #18014](https://github.com/BerriAI/litellm/pull/18014)
+- **Fix model extraction** - Fix get_model_from_request() to extract model ID from Vertex AI passthrough URLs - [PR #18097](https://github.com/BerriAI/litellm/pull/18097)
+
+---
+
+## Performance / Loadbalancing / Reliability improvements
+
+- **Lazy Imports** - Use per-attribute lazy imports and extract shared constants - [PR #17994](https://github.com/BerriAI/litellm/pull/17994)
+- **Lazy Load HTTP Handlers** - Lazy load http handlers - [PR #17997](https://github.com/BerriAI/litellm/pull/17997)
+- **Lazy Load Caches** - Lazy load caches - [PR #18001](https://github.com/BerriAI/litellm/pull/18001)
+- **Lazy Load Types** - Lazy load bedrock types, .types.utils, GuardrailItem - [PR #18053](https://github.com/BerriAI/litellm/pull/18053), [PR #18054](https://github.com/BerriAI/litellm/pull/18054), [PR #18072](https://github.com/BerriAI/litellm/pull/18072)
+- **Lazy Load Configs** - Lazy load 41 configuration classes - [PR #18267](https://github.com/BerriAI/litellm/pull/18267)
+- **Lazy Load Client Decorators** - Lazy load heavy client decorator imports - [PR #18064](https://github.com/BerriAI/litellm/pull/18064)
+- **Prisma Build Time** - Download Prisma binaries at build time instead of runtime for security restricted environments - [PR #17695](https://github.com/BerriAI/litellm/pull/17695)
+- **Docker Alpine** - Add libsndfile to Alpine image for ARM64 audio processing - [PR #18092](https://github.com/BerriAI/litellm/pull/18092)
+- **Security** - Prevent LiteLLM API key leakage on /health endpoint failures - [PR #18133](https://github.com/BerriAI/litellm/pull/18133)
+
+---
+
+## Documentation Updates
+
+- **SAP Docs** - Update SAP documentation - [PR #17974](https://github.com/BerriAI/litellm/pull/17974)
+- **Pydantic AI Agents** - Add docs on using pydantic ai agents with LiteLLM A2A gateway - [PR #18026](https://github.com/BerriAI/litellm/pull/18026)
+- **Vertex AI Agent Engine** - Add Vertex AI Agent Engine documentation - [PR #18027](https://github.com/BerriAI/litellm/pull/18027)
+- **Router Order** - Add router order parameter documentation - [PR #18045](https://github.com/BerriAI/litellm/pull/18045)
+- **Secret Manager Settings** - Improve secret manager settings documentation - [PR #18235](https://github.com/BerriAI/litellm/pull/18235)
+- **Gemini 3 Flash** - Add version requirement in Gemini 3 Flash blog - [PR #18227](https://github.com/BerriAI/litellm/pull/18227)
+- **README** - Expand Responses API section and update endpoints - [PR #17354](https://github.com/BerriAI/litellm/pull/17354)
+- **Amazon Nova** - Add Amazon Nova to sidebar and supported models - [PR #18220](https://github.com/BerriAI/litellm/pull/18220)
+- **Benchmarks** - Add infrastructure recommendations to benchmarks documentation - [PR #18264](https://github.com/BerriAI/litellm/pull/18264)
+- **Broken Links** - Fix broken link corrections - [PR #18104](https://github.com/BerriAI/litellm/pull/18104)
+- **README Fixes** - Various README improvements - [PR #18206](https://github.com/BerriAI/litellm/pull/18206)
+
+---
+
+## Infrastructure / CI/CD
+
+- **PR Templates** - Add LiteLLM team PR template and CI/CD rules - [PR #17983](https://github.com/BerriAI/litellm/pull/17983), [PR #17985](https://github.com/BerriAI/litellm/pull/17985)
+- **Issue Labeling** - Improve issue labeling with component dropdown and more provider keywords - [PR #17957](https://github.com/BerriAI/litellm/pull/17957)
+- **PR Template Cleanup** - Remove redundant fields from PR template - [PR #17956](https://github.com/BerriAI/litellm/pull/17956)
+- **Dependencies** - Bump altcha-lib from 1.3.0 to 1.4.1 - [PR #18017](https://github.com/BerriAI/litellm/pull/18017)
+
+---
+
+## New Contributors
+
+* @dongbin-lunark made their first contribution in [PR #17757](https://github.com/BerriAI/litellm/pull/17757)
+* @qdrddr made their first contribution in [PR #18004](https://github.com/BerriAI/litellm/pull/18004)
+* @donicrosby made their first contribution in [PR #17962](https://github.com/BerriAI/litellm/pull/17962)
+* @NicolaivdSmagt made their first contribution in [PR #17992](https://github.com/BerriAI/litellm/pull/17992)
+* @Reapor-Yurnero made their first contribution in [PR #18085](https://github.com/BerriAI/litellm/pull/18085)
+* @jk-f5 made their first contribution in [PR #18086](https://github.com/BerriAI/litellm/pull/18086)
+* @castrapel made their first contribution in [PR #18077](https://github.com/BerriAI/litellm/pull/18077)
+* @dtikhonov made their first contribution in [PR #17484](https://github.com/BerriAI/litellm/pull/17484)
+* @opleonnn made their first contribution in [PR #18175](https://github.com/BerriAI/litellm/pull/18175)
+* @eurogig made their first contribution in [PR #18084](https://github.com/BerriAI/litellm/pull/18084)
+
+---
+
+## Full Changelog
+
+**[View complete changelog on GitHub](https://github.com/BerriAI/litellm/compare/v1.80.10-nightly...v1.80.11)**
+
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index ead5d78e606..b6b8fe1223d 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -42,6 +42,7 @@ const sidebars = {
label: "Guardrails",
items: [
"proxy/guardrails/quick_start",
+ "proxy/guardrails/guardrail_load_balancing",
{
type: "category",
"label": "Contributing to Guardrails",
@@ -52,6 +53,7 @@ const sidebars = {
]
},
"proxy/guardrails/test_playground",
+ "proxy/guardrails/litellm_content_filter",
...[
"proxy/guardrails/aim_security",
"proxy/guardrails/onyx_security",
@@ -63,7 +65,6 @@ const sidebars = {
"proxy/guardrails/grayswan",
"proxy/guardrails/hiddenlayer",
"proxy/guardrails/lasso_security",
- "proxy/guardrails/litellm_content_filter",
"proxy/guardrails/guardrails_ai",
"proxy/guardrails/lakera_ai",
"proxy/guardrails/model_armor",
@@ -288,7 +289,7 @@ const sidebars = {
label: "All Endpoints (Swagger)",
href: "https://litellm-api.up.railway.app/",
},
- "proxy/enterprise",
+ "proxy/enterprise",
{
type: "category",
label: "Authentication",
@@ -469,10 +470,10 @@ const sidebars = {
"proxy/managed_finetuning",
]
},
- "generateContent",
- "apply_guardrail",
- "bedrock_invoke",
- "interactions",
+ "generateContent",
+ "apply_guardrail",
+ "bedrock_invoke",
+ "interactions",
{
type: "category",
label: "/images",
@@ -544,6 +545,7 @@ const sidebars = {
"search/dataforseo",
"search/firecrawl",
"search/searxng",
+ "search/linkup",
]
},
"skills",
@@ -662,6 +664,7 @@ const sidebars = {
"providers/bedrock_agents",
"providers/bedrock_writer",
"providers/bedrock_batches",
+ "providers/aws_polly",
"providers/bedrock_vector_store",
]
},
@@ -669,6 +672,7 @@ const sidebars = {
"providers/ai21",
"providers/aiml",
"providers/aleph_alpha",
+ "providers/amazon_nova",
"providers/anyscale",
"providers/baseten",
"providers/bytez",
@@ -780,6 +784,7 @@ const sidebars = {
]
},
"providers/xai",
+ "providers/xiaomi_mimo",
"providers/xinference",
"providers/zai",
],
diff --git a/enterprise/dist/litellm_enterprise-0.1.26-py3-none-any.whl b/enterprise/dist/litellm_enterprise-0.1.26-py3-none-any.whl
new file mode 100644
index 00000000000..e4cfac65530
Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.26-py3-none-any.whl differ
diff --git a/enterprise/dist/litellm_enterprise-0.1.26.tar.gz b/enterprise/dist/litellm_enterprise-0.1.26.tar.gz
new file mode 100644
index 00000000000..c8e0081ff11
Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.26.tar.gz differ
diff --git a/enterprise/dist/litellm_enterprise-0.1.27-py3-none-any.whl b/enterprise/dist/litellm_enterprise-0.1.27-py3-none-any.whl
new file mode 100644
index 00000000000..0274d62e16e
Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.27-py3-none-any.whl differ
diff --git a/enterprise/dist/litellm_enterprise-0.1.27.tar.gz b/enterprise/dist/litellm_enterprise-0.1.27.tar.gz
new file mode 100644
index 00000000000..d802b5a89d5
Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.27.tar.gz differ
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
index 1fe82c2c188..61e0745bab1 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
@@ -5,7 +5,7 @@ Base class for sending emails to user after creating keys or invite links
import json
import os
-from typing import List, Optional
+from typing import List, Literal, Optional
from litellm_enterprise.types.enterprise_callbacks.send_emails import (
EmailEvent,
@@ -15,6 +15,7 @@ from litellm_enterprise.types.enterprise_callbacks.send_emails import (
)
from litellm._logging import verbose_proxy_logger
+from litellm.caching.caching import DualCache
from litellm.integrations.custom_logger import CustomLogger
from litellm.integrations.email_templates.email_footer import EMAIL_FOOTER
from litellm.integrations.email_templates.key_created_email import (
@@ -26,9 +27,17 @@ from litellm.integrations.email_templates.key_rotated_email import (
from litellm.integrations.email_templates.user_invitation_email import (
USER_INVITATION_EMAIL_TEMPLATE,
)
-from litellm.proxy._types import InvitationNew, UserAPIKeyAuth, WebhookEvent
+from litellm.integrations.email_templates.templates import (
+ MAX_BUDGET_ALERT_EMAIL_TEMPLATE,
+ SOFT_BUDGET_ALERT_EMAIL_TEMPLATE,
+)
+from litellm.proxy._types import CallInfo, InvitationNew, UserAPIKeyAuth, WebhookEvent
from litellm.secret_managers.main import get_secret_bool
from litellm.types.integrations.slack_alerting import LITELLM_LOGO_URL
+from litellm.constants import (
+ EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE,
+ EMAIL_BUDGET_ALERT_TTL,
+)
class BaseEmailLogger(CustomLogger):
@@ -40,6 +49,21 @@ class BaseEmailLogger(CustomLogger):
EmailEvent.virtual_key_rotated: "LiteLLM: {event_message}",
}
+ def __init__(
+ self,
+ internal_usage_cache: Optional[DualCache] = None,
+ **kwargs,
+ ):
+ """
+ Initialize BaseEmailLogger
+
+ Args:
+ internal_usage_cache: DualCache instance for preventing duplicate alerts
+ **kwargs: Additional arguments passed to CustomLogger
+ """
+ super().__init__(**kwargs)
+ self.internal_usage_cache = internal_usage_cache or DualCache()
+
async def send_user_invitation_email(self, event: WebhookEvent):
"""
Send email to user after inviting them to the team
@@ -154,6 +178,218 @@ class BaseEmailLogger(CustomLogger):
)
pass
+ async def send_soft_budget_alert_email(self, event: WebhookEvent):
+ """
+ Send email to user when soft budget is crossed
+ """
+ email_params = await self._get_email_params(
+ email_event=EmailEvent.soft_budget_crossed, # Reuse existing event type for subject template
+ user_id=event.user_id,
+ user_email=event.user_email,
+ event_message=event.event_message,
+ )
+
+ verbose_proxy_logger.debug(
+ f"send_soft_budget_alert_email_event: {json.dumps(event.model_dump(exclude_none=True), indent=4, default=str)}"
+ )
+
+ # Format budget values
+ soft_budget_str = f"${event.soft_budget}" if event.soft_budget is not None else "N/A"
+ spend_str = f"${event.spend}" if event.spend is not None else "$0.00"
+ max_budget_info = ""
+ if event.max_budget is not None:
+ max_budget_info = f"Maximum Budget: ${event.max_budget}
"
+
+ email_html_content = SOFT_BUDGET_ALERT_EMAIL_TEMPLATE.format(
+ email_logo_url=email_params.logo_url,
+ recipient_email=email_params.recipient_email,
+ soft_budget=soft_budget_str,
+ spend=spend_str,
+ max_budget_info=max_budget_info,
+ base_url=email_params.base_url,
+ email_support_contact=email_params.support_contact,
+ )
+ await self.send_email(
+ from_email=self.DEFAULT_LITELLM_EMAIL,
+ to_email=[email_params.recipient_email],
+ subject=email_params.subject,
+ html_body=email_html_content,
+ )
+ pass
+
+ async def send_max_budget_alert_email(self, event: WebhookEvent):
+ """
+ Send email to user when max budget alert threshold is reached
+ """
+ email_params = await self._get_email_params(
+ email_event=EmailEvent.max_budget_alert,
+ user_id=event.user_id,
+ user_email=event.user_email,
+ event_message=event.event_message,
+ )
+
+ verbose_proxy_logger.debug(
+ f"send_max_budget_alert_email_event: {json.dumps(event.model_dump(exclude_none=True), indent=4, default=str)}"
+ )
+
+ # Format budget values
+ spend_str = f"${event.spend}" if event.spend is not None else "$0.00"
+ max_budget_str = f"${event.max_budget}" if event.max_budget is not None else "N/A"
+
+ # Calculate percentage and alert threshold
+ percentage = int(EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE * 100)
+ alert_threshold_str = f"${event.max_budget * EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE:.2f}" if event.max_budget is not None else "N/A"
+
+ email_html_content = MAX_BUDGET_ALERT_EMAIL_TEMPLATE.format(
+ email_logo_url=email_params.logo_url,
+ recipient_email=email_params.recipient_email,
+ percentage=percentage,
+ spend=spend_str,
+ max_budget=max_budget_str,
+ alert_threshold=alert_threshold_str,
+ base_url=email_params.base_url,
+ email_support_contact=email_params.support_contact,
+ )
+ await self.send_email(
+ from_email=self.DEFAULT_LITELLM_EMAIL,
+ to_email=[email_params.recipient_email],
+ subject=email_params.subject,
+ html_body=email_html_content,
+ )
+ pass
+
+ async def budget_alerts(
+ self,
+ type: Literal[
+ "token_budget",
+ "soft_budget",
+ "max_budget_alert",
+ "user_budget",
+ "team_budget",
+ "organization_budget",
+ "proxy_budget",
+ "projected_limit_exceeded",
+ ],
+ user_info: CallInfo,
+ ):
+ """
+ Send a budget alert via email
+
+ Args:
+ type: The type of budget alert to send
+ user_info: The user info to send the alert for
+ """
+ ## PREVENTITIVE ALERTING ##
+ # - Alert once within 24hr period
+ # - Cache this information
+ # - Don't re-alert, if alert already sent
+ _cache: DualCache = self.internal_usage_cache
+
+ # percent of max_budget left to spend
+ if user_info.max_budget is None and user_info.soft_budget is None:
+ return
+
+ # For soft_budget alerts, check if we've already sent an alert
+ if type == "soft_budget":
+ if user_info.soft_budget is not None and user_info.spend >= user_info.soft_budget:
+ # Generate cache key based on event type and identifier
+ _id = user_info.token or user_info.user_id or "default_id"
+ _cache_key = f"email_budget_alerts:soft_budget_crossed:{_id}"
+
+ # Check if we've already sent this alert
+ result = await _cache.async_get_cache(key=_cache_key)
+ if result is None:
+ # Create WebhookEvent for soft budget alert
+ event_message = f"Soft Budget Crossed - Total Soft Budget: ${user_info.soft_budget}"
+ webhook_event = WebhookEvent(
+ event="soft_budget_crossed",
+ event_message=event_message,
+ spend=user_info.spend,
+ max_budget=user_info.max_budget,
+ soft_budget=user_info.soft_budget,
+ token=user_info.token,
+ customer_id=user_info.customer_id,
+ user_id=user_info.user_id,
+ team_id=user_info.team_id,
+ team_alias=user_info.team_alias,
+ organization_id=user_info.organization_id,
+ user_email=user_info.user_email,
+ key_alias=user_info.key_alias,
+ projected_exceeded_date=user_info.projected_exceeded_date,
+ projected_spend=user_info.projected_spend,
+ event_group=user_info.event_group,
+ )
+
+ try:
+ await self.send_soft_budget_alert_email(webhook_event)
+
+ # Cache the alert to prevent duplicate sends
+ await _cache.async_set_cache(
+ key=_cache_key,
+ value="SENT",
+ ttl=EMAIL_BUDGET_ALERT_TTL,
+ )
+ except Exception as e:
+ verbose_proxy_logger.error(
+ f"Error sending soft budget alert email: {e}",
+ exc_info=True,
+ )
+ return
+
+ # For max_budget_alert, check if we've already sent an alert
+ if type == "max_budget_alert":
+ if user_info.max_budget is not None and user_info.spend is not None:
+ alert_threshold = user_info.max_budget * EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE
+
+ # Only alert if we've crossed the threshold but haven't exceeded max_budget yet
+ if user_info.spend >= alert_threshold and user_info.spend < user_info.max_budget:
+ # Generate cache key based on event type and identifier
+ _id = user_info.token or user_info.user_id or "default_id"
+ _cache_key = f"email_budget_alerts:max_budget_alert:{_id}"
+
+ # Check if we've already sent this alert
+ result = await _cache.async_get_cache(key=_cache_key)
+ if result is None:
+ # Calculate percentage
+ percentage = int(EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE * 100)
+
+ # Create WebhookEvent for max budget alert
+ event_message = f"Max Budget Alert - {percentage}% of Maximum Budget Reached"
+ webhook_event = WebhookEvent(
+ event="max_budget_alert",
+ event_message=event_message,
+ spend=user_info.spend,
+ max_budget=user_info.max_budget,
+ soft_budget=user_info.soft_budget,
+ token=user_info.token,
+ customer_id=user_info.customer_id,
+ user_id=user_info.user_id,
+ team_id=user_info.team_id,
+ team_alias=user_info.team_alias,
+ organization_id=user_info.organization_id,
+ user_email=user_info.user_email,
+ key_alias=user_info.key_alias,
+ projected_exceeded_date=user_info.projected_exceeded_date,
+ projected_spend=user_info.projected_spend,
+ event_group=user_info.event_group,
+ )
+
+ try:
+ await self.send_max_budget_alert_email(webhook_event)
+
+ # Cache the alert to prevent duplicate sends
+ await _cache.async_set_cache(
+ key=_cache_key,
+ value="SENT",
+ ttl=EMAIL_BUDGET_ALERT_TTL,
+ )
+ except Exception as e:
+ verbose_proxy_logger.error(
+ f"Error sending max budget alert email: {e}",
+ exc_info=True,
+ )
+ return
+
async def _get_email_params(
self,
email_event: EmailEvent,
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/resend_email.py b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/resend_email.py
index 8119e4a7ef5..7593e66aa47 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/resend_email.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/resend_email.py
@@ -19,7 +19,8 @@ RESEND_API_ENDPOINT = "https://api.resend.com/emails"
class ResendEmailLogger(BaseEmailLogger):
- def __init__(self):
+ def __init__(self, internal_usage_cache=None, **kwargs):
+ super().__init__(internal_usage_cache=internal_usage_cache, **kwargs)
self.async_httpx_client = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/sendgrid_email.py b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/sendgrid_email.py
index dfde9ce329a..8fc2d66d531 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/sendgrid_email.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/sendgrid_email.py
@@ -27,7 +27,8 @@ class SendGridEmailLogger(BaseEmailLogger):
- SENDGRID_API_KEY
"""
- def __init__(self):
+ def __init__(self, internal_usage_cache=None, **kwargs):
+ super().__init__(internal_usage_cache=internal_usage_cache, **kwargs)
self.async_httpx_client = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/smtp_email.py b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/smtp_email.py
index 4ede8ee59fe..8efdaf231b7 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/smtp_email.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/smtp_email.py
@@ -21,7 +21,8 @@ class SMTPEmailLogger(BaseEmailLogger):
- SMTP_SENDER_EMAIL
"""
- def __init__(self):
+ def __init__(self, internal_usage_cache=None, **kwargs):
+ super().__init__(internal_usage_cache=internal_usage_cache, **kwargs)
verbose_logger.debug("SMTP Email Logger initialized....")
async def send_email(
diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py
new file mode 100644
index 00000000000..4ee6a89cc98
--- /dev/null
+++ b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py
@@ -0,0 +1,110 @@
+"""
+Polls LiteLLM_ManagedObjectTable to check if the response is complete.
+Cost tracking is handled automatically by litellm.aget_responses().
+"""
+
+from typing import TYPE_CHECKING
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+
+if TYPE_CHECKING:
+ from litellm.proxy.utils import PrismaClient, ProxyLogging
+ from litellm.router import Router
+
+
+class CheckResponsesCost:
+ def __init__(
+ self,
+ proxy_logging_obj: "ProxyLogging",
+ prisma_client: "PrismaClient",
+ llm_router: "Router",
+ ):
+ from litellm.proxy.utils import PrismaClient, ProxyLogging
+ from litellm.router import Router
+
+ self.proxy_logging_obj: ProxyLogging = proxy_logging_obj
+ self.prisma_client: PrismaClient = prisma_client
+ self.llm_router: Router = llm_router
+
+ async def check_responses_cost(self):
+ """
+ Check if background responses are complete and track their cost.
+ - Get all status="queued" or "in_progress" and file_purpose="response" jobs
+ - Query the provider to check if response is complete
+ - Cost is automatically tracked by litellm.aget_responses()
+ - Mark completed/failed/cancelled responses as complete in the database
+ """
+ jobs = await self.prisma_client.db.litellm_managedobjecttable.find_many(
+ where={
+ "status": {"in": ["queued", "in_progress"]},
+ "file_purpose": "response",
+ }
+ )
+
+ verbose_proxy_logger.debug(f"Found {len(jobs)} response jobs to check")
+ completed_jobs = []
+
+ for job in jobs:
+ unified_object_id = job.unified_object_id
+
+ try:
+ from litellm.proxy.hooks.responses_id_security import (
+ ResponsesIDSecurity,
+ )
+
+ # Get the stored response object to extract model information
+ stored_response = job.file_object
+ model_name = stored_response.get("model", None)
+
+ # Decrypt the response ID
+ responses_id_security, _, _ = ResponsesIDSecurity()._decrypt_response_id(unified_object_id)
+
+ # Prepare metadata with model information for cost tracking
+ litellm_metadata = {
+ "user_api_key_user_id": job.created_by or "default-user-id",
+ }
+
+ # Add model information if available
+ if model_name:
+ litellm_metadata["model"] = model_name
+ litellm_metadata["model_group"] = model_name # Use same value for model_group
+
+ response = await litellm.aget_responses(
+ response_id=responses_id_security,
+ litellm_metadata=litellm_metadata,
+ )
+
+ verbose_proxy_logger.debug(
+ f"Response {unified_object_id} status: {response.status}, model: {model_name}"
+ )
+
+ except Exception as e:
+ verbose_proxy_logger.info(
+ f"Skipping job {unified_object_id} due to error: {e}"
+ )
+ continue
+
+ # Check if response is in a terminal state
+ if response.status == "completed":
+ verbose_proxy_logger.info(
+ f"Response {unified_object_id} is complete. Cost automatically tracked by aget_responses."
+ )
+ completed_jobs.append(job)
+
+ elif response.status in ["failed", "cancelled"]:
+ verbose_proxy_logger.info(
+ f"Response {unified_object_id} has status {response.status}, marking as complete"
+ )
+ completed_jobs.append(job)
+
+ # Mark completed jobs in the database
+ if len(completed_jobs) > 0:
+ await self.prisma_client.db.litellm_managedobjecttable.update_many(
+ where={"id": {"in": [job.id for job in completed_jobs]}},
+ data={"status": "completed"},
+ )
+ verbose_proxy_logger.info(
+ f"Marked {len(completed_jobs)} response jobs as completed"
+ )
+
diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
index e12be6baf5d..a83d7e224b5 100644
--- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
+++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py
@@ -23,7 +23,9 @@ from litellm.proxy._types import (
from litellm.proxy.openai_files_endpoints.common_utils import (
_is_base64_encoded_unified_file_id,
get_batch_id_from_unified_batch_id,
+ get_content_type_from_file_object,
get_model_id_from_unified_batch_id,
+ normalize_mime_type_for_provider,
)
from litellm.types.llms.openai import (
AllMessageValues,
@@ -33,6 +35,7 @@ from litellm.types.llms.openai import (
FileObject,
OpenAIFileObject,
OpenAIFilesPurpose,
+ ResponsesAPIResponse,
)
from litellm.types.utils import (
CallTypesLiteral,
@@ -41,10 +44,6 @@ from litellm.types.utils import (
LLMResponseTypes,
SpecialEnums,
)
-from litellm.proxy.openai_files_endpoints.common_utils import (
- get_content_type_from_file_object,
- normalize_mime_type_for_provider,
-)
if TYPE_CHECKING:
from litellm.types.llms.openai import HttpxBinaryResponseContent
@@ -133,10 +132,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
async def store_unified_object_id(
self,
unified_object_id: str,
- file_object: Union[LiteLLMBatch, LiteLLMFineTuningJob],
+ file_object: Union[LiteLLMBatch, LiteLLMFineTuningJob, "ResponsesAPIResponse"],
litellm_parent_otel_span: Optional[Span],
model_object_id: str,
- file_purpose: Literal["batch", "fine-tune"],
+ file_purpose: Literal["batch", "fine-tune", "response"],
user_api_key_dict: UserAPIKeyAuth,
) -> None:
verbose_logger.info(
@@ -946,7 +945,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
# File is stored in a storage backend, download and convert to base64
try:
- from litellm.llms.base_llm.files.storage_backend_factory import get_storage_backend
+ from litellm.llms.base_llm.files.storage_backend_factory import (
+ get_storage_backend,
+ )
storage_backend_name = db_file.storage_backend
storage_url = db_file.storage_url
diff --git a/enterprise/litellm_enterprise/types/enterprise_callbacks/send_emails.py b/enterprise/litellm_enterprise/types/enterprise_callbacks/send_emails.py
index 736aaff1f75..380b0a6facb 100644
--- a/enterprise/litellm_enterprise/types/enterprise_callbacks/send_emails.py
+++ b/enterprise/litellm_enterprise/types/enterprise_callbacks/send_emails.py
@@ -36,6 +36,8 @@ class EmailEvent(str, enum.Enum):
virtual_key_created = "Virtual Key Created"
new_user_invitation = "New User Invitation"
virtual_key_rotated = "Virtual Key Rotated"
+ soft_budget_crossed = "Soft Budget Crossed"
+ max_budget_alert = "Max Budget Alert"
class EmailEventSettings(BaseModel):
event: EmailEvent
@@ -51,6 +53,8 @@ class DefaultEmailSettings(BaseModel):
EmailEvent.virtual_key_created: True, # On by default
EmailEvent.new_user_invitation: True, # On by default
EmailEvent.virtual_key_rotated: True, # On by default
+ EmailEvent.soft_budget_crossed: True, # On by default
+ EmailEvent.max_budget_alert: True, # On by default
}
)
def to_dict(self) -> Dict[str, bool]:
diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml
index 2bcd8d33adc..1f3da432574 100644
--- a/enterprise/pyproject.toml
+++ b/enterprise/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm-enterprise"
-version = "0.1.25"
+version = "0.1.27"
description = "Package for LiteLLM Enterprise features"
authors = ["BerriAI"]
readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
-version = "0.1.25"
+version = "0.1.27"
version_files = [
"pyproject.toml:version",
"../requirements.txt:litellm-enterprise==",
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251220144550_schema_update/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251220144550_schema_update/migration.sql
new file mode 100644
index 00000000000..b40defec309
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251220144550_schema_update/migration.sql
@@ -0,0 +1,20 @@
+-- CreateTable
+CREATE TABLE "LiteLLM_SkillsTable" (
+ "skill_id" TEXT NOT NULL,
+ "display_title" TEXT,
+ "description" TEXT,
+ "instructions" TEXT,
+ "source" TEXT NOT NULL DEFAULT 'custom',
+ "latest_version" TEXT,
+ "file_content" BYTEA,
+ "file_name" TEXT,
+ "file_type" TEXT,
+ "metadata" JSONB DEFAULT '{}',
+ "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ "created_by" TEXT,
+ "updated_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+ "updated_by" TEXT,
+
+ CONSTRAINT "LiteLLM_SkillsTable_pkey" PRIMARY KEY ("skill_id")
+);
+
diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
index fd77a86f42c..aac0b5b35de 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
+++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
@@ -727,4 +727,22 @@ model LiteLLM_UISettings {
ui_settings Json
created_at DateTime @default(now())
updated_at DateTime @updatedAt
+}
+
+// Skills table for storing LiteLLM-managed skills
+model LiteLLM_SkillsTable {
+ skill_id String @id @default(uuid())
+ display_title String?
+ description String?
+ instructions String? // The skill instructions/prompt (from SKILL.md)
+ source String @default("custom") // "custom" or "anthropic"
+ latest_version String?
+ file_content Bytes? // Binary content of the skill files (zip)
+ file_name String? // Original filename
+ file_type String? // MIME type (e.g., "application/zip")
+ metadata Json? @default("{}")
+ created_at DateTime @default(now())
+ created_by String?
+ updated_at DateTime @default(now()) @updatedAt
+ updated_by String?
}
\ No newline at end of file
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index 674e112890a..7c11a04fca8 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm-proxy-extras"
-version = "0.4.14"
+version = "0.4.16"
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 = "0.4.14"
+version = "0.4.16"
version_files = [
"pyproject.toml:version",
"../requirements.txt:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index b71240777f9..b20b3c5f8e1 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -134,6 +134,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
"weave_otel",
"pagerduty",
"humanloop",
+ "azure_sentinel",
"gcs_pubsub",
"agentops",
"anthropic_cache_control_hook",
@@ -557,6 +558,8 @@ ovhcloud_embedding_models: Set = set()
lemonade_models: Set = set()
docker_model_runner_models: Set = set()
amazon_nova_models: Set = set()
+stability_models: Set = set()
+github_copilot_models: Set = set()
def is_bedrock_pricing_only_model(key: str) -> bool:
@@ -801,6 +804,10 @@ def add_known_models():
docker_model_runner_models.add(key)
elif value.get("litellm_provider") == "amazon_nova":
amazon_nova_models.add(key)
+ elif value.get("litellm_provider") == "stability":
+ stability_models.add(key)
+ elif value.get("litellm_provider") == "github_copilot":
+ github_copilot_models.add(key)
add_known_models()
@@ -1003,6 +1010,8 @@ models_by_provider: dict = {
"lemonade": lemonade_models,
"clarifai": clarifai_models,
"amazon_nova": amazon_nova_models,
+ "stability": stability_models,
+ "github_copilot": github_copilot_models,
}
# mapping for those models which have larger equivalents
@@ -1054,47 +1063,8 @@ from .utils import client
# Note: Most other utils imports are lazy-loaded via __getattr__ to avoid loading utils.py
# (which imports tiktoken) at import time
-from .llms.bytez.chat.transformation import BytezChatConfig
from .llms.custom_llm import CustomLLM
-from .llms.aiohttp_openai.chat.transformation import AiohttpOpenAIChatConfig
-from .llms.galadriel.chat.transformation import GaladrielChatConfig
-from .llms.github.chat.transformation import GithubChatConfig
-from .llms.compactifai.chat.transformation import CompactifAIChatConfig
-from .llms.empower.chat.transformation import EmpowerChatConfig
-from .llms.huggingface.chat.transformation import HuggingFaceChatConfig
-from .llms.huggingface.embedding.transformation import HuggingFaceEmbeddingConfig
-from .llms.oobabooga.chat.transformation import OobaboogaConfig
-from .llms.maritalk import MaritalkConfig
-from .llms.openrouter.chat.transformation import OpenrouterConfig
-from .llms.datarobot.chat.transformation import DataRobotConfig
-from .llms.anthropic.chat.transformation import AnthropicConfig
from .llms.anthropic.common_utils import AnthropicModelInfo
-from .llms.azure_ai.anthropic.transformation import AzureAnthropicConfig
-from .llms.groq.stt.transformation import GroqSTTConfig
-from .llms.anthropic.completion.transformation import AnthropicTextConfig
-from .llms.triton.completion.transformation import TritonConfig
-from .llms.triton.completion.transformation import TritonGenerateConfig
-from .llms.triton.completion.transformation import TritonInferConfig
-from .llms.triton.embedding.transformation import TritonEmbeddingConfig
-from .llms.huggingface.rerank.transformation import HuggingFaceRerankConfig
-from .llms.databricks.chat.transformation import DatabricksConfig
-from .llms.databricks.embed.transformation import DatabricksEmbeddingConfig
-from .llms.predibase.chat.transformation import PredibaseConfig
-from .llms.replicate.chat.transformation import ReplicateConfig
-from .llms.snowflake.chat.transformation import SnowflakeConfig
-from .llms.cohere.rerank.transformation import CohereRerankConfig
-from .llms.cohere.rerank_v2.transformation import CohereRerankV2Config
-from .llms.azure_ai.rerank.transformation import AzureAIRerankConfig
-from .llms.infinity.rerank.transformation import InfinityRerankConfig
-from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig
-from .llms.deepinfra.rerank.transformation import DeepinfraRerankConfig
-from .llms.hosted_vllm.rerank.transformation import HostedVLLMRerankConfig
-from .llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig
-from .llms.nvidia_nim.rerank.ranking_transformation import NvidiaNimRankingConfig
-from .llms.vertex_ai.rerank.transformation import VertexAIRerankConfig
-from .llms.fireworks_ai.rerank.transformation import FireworksAIRerankConfig
-from .llms.voyage.rerank.transformation import VoyageRerankConfig
-from .llms.clarifai.chat.transformation import ClarifaiConfig
from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config
from .llms.meta_llama.chat.transformation import LlamaAPIConfig
from .llms.anthropic.experimental_pass_through.messages.transformation import (
@@ -1194,9 +1164,9 @@ from .llms.bedrock.chat.invoke_transformations.amazon_openai_transformation impo
AmazonBedrockOpenAIConfig,
)
-from .llms.bedrock.image.amazon_stability1_transformation import AmazonStabilityConfig
-from .llms.bedrock.image.amazon_stability3_transformation import AmazonStability3Config
-from .llms.bedrock.image.amazon_nova_canvas_transformation import AmazonNovaCanvasConfig
+from .llms.bedrock.image_generation.amazon_stability1_transformation import AmazonStabilityConfig
+from .llms.bedrock.image_generation.amazon_stability3_transformation import AmazonStability3Config
+from .llms.bedrock.image_generation.amazon_nova_canvas_transformation import AmazonNovaCanvasConfig
from .llms.bedrock.embed.amazon_titan_g1_transformation import AmazonTitanG1Config
from .llms.bedrock.embed.amazon_titan_multimodal_transformation import (
AmazonTitanMultimodalEmbeddingG1Config,
@@ -1502,6 +1472,50 @@ if TYPE_CHECKING:
from litellm.types.utils import ModelInfo as _ModelInfoType
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.caching.caching import Cache
+
+ # Type stubs for lazy-loaded configs to help mypy
+ from .llms.bedrock.chat.converse_transformation import AmazonConverseConfig as AmazonConverseConfig
+ from .llms.openai_like.chat.handler import OpenAILikeChatConfig as OpenAILikeChatConfig
+ from .llms.galadriel.chat.transformation import GaladrielChatConfig as GaladrielChatConfig
+ from .llms.github.chat.transformation import GithubChatConfig as GithubChatConfig
+ from .llms.azure_ai.anthropic.transformation import AzureAnthropicConfig as AzureAnthropicConfig
+ from .llms.bytez.chat.transformation import BytezChatConfig as BytezChatConfig
+ from .llms.compactifai.chat.transformation import CompactifAIChatConfig as CompactifAIChatConfig
+ from .llms.empower.chat.transformation import EmpowerChatConfig as EmpowerChatConfig
+ from .llms.aiohttp_openai.chat.transformation import AiohttpOpenAIChatConfig as AiohttpOpenAIChatConfig
+ from .llms.huggingface.chat.transformation import HuggingFaceChatConfig as HuggingFaceChatConfig
+ from .llms.huggingface.embedding.transformation import HuggingFaceEmbeddingConfig as HuggingFaceEmbeddingConfig
+ from .llms.oobabooga.chat.transformation import OobaboogaConfig as OobaboogaConfig
+ from .llms.maritalk import MaritalkConfig as MaritalkConfig
+ from .llms.openrouter.chat.transformation import OpenrouterConfig as OpenrouterConfig
+ from .llms.datarobot.chat.transformation import DataRobotConfig as DataRobotConfig
+ from .llms.anthropic.chat.transformation import AnthropicConfig as AnthropicConfig
+ from .llms.anthropic.completion.transformation import AnthropicTextConfig as AnthropicTextConfig
+ from .llms.groq.stt.transformation import GroqSTTConfig as GroqSTTConfig
+ from .llms.triton.completion.transformation import TritonConfig as TritonConfig
+ from .llms.triton.completion.transformation import TritonGenerateConfig as TritonGenerateConfig
+ from .llms.triton.completion.transformation import TritonInferConfig as TritonInferConfig
+ from .llms.triton.embedding.transformation import TritonEmbeddingConfig as TritonEmbeddingConfig
+ from .llms.huggingface.rerank.transformation import HuggingFaceRerankConfig as HuggingFaceRerankConfig
+ from .llms.databricks.chat.transformation import DatabricksConfig as DatabricksConfig
+ from .llms.databricks.embed.transformation import DatabricksEmbeddingConfig as DatabricksEmbeddingConfig
+ from .llms.predibase.chat.transformation import PredibaseConfig as PredibaseConfig
+ from .llms.replicate.chat.transformation import ReplicateConfig as ReplicateConfig
+ from .llms.snowflake.chat.transformation import SnowflakeConfig as SnowflakeConfig
+ from .llms.cohere.rerank.transformation import CohereRerankConfig as CohereRerankConfig
+ from .llms.cohere.rerank_v2.transformation import CohereRerankV2Config as CohereRerankV2Config
+ from .llms.azure_ai.rerank.transformation import AzureAIRerankConfig as AzureAIRerankConfig
+ from .llms.infinity.rerank.transformation import InfinityRerankConfig as InfinityRerankConfig
+ from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig as JinaAIRerankConfig
+ from .llms.deepinfra.rerank.transformation import DeepinfraRerankConfig as DeepinfraRerankConfig
+ from .llms.hosted_vllm.rerank.transformation import HostedVLLMRerankConfig as HostedVLLMRerankConfig
+ from .llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig as NvidiaNimRerankConfig
+ from .llms.nvidia_nim.rerank.ranking_transformation import NvidiaNimRankingConfig as NvidiaNimRankingConfig
+ from .llms.vertex_ai.rerank.transformation import VertexAIRerankConfig as VertexAIRerankConfig
+ from .llms.fireworks_ai.rerank.transformation import FireworksAIRerankConfig as FireworksAIRerankConfig
+ from .llms.voyage.rerank.transformation import VoyageRerankConfig as VoyageRerankConfig
+ from .llms.clarifai.chat.transformation import ClarifaiConfig as ClarifaiConfig
+ from .llms.ai21.chat.transformation import AI21ChatConfig as AI21ChatConfig
from litellm.caching.llm_caching_handler import LLMClientCache
from litellm.types.llms.bedrock import COHERE_EMBEDDING_INPUT_TYPES
from litellm.types.utils import (
@@ -1555,9 +1569,7 @@ if TYPE_CHECKING:
module_level_aclient: AsyncHTTPHandler
module_level_client: HTTPHandler
- # LLM config classes - lazy loaded only
- AmazonConverseConfig: Type[Any]
- OpenAILikeChatConfig: Type[Any]
+ # Note: AmazonConverseConfig and OpenAILikeChatConfig are imported above in TYPE_CHECKING block
def __getattr__(name: str) -> Any:
diff --git a/litellm/_lazy_imports.py b/litellm/_lazy_imports.py
index 94b3e4a8da1..6f96f9f8ff3 100644
--- a/litellm/_lazy_imports.py
+++ b/litellm/_lazy_imports.py
@@ -1,5 +1,6 @@
-from typing import Any, Optional, cast
import sys
+from typing import Any, Optional, cast
+
def _get_litellm_globals() -> dict:
"""Helper to get the globals dictionary of the litellm module."""
@@ -158,6 +159,45 @@ DOTPROMPT_NAMES = (
LLM_CONFIG_NAMES = (
"AmazonConverseConfig",
"OpenAILikeChatConfig",
+ "GaladrielChatConfig",
+ "GithubChatConfig",
+ "AzureAnthropicConfig",
+ "BytezChatConfig",
+ "CompactifAIChatConfig",
+ "EmpowerChatConfig",
+ "AiohttpOpenAIChatConfig",
+ "HuggingFaceChatConfig",
+ "HuggingFaceEmbeddingConfig",
+ "OobaboogaConfig",
+ "MaritalkConfig",
+ "OpenrouterConfig",
+ "DataRobotConfig",
+ "AnthropicConfig",
+ "AnthropicTextConfig",
+ "GroqSTTConfig",
+ "TritonConfig",
+ "TritonGenerateConfig",
+ "TritonInferConfig",
+ "TritonEmbeddingConfig",
+ "HuggingFaceRerankConfig",
+ "DatabricksConfig",
+ "DatabricksEmbeddingConfig",
+ "PredibaseConfig",
+ "ReplicateConfig",
+ "SnowflakeConfig",
+ "CohereRerankConfig",
+ "CohereRerankV2Config",
+ "AzureAIRerankConfig",
+ "InfinityRerankConfig",
+ "JinaAIRerankConfig",
+ "DeepinfraRerankConfig",
+ "HostedVLLMRerankConfig",
+ "NvidiaNimRerankConfig",
+ "NvidiaNimRankingConfig",
+ "VertexAIRerankConfig",
+ "FireworksAIRerankConfig",
+ "VoyageRerankConfig",
+ "ClarifaiConfig",
)
# Types that support lazy loading via _lazy_import_types
@@ -223,7 +263,9 @@ def _lazy_import_utils(name: str) -> Any: # noqa: PLR0915
return _supports_response_schema
if name == "supports_parallel_function_calling":
- from .utils import supports_parallel_function_calling as _supports_parallel_function_calling
+ from .utils import (
+ supports_parallel_function_calling as _supports_parallel_function_calling,
+ )
_globals["supports_parallel_function_calling"] = _supports_parallel_function_calling
return _supports_parallel_function_calling
@@ -389,7 +431,9 @@ def _lazy_import_cost_calculator(name: str) -> Any:
return _cost_per_token
if name == "response_cost_calculator":
- from .cost_calculator import response_cost_calculator as _response_cost_calculator
+ from .cost_calculator import (
+ response_cost_calculator as _response_cost_calculator,
+ )
_globals["response_cost_calculator"] = _response_cost_calculator
return _response_cost_calculator
@@ -461,9 +505,7 @@ def _lazy_import_types_utils(name: str) -> Any:
return _CredentialItem
if name == "PriorityReservationDict":
- from .types.utils import (
- PriorityReservationDict as _PriorityReservationDict,
- )
+ from .types.utils import PriorityReservationDict as _PriorityReservationDict
_globals["PriorityReservationDict"] = _PriorityReservationDict
return _PriorityReservationDict
@@ -483,9 +525,7 @@ def _lazy_import_types_utils(name: str) -> Any:
return _SearchProviders
if name == "GenericStreamingChunk":
- from .types.utils import (
- GenericStreamingChunk as _GenericStreamingChunk,
- )
+ from .types.utils import GenericStreamingChunk as _GenericStreamingChunk
_globals["GenericStreamingChunk"] = _GenericStreamingChunk
return _GenericStreamingChunk
@@ -529,13 +569,17 @@ def _lazy_import_llm_client_cache(name: str) -> Any:
_globals = _get_litellm_globals()
if name == "LLMClientCache":
- from litellm.caching.llm_caching_handler import LLMClientCache as _LLMClientCache
+ from litellm.caching.llm_caching_handler import (
+ LLMClientCache as _LLMClientCache,
+ )
_globals["LLMClientCache"] = _LLMClientCache
return _LLMClientCache
if name == "in_memory_llm_clients_cache":
- from litellm.caching.llm_caching_handler import LLMClientCache as _LLMClientCache
+ from litellm.caching.llm_caching_handler import (
+ LLMClientCache as _LLMClientCache,
+ )
instance = _LLMClientCache()
# Only populate the requested singleton name to keep lazy-import
@@ -555,7 +599,9 @@ def _lazy_import_litellm_logging(name: str) -> Any:
return _Logging
if name == "modify_integration":
- from litellm.litellm_core_utils.litellm_logging import modify_integration as _modify_integration
+ from litellm.litellm_core_utils.litellm_logging import (
+ modify_integration as _modify_integration,
+ )
_globals["modify_integration"] = _modify_integration
return _modify_integration
@@ -630,9 +676,7 @@ def _lazy_import_types(name: str) -> Any:
_globals = _get_litellm_globals()
if name == "GuardrailItem":
- from litellm.types.guardrails import (
- GuardrailItem as _GuardrailItem,
- )
+ from litellm.types.guardrails import GuardrailItem as _GuardrailItem
_globals["GuardrailItem"] = _GuardrailItem
return _GuardrailItem
@@ -640,7 +684,7 @@ def _lazy_import_types(name: str) -> Any:
raise AttributeError(f"Types lazy import: unknown attribute {name!r}")
-def _lazy_import_llm_configs(name: str) -> Any:
+def _lazy_import_llm_configs(name: str) -> Any: # noqa: PLR0915
"""Lazy import for LLM config classes."""
_globals = _get_litellm_globals()
@@ -660,4 +704,306 @@ def _lazy_import_llm_configs(name: str) -> Any:
_globals["OpenAILikeChatConfig"] = _OpenAILikeChatConfig
return _OpenAILikeChatConfig
+ if name == "GaladrielChatConfig":
+ from .llms.galadriel.chat.transformation import (
+ GaladrielChatConfig as _GaladrielChatConfig,
+ )
+
+ _globals["GaladrielChatConfig"] = _GaladrielChatConfig
+ return _GaladrielChatConfig
+
+ if name == "GithubChatConfig":
+ from .llms.github.chat.transformation import (
+ GithubChatConfig as _GithubChatConfig,
+ )
+
+ _globals["GithubChatConfig"] = _GithubChatConfig
+ return _GithubChatConfig
+
+ if name == "AzureAnthropicConfig":
+ from .llms.azure_ai.anthropic.transformation import (
+ AzureAnthropicConfig as _AzureAnthropicConfig,
+ )
+
+ _globals["AzureAnthropicConfig"] = _AzureAnthropicConfig
+ return _AzureAnthropicConfig
+
+ if name == "BytezChatConfig":
+ from .llms.bytez.chat.transformation import BytezChatConfig as _BytezChatConfig
+
+ _globals["BytezChatConfig"] = _BytezChatConfig
+ return _BytezChatConfig
+
+ if name == "CompactifAIChatConfig":
+ from .llms.compactifai.chat.transformation import (
+ CompactifAIChatConfig as _CompactifAIChatConfig,
+ )
+
+ _globals["CompactifAIChatConfig"] = _CompactifAIChatConfig
+ return _CompactifAIChatConfig
+
+ if name == "EmpowerChatConfig":
+ from .llms.empower.chat.transformation import (
+ EmpowerChatConfig as _EmpowerChatConfig,
+ )
+
+ _globals["EmpowerChatConfig"] = _EmpowerChatConfig
+ return _EmpowerChatConfig
+
+ if name == "AiohttpOpenAIChatConfig":
+ from .llms.aiohttp_openai.chat.transformation import (
+ AiohttpOpenAIChatConfig as _AiohttpOpenAIChatConfig,
+ )
+
+ _globals["AiohttpOpenAIChatConfig"] = _AiohttpOpenAIChatConfig
+ return _AiohttpOpenAIChatConfig
+
+ if name == "HuggingFaceChatConfig":
+ from .llms.huggingface.chat.transformation import (
+ HuggingFaceChatConfig as _HuggingFaceChatConfig,
+ )
+
+ _globals["HuggingFaceChatConfig"] = _HuggingFaceChatConfig
+ return _HuggingFaceChatConfig
+
+ if name == "HuggingFaceEmbeddingConfig":
+ from .llms.huggingface.embedding.transformation import (
+ HuggingFaceEmbeddingConfig as _HuggingFaceEmbeddingConfig,
+ )
+
+ _globals["HuggingFaceEmbeddingConfig"] = _HuggingFaceEmbeddingConfig
+ return _HuggingFaceEmbeddingConfig
+
+ if name == "OobaboogaConfig":
+ from .llms.oobabooga.chat.transformation import (
+ OobaboogaConfig as _OobaboogaConfig,
+ )
+
+ _globals["OobaboogaConfig"] = _OobaboogaConfig
+ return _OobaboogaConfig
+
+ if name == "MaritalkConfig":
+ from .llms.maritalk import MaritalkConfig as _MaritalkConfig
+
+ _globals["MaritalkConfig"] = _MaritalkConfig
+ return _MaritalkConfig
+
+ if name == "OpenrouterConfig":
+ from .llms.openrouter.chat.transformation import (
+ OpenrouterConfig as _OpenrouterConfig,
+ )
+
+ _globals["OpenrouterConfig"] = _OpenrouterConfig
+ return _OpenrouterConfig
+
+ if name == "DataRobotConfig":
+ from .llms.datarobot.chat.transformation import (
+ DataRobotConfig as _DataRobotConfig,
+ )
+
+ _globals["DataRobotConfig"] = _DataRobotConfig
+ return _DataRobotConfig
+
+ if name == "AnthropicConfig":
+ from .llms.anthropic.chat.transformation import (
+ AnthropicConfig as _AnthropicConfig,
+ )
+
+ _globals["AnthropicConfig"] = _AnthropicConfig
+ return _AnthropicConfig
+
+ if name == "AnthropicTextConfig":
+ from .llms.anthropic.completion.transformation import (
+ AnthropicTextConfig as _AnthropicTextConfig,
+ )
+
+ _globals["AnthropicTextConfig"] = _AnthropicTextConfig
+ return _AnthropicTextConfig
+
+ if name == "GroqSTTConfig":
+ from .llms.groq.stt.transformation import GroqSTTConfig as _GroqSTTConfig
+
+ _globals["GroqSTTConfig"] = _GroqSTTConfig
+ return _GroqSTTConfig
+
+ if name == "TritonConfig":
+ from .llms.triton.completion.transformation import TritonConfig as _TritonConfig
+
+ _globals["TritonConfig"] = _TritonConfig
+ return _TritonConfig
+
+ if name == "TritonGenerateConfig":
+ from .llms.triton.completion.transformation import (
+ TritonGenerateConfig as _TritonGenerateConfig,
+ )
+
+ _globals["TritonGenerateConfig"] = _TritonGenerateConfig
+ return _TritonGenerateConfig
+
+ if name == "TritonInferConfig":
+ from .llms.triton.completion.transformation import (
+ TritonInferConfig as _TritonInferConfig,
+ )
+
+ _globals["TritonInferConfig"] = _TritonInferConfig
+ return _TritonInferConfig
+
+ if name == "TritonEmbeddingConfig":
+ from .llms.triton.embedding.transformation import (
+ TritonEmbeddingConfig as _TritonEmbeddingConfig,
+ )
+
+ _globals["TritonEmbeddingConfig"] = _TritonEmbeddingConfig
+ return _TritonEmbeddingConfig
+
+ if name == "HuggingFaceRerankConfig":
+ from .llms.huggingface.rerank.transformation import (
+ HuggingFaceRerankConfig as _HuggingFaceRerankConfig,
+ )
+
+ _globals["HuggingFaceRerankConfig"] = _HuggingFaceRerankConfig
+ return _HuggingFaceRerankConfig
+
+ if name == "DatabricksConfig":
+ from .llms.databricks.chat.transformation import (
+ DatabricksConfig as _DatabricksConfig,
+ )
+
+ _globals["DatabricksConfig"] = _DatabricksConfig
+ return _DatabricksConfig
+
+ if name == "DatabricksEmbeddingConfig":
+ from .llms.databricks.embed.transformation import (
+ DatabricksEmbeddingConfig as _DatabricksEmbeddingConfig,
+ )
+
+ _globals["DatabricksEmbeddingConfig"] = _DatabricksEmbeddingConfig
+ return _DatabricksEmbeddingConfig
+
+ if name == "PredibaseConfig":
+ from .llms.predibase.chat.transformation import (
+ PredibaseConfig as _PredibaseConfig,
+ )
+
+ _globals["PredibaseConfig"] = _PredibaseConfig
+ return _PredibaseConfig
+
+ if name == "ReplicateConfig":
+ from .llms.replicate.chat.transformation import (
+ ReplicateConfig as _ReplicateConfig,
+ )
+
+ _globals["ReplicateConfig"] = _ReplicateConfig
+ return _ReplicateConfig
+
+ if name == "SnowflakeConfig":
+ from .llms.snowflake.chat.transformation import (
+ SnowflakeConfig as _SnowflakeConfig,
+ )
+
+ _globals["SnowflakeConfig"] = _SnowflakeConfig
+ return _SnowflakeConfig
+
+ if name == "CohereRerankConfig":
+ from .llms.cohere.rerank.transformation import (
+ CohereRerankConfig as _CohereRerankConfig,
+ )
+
+ _globals["CohereRerankConfig"] = _CohereRerankConfig
+ return _CohereRerankConfig
+
+ if name == "CohereRerankV2Config":
+ from .llms.cohere.rerank_v2.transformation import (
+ CohereRerankV2Config as _CohereRerankV2Config,
+ )
+
+ _globals["CohereRerankV2Config"] = _CohereRerankV2Config
+ return _CohereRerankV2Config
+
+ if name == "AzureAIRerankConfig":
+ from .llms.azure_ai.rerank.transformation import (
+ AzureAIRerankConfig as _AzureAIRerankConfig,
+ )
+
+ _globals["AzureAIRerankConfig"] = _AzureAIRerankConfig
+ return _AzureAIRerankConfig
+
+ if name == "InfinityRerankConfig":
+ from .llms.infinity.rerank.transformation import (
+ InfinityRerankConfig as _InfinityRerankConfig,
+ )
+
+ _globals["InfinityRerankConfig"] = _InfinityRerankConfig
+ return _InfinityRerankConfig
+
+ if name == "JinaAIRerankConfig":
+ from .llms.jina_ai.rerank.transformation import (
+ JinaAIRerankConfig as _JinaAIRerankConfig,
+ )
+
+ _globals["JinaAIRerankConfig"] = _JinaAIRerankConfig
+ return _JinaAIRerankConfig
+
+ if name == "DeepinfraRerankConfig":
+ from .llms.deepinfra.rerank.transformation import (
+ DeepinfraRerankConfig as _DeepinfraRerankConfig,
+ )
+
+ _globals["DeepinfraRerankConfig"] = _DeepinfraRerankConfig
+ return _DeepinfraRerankConfig
+
+ if name == "HostedVLLMRerankConfig":
+ from .llms.hosted_vllm.rerank.transformation import (
+ HostedVLLMRerankConfig as _HostedVLLMRerankConfig,
+ )
+
+ _globals["HostedVLLMRerankConfig"] = _HostedVLLMRerankConfig
+ return _HostedVLLMRerankConfig
+
+ if name == "NvidiaNimRerankConfig":
+ from .llms.nvidia_nim.rerank.transformation import (
+ NvidiaNimRerankConfig as _NvidiaNimRerankConfig,
+ )
+
+ _globals["NvidiaNimRerankConfig"] = _NvidiaNimRerankConfig
+ return _NvidiaNimRerankConfig
+
+ if name == "NvidiaNimRankingConfig":
+ from .llms.nvidia_nim.rerank.ranking_transformation import (
+ NvidiaNimRankingConfig as _NvidiaNimRankingConfig,
+ )
+
+ _globals["NvidiaNimRankingConfig"] = _NvidiaNimRankingConfig
+ return _NvidiaNimRankingConfig
+
+ if name == "VertexAIRerankConfig":
+ from .llms.vertex_ai.rerank.transformation import (
+ VertexAIRerankConfig as _VertexAIRerankConfig,
+ )
+
+ _globals["VertexAIRerankConfig"] = _VertexAIRerankConfig
+ return _VertexAIRerankConfig
+
+ if name == "FireworksAIRerankConfig":
+ from .llms.fireworks_ai.rerank.transformation import (
+ FireworksAIRerankConfig as _FireworksAIRerankConfig,
+ )
+
+ _globals["FireworksAIRerankConfig"] = _FireworksAIRerankConfig
+ return _FireworksAIRerankConfig
+
+ if name == "VoyageRerankConfig":
+ from .llms.voyage.rerank.transformation import (
+ VoyageRerankConfig as _VoyageRerankConfig,
+ )
+
+ _globals["VoyageRerankConfig"] = _VoyageRerankConfig
+ return _VoyageRerankConfig
+
+ if name == "ClarifaiConfig":
+ from .llms.clarifai.chat.transformation import ClarifaiConfig as _ClarifaiConfig
+
+ _globals["ClarifaiConfig"] = _ClarifaiConfig
+ return _ClarifaiConfig
+
raise AttributeError(f"LLM config lazy import: unknown attribute {name!r}")
\ No newline at end of file
diff --git a/litellm/anthropic_interface/messages/__init__.py b/litellm/anthropic_interface/messages/__init__.py
index 16bb5f3d462..d7ff53a1763 100644
--- a/litellm/anthropic_interface/messages/__init__.py
+++ b/litellm/anthropic_interface/messages/__init__.py
@@ -37,6 +37,7 @@ async def acreate(
tools: Optional[List[Dict]] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
+ container: Optional[Dict] = None,
**kwargs
) -> Union[AnthropicMessagesResponse, AsyncIterator]:
"""
@@ -56,6 +57,7 @@ async def acreate(
tools (List[Dict], optional): List of tool definitions
top_k (int, optional): Top K sampling parameter
top_p (float, optional): Nucleus sampling parameter
+ container (Dict, optional): Container config with skills for code execution
**kwargs: Additional arguments
Returns:
@@ -75,6 +77,7 @@ async def acreate(
tools=tools,
top_k=top_k,
top_p=top_p,
+ container=container,
**kwargs,
)
@@ -93,6 +96,7 @@ def create(
tools: Optional[List[Dict]] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
+ container: Optional[Dict] = None,
**kwargs
) -> Union[
AnthropicMessagesResponse,
@@ -135,5 +139,6 @@ def create(
tools=tools,
top_k=top_k,
top_p=top_p,
+ container=container,
**kwargs,
)
diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py
index 8d6a7296385..ea7e3f5a979 100644
--- a/litellm/caching/redis_cache.py
+++ b/litellm/caching/redis_cache.py
@@ -10,6 +10,7 @@ Has 4 primary methods:
import ast
import asyncio
+import hashlib
import inspect
import json
import time
@@ -145,9 +146,17 @@ class RedisCache(BaseCache):
except Exception:
pass
- ### ASYNC HEALTH PING ###
+ self._setup_health_pings()
+
+ if litellm.default_redis_ttl is not None:
+ super().__init__(default_ttl=int(litellm.default_redis_ttl))
+ else:
+ super().__init__() # defaults to 60s
+
+ def _setup_health_pings(self):
+ """Setup async and sync health pings for Redis."""
+ # ASYNC HEALTH PING
try:
- # asyncio.get_running_loop().create_task(self.ping())
_ = asyncio.get_running_loop().create_task(self.ping())
except Exception as e:
if "no running event loop" in str(e):
@@ -159,8 +168,9 @@ class RedisCache(BaseCache):
"Error connecting to Async Redis client - {}".format(str(e)),
extra={"error": str(e)},
)
+ self._handle_async_ping_error(e)
- ### SYNC HEALTH PING ###
+ # SYNC HEALTH PING
try:
if hasattr(self.redis_client, "ping"):
self.redis_client.ping() # type: ignore
@@ -168,11 +178,53 @@ class RedisCache(BaseCache):
verbose_logger.error(
"Error connecting to Sync Redis client", extra={"error": str(e)}
)
+ self._handle_sync_ping_error(e)
- if litellm.default_redis_ttl is not None:
- super().__init__(default_ttl=int(litellm.default_redis_ttl))
- else:
- super().__init__() # defaults to 60s
+ def _handle_async_ping_error(self, e: Exception):
+ """Handle async ping error with service failure hook."""
+ try:
+ loop = asyncio.get_running_loop()
+ start_time = time.time()
+ end_time = start_time
+ loop.create_task(
+ self.service_logger_obj.async_service_failure_hook(
+ service=ServiceTypes.REDIS,
+ duration=end_time - start_time,
+ error=e,
+ call_type="redis_async_ping",
+ )
+ )
+ except Exception:
+ pass
+
+ def _handle_sync_ping_error(self, e: Exception):
+ """Handle sync ping error with service failure hook."""
+ try:
+ loop = asyncio.get_running_loop()
+ start_time = time.time()
+ end_time = start_time
+ loop.create_task(
+ self.service_logger_obj.async_service_failure_hook(
+ service=ServiceTypes.REDIS,
+ duration=end_time - start_time,
+ error=e,
+ call_type="redis_sync_ping",
+ )
+ )
+ except Exception:
+ pass
+
+ def _get_async_client_cache_key(self) -> str:
+ """
+ Generate a cache key for the async Redis client based on connection parameters.
+ This ensures different Redis configurations use different cached clients.
+ """
+ # Create a stable representation of redis_kwargs for hashing
+ # Sort keys to ensure consistent hash regardless of parameter order
+ sorted_kwargs = sorted(self.redis_kwargs.items())
+ kwargs_str = json.dumps(sorted_kwargs, sort_keys=True)
+ kwargs_hash = hashlib.sha256(kwargs_str.encode()).hexdigest()[:16]
+ return f"async-redis-client-{kwargs_hash}"
def init_async_client(
self,
@@ -181,7 +233,8 @@ class RedisCache(BaseCache):
from .._redis import get_redis_async_client, get_redis_connection_pool
- cached_client = in_memory_llm_clients_cache.get_cache(key="async-redis-client")
+ cache_key = self._get_async_client_cache_key()
+ cached_client = in_memory_llm_clients_cache.get_cache(key=cache_key)
if cached_client is not None:
redis_async_client = cast(
Union[async_redis_client, async_redis_cluster_client], cached_client
@@ -193,7 +246,7 @@ class RedisCache(BaseCache):
connection_pool=self.async_redis_conn_pool, **self.redis_kwargs
)
in_memory_llm_clients_cache.set_cache(
- key="async-redis-client", value=redis_async_client
+ key=cache_key, value=redis_async_client
)
self.redis_async_client = redis_async_client # type: ignore
diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py
index 612bec239ba..55a8e665bbd 100644
--- a/litellm/completion_extras/litellm_responses_transformation/transformation.py
+++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py
@@ -167,24 +167,27 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
)
elif role == "tool":
# Convert tool message to function call output format
- # Transform content to responses format (handles str, list, and other types)
- # _convert_content_to_responses_format always returns List[Dict[str, Any]]
+ # The Responses API expects 'output' to be a list with input_text/input_image types
+ # Using list format for consistency across text and multimodal content
+ tool_output: List[Dict[str, Any]]
if content is None:
- transformed_output: list[dict[str, Any]] = []
- elif isinstance(content, (str, list)):
- transformed_output = self._convert_content_to_responses_format(
- content, "tool"
+ tool_output = []
+ elif isinstance(content, str):
+ # Convert string to list with input_text
+ tool_output = [{"type": "input_text", "text": content}]
+ elif isinstance(content, list):
+ # Transform list content to Responses API format
+ tool_output = self._convert_content_to_responses_format(
+ content, "user" # Use "user" role to get input_* types
)
else:
- # Fallback: convert unexpected types to string first
- transformed_output = self._convert_content_to_responses_format(
- str(content), "tool"
- )
+ # Fallback: convert unexpected types to input_text
+ tool_output = [{"type": "input_text", "text": str(content)}]
input_items.append(
{
"type": "function_call_output",
"call_id": tool_call_id,
- "output": transformed_output,
+ "output": tool_output,
}
)
elif role == "assistant" and tool_calls and isinstance(tool_calls, list):
@@ -345,6 +348,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
index = 0
reasoning_content: Optional[str] = None
+ # Collect all tool calls to put them in a single choice
+ # (Chat Completions API expects all tool calls in one message)
+ accumulated_tool_calls: List[Dict[str, Any]] = []
+ tool_call_index = 0
+
for item in output_items:
if isinstance(item, ResponseReasoningItem):
for summary_item in item.summary:
@@ -378,20 +386,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
tool_call_dict = LiteLLMCompletionResponsesConfig.convert_response_function_tool_call_to_chat_completion_tool_call(
tool_call_item=item,
- index=index,
+ index=tool_call_index,
)
-
- msg = Message(
- content=None,
- tool_calls=[tool_call_dict],
- reasoning_content=reasoning_content,
- )
-
- choices.append(
- Choices(message=msg, finish_reason="tool_calls", index=index)
- )
- reasoning_content = None # flush reasoning content
- index += 1
+ accumulated_tool_calls.append(tool_call_dict)
+ tool_call_index += 1
elif isinstance(item, dict) and handle_raw_dict_callback is not None:
# Handle raw dict responses (e.g., from GPT-5 Codex)
@@ -401,6 +399,18 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
else:
pass # don't fail request if item in list is not supported
+ # If we accumulated tool calls, create a single choice with all of them
+ if accumulated_tool_calls:
+ msg = Message(
+ content=None,
+ tool_calls=accumulated_tool_calls,
+ reasoning_content=reasoning_content,
+ )
+ choices.append(
+ Choices(message=msg, finish_reason="tool_calls", index=index)
+ )
+ reasoning_content = None
+
return choices
def transform_response( # noqa: PLR0915
@@ -492,7 +502,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
def _convert_content_str_to_input_text(
self, content: str, role: str
) -> Dict[str, Any]:
- if role == "user" or role == "system":
+ if role == "user" or role == "system" or role == "tool":
return {"type": "input_text", "text": content}
else:
return {"type": "output_text", "text": content}
diff --git a/litellm/constants.py b/litellm/constants.py
index 38d3e8a1753..511cbafc748 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -313,6 +313,8 @@ DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE = os.getenv(
"DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE", "streaming.chunk.yield"
)
+EMAIL_BUDGET_ALERT_TTL = int(os.getenv("EMAIL_BUDGET_ALERT_TTL", 24 * 60 * 60)) # 24 hours in seconds
+EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE = float(os.getenv("EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE", 0.8)) # 80% of max budget
############### LLM Provider Constants ###############
### ANTHROPIC CONSTANTS ###
ANTHROPIC_SKILLS_API_BETA_VERSION = "skills-2025-10-02"
@@ -890,6 +892,7 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[
"qwen2",
"twelvelabs",
"openai",
+ "stability",
]
BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[
diff --git a/litellm/images/main.py b/litellm/images/main.py
index 7ab496db0b7..03c0e36ad93 100644
--- a/litellm/images/main.py
+++ b/litellm/images/main.py
@@ -33,6 +33,7 @@ from litellm.main import (
base_llm_aiohttp_handler,
base_llm_http_handler,
bedrock_image_generation,
+ bedrock_image_edit,
openai_chat_completions,
openai_image_variations,
)
@@ -670,7 +671,7 @@ def image_variation(
@client
-def image_edit(
+def image_edit( # noqa: PLR0915
image: Union[FileTypes, List[FileTypes]],
prompt: str,
model: Optional[str] = None,
@@ -695,6 +696,29 @@ def image_edit(
"""
local_vars = locals()
try:
+ openai_params = [
+ "user",
+ "request_timeout",
+ "api_base",
+ "api_version",
+ "api_key",
+ "deployment_id",
+ "organization",
+ "base_url",
+ "default_headers",
+ "timeout",
+ "max_retries",
+ "n",
+ "quality",
+ "size",
+ "style",
+ "async_call",
+ ]
+ litellm_params_list = all_litellm_params
+ default_params = openai_params + litellm_params_list
+ non_default_params = {
+ k: v for k, v in kwargs.items() if k not in default_params
+ } # model-specific params - pass them straight to the model/provider
litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
_is_async = kwargs.pop("async_call", False) is True
@@ -788,13 +812,14 @@ def image_edit(
image_edit_optional_params: ImageEditOptionalRequestParams = (
_get_ImageEditRequestUtils().get_requested_image_edit_optional_param(local_vars)
)
-
# Get optional parameters for the responses API
image_edit_request_params: Dict = (
_get_ImageEditRequestUtils().get_optional_params_image_edit(
model=model,
image_edit_provider_config=image_edit_provider_config,
image_edit_optional_params=image_edit_optional_params,
+ drop_params=kwargs.get("drop_params"),
+ additional_drop_params=kwargs.get("additional_drop_params"),
)
)
@@ -810,6 +835,42 @@ def image_edit(
custom_llm_provider=custom_llm_provider,
)
+ # Route bedrock to its specific handler (AWS signing required)
+ if custom_llm_provider == "bedrock":
+ if model is None:
+ raise Exception("Model needs to be set for bedrock")
+ image_edit_request_params.update(non_default_params)
+ return bedrock_image_edit.image_edit( # type: ignore
+ model=model,
+ image=images,
+ prompt=prompt,
+ timeout=timeout,
+ logging_obj=litellm_logging_obj,
+ optional_params=image_edit_request_params,
+ model_response=ImageResponse(),
+ aimage_edit=_is_async,
+ client=kwargs.get("client"),
+ api_base=kwargs.get("api_base"),
+ extra_headers=extra_headers,
+ api_key=kwargs.get("api_key"),
+ )
+ elif custom_llm_provider == "stability":
+ image_edit_request_params.update(non_default_params)
+ return base_llm_http_handler.image_edit_handler(
+ model=model,
+ image=images,
+ prompt=prompt,
+ image_edit_provider_config=image_edit_provider_config,
+ image_edit_optional_request_params=image_edit_request_params,
+ custom_llm_provider=custom_llm_provider,
+ litellm_params=litellm_params,
+ logging_obj=litellm_logging_obj,
+ extra_headers=extra_headers,
+ extra_body=extra_body,
+ timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
+ _is_async=_is_async,
+ client=kwargs.get("client"),
+ )
# Call the handler with _is_async flag instead of directly calling the async handler
return base_llm_http_handler.image_edit_handler(
model=model,
diff --git a/litellm/images/utils.py b/litellm/images/utils.py
index 7b1875c4932..fa271b61b6a 100644
--- a/litellm/images/utils.py
+++ b/litellm/images/utils.py
@@ -1,5 +1,5 @@
from io import BufferedReader, BytesIO
-from typing import Any, Dict, cast, get_type_hints
+from typing import Any, Dict, List, Optional, cast, get_type_hints
import litellm
from litellm.litellm_core_utils.token_counter import get_image_type
@@ -14,41 +14,53 @@ class ImageEditRequestUtils:
model: str,
image_edit_provider_config: BaseImageEditConfig,
image_edit_optional_params: ImageEditOptionalRequestParams,
+ drop_params: Optional[bool] = None,
+ additional_drop_params: Optional[List[str]] = None,
) -> Dict:
"""
Get optional parameters for the image edit API.
Args:
- params: Dictionary of all parameters
model: The model name
image_edit_provider_config: The provider configuration for image edit API
+ image_edit_optional_params: The optional parameters for the image edit API
+ drop_params: If True, silently drop unsupported parameters instead of raising
+ additional_drop_params: List of additional parameter names to drop
Returns:
A dictionary of supported parameters for the image edit API
"""
- # Remove None values and internal parameters
-
- # Get supported parameters for the model
supported_params = image_edit_provider_config.get_supported_openai_params(model)
- # Check for unsupported parameters
+ should_drop = litellm.drop_params is True or drop_params is True
+
+ filtered_optional_params = dict(image_edit_optional_params)
+ if additional_drop_params:
+ for param in additional_drop_params:
+ filtered_optional_params.pop(param, None)
+
unsupported_params = [
param
- for param in image_edit_optional_params
+ for param in filtered_optional_params
if param not in supported_params
]
if unsupported_params:
- raise litellm.UnsupportedParamsError(
- model=model,
- message=f"The following parameters are not supported for model {model}: {', '.join(unsupported_params)}",
- )
+ if should_drop:
+ for param in unsupported_params:
+ filtered_optional_params.pop(param, None)
+ else:
+ raise litellm.UnsupportedParamsError(
+ model=model,
+ message=f"The following parameters are not supported for model {model}: {', '.join(unsupported_params)}",
+ )
- # Map parameters to provider-specific format
mapped_params = image_edit_provider_config.map_openai_params(
- image_edit_optional_params=image_edit_optional_params,
+ image_edit_optional_params=cast(
+ ImageEditOptionalRequestParams, filtered_optional_params
+ ),
model=model,
- drop_params=litellm.drop_params,
+ drop_params=should_drop,
)
return mapped_params
@@ -70,7 +82,6 @@ class ImageEditRequestUtils:
filtered_params = {
k: v for k, v in params.items() if k in valid_keys and v is not None
}
-
return cast(ImageEditOptionalRequestParams, filtered_params)
@staticmethod
diff --git a/litellm/integrations/SlackAlerting/budget_alert_types.py b/litellm/integrations/SlackAlerting/budget_alert_types.py
index dadfef3fc40..205c5c89e35 100644
--- a/litellm/integrations/SlackAlerting/budget_alert_types.py
+++ b/litellm/integrations/SlackAlerting/budget_alert_types.py
@@ -77,8 +77,9 @@ class ProjectedLimitExceededAlert(BaseBudgetAlertType):
def get_budget_alert_type(
type: Literal[
"token_budget",
- "soft_budget",
"user_budget",
+ "soft_budget",
+ "max_budget_alert",
"team_budget",
"organization_budget",
"proxy_budget",
@@ -91,6 +92,7 @@ def get_budget_alert_type(
"proxy_budget": ProxyBudgetAlert(),
"soft_budget": SoftBudgetAlert(),
"user_budget": UserBudgetAlert(),
+ "max_budget_alert": TokenBudgetAlert(),
"team_budget": TeamBudgetAlert(),
"organization_budget": OrganizationBudgetAlert(),
"token_budget": TokenBudgetAlert(),
diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py
index 0e691e2c43f..0c36e15db01 100644
--- a/litellm/integrations/SlackAlerting/slack_alerting.py
+++ b/litellm/integrations/SlackAlerting/slack_alerting.py
@@ -531,8 +531,9 @@ class SlackAlerting(CustomBatchLogger):
self,
type: Literal[
"token_budget",
- "soft_budget",
"user_budget",
+ "soft_budget",
+ "max_budget_alert",
"team_budget",
"organization_budget",
"proxy_budget",
diff --git a/litellm/integrations/arize/arize_phoenix.py b/litellm/integrations/arize/arize_phoenix.py
index 4a6e0cec8ca..cd345a7f76d 100644
--- a/litellm/integrations/arize/arize_phoenix.py
+++ b/litellm/integrations/arize/arize_phoenix.py
@@ -1,12 +1,10 @@
import os
from typing import TYPE_CHECKING, Any, Optional, Union
-from datetime import datetime
from litellm._logging import verbose_logger
from litellm.integrations.arize import _utils
from litellm.integrations.arize._utils import ArizeOTELAttributes
from litellm.types.integrations.arize_phoenix import ArizePhoenixConfig
-from litellm.types.services import ServiceLoggerPayload
from litellm.integrations.opentelemetry import OpenTelemetry
if TYPE_CHECKING:
@@ -35,13 +33,19 @@ class ArizePhoenixLogger(OpenTelemetry):
@staticmethod
def set_arize_phoenix_attributes(span: Span, kwargs, response_obj):
_utils.set_attributes(span, kwargs, response_obj, ArizeOTELAttributes)
+
+ # Set project name on the span for all traces to go to custom Phoenix projects
+ config = ArizePhoenixLogger.get_arize_phoenix_config()
+ if config.project_name:
+ from litellm.integrations.opentelemetry_utils.base_otel_llm_obs_attributes import safe_set_attribute
+ safe_set_attribute(span, "openinference.project.name", config.project_name)
+
return
@staticmethod
def get_arize_phoenix_config() -> ArizePhoenixConfig:
"""
Retrieves the Arize Phoenix configuration based on environment variables.
-
Returns:
ArizePhoenixConfig: A Pydantic model containing Arize Phoenix configuration.
"""
@@ -95,7 +99,7 @@ class ArizePhoenixLogger(OpenTelemetry):
"PHOENIX_API_KEY must be set when using Phoenix Cloud (app.phoenix.arize.com)."
)
- project_name = os.environ.get("PHOENIX_PROJECT_NAME", "litellm-project")
+ project_name = os.environ.get("PHOENIX_PROJECT_NAME", "default")
return ArizePhoenixConfig(
otlp_auth_headers=otlp_auth_headers,
@@ -103,34 +107,8 @@ class ArizePhoenixLogger(OpenTelemetry):
endpoint=endpoint,
project_name=project_name,
)
-
- async def async_service_success_hook(
- self,
- payload: ServiceLoggerPayload,
- parent_otel_span: Optional[Span] = None,
- start_time: Optional[Union[datetime, float]] = None,
- end_time: Optional[Union[datetime, float]] = None,
- event_metadata: Optional[dict] = None,
- ):
- pass # suppress additional spans
-
- async def async_service_failure_hook(
- self,
- payload: ServiceLoggerPayload,
- error: Optional[str] = "",
- parent_otel_span: Optional[Span] = None,
- start_time: Optional[Union[datetime, float]] = None,
- end_time: Optional[Union[float, datetime]] = None,
- event_metadata: Optional[dict] = None,
- ):
- pass # suppress additional spans
-
- def create_litellm_proxy_request_started_span(
- self,
- start_time: datetime,
- headers: dict,
- ):
- pass # suppress additional spans
+
+ ## cannot suppress additional proxy server spans, removed previous methods.
async def async_health_check(self):
diff --git a/litellm/integrations/azure_sentinel/__init__.py b/litellm/integrations/azure_sentinel/__init__.py
new file mode 100644
index 00000000000..46f2fed0a97
--- /dev/null
+++ b/litellm/integrations/azure_sentinel/__init__.py
@@ -0,0 +1,4 @@
+from litellm.integrations.azure_sentinel.azure_sentinel import AzureSentinelLogger
+
+__all__ = ["AzureSentinelLogger"]
+
diff --git a/litellm/integrations/azure_sentinel/azure_sentinel.py b/litellm/integrations/azure_sentinel/azure_sentinel.py
new file mode 100644
index 00000000000..875432de876
--- /dev/null
+++ b/litellm/integrations/azure_sentinel/azure_sentinel.py
@@ -0,0 +1,304 @@
+"""
+Azure Sentinel Integration - sends logs to Azure Log Analytics using Logs Ingestion API
+
+Azure Sentinel uses Log Analytics workspaces for data storage. This integration sends
+LiteLLM logs to the Log Analytics workspace using the Azure Monitor Logs Ingestion API.
+
+Reference API: https://learn.microsoft.com/en-us/azure/azure-monitor/logs/logs-ingestion-api-overview
+
+`async_log_success_event` - used by litellm proxy to send logs to Azure Sentinel
+`async_log_failure_event` - used by litellm proxy to send failure logs to Azure Sentinel
+
+For batching specific details see CustomBatchLogger class
+"""
+
+import asyncio
+import os
+import traceback
+from typing import List, Optional
+
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_batch_logger import CustomBatchLogger
+from litellm.llms.custom_httpx.http_handler import (
+ get_async_httpx_client,
+ httpxSpecialProvider,
+)
+from litellm.types.utils import StandardLoggingPayload
+
+
+class AzureSentinelLogger(CustomBatchLogger):
+ """
+ Logger that sends LiteLLM logs to Azure Sentinel via Azure Monitor Logs Ingestion API
+ """
+
+ def __init__(
+ self,
+ dcr_immutable_id: Optional[str] = None,
+ stream_name: Optional[str] = None,
+ endpoint: Optional[str] = None,
+ tenant_id: Optional[str] = None,
+ client_id: Optional[str] = None,
+ client_secret: Optional[str] = None,
+ **kwargs,
+ ):
+ """
+ Initialize Azure Sentinel logger using Logs Ingestion API
+
+ Args:
+ dcr_immutable_id (str, optional): Data Collection Rule (DCR) Immutable ID.
+ If not provided, will use AZURE_SENTINEL_DCR_IMMUTABLE_ID env var.
+ stream_name (str, optional): Stream name from DCR (e.g., "Custom-LiteLLM").
+ If not provided, will use AZURE_SENTINEL_STREAM_NAME env var or default to "Custom-LiteLLM".
+ endpoint (str, optional): Data Collection Endpoint (DCE) or DCR ingestion endpoint.
+ If not provided, will use AZURE_SENTINEL_ENDPOINT env var.
+ tenant_id (str, optional): Azure Tenant ID for OAuth2 authentication.
+ If not provided, will use AZURE_SENTINEL_TENANT_ID or AZURE_TENANT_ID env var.
+ client_id (str, optional): Azure Client ID (Application ID) for OAuth2 authentication.
+ If not provided, will use AZURE_SENTINEL_CLIENT_ID or AZURE_CLIENT_ID env var.
+ client_secret (str, optional): Azure Client Secret for OAuth2 authentication.
+ If not provided, will use AZURE_SENTINEL_CLIENT_SECRET or AZURE_CLIENT_SECRET env var.
+ """
+ self.async_httpx_client = get_async_httpx_client(
+ llm_provider=httpxSpecialProvider.LoggingCallback
+ )
+
+ self.dcr_immutable_id = (
+ dcr_immutable_id or os.getenv("AZURE_SENTINEL_DCR_IMMUTABLE_ID")
+ )
+ self.stream_name = stream_name or os.getenv(
+ "AZURE_SENTINEL_STREAM_NAME", "Custom-LiteLLM"
+ )
+ self.endpoint = endpoint or os.getenv("AZURE_SENTINEL_ENDPOINT")
+ self.tenant_id = tenant_id or os.getenv("AZURE_SENTINEL_TENANT_ID") or os.getenv(
+ "AZURE_TENANT_ID"
+ )
+ self.client_id = client_id or os.getenv("AZURE_SENTINEL_CLIENT_ID") or os.getenv(
+ "AZURE_CLIENT_ID"
+ )
+ self.client_secret = (
+ client_secret
+ or os.getenv("AZURE_SENTINEL_CLIENT_SECRET")
+ or os.getenv("AZURE_CLIENT_SECRET")
+ )
+
+ if not self.dcr_immutable_id:
+ raise ValueError(
+ "AZURE_SENTINEL_DCR_IMMUTABLE_ID is required. Set it as an environment variable or pass dcr_immutable_id parameter."
+ )
+ if not self.endpoint:
+ raise ValueError(
+ "AZURE_SENTINEL_ENDPOINT is required. Set it as an environment variable or pass endpoint parameter."
+ )
+ if not self.tenant_id:
+ raise ValueError(
+ "AZURE_SENTINEL_TENANT_ID or AZURE_TENANT_ID is required. Set it as an environment variable or pass tenant_id parameter."
+ )
+ if not self.client_id:
+ raise ValueError(
+ "AZURE_SENTINEL_CLIENT_ID or AZURE_CLIENT_ID is required. Set it as an environment variable or pass client_id parameter."
+ )
+ if not self.client_secret:
+ raise ValueError(
+ "AZURE_SENTINEL_CLIENT_SECRET or AZURE_CLIENT_SECRET is required. Set it as an environment variable or pass client_secret parameter."
+ )
+
+ # Build API endpoint: {Endpoint}/dataCollectionRules/{DCR Immutable ID}/streams/{Stream Name}?api-version=2023-01-01
+ self.api_endpoint = (
+ f"{self.endpoint.rstrip('/')}/dataCollectionRules/{self.dcr_immutable_id}/streams/{self.stream_name}?api-version=2023-01-01"
+ )
+
+ # OAuth2 scope for Azure Monitor
+ self.oauth_scope = "https://monitor.azure.com/.default"
+ self.oauth_token: Optional[str] = None
+ self.oauth_token_expires_at: Optional[float] = None
+
+ self.flush_lock = asyncio.Lock()
+ super().__init__(**kwargs, flush_lock=self.flush_lock)
+ asyncio.create_task(self.periodic_flush())
+ self.log_queue: List[StandardLoggingPayload] = []
+
+ async def _get_oauth_token(self) -> str:
+ """
+ Get OAuth2 Bearer token for Azure Monitor Logs Ingestion API
+
+ Returns:
+ Bearer token string
+ """
+ # Check if we have a valid cached token
+ import time
+
+ if (
+ self.oauth_token
+ and self.oauth_token_expires_at
+ and time.time() < self.oauth_token_expires_at - 60
+ ): # Refresh 60 seconds before expiry
+ return self.oauth_token
+
+ # Get new token using client credentials flow
+ assert self.tenant_id is not None, "tenant_id is required"
+ assert self.client_id is not None, "client_id is required"
+ assert self.client_secret is not None, "client_secret is required"
+
+ token_url = f"https://login.microsoftonline.com/{self.tenant_id}/oauth2/v2.0/token"
+
+ token_data = {
+ "client_id": self.client_id,
+ "client_secret": self.client_secret,
+ "scope": self.oauth_scope,
+ "grant_type": "client_credentials",
+ }
+
+ response = await self.async_httpx_client.post(
+ url=token_url,
+ data=token_data,
+ headers={"Content-Type": "application/x-www-form-urlencoded"},
+ )
+
+ if response.status_code != 200:
+ raise Exception(
+ f"Failed to get OAuth2 token: {response.status_code} - {response.text}"
+ )
+
+ token_response = response.json()
+ self.oauth_token = token_response.get("access_token")
+ expires_in = token_response.get("expires_in", 3600)
+
+ if not self.oauth_token:
+ raise Exception("OAuth2 token response did not contain access_token")
+
+ # Cache token expiry time
+ import time
+
+ self.oauth_token_expires_at = time.time() + expires_in
+
+ return self.oauth_token
+
+ async def async_log_success_event(
+ self, kwargs, response_obj, start_time, end_time
+ ):
+ """
+ Async Log success events to Azure Sentinel
+
+ - Gets StandardLoggingPayload from kwargs
+ - Adds to batch queue
+ - Flushes based on CustomBatchLogger settings
+
+ Raises:
+ Raises a NON Blocking verbose_logger.exception if an error occurs
+ """
+ try:
+ verbose_logger.debug(
+ "Azure Sentinel: Logging - Enters logging function for model %s", kwargs
+ )
+ standard_logging_payload = kwargs.get("standard_logging_object", None)
+
+ if standard_logging_payload is None:
+ verbose_logger.warning(
+ "Azure Sentinel: standard_logging_object not found in kwargs"
+ )
+ return
+
+ self.log_queue.append(standard_logging_payload)
+
+ if len(self.log_queue) >= self.batch_size:
+ await self.async_send_batch()
+
+ except Exception as e:
+ verbose_logger.exception(
+ f"Azure Sentinel Layer Error - {str(e)}\n{traceback.format_exc()}"
+ )
+ pass
+
+ async def async_log_failure_event(
+ self, kwargs, response_obj, start_time, end_time
+ ):
+ """
+ Async Log failure events to Azure Sentinel
+
+ - Gets StandardLoggingPayload from kwargs
+ - Adds to batch queue
+ - Flushes based on CustomBatchLogger settings
+
+ Raises:
+ Raises a NON Blocking verbose_logger.exception if an error occurs
+ """
+ try:
+ verbose_logger.debug(
+ "Azure Sentinel: Logging - Enters failure logging function for model %s",
+ kwargs,
+ )
+ standard_logging_payload = kwargs.get("standard_logging_object", None)
+
+ if standard_logging_payload is None:
+ verbose_logger.warning(
+ "Azure Sentinel: standard_logging_object not found in kwargs"
+ )
+ return
+
+ self.log_queue.append(standard_logging_payload)
+
+ if len(self.log_queue) >= self.batch_size:
+ await self.async_send_batch()
+
+ except Exception as e:
+ verbose_logger.exception(
+ f"Azure Sentinel Layer Error - {str(e)}\n{traceback.format_exc()}"
+ )
+ pass
+
+ async def async_send_batch(self):
+ """
+ Sends the batch of logs to Azure Monitor Logs Ingestion API
+
+ Raises:
+ Raises a NON Blocking verbose_logger.exception if an error occurs
+ """
+ try:
+ if not self.log_queue:
+ return
+
+ verbose_logger.debug(
+ "Azure Sentinel - about to flush %s events", len(self.log_queue)
+ )
+
+ from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
+ # Get OAuth2 token
+ bearer_token = await self._get_oauth_token()
+
+ # Convert log queue to JSON array format expected by Logs Ingestion API
+ # Each log entry should be a JSON object in the array
+ body = safe_dumps(self.log_queue)
+
+ # Set headers for Logs Ingestion API
+ headers = {
+ "Authorization": f"Bearer {bearer_token}",
+ "Content-Type": "application/json",
+ }
+
+ # Send the request
+ response = await self.async_httpx_client.post(
+ url=self.api_endpoint, data=body.encode("utf-8"), headers=headers
+ )
+
+ if response.status_code not in [200, 204]:
+ verbose_logger.error(
+ "Azure Sentinel API error: status_code=%s, response=%s",
+ response.status_code,
+ response.text,
+ )
+ raise Exception(
+ f"Failed to send logs to Azure Sentinel: {response.status_code} - {response.text}"
+ )
+
+ verbose_logger.debug(
+ "Azure Sentinel: Response from API status_code: %s",
+ response.status_code,
+ )
+
+ except Exception as e:
+ verbose_logger.exception(
+ f"Azure Sentinel Error sending batch API - {str(e)}\n{traceback.format_exc()}"
+ )
+ finally:
+ self.log_queue.clear()
diff --git a/litellm/integrations/azure_sentinel/example_standard_logging_payload.json b/litellm/integrations/azure_sentinel/example_standard_logging_payload.json
new file mode 100644
index 00000000000..a9ef7d8557b
--- /dev/null
+++ b/litellm/integrations/azure_sentinel/example_standard_logging_payload.json
@@ -0,0 +1,179 @@
+{
+ "id": "chatcmpl-2299b6a2-82a3-465a-b47c-04e685a2227f",
+ "trace_id": "97311c60-9a61-4f48-a814-70139ee57868",
+ "call_type": "acompletion",
+ "cache_hit": null,
+ "stream": true,
+ "status": "success",
+ "custom_llm_provider": "openai",
+ "saved_cache_cost": 0.0,
+ "startTime": 1766000068.28466,
+ "endTime": 1766000070.07935,
+ "completionStartTime": 1766000070.07935,
+ "response_time": 1.79468512535095,
+ "model": "gpt-4o",
+ "metadata": {
+ "user_api_key_hash": null,
+ "user_api_key_alias": null,
+ "user_api_key_team_id": null,
+ "user_api_key_org_id": null,
+ "user_api_key_user_id": null,
+ "user_api_key_team_alias": null,
+ "user_api_key_user_email": null,
+ "spend_logs_metadata": null,
+ "requester_ip_address": null,
+ "requester_metadata": null,
+ "user_api_key_end_user_id": null,
+ "prompt_management_metadata": null,
+ "applied_guardrails": [],
+ "mcp_tool_call_metadata": null,
+ "vector_store_request_metadata": null,
+ "guardrail_information": null
+ },
+ "cache_key": null,
+ "response_cost": 0.00022500000000000002,
+ "total_tokens": 30,
+ "prompt_tokens": 10,
+ "completion_tokens": 20,
+ "request_tags": [],
+ "end_user": "",
+ "api_base": "",
+ "model_group": "",
+ "model_id": "",
+ "requester_ip_address": null,
+ "messages": [
+ {
+ "role": "user",
+ "content": "Hello, world!"
+ }
+ ],
+ "response": {
+ "id": "chatcmpl-2299b6a2-82a3-465a-b47c-04e685a2227f",
+ "created": 1742855151,
+ "model": "gpt-4o",
+ "object": "chat.completion",
+ "system_fingerprint": null,
+ "choices": [
+ {
+ "finish_reason": "stop",
+ "index": 0,
+ "message": {
+ "content": "hi",
+ "role": "assistant",
+ "tool_calls": null,
+ "function_call": null,
+ "provider_specific_fields": null
+ }
+ }
+ ],
+ "usage": {
+ "completion_tokens": 20,
+ "prompt_tokens": 10,
+ "total_tokens": 30,
+ "completion_tokens_details": null,
+ "prompt_tokens_details": null
+ }
+ },
+ "model_parameters": {},
+ "hidden_params": {
+ "model_id": null,
+ "cache_key": null,
+ "api_base": "https://api.openai.com",
+ "response_cost": 0.00022500000000000002,
+ "additional_headers": {},
+ "litellm_overhead_time_ms": null,
+ "batch_models": null,
+ "litellm_model_name": "gpt-4o"
+ },
+ "model_map_information": {
+ "model_map_key": "gpt-4o",
+ "model_map_value": {
+ "key": "gpt-4o",
+ "max_tokens": 16384,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 16384,
+ "input_cost_per_token": 2.5e-06,
+ "cache_creation_input_token_cost": null,
+ "cache_read_input_token_cost": 1.25e-06,
+ "input_cost_per_character": null,
+ "input_cost_per_token_above_128k_tokens": null,
+ "input_cost_per_query": null,
+ "input_cost_per_second": null,
+ "input_cost_per_audio_token": null,
+ "input_cost_per_token_batches": 1.25e-06,
+ "output_cost_per_token_batches": 5e-06,
+ "output_cost_per_token": 1e-05,
+ "output_cost_per_audio_token": null,
+ "output_cost_per_character": null,
+ "output_cost_per_token_above_128k_tokens": null,
+ "output_cost_per_character_above_128k_tokens": null,
+ "output_cost_per_second": null,
+ "output_cost_per_image": null,
+ "output_vector_size": null,
+ "litellm_provider": "openai",
+ "mode": "chat",
+ "supports_system_messages": true,
+ "supports_response_schema": true,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_assistant_prefill": false,
+ "supports_prompt_caching": true,
+ "supports_audio_input": false,
+ "supports_audio_output": false,
+ "supports_pdf_input": false,
+ "supports_embedding_image_input": false,
+ "supports_native_streaming": null,
+ "supports_web_search": true,
+ "search_context_cost_per_query": {
+ "search_context_size_low": 0.03,
+ "search_context_size_medium": 0.035,
+ "search_context_size_high": 0.05
+ },
+ "tpm": null,
+ "rpm": null,
+ "supported_openai_params": [
+ "frequency_penalty",
+ "logit_bias",
+ "logprobs",
+ "top_logprobs",
+ "max_tokens",
+ "max_completion_tokens",
+ "modalities",
+ "prediction",
+ "n",
+ "presence_penalty",
+ "seed",
+ "stop",
+ "stream",
+ "stream_options",
+ "temperature",
+ "top_p",
+ "tools",
+ "tool_choice",
+ "function_call",
+ "functions",
+ "max_retries",
+ "extra_headers",
+ "parallel_tool_calls",
+ "audio",
+ "response_format",
+ "user"
+ ]
+ }
+ },
+ "error_str": null,
+ "error_information": {
+ "error_code": "",
+ "error_class": "",
+ "llm_provider": "",
+ "traceback": "",
+ "error_message": ""
+ },
+ "response_cost_failure_debug_info": null,
+ "guardrail_information": null,
+ "standard_built_in_tools_params": {
+ "web_search_options": null,
+ "file_search": null
+ }
+ }
diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py
index 6892ba3426a..fe0ce208ee6 100644
--- a/litellm/integrations/custom_guardrail.py
+++ b/litellm/integrations/custom_guardrail.py
@@ -240,6 +240,28 @@ class CustomGuardrail(CustomLogger):
return metadata["disable_global_guardrail"]
return False
+ def _is_valid_response_type(self, result: Any) -> bool:
+ """
+ Check if result is a valid LLMResponseTypes instance.
+
+ Safely handles TypedDict types which don't support isinstance checks.
+ For non-LiteLLM responses (like passthrough httpx.Response), returns True
+ to allow them through.
+ """
+ if result is None:
+ return False
+
+ try:
+ # Try isinstance check on valid types that support it
+ response_types = get_args(LLMResponseTypes)
+ return isinstance(result, response_types)
+ except TypeError as e:
+ # TypedDict types don't support isinstance checks
+ # In this case, we can't validate the type, so we allow it through
+ if "TypedDict" in str(e):
+ return True
+ raise
+
def get_guardrail_from_metadata(
self, data: dict
) -> Union[List[str], List[Dict[str, DynamicGuardrailParams]]]:
@@ -342,7 +364,7 @@ class CustomGuardrail(CustomLogger):
response=response,
)
- if result is None or not isinstance(result, get_args(LLMResponseTypes)):
+ if not self._is_valid_response_type(result):
return response
return result
diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py
index 6771999cd35..4c4e6fa6342 100644
--- a/litellm/integrations/custom_logger.py
+++ b/litellm/integrations/custom_logger.py
@@ -32,6 +32,8 @@ from litellm.types.utils import (
)
if TYPE_CHECKING:
+ from fastapi import HTTPException
+
from litellm.caching.caching import DualCache
from opentelemetry.trace import Span as _Span
@@ -348,7 +350,20 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
original_exception: Exception,
user_api_key_dict: UserAPIKeyAuth,
traceback_str: Optional[str] = None,
- ):
+ ) -> Optional["HTTPException"]:
+ """
+ Called after an LLM API call fails. Can return or raise HTTPException to transform error responses.
+
+ Args:
+ - request_data: dict - The request data.
+ - original_exception: Exception - The original exception that occurred.
+ - user_api_key_dict: UserAPIKeyAuth - The user API key dictionary.
+ - traceback_str: Optional[str] - The traceback string.
+
+ Returns:
+ - Optional[HTTPException]: Return an HTTPException to transform the error response sent to the client.
+ Return None to use the original exception.
+ """
pass
async def async_post_call_success_hook(
diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py
index 21e1d562224..503e8d8c87a 100644
--- a/litellm/integrations/datadog/datadog.py
+++ b/litellm/integrations/datadog/datadog.py
@@ -27,6 +27,13 @@ import litellm
from litellm._logging import verbose_logger
from litellm._uuid import uuid
from litellm.integrations.custom_batch_logger import CustomBatchLogger
+from litellm.integrations.datadog.datadog_handler import (
+ get_datadog_hostname,
+ get_datadog_service,
+ get_datadog_source,
+ get_datadog_tags,
+)
+from litellm.litellm_core_utils.dd_tracing import tracer
from litellm.llms.custom_httpx.http_handler import (
_get_httpx_client,
get_async_httpx_client,
@@ -67,23 +74,23 @@ class DataDogLogger(
Optional environment variables (DataDog Agent):
`LITELLM_DD_AGENT_HOST` - hostname or IP of DataDog agent, example = `"localhost"`
`LITELLM_DD_AGENT_PORT` - port of DataDog agent (default: 10518 for logs)
-
+
Note: We use LITELLM_DD_AGENT_HOST instead of DD_AGENT_HOST to avoid conflicts
with ddtrace which automatically sets DD_AGENT_HOST for APM tracing.
"""
try:
verbose_logger.debug("Datadog: in init datadog logger")
-
+
#########################################################
# Handle datadog_params set as litellm.datadog_params
#########################################################
dict_datadog_params = self._get_datadog_params()
kwargs.update(dict_datadog_params)
-
+
self.async_client = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)
-
+
# Configure DataDog endpoint (Agent or Direct API)
# Use LITELLM_DD_AGENT_HOST to avoid conflicts with ddtrace's DD_AGENT_HOST
dd_agent_host = os.getenv("LITELLM_DD_AGENT_HOST")
@@ -91,7 +98,7 @@ class DataDogLogger(
self._configure_dd_agent(dd_agent_host=dd_agent_host)
else:
self._configure_dd_direct_api()
-
+
# Optional override for testing
self._apply_dd_base_url_override()
self.sync_client = _get_httpx_client()
@@ -118,17 +125,21 @@ class DataDogLogger(
dict_datadog_params = litellm.datadog_params.model_dump()
elif isinstance(litellm.datadog_params, Dict):
# only allow params that are of DatadogInitParams
- dict_datadog_params = DatadogInitParams(**litellm.datadog_params).model_dump()
+ dict_datadog_params = DatadogInitParams(
+ **litellm.datadog_params
+ ).model_dump()
return dict_datadog_params
def _configure_dd_agent(self, dd_agent_host: str) -> None:
"""
Configure DataDog Agent for log forwarding
-
+
Args:
dd_agent_host: Hostname or IP of DataDog agent
"""
- dd_agent_port = os.getenv("LITELLM_DD_AGENT_PORT", "10518") # default port for logs
+ dd_agent_port = os.getenv(
+ "LITELLM_DD_AGENT_PORT", "10518"
+ ) # default port for logs
self.intake_url = f"http://{dd_agent_host}:{dd_agent_port}/api/v2/logs"
self.DD_API_KEY = os.getenv("DD_API_KEY") # Optional when using agent
verbose_logger.debug(f"Datadog: Using DD Agent at {self.intake_url}")
@@ -136,7 +147,7 @@ class DataDogLogger(
def _configure_dd_direct_api(self) -> None:
"""
Configure direct DataDog API connection
-
+
Raises:
Exception: If required environment variables are not set
"""
@@ -144,11 +155,9 @@ class DataDogLogger(
raise Exception("DD_API_KEY is not set, set 'DD_API_KEY=<>")
if os.getenv("DD_SITE", None) is None:
raise Exception("DD_SITE is not set in .env, set 'DD_SITE=<>")
-
+
self.DD_API_KEY = os.getenv("DD_API_KEY")
- self.intake_url = (
- f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs"
- )
+ self.intake_url = f"https://http-intake.logs.{os.getenv('DD_SITE')}/api/v2/logs"
def _apply_dd_base_url_override(self) -> None:
"""
@@ -270,7 +279,7 @@ class DataDogLogger(
# Add API key if available (required for direct API, optional for agent)
if self.DD_API_KEY:
headers["DD-API-KEY"] = self.DD_API_KEY
-
+
response = self.sync_client.post(
url=self.intake_url,
json=dd_payload, # type: ignore
@@ -318,18 +327,18 @@ class DataDogLogger(
status: DataDogStatus,
) -> DatadogPayload:
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
json_payload = safe_dumps(standard_logging_object)
verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
dd_payload = DatadogPayload(
- ddsource=self._get_datadog_source(),
- ddtags=self._get_datadog_tags(
- standard_logging_object=standard_logging_object
- ),
- hostname=self._get_datadog_hostname(),
+ ddsource=get_datadog_source(),
+ ddtags=get_datadog_tags(standard_logging_object=standard_logging_object),
+ hostname=get_datadog_hostname(),
message=json_payload,
- service=self._get_datadog_service(),
+ service=get_datadog_service(),
status=status,
)
+ self._add_trace_context_to_payload(dd_payload=dd_payload)
return dd_payload
def create_datadog_logging_payload(
@@ -384,18 +393,19 @@ class DataDogLogger(
import gzip
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
compressed_data = gzip.compress(safe_dumps(data).encode("utf-8"))
-
+
# Build headers
headers = {
"Content-Encoding": "gzip",
"Content-Type": "application/json",
}
-
+
# Add API key if available (required for direct API, optional for agent)
if self.DD_API_KEY:
headers["DD-API-KEY"] = self.DD_API_KEY
-
+
response = await self.async_client.post(
url=self.intake_url,
data=compressed_data, # type: ignore
@@ -421,13 +431,14 @@ class DataDogLogger(
_payload_dict = payload.model_dump()
_payload_dict.update(event_metadata or {})
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
_dd_message_str = safe_dumps(_payload_dict)
_dd_payload = DatadogPayload(
- ddsource=self._get_datadog_source(),
- ddtags=self._get_datadog_tags(),
- hostname=self._get_datadog_hostname(),
+ ddsource=get_datadog_source(),
+ ddtags=get_datadog_tags(),
+ hostname=get_datadog_hostname(),
message=_dd_message_str,
- service=self._get_datadog_service(),
+ service=get_datadog_service(),
status=DataDogStatus.WARN,
)
@@ -462,13 +473,14 @@ class DataDogLogger(
_payload_dict.update(event_metadata or {})
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
_dd_message_str = safe_dumps(_payload_dict)
_dd_payload = DatadogPayload(
- ddsource=self._get_datadog_source(),
- ddtags=self._get_datadog_tags(),
- hostname=self._get_datadog_hostname(),
+ ddsource=get_datadog_source(),
+ ddtags=get_datadog_tags(),
+ hostname=get_datadog_hostname(),
message=_dd_message_str,
- service=self._get_datadog_service(),
+ service=get_datadog_service(),
status=DataDogStatus.INFO,
)
@@ -530,7 +542,6 @@ class DataDogLogger(
else:
clean_metadata[key] = value
-
# Build the initial payload
payload = {
"id": id,
@@ -550,68 +561,70 @@ class DataDogLogger(
}
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
json_payload = safe_dumps(payload)
verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
dd_payload = DatadogPayload(
- ddsource=self._get_datadog_source(),
- ddtags=self._get_datadog_tags(),
- hostname=self._get_datadog_hostname(),
+ ddsource=get_datadog_source(),
+ ddtags=get_datadog_tags(),
+ hostname=get_datadog_hostname(),
message=json_payload,
- service=self._get_datadog_service(),
+ service=get_datadog_service(),
status=DataDogStatus.INFO,
)
return dd_payload
- @staticmethod
- def _get_datadog_tags(
- standard_logging_object: Optional[StandardLoggingPayload] = None,
- ) -> str:
- """
- Get the datadog tags for the request
+ def _add_trace_context_to_payload(
+ self,
+ dd_payload: DatadogPayload,
+ ) -> None:
+ """Attach Datadog APM trace context if one is active."""
- DD tags need to be as follows:
- - tags: ["user_handle:dog@gmail.com", "app_version:1.0.0"]
- """
- base_tags = {
- "env": os.getenv("DD_ENV", "unknown"),
- "service": os.getenv("DD_SERVICE", "litellm"),
- "version": os.getenv("DD_VERSION", "unknown"),
- "HOSTNAME": DataDogLogger._get_datadog_hostname(),
- "POD_NAME": os.getenv("POD_NAME", "unknown"),
- }
+ try:
+ trace_context = self._get_active_trace_context()
+ if trace_context is None:
+ return
- tags = [f"{k}:{v}" for k, v in base_tags.items()]
-
- if standard_logging_object:
- _request_tags: List[str] = (
- standard_logging_object.get("request_tags", []) or []
+ dd_payload["dd.trace_id"] = trace_context["trace_id"]
+ span_id = trace_context.get("span_id")
+ if span_id is not None:
+ dd_payload["dd.span_id"] = span_id
+ except Exception:
+ verbose_logger.exception(
+ "Datadog: Failed to attach trace context to payload"
)
- request_tags = [f"request_tag:{tag}" for tag in _request_tags]
- tags.extend(request_tags)
- return ",".join(tags)
+ def _get_active_trace_context(self) -> Optional[Dict[str, str]]:
+ try:
+ current_span = None
+ current_span_fn = getattr(tracer, "current_span", None)
+ if callable(current_span_fn):
+ current_span = current_span_fn()
- @staticmethod
- def _get_datadog_source():
- return os.getenv("DD_SOURCE", "litellm")
+ if current_span is None:
+ current_root_span_fn = getattr(tracer, "current_root_span", None)
+ if callable(current_root_span_fn):
+ current_span = current_root_span_fn()
- @staticmethod
- def _get_datadog_service():
- return os.getenv("DD_SERVICE", "litellm-server")
+ if current_span is None:
+ return None
- @staticmethod
- def _get_datadog_hostname():
- return os.getenv("HOSTNAME", "")
+ trace_id = getattr(current_span, "trace_id", None)
+ if trace_id is None:
+ return None
- @staticmethod
- def _get_datadog_env():
- return os.getenv("DD_ENV", "unknown")
-
- @staticmethod
- def _get_datadog_pod_name():
- return os.getenv("POD_NAME", "unknown")
+ span_id = getattr(current_span, "span_id", None)
+ trace_context: Dict[str, str] = {"trace_id": str(trace_id)}
+ if span_id is not None:
+ trace_context["span_id"] = str(span_id)
+ return trace_context
+ except Exception:
+ verbose_logger.exception(
+ "Datadog: Failed to retrieve active trace context from tracer"
+ )
+ return None
async def async_health_check(self) -> IntegrationHealthCheckStatus:
"""
@@ -651,4 +664,4 @@ class DataDogLogger(
start_time_utc: Optional[datetimeObj],
end_time_utc: Optional[datetimeObj],
) -> Optional[dict]:
- pass
\ No newline at end of file
+ pass
diff --git a/litellm/integrations/datadog/datadog_handler.py b/litellm/integrations/datadog/datadog_handler.py
new file mode 100644
index 00000000000..26fab77759e
--- /dev/null
+++ b/litellm/integrations/datadog/datadog_handler.py
@@ -0,0 +1,50 @@
+"""Shared helpers for Datadog integrations."""
+
+from __future__ import annotations
+
+import os
+from typing import List, Optional
+
+from litellm.types.utils import StandardLoggingPayload
+
+
+def get_datadog_source() -> str:
+ return os.getenv("DD_SOURCE", "litellm")
+
+
+def get_datadog_service() -> str:
+ return os.getenv("DD_SERVICE", "litellm-server")
+
+
+def get_datadog_hostname() -> str:
+ return os.getenv("HOSTNAME", "")
+
+
+def get_datadog_env() -> str:
+ return os.getenv("DD_ENV", "unknown")
+
+
+def get_datadog_pod_name() -> str:
+ return os.getenv("POD_NAME", "unknown")
+
+
+def get_datadog_tags(
+ standard_logging_object: Optional[StandardLoggingPayload] = None,
+) -> str:
+ """Build Datadog tags string used by multiple integrations."""
+
+ base_tags = {
+ "env": get_datadog_env(),
+ "service": get_datadog_service(),
+ "version": os.getenv("DD_VERSION", "unknown"),
+ "HOSTNAME": get_datadog_hostname(),
+ "POD_NAME": get_datadog_pod_name(),
+ }
+
+ tags: List[str] = [f"{k}:{v}" for k, v in base_tags.items()]
+
+ if standard_logging_object:
+ request_tags = standard_logging_object.get("request_tags", []) or []
+ tags.extend(f"request_tag:{tag}" for tag in request_tags)
+
+ return ",".join(tags)
diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py
index b44762d0af8..65ed8a795c0 100644
--- a/litellm/integrations/datadog/datadog_llm_obs.py
+++ b/litellm/integrations/datadog/datadog_llm_obs.py
@@ -18,7 +18,10 @@ 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.integrations.datadog.datadog_handler import (
+ get_datadog_service,
+ get_datadog_tags,
+)
from litellm.litellm_core_utils.dd_tracing import tracer
from litellm.litellm_core_utils.prompt_templates.common_utils import (
handle_any_messages_to_chat_completion_str_messages_conversion,
@@ -36,7 +39,7 @@ from litellm.types.utils import (
)
-class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
+class DataDogLLMObsLogger(CustomBatchLogger):
def __init__(self, **kwargs):
try:
verbose_logger.debug("DataDogLLMObs: Initializing logger")
@@ -142,8 +145,8 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
"data": DDIntakePayload(
type="span",
attributes=DDSpanAttributes(
- ml_app=self._get_datadog_service(),
- tags=[self._get_datadog_tags()],
+ ml_app=get_datadog_service(),
+ tags=[get_datadog_tags()],
spans=self.log_queue,
),
),
@@ -243,9 +246,7 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger):
duration=int((end_time - start_time).total_seconds() * 1e9),
metrics=metrics,
status="error" if error_info else "ok",
- tags=[
- self._get_datadog_tags(standard_logging_object=standard_logging_payload)
- ],
+ tags=[get_datadog_tags(standard_logging_object=standard_logging_payload)],
)
apm_trace_id = self._get_apm_trace_id()
diff --git a/litellm/integrations/email_templates/templates.py b/litellm/integrations/email_templates/templates.py
index 7029e8ce12a..5de23db0f24 100644
--- a/litellm/integrations/email_templates/templates.py
+++ b/litellm/integrations/email_templates/templates.py
@@ -60,3 +60,51 @@ USER_INVITED_EMAIL_TEMPLATE = """
Best,
The LiteLLM team
"""
+
+SOFT_BUDGET_ALERT_EMAIL_TEMPLATE = """
+ 
+
+ Hi {recipient_email},
+
+ Your LiteLLM API key has crossed its soft budget limit of {soft_budget}.
+
+ Current Spend: {spend}
+ Soft Budget: {soft_budget}
+ {max_budget_info}
+
+
+ ⚠️ Note: Your API requests will continue to work, but you should monitor your usage closely.
+ If you reach your maximum budget, requests will be rejected.
+
+
+ You can view your usage and manage your budget in the LiteLLM Dashboard.
+
+ If you have any questions, please send an email to {email_support_contact}
+
+ Best,
+ The LiteLLM team
+"""
+
+MAX_BUDGET_ALERT_EMAIL_TEMPLATE = """
+
+
+ Hi {recipient_email},
+
+ Your LiteLLM API key has reached {percentage}% of its maximum budget.
+
+ Current Spend: {spend}
+ Maximum Budget: {max_budget}
+ Alert Threshold: {alert_threshold} ({percentage}%)
+
+
+ ⚠️ Warning: You are approaching your maximum budget limit.
+ Once you reach your maximum budget of {max_budget}, all API requests will be rejected.
+
+
+ You can view your usage and manage your budget in the LiteLLM Dashboard.
+
+ If you have any questions, please send an email to {email_support_contact}
+
+ Best,
+ The LiteLLM team
+"""
\ No newline at end of file
diff --git a/litellm/integrations/langfuse/langfuse_prompt_management.py b/litellm/integrations/langfuse/langfuse_prompt_management.py
index adc8ae61d01..8f73eabad44 100644
--- a/litellm/integrations/langfuse/langfuse_prompt_management.py
+++ b/litellm/integrations/langfuse/langfuse_prompt_management.py
@@ -294,6 +294,11 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge
self.async_log_success_event, kwargs, response_obj, start_time, end_time
)
+ def log_failure_event(self, kwargs, response_obj, start_time, end_time):
+ return run_async_function(
+ self.async_log_failure_event, kwargs, response_obj, start_time, end_time
+ )
+
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
standard_callback_dynamic_params = kwargs.get(
"standard_callback_dynamic_params"
diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py
index 0d6c0a0c641..93dce578fe1 100644
--- a/litellm/integrations/opentelemetry.py
+++ b/litellm/integrations/opentelemetry.py
@@ -1994,10 +1994,7 @@ class OpenTelemetry(CustomLogger):
"""
Create a span for the received proxy server request.
"""
- # don't create proxy parent spans for arize phoenix - [TODO]: figure out a better way to handle this
- if self.callback_name == "arize_phoenix":
- return None
-
+
return self.tracer.start_span(
name="Received Proxy Server Request",
start_time=self._to_ns(start_time),
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index 4ce818f0cef..20f1357a1c8 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -815,7 +815,20 @@ class PrometheusLogger(CustomLogger):
user_api_key_auth_metadata: Optional[dict] = standard_logging_payload[
"metadata"
].get("user_api_key_auth_metadata")
+
+ # Include top-level metadata fields (excluding nested dictionaries)
+ # This allows accessing fields like requester_ip_address from top-level metadata
+ top_level_metadata = standard_logging_payload.get("metadata", {})
+ top_level_fields: Dict[str, Any] = {}
+ if isinstance(top_level_metadata, dict):
+ top_level_fields = {
+ k: v
+ for k, v in top_level_metadata.items()
+ if not isinstance(v, dict) # Exclude nested dicts to avoid conflicts
+ }
+
combined_metadata: Dict[str, Any] = {
+ **top_level_fields, # Include top-level fields first
**(_requester_metadata if _requester_metadata else {}),
**(user_api_key_auth_metadata if user_api_key_auth_metadata else {}),
}
diff --git a/litellm/interactions/http_handler.py b/litellm/interactions/http_handler.py
index 5555b1d7e32..4b4ed9be4db 100644
--- a/litellm/interactions/http_handler.py
+++ b/litellm/interactions/http_handler.py
@@ -4,7 +4,6 @@ HTTP Handler for Interactions API requests.
This module handles the HTTP communication for the Google Interactions API.
"""
-import json
from typing import (
Any,
AsyncIterator,
@@ -18,7 +17,6 @@ from typing import (
import httpx
import litellm
-from litellm._logging import verbose_logger
from litellm.constants import request_timeout
from litellm.interactions.streaming_iterator import (
InteractionsAPIStreamingIterator,
diff --git a/litellm/interactions/streaming_iterator.py b/litellm/interactions/streaming_iterator.py
index ad18477663c..f65d08d3ca9 100644
--- a/litellm/interactions/streaming_iterator.py
+++ b/litellm/interactions/streaming_iterator.py
@@ -8,11 +8,10 @@ from the Google Interactions API, similar to the responses API streaming iterato
import asyncio
import json
from datetime import datetime
-from typing import Any, Dict, Iterator, Optional
+from typing import Any, Dict, Optional
import httpx
-import litellm
from litellm._logging import verbose_logger
from litellm.constants import STREAM_SSE_DONE_STRING
from litellm.litellm_core_utils.asyncify import run_async_function
@@ -22,7 +21,6 @@ from litellm.litellm_core_utils.llm_response_utils.get_api_base import get_api_b
from litellm.litellm_core_utils.thread_pool_executor import executor
from litellm.llms.base_llm.interactions.transformation import BaseInteractionsAPIConfig
from litellm.types.interactions import (
- InteractionsAPIResponse,
InteractionsAPIStreamingResponse,
)
from litellm.utils import CustomStreamWrapper
diff --git a/litellm/litellm_core_utils/api_route_to_call_types.py b/litellm/litellm_core_utils/api_route_to_call_types.py
index 35f83de1dd7..4146ff6d6a6 100644
--- a/litellm/litellm_core_utils/api_route_to_call_types.py
+++ b/litellm/litellm_core_utils/api_route_to_call_types.py
@@ -5,10 +5,12 @@ This dictionary maps each API endpoint to the CallTypes that can be used for tha
Each route can have both async (prefixed with 'a') and sync call types.
"""
+from typing import List, Optional
+
from litellm.types.utils import API_ROUTE_TO_CALL_TYPES, CallTypes
-def get_call_types_for_route(route: str) -> list:
+def get_call_types_for_route(route: str) -> Optional[List[CallTypes]]:
"""
Get the list of CallTypes for a given API route.
@@ -16,9 +18,9 @@ def get_call_types_for_route(route: str) -> list:
route: API route path (e.g., "/chat/completions")
Returns:
- List of CallTypes for that route, or empty list if route not found
+ List of CallTypes for that route, or None if route not found
"""
- return API_ROUTE_TO_CALL_TYPES.get(route, [])
+ return API_ROUTE_TO_CALL_TYPES.get(route, None)
def get_routes_for_call_type(call_type: CallTypes) -> list:
diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py
index 7bf95ca3404..1517d1e776d 100644
--- a/litellm/litellm_core_utils/exception_mapping_utils.py
+++ b/litellm/litellm_core_utils/exception_mapping_utils.py
@@ -78,9 +78,7 @@ class ExceptionCheckers:
"is longer than the model's context length",
"input tokens exceed the configured limit",
"`inputs` tokens + `max_new_tokens` must be",
- # Gemini pattern: "The input token count exceeds the maximum number of tokens allowed"
- # See: https://github.com/BerriAI/litellm/issues/XXXX
- "input token count exceeds the maximum number of tokens allowed",
+ "exceeds the maximum number of tokens allowed", # Gemini
]
for substring in known_exception_substrings:
if substring in _error_str_lowercase:
@@ -1262,6 +1260,14 @@ def exception_type( # type: ignore # noqa: PLR0915
model=model,
llm_provider=custom_llm_provider,
)
+ elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str):
+ exception_mapping_worked = True
+ raise ContextWindowExceededError(
+ message=f"ContextWindowExceededError: {custom_llm_provider.capitalize()}Exception - {error_str}",
+ model=model,
+ llm_provider=custom_llm_provider,
+ litellm_debug_info=extra_information,
+ )
elif (
"None Unknown Error." in error_str
or "Content has no parts." in error_str
diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py
index 36508e021e7..a23fce891b9 100644
--- a/litellm/litellm_core_utils/get_llm_provider_logic.py
+++ b/litellm/litellm_core_utils/get_llm_provider_logic.py
@@ -5,6 +5,7 @@ import httpx
import litellm
from litellm.constants import REPLICATE_MODEL_NAME_WITH_ID_LENGTH
from litellm.secret_managers.main import get_secret, get_secret_str
+from litellm.llms.openai_like.json_loader import JSONProviderRegistry
from ..types.router import LiteLLM_Params
@@ -155,6 +156,17 @@ def get_llm_provider( # noqa: PLR0915
if api_key and api_key.startswith("os.environ/"):
dynamic_api_key = get_secret_str(api_key)
+
+ # Check JSON-configured providers FIRST (before enum-based provider_list)
+ provider_prefix = model.split("/", 1)[0]
+ if len(model.split("/")) > 1 and JSONProviderRegistry.exists(provider_prefix):
+ return _get_openai_compatible_provider_info(
+ model=model,
+ api_base=api_base,
+ api_key=api_key,
+ dynamic_api_key=dynamic_api_key,
+ )
+
# check if llm provider part of model name
if (
diff --git a/litellm/litellm_core_utils/get_model_cost_map.py b/litellm/litellm_core_utils/get_model_cost_map.py
index b6a3a243c46..9b86f4ca2f0 100644
--- a/litellm/litellm_core_utils/get_model_cost_map.py
+++ b/litellm/litellm_core_utils/get_model_cost_map.py
@@ -18,14 +18,15 @@ def get_model_cost_map(url: str) -> dict:
os.getenv("LITELLM_LOCAL_MODEL_COST_MAP", False)
or os.getenv("LITELLM_LOCAL_MODEL_COST_MAP", False) == "True"
):
- import importlib.resources
+ from importlib.resources import files
import json
- with importlib.resources.open_text(
- "litellm", "model_prices_and_context_window_backup.json"
- ) as f:
- content = json.load(f)
- return content
+ content = json.loads(
+ files("litellm")
+ .joinpath("model_prices_and_context_window_backup.json")
+ .read_text(encoding="utf-8")
+ )
+ return content
try:
response = httpx.get(
@@ -35,11 +36,12 @@ def get_model_cost_map(url: str) -> dict:
content = response.json()
return content
except Exception:
- import importlib.resources
+ from importlib.resources import files
import json
- with importlib.resources.open_text(
- "litellm", "model_prices_and_context_window_backup.json"
- ) as f:
- content = json.load(f)
- return content
+ content = json.loads(
+ files("litellm")
+ .joinpath("model_prices_and_context_window_backup.json")
+ .read_text(encoding="utf-8")
+ )
+ return content
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index f2f6a785969..378c201f7a3 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -127,6 +127,7 @@ from litellm.utils import _get_base_model_from_metadata, executor, print_verbose
from ..integrations.argilla import ArgillaLogger
from ..integrations.arize.arize_phoenix import ArizePhoenixLogger
from ..integrations.athina import AthinaLogger
+from ..integrations.azure_sentinel.azure_sentinel import AzureSentinelLogger
from ..integrations.azure_storage.azure_storage import AzureBlobStorageLogger
from ..integrations.custom_prompt_management import CustomPromptManagement
from ..integrations.datadog.datadog import DataDogLogger
@@ -917,9 +918,11 @@ class Logging(LiteLLMLoggingBaseClass):
raw_request_body=self._get_raw_request_body(
additional_args.get("complete_input_dict", {})
),
+ # NOTE: setting ignore_sensitive_headers to True will cause
+ # the Authorization header to be leaked when calls to the health
+ # endpoint are made and fail.
raw_request_headers=self._get_masked_headers(
additional_args.get("headers", {}) or {},
- ignore_sensitive_headers=True,
),
error=None,
)
@@ -3548,6 +3551,14 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
_datadog_llm_obs_logger = DataDogLLMObsLogger()
_in_memory_loggers.append(_datadog_llm_obs_logger)
return _datadog_llm_obs_logger # type: ignore
+ elif logging_integration == "azure_sentinel":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, AzureSentinelLogger):
+ return callback # type: ignore
+
+ _azure_sentinel_logger = AzureSentinelLogger()
+ _in_memory_loggers.append(_azure_sentinel_logger)
+ return _azure_sentinel_logger # type: ignore
elif logging_integration == "gcs_bucket":
for callback in _in_memory_loggers:
if isinstance(callback, GCSBucketLogger):
@@ -4052,6 +4063,10 @@ def get_custom_logger_compatible_class( # noqa: PLR0915
for callback in _in_memory_loggers:
if isinstance(callback, DataDogLLMObsLogger):
return callback
+ elif logging_integration == "azure_sentinel":
+ for callback in _in_memory_loggers:
+ if isinstance(callback, AzureSentinelLogger):
+ return callback
elif logging_integration == "gcs_bucket":
for callback in _in_memory_loggers:
if isinstance(callback, GCSBucketLogger):
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index ef2183a4556..232d9bfc5d1 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -674,7 +674,7 @@ class CostCalculatorUtils:
from litellm.llms.azure_ai.image_generation.cost_calculator import (
cost_calculator as azure_ai_image_cost_calculator,
)
- from litellm.llms.bedrock.image.cost_calculator import (
+ from litellm.llms.bedrock.image_generation.cost_calculator import (
cost_calculator as bedrock_image_cost_calculator,
)
from litellm.llms.gemini.image_generation.cost_calculator import (
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 652692c7b8d..6cc6c229f56 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -1572,6 +1572,21 @@ def convert_to_gemini_tool_call_result(
return _part
+def _sanitize_anthropic_tool_use_id(tool_use_id: str) -> str:
+ """
+ Sanitize tool_use_id to match Anthropic's required pattern: ^[a-zA-Z0-9_-]+$
+
+ Anthropic requires tool_use_id to only contain alphanumeric characters, underscores, and hyphens.
+ This function replaces any invalid characters with underscores.
+ """
+ # Replace any character that's not alphanumeric, underscore, or hyphen with underscore
+ sanitized = re.sub(r'[^a-zA-Z0-9_-]', '_', tool_use_id)
+ # Ensure it's not empty (fallback to a default if needed)
+ if not sanitized:
+ sanitized = "tool_use_id"
+ return sanitized
+
+
def convert_to_anthropic_tool_result(
message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage],
) -> AnthropicMessagesToolResultParam:
@@ -1639,18 +1654,22 @@ def convert_to_anthropic_tool_result(
if message["role"] == "tool":
tool_message: ChatCompletionToolMessage = message
tool_call_id: str = tool_message["tool_call_id"]
+ # Sanitize tool_use_id to match Anthropic's pattern requirement: ^[a-zA-Z0-9_-]+$
+ sanitized_tool_use_id = _sanitize_anthropic_tool_use_id(tool_call_id)
# We can't determine from openai message format whether it's a successful or
# error call result so default to the successful result template
anthropic_tool_result = AnthropicMessagesToolResultParam(
- type="tool_result", tool_use_id=tool_call_id, content=anthropic_content
+ type="tool_result", tool_use_id=sanitized_tool_use_id, content=anthropic_content
)
if message["role"] == "function":
function_message: ChatCompletionFunctionMessage = message
tool_call_id = function_message.get("tool_call_id") or str(uuid.uuid4())
+ # Sanitize tool_use_id to match Anthropic's pattern requirement: ^[a-zA-Z0-9_-]+$
+ sanitized_tool_use_id = _sanitize_anthropic_tool_use_id(tool_call_id)
anthropic_tool_result = AnthropicMessagesToolResultParam(
- type="tool_result", tool_use_id=tool_call_id, content=anthropic_content
+ type="tool_result", tool_use_id=sanitized_tool_use_id, content=anthropic_content
)
if anthropic_tool_result is None:
diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
index 094b5842f07..9d50cc4d92d 100644
--- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py
+++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
@@ -43,7 +43,6 @@ if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.types.llms.anthropic_messages.anthropic_response import (
AnthropicMessagesResponse,
- AnthropicResponseTextBlock,
)
@@ -253,20 +252,39 @@ class AnthropicMessagesHandler(BaseTranslation):
task_mappings: List[Tuple[int, Optional[int]]] = []
# Track (content_index, None) for each text
- response_content = response.get("content", [])
+ # Handle both dict and object responses
+ response_content: List[Any] = []
+ if isinstance(response, dict):
+ response_content = response.get("content", []) or []
+ elif hasattr(response, "content"):
+ content = getattr(response, "content", None)
+ response_content = content or []
+ else:
+ response_content = []
+
if not response_content:
return response
# Step 1: Extract all text content and tool calls from response
for content_idx, content_block in enumerate(response_content):
- # Check if this is a text or tool_use block by checking the 'type' field
- if isinstance(content_block, dict) and content_block.get("type") in [
- "text",
- "tool_use",
- ]:
- # Cast to dict to handle the union type properly
+ # Handle both dict and Pydantic object content blocks
+ block_dict: Dict[str, Any] = {}
+ if isinstance(content_block, dict):
+ block_type = content_block.get("type")
+ block_dict = cast(Dict[str, Any], content_block)
+ elif hasattr(content_block, "type"):
+ block_type = getattr(content_block, "type", None)
+ # Convert Pydantic object to dict for processing
+ if hasattr(content_block, "model_dump"):
+ block_dict = content_block.model_dump()
+ else:
+ block_dict = {"type": block_type, "text": getattr(content_block, "text", None)}
+ else:
+ continue
+
+ if block_type in ["text", "tool_use"]:
self._extract_output_text_and_images(
- content_block=cast(Dict[str, Any], content_block),
+ content_block=block_dict,
content_idx=content_idx,
texts_to_check=texts_to_check,
images_to_check=images_to_check,
@@ -530,7 +548,11 @@ class AnthropicMessagesHandler(BaseTranslation):
Override this method to customize text content detection.
"""
- response_content = response.get("content", [])
+ if isinstance(response, dict):
+ response_content = response.get("content", [])
+ else:
+ response_content = getattr(response, "content", None) or []
+
if not response_content:
return False
for content_block in response_content:
@@ -590,7 +612,16 @@ class AnthropicMessagesHandler(BaseTranslation):
mapping = task_mappings[task_idx]
content_idx = cast(int, mapping[0])
- response_content = response.get("content", [])
+ # Handle both dict and object responses
+ response_content: List[Any] = []
+ if isinstance(response, dict):
+ response_content = response.get("content", []) or []
+ elif hasattr(response, "content"):
+ content = getattr(response, "content", None)
+ response_content = content or []
+ else:
+ continue
+
if not response_content:
continue
@@ -601,7 +632,11 @@ class AnthropicMessagesHandler(BaseTranslation):
content_block = response_content[content_idx]
# Verify it's a text block and update the text field
- if isinstance(content_block, dict) and content_block.get("type") == "text":
- # Cast to dict to handle the union type properly for assignment
- content_block = cast("AnthropicResponseTextBlock", content_block)
- content_block["text"] = guardrail_response
+ # Handle both dict and Pydantic object content blocks
+ if isinstance(content_block, dict):
+ if content_block.get("type") == "text":
+ cast(Dict[str, Any], content_block)["text"] = guardrail_response
+ elif hasattr(content_block, "type") and getattr(content_block, "type", None) == "text":
+ # Update Pydantic object's text attribute
+ if hasattr(content_block, "text"):
+ content_block.text = guardrail_response
diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py
index ffe8cb309f3..26e6016095e 100644
--- a/litellm/llms/anthropic/chat/handler.py
+++ b/litellm/llms/anthropic/chat/handler.py
@@ -692,12 +692,15 @@ class ModelResponseIterator:
text = content_block_start["content_block"]["text"]
elif content_block_start["content_block"]["type"] == "tool_use" or content_block_start["content_block"]["type"] == "server_tool_use":
self.tool_index += 1
+ # Use empty string for arguments in content_block_start - actual arguments
+ # come in subsequent content_block_delta chunks and get accumulated.
+ # Using str(input) here would prepend '{}' causing invalid JSON accumulation.
tool_use = ChatCompletionToolCallChunk(
id=content_block_start["content_block"]["id"],
type="function",
function=ChatCompletionToolCallFunctionChunk(
name=content_block_start["content_block"]["name"],
- arguments=str(content_block_start["content_block"]["input"]),
+ arguments="",
),
index=self.tool_index,
)
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
index 4c202b9eec0..8868fabdcef 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
@@ -169,7 +169,7 @@ class LiteLLMAnthropicMessagesAdapter:
"""
Which anthropic params, we need to translate to the openai format.
"""
- return ["messages", "metadata", "system", "tool_choice", "tools"]
+ return ["messages", "metadata", "system", "tool_choice", "tools", "thinking"]
def translate_anthropic_messages_to_openai( # noqa: PLR0915
self,
@@ -420,6 +420,35 @@ class LiteLLMAnthropicMessagesAdapter:
return new_messages
+ def translate_anthropic_thinking_to_openai(
+ self, thinking: Dict[str, Any]
+ ) -> Optional[str]:
+ """
+ Translate Anthropic's thinking parameter to OpenAI's reasoning_effort.
+
+ Anthropic thinking format: {'type': 'enabled'|'disabled', 'budget_tokens': int}
+ OpenAI reasoning_effort: 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' | 'default'
+ """
+ if not isinstance(thinking, dict):
+ return None
+
+ thinking_type = thinking.get("type", "disabled")
+
+ if thinking_type == "disabled":
+ return None
+ elif thinking_type == "enabled":
+ budget_tokens = thinking.get("budget_tokens", 0)
+ if budget_tokens >= 10000:
+ return "high"
+ elif budget_tokens >= 5000:
+ return "medium"
+ elif budget_tokens >= 2000:
+ return "low"
+ else:
+ return "minimal"
+
+ return None
+
def translate_anthropic_tool_choice_to_openai(
self, tool_choice: AnthropicMessagesToolChoice
) -> ChatCompletionToolChoiceValues:
@@ -529,6 +558,16 @@ class LiteLLMAnthropicMessagesAdapter:
tools=cast(List[AllAnthropicToolsValues], tools)
)
+ ## CONVERT THINKING
+ if "thinking" in anthropic_message_request:
+ thinking = anthropic_message_request["thinking"]
+ if thinking:
+ reasoning_effort = self.translate_anthropic_thinking_to_openai(
+ thinking=cast(Dict[str, Any], thinking)
+ )
+ if reasoning_effort:
+ new_kwargs["reasoning_effort"] = reasoning_effort
+
translatable_params = self.translatable_anthropic_params()
for k, v in anthropic_message_request.items():
if k not in translatable_params: # pass remaining params as is
@@ -701,9 +740,7 @@ class LiteLLMAnthropicMessagesAdapter:
from litellm.types.llms.anthropic import TextBlock, ToolUseBlock
for choice in choices:
- if choice.delta.content is not None and len(choice.delta.content) > 0:
- return "text", TextBlock(type="text", text="")
- elif (
+ if (
choice.delta.tool_calls is not None
and len(choice.delta.tool_calls) > 0
and choice.delta.tool_calls[0].function is not None
@@ -714,6 +751,8 @@ class LiteLLMAnthropicMessagesAdapter:
name=choice.delta.tool_calls[0].function.name or "",
input={}, # type: ignore[typeddict-item]
)
+ elif choice.delta.content is not None and len(choice.delta.content) > 0:
+ return "text", TextBlock(type="text", text="")
elif isinstance(choice, StreamingChoices) and hasattr(
choice.delta, "thinking_blocks"
):
@@ -757,7 +796,7 @@ class LiteLLMAnthropicMessagesAdapter:
for choice in choices:
if choice.delta.content is not None and len(choice.delta.content) > 0:
text += choice.delta.content
- elif choice.delta.tool_calls is not None:
+ if choice.delta.tool_calls is not None:
partial_json = ""
for tool in choice.delta.tool_calls:
if (
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
index cc9334ae68b..908b46c11e2 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
@@ -119,6 +119,7 @@ def anthropic_messages_handler(
tools: Optional[List[Dict]] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
+ container: Optional[Dict] = None,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
client: Optional[AsyncHTTPHandler] = None,
@@ -131,6 +132,9 @@ def anthropic_messages_handler(
]:
"""
Makes Anthropic `/v1/messages` API calls In the Anthropic API Spec
+
+ Args:
+ container: Container config with skills for code execution
"""
from litellm.types.utils import LlmProviders
diff --git a/litellm/llms/aws_polly/__init__.py b/litellm/llms/aws_polly/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/llms/aws_polly/text_to_speech/__init__.py b/litellm/llms/aws_polly/text_to_speech/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/llms/aws_polly/text_to_speech/transformation.py b/litellm/llms/aws_polly/text_to_speech/transformation.py
new file mode 100644
index 00000000000..dc6c40000f1
--- /dev/null
+++ b/litellm/llms/aws_polly/text_to_speech/transformation.py
@@ -0,0 +1,391 @@
+"""
+AWS Polly Text-to-Speech transformation
+
+Maps OpenAI TTS spec to AWS Polly SynthesizeSpeech API
+Reference: https://docs.aws.amazon.com/polly/latest/dg/API_SynthesizeSpeech.html
+"""
+
+import json
+from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Tuple, Union
+
+import httpx
+
+from litellm.llms.base_llm.text_to_speech.transformation import (
+ BaseTextToSpeechConfig,
+ TextToSpeechRequestData,
+)
+from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+ from litellm.types.llms.openai import HttpxBinaryResponseContent
+else:
+ LiteLLMLoggingObj = Any
+ HttpxBinaryResponseContent = Any
+
+
+class AWSPollyTextToSpeechConfig(BaseTextToSpeechConfig, BaseAWSLLM):
+ """
+ Configuration for AWS Polly Text-to-Speech
+
+ Reference: https://docs.aws.amazon.com/polly/latest/dg/API_SynthesizeSpeech.html
+ """
+
+ def __init__(self):
+ BaseTextToSpeechConfig.__init__(self)
+ BaseAWSLLM.__init__(self)
+
+ # Default settings
+ DEFAULT_VOICE = "Joanna"
+ DEFAULT_ENGINE = "neural"
+ DEFAULT_OUTPUT_FORMAT = "mp3"
+ DEFAULT_REGION = "us-east-1"
+
+ # Voice name mappings from OpenAI voices to Polly voices
+ VOICE_MAPPINGS = {
+ "alloy": "Joanna", # US English female
+ "echo": "Matthew", # US English male
+ "fable": "Amy", # British English female
+ "onyx": "Brian", # British English male
+ "nova": "Ivy", # US English female (child)
+ "shimmer": "Kendra", # US English female
+ }
+
+ # Response format mappings from OpenAI to Polly
+ FORMAT_MAPPINGS = {
+ "mp3": "mp3",
+ "opus": "ogg_vorbis",
+ "aac": "mp3", # Polly doesn't support AAC, use MP3
+ "flac": "mp3", # Polly doesn't support FLAC, use MP3
+ "wav": "pcm",
+ "pcm": "pcm",
+ }
+
+ # Valid Polly engines
+ VALID_ENGINES = {"standard", "neural", "long-form", "generative"}
+
+ def dispatch_text_to_speech(
+ self,
+ model: str,
+ input: str,
+ voice: Optional[Union[str, Dict]],
+ optional_params: Dict,
+ litellm_params_dict: Dict,
+ logging_obj: "LiteLLMLoggingObj",
+ timeout: Union[float, httpx.Timeout],
+ extra_headers: Optional[Dict[str, Any]],
+ base_llm_http_handler: Any,
+ aspeech: bool,
+ api_base: Optional[str],
+ api_key: Optional[str],
+ **kwargs: Any,
+ ) -> Union[
+ "HttpxBinaryResponseContent",
+ Coroutine[Any, Any, "HttpxBinaryResponseContent"],
+ ]:
+ """
+ Dispatch method to handle AWS Polly TTS requests
+
+ This method encapsulates AWS-specific credential resolution and parameter handling
+
+ Args:
+ base_llm_http_handler: The BaseLLMHTTPHandler instance from main.py
+ """
+ # Get AWS region from kwargs or environment
+ aws_region_name = kwargs.get("aws_region_name") or self._get_aws_region_name_for_polly(
+ optional_params=optional_params
+ )
+
+ # Convert voice to string if it's a dict
+ voice_str: Optional[str] = None
+ if isinstance(voice, str):
+ voice_str = voice
+ elif isinstance(voice, dict):
+ voice_str = voice.get("name") if voice else None
+
+ # Update litellm_params with resolved values
+ # Note: AWS credentials (aws_access_key_id, aws_secret_access_key, etc.)
+ # are already in litellm_params_dict via get_litellm_params() in main.py
+ litellm_params_dict["aws_region_name"] = aws_region_name
+ litellm_params_dict["api_base"] = api_base
+ litellm_params_dict["api_key"] = api_key
+
+ # Call the text_to_speech_handler
+ response = base_llm_http_handler.text_to_speech_handler(
+ model=model,
+ input=input,
+ voice=voice_str,
+ text_to_speech_provider_config=self,
+ text_to_speech_optional_params=optional_params,
+ custom_llm_provider="aws_polly",
+ litellm_params=litellm_params_dict,
+ logging_obj=logging_obj,
+ timeout=timeout,
+ extra_headers=extra_headers,
+ client=None,
+ _is_async=aspeech,
+ )
+
+ return response
+
+ def _get_aws_region_name_for_polly(self, optional_params: Dict) -> str:
+ """Get AWS region name for Polly API calls."""
+ aws_region_name = optional_params.get("aws_region_name")
+ if aws_region_name is None:
+ aws_region_name = self.get_aws_region_name_for_non_llm_api_calls()
+ return aws_region_name
+
+ def get_supported_openai_params(self, model: str) -> list:
+ """
+ AWS Polly TTS supports these OpenAI parameters
+ """
+ return ["voice", "response_format", "speed"]
+
+ def map_openai_params(
+ self,
+ model: str,
+ optional_params: Dict,
+ voice: Optional[Union[str, Dict]] = None,
+ drop_params: bool = False,
+ kwargs: Dict = {},
+ ) -> Tuple[Optional[str], Dict]:
+ """
+ Map OpenAI parameters to AWS Polly parameters
+ """
+ mapped_params = {}
+
+ # Map voice - support both native Polly voices and OpenAI voice mappings
+ mapped_voice: Optional[str] = None
+ if isinstance(voice, str):
+ if voice in self.VOICE_MAPPINGS:
+ # OpenAI voice -> Polly voice
+ mapped_voice = self.VOICE_MAPPINGS[voice]
+ else:
+ # Assume it's already a Polly voice name
+ mapped_voice = voice
+
+ # Map response format
+ if "response_format" in optional_params:
+ format_name = optional_params["response_format"]
+ if format_name in self.FORMAT_MAPPINGS:
+ mapped_params["output_format"] = self.FORMAT_MAPPINGS[format_name]
+ else:
+ mapped_params["output_format"] = format_name
+ else:
+ mapped_params["output_format"] = self.DEFAULT_OUTPUT_FORMAT
+
+ # Extract engine from model name (e.g., "aws_polly/neural" -> "neural")
+ engine = self._extract_engine_from_model(model)
+ mapped_params["engine"] = engine
+
+ # Pass through Polly-specific parameters (use AWS API casing)
+ if "language_code" in kwargs:
+ mapped_params["LanguageCode"] = kwargs["language_code"]
+ if "lexicon_names" in kwargs:
+ mapped_params["LexiconNames"] = kwargs["lexicon_names"]
+ if "sample_rate" in kwargs:
+ mapped_params["SampleRate"] = kwargs["sample_rate"]
+
+ return mapped_voice, mapped_params
+
+ def _extract_engine_from_model(self, model: str) -> str:
+ """
+ Extract engine from model name.
+
+ Examples:
+ - aws_polly/neural -> neural
+ - aws_polly/standard -> standard
+ - aws_polly/long-form -> long-form
+ - aws_polly -> neural (default)
+ """
+ if "/" in model:
+ parts = model.split("/")
+ if len(parts) >= 2:
+ engine = parts[1].lower()
+ if engine in self.VALID_ENGINES:
+ return engine
+ return self.DEFAULT_ENGINE
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ ) -> dict:
+ """
+ Validate AWS environment and set up headers.
+ AWS SigV4 signing will be done in transform_text_to_speech_request.
+ """
+ validated_headers = headers.copy()
+ validated_headers["Content-Type"] = "application/json"
+ return validated_headers
+
+ def get_complete_url(
+ self,
+ model: str,
+ api_base: Optional[str],
+ litellm_params: dict,
+ ) -> str:
+ """
+ Get the complete URL for AWS Polly SynthesizeSpeech request
+
+ Polly endpoint format:
+ https://polly.{region}.amazonaws.com/v1/speech
+ """
+ if api_base is not None:
+ return api_base.rstrip("/") + "/v1/speech"
+
+ aws_region_name = litellm_params.get("aws_region_name", self.DEFAULT_REGION)
+ return f"https://polly.{aws_region_name}.amazonaws.com/v1/speech"
+
+ def is_ssml_input(self, input: str) -> bool:
+ """
+ Returns True if input is SSML, False otherwise.
+
+ Based on AWS Polly SSML requirements - must contain tag.
+ """
+ return "" in input or " Tuple[Dict[str, str], str]:
+ """
+ Sign the AWS Polly request using SigV4.
+
+ Returns:
+ Tuple of (signed_headers, json_body_string)
+ """
+ try:
+ from botocore.auth import SigV4Auth
+ from botocore.awsrequest import AWSRequest
+ except ImportError:
+ raise ImportError("Missing boto3 to call AWS Polly. Run 'pip install boto3'.")
+
+ # Get AWS region
+ aws_region_name = litellm_params.get("aws_region_name", self.DEFAULT_REGION)
+
+ # Get AWS credentials
+ credentials = self.get_credentials(
+ aws_access_key_id=litellm_params.get("aws_access_key_id"),
+ aws_secret_access_key=litellm_params.get("aws_secret_access_key"),
+ aws_session_token=litellm_params.get("aws_session_token"),
+ aws_region_name=aws_region_name,
+ aws_session_name=litellm_params.get("aws_session_name"),
+ aws_profile_name=litellm_params.get("aws_profile_name"),
+ aws_role_name=litellm_params.get("aws_role_name"),
+ aws_web_identity_token=litellm_params.get("aws_web_identity_token"),
+ aws_sts_endpoint=litellm_params.get("aws_sts_endpoint"),
+ aws_external_id=litellm_params.get("aws_external_id"),
+ )
+
+ # Serialize request body to JSON
+ json_body = json.dumps(request_body)
+
+ # Create headers for signing
+ headers = {
+ "Content-Type": "application/json",
+ }
+
+ # Create AWS request for signing
+ aws_request = AWSRequest(
+ method="POST",
+ url=endpoint_url,
+ data=json_body,
+ headers=headers,
+ )
+
+ # Sign the request
+ SigV4Auth(credentials, "polly", aws_region_name).add_auth(aws_request)
+
+ # Return signed headers and body
+ return dict(aws_request.headers), json_body
+
+ def transform_text_to_speech_request(
+ self,
+ model: str,
+ input: str,
+ voice: Optional[str],
+ optional_params: Dict,
+ litellm_params: Dict,
+ headers: dict,
+ ) -> TextToSpeechRequestData:
+ """
+ Transform OpenAI TTS request to AWS Polly SynthesizeSpeech format.
+
+ Supports:
+ - Native Polly voices (Joanna, Matthew, etc.)
+ - OpenAI voice mapping (alloy, echo, etc.)
+ - SSML input (auto-detected via tag)
+ - Multiple engines (neural, standard, long-form, generative)
+
+ Returns:
+ TextToSpeechRequestData: Contains signed request for Polly API
+ """
+ # Get voice (already mapped in main.py, or use default)
+ polly_voice = voice or self.DEFAULT_VOICE
+
+ # Get output format
+ output_format = optional_params.get("output_format", self.DEFAULT_OUTPUT_FORMAT)
+
+ # Get engine
+ engine = optional_params.get("engine", self.DEFAULT_ENGINE)
+
+ # Build request body
+ request_body: Dict[str, Any] = {
+ "Engine": engine,
+ "OutputFormat": output_format,
+ "Text": input,
+ "VoiceId": polly_voice,
+ }
+
+ # Auto-detect SSML
+ if self.is_ssml_input(input):
+ request_body["TextType"] = "ssml"
+ else:
+ request_body["TextType"] = "text"
+
+ # Add optional Polly parameters (already in AWS casing from map_openai_params)
+ for key in ["LanguageCode", "LexiconNames", "SampleRate"]:
+ if key in optional_params:
+ request_body[key] = optional_params[key]
+
+ # Get endpoint URL
+ endpoint_url = self.get_complete_url(
+ model=model,
+ api_base=litellm_params.get("api_base"),
+ litellm_params=litellm_params,
+ )
+
+ # Sign the request with AWS SigV4
+ signed_headers, json_body = self._sign_polly_request(
+ request_body=request_body,
+ endpoint_url=endpoint_url,
+ litellm_params=litellm_params,
+ )
+
+ # Return as ssml_body so the handler uses data= instead of json=
+ # This preserves the exact JSON string that was signed
+ return TextToSpeechRequestData(
+ ssml_body=json_body,
+ headers=signed_headers,
+ )
+
+ def transform_text_to_speech_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: "LiteLLMLoggingObj",
+ ) -> "HttpxBinaryResponseContent":
+ """
+ Transform AWS Polly response to standard format.
+
+ Polly returns the audio data directly in the response body.
+ """
+ from litellm.types.llms.openai import HttpxBinaryResponseContent
+
+ return HttpxBinaryResponseContent(raw_response)
+
diff --git a/litellm/llms/azure/realtime/handler.py b/litellm/llms/azure/realtime/handler.py
index 217a05c83a4..e533978e07a 100644
--- a/litellm/llms/azure/realtime/handler.py
+++ b/litellm/llms/azure/realtime/handler.py
@@ -94,7 +94,7 @@ class AzureOpenAIRealtime(AzureChatCompletion):
ssl_context = get_shared_realtime_ssl_context()
async with websockets.connect( # type: ignore
url,
- extra_headers={
+ additional_headers={
"api-key": api_key, # type: ignore
},
max_size=REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES,
diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py
index 816b93edd20..71d21001cc3 100644
--- a/litellm/llms/bedrock/base_aws_llm.py
+++ b/litellm/llms/bedrock/base_aws_llm.py
@@ -357,6 +357,18 @@ class BaseAWSLLM:
model_id = BaseAWSLLM._get_model_id_from_model_with_spec(
model_id, spec="openai"
)
+ elif provider == "qwen2" and "qwen2/" in model_id:
+ model_id = BaseAWSLLM._get_model_id_from_model_with_spec(
+ model_id, spec="qwen2"
+ )
+ elif provider == "qwen3" and "qwen3/" in model_id:
+ model_id = BaseAWSLLM._get_model_id_from_model_with_spec(
+ model_id, spec="qwen3"
+ )
+ elif provider == "stability" and "stability/" in model_id:
+ model_id = BaseAWSLLM._get_model_id_from_model_with_spec(
+ model_id, spec="stability"
+ )
return model_id
@staticmethod
diff --git a/litellm/llms/bedrock/embed/amazon_nova_transformation.py b/litellm/llms/bedrock/embed/amazon_nova_transformation.py
index ada49d0ff21..3e5686c46fb 100644
--- a/litellm/llms/bedrock/embed/amazon_nova_transformation.py
+++ b/litellm/llms/bedrock/embed/amazon_nova_transformation.py
@@ -46,6 +46,39 @@ class AmazonNovaEmbeddingConfig:
elif k in self.get_supported_openai_params():
optional_params[k] = v
return optional_params
+
+ def _parse_data_url(self, data_url: str) -> tuple:
+ """
+ Parse a data URL to extract the media type and base64 data.
+
+ Args:
+ data_url: Data URL in format: data:image/jpeg;base64,/9j/4AAQ...
+
+ Returns:
+ tuple: (media_type, base64_data)
+ media_type: e.g., "image/jpeg", "video/mp4", "audio/mpeg"
+ base64_data: The base64-encoded data without the prefix
+ """
+ if not data_url.startswith("data:"):
+ raise ValueError(f"Invalid data URL format: {data_url[:50]}...")
+
+ # Split by comma to separate metadata from data
+ # Format: data:image/jpeg;base64,
+ if "," not in data_url:
+ raise ValueError(f"Invalid data URL format (missing comma): {data_url[:50]}...")
+
+ metadata, base64_data = data_url.split(",", 1)
+
+ # Extract media type from metadata
+ # Remove 'data:' prefix and ';base64' suffix
+ metadata = metadata[5:] # Remove 'data:'
+
+ if ";" in metadata:
+ media_type = metadata.split(";")[0]
+ else:
+ media_type = metadata
+
+ return media_type, base64_data
def _transform_request(
self,
@@ -99,15 +132,58 @@ class AmazonNovaEmbeddingConfig:
if "embeddingDimension" not in embedding_params:
embedding_params["embeddingDimension"] = 3072
- # For text input, add basic text structure if user hasn't provided text/image/video/audio
+ # For text/media input, add basic structure if user hasn't provided text/image/video/audio
if "text" not in embedding_params and "image" not in embedding_params and "video" not in embedding_params and "audio" not in embedding_params:
- # Default to text if no modality specified
- if input.startswith("s3://"):
+ # Check if input is a data URL (e.g., data:image/jpeg;base64,...)
+ if input.startswith("data:"):
+ # Parse the data URL to extract media type and base64 data
+ media_type, base64_data = self._parse_data_url(input)
+
+ if media_type.startswith("image/"):
+ # Extract image format from MIME type (e.g., image/jpeg -> jpeg)
+ image_format = media_type.split("/")[1].lower()
+ # Nova API expects specific formats
+ if image_format == "jpg":
+ image_format = "jpeg"
+
+ embedding_params["image"] = {
+ "format": image_format,
+ "source": {
+ "bytes": base64_data
+ }
+ }
+ elif media_type.startswith("video/"):
+ # Handle video data URLs
+ video_format = media_type.split("/")[1].lower()
+ embedding_params["video"] = {
+ "format": video_format,
+ "source": {
+ "bytes": base64_data
+ }
+ }
+ elif media_type.startswith("audio/"):
+ # Handle audio data URLs
+ audio_format = media_type.split("/")[1].lower()
+ embedding_params["audio"] = {
+ "format": audio_format,
+ "source": {
+ "bytes": base64_data
+ }
+ }
+ else:
+ # Fallback to text for unknown types
+ embedding_params["text"] = {
+ "value": input,
+ "truncationMode": "END"
+ }
+ elif input.startswith("s3://"):
+ # S3 URL - default to text for now, user should specify modality
embedding_params["text"] = {
"source": {"s3Location": {"uri": input}},
"truncationMode": "END" # Required by Nova API
}
else:
+ # Plain text input
embedding_params["text"] = {
"value": input,
"truncationMode": "END" # Required by Nova API
diff --git a/litellm/llms/bedrock/image_edit/__init__.py b/litellm/llms/bedrock/image_edit/__init__.py
new file mode 100644
index 00000000000..f3a0e61067d
--- /dev/null
+++ b/litellm/llms/bedrock/image_edit/__init__.py
@@ -0,0 +1,10 @@
+"""
+Bedrock Image Edit Module
+
+Handles image edit operations for Bedrock stability models.
+"""
+
+from .handler import BedrockImageEdit
+
+__all__ = ["BedrockImageEdit"]
+
diff --git a/litellm/llms/bedrock/image_edit/handler.py b/litellm/llms/bedrock/image_edit/handler.py
new file mode 100644
index 00000000000..b4b6c8d7622
--- /dev/null
+++ b/litellm/llms/bedrock/image_edit/handler.py
@@ -0,0 +1,310 @@
+"""
+Bedrock Image Edit Handler
+
+Handles image edit requests for Bedrock stability models.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import httpx
+from pydantic import BaseModel
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging
+from litellm.llms.bedrock.image_edit.stability_transformation import (
+ BedrockStabilityImageEditConfig,
+)
+from litellm.llms.custom_httpx.http_handler import (
+ AsyncHTTPHandler,
+ HTTPHandler,
+ _get_httpx_client,
+ get_async_httpx_client,
+)
+from litellm.types.utils import ImageResponse
+
+from ..base_aws_llm import BaseAWSLLM
+from ..common_utils import BedrockError
+
+if TYPE_CHECKING:
+ from botocore.awsrequest import AWSPreparedRequest
+else:
+ AWSPreparedRequest = Any
+
+
+class BedrockImageEditPreparedRequest(BaseModel):
+ """
+ Internal/Helper class for preparing the request for bedrock image edit
+ """
+
+ endpoint_url: str
+ prepped: AWSPreparedRequest
+ body: bytes
+ data: dict
+
+
+class BedrockImageEdit(BaseAWSLLM):
+ """
+ Bedrock Image Edit handler
+ """
+
+ @classmethod
+ def get_config_class(cls, model: str | None):
+ if BedrockStabilityImageEditConfig._is_stability_edit_model(model):
+ return BedrockStabilityImageEditConfig
+ else:
+ raise ValueError(f"Unsupported model for bedrock image edit: {model}")
+
+ def image_edit(
+ self,
+ model: str,
+ image: list,
+ prompt: str,
+ model_response: ImageResponse,
+ optional_params: dict,
+ logging_obj: LitellmLogging,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ aimage_edit: bool = False,
+ api_base: Optional[str] = None,
+ extra_headers: Optional[dict] = None,
+ client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+ api_key: Optional[str] = None,
+ ):
+ prepared_request = self._prepare_request(
+ model=model,
+ image=image,
+ prompt=prompt,
+ optional_params=optional_params,
+ api_base=api_base,
+ extra_headers=extra_headers,
+ logging_obj=logging_obj,
+ api_key=api_key,
+ )
+
+ if aimage_edit is True:
+ return self.async_image_edit(
+ prepared_request=prepared_request,
+ timeout=timeout,
+ model=model,
+ logging_obj=logging_obj,
+ prompt=prompt,
+ model_response=model_response,
+ client=(
+ client
+ if client is not None and isinstance(client, AsyncHTTPHandler)
+ else None
+ ),
+ )
+
+ if client is None or not isinstance(client, HTTPHandler):
+ client = _get_httpx_client()
+ try:
+ response = client.post(url=prepared_request.endpoint_url, headers=prepared_request.prepped.headers, data=prepared_request.body) # type: ignore
+ response.raise_for_status()
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise BedrockError(status_code=error_code, message=err.response.text)
+ except httpx.TimeoutException:
+ raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+ ### FORMAT RESPONSE TO OPENAI FORMAT ###
+ model_response = self._transform_response_dict_to_openai_response(
+ model_response=model_response,
+ model=model,
+ logging_obj=logging_obj,
+ prompt=prompt,
+ response=response,
+ data=prepared_request.data,
+ )
+ return model_response
+
+ async def async_image_edit(
+ self,
+ prepared_request: BedrockImageEditPreparedRequest,
+ timeout: Optional[Union[float, httpx.Timeout]],
+ model: str,
+ logging_obj: LitellmLogging,
+ prompt: str,
+ model_response: ImageResponse,
+ client: Optional[AsyncHTTPHandler] = None,
+ ) -> ImageResponse:
+ """
+ Asynchronous handler for bedrock image edit
+ """
+ async_client = client or get_async_httpx_client(
+ llm_provider=litellm.LlmProviders.BEDROCK,
+ params={"timeout": timeout},
+ )
+
+ try:
+ response = await async_client.post(url=prepared_request.endpoint_url, headers=prepared_request.prepped.headers, data=prepared_request.body) # type: ignore
+ response.raise_for_status()
+ except httpx.HTTPStatusError as err:
+ error_code = err.response.status_code
+ raise BedrockError(status_code=error_code, message=err.response.text)
+ except httpx.TimeoutException:
+ raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+ ### FORMAT RESPONSE TO OPENAI FORMAT ###
+ model_response = self._transform_response_dict_to_openai_response(
+ model=model,
+ logging_obj=logging_obj,
+ prompt=prompt,
+ response=response,
+ data=prepared_request.data,
+ model_response=model_response,
+ )
+ return model_response
+
+ def _prepare_request(
+ self,
+ model: str,
+ image: list,
+ prompt: str,
+ optional_params: dict,
+ api_base: Optional[str],
+ extra_headers: Optional[dict],
+ logging_obj: LitellmLogging,
+ api_key: Optional[str],
+ ) -> BedrockImageEditPreparedRequest:
+ """
+ Prepare the request body, headers, and endpoint URL for the Bedrock Image Edit API
+
+ Args:
+ model (str): The model to use for the image edit
+ image (list): The images to edit
+ prompt (str): The prompt for the edit
+ optional_params (dict): The optional parameters for the image edit
+ api_base (Optional[str]): The base URL for the Bedrock API
+ extra_headers (Optional[dict]): The extra headers to include in the request
+ logging_obj (LitellmLogging): The logging object to use for logging
+ api_key (Optional[str]): The API key to use
+
+ Returns:
+ BedrockImageEditPreparedRequest: The prepared request object
+ """
+ boto3_credentials_info = self._get_boto_credentials_from_optional_params(
+ optional_params, model
+ )
+
+ # Use the existing ARN-aware provider detection method
+ bedrock_provider = self.get_bedrock_invoke_provider(model)
+ ### SET RUNTIME ENDPOINT ###
+ modelId = self.get_bedrock_model_id(
+ model=model,
+ provider=bedrock_provider,
+ optional_params=optional_params,
+ )
+ _, proxy_endpoint_url = self.get_runtime_endpoint(
+ api_base=api_base,
+ aws_bedrock_runtime_endpoint=boto3_credentials_info.aws_bedrock_runtime_endpoint,
+ aws_region_name=boto3_credentials_info.aws_region_name,
+ )
+ proxy_endpoint_url = f"{proxy_endpoint_url}/model/{modelId}/invoke"
+ data = self._get_request_body(
+ model=model,
+ image=image,
+ prompt=prompt,
+ optional_params=optional_params,
+ )
+
+ # Make POST Request
+ body = json.dumps(data).encode("utf-8")
+ headers = {"Content-Type": "application/json"}
+ if extra_headers is not None:
+ headers = {"Content-Type": "application/json", **extra_headers}
+
+ prepped = self.get_request_headers(
+ credentials=boto3_credentials_info.credentials,
+ aws_region_name=boto3_credentials_info.aws_region_name,
+ extra_headers=extra_headers,
+ endpoint_url=proxy_endpoint_url,
+ data=body,
+ headers=headers,
+ api_key=api_key,
+ )
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=prompt,
+ api_key="",
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": proxy_endpoint_url,
+ "headers": prepped.headers,
+ },
+ )
+ return BedrockImageEditPreparedRequest(
+ endpoint_url=proxy_endpoint_url,
+ prepped=prepped,
+ body=body,
+ data=data,
+ )
+
+ def _get_request_body(
+ self,
+ model: str,
+ image: list,
+ prompt: str,
+ optional_params: dict,
+ ) -> dict:
+ """
+ Get the request body for the Bedrock Image Edit API
+
+ Checks the model/provider and transforms the request body accordingly
+
+ Returns:
+ dict: The request body to use for the Bedrock Image Edit API
+ """
+ config_class = self.get_config_class(model=model)
+ config_instance = config_class()
+ request_body = config_instance.transform_image_edit_request(
+ model=model,
+ prompt=prompt,
+ image=image[0] if image else None,
+ image_edit_optional_request_params=optional_params,
+ litellm_params={},
+ headers={},
+ )
+ return dict(request_body)
+
+ def _transform_response_dict_to_openai_response(
+ self,
+ model_response: ImageResponse,
+ model: str,
+ logging_obj: LitellmLogging,
+ prompt: str,
+ response: httpx.Response,
+ data: dict,
+ ) -> ImageResponse:
+ """
+ Transforms the Image Edit response from Bedrock to OpenAI format
+ """
+
+ ## LOGGING
+ if logging_obj is not None:
+ logging_obj.post_call(
+ input=prompt,
+ api_key="",
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
+ )
+ verbose_logger.debug("raw model_response: %s", response.text)
+ response_dict = response.json()
+ if response_dict is None:
+ raise ValueError("Error in response object format, got None")
+
+ config_class = self.get_config_class(model=model)
+ config_instance = config_class()
+
+ model_response = config_instance.transform_image_edit_response(
+ model=model,
+ raw_response=response,
+ logging_obj=logging_obj,
+ )
+
+ return model_response
+
diff --git a/litellm/llms/bedrock/image_edit/stability_transformation.py b/litellm/llms/bedrock/image_edit/stability_transformation.py
new file mode 100644
index 00000000000..bcaf0923f69
--- /dev/null
+++ b/litellm/llms/bedrock/image_edit/stability_transformation.py
@@ -0,0 +1,377 @@
+"""
+Bedrock Stability AI Image Edit Transformation
+
+Handles transformation between OpenAI-compatible format and Bedrock Stability AI Image Edit API format.
+
+Supported models:
+- stability.stable-conservative-upscale-v1:0
+- stability.stable-creative-upscale-v1:0
+- stability.stable-fast-upscale-v1:0
+- stability.stable-outpaint-v1:0
+- stability.stable-image-control-sketch-v1:0
+- stability.stable-image-control-structure-v1:0
+- stability.stable-image-erase-object-v1:0
+- stability.stable-image-inpaint-v1:0
+- stability.stable-image-remove-background-v1:0
+- stability.stable-image-search-recolor-v1:0
+- stability.stable-image-search-replace-v1:0
+- stability.stable-image-style-guide-v1:0
+- stability.stable-style-transfer-v1:0
+
+API Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html
+"""
+
+import json
+import base64
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
+
+import httpx
+
+from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
+from litellm.types.images.main import ImageEditOptionalRequestParams
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.llms.stability import (
+ OPENAI_SIZE_TO_STABILITY_ASPECT_RATIO,
+)
+from litellm.types.utils import FileTypes, ImageObject, ImageResponse
+from litellm.utils import get_model_info
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+ LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+ LiteLLMLoggingObj = Any
+
+
+class BedrockStabilityImageEditConfig(BaseImageEditConfig):
+ """
+ Configuration for Bedrock Stability AI image edit.
+
+ Supports all Stability image edit operations through Bedrock.
+ """
+
+ @classmethod
+ def _is_stability_edit_model(cls, model: Optional[str] = None) -> bool:
+ """
+ Returns True if the model is a Bedrock Stability edit model.
+
+ Bedrock Stability edit models follow this pattern:
+ stability.stable-conservative-upscale-v1:0
+ stability.stable-creative-upscale-v1:0
+ stability.stable-fast-upscale-v1:0
+ stability.stable-outpaint-v1:0
+ stability.stable-image-inpaint-v1:0
+ stability.stable-image-erase-object-v1:0
+ etc.
+ """
+ if model:
+ model_lower = model.lower()
+ if "stability." in model_lower and any([
+ "upscale" in model_lower,
+ "outpaint" in model_lower,
+ "inpaint" in model_lower,
+ "erase" in model_lower,
+ "remove-background" in model_lower,
+ "search-recolor" in model_lower,
+ "search-replace" in model_lower,
+ "control-sketch" in model_lower,
+ "control-structure" in model_lower,
+ "style-guide" in model_lower,
+ "style-transfer" in model_lower,
+ ]):
+ return True
+ return False
+
+ def get_supported_openai_params(
+ self, model: str
+ ) -> list:
+ """
+ Return list of OpenAI params supported by Bedrock Stability.
+ """
+ return [
+ "n", # Number of images (Stability always returns 1, we can loop)
+ "size", # Maps to aspect_ratio
+ "response_format", # b64_json or url (Stability only returns b64)
+ "mask",
+ ]
+
+ def map_openai_params(
+ self,
+ image_edit_optional_params: ImageEditOptionalRequestParams,
+ model: str,
+ drop_params: bool,
+ ) -> Dict:
+ """
+ Map OpenAI parameters to Bedrock Stability parameters.
+
+ OpenAI -> Stability mappings:
+ - size -> aspect_ratio
+ - n -> (handled separately, Stability returns 1 image per request)
+ """
+ supported_params = self.get_supported_openai_params(model)
+ # Define mapping from OpenAI params to Stability params
+ param_mapping = {
+ "size": "aspect_ratio",
+ # "n" and "response_format" are handled separately
+ }
+
+ # Create a copy to not mutate original - convert TypedDict to regular dict
+ mapped_params: Dict[str, Any] = dict(image_edit_optional_params)
+
+ for k, v in image_edit_optional_params.items():
+ if k in param_mapping:
+ # Map param if mapping exists and value is valid
+ if k == "size" and v in OPENAI_SIZE_TO_STABILITY_ASPECT_RATIO:
+ mapped_params[param_mapping[k]] = OPENAI_SIZE_TO_STABILITY_ASPECT_RATIO[v] # type: ignore
+ # Don't copy "size" itself to final dict
+ elif k == "n":
+ # Store for logic but do not add to outgoing params
+ mapped_params["_n"] = v
+ elif k == "response_format":
+ # Only b64 supported at Stability; store for postprocessing
+ mapped_params["_response_format"] = v
+ elif k not in supported_params:
+ if not drop_params:
+ raise ValueError(
+ f"Parameter {k} is not supported for model {model}. "
+ f"Supported parameters are {supported_params}. "
+ f"Set drop_params=True to drop unsupported parameters."
+ )
+ # Otherwise, param will simply be dropped
+ else:
+ # param is supported and not mapped, keep as-is
+ continue
+
+ # Remove OpenAI params that have been mapped unless they're in stability
+ for mapped in ["size", "n", "response_format"]:
+ if mapped in mapped_params:
+ del mapped_params[mapped]
+
+ return mapped_params
+
+ def transform_image_edit_request(
+ self,
+ model: str,
+ prompt: str,
+ image: FileTypes,
+ image_edit_optional_request_params: Dict,
+ litellm_params: GenericLiteLLMParams,
+ headers: dict,
+ ) -> Tuple[Dict, Any]:
+ """
+ Transform OpenAI-style request to Bedrock Stability request format.
+
+ Returns the request body dict that will be JSON-encoded by the handler.
+ """
+ # Build Bedrock Stability request
+ data: Dict[str, Any] = {
+ "prompt": prompt,
+ "output_format": "png", # Default to PNG
+ }
+
+ # Convert image to base64
+ image_b64: str
+ if hasattr(image, 'read') and callable(getattr(image, 'read', None)):
+ # File-like object (e.g., BufferedReader from open())
+ image_bytes = image.read() # type: ignore
+ image_b64 = base64.b64encode(image_bytes).decode('utf-8') # type: ignore
+ elif isinstance(image, bytes):
+ # Raw bytes
+ image_b64 = base64.b64encode(image).decode('utf-8')
+ elif isinstance(image, str):
+ # Already a base64 string
+ image_b64 = image
+ else:
+ # Try to handle as bytes
+ image_b64 = base64.b64encode(bytes(image)).decode('utf-8') # type: ignore
+
+ data["image"] = image_b64
+
+ # Add optional params (already mapped in map_openai_params)
+ for key, value in image_edit_optional_request_params.items(): # type: ignore
+ # Skip internal params (prefixed with _)
+ if key.startswith("_") or value is None:
+ continue
+
+ # File-like optional params (mask, init_image, style_image, etc.)
+ if key in ["mask", "init_image", "style_image"]:
+ # Handle case where value might be in a list
+ file_value = value
+ if isinstance(value, list) and len(value) > 0:
+ file_value = value[0]
+
+ if hasattr(file_value, 'read') and callable(getattr(file_value, 'read', None)):
+ file_bytes = file_value.read() # type: ignore
+ elif isinstance(file_value, bytes):
+ file_bytes = file_value
+ elif isinstance(file_value, str):
+ # Already a base64 string
+ data[key] = file_value
+ continue
+ else:
+ file_bytes = file_value # type: ignore
+
+ if isinstance(file_bytes, bytes):
+ file_b64 = base64.b64encode(file_bytes).decode('utf-8')
+ else:
+ file_b64 = str(file_bytes)
+ data[key] = file_b64
+ continue
+
+ # Supported text fields
+ if key in [
+ "negative_prompt",
+ "aspect_ratio",
+ "seed",
+ "output_format",
+ "model",
+ "mode",
+ "strength",
+ "style_preset",
+ "creativity",
+ "control_strength",
+ "grow_mask",
+ "left",
+ "right",
+ "up",
+ "down",
+ "select_prompt",
+ "search_prompt",
+ "fidelity",
+ "composition_fidelity",
+ "style_strength",
+ "change_strength",
+ ]:
+ data[key] = value # type: ignore
+
+ return data, {}
+
+ def transform_image_edit_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ api_key: Optional[str] = None,
+ json_mode: Optional[bool] = None,
+ ) -> ImageResponse:
+ """
+ Transform Bedrock Stability response to OpenAI-compatible ImageResponse.
+
+ Bedrock returns: {"images": ["base64..."], "finish_reasons": [null], "seeds": [123]}
+ OpenAI expects: {"data": [{"b64_json": "base64..."}], "created": timestamp}
+ """
+ try:
+ response_data = raw_response.json()
+ with open("response_data.json", "w") as f:
+ json.dump(response_data, f)
+ except Exception as e:
+ raise self.get_error_class(
+ error_message=f"Error parsing Bedrock Stability response: {e}",
+ status_code=raw_response.status_code,
+ headers=raw_response.headers,
+ )
+
+ # Check for errors in response
+ if "errors" in response_data:
+ raise self.get_error_class(
+ error_message=f"Bedrock Stability error: {response_data['errors']}",
+ status_code=raw_response.status_code,
+ headers=raw_response.headers,
+ )
+
+ # Check finish_reasons
+ finish_reasons = response_data.get("finish_reasons", [])
+ if finish_reasons and finish_reasons[0]:
+ raise self.get_error_class(
+ error_message=f"Bedrock Stability error: {finish_reasons[0]}",
+ status_code=400,
+ headers=raw_response.headers,
+ )
+
+ model_response = ImageResponse()
+ if not model_response.data:
+ model_response.data = []
+
+ # Extract images from response
+ images = response_data.get("images", [])
+ if images:
+ for image_b64 in images:
+ if image_b64:
+ model_response.data.append(
+ ImageObject(
+ b64_json=image_b64,
+ url=None,
+ revised_prompt=None,
+ )
+ )
+
+ if not hasattr(model_response, "_hidden_params"):
+ model_response._hidden_params = {}
+ if "additional_headers" not in model_response._hidden_params:
+ model_response._hidden_params["additional_headers"] = {}
+
+ # Set cost based on model
+ model_info = get_model_info(model, custom_llm_provider="bedrock")
+ cost_per_image = model_info.get("output_cost_per_image", 0)
+ if cost_per_image is not None:
+ model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"] = float(cost_per_image)
+
+ return model_response
+
+ def use_multipart_form_data(self) -> bool:
+ """
+ Bedrock Stability uses JSON format, not multipart/form-data.
+ """
+ return False
+
+ def get_complete_url(
+ self,
+ model: str,
+ api_base: Optional[str],
+ litellm_params: dict,
+ ) -> str:
+ """
+ Get the complete URL for the Bedrock Image Edit API.
+
+ For Bedrock, this is handled by the handler which constructs the endpoint URL
+ based on the model ID and AWS region. This method is required by the base class
+ but the actual URL construction happens in BedrockImageEdit.image_edit().
+
+ Returns a placeholder - the real endpoint is constructed in the handler.
+ """
+ # Bedrock URLs are constructed in the handler using boto3
+ # This is a placeholder for the abstract method requirement
+ return "bedrock://image-edit"
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ """
+ Validate environment for Bedrock Stability image edit.
+
+ For Bedrock, AWS credentials are managed by the BaseAWSLLM class.
+ This method validates that headers are properly set up.
+
+ Args:
+ headers: The request headers to validate/update
+ model: The model name being used
+ api_key: Optional API key (not used for Bedrock, which uses AWS credentials)
+
+ Returns:
+ Updated headers dict
+ """
+ if headers is None:
+ headers = {}
+
+ # Bedrock uses AWS credentials, not API keys
+ # Headers are set up by the handler's get_request_headers() method
+ # This just ensures basic headers are present
+ if "Content-Type" not in headers:
+ headers["Content-Type"] = "application/json"
+
+ return headers
+
diff --git a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py b/litellm/llms/bedrock/image_generation/amazon_nova_canvas_transformation.py
similarity index 100%
rename from litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py
rename to litellm/llms/bedrock/image_generation/amazon_nova_canvas_transformation.py
diff --git a/litellm/llms/bedrock/image/amazon_stability1_transformation.py b/litellm/llms/bedrock/image_generation/amazon_stability1_transformation.py
similarity index 100%
rename from litellm/llms/bedrock/image/amazon_stability1_transformation.py
rename to litellm/llms/bedrock/image_generation/amazon_stability1_transformation.py
diff --git a/litellm/llms/bedrock/image/amazon_stability3_transformation.py b/litellm/llms/bedrock/image_generation/amazon_stability3_transformation.py
similarity index 100%
rename from litellm/llms/bedrock/image/amazon_stability3_transformation.py
rename to litellm/llms/bedrock/image_generation/amazon_stability3_transformation.py
diff --git a/litellm/llms/bedrock/image/amazon_titan_transformation.py b/litellm/llms/bedrock/image_generation/amazon_titan_transformation.py
similarity index 100%
rename from litellm/llms/bedrock/image/amazon_titan_transformation.py
rename to litellm/llms/bedrock/image_generation/amazon_titan_transformation.py
diff --git a/litellm/llms/bedrock/image/cost_calculator.py b/litellm/llms/bedrock/image_generation/cost_calculator.py
similarity index 87%
rename from litellm/llms/bedrock/image/cost_calculator.py
rename to litellm/llms/bedrock/image_generation/cost_calculator.py
index bc1a57b8aec..b04acc3e809 100644
--- a/litellm/llms/bedrock/image/cost_calculator.py
+++ b/litellm/llms/bedrock/image_generation/cost_calculator.py
@@ -1,6 +1,6 @@
from typing import Optional
-from litellm.llms.bedrock.image.image_handler import BedrockImageGeneration
+from litellm.llms.bedrock.image_generation.image_handler import BedrockImageGeneration
from litellm.types.utils import ImageResponse
diff --git a/litellm/llms/bedrock/image/image_handler.py b/litellm/llms/bedrock/image_generation/image_handler.py
similarity index 90%
rename from litellm/llms/bedrock/image/image_handler.py
rename to litellm/llms/bedrock/image_generation/image_handler.py
index 89e37bbdd8d..0a4cde90b27 100644
--- a/litellm/llms/bedrock/image/image_handler.py
+++ b/litellm/llms/bedrock/image_generation/image_handler.py
@@ -9,13 +9,13 @@ from pydantic import BaseModel
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging
-from litellm.llms.bedrock.image.amazon_nova_canvas_transformation import (
+from litellm.llms.bedrock.image_generation.amazon_nova_canvas_transformation import (
AmazonNovaCanvasConfig,
)
-from litellm.llms.bedrock.image.amazon_stability3_transformation import (
+from litellm.llms.bedrock.image_generation.amazon_stability3_transformation import (
AmazonStability3Config,
)
-from litellm.llms.bedrock.image.amazon_titan_transformation import (
+from litellm.llms.bedrock.image_generation.amazon_titan_transformation import (
AmazonTitanImageGenerationConfig,
)
from litellm.llms.custom_httpx.http_handler import (
@@ -170,6 +170,21 @@ class BedrockImageGeneration(BaseAWSLLM):
)
return model_response
+ def _extract_headers_from_optional_params(self, optional_params: dict) -> dict:
+ """
+ Extract guardrail parameters from optional_params and convert them to headers.
+ """
+ headers = {}
+ guardrail_identifier = optional_params.pop("guardrailIdentifier", None)
+ guardrail_version = optional_params.pop("guardrailVersion", None)
+
+ if guardrail_identifier is not None:
+ headers["x-amz-bedrock-guardrail-identifier"] = guardrail_identifier
+ if guardrail_version is not None:
+ headers["x-amz-bedrock-guardrail-version"] = guardrail_version
+
+ return headers
+
def _prepare_request(
self,
model: str,
@@ -228,6 +243,10 @@ class BedrockImageGeneration(BaseAWSLLM):
if extra_headers is not None:
headers = {"Content-Type": "application/json", **extra_headers}
+ # Extract guardrail parameters and add them as headers
+ guardrail_headers = self._extract_headers_from_optional_params(optional_params)
+ headers.update(guardrail_headers)
+
prepped = self.get_request_headers(
credentials=boto3_credentials_info.credentials,
aws_region_name=boto3_credentials_info.aws_region_name,
diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py
index 1b4d04af80a..7fdb78c1670 100644
--- a/litellm/llms/custom_httpx/http_handler.py
+++ b/litellm/llms/custom_httpx/http_handler.py
@@ -769,7 +769,7 @@ class AsyncHTTPHandler:
connector_kwargs["ssl"] = ssl_context
elif ssl_verify is False:
# Priority 2: Explicitly disable SSL verification
- connector_kwargs["verify_ssl"] = False
+ connector_kwargs["ssl"] = False
return connector_kwargs
diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py
index 4b38f542159..34ea598a655 100644
--- a/litellm/llms/custom_httpx/llm_http_handler.py
+++ b/litellm/llms/custom_httpx/llm_http_handler.py
@@ -3646,7 +3646,7 @@ class BaseLLMHTTPHandler:
ssl_context = get_shared_realtime_ssl_context()
async with websockets.connect( # type: ignore
url,
- extra_headers=headers,
+ additional_headers=headers,
max_size=REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES,
ssl=ssl_context,
) as backend_ws:
@@ -3761,7 +3761,7 @@ class BaseLLMHTTPHandler:
input=prompt,
api_key="",
additional_args={
- "complete_input_dict": data,
+ "complete_input_dict": files,
"api_base": api_base,
"headers": headers,
},
diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py
index ac3be0c3518..2b7f5dd5995 100644
--- a/litellm/llms/databricks/chat/transformation.py
+++ b/litellm/llms/databricks/chat/transformation.py
@@ -2,6 +2,7 @@
Translates from OpenAI's `/v1/chat/completions` to Databricks' `/chat/completions`
"""
+import os
from typing import (
TYPE_CHECKING,
Any,
@@ -26,7 +27,7 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo
_should_convert_tool_call_to_json_mode,
)
from litellm.litellm_core_utils.prompt_templates.common_utils import (
- strip_name_from_message
+ strip_name_from_message,
)
from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
from litellm.types.llms.anthropic import AllAnthropicToolsValues
@@ -124,12 +125,24 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
+ # Check for custom user agent in optional_params or environment
+ # This allows partners building on LiteLLM to set their own telemetry
+ # Use pop() to remove these keys so they don't get sent to the API
+ custom_user_agent = (
+ optional_params.pop("user_agent", None)
+ or optional_params.pop("databricks_user_agent", None)
+ or litellm_params.get("user_agent")
+ or os.getenv("LITELLM_USER_AGENT")
+ or os.getenv("DATABRICKS_USER_AGENT")
+ )
+
api_base, headers = self.databricks_validate_environment(
api_base=api_base,
api_key=api_key,
endpoint_type="chat_completions",
custom_endpoint=False,
headers=headers,
+ custom_user_agent=custom_user_agent,
)
# Ensure Content-Type header is set
headers["Content-Type"] = "application/json"
@@ -173,9 +186,9 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
# Build DatabricksFunction explicitly to avoid parameter conflicts
function_params: DatabricksFunction = {
"name": tool["name"],
- "parameters": cast(dict, tool.get("input_schema") or {})
+ "parameters": cast(dict, tool.get("input_schema") or {}),
}
-
+
# Only add description if it exists
description = tool.get("description")
if description is not None:
@@ -229,7 +242,7 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
Databricks supports Anthropic-style cache control for Claude models.
Databricks ignores the cache_control flag with other models.
"""
- # TODO: Think about how to best design the request transformation so that
+ # TODO: Think about how to best design the request transformation so that
# every request doesn't have to be transformed for to OpenAI and Anthropic request formats.
return messages, tools
@@ -347,15 +360,17 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
messages=new_messages, model=model, is_async=cast(Literal[False], False)
)
- def _move_cache_control_into_string_content_block(self, message: AllMessageValues) -> AllMessageValues:
+ def _move_cache_control_into_string_content_block(
+ self, message: AllMessageValues
+ ) -> AllMessageValues:
"""
Moves message-level cache_control into a content block when content is a string.
-
+
Transforms:
{"role": "user", "content": "text", "cache_control": {...}}
Into:
{"role": "user", "content": [{"type": "text", "text": "text", "cache_control": {...}}]}
-
+
This is required for Anthropic's prompt caching API when cache_control is specified
at the message level but content is a simple string (not already an array of content blocks).
"""
@@ -371,7 +386,6 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
}
]
return cast(AllMessageValues, transformed_message)
-
@staticmethod
def extract_content_str(
@@ -509,9 +523,9 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
reasoning_content=reasoning_content,
thinking_blocks=thinking_blocks,
tool_calls=choice["message"].get("tool_calls"),
- provider_specific_fields={"citations": citations}
- if citations is not None
- else None,
+ provider_specific_fields=(
+ {"citations": citations} if citations is not None else None
+ ),
)
if finish_reason is None:
@@ -543,12 +557,15 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
- ## LOGGING
+ # Redact sensitive data before logging to prevent credential leakage
+ redacted_request_data = self.redact_sensitive_data(request_data)
+
+ ## LOGGING - Never log actual API keys
logging_obj.post_call(
input=messages,
- api_key=api_key,
+ api_key="[REDACTED]",
original_response=raw_response.text,
- additional_args={"complete_input_dict": request_data},
+ additional_args={"complete_input_dict": redacted_request_data},
)
## RESPONSE OBJECT
diff --git a/litellm/llms/databricks/common_utils.py b/litellm/llms/databricks/common_utils.py
index 1353b5b13f6..608f29a03a7 100644
--- a/litellm/llms/databricks/common_utils.py
+++ b/litellm/llms/databricks/common_utils.py
@@ -1,4 +1,18 @@
-from typing import Literal, Optional, Tuple
+"""
+Databricks integration utilities for LiteLLM.
+
+This module provides authentication, telemetry, and security utilities
+for the Databricks LLM provider integration.
+
+Authentication priority:
+1. OAuth M2M (DATABRICKS_CLIENT_ID + DATABRICKS_CLIENT_SECRET) - Recommended for production
+2. PAT (DATABRICKS_API_KEY) - Supported for development
+3. Databricks SDK automatic auth - Fallback (uses unified auth)
+"""
+
+import os
+import re
+from typing import Any, Dict, Literal, Optional, Tuple
from litellm.llms.base_llm.chat.transformation import BaseLLMException
@@ -8,17 +22,175 @@ class DatabricksException(BaseLLMException):
class DatabricksBase:
+ """
+ Base class for Databricks integration with authentication,
+ telemetry, and security utilities.
+ """
+
+ # Patterns to redact in logs
+ SENSITIVE_PATTERNS = [
+ (re.compile(r"(Bearer\s+)[A-Za-z0-9\-_\.]+", re.IGNORECASE), r"\1[REDACTED]"),
+ (re.compile(r"(Authorization:\s*)[^\s,}]+", re.IGNORECASE), r"\1[REDACTED]"),
+ (
+ re.compile(r'(api[_-]?key["\s:=]+)[^\s,}"\']+', re.IGNORECASE),
+ r"\1[REDACTED]",
+ ),
+ (
+ re.compile(r'(client[_-]?secret["\s:=]+)[^\s,}"\']+', re.IGNORECASE),
+ r"\1[REDACTED]",
+ ),
+ (re.compile(r"(dapi[a-zA-Z0-9]{32,})", re.IGNORECASE), r"[REDACTED_PAT]"),
+ (
+ re.compile(r'(access[_-]?token["\s:=]+)[^\s,}"\']+', re.IGNORECASE),
+ r"\1[REDACTED]",
+ ),
+ ]
+
+ @classmethod
+ def redact_sensitive_data(cls, data: Any) -> Any:
+ """
+ Redact sensitive information (tokens, secrets) from data before logging.
+
+ Handles strings, dicts, and lists recursively. Keys containing sensitive
+ terms (authorization, api_key, token, secret, password, credential) are
+ fully redacted.
+
+ Args:
+ data: String, dict, or other data structure to redact
+
+ Returns:
+ Redacted version of the data safe for logging
+ """
+ if data is None:
+ return None
+
+ if isinstance(data, str):
+ result = data
+ for pattern, replacement in cls.SENSITIVE_PATTERNS:
+ result = pattern.sub(replacement, result)
+ return result
+
+ if isinstance(data, dict):
+ redacted = {}
+ for key, value in data.items():
+ lower_key = key.lower()
+ if any(
+ sensitive in lower_key
+ for sensitive in [
+ "authorization",
+ "api_key",
+ "apikey",
+ "token",
+ "secret",
+ "password",
+ "credential",
+ ]
+ ):
+ redacted[key] = "[REDACTED]"
+ else:
+ redacted[key] = cls.redact_sensitive_data(value)
+ return redacted
+
+ if isinstance(data, list):
+ return [cls.redact_sensitive_data(item) for item in data]
+
+ return data
+
+ @classmethod
+ def redact_headers_for_logging(cls, headers: Dict[str, str]) -> Dict[str, str]:
+ """
+ Create a copy of headers with sensitive values redacted for safe logging.
+
+ Shows first 8 characters of sensitive values for debugging purposes,
+ with the rest redacted.
+
+ Args:
+ headers: HTTP headers dictionary
+
+ Returns:
+ New dictionary with sensitive headers redacted
+ """
+ if not headers:
+ return {}
+
+ redacted = {}
+ sensitive_headers = {
+ "authorization",
+ "x-api-key",
+ "api-key",
+ "x-databricks-token",
+ }
+
+ for key, value in headers.items():
+ if key.lower() in sensitive_headers:
+ if len(value) > 10:
+ redacted[key] = f"{value[:8]}...[REDACTED]"
+ else:
+ redacted[key] = "[REDACTED]"
+ else:
+ redacted[key] = value
+
+ return redacted
+
+ @staticmethod
+ def _build_user_agent(custom_user_agent: Optional[str] = None) -> str:
+ """
+ Build the User-Agent string for Databricks API calls.
+
+ If a custom user agent is provided, the partner name (part before /)
+ is extracted and prefixed to the litellm user agent with an underscore.
+ The custom version is ignored; LiteLLM's version is always used.
+
+ Args:
+ custom_user_agent: Optional custom user agent string (e.g., "mycompany/1.0.0")
+
+ Returns:
+ User-Agent string in format:
+ - Default: "litellm/{version}"
+ - With custom: "{partner}_litellm/{version}"
+
+ Examples:
+ - None -> "litellm/1.79.1"
+ - "mycompany/1.0.0" -> "mycompany_litellm/1.79.1"
+ - "partner_product/2.0.0" -> "partner_product_litellm/1.79.1"
+ - "acme" -> "acme_litellm/1.79.1"
+ """
+ try:
+ from litellm._version import version
+ except Exception:
+ version = "0.0.0"
+
+ if custom_user_agent:
+ custom_user_agent = custom_user_agent.strip()
+
+ # Extract partner name (part before / if present)
+ if "/" in custom_user_agent:
+ partner_name = custom_user_agent.split("/")[0].strip()
+ else:
+ partner_name = custom_user_agent
+
+ # Validate partner name: alphanumeric, underscore, hyphen only
+ if (
+ partner_name
+ and partner_name.replace("_", "").replace("-", "").isalnum()
+ ):
+ return f"{partner_name}_litellm/{version}"
+
+ # Default: just litellm
+ return f"litellm/{version}"
+
def _get_api_base(self, api_base: Optional[str]) -> str:
+ """
+ Get the Databricks API base URL.
+
+ If not provided, attempts to get it from the Databricks SDK.
+ """
if api_base is None:
try:
from databricks.sdk import WorkspaceClient
databricks_client = WorkspaceClient()
-
- api_base = (
- api_base or f"{databricks_client.config.host}/serving-endpoints"
- )
-
+ api_base = f"{databricks_client.config.host}/serving-endpoints"
return api_base
except ImportError:
raise DatabricksException(
@@ -30,12 +202,87 @@ class DatabricksBase:
)
return api_base
+ def _get_oauth_m2m_token(
+ self,
+ api_base: str,
+ client_id: str,
+ client_secret: str,
+ ) -> str:
+ """
+ Obtain an OAuth M2M access token using client credentials flow.
+
+ This is the recommended authentication method for production integrations
+ per Databricks Partner requirements.
+
+ Args:
+ api_base: Databricks workspace URL
+ client_id: OAuth client ID (Service Principal application ID)
+ client_secret: OAuth client secret
+
+ Returns:
+ Access token string
+
+ Raises:
+ DatabricksException: If token request fails
+ """
+ import requests
+
+ # Extract workspace URL from api_base
+ workspace_url = api_base.rstrip("/")
+ if "/serving-endpoints" in workspace_url:
+ workspace_url = workspace_url.replace("/serving-endpoints", "")
+
+ token_url = f"{workspace_url}/oidc/v1/token"
+
+ try:
+ response = requests.post(
+ token_url,
+ data={
+ "grant_type": "client_credentials",
+ "scope": "all-apis",
+ },
+ auth=(client_id, client_secret),
+ headers={"Content-Type": "application/x-www-form-urlencoded"},
+ timeout=30,
+ )
+ except requests.RequestException as e:
+ raise DatabricksException(
+ status_code=500,
+ message=f"OAuth M2M token request failed: {str(e)}",
+ )
+
+ if response.status_code != 200:
+ raise DatabricksException(
+ status_code=response.status_code,
+ message=f"OAuth M2M token request failed: {response.text}",
+ )
+
+ token_data = response.json()
+ return token_data["access_token"]
+
def _get_databricks_credentials(
self, api_key: Optional[str], api_base: Optional[str], headers: Optional[dict]
) -> Tuple[str, dict]:
+ """
+ Get Databricks credentials using the Databricks SDK.
+
+ Also registers LiteLLM as a partner for proper telemetry attribution
+ in Databricks system.access.audit table.
+
+ Args:
+ api_key: Optional API key (PAT)
+ api_base: Optional API base URL
+ headers: Optional existing headers
+
+ Returns:
+ Tuple of (api_base, headers)
+ """
headers = headers or {"Content-Type": "application/json"}
try:
- from databricks.sdk import WorkspaceClient
+ from databricks.sdk import WorkspaceClient, useragent
+
+ # Register LiteLLM as partner for Databricks telemetry attribution
+ useragent.with_partner("litellm")
databricks_client = WorkspaceClient()
@@ -66,14 +313,53 @@ class DatabricksBase:
endpoint_type: Literal["chat_completions", "embeddings"],
custom_endpoint: Optional[bool],
headers: Optional[dict],
+ custom_user_agent: Optional[str] = None,
) -> Tuple[str, dict]:
- if api_key is None and not headers: # handle empty headers
+ """
+ Validate and configure the Databricks environment.
+
+ Authentication priority:
+ 1. OAuth M2M (DATABRICKS_CLIENT_ID + DATABRICKS_CLIENT_SECRET) - Recommended
+ 2. PAT (DATABRICKS_API_KEY) - Supported for development
+ 3. Databricks SDK automatic auth - Fallback (uses unified auth)
+
+ Args:
+ api_key: Personal access token (PAT)
+ api_base: Databricks workspace URL with /serving-endpoints
+ endpoint_type: Type of endpoint (chat_completions or embeddings)
+ custom_endpoint: Whether using a custom endpoint URL
+ headers: Existing headers dict
+ custom_user_agent: Optional custom user agent to prefix
+
+ Returns:
+ Tuple of (api_base, headers) with authentication configured
+ """
+ from litellm._logging import verbose_logger
+
+ # Check for OAuth M2M credentials (recommended for production)
+ client_id = os.getenv("DATABRICKS_CLIENT_ID")
+ client_secret = os.getenv("DATABRICKS_CLIENT_SECRET")
+
+ # Determine api_base first
+ if api_base is None:
+ api_base = os.getenv("DATABRICKS_API_BASE")
+
+ if client_id and client_secret and api_base:
+ # Use OAuth M2M flow (preferred for production)
+ verbose_logger.debug("Using OAuth M2M authentication for Databricks")
+ access_token = self._get_oauth_m2m_token(api_base, client_id, client_secret)
+ headers = headers or {}
+ headers["Authorization"] = f"Bearer {access_token}"
+ headers["Content-Type"] = "application/json"
+ elif api_key is None and not headers:
if custom_endpoint is True:
raise DatabricksException(
status_code=400,
message="Missing API Key - A call is being made to LLM Provider but no key is set either in the environment variables ({LLM_PROVIDER}_API_KEY) or via params",
)
else:
+ # Fallback to Databricks SDK (registers partner telemetry)
+ verbose_logger.debug("Using Databricks SDK for authentication")
api_base, headers = self._get_databricks_credentials(
api_base=api_base, api_key=api_key, headers=headers
)
@@ -101,8 +387,17 @@ class DatabricksBase:
if api_key is not None:
headers["Authorization"] = f"Bearer {api_key}"
+ # Set User-Agent with optional custom prefix
+ headers["User-Agent"] = self._build_user_agent(custom_user_agent)
+
+ # Debug logging with redaction (never log actual tokens)
+ verbose_logger.debug(
+ f"Databricks request headers: {self.redact_headers_for_logging(headers)}"
+ )
+
if endpoint_type == "chat_completions" and custom_endpoint is not True:
api_base = "{}/chat/completions".format(api_base)
elif endpoint_type == "embeddings" and custom_endpoint is not True:
api_base = "{}/embeddings".format(api_base)
+
return api_base, headers
diff --git a/litellm/llms/databricks/embed/handler.py b/litellm/llms/databricks/embed/handler.py
index 2eabcdbc866..227824f72d0 100644
--- a/litellm/llms/databricks/embed/handler.py
+++ b/litellm/llms/databricks/embed/handler.py
@@ -2,6 +2,7 @@
Calling logic for Databricks embeddings
"""
+import os
from typing import Optional
from litellm.utils import EmbeddingResponse
@@ -26,12 +27,23 @@ class DatabricksEmbeddingHandler(OpenAILikeEmbeddingHandler, DatabricksBase):
custom_endpoint: Optional[bool] = None,
headers: Optional[dict] = None,
) -> EmbeddingResponse:
+ # Check for custom user agent in optional_params or environment
+ # This allows partners building on LiteLLM to set their own telemetry
+ # Use pop() to remove these keys so they don't get sent to the API
+ custom_user_agent = (
+ optional_params.pop("user_agent", None)
+ or optional_params.pop("databricks_user_agent", None)
+ or os.getenv("LITELLM_USER_AGENT")
+ or os.getenv("DATABRICKS_USER_AGENT")
+ )
+
api_base, headers = self.databricks_validate_environment(
api_base=api_base,
api_key=api_key,
endpoint_type="embeddings",
custom_endpoint=custom_endpoint,
headers=headers,
+ custom_user_agent=custom_user_agent,
)
return super().embedding(
model=model,
diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py
index 81b34f80ea0..86bcd94450f 100644
--- a/litellm/llms/fireworks_ai/chat/transformation.py
+++ b/litellm/llms/fireworks_ai/chat/transformation.py
@@ -240,12 +240,43 @@ class FireworksAIConfig(OpenAIGPTConfig):
return messages
def get_provider_info(self, model: str) -> ProviderSpecificModelInfo:
- provider_specific_model_info = ProviderSpecificModelInfo(
- supports_function_calling=True,
- supports_prompt_caching=True, # https://docs.fireworks.ai/guides/prompt-caching
- supports_pdf_input=True, # via document inlining
- supports_vision=True, # via document inlining
+ # Models that support reasoning_effort
+ reasoning_supported_models = [
+ "qwen3-8b",
+ "qwen3-32b",
+ "qwen3-coder-480b-a35b-instruct",
+ "deepseek-v3p1",
+ "deepseek-v3p2",
+ "glm-4p5",
+ "glm-4p5-air",
+ "glm-4p6",
+ "gpt-oss-120b",
+ "gpt-oss-20b",
+ ]
+
+ # Normalize model name - remove prefix if present
+ normalized_model = model
+ if model.startswith("fireworks_ai/"):
+ normalized_model = model.replace("fireworks_ai/", "")
+ if normalized_model.startswith("accounts/fireworks/models/"):
+ normalized_model = normalized_model.replace("accounts/fireworks/models/", "")
+
+ # Check if model supports reasoning
+ supports_reasoning_value = any(
+ reasoning_model in normalized_model for reasoning_model in reasoning_supported_models
)
+
+ provider_specific_model_info: ProviderSpecificModelInfo = {
+ "supports_function_calling": True,
+ "supports_prompt_caching": True, # https://docs.fireworks.ai/guides/prompt-caching
+ "supports_pdf_input": True, # via document inlining
+ "supports_vision": True, # via document inlining
+ }
+
+ # Only include supports_reasoning if True
+ if supports_reasoning_value:
+ provider_specific_model_info["supports_reasoning"] = True
+
return provider_specific_model_info
def transform_request(
diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py
index bc32aca6554..d8692bb6a3a 100644
--- a/litellm/llms/gemini/google_genai/transformation.py
+++ b/litellm/llms/gemini/google_genai/transformation.py
@@ -75,6 +75,7 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
"seed",
"response_mime_type",
"response_schema",
+ "response_json_schema",
"routing_config",
"model_selection_config",
"safety_settings",
@@ -105,13 +106,37 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
Returns:
Mapped parameters for the provider
"""
+ from litellm.llms.vertex_ai.gemini.transformation import (
+ _camel_to_snake,
+ _snake_to_camel,
+ )
+
_generate_content_config_dict: Dict[str, Any] = {}
supported_google_genai_params = (
self.get_supported_generate_content_optional_params(model)
)
+ # Create a set with both camelCase and snake_case versions for faster lookup
+ supported_params_set = set(supported_google_genai_params)
+ supported_params_set.update(_snake_to_camel(p) for p in supported_google_genai_params)
+ supported_params_set.update(_camel_to_snake(p) for p in supported_google_genai_params if "_" not in p)
+
for param, value in generate_content_config_dict.items():
- if param in supported_google_genai_params:
- _generate_content_config_dict[param] = value
+ # Google GenAI API expects camelCase, so we'll always output in camelCase
+ # Check if param (or its variants) is supported
+ param_snake = _camel_to_snake(param)
+ param_camel = _snake_to_camel(param)
+
+ # Check if param is supported in any format
+ is_supported = (
+ param in supported_google_genai_params or
+ param_snake in supported_google_genai_params or
+ param_camel in supported_google_genai_params
+ )
+
+ if is_supported:
+ # Always output in camelCase for Google GenAI API
+ output_key = param_camel if param != param_camel else param
+ _generate_content_config_dict[output_key] = value
return _generate_content_config_dict
def validate_environment(
diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py
index 2d8d82e6ad8..63b835df9d0 100644
--- a/litellm/llms/gemini/image_generation/transformation.py
+++ b/litellm/llms/gemini/image_generation/transformation.py
@@ -11,7 +11,12 @@ from litellm.types.llms.openai import (
AllMessageValues,
OpenAIImageGenerationOptionalParams,
)
-from litellm.types.utils import ImageObject, ImageResponse
+from litellm.types.utils import (
+ ImageObject,
+ ImageResponse,
+ ImageUsage,
+ ImageUsageInputTokensDetails,
+)
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
@@ -73,6 +78,33 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
"896x1280": "3:4",
}
return aspect_ratio_map.get(size, "1:1")
+
+ def _transform_image_usage(self, usage_metadata: dict) -> ImageUsage:
+ """
+ Transform Gemini usageMetadata to ImageUsage format
+ """
+ input_tokens_details = ImageUsageInputTokensDetails(
+ image_tokens=0,
+ text_tokens=0,
+ )
+
+ # Extract detailed token counts from promptTokensDetails
+ tokens_details = usage_metadata.get("promptTokensDetails", [])
+ for details in tokens_details:
+ if isinstance(details, dict):
+ modality = details.get("modality")
+ token_count = details.get("tokenCount", 0)
+ if modality == "TEXT":
+ input_tokens_details.text_tokens = token_count
+ elif modality == "IMAGE":
+ input_tokens_details.image_tokens = token_count
+
+ return ImageUsage(
+ input_tokens=usage_metadata.get("promptTokenCount", 0),
+ input_tokens_details=input_tokens_details,
+ output_tokens=usage_metadata.get("candidatesTokenCount", 0),
+ total_tokens=usage_metadata.get("totalTokenCount", 0),
+ )
def get_complete_url(
self,
@@ -227,6 +259,10 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
b64_json=inline_data["data"],
url=None,
))
+
+ # Extract usage metadata for Gemini models
+ if "usageMetadata" in response_data:
+ model_response.usage = self._transform_image_usage(response_data["usageMetadata"])
else:
# Original Imagen format - predictions with generated images
predictions = response_data.get("predictions", [])
diff --git a/litellm/llms/linkup/__init__.py b/litellm/llms/linkup/__init__.py
new file mode 100644
index 00000000000..b1553a17379
--- /dev/null
+++ b/litellm/llms/linkup/__init__.py
@@ -0,0 +1,7 @@
+"""
+Linkup API integration module.
+"""
+from litellm.llms.linkup.search.transformation import LinkupSearchConfig
+
+__all__ = ["LinkupSearchConfig"]
+
diff --git a/litellm/llms/linkup/search/__init__.py b/litellm/llms/linkup/search/__init__.py
new file mode 100644
index 00000000000..b47af3f3057
--- /dev/null
+++ b/litellm/llms/linkup/search/__init__.py
@@ -0,0 +1,7 @@
+"""
+Linkup Search API module.
+"""
+from litellm.llms.linkup.search.transformation import LinkupSearchConfig
+
+__all__ = ["LinkupSearchConfig"]
+
diff --git a/litellm/llms/linkup/search/transformation.py b/litellm/llms/linkup/search/transformation.py
new file mode 100644
index 00000000000..bbe76664b4c
--- /dev/null
+++ b/litellm/llms/linkup/search/transformation.py
@@ -0,0 +1,206 @@
+"""
+Calls Linkup's /search endpoint to search the web.
+
+Linkup API Reference: https://docs.linkup.so/pages/documentation/api-reference/endpoint/post-search
+"""
+from typing import Dict, List, Literal, Optional, TypedDict, Union
+
+import httpx
+
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.search.transformation import (
+ BaseSearchConfig,
+ SearchResponse,
+ SearchResult,
+)
+from litellm.secret_managers.main import get_secret_str
+
+
+class _LinkupSearchRequestRequired(TypedDict):
+ """Required fields for Linkup Search API request."""
+
+ q: str # Required - The natural language question for which you want to retrieve context
+ depth: Literal["deep", "standard"] # Required - Defines the precision of the search
+ outputType: Literal[
+ "searchResults", "sourcedAnswer", "structured"
+ ] # Required - The type of output
+
+
+class LinkupSearchRequest(_LinkupSearchRequestRequired, total=False):
+ """
+ Linkup Search API request format.
+ Based on: https://docs.linkup.so/pages/documentation/api-reference/endpoint/post-search
+ """
+
+ structuredOutputSchema: str # Required only when outputType is "structured"
+ includeSources: bool # Optional - Include sources in response (default false)
+ includeImages: bool # Optional - Include images in results (default false)
+ fromDate: str # Optional - Start date for results (YYYY-MM-DD)
+ toDate: str # Optional - End date for results (YYYY-MM-DD)
+ includeDomains: List[str] # Optional - Domains to search on (max 100)
+ excludeDomains: List[str] # Optional - Domains to exclude
+ includeInlineCitations: bool # Optional - Include inline citations (default false)
+ maxResults: int # Optional - Maximum number of results to return
+
+
+class LinkupSearchConfig(BaseSearchConfig):
+ LINKUP_API_BASE = "https://api.linkup.so/v1"
+
+ @staticmethod
+ def ui_friendly_name() -> str:
+ return "Linkup"
+
+ def validate_environment(
+ self,
+ headers: Dict,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ **kwargs,
+ ) -> Dict:
+ """
+ Validate environment and return headers.
+ """
+ api_key = api_key or get_secret_str("LINKUP_API_KEY")
+ if not api_key:
+ raise ValueError(
+ "LINKUP_API_KEY is not set. Set `LINKUP_API_KEY` environment variable."
+ )
+ headers["Authorization"] = f"Bearer {api_key}"
+ headers["Content-Type"] = "application/json"
+ return headers
+
+ def get_complete_url(
+ self,
+ api_base: Optional[str],
+ optional_params: dict,
+ data: Optional[Union[Dict, List[Dict]]] = None,
+ **kwargs,
+ ) -> str:
+ """
+ Get complete URL for Search endpoint.
+ """
+ api_base = (
+ api_base or get_secret_str("LINKUP_API_BASE") or self.LINKUP_API_BASE
+ )
+
+ # Append "/search" to the api base if it's not already there
+ if not api_base.endswith("/search"):
+ api_base = f"{api_base}/search"
+
+ return api_base
+
+ def transform_search_request(
+ self,
+ query: Union[str, List[str]],
+ optional_params: dict,
+ **kwargs,
+ ) -> Dict:
+ """
+ Transform Search request to Linkup API format.
+
+ Transforms Perplexity unified spec parameters:
+ - query -> q
+ - max_results -> maxResults
+ - search_domain_filter -> includeDomains
+ - country -> (not directly supported)
+ - max_tokens_per_page -> (not applicable)
+
+ All other Linkup-specific parameters are passed through as-is.
+
+ Args:
+ query: Search query (string or list of strings). Linkup only supports single string queries.
+ optional_params: Optional parameters for the request
+
+ Returns:
+ Dict with typed request data following LinkupSearchRequest spec
+ """
+ if isinstance(query, list):
+ # Linkup only supports single string queries, join with spaces
+ query = " ".join(query)
+
+ request_data: LinkupSearchRequest = {
+ "q": query,
+ "depth": optional_params.get("depth", "standard"),
+ "outputType": optional_params.get("outputType", "searchResults"),
+ }
+
+ # Transform Perplexity unified spec parameters to Linkup format
+ if "max_results" in optional_params:
+ request_data["maxResults"] = optional_params["max_results"]
+
+ if "search_domain_filter" in optional_params:
+ request_data["includeDomains"] = optional_params["search_domain_filter"]
+
+ # Convert to dict before dynamic key assignments
+ result_data = dict(request_data)
+
+ # pass through all other parameters as-is
+ for param, value in optional_params.items():
+ if (
+ param not in self.get_supported_perplexity_optional_params()
+ and param not in result_data
+ ):
+ result_data[param] = value
+
+ return result_data
+
+ def transform_search_response(
+ self,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ **kwargs,
+ ) -> SearchResponse:
+ """
+ Transform Linkup API response to LiteLLM unified SearchResponse format.
+
+ Linkup -> LiteLLM mappings:
+ - results[].name -> SearchResult.title
+ - results[].url -> SearchResult.url
+ - results[].content -> SearchResult.snippet
+ - No date field in results (set to None)
+ - No last_updated field in Linkup response (set to None)
+
+ Args:
+ raw_response: Raw httpx response from Linkup API
+ logging_obj: Logging object for tracking
+
+ Returns:
+ SearchResponse with standardized format
+ """
+ response_json = raw_response.json()
+
+ # Transform results to SearchResult objects
+ results = []
+
+ # Process results array
+ raw_results = response_json.get("results", [])
+
+ for result in raw_results:
+ # Handle both text and image result types
+ result_type = result.get("type", "text")
+
+ if result_type == "text":
+ search_result = SearchResult(
+ title=result.get("name", ""),
+ url=result.get("url", ""),
+ snippet=result.get("content", ""),
+ date=None,
+ last_updated=None,
+ )
+ results.append(search_result)
+ elif result_type == "image":
+ # For image results, use the URL as both title and snippet if name not provided
+ search_result = SearchResult(
+ title=result.get("name", result.get("url", "")),
+ url=result.get("url", ""),
+ snippet=result.get("content", ""),
+ date=None,
+ last_updated=None,
+ )
+ results.append(search_result)
+
+ return SearchResponse(
+ results=results,
+ object="search",
+ )
+
diff --git a/litellm/llms/litellm_proxy/skills/README.md b/litellm/llms/litellm_proxy/skills/README.md
new file mode 100644
index 00000000000..1dfeff1a42c
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/README.md
@@ -0,0 +1,381 @@
+# LiteLLM Skills - Database-Backed Skills Storage
+
+This module provides database-backed skills storage as an alternative to Anthropic's cloud-based Skills API. It enables using skills with **any LLM provider** (Bedrock, OpenAI, Azure, etc.) by storing skills locally and converting them to tools + system prompt injection.
+
+## Architecture
+
+```mermaid
+flowchart TB
+ subgraph "Skill Creation"
+ A[User creates skill with ZIP file] --> B{custom_llm_provider?}
+ B -->|anthropic| C[Forward to Anthropic API]
+ B -->|litellm_proxy| D[Store in LiteLLM Database]
+
+ D --> E[Extract & store:
- display_title
- description
- instructions
- file_content ZIP]
+ end
+
+ subgraph "Skill Usage in Messages API"
+ F[Request with container.skills] --> G[SkillsInjectionHook]
+ G --> H{skill_id prefix?}
+
+ H -->|"litellm:skill_abc"| I[Fetch from LiteLLM DB]
+ H -->|"skill_xyz" no prefix| J[Pass to Anthropic as native skill]
+
+ I --> K{Model provider?}
+ K -->|Anthropic API| L[Convert to tools]
+ K -->|Bedrock/OpenAI/etc| M[Convert to tools +
Inject SKILL.md into system prompt]
+
+ J --> N[Keep in container.skills]
+ end
+
+ subgraph "Skill Resolution for Non-Anthropic"
+ M --> O[Extract SKILL.md from ZIP]
+ O --> P[Add to system prompt:
# Available Skills
## Skill: My Skill
SKILL.md content...]
+ P --> Q[Create OpenAI-style tool:
type: function
name: skill_id
description: instructions]
+ Q --> R[Send to LLM Provider]
+ end
+```
+
+## Automatic Code Execution
+
+For skills that include executable code (Python files), LiteLLM automatically handles:
+
+1. **Pre-call hook** (`async_pre_call_hook`): Adds `litellm_code_execution` tool, injects SKILL.md content
+2. **Post-call hook** (`async_post_call_success_deployment_hook`): Detects tool calls, executes code in Docker sandbox, continues loop
+3. **Returns files**: Generated files (GIFs, images, etc.) returned directly on response
+
+```mermaid
+sequenceDiagram
+ participant User
+ participant LiteLLM as LiteLLM SDK
+ participant PreHook as async_pre_call_hook
+ participant LLM as LLM Provider
+ participant PostHook as async_post_call_success_deployment_hook
+ participant Sandbox as Docker Sandbox
+
+ User->>LiteLLM: litellm.acompletion(model, messages, container={skills: [...]})
+
+ Note over LiteLLM,PreHook: PRE-CALL HOOK
+ LiteLLM->>PreHook: Intercept request
+ PreHook->>PreHook: Fetch skill from DB (litellm:skill_id)
+ PreHook->>PreHook: Extract SKILL.md from ZIP
+ PreHook->>PreHook: Inject SKILL.md into system prompt
+ PreHook->>PreHook: Add litellm_code_execution tool
+ PreHook->>PreHook: Store skill files in metadata
+ PreHook-->>LiteLLM: Modified request
+
+ LiteLLM->>LLM: Forward to provider (OpenAI/Bedrock/etc)
+ LLM-->>LiteLLM: Response with tool_calls
+
+ Note over LiteLLM,PostHook: POST-CALL HOOK (Agentic Loop)
+ LiteLLM->>PostHook: Check response
+
+ loop Until no more tool calls
+ PostHook->>PostHook: Check for litellm_code_execution tool call
+ alt Has code execution tool call
+ PostHook->>Sandbox: Execute Python code
+ Sandbox->>Sandbox: Copy skill files to /sandbox
+ Sandbox->>Sandbox: Install requirements.txt
+ Sandbox->>Sandbox: Run code
+ Sandbox-->>PostHook: Result + generated files
+ PostHook->>PostHook: Add tool result to messages
+ PostHook->>LLM: Make another LLM call
+ LLM-->>PostHook: New response
+ else No code execution
+ PostHook->>PostHook: Break loop
+ end
+ end
+
+ PostHook->>PostHook: Attach files to response._litellm_generated_files
+ PostHook-->>LiteLLM: Modified response with files
+ LiteLLM-->>User: Final response with generated files
+```
+
+```python
+import litellm
+from litellm.proxy.hooks.litellm_skills import SkillsInjectionHook
+
+# Register the hook (done once at startup)
+hook = SkillsInjectionHook()
+litellm.callbacks.append(hook)
+
+# ONE request - LiteLLM handles everything automatically
+# The container parameter triggers the SkillsInjectionHook
+response = await litellm.acompletion(
+ model="gpt-4o-mini",
+ messages=[{"role": "user", "content": "Create a bouncing ball GIF"}],
+ container={
+ "skills": [{"type": "custom", "skill_id": "litellm:skill_abc123"}]
+ },
+)
+
+# Files are attached directly to response
+generated_files = response._litellm_generated_files
+for f in generated_files:
+ print(f"Generated: {f['name']} ({f['size']} bytes)")
+ # f['content_base64'] contains the file data
+```
+
+This mimics Anthropic's behavior - no manual agentic loop needed!
+
+### How it works
+
+The `SkillsInjectionHook` uses two hooks:
+
+1. **`async_pre_call_hook`** (proxy only): Transforms the request before LLM call
+ - Fetches skills from DB
+ - Injects SKILL.md into system prompt
+ - Adds `litellm_code_execution` tool
+ - Sets `_litellm_code_execution_enabled=True` in metadata
+
+2. **`async_post_call_success_deployment_hook`** (SDK + proxy): Called after LLM response
+ - Checks if response has `litellm_code_execution` tool call
+ - Executes code in Docker sandbox
+ - Adds result to messages, makes another LLM call
+ - Repeats until model gives final response
+ - Attaches generated files to `response._litellm_generated_files`
+
+## File Structure
+
+```
+litellm/llms/litellm_proxy/skills/
+├── __init__.py # Exports all skill components
+├── handler.py # LiteLLMSkillsHandler - database CRUD operations (Prisma)
+├── transformation.py # LiteLLMSkillsTransformationHandler - SDK transformation layer
+├── prompt_injection.py # SkillPromptInjectionHandler - SKILL.md extraction and injection
+├── sandbox_executor.py # SkillsSandboxExecutor - Docker sandbox code execution
+├── code_execution.py # CodeExecutionHandler - automatic agentic loop
+└── README.md # This file
+
+litellm/proxy/hooks/litellm_skills/
+├── __init__.py # Re-exports from SDK + SkillsInjectionHook
+└── main.py # SkillsInjectionHook - CustomLogger hook for proxy
+```
+
+## Components
+
+### 1. `handler.py` - LiteLLMSkillsHandler
+
+Database operations for skills CRUD:
+
+```python
+from litellm.llms.litellm_proxy.skills import LiteLLMSkillsHandler
+
+# Create skill
+skill = await LiteLLMSkillsHandler.create_skill(
+ data=NewSkillRequest(
+ display_title="My Skill",
+ description="A helpful skill",
+ instructions="Use this skill when...",
+ file_content=zip_bytes, # ZIP file content
+ file_name="my-skill.zip",
+ file_type="application/zip",
+ ),
+ user_id="user_123"
+)
+
+# List skills
+skills = await LiteLLMSkillsHandler.list_skills(limit=10, offset=0)
+
+# Get skill
+skill = await LiteLLMSkillsHandler.get_skill(skill_id="skill_abc123")
+
+# Delete skill
+await LiteLLMSkillsHandler.delete_skill(skill_id="skill_abc123")
+```
+
+### 2. `transformation.py` - LiteLLMSkillsTransformationHandler
+
+SDK-level transformation layer that wraps handler operations:
+
+```python
+from litellm.llms.litellm_proxy.skills import LiteLLMSkillsTransformationHandler
+
+handler = LiteLLMSkillsTransformationHandler()
+
+# Async create
+skill = await handler.create_skill_handler(
+ display_title="My Skill",
+ files=[zip_file],
+ _is_async=True
+)
+```
+
+## Skill ZIP Format
+
+Skills must be packaged as ZIP files with a `SKILL.md` file:
+
+```
+my-skill.zip
+└── my-skill/
+ └── SKILL.md
+```
+
+### SKILL.md Format
+
+```markdown
+---
+name: my-skill
+description: A brief description of what this skill does
+---
+
+# My Skill
+
+Detailed instructions for the LLM on how to use this skill.
+
+## Usage
+
+When the user asks about X, use this skill to...
+
+## Examples
+
+- Example 1: ...
+- Example 2: ...
+```
+
+## SDK Usage
+
+### Create Skill in LiteLLM Database
+
+```python
+import litellm
+
+# Create skill stored in LiteLLM DB
+skill = litellm.create_skill(
+ display_title="Data Analysis Skill",
+ files=[open("data-analysis.zip", "rb")],
+ custom_llm_provider="litellm_proxy", # Store in LiteLLM DB
+)
+
+print(f"Created skill: {skill.id}") # skill_abc123
+```
+
+### Use Skill with Any Provider
+
+```python
+import litellm
+
+# Use LiteLLM-stored skill with Bedrock
+response = litellm.completion(
+ model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+ messages=[{"role": "user", "content": "Analyze this data..."}],
+ container={
+ "skills": [
+ {"type": "custom", "skill_id": "litellm:skill_abc123"} # litellm: prefix
+ ]
+ }
+)
+```
+
+## How Skill Resolution Works
+
+### Step 1: Request with Skills
+
+```python
+{
+ "model": "bedrock/claude-3-sonnet",
+ "messages": [{"role": "user", "content": "Help me analyze data"}],
+ "container": {
+ "skills": [
+ {"type": "custom", "skill_id": "litellm:skill_abc123"}
+ ]
+ }
+}
+```
+
+### Step 2: SkillsInjectionHook Processing
+
+The hook (`litellm/proxy/hooks/litellm_skills/main.py`) intercepts the request:
+
+1. **Detects `litellm:` prefix** → Fetches skill from database
+2. **Checks model provider** → Bedrock is not Anthropic
+3. **Extracts SKILL.md** from stored ZIP file
+4. **Converts skill to tool** + **Injects content into system prompt**
+
+### Step 3: Transformed Request
+
+```python
+{
+ "model": "bedrock/claude-3-sonnet",
+ "messages": [
+ {
+ "role": "system",
+ "content": """
+---
+
+# Available Skills
+
+## Skill: Data Analysis Skill
+
+# Data Analysis Skill
+
+This skill helps with data analysis tasks...
+
+## Usage
+When the user asks about data analysis...
+"""
+ },
+ {"role": "user", "content": "Help me analyze data"}
+ ],
+ "tools": [
+ {
+ "type": "function",
+ "function": {
+ "name": "skill_abc123",
+ "description": "This skill helps with data analysis tasks...",
+ "parameters": {"type": "object", "properties": {}, "required": []}
+ }
+ }
+ ]
+ # container is removed for non-Anthropic providers
+}
+```
+
+## Database Schema
+
+Skills are stored in `LiteLLM_SkillsTable`:
+
+```prisma
+model LiteLLM_SkillsTable {
+ skill_id String @id @default(uuid())
+ display_title String?
+ description String?
+ instructions String?
+ source String @default("custom")
+ latest_version String?
+ metadata Json? @default("{}")
+ file_content Bytes? // ZIP file binary content
+ file_name String? // Original filename
+ file_type String? // MIME type
+ created_at DateTime @default(now())
+ created_by String?
+ updated_at DateTime @default(now()) @updatedAt
+ updated_by String?
+}
+```
+
+## Routing Summary
+
+| Scenario | custom_llm_provider | skill_id Format | Behavior |
+|----------|---------------------|-----------------|----------|
+| Create skill on Anthropic | `anthropic` | N/A | Forward to Anthropic API |
+| Create skill in LiteLLM DB | `litellm_proxy` | N/A | Store in database |
+| Use Anthropic native skill | N/A | `skill_xyz` | Pass to Anthropic container.skills |
+| Use LiteLLM skill on Anthropic | N/A | `litellm:skill_abc` | Convert to tools |
+| Use LiteLLM skill on Bedrock/OpenAI | N/A | `litellm:skill_abc` | Convert to tools + inject SKILL.md |
+
+## Testing
+
+Run the tests:
+
+```bash
+pytest tests/proxy_unit_tests/test_skills_db.py -v
+```
+
+Tests cover:
+- Creating skills with file content
+- Listing and retrieving skills
+- Deleting skills
+- Hook resolution with ZIP file extraction
+- System prompt injection for non-Anthropic models
+
diff --git a/litellm/llms/litellm_proxy/skills/__init__.py b/litellm/llms/litellm_proxy/skills/__init__.py
new file mode 100644
index 00000000000..5fb29e96bb9
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/__init__.py
@@ -0,0 +1,54 @@
+"""
+LiteLLM Proxy Skills - Database-backed skills storage and execution
+
+This module provides:
+- Database-backed skills storage (alternative to Anthropic's cloud-based skills API)
+- Skill content extraction and prompt injection
+- Sandboxed code execution for skills
+- Automatic code execution handler
+
+Main components:
+- handler.py: LiteLLMSkillsHandler - database CRUD operations
+- transformation.py: LiteLLMSkillsTransformationHandler - SDK transformation layer
+- prompt_injection.py: SkillPromptInjectionHandler - SKILL.md extraction and injection
+- sandbox_executor.py: SkillsSandboxExecutor - Docker sandbox execution
+- code_execution.py: CodeExecutionHandler - automatic agentic loop
+"""
+
+from litellm.llms.litellm_proxy.skills.code_execution import (
+ LITELLM_CODE_EXECUTION_TOOL,
+ CodeExecutionHandler,
+ LiteLLMInternalTools,
+ add_code_execution_tool,
+ code_execution_handler,
+ get_litellm_code_execution_tool,
+ has_code_execution_tool,
+)
+from litellm.llms.litellm_proxy.skills.constants import (
+ DEFAULT_MAX_ITERATIONS,
+ DEFAULT_SANDBOX_TIMEOUT,
+)
+from litellm.llms.litellm_proxy.skills.handler import LiteLLMSkillsHandler
+from litellm.llms.litellm_proxy.skills.prompt_injection import (
+ SkillPromptInjectionHandler,
+)
+from litellm.llms.litellm_proxy.skills.sandbox_executor import SkillsSandboxExecutor
+from litellm.llms.litellm_proxy.skills.transformation import (
+ LiteLLMSkillsTransformationHandler,
+)
+
+__all__ = [
+ "LiteLLMSkillsHandler",
+ "LiteLLMSkillsTransformationHandler",
+ "SkillPromptInjectionHandler",
+ "SkillsSandboxExecutor",
+ "CodeExecutionHandler",
+ "LiteLLMInternalTools",
+ "LITELLM_CODE_EXECUTION_TOOL",
+ "get_litellm_code_execution_tool",
+ "code_execution_handler",
+ "has_code_execution_tool",
+ "add_code_execution_tool",
+ "DEFAULT_MAX_ITERATIONS",
+ "DEFAULT_SANDBOX_TIMEOUT",
+]
diff --git a/litellm/llms/litellm_proxy/skills/code_execution.py b/litellm/llms/litellm_proxy/skills/code_execution.py
new file mode 100644
index 00000000000..d307b8b36d9
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/code_execution.py
@@ -0,0 +1,311 @@
+"""
+Automatic Code Execution Handler for LiteLLM Skills
+
+When `litellm_code_execution` tool is present, this handler automatically:
+1. Makes the LLM call
+2. Executes any code the model generates
+3. Continues the conversation with results
+4. Returns final response with generated files inline (base64)
+
+This mimics Anthropic's behavior where code execution happens automatically.
+Generated files are returned directly in the response - no separate storage needed.
+"""
+
+import base64
+import json
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+from litellm._logging import verbose_logger
+
+
+class LiteLLMInternalTools(str, Enum):
+ """
+ Enum for internal LiteLLM tools that are injected into requests.
+
+ These tools are handled automatically by LiteLLM hooks and are not
+ passed to the underlying LLM provider directly.
+ """
+ CODE_EXECUTION = "litellm_code_execution"
+
+
+def get_litellm_code_execution_tool() -> Dict[str, Any]:
+ """
+ Returns the litellm_code_execution tool definition in OpenAI format.
+
+ This tool enables automatic code execution in a sandboxed environment
+ when skills include executable Python code.
+ """
+ return {
+ "type": "function",
+ "function": {
+ "name": LiteLLMInternalTools.CODE_EXECUTION.value,
+ "description": "Execute Python code in a sandboxed environment. Use this to run code that generates files, processes data, or performs computations. Generated files will be returned directly.",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "code": {
+ "type": "string",
+ "description": "Python code to execute"
+ }
+ },
+ "required": ["code"]
+ }
+ }
+ }
+
+
+def get_litellm_code_execution_tool_anthropic() -> Dict[str, Any]:
+ """
+ Returns the litellm_code_execution tool definition in Anthropic/messages API format.
+
+ This tool enables automatic code execution in a sandboxed environment
+ when skills include executable Python code.
+ """
+ return {
+ "name": LiteLLMInternalTools.CODE_EXECUTION.value,
+ "description": "Execute Python code in a sandboxed environment. Use this to run code that generates files, processes data, or performs computations. Generated files will be returned directly.",
+ "input_schema": {
+ "type": "object",
+ "properties": {
+ "code": {
+ "type": "string",
+ "description": "Python code to execute"
+ }
+ },
+ "required": ["code"]
+ }
+ }
+
+
+# Singleton tool definition for backwards compatibility
+LITELLM_CODE_EXECUTION_TOOL = get_litellm_code_execution_tool()
+
+
+class CodeExecutionHandler:
+ """
+ Handles automatic code execution for LiteLLM skills.
+
+ When enabled, this handler intercepts LLM responses with code execution
+ tool calls, executes them in a sandbox, and continues the conversation
+ automatically until completion.
+ """
+
+ def __init__(
+ self,
+ max_iterations: Optional[int] = None,
+ sandbox_timeout: Optional[int] = None,
+ ):
+ from litellm.llms.litellm_proxy.skills.constants import (
+ DEFAULT_MAX_ITERATIONS,
+ DEFAULT_SANDBOX_TIMEOUT,
+ )
+
+ self.max_iterations = max_iterations or DEFAULT_MAX_ITERATIONS
+ self.sandbox_timeout = sandbox_timeout or DEFAULT_SANDBOX_TIMEOUT
+
+ async def execute_with_code_execution(
+ self,
+ model: str,
+ messages: List[Dict],
+ tools: List[Dict],
+ skill_files: Dict[str, bytes],
+ skill_id: Optional[str] = None,
+ **kwargs,
+ ) -> Dict[str, Any]:
+ """
+ Execute an LLM call with automatic code execution handling.
+
+ This method:
+ 1. Makes the initial LLM call
+ 2. If model calls litellm_code_execution, executes the code
+ 3. Continues conversation with results
+ 4. Repeats until model stops calling tools
+ 5. Returns final response with generated files inline
+
+ Args:
+ model: Model to use
+ messages: Initial messages
+ tools: Tools including litellm_code_execution
+ skill_files: Dict of skill files for execution
+ skill_id: Optional skill ID for tracking
+ **kwargs: Additional args for litellm.acompletion
+
+ Returns:
+ Dict with:
+ - response: Final LLM response
+ - files: List of generated files with content (base64)
+ - execution_results: List of code execution results
+ """
+ import litellm
+ from litellm.llms.litellm_proxy.skills.sandbox_executor import (
+ SkillsSandboxExecutor,
+ )
+
+ current_messages = list(messages)
+ generated_files: List[Dict[str, Any]] = [] # Files returned directly
+ execution_results: List[Dict] = []
+
+ executor = SkillsSandboxExecutor(timeout=self.sandbox_timeout)
+ response: Any = None # Initialize to avoid possibly unbound error
+
+ for iteration in range(self.max_iterations):
+ verbose_logger.debug(
+ f"CodeExecutionHandler: Iteration {iteration + 1}/{self.max_iterations}"
+ )
+
+ # Make LLM call
+ response = await litellm.acompletion(
+ model=model,
+ messages=current_messages,
+ tools=tools,
+ **kwargs,
+ )
+
+ assistant_message = response.choices[0].message # type: ignore
+ stop_reason = response.choices[0].finish_reason # type: ignore
+
+ # Build assistant message for conversation history
+ assistant_msg_dict: Dict[str, Any] = {
+ "role": "assistant",
+ "content": assistant_message.content,
+ }
+ if assistant_message.tool_calls:
+ assistant_msg_dict["tool_calls"] = [
+ {
+ "id": tc.id,
+ "type": "function",
+ "function": {
+ "name": tc.function.name,
+ "arguments": tc.function.arguments
+ }
+ }
+ for tc in assistant_message.tool_calls
+ ]
+ current_messages.append(assistant_msg_dict)
+
+ # Check if we're done (no tool calls or not tool_calls finish reason)
+ if stop_reason != "tool_calls" or not assistant_message.tool_calls:
+ verbose_logger.debug(
+ f"CodeExecutionHandler: Completed after {iteration + 1} iterations"
+ )
+ return {
+ "response": response,
+ "files": generated_files, # Files returned directly with base64 content
+ "execution_results": execution_results,
+ "messages": current_messages,
+ }
+
+ # Handle tool calls
+ for tool_call in assistant_message.tool_calls:
+ tool_name = tool_call.function.name
+
+ if tool_name == LiteLLMInternalTools.CODE_EXECUTION.value:
+ # Execute code in sandbox
+ try:
+ args = json.loads(tool_call.function.arguments)
+ code = args.get("code", "")
+
+ verbose_logger.debug(
+ f"CodeExecutionHandler: Executing code ({len(code)} chars)"
+ )
+
+ exec_result = executor.execute(
+ code=code,
+ skill_files=skill_files,
+ )
+
+ verbose_logger.debug(
+ f"CodeExecutionHandler: Execution result: {exec_result}"
+ )
+
+ execution_results.append({
+ "iteration": iteration,
+ "success": exec_result["success"],
+ "output": exec_result["output"],
+ "error": exec_result["error"],
+ "files": [f["name"] for f in exec_result["files"]],
+ })
+
+ # Build tool result content
+ tool_result = exec_result["output"] or ""
+
+ # Collect generated files (returned directly, no storage)
+ if exec_result["files"]:
+ tool_result += "\n\nGenerated files:"
+ for f in exec_result["files"]:
+ file_content = base64.b64decode(f["content_base64"])
+ # Add to generated files list (returned in response)
+ generated_files.append({
+ "name": f["name"],
+ "mime_type": f["mime_type"],
+ "content_base64": f["content_base64"],
+ "size": len(file_content),
+ })
+ tool_result += f"\n- {f['name']} ({len(file_content)} bytes)"
+
+ verbose_logger.debug(
+ f"CodeExecutionHandler: Generated file {f['name']} ({len(file_content)} bytes)"
+ )
+
+ if exec_result["error"]:
+ tool_result += f"\n\nError:\n{exec_result['error']}"
+
+ except Exception as e:
+ tool_result = f"Code execution failed: {str(e)}"
+ execution_results.append({
+ "iteration": iteration,
+ "success": False,
+ "error": str(e),
+ })
+
+ # Add tool result to messages
+ current_messages.append({
+ "role": "tool",
+ "tool_call_id": tool_call.id,
+ "content": tool_result,
+ })
+ else:
+ # Non-code-execution tool - pass through
+ # In a full implementation, this would call other tool handlers
+ current_messages.append({
+ "role": "tool",
+ "tool_call_id": tool_call.id,
+ "content": f"Tool '{tool_name}' not handled by code execution handler",
+ })
+
+ # Max iterations reached
+ verbose_logger.warning(
+ f"CodeExecutionHandler: Max iterations ({self.max_iterations}) reached"
+ )
+ return {
+ "response": response,
+ "files": generated_files,
+ "execution_results": execution_results,
+ "messages": current_messages,
+ "max_iterations_reached": True,
+ }
+
+
+def has_code_execution_tool(tools: Optional[List[Dict]]) -> bool:
+ """Check if litellm_code_execution tool is in the tools list."""
+ if not tools:
+ return False
+ for tool in tools:
+ func = tool.get("function", {})
+ if func.get("name") == LiteLLMInternalTools.CODE_EXECUTION.value:
+ return True
+ return False
+
+
+def add_code_execution_tool(tools: Optional[List[Dict]]) -> List[Dict]:
+ """Add litellm_code_execution tool if not already present."""
+ tools = tools or []
+ if not has_code_execution_tool(tools):
+ tools.append(LITELLM_CODE_EXECUTION_TOOL)
+ return tools
+
+
+# Global handler instance
+code_execution_handler = CodeExecutionHandler()
+
diff --git a/litellm/llms/litellm_proxy/skills/constants.py b/litellm/llms/litellm_proxy/skills/constants.py
new file mode 100644
index 00000000000..a2be6961db6
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/constants.py
@@ -0,0 +1,13 @@
+"""
+Constants for LiteLLM Skills
+
+Centralized constants for skills processing, code execution, and sandbox configuration.
+"""
+
+# Code execution loop settings
+DEFAULT_MAX_ITERATIONS: int = 10
+"""Maximum number of iterations for the automatic code execution loop."""
+
+DEFAULT_SANDBOX_TIMEOUT: int = 120
+"""Default timeout in seconds for sandbox code execution."""
+
diff --git a/litellm/llms/litellm_proxy/skills/handler.py b/litellm/llms/litellm_proxy/skills/handler.py
new file mode 100644
index 00000000000..f44ac4cda92
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/handler.py
@@ -0,0 +1,219 @@
+"""
+Handler for LiteLLM database-backed skills operations.
+
+This module contains the actual database operations for skills CRUD.
+Used by the transformation layer and skills injection hook.
+"""
+
+import uuid
+from typing import Any, Dict, List, Optional
+
+from litellm._logging import verbose_logger
+from litellm.proxy._types import LiteLLM_SkillsTable, NewSkillRequest
+
+
+def _prisma_skill_to_litellm(prisma_skill) -> LiteLLM_SkillsTable:
+ """
+ Convert a Prisma skill record to LiteLLM_SkillsTable.
+
+ Handles Base64 decoding of file_content field.
+ """
+ import base64
+
+ data = prisma_skill.model_dump()
+
+ # Decode Base64 file_content back to bytes
+ # model_dump() converts Base64 field to base64-encoded string
+ if data.get("file_content") is not None:
+ if isinstance(data["file_content"], str):
+ data["file_content"] = base64.b64decode(data["file_content"])
+ elif isinstance(data["file_content"], bytes):
+ # Already bytes, no conversion needed
+ pass
+
+ return LiteLLM_SkillsTable(**data)
+
+
+class LiteLLMSkillsHandler:
+ """
+ Handler for LiteLLM database-backed skills operations.
+
+ This class provides static methods for CRUD operations on skills
+ stored in the LiteLLM proxy database (LiteLLM_SkillsTable).
+ """
+
+ @staticmethod
+ async def _get_prisma_client():
+ """Get the prisma client from proxy server."""
+ from litellm.proxy.proxy_server import prisma_client
+
+ if prisma_client is None:
+ raise ValueError(
+ "Prisma client is not initialized. "
+ "Database connection required for LiteLLM skills."
+ )
+ return prisma_client
+
+ @staticmethod
+ async def create_skill(
+ data: NewSkillRequest,
+ user_id: Optional[str] = None,
+ ) -> LiteLLM_SkillsTable:
+ """
+ Create a new skill in the LiteLLM database.
+
+ Args:
+ data: NewSkillRequest with skill details
+ user_id: Optional user ID for tracking
+
+ Returns:
+ LiteLLM_SkillsTable record
+ """
+ prisma_client = await LiteLLMSkillsHandler._get_prisma_client()
+
+ skill_id = f"litellm_skill_{uuid.uuid4()}"
+
+ skill_data: Dict[str, Any] = {
+ "skill_id": skill_id,
+ "display_title": data.display_title,
+ "description": data.description,
+ "instructions": data.instructions,
+ "source": "custom",
+ "created_by": user_id,
+ "updated_by": user_id,
+ }
+
+ # Handle metadata
+ if data.metadata is not None:
+ from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
+
+ skill_data["metadata"] = safe_dumps(data.metadata)
+
+ # Handle file content - wrap bytes in Base64 for Prisma
+ if data.file_content is not None:
+ from prisma.fields import Base64
+
+ skill_data["file_content"] = Base64.encode(data.file_content)
+ if data.file_name is not None:
+ skill_data["file_name"] = data.file_name
+ if data.file_type is not None:
+ skill_data["file_type"] = data.file_type
+
+ verbose_logger.debug(
+ f"LiteLLMSkillsHandler: Creating skill {skill_id} with title={data.display_title}"
+ )
+
+ new_skill = await prisma_client.db.litellm_skillstable.create(data=skill_data)
+
+ return _prisma_skill_to_litellm(new_skill)
+
+ @staticmethod
+ async def list_skills(
+ limit: int = 20,
+ offset: int = 0,
+ ) -> List[LiteLLM_SkillsTable]:
+ """
+ List skills from the LiteLLM database.
+
+ Args:
+ limit: Maximum number of skills to return
+ offset: Number of skills to skip
+
+ Returns:
+ List of LiteLLM_SkillsTable records
+ """
+ prisma_client = await LiteLLMSkillsHandler._get_prisma_client()
+
+ verbose_logger.debug(
+ f"LiteLLMSkillsHandler: Listing skills with limit={limit}, offset={offset}"
+ )
+
+ skills = await prisma_client.db.litellm_skillstable.find_many(
+ take=limit,
+ skip=offset,
+ order={"created_at": "desc"},
+ )
+
+ return [_prisma_skill_to_litellm(s) for s in skills]
+
+ @staticmethod
+ async def get_skill(skill_id: str) -> LiteLLM_SkillsTable:
+ """
+ Get a skill by ID from the LiteLLM database.
+
+ Args:
+ skill_id: The skill ID to retrieve
+
+ Returns:
+ LiteLLM_SkillsTable record
+
+ Raises:
+ ValueError: If skill not found
+ """
+ prisma_client = await LiteLLMSkillsHandler._get_prisma_client()
+
+ verbose_logger.debug(f"LiteLLMSkillsHandler: Getting skill {skill_id}")
+
+ skill = await prisma_client.db.litellm_skillstable.find_unique(
+ where={"skill_id": skill_id}
+ )
+
+ if skill is None:
+ raise ValueError(f"Skill not found: {skill_id}")
+
+ return _prisma_skill_to_litellm(skill)
+
+ @staticmethod
+ async def delete_skill(skill_id: str) -> Dict[str, str]:
+ """
+ Delete a skill by ID from the LiteLLM database.
+
+ Args:
+ skill_id: The skill ID to delete
+
+ Returns:
+ Dict with id and type of deleted skill
+
+ Raises:
+ ValueError: If skill not found
+ """
+ prisma_client = await LiteLLMSkillsHandler._get_prisma_client()
+
+ verbose_logger.debug(f"LiteLLMSkillsHandler: Deleting skill {skill_id}")
+
+ # Check if skill exists
+ skill = await prisma_client.db.litellm_skillstable.find_unique(
+ where={"skill_id": skill_id}
+ )
+
+ if skill is None:
+ raise ValueError(f"Skill not found: {skill_id}")
+
+ # Delete the skill
+ await prisma_client.db.litellm_skillstable.delete(where={"skill_id": skill_id})
+
+ return {"id": skill_id, "type": "skill_deleted"}
+
+ @staticmethod
+ async def fetch_skill_from_db(skill_id: str) -> Optional[LiteLLM_SkillsTable]:
+ """
+ Fetch a skill from the database (used by skills injection hook).
+
+ This is a convenience method that returns None instead of raising
+ an exception if the skill is not found.
+
+ Args:
+ skill_id: The skill ID to fetch
+
+ Returns:
+ LiteLLM_SkillsTable or None if not found
+ """
+ try:
+ return await LiteLLMSkillsHandler.get_skill(skill_id)
+ except ValueError:
+ return None
+ except Exception as e:
+ verbose_logger.warning(
+ f"LiteLLMSkillsHandler: Error fetching skill {skill_id}: {e}"
+ )
+ return None
diff --git a/litellm/llms/litellm_proxy/skills/prompt_injection.py b/litellm/llms/litellm_proxy/skills/prompt_injection.py
new file mode 100644
index 00000000000..17469274c1c
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/prompt_injection.py
@@ -0,0 +1,305 @@
+"""
+Prompt Injection Handler for LiteLLM Skills
+
+Handles extraction of skill content (SKILL.md) from stored ZIP files
+and injection into the system prompt for non-Anthropic models.
+"""
+
+import zipfile
+from io import BytesIO
+from typing import Any, Dict, List, Optional
+
+from litellm._logging import verbose_logger
+from litellm.proxy._types import LiteLLM_SkillsTable
+
+
+class SkillPromptInjectionHandler:
+ """
+ Handles skill content extraction and system prompt injection.
+
+ Responsibilities:
+ - Extract SKILL.md content from skill ZIP files
+ - Extract ALL files from ZIP for code execution
+ - Inject skill content into system message
+ - Create execute_code tool definition
+ """
+
+ def extract_skill_content(self, skill: LiteLLM_SkillsTable) -> Optional[str]:
+ """
+ Extract skill content from the stored zip file.
+
+ Looks for SKILL.md or README.md in the zip and returns its content.
+ This content describes the skill's capabilities and instructions.
+
+ Args:
+ skill: The skill from LiteLLM database
+
+ Returns:
+ The skill content as a string, or None if not available
+ """
+ if not skill.file_content:
+ return skill.instructions
+
+ try:
+ zip_buffer = BytesIO(skill.file_content)
+ with zipfile.ZipFile(zip_buffer, "r") as zf:
+ # Look for SKILL.md first
+ for name in zf.namelist():
+ if name.endswith("SKILL.md"):
+ content = zf.read(name).decode("utf-8")
+ if content:
+ return f"## Skill: {skill.display_title or skill.skill_id}\n\n{content}"
+
+ # Fall back to README.md
+ for name in zf.namelist():
+ if name.endswith("README.md"):
+ content = zf.read(name).decode("utf-8")
+ if content:
+ return f"## Skill: {skill.display_title or skill.skill_id}\n\n{content}"
+
+ # Fall back to any .md file
+ for name in zf.namelist():
+ if name.endswith(".md"):
+ content = zf.read(name).decode("utf-8")
+ if content:
+ return f"## Skill: {skill.display_title or skill.skill_id}\n\n{content}"
+ except Exception as e:
+ verbose_logger.warning(
+ f"SkillPromptInjectionHandler: Error extracting content from skill {skill.skill_id}: {e}"
+ )
+
+ return skill.instructions
+
+ def extract_all_files(self, skill: LiteLLM_SkillsTable) -> Dict[str, bytes]:
+ """
+ Extract ALL files from skill ZIP for code execution.
+
+ Returns a dict mapping file paths to their binary content.
+ The paths have the skill folder prefix removed (e.g., "slack-gif-creator/core/..." -> "core/...").
+
+ Args:
+ skill: The skill from LiteLLM database
+
+ Returns:
+ Dict mapping file paths to binary content
+ """
+ files: Dict[str, bytes] = {}
+
+ if not skill.file_content:
+ return files
+
+ try:
+ zip_buffer = BytesIO(skill.file_content)
+ with zipfile.ZipFile(zip_buffer, "r") as zf:
+ for name in zf.namelist():
+ # Skip directories
+ if name.endswith("/"):
+ continue
+
+ # Remove skill folder prefix (first path component)
+ parts = name.split("/")
+ if len(parts) > 1:
+ clean_path = "/".join(parts[1:])
+ else:
+ clean_path = name
+
+ if clean_path:
+ files[clean_path] = zf.read(name)
+ except Exception as e:
+ verbose_logger.warning(
+ f"SkillPromptInjectionHandler: Error extracting files from skill {skill.skill_id}: {e}"
+ )
+
+ return files
+
+ def inject_skill_content_to_messages(
+ self, data: dict, skill_contents: List[str], use_anthropic_format: bool = False
+ ) -> dict:
+ """
+ Inject skill content into the system prompt.
+
+ For Anthropic messages API (use_anthropic_format=True):
+ - Injects into top-level 'system' parameter (not in messages array)
+
+ For OpenAI-style APIs (use_anthropic_format=False):
+ - Injects into messages array with role="system"
+
+ Args:
+ data: The request data dict
+ skill_contents: List of skill content strings to inject
+ use_anthropic_format: If True, use top-level 'system' param for Anthropic
+
+ Returns:
+ Modified data dict with skill content in system prompt
+ """
+ if not skill_contents:
+ return data
+
+ # Build the skill injection text
+ skill_section = "\n\n---\n\n# Available Skills\n\n" + "\n\n---\n\n".join(skill_contents)
+
+ if use_anthropic_format:
+ # Anthropic messages API: use top-level 'system' parameter
+ current_system = data.get("system", "")
+ if current_system:
+ data["system"] = current_system + skill_section
+ else:
+ data["system"] = skill_section.strip()
+ return data
+
+ # OpenAI-style: inject into messages array
+ messages = data.get("messages", [])
+ if not messages:
+ return data
+
+ # Find or create system message
+ system_msg_idx = None
+ for i, msg in enumerate(messages):
+ if isinstance(msg, dict) and msg.get("role") == "system":
+ system_msg_idx = i
+ break
+
+ if system_msg_idx is not None:
+ # Append to existing system message
+ current_content = messages[system_msg_idx].get("content", "")
+ messages[system_msg_idx]["content"] = current_content + skill_section
+ else:
+ # Create new system message at the beginning
+ messages.insert(0, {"role": "system", "content": skill_section.strip()})
+
+ data["messages"] = messages
+ return data
+
+ def create_execute_code_tool(self, skill_modules: List[str]) -> Dict[str, Any]:
+ """
+ Create the execute_code tool definition.
+
+ This tool allows the model to execute Python code with access
+ to the skill's modules (e.g., 'from core.gif_builder import GIFBuilder').
+
+ Args:
+ skill_modules: List of available module paths (e.g., ["core/gif_builder.py"])
+
+ Returns:
+ OpenAI-style tool definition
+ """
+ # Format module list for description
+ module_examples = []
+ for mod in skill_modules[:5]: # Limit to 5 examples
+ if mod.endswith(".py"):
+ # Convert path to import: "core/gif_builder.py" -> "from core.gif_builder import ..."
+ import_path = mod.replace("/", ".").replace(".py", "")
+ module_examples.append(f"from {import_path} import ...")
+
+ module_hint = ""
+ if module_examples:
+ module_hint = f" Available modules: {', '.join(module_examples)}"
+
+ return {
+ "type": "function",
+ "function": {
+ "name": "execute_code",
+ "description": f"Execute Python code in a sandboxed environment. Generated files will be returned.{module_hint}",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "code": {
+ "type": "string",
+ "description": "Python code to execute. You can import skill modules and use standard libraries."
+ }
+ },
+ "required": ["code"]
+ }
+ }
+ }
+
+ def convert_skill_to_tool(self, skill: LiteLLM_SkillsTable) -> Dict[str, Any]:
+ """
+ Convert a LiteLLM skill to an OpenAI-style tool.
+
+ The skill's instructions are used as the function description,
+ allowing the model to understand when and how to use the skill.
+
+ Args:
+ skill: The skill from LiteLLM database
+
+ Returns:
+ OpenAI-style tool definition
+ """
+ # Create a function name from skill_id (sanitize for function naming)
+ func_name = skill.skill_id.replace("-", "_").replace(" ", "_")
+
+ # Use instructions as description, fall back to description or title
+ description = (
+ skill.instructions
+ or skill.description
+ or skill.display_title
+ or f"Skill: {skill.skill_id}"
+ )
+
+ # Truncate description if too long (OpenAI has limits)
+ max_desc_length = 1024
+ if len(description) > max_desc_length:
+ description = description[: max_desc_length - 3] + "..."
+
+ tool: Dict[str, Any] = {
+ "type": "function",
+ "function": {
+ "name": func_name,
+ "description": description,
+ "parameters": {
+ "type": "object",
+ "properties": {},
+ "required": [],
+ },
+ },
+ }
+
+ # If skill has metadata with parameter definitions, use them
+ if skill.metadata and isinstance(skill.metadata, dict):
+ params = skill.metadata.get("parameters")
+ if params and isinstance(params, dict):
+ tool["function"]["parameters"] = params
+
+ return tool
+
+ def convert_skill_to_anthropic_tool(self, skill: LiteLLM_SkillsTable) -> Dict[str, Any]:
+ """
+ Convert a LiteLLM skill to an Anthropic-style tool (messages API format).
+
+ Args:
+ skill: The skill from LiteLLM database
+
+ Returns:
+ Anthropic-style tool definition with name, description, input_schema
+ """
+ func_name = skill.skill_id.replace("-", "_").replace(" ", "_")
+
+ description = (
+ skill.instructions
+ or skill.description
+ or skill.display_title
+ or f"Skill: {skill.skill_id}"
+ )
+
+ max_desc_length = 1024
+ if len(description) > max_desc_length:
+ description = description[: max_desc_length - 3] + "..."
+
+ input_schema: Dict[str, Any] = {
+ "type": "object",
+ "properties": {},
+ "required": [],
+ }
+
+ if skill.metadata and isinstance(skill.metadata, dict):
+ params = skill.metadata.get("parameters")
+ if params and isinstance(params, dict):
+ input_schema = params
+
+ return {
+ "name": func_name,
+ "description": description,
+ "input_schema": input_schema,
+ }
+
diff --git a/litellm/llms/litellm_proxy/skills/sandbox_executor.py b/litellm/llms/litellm_proxy/skills/sandbox_executor.py
new file mode 100644
index 00000000000..7676ade5cd0
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/sandbox_executor.py
@@ -0,0 +1,286 @@
+"""
+Sandbox Executor for LiteLLM Skills
+
+Executes skill code in a sandboxed environment using llm-sandbox.
+Supports Docker, Podman, and Kubernetes backends.
+"""
+
+import base64
+import os
+from typing import Any, Dict, List, Optional
+
+from litellm._logging import verbose_logger
+
+
+class SkillsSandboxExecutor:
+ """
+ Executes skill code in llm-sandbox Docker container.
+
+ Responsibilities:
+ - Create sandbox session with skill files
+ - Install requirements
+ - Execute model-generated code
+ - Collect generated files (GIFs, images, etc.)
+ """
+
+ def __init__(
+ self,
+ timeout: int = 60,
+ backend: str = "docker",
+ image: Optional[str] = None,
+ ):
+ """
+ Initialize the sandbox executor.
+
+ Args:
+ timeout: Maximum execution time in seconds
+ backend: Sandbox backend ("docker", "podman", "kubernetes")
+ image: Custom Docker image (default: uses llm-sandbox default)
+ """
+ self.timeout = timeout
+ self.backend = backend
+ self.image = image
+ self._session = None
+
+ def execute(
+ self,
+ code: str,
+ skill_files: Dict[str, bytes],
+ requirements: Optional[str] = None,
+ ) -> Dict[str, Any]:
+ """
+ Execute code with skill files in sandbox.
+
+ Args:
+ code: Python code to execute
+ skill_files: Dict mapping file paths to binary content
+ requirements: Optional requirements.txt content
+
+ Returns:
+ {
+ "success": bool,
+ "output": str,
+ "error": str (if failed),
+ "files": [{"name": str, "content_base64": str, "mime_type": str}]
+ }
+ """
+ try:
+ from llm_sandbox import SandboxSession
+ except ImportError:
+ verbose_logger.error(
+ "SkillsSandboxExecutor: llm-sandbox not installed. "
+ "Install with: pip install llm-sandbox"
+ )
+ return {
+ "success": False,
+ "output": "",
+ "error": "llm-sandbox not installed. Install with: pip install llm-sandbox",
+ "files": [],
+ }
+
+ try:
+ # Create sandbox session
+ session_kwargs: Dict[str, Any] = {
+ "lang": "python",
+ "verbose": False,
+ }
+
+ if self.image:
+ session_kwargs["image"] = self.image
+
+ with SandboxSession(**session_kwargs) as session:
+ # 1. Copy skill files into sandbox using copy_to_runtime
+ import tempfile
+
+ # Create a temp directory to stage files
+ with tempfile.TemporaryDirectory() as tmpdir:
+ for path, content in skill_files.items():
+ # Create the file in temp directory
+ local_path = os.path.join(tmpdir, path)
+ os.makedirs(os.path.dirname(local_path), exist_ok=True)
+ with open(local_path, "wb") as f:
+ f.write(content)
+
+ # Copy to sandbox
+ sandbox_path = f"/sandbox/{path}"
+ session.copy_to_runtime(local_path, sandbox_path)
+
+ verbose_logger.debug(
+ f"SkillsSandboxExecutor: Copied {len(skill_files)} files to sandbox"
+ )
+
+ # 2. Install requirements if present
+ req_packages = None
+ if requirements:
+ req_packages = requirements.strip().replace("\n", " ")
+ elif "requirements.txt" in skill_files:
+ req_content = skill_files["requirements.txt"].decode("utf-8")
+ req_packages = req_content.strip().replace("\n", " ")
+
+ if req_packages:
+ # Run pip install as code
+ pip_code = f"""
+import subprocess
+subprocess.run(['pip', 'install'] + '{req_packages}'.split(), check=True)
+"""
+ result = session.run(pip_code)
+ verbose_logger.debug(
+ "SkillsSandboxExecutor: Installed requirements"
+ )
+
+ # 3. Execute the code
+ # Wrap code to run from /sandbox directory
+ wrapped_code = f"""
+import os
+os.chdir('/sandbox')
+import sys
+sys.path.insert(0, '/sandbox')
+
+{code}
+"""
+ result = session.run(wrapped_code)
+
+ success = result.exit_code == 0
+ output = result.stdout or ""
+ error = result.stderr or ""
+
+ if success:
+ verbose_logger.debug(
+ "SkillsSandboxExecutor: Code execution succeeded"
+ )
+ else:
+ verbose_logger.debug(
+ f"SkillsSandboxExecutor: Code execution failed with exit code {result.exit_code}"
+ )
+ verbose_logger.debug(
+ f"SkillsSandboxExecutor: stderr: {error[:500] if error else 'No stderr'}"
+ )
+ verbose_logger.debug(
+ f"SkillsSandboxExecutor: stdout: {output[:500] if output else 'No stdout'}"
+ )
+
+ # 4. Collect generated files
+ generated_files = self._collect_generated_files(session, skill_files)
+
+ return {
+ "success": success,
+ "output": output,
+ "error": error,
+ "files": generated_files,
+ }
+
+ except Exception as e:
+ verbose_logger.error(
+ f"SkillsSandboxExecutor: Execution failed: {e}"
+ )
+ return {
+ "success": False,
+ "output": "",
+ "error": str(e),
+ "files": [],
+ }
+
+ def _collect_generated_files(
+ self,
+ session: Any,
+ original_files: Dict[str, bytes],
+ ) -> List[Dict[str, Any]]:
+ """
+ Collect files generated during execution.
+
+ Looks for new files in /sandbox that weren't in the original skill files.
+ Focuses on common output types: GIF, PNG, JPG, PDF, CSV, etc.
+
+ Args:
+ session: The sandbox session
+ original_files: Original skill files (to exclude)
+
+ Returns:
+ List of generated files with base64 content
+ """
+ generated_files: List[Dict[str, Any]] = []
+
+ try:
+ import tempfile
+
+ # List files in /sandbox using Python code
+ list_code = """
+import os
+import json
+files = []
+for root, dirs, filenames in os.walk('/sandbox'):
+ for f in filenames:
+ if f.endswith(('.gif', '.png', '.jpg', '.jpeg', '.pdf', '.csv', '.json')):
+ files.append(os.path.join(root, f))
+print(json.dumps(files))
+"""
+ result = session.run(list_code)
+
+ if result.exit_code == 0 and result.stdout:
+ import json
+ try:
+ filepaths = json.loads(result.stdout.strip())
+ except json.JSONDecodeError:
+ filepaths = []
+
+ for filepath in filepaths:
+ if not filepath:
+ continue
+
+ # Get relative path
+ rel_path = filepath.replace("/sandbox/", "")
+
+ # Skip if it was an original file
+ if rel_path in original_files:
+ continue
+
+ # Copy file from sandbox using copy_from_runtime
+ with tempfile.NamedTemporaryFile(delete=False) as tmp:
+ tmp_path = tmp.name
+
+ try:
+ session.copy_from_runtime(filepath, tmp_path)
+
+ with open(tmp_path, "rb") as f:
+ content = f.read()
+
+ content_b64 = base64.b64encode(content).decode("utf-8")
+ generated_files.append({
+ "name": os.path.basename(filepath),
+ "path": rel_path,
+ "content_base64": content_b64,
+ "mime_type": self._get_mime_type(filepath),
+ })
+
+ verbose_logger.debug(
+ f"SkillsSandboxExecutor: Collected generated file: {rel_path}"
+ )
+ except Exception as e:
+ verbose_logger.warning(
+ f"SkillsSandboxExecutor: Error copying file {filepath}: {e}"
+ )
+ finally:
+ if os.path.exists(tmp_path):
+ os.unlink(tmp_path)
+
+ except Exception as e:
+ verbose_logger.warning(
+ f"SkillsSandboxExecutor: Error collecting generated files: {e}"
+ )
+
+ return generated_files
+
+ def _get_mime_type(self, filename: str) -> str:
+ """Get MIME type for a file based on extension."""
+ ext = filename.lower().split(".")[-1]
+ return {
+ "gif": "image/gif",
+ "png": "image/png",
+ "jpg": "image/jpeg",
+ "jpeg": "image/jpeg",
+ "pdf": "application/pdf",
+ "csv": "text/csv",
+ "json": "application/json",
+ "txt": "text/plain",
+ }.get(ext, "application/octet-stream")
+
diff --git a/litellm/llms/litellm_proxy/skills/transformation.py b/litellm/llms/litellm_proxy/skills/transformation.py
new file mode 100644
index 00000000000..e7c999eacec
--- /dev/null
+++ b/litellm/llms/litellm_proxy/skills/transformation.py
@@ -0,0 +1,336 @@
+"""
+Transformation handler for LiteLLM database-backed skills.
+
+This module provides the SDK-level transformation layer that converts
+API requests to database operations via LiteLLMSkillsHandler.
+
+Pattern follows litellm/llms/litellm_proxy/responses/transformation.py
+"""
+
+from typing import TYPE_CHECKING, Any, Coroutine, Dict, List, Optional, Union
+
+from litellm.types.llms.anthropic_skills import (
+ DeleteSkillResponse,
+ ListSkillsResponse,
+ Skill,
+)
+from litellm.types.utils import LlmProviders
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+
+class LiteLLMSkillsTransformationHandler:
+ """
+ Transformation handler for skills API requests to LiteLLM database operations.
+
+ This is used when custom_llm_provider="litellm_proxy" to store/retrieve skills
+ from the LiteLLM proxy database instead of calling an external API.
+ """
+
+ @property
+ def custom_llm_provider(self) -> str:
+ """Return the provider name for logging."""
+ return LlmProviders.LITELLM_PROXY.value
+
+ def create_skill_handler(
+ self,
+ display_title: Optional[str] = None,
+ description: Optional[str] = None,
+ instructions: Optional[str] = None,
+ files: Optional[List[Any]] = None,
+ file_content: Optional[bytes] = None,
+ file_name: Optional[str] = None,
+ file_type: Optional[str] = None,
+ metadata: Optional[Dict[str, Any]] = None,
+ user_id: Optional[str] = None,
+ _is_async: bool = False,
+ logging_obj: Optional["LiteLLMLoggingObj"] = None,
+ litellm_call_id: Optional[str] = None,
+ **kwargs,
+ ) -> Union[Skill, Coroutine[Any, Any, Skill]]:
+ """
+ Create a skill in LiteLLM database.
+
+ Args:
+ display_title: Display title for the skill
+ description: Description of the skill
+ instructions: Instructions/prompt for the skill
+ files: Files to upload - list of tuples (filename, content, content_type)
+ file_content: Binary content of skill files (alternative to files)
+ file_name: Original filename (alternative to files)
+ file_type: MIME type (alternative to files)
+ metadata: Additional metadata
+ user_id: User ID for tracking
+ _is_async: Whether to return a coroutine
+
+ Returns:
+ Skill object or coroutine that returns Skill
+ """
+ # Pre-call logging
+ if logging_obj:
+ logging_obj.update_environment_variables(
+ model=None,
+ optional_params={"display_title": display_title},
+ litellm_params={"litellm_call_id": litellm_call_id},
+ custom_llm_provider=self.custom_llm_provider,
+ )
+
+ # Extract file content from files parameter if provided
+ # files is a list of tuples: [(filename, content, content_type), ...]
+ if files and not file_content:
+ if isinstance(files, list) and len(files) > 0:
+ first_file = files[0]
+ if isinstance(first_file, tuple) and len(first_file) >= 2:
+ file_name = first_file[0]
+ file_content = first_file[1]
+ file_type = first_file[2] if len(first_file) > 2 else "application/zip"
+
+ if _is_async:
+ return self._async_create_skill(
+ display_title=display_title,
+ description=description,
+ instructions=instructions,
+ file_content=file_content,
+ file_name=file_name,
+ file_type=file_type,
+ metadata=metadata,
+ user_id=user_id,
+ )
+
+ import asyncio
+ return asyncio.get_event_loop().run_until_complete(
+ self._async_create_skill(
+ display_title=display_title,
+ description=description,
+ instructions=instructions,
+ file_content=file_content,
+ file_name=file_name,
+ file_type=file_type,
+ metadata=metadata,
+ user_id=user_id,
+ )
+ )
+
+ async def _async_create_skill(
+ self,
+ display_title: Optional[str] = None,
+ description: Optional[str] = None,
+ instructions: Optional[str] = None,
+ file_content: Optional[bytes] = None,
+ file_name: Optional[str] = None,
+ file_type: Optional[str] = None,
+ metadata: Optional[Dict[str, Any]] = None,
+ user_id: Optional[str] = None,
+ ) -> Skill:
+ """Async implementation of create_skill."""
+ # Lazy import to avoid SDK dependency on proxy
+ from litellm.llms.litellm_proxy.skills.handler import LiteLLMSkillsHandler
+ from litellm.proxy._types import NewSkillRequest
+
+ skill_request = NewSkillRequest(
+ display_title=display_title,
+ description=description,
+ instructions=instructions,
+ file_content=file_content,
+ file_name=file_name,
+ file_type=file_type,
+ metadata=metadata,
+ )
+
+ db_skill = await LiteLLMSkillsHandler.create_skill(
+ data=skill_request,
+ user_id=user_id,
+ )
+
+ return self._db_skill_to_response(db_skill)
+
+ def list_skills_handler(
+ self,
+ limit: int = 20,
+ offset: int = 0,
+ _is_async: bool = False,
+ logging_obj: Optional["LiteLLMLoggingObj"] = None,
+ litellm_call_id: Optional[str] = None,
+ **kwargs,
+ ) -> Union[ListSkillsResponse, Coroutine[Any, Any, ListSkillsResponse]]:
+ """
+ List skills from LiteLLM database.
+
+ Args:
+ limit: Maximum number of skills to return
+ offset: Number of skills to skip
+ _is_async: Whether to return a coroutine
+ logging_obj: LiteLLM logging object
+ litellm_call_id: Call ID for logging
+
+ Returns:
+ ListSkillsResponse or coroutine that returns ListSkillsResponse
+ """
+ # Pre-call logging
+ if logging_obj:
+ logging_obj.update_environment_variables(
+ model=None,
+ optional_params={"limit": limit, "offset": offset},
+ litellm_params={"litellm_call_id": litellm_call_id},
+ custom_llm_provider=self.custom_llm_provider,
+ )
+
+ if _is_async:
+ return self._async_list_skills(limit=limit, offset=offset)
+
+ import asyncio
+ return asyncio.get_event_loop().run_until_complete(
+ self._async_list_skills(limit=limit, offset=offset)
+ )
+
+ async def _async_list_skills(
+ self,
+ limit: int = 20,
+ offset: int = 0,
+ ) -> ListSkillsResponse:
+ """Async implementation of list_skills."""
+ # Lazy import to avoid SDK dependency on proxy
+ from litellm.llms.litellm_proxy.skills.handler import LiteLLMSkillsHandler
+
+ db_skills = await LiteLLMSkillsHandler.list_skills(
+ limit=limit,
+ offset=offset,
+ )
+
+ skills = [self._db_skill_to_response(s) for s in db_skills]
+ return ListSkillsResponse(
+ data=skills,
+ has_more=len(skills) >= limit,
+ next_page=None,
+ )
+
+ def get_skill_handler(
+ self,
+ skill_id: str,
+ _is_async: bool = False,
+ logging_obj: Optional["LiteLLMLoggingObj"] = None,
+ litellm_call_id: Optional[str] = None,
+ **kwargs,
+ ) -> Union[Skill, Coroutine[Any, Any, Skill]]:
+ """
+ Get a skill from LiteLLM database.
+
+ Args:
+ skill_id: The skill ID to retrieve
+ _is_async: Whether to return a coroutine
+ logging_obj: LiteLLM logging object
+ litellm_call_id: Call ID for logging
+
+ Returns:
+ Skill or coroutine that returns Skill
+ """
+ # Pre-call logging
+ if logging_obj:
+ logging_obj.update_environment_variables(
+ model=None,
+ optional_params={"skill_id": skill_id},
+ litellm_params={"litellm_call_id": litellm_call_id},
+ custom_llm_provider=self.custom_llm_provider,
+ )
+
+ if _is_async:
+ return self._async_get_skill(skill_id=skill_id)
+
+ import asyncio
+ return asyncio.get_event_loop().run_until_complete(
+ self._async_get_skill(skill_id=skill_id)
+ )
+
+ async def _async_get_skill(self, skill_id: str) -> Skill:
+ """Async implementation of get_skill."""
+ # Lazy import to avoid SDK dependency on proxy
+ from litellm.llms.litellm_proxy.skills.handler import LiteLLMSkillsHandler
+
+ db_skill = await LiteLLMSkillsHandler.get_skill(skill_id=skill_id)
+ return self._db_skill_to_response(db_skill)
+
+ def delete_skill_handler(
+ self,
+ skill_id: str,
+ _is_async: bool = False,
+ logging_obj: Optional["LiteLLMLoggingObj"] = None,
+ litellm_call_id: Optional[str] = None,
+ **kwargs,
+ ) -> Union[DeleteSkillResponse, Coroutine[Any, Any, DeleteSkillResponse]]:
+ """
+ Delete a skill from LiteLLM database.
+
+ Args:
+ skill_id: The skill ID to delete
+ _is_async: Whether to return a coroutine
+ logging_obj: LiteLLM logging object
+ litellm_call_id: Call ID for logging
+
+ Returns:
+ DeleteSkillResponse or coroutine that returns DeleteSkillResponse
+ """
+ # Pre-call logging
+ if logging_obj:
+ logging_obj.update_environment_variables(
+ model=None,
+ optional_params={"skill_id": skill_id},
+ litellm_params={"litellm_call_id": litellm_call_id},
+ custom_llm_provider=self.custom_llm_provider,
+ )
+
+ if _is_async:
+ return self._async_delete_skill(skill_id=skill_id)
+
+ import asyncio
+ return asyncio.get_event_loop().run_until_complete(
+ self._async_delete_skill(skill_id=skill_id)
+ )
+
+ async def _async_delete_skill(self, skill_id: str) -> DeleteSkillResponse:
+ """Async implementation of delete_skill."""
+ # Lazy import to avoid SDK dependency on proxy
+ from litellm.llms.litellm_proxy.skills.handler import LiteLLMSkillsHandler
+
+ result = await LiteLLMSkillsHandler.delete_skill(skill_id=skill_id)
+ return DeleteSkillResponse(
+ id=result["id"],
+ type=result.get("type", "skill_deleted"),
+ )
+
+ def _db_skill_to_response(self, db_skill: Any) -> Skill:
+ """
+ Convert a database skill record to Anthropic-compatible Skill response.
+
+ Args:
+ db_skill: LiteLLM_SkillsTable record
+
+ Returns:
+ Skill object
+ """
+ created_at = ""
+ updated_at = ""
+
+ if hasattr(db_skill, "created_at") and db_skill.created_at:
+ created_at = (
+ db_skill.created_at.isoformat()
+ if hasattr(db_skill.created_at, "isoformat")
+ else str(db_skill.created_at)
+ )
+ if hasattr(db_skill, "updated_at") and db_skill.updated_at:
+ updated_at = (
+ db_skill.updated_at.isoformat()
+ if hasattr(db_skill.updated_at, "isoformat")
+ else str(db_skill.updated_at)
+ )
+
+ return Skill(
+ id=db_skill.skill_id,
+ created_at=created_at,
+ updated_at=updated_at,
+ display_title=db_skill.display_title,
+ latest_version=db_skill.latest_version,
+ source=db_skill.source or "custom",
+ type="skill",
+ )
+
diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py
index 87e3eece14c..6c573894f69 100644
--- a/litellm/llms/openai/chat/guardrail_translation/handler.py
+++ b/litellm/llms/openai/chat/guardrail_translation/handler.py
@@ -21,13 +21,7 @@ from litellm._logging import verbose_proxy_logger
from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
from litellm.main import stream_chunk_builder
from litellm.types.llms.openai import ChatCompletionToolParam
-from litellm.types.utils import (
- Choices,
- GenericGuardrailAPIInputs,
- ModelResponse,
- ModelResponseStream,
- StreamingChoices,
-)
+from litellm.types.utils import Choices, GenericGuardrailAPIInputs, ModelResponse, ModelResponseStream, StreamingChoices
if TYPE_CHECKING:
from litellm.integrations.custom_guardrail import CustomGuardrail
@@ -162,6 +156,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
url = image_url.get("url")
if url:
images_to_check.append(url)
+ elif isinstance(image_url, str):
+ images_to_check.append(image_url)
# Extract tool calls (typically in assistant messages)
tool_calls = message.get("tool_calls", None)
diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py
index e04def0d9c8..4d623097478 100644
--- a/litellm/llms/openai/openai.py
+++ b/litellm/llms/openai/openai.py
@@ -555,9 +555,13 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
provider_config: Optional[BaseConfig] = None
if custom_llm_provider is not None and model is not None:
- provider_config = ProviderConfigManager.get_provider_chat_config(
- model=model, provider=LlmProviders(custom_llm_provider)
- )
+ try:
+ provider_config = ProviderConfigManager.get_provider_chat_config(
+ model=model, provider=LlmProviders(custom_llm_provider)
+ )
+ except ValueError:
+ # JSON-configured providers may not be in LlmProviders enum
+ provider_config = None
if provider_config is None:
provider_config = OpenAIConfig()
diff --git a/litellm/llms/openai/realtime/handler.py b/litellm/llms/openai/realtime/handler.py
index 882309bb2fa..3ae4d2bc9f7 100644
--- a/litellm/llms/openai/realtime/handler.py
+++ b/litellm/llms/openai/realtime/handler.py
@@ -59,7 +59,7 @@ class OpenAIRealtime(OpenAIChatCompletion):
ssl_context = get_shared_realtime_ssl_context()
async with websockets.connect( # type: ignore
url,
- extra_headers={
+ additional_headers={
"Authorization": f"Bearer {api_key}", # type: ignore
"OpenAI-Beta": "realtime=v1",
},
diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py
index 4480ec497c7..9b8f15c7623 100644
--- a/litellm/llms/openai/responses/guardrail_translation/handler.py
+++ b/litellm/llms/openai/responses/guardrail_translation/handler.py
@@ -30,7 +30,7 @@ Output: response.output is List[GenericResponseOutputItem] where each has:
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
-from openai.types.responses import ResponseFunctionToolCall
+from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
from pydantic import BaseModel
from litellm._logging import verbose_proxy_logger
@@ -299,8 +299,25 @@ class OpenAIResponsesHandler(BaseTranslation):
task_mappings: List[Tuple[int, int]] = []
# Track (output_item_index, content_index) for each text
+ # Handle both dict and Pydantic object responses
+ if isinstance(response, dict):
+ response_output = response.get("output", [])
+ elif hasattr(response, "output"):
+ response_output = response.output or []
+ else:
+ verbose_proxy_logger.debug(
+ "OpenAI Responses API: No output found in response"
+ )
+ return response
+
+ if not response_output:
+ verbose_proxy_logger.debug(
+ "OpenAI Responses API: Empty output in response"
+ )
+ return response
+
# Step 1: Extract all text content and tool calls from response output
- for output_idx, output_item in enumerate(response.output):
+ for output_idx, output_item in enumerate(response_output):
self._extract_output_text_and_images(
output_item=output_item,
output_idx=output_idx,
@@ -538,13 +555,18 @@ class OpenAIResponsesHandler(BaseTranslation):
content: Optional[Union[List[OutputText], List[dict]]] = None
if isinstance(output_item, BaseModel):
try:
+ output_item_dump = output_item.model_dump()
generic_response_output_item = GenericResponseOutputItem.model_validate(
- output_item.model_dump()
+ output_item_dump
)
if generic_response_output_item.content:
content = generic_response_output_item.content
except Exception:
- return
+ # Try to extract content directly from output_item if validation fails
+ if hasattr(output_item, "content") and output_item.content:
+ content = output_item.content
+ else:
+ return
elif isinstance(output_item, dict):
content = output_item.get("content", [])
else:
@@ -582,22 +604,53 @@ class OpenAIResponsesHandler(BaseTranslation):
Override this method to customize how responses are applied.
"""
+ # Handle both dict and Pydantic object responses
+ if isinstance(response, dict):
+ response_output = response.get("output", [])
+ elif hasattr(response, "output"):
+ response_output = response.output or []
+ else:
+ return
+
for task_idx, guardrail_response in enumerate(responses):
mapping = task_mappings[task_idx]
output_idx = cast(int, mapping[0])
content_idx = cast(int, mapping[1])
- output_item = response.output[output_idx]
+ if output_idx >= len(response_output):
+ continue
- # Handle both GenericResponseOutputItem and dict
+ output_item = response_output[output_idx]
+
+ # Handle both GenericResponseOutputItem, BaseModel, and dict
if isinstance(output_item, GenericResponseOutputItem):
- content_item = output_item.content[content_idx]
- if isinstance(content_item, OutputText):
- content_item.text = guardrail_response
- elif isinstance(content_item, dict):
- content_item["text"] = guardrail_response
+ if output_item.content and content_idx < len(output_item.content):
+ content_item = output_item.content[content_idx]
+ if isinstance(content_item, OutputText):
+ content_item.text = guardrail_response
+ elif isinstance(content_item, dict):
+ content_item["text"] = guardrail_response
+ elif isinstance(output_item, BaseModel):
+ # Handle other Pydantic models by converting to GenericResponseOutputItem
+ try:
+ generic_item = GenericResponseOutputItem.model_validate(
+ output_item.model_dump()
+ )
+ if generic_item.content and content_idx < len(generic_item.content):
+ content_item = generic_item.content[content_idx]
+ if isinstance(content_item, OutputText):
+ content_item.text = guardrail_response
+ # Update the original response output
+ if hasattr(output_item, "content") and output_item.content:
+ original_content = output_item.content[content_idx]
+ if hasattr(original_content, "text"):
+ original_content.text = guardrail_response
+ except Exception:
+ pass
elif isinstance(output_item, dict):
content = output_item.get("content", [])
if content and content_idx < len(content):
if isinstance(content[content_idx], dict):
content[content_idx]["text"] = guardrail_response
+ elif hasattr(content[content_idx], "text"):
+ content[content_idx].text = guardrail_response
diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py
index 7ccec074703..96598c1dfe6 100644
--- a/litellm/llms/openai/responses/transformation.py
+++ b/litellm/llms/openai/responses/transformation.py
@@ -96,8 +96,8 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
validated_input.append(item.model_dump(exclude_none=True))
elif isinstance(item, dict):
# Handle reasoning items specifically to filter out status=None
- verbose_logger.debug(f"Handling reasoning item: {item}")
if item.get("type") == "reasoning":
+ verbose_logger.debug(f"Handling reasoning item: {item}")
# Type assertion since we know it's a dict at this point
dict_item = cast(Dict[str, Any], item)
filtered_item = self._handle_reasoning_item(dict_item)
@@ -411,7 +411,6 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
)
raw_response_headers = dict(raw_response.headers)
processed_headers = process_response_headers(raw_response_headers)
-
response = ResponsesAPIResponse(**raw_response_json)
response._hidden_params["additional_headers"] = processed_headers
response._hidden_params["headers"] = raw_response_headers
diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json
index 2d801506d5f..d9351c8b6b8 100644
--- a/litellm/llms/openai_like/providers.json
+++ b/litellm/llms/openai_like/providers.json
@@ -18,5 +18,12 @@
"veniceai": {
"base_url": "https://api.venice.ai/api/v1",
"api_key_env": "VENICE_AI_API_KEY"
+ },
+ "xiaomi_mimo": {
+ "base_url": "https://api.xiaomimimo.com/v1",
+ "api_key_env": "XIAOMI_MIMO_API_KEY",
+ "param_mappings": {
+ "max_completion_tokens": "max_tokens"
+ }
}
-}
+}
\ No newline at end of file
diff --git a/litellm/llms/stability/image_edit/__init__.py b/litellm/llms/stability/image_edit/__init__.py
new file mode 100644
index 00000000000..5a9eb2e02b9
--- /dev/null
+++ b/litellm/llms/stability/image_edit/__init__.py
@@ -0,0 +1,37 @@
+"""
+Stability AI Image Edit Module
+
+Factory function for getting the appropriate config class.
+"""
+
+from litellm.llms.base_llm.image_edit.transformation import (
+ BaseImageEditConfig,
+)
+
+from .transformations import StabilityImageEditConfig
+
+__all__ = [
+ "StabilityImageEditConfig",
+ "get_stability_image_edit_config",
+]
+
+
+def get_stability_image_edit_config(model: str) -> BaseImageEditConfig:
+ """
+ Get the appropriate Stability AI config for the given model.
+
+ Currently all models use the same config class, but this factory
+ allows for model-specific configs in the future.
+
+ Args:
+ model: The model name (e.g., "stability/inpaint", "stability/outpaint")
+
+ Returns:
+ BaseImageEditConfig instance for Stability AI
+ """
+ # For now, all models use the same config
+ # In the future, we could have model-specific configs:
+ # - StabilityInpaintConfig for Inpaint models
+ # - StabilityOutpaintConfig for Outpaint models
+ # - etc.
+ return StabilityImageEditConfig()
diff --git a/litellm/llms/stability/image_edit/transformations.py b/litellm/llms/stability/image_edit/transformations.py
new file mode 100644
index 00000000000..173fae2d6fd
--- /dev/null
+++ b/litellm/llms/stability/image_edit/transformations.py
@@ -0,0 +1,314 @@
+"""
+Stability AI Image Edit Config
+
+Handles transformation between OpenAI-compatible format and Stability AI API format.
+
+API Reference: https://platform.stability.ai/docs/api-reference
+"""
+
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+import httpx
+from httpx._types import RequestFiles
+
+from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.images.main import ImageEditOptionalRequestParams
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.llms.stability import (
+ OPENAI_SIZE_TO_STABILITY_ASPECT_RATIO,
+ STABILITY_EDIT_ENDPOINTS,
+)
+from litellm.types.utils import FileTypes, ImageObject, ImageResponse
+from litellm.utils import get_model_info
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+ LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+ LiteLLMLoggingObj = Any
+
+
+class StabilityImageEditConfig(BaseImageEditConfig):
+ """
+ Configuration for Stability AI image edit.
+
+ Supports:
+ - Stable Diffusion 3 (SD3, SD3.5) Image Edit
+ """
+
+ DEFAULT_BASE_URL: str = "https://api.stability.ai"
+
+ def get_supported_openai_params(
+ self, model: str
+ ) -> List[str]:
+ """
+ Return list of OpenAI params supported by Stability AI.
+
+ https://platform.stability.ai/docs/api-reference
+ """
+ return [
+ "n", # Number of images (Stability always returns 1, we can loop)
+ "size", # Maps to aspect_ratio
+ "response_format", # b64_json or url (Stability only returns b64)
+ "mask"
+ ]
+
+ def map_openai_params(
+ self,
+ image_edit_optional_params: ImageEditOptionalRequestParams,
+ model: str,
+ drop_params: bool,
+ ) -> Dict:
+ """
+ Map OpenAI parameters to Stability AI parameters.
+
+ OpenAI -> Stability mappings:
+ - size -> aspect_ratio
+ - n -> (handled separately, Stability returns 1 image per request)
+ """
+ supported_params = self.get_supported_openai_params(model)
+ # Define mapping from OpenAI params to Stability params
+ param_mapping = {
+ "size": "aspect_ratio",
+ # "n" and "response_format" are handled separately
+ }
+
+ # Create a copy to not mutate original - convert TypedDict to regular dict
+ mapped_params: Dict[str, Any] = dict(image_edit_optional_params)
+
+ for k, v in image_edit_optional_params.items():
+ if k in param_mapping:
+ # Map param if mapping exists and value is valid
+ if k == "size" and v in OPENAI_SIZE_TO_STABILITY_ASPECT_RATIO:
+ mapped_params[param_mapping[k]] = OPENAI_SIZE_TO_STABILITY_ASPECT_RATIO[v] # type: ignore
+ # Don't copy "size" itself to final dict
+ elif k == "n":
+ # Store for logic but do not add to outgoing params
+ mapped_params["_n"] = v
+ elif k == "response_format":
+ # Only b64 supported at Stability; store for postprocessing
+ mapped_params["_response_format"] = v
+ elif k not in supported_params:
+ if not drop_params:
+ raise ValueError(
+ f"Parameter {k} is not supported for model {model}. "
+ f"Supported parameters are {supported_params}. "
+ f"Set drop_params=True to drop unsupported parameters."
+ )
+ # Otherwise, param will simply be dropped
+ else:
+ # param is supported and not mapped, keep as-is
+ continue
+
+ # Remove OpenAI params that have been mapped unless they're in stability
+ for mapped in ["size", "n", "response_format"]:
+ if mapped in mapped_params:
+ del mapped_params[mapped]
+
+ return mapped_params
+
+ def _get_model_endpoint(self, model: str) -> str:
+ """
+ Get the API endpoint for a given model.
+ """
+ # Remove "stability/" prefix if present
+ model_name = model.lower()
+ if model_name.startswith("stability/"):
+ model_name = model_name[10:] # Remove "stability/" prefix
+
+ # Check if model is in our mapping
+ for key, endpoint in STABILITY_EDIT_ENDPOINTS.items():
+ if key in model_name:
+ return endpoint
+
+ # Default to SD3 endpoint
+ return "/v2beta/stable-image/edit/inpaint"
+
+ def get_complete_url(
+ self,
+ model: str,
+ api_base: Optional[str],
+ litellm_params: dict,
+ ) -> str:
+ """
+ Get the complete URL for the Stability AI API request.
+ """
+ base_url: str = (
+ api_base
+ or get_secret_str("STABILITY_API_BASE")
+ or litellm_params.get("api_base", None)
+ or self.DEFAULT_BASE_URL
+ )
+ base_url = base_url.rstrip("/")
+
+ endpoint = self._get_model_endpoint(model)
+ return f"{base_url}{endpoint}"
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ api_key: Optional[str] = None,
+ ) -> dict:
+ """
+ Validate environment and set up headers for Stability AI.
+ """
+ final_api_key: Optional[str] = api_key or get_secret_str("STABILITY_API_KEY")
+
+ if not final_api_key:
+ raise ValueError(
+ "STABILITY_API_KEY is not set. "
+ "Please set it via environment variable or pass api_key parameter."
+ )
+
+ headers["Authorization"] = f"Bearer {final_api_key}"
+ headers["Accept"] = "application/json"
+ return headers
+
+ def transform_image_edit_request(
+ self,
+ model: str,
+ prompt: str,
+ image: FileTypes,
+ image_edit_optional_request_params: Dict,
+ litellm_params: GenericLiteLLMParams,
+ headers: dict,
+ ) -> Tuple[Dict, RequestFiles]:
+ """
+ Transform OpenAI-style request to Stability AI request format.
+
+ Note: Stability AI uses multipart/form-data, but the HTTP handler
+ will handle the conversion from dict to form data.
+ """
+ # Build Stability request
+ # Populate multipart form-data as separate text fields (data) and files.
+ # Stability expects prompt/output_format/etc. as normal form fields, not file parts.
+ data: Dict[str, Any] = {
+ "prompt": prompt,
+ "output_format": "png", # Default to PNG
+ }
+ # Handle image parameter - could be a single file or list
+ image_file = image[0] if isinstance(image, list) else image # type: ignore
+ files: Dict[str, Any] = {"image": image_file}
+
+ # Add optional params (already mapped in map_openai_params)
+ for key, value in image_edit_optional_request_params.items(): # type: ignore
+ # Skip internal params (prefixed with _)
+ if key.startswith("_") or value is None:
+ continue
+
+ # File-like optional param
+ if key == "mask":
+ # Handle case where mask might be in a list
+ mask_value = value
+ if isinstance(value, list) and len(value) > 0:
+ mask_value = value[0]
+ files["mask"] = mask_value # type: ignore
+ continue
+
+ # File-like optional params (init_image, style_image, etc.)
+ if key in ["init_image", "style_image"]:
+ # Handle case where value might be in a list
+ file_value = value
+ if isinstance(value, list) and len(value) > 0:
+ file_value = value[0]
+ files[key] = file_value # type: ignore
+ continue
+
+ # Supported text fields
+ if key in [
+ "negative_prompt",
+ "aspect_ratio",
+ "seed",
+ "mode",
+ "strength",
+ "style_preset",
+ "left",
+ "bottom",
+ "right",
+ "top",
+ "creativity",
+ "search_prompt",
+ "grow_mask",
+ "select_prompt",
+ "control_strength",
+ "composition_fidelity",
+ "change_strength"
+ ]:
+ data[key] = value # type: ignore
+
+ return data, files
+
+ def transform_image_edit_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ api_key: Optional[str] = None,
+ json_mode: Optional[bool] = None,
+ ) -> ImageResponse:
+ """
+ Transform Stability AI response to OpenAI-compatible ImageResponse.
+
+ Stability returns: {"image": "base64...", "finish_reason": "SUCCESS", "seed": 123}
+ OpenAI expects: {"data": [{"b64_json": "base64..."}], "created": timestamp}
+ """
+ try:
+ response_data = raw_response.json()
+ except Exception as e:
+ raise self.get_error_class(
+ error_message=f"Error parsing Stability AI response: {e}",
+ status_code=raw_response.status_code,
+ headers=raw_response.headers,
+ )
+
+ # Check for errors in response
+ if "errors" in response_data:
+ raise self.get_error_class(
+ error_message=f"Stability AI error: {response_data['errors']}",
+ status_code=raw_response.status_code,
+ headers=raw_response.headers,
+ )
+
+ # Check finish_reason
+ finish_reason = response_data.get("finish_reason", "")
+ if finish_reason == "CONTENT_FILTERED":
+ raise self.get_error_class(
+ error_message="Content was filtered by Stability AI safety systems",
+ status_code=400,
+ headers=raw_response.headers,
+ )
+
+ model_response = ImageResponse()
+ if not model_response.data:
+ model_response.data = []
+
+ # Extract image from response
+ image_b64 = response_data.get("image")
+ if image_b64:
+ model_response.data.append(
+ ImageObject(
+ b64_json=image_b64,
+ url=None,
+ revised_prompt=None,
+ )
+ )
+
+ if not hasattr(model_response, "_hidden_params"):
+ model_response._hidden_params = {}
+ if "additional_headers" not in model_response._hidden_params:
+ model_response._hidden_params["additional_headers"] = {}
+ # Override: fetch model-cost from model_cost map based on the provided model name
+ model_info = get_model_info(model, custom_llm_provider="stability")
+ cost_per_image = model_info.get("output_cost_per_image", 0)
+ if cost_per_image is not None:
+ model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"] = float(cost_per_image)
+ return model_response
+
+ def use_multipart_form_data(self) -> bool:
+ """
+ Stability AI requires multipart/form-data for image generation.
+ """
+ return True
diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py
index 6bb11430f20..03fa5b98928 100644
--- a/litellm/llms/vertex_ai/common_utils.py
+++ b/litellm/llms/vertex_ai/common_utils.py
@@ -640,14 +640,28 @@ def add_object_type(schema):
if properties is not None:
if "required" in schema and schema["required"] is None:
schema.pop("required", None)
- schema["type"] = "object"
- for name, value in properties.items():
- add_object_type(value)
+ # Gemini doesn't accept empty properties for object types
+ # If properties is empty, remove it and the type field
+ if not properties:
+ schema.pop("properties", None)
+ schema.pop("type", None)
+ schema.pop("required", None)
+ else:
+ schema["type"] = "object"
+ for name, value in properties.items():
+ add_object_type(value)
items = schema.get("items", None)
if items is not None:
add_object_type(items)
+ for key in ["anyOf", "oneOf", "allOf"]:
+ values = schema.get(key, None)
+ if values is not None and isinstance(values, list):
+ for value in values:
+ if isinstance(value, dict):
+ add_object_type(value)
+
def strip_field(schema, field_name: str):
schema.pop(field_name, None)
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index feae8395178..b1810b40cf9 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -228,12 +228,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
Gemini 3 models include:
- gemini-3-pro-preview
+ - gemini-3-flash
+ - gemini-3-flash-preview (Gemini 3 Flash)
- Any future Gemini 3.x models
"""
# Check for Gemini 3 models
if "gemini-3" in model:
return True
-
return False
def _supports_penalty_parameters(self, model: str) -> bool:
@@ -685,22 +686,40 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
Returns:
GeminiThinkingConfig with thinkingLevel and includeThoughts
"""
+ # Check if this is gemini-3-flash which supports MINIMAL thinking level
+ is_gemini3flash= model and (
+ "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
+ )
if reasoning_effort == "minimal":
- return {"thinkingLevel": "low", "includeThoughts": True}
+ if is_gemini3flash:
+ return {"thinkingLevel": "minimal", "includeThoughts": True}
+ else:
+ return {"thinkingLevel": "low", "includeThoughts": True}
elif reasoning_effort == "low":
return {"thinkingLevel": "low", "includeThoughts": True}
elif reasoning_effort == "medium":
- return {
- "thinkingLevel": "high",
- "includeThoughts": True,
- } # medium is not out yet
+ # For gemini-3-flash-preview, medium maps to "medium", otherwise "high"
+ if is_gemini3flash:
+ return {"thinkingLevel": "medium", "includeThoughts": True}
+ else:
+ return {
+ "thinkingLevel": "high",
+ "includeThoughts": True,
+ } # medium is not out yet for other models
elif reasoning_effort == "high":
return {"thinkingLevel": "high", "includeThoughts": True}
elif reasoning_effort == "disable":
- # Gemini 3 cannot fully disable thinking, so we use "low" but hide thoughts
- return {"thinkingLevel": "low", "includeThoughts": False}
+ # Gemini 3 cannot fully disable thinking, so we use "minimal" for gemini-3-flash-preview, "low" for others
+ if is_gemini3flash:
+ return {"thinkingLevel": "minimal", "includeThoughts": False}
+ else:
+ return {"thinkingLevel": "low", "includeThoughts": False}
elif reasoning_effort == "none":
- return {"thinkingLevel": "low", "includeThoughts": False}
+ # For gemini-3-flash-preview, use "minimal" instead of "low"
+ if is_gemini3flash:
+ return {"thinkingLevel": "minimal", "includeThoughts": False}
+ else:
+ return {"thinkingLevel": "low", "includeThoughts": False}
else:
raise ValueError(f"Invalid reasoning effort: {reasoning_effort}")
@@ -751,17 +770,38 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
@staticmethod
def _map_thinking_param(
thinking_param: AnthropicThinkingParam,
+ model: Optional[str] = None,
) -> GeminiThinkingConfig:
thinking_enabled = thinking_param.get("type") == "enabled"
thinking_budget = thinking_param.get("budget_tokens")
params: GeminiThinkingConfig = {}
- if thinking_enabled and not VertexGeminiConfig._is_thinking_budget_zero(
- thinking_budget
- ):
- params["includeThoughts"] = True
- if thinking_budget is not None and isinstance(thinking_budget, int):
- params["thinkingBudget"] = thinking_budget
+
+ # For Gemini 3+ models, use thinkingLevel instead of thinkingBudget
+ if model and VertexGeminiConfig._is_gemini_3_or_newer(model):
+ if thinking_enabled:
+ if thinking_budget is None or thinking_budget == 0:
+ params["includeThoughts"] = False
+ else:
+ params["includeThoughts"] = True
+ if thinking_budget >= 10000:
+ is_gemini3flash = "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
+ params["thinkingLevel"] = "minimal" if is_gemini3flash else "low"
+ else:
+ is_gemini3flash = "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
+ params["thinkingLevel"] = "minimal" if is_gemini3flash else "low"
+ else:
+ # Thinking disabled
+ params["includeThoughts"] = False
+ else:
+ # For older Gemini models, use thinkingBudget
+ if thinking_enabled and not VertexGeminiConfig._is_thinking_budget_zero(
+ thinking_budget
+ ):
+ params["includeThoughts"] = True
+ if thinking_budget is not None and isinstance(thinking_budget, int):
+ params["thinkingBudget"] = thinking_budget
+
return params
def map_response_modalities(self, value: list) -> list:
@@ -938,7 +978,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
optional_params[
"thinkingConfig"
] = VertexGeminiConfig._map_thinking_param(
- cast(AnthropicThinkingParam, value)
+ cast(AnthropicThinkingParam, value),
+ model=model,
)
elif param == "modalities" and isinstance(value, list):
response_modalities = self.map_response_modalities(value)
@@ -970,7 +1011,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
"thinkingLevel" not in thinking_config
and "thinkingBudget" not in thinking_config
):
- thinking_config["thinkingLevel"] = "low"
+ # For gemini-3-flash-preview, default to "minimal" to match Gemini 2.5 Flash behavior
+ # For other Gemini 3 models, default to "low"
+ is_gemini3flash = "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower()
+ thinking_config["thinkingLevel"] = "minimal" if is_gemini3flash else "low"
optional_params["thinkingConfig"] = thinking_config
return optional_params
@@ -1432,6 +1476,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
cached_tokens: Optional[int] = None
audio_tokens: Optional[int] = None
text_tokens: Optional[int] = None
+ image_tokens: Optional[int] = None
prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
reasoning_tokens: Optional[int] = None
response_tokens: Optional[int] = None
@@ -1482,6 +1527,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
audio_tokens = detail.get("tokenCount", 0)
elif detail["modality"] == "TEXT":
text_tokens = detail.get("tokenCount", 0)
+ elif detail["modality"] == "IMAGE":
+ image_tokens = detail.get("tokenCount", 0)
if "thoughtsTokenCount" in usage_metadata:
reasoning_tokens = usage_metadata["thoughtsTokenCount"]
# Also add reasoning tokens to response_tokens_details
@@ -1502,6 +1549,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
cached_tokens=cached_tokens,
audio_tokens=audio_tokens,
text_tokens=text_tokens,
+ image_tokens=image_tokens,
)
completion_tokens = response_tokens or completion_response["usageMetadata"].get(
diff --git a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
index 469340f6bba..d575c5862e8 100644
--- a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
+++ b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
@@ -8,7 +8,6 @@ import httpx
from httpx._types import RequestFiles
import litellm
-
from litellm.images.utils import ImageEditRequestUtils
from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
@@ -94,10 +93,22 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM):
headers: dict,
model: str,
api_key: Optional[str] = None,
+ litellm_params: Optional[dict] = None,
+ api_base: Optional[str] = None,
) -> dict:
headers = headers or {}
- vertex_project = self._resolve_vertex_project()
- vertex_credentials = self._resolve_vertex_credentials()
+ litellm_params = litellm_params or {}
+
+ # If a custom api_base is provided, skip credential validation
+ # This allows users to use proxies or mock endpoints without needing Vertex AI credentials
+ _api_base = litellm_params.get("api_base") or api_base
+ if _api_base is not None:
+ return headers
+
+ # First check litellm_params (where vertex_ai_project/vertex_ai_credentials are passed)
+ # then fall back to environment variables and other sources
+ vertex_project = self.safe_get_vertex_ai_project(litellm_params) or self._resolve_vertex_project()
+ vertex_credentials = self.safe_get_vertex_ai_credentials(litellm_params) or self._resolve_vertex_credentials()
access_token, _ = self._ensure_access_token(
credentials=vertex_credentials,
project_id=vertex_project,
@@ -114,19 +125,27 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM):
"""
Get the complete URL for Vertex AI Gemini generateContent API
"""
- vertex_project = self._resolve_vertex_project()
- vertex_location = self._resolve_vertex_location()
-
- if not vertex_project or not vertex_location:
- raise ValueError("vertex_project and vertex_location are required for Vertex AI")
-
# Use the model name as provided, handling vertex_ai prefix
model_name = model
if model.startswith("vertex_ai/"):
model_name = model.replace("vertex_ai/", "")
+ # If a custom api_base is provided, use it directly
+ # This allows users to use proxies or mock endpoints
if api_base:
- base_url = api_base.rstrip("/")
+ return api_base.rstrip("/")
+
+ # First check litellm_params (where vertex_ai_project/vertex_ai_location are passed)
+ # then fall back to environment variables and other sources
+ vertex_project = self.safe_get_vertex_ai_project(litellm_params) or self._resolve_vertex_project()
+ vertex_location = self.safe_get_vertex_ai_location(litellm_params) or self._resolve_vertex_location()
+
+ if not vertex_project or not vertex_location:
+ raise ValueError("vertex_project and vertex_location are required for Vertex AI")
+
+ # Handle global location differently (no region prefix in URL)
+ if vertex_location == "global":
+ base_url = "https://aiplatform.googleapis.com"
else:
base_url = f"https://{vertex_location}-aiplatform.googleapis.com"
diff --git a/litellm/llms/vertex_ai/multimodal_embeddings/transformation.py b/litellm/llms/vertex_ai/multimodal_embeddings/transformation.py
index 5bf02ad765f..2cb2ac9ed8f 100644
--- a/litellm/llms/vertex_ai/multimodal_embeddings/transformation.py
+++ b/litellm/llms/vertex_ai/multimodal_embeddings/transformation.py
@@ -58,36 +58,81 @@ class VertexAIMultimodalEmbeddingConfig(BaseEmbeddingConfig):
headers.update(default_headers)
return headers
+ def _is_gcs_uri(self, input_str: str) -> bool:
+ """Check if the input string is a GCS URI."""
+ return "gs://" in input_str
+
+ def _is_video(self, input_str: str) -> bool:
+ """Check if the input string represents a video (mp4)."""
+ return "mp4" in input_str
+
+ def _is_media_input(self, input_str: str) -> bool:
+ """Check if the input string is a media element (GCS URI or base64 image)."""
+ return self._is_gcs_uri(input_str) or is_base64_encoded(s=input_str)
+
+ def _create_image_instance(self, input_str: str) -> InstanceImage:
+ """Create an InstanceImage from a GCS URI or base64 string."""
+ if self._is_gcs_uri(input_str):
+ return InstanceImage(gcsUri=input_str)
+ else:
+ return InstanceImage(
+ bytesBase64Encoded=(
+ input_str.split(",")[1] if "," in input_str else input_str
+ )
+ )
+
+ def _create_video_instance(self, input_str: str) -> InstanceVideo:
+ """Create an InstanceVideo from a GCS URI."""
+ return InstanceVideo(gcsUri=input_str)
+
def _process_input_element(self, input_element: str) -> Instance:
"""
- Process the input element for multimodal embedding requests. checks if the if the input is gcs uri, base64 encoded image or plain text.
+ Process a single input element for multimodal embedding requests.
+ Detects if the input is a GCS URI, base64 encoded image, or plain text.
Args:
input_element (str): The input element to process.
Returns:
- Dict[str, Any]: A dictionary representing the processed input element.
+ Instance: A dictionary representing the processed input element.
"""
if len(input_element) == 0:
return Instance(text=input_element)
- elif "gs://" in input_element:
- if "mp4" in input_element:
- return Instance(video=InstanceVideo(gcsUri=input_element))
+ elif self._is_gcs_uri(input_element):
+ if self._is_video(input_element):
+ return Instance(video=self._create_video_instance(input_element))
else:
- return Instance(image=InstanceImage(gcsUri=input_element))
+ return Instance(image=self._create_image_instance(input_element))
elif is_base64_encoded(s=input_element):
- return Instance(
- image=InstanceImage(
- bytesBase64Encoded=(
- input_element.split(",")[1]
- if "," in input_element
- else input_element
- )
- )
- )
+ return Instance(image=self._create_image_instance(input_element))
else:
return Instance(text=input_element)
+ def _try_merge_text_with_media(
+ self, text_str: str, next_elem: Optional[str]
+ ) -> tuple[Instance, bool]:
+ """
+ Try to merge a text element with a following media element into a single instance.
+
+ Args:
+ text_str: The text string to potentially merge.
+ next_elem: The next element in the input list (may be media).
+
+ Returns:
+ A tuple of (Instance, consumed_next) where consumed_next indicates
+ if the next element was merged into this instance.
+ """
+ instance_args: Instance = {"text": text_str}
+
+ if next_elem and isinstance(next_elem, str) and self._is_media_input(next_elem):
+ if self._is_gcs_uri(next_elem) and self._is_video(next_elem):
+ instance_args["video"] = self._create_video_instance(next_elem)
+ else:
+ instance_args["image"] = self._create_image_instance(next_elem)
+ return instance_args, True
+
+ return instance_args, False
+
def process_openai_embedding_input(
self, _input: Union[list, str]
) -> List[Instance]:
@@ -98,50 +143,33 @@ class VertexAIMultimodalEmbeddingConfig(BaseEmbeddingConfig):
_input (Union[list, str]): The input data to process.
Returns:
- Union[Instance, List[Instance]]: Either a single Instance or list of Instance objects.
+ List[Instance]: List of Instance objects for the embedding request.
"""
_input_list = [_input] if not isinstance(_input, list) else _input
- processed_instances = []
+ processed_instances: List[Instance] = []
i = 0
while i < len(_input_list):
current = _input_list[i]
-
- # Look ahead for potential media elements
next_elem = _input_list[i + 1] if i + 1 < len(_input_list) else None
- # If current is a text and next is a GCS URI, or current is a GCS URI
if isinstance(current, str):
- instance_args: Instance = {}
-
- # Process current element
- if "gs://" not in current:
- instance_args["text"] = current
- elif "mp4" in current:
- instance_args["video"] = InstanceVideo(gcsUri=current)
+ if self._is_media_input(current):
+ # Current element is media - process it standalone
+ processed_instances.append(self._process_input_element(current))
+ i += 1
else:
- instance_args["image"] = InstanceImage(gcsUri=current)
-
- # Check next element if it's a GCS URI
- if next_elem and isinstance(next_elem, str) and "gs://" in next_elem:
- if "mp4" in next_elem:
- instance_args["video"] = InstanceVideo(gcsUri=next_elem)
- else:
- instance_args["image"] = InstanceImage(gcsUri=next_elem)
- i += 2 # Skip next element since we processed it
- else:
- i += 1 # Move to next element
-
- processed_instances.append(instance_args)
- continue
-
- # Handle dict or other types
- if isinstance(current, dict):
- instance = Instance(**current)
- processed_instances.append(instance)
+ # Current element is text - try to merge with next media element
+ instance, consumed_next = self._try_merge_text_with_media(
+ text_str=current, next_elem=next_elem
+ )
+ processed_instances.append(instance)
+ i += 2 if consumed_next else 1
+ elif isinstance(current, dict):
+ processed_instances.append(Instance(**current))
+ i += 1
else:
raise ValueError(f"Unsupported input type: {type(current)}")
- i += 1
return processed_instances
diff --git a/litellm/llms/vertex_ai/ocr/common_utils.py b/litellm/llms/vertex_ai/ocr/common_utils.py
new file mode 100644
index 00000000000..dc2c07420bf
--- /dev/null
+++ b/litellm/llms/vertex_ai/ocr/common_utils.py
@@ -0,0 +1,41 @@
+"""
+Common utilities for Vertex AI OCR providers.
+
+This module provides routing logic to determine which OCR configuration to use
+based on the model name.
+"""
+
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:
+ from litellm.llms.base_llm.ocr.transformation import BaseOCRConfig
+
+
+def get_vertex_ai_ocr_config(model: str) -> Optional["BaseOCRConfig"]:
+ """
+ Determine which Vertex AI OCR configuration to use based on the model name.
+
+ Vertex AI supports multiple OCR services:
+ - Vertex AI OCR: vertex_ai/
+
+ Args:
+ model: The model name (e.g., "vertex_ai/ocr/")
+
+ Returns:
+ OCR configuration instance for the specified model
+
+ Examples:
+ >>> get_vertex_ai_ocr_config("vertex_ai/deepseek-ai/deepseek-ocr-maas")
+
+
+ >>> get_vertex_ai_ocr_config("vertex_ai/ocr/mistral-ocr-maas")
+
+ """
+ from litellm.llms.vertex_ai.ocr.deepseek_transformation import (
+ VertexAIDeepSeekOCRConfig,
+ )
+ from litellm.llms.vertex_ai.ocr.transformation import VertexAIOCRConfig
+ if "deepseek" in model:
+ return VertexAIDeepSeekOCRConfig()
+ return VertexAIOCRConfig()
+
diff --git a/litellm/llms/vertex_ai/ocr/deepseek_transformation.py b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py
new file mode 100644
index 00000000000..b16f73af3f6
--- /dev/null
+++ b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py
@@ -0,0 +1,394 @@
+"""
+Vertex AI DeepSeek OCR transformation implementation.
+"""
+import json
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+import httpx
+
+from litellm._logging import verbose_logger
+from litellm.llms.base_llm.ocr.transformation import (
+ BaseOCRConfig,
+ DocumentType,
+ OCRPage,
+ OCRRequestData,
+ OCRResponse,
+ OCRUsageInfo,
+)
+from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
+
+if TYPE_CHECKING:
+ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+else:
+ LiteLLMLoggingObj = Any
+
+
+class VertexAIDeepSeekOCRConfig(BaseOCRConfig):
+ """
+ Vertex AI DeepSeek OCR transformation configuration.
+
+ Vertex AI DeepSeek OCR uses the chat completion API format through the openapi endpoint.
+ This transformation converts OCR requests to chat completion format and vice versa.
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+ self.vertex_base = VertexBase()
+
+ def validate_environment(
+ self,
+ headers: Dict,
+ model: str,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ litellm_params: Optional[dict] = None,
+ **kwargs,
+ ) -> Dict:
+ """
+ Validate environment and return headers for Vertex AI OCR.
+
+ Vertex AI uses Bearer token authentication with access token from credentials.
+ """
+ # Extract Vertex AI parameters using safe helpers from VertexBase
+ # Use safe_get_* methods that don't mutate litellm_params dict
+ litellm_params = litellm_params or {}
+
+ vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params=litellm_params)
+ vertex_credentials = VertexBase.safe_get_vertex_ai_credentials(litellm_params=litellm_params)
+
+ # Get access token from Vertex credentials
+ access_token, project_id = self.vertex_base.get_access_token(
+ credentials=vertex_credentials,
+ project_id=vertex_project,
+ )
+
+ headers = {
+ "Authorization": f"Bearer {access_token}",
+ "Content-Type": "application/json",
+ **headers,
+ }
+
+ return headers
+
+ def get_complete_url(
+ self,
+ api_base: Optional[str],
+ model: str,
+ optional_params: dict,
+ litellm_params: Optional[dict] = None,
+ **kwargs,
+ ) -> str:
+ """
+ Get complete URL for Vertex AI DeepSeek OCR endpoint.
+
+ Vertex AI endpoint format:
+ https://{location}-aiplatform.googleapis.com/v1/projects/{project}/locations/{location}/endpoints/openapi/chat/completions
+
+ Args:
+ api_base: Vertex AI API base URL (optional)
+ model: Model name (e.g., "deepseek-ai/deepseek-ocr-maas")
+ optional_params: Optional parameters
+ litellm_params: LiteLLM parameters containing vertex_project, vertex_location
+
+ Returns: Complete URL for Vertex AI OCR endpoint
+ """
+ # Extract Vertex AI parameters using safe helpers from VertexBase
+ # Use safe_get_* methods that don't mutate litellm_params dict
+ litellm_params = litellm_params or {}
+
+ vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params=litellm_params)
+ vertex_location = VertexBase.safe_get_vertex_ai_location(litellm_params=litellm_params)
+
+ if vertex_project is None:
+ raise ValueError(
+ "Missing vertex_project - Set VERTEXAI_PROJECT environment variable or pass vertex_project parameter"
+ )
+
+ if vertex_location is None:
+ vertex_location = "us-central1"
+
+ # Get API base URL
+ if api_base is None:
+ api_base = "https://aiplatform.googleapis.com"
+
+ # Ensure no trailing slash
+ api_base = api_base.rstrip("/")
+
+ # Vertex AI DeepSeek OCR endpoint format
+ # Format: https://{region}-aiplatform.googleapis.com/v1/projects/{project}/locations/{region}/endpoints/openapi/chat/completions
+ return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/endpoints/openapi/chat/completions"
+
+ def transform_ocr_request(
+ self,
+ model: str,
+ document: DocumentType,
+ optional_params: dict,
+ headers: dict,
+ **kwargs,
+ ) -> OCRRequestData:
+ """
+ Transform OCR request to chat completion format for Vertex AI DeepSeek OCR.
+
+ Converts OCR document format to chat completion messages format:
+ - Input: {"type": "image_url", "image_url": "gs://..."}
+ - Output: {"model": "deepseek-ai/deepseek-ocr-maas", "messages": [{"role": "user", "content": [{"type": "image_url", "image_url": "gs://..."}]}]}
+
+ Args:
+ model: Model name (e.g., "deepseek-ai/deepseek-ocr-maas")
+ document: Document dict from user (Mistral OCR format)
+ optional_params: Already mapped optional parameters
+ headers: Request headers
+ **kwargs: Additional arguments
+
+ Returns:
+ OCRRequestData with JSON data in chat completion format
+ """
+ verbose_logger.debug("Vertex AI DeepSeek OCR transform_ocr_request (sync) called")
+
+ if not isinstance(document, dict):
+ raise ValueError(f"Expected document dict, got {type(document)}")
+
+ # Extract document type and URL
+ doc_type = document.get("type")
+ image_url = None
+ document_url = None
+
+ if doc_type == "image_url":
+ image_url = document.get("image_url", "")
+ elif doc_type == "document_url":
+ document_url = document.get("document_url", "")
+ else:
+ raise ValueError(f"Unsupported document type: {doc_type}. Expected 'image_url' or 'document_url'")
+
+ # Build chat completion message content
+ content_item = {}
+ if image_url:
+ content_item = {
+ "type": "image_url",
+ "image_url": image_url
+ }
+ elif document_url:
+ # For document URLs, we use image_url type as well (Vertex AI supports both)
+ content_item = {
+ "type": "image_url",
+ "image_url": document_url
+ }
+
+ # Build chat completion request
+ data = {
+ "model": "deepseek-ai/" + model,
+ "messages": [
+ {
+ "role": "user",
+ "content": [content_item]
+ }
+ ]
+ }
+
+ # Add optional parameters (stream, temperature, etc.)
+ # Filter out OCR-specific params that don't apply to chat completion
+ chat_completion_params = {}
+ for key, value in optional_params.items():
+ # Include common chat completion params
+ if key in ["stream", "temperature", "max_tokens", "top_p", "n", "stop"]:
+ chat_completion_params[key] = value
+
+ data.update(chat_completion_params)
+
+ verbose_logger.debug("Vertex AI DeepSeek OCR: Transformed request to chat completion format")
+
+ return OCRRequestData(data=data, files=None)
+
+ async def async_transform_ocr_request(
+ self,
+ model: str,
+ document: DocumentType,
+ optional_params: dict,
+ headers: dict,
+ **kwargs,
+ ) -> OCRRequestData:
+ """
+ Transform OCR request to chat completion format for Vertex AI DeepSeek OCR (async).
+
+ Same as sync version - no async-specific logic needed.
+
+ Args:
+ model: Model name
+ document: Document dict from user
+ optional_params: Already mapped optional parameters
+ headers: Request headers
+ **kwargs: Additional arguments
+
+ Returns:
+ OCRRequestData with JSON data in chat completion format
+ """
+ return self.transform_ocr_request(
+ model=model,
+ document=document,
+ optional_params=optional_params,
+ headers=headers,
+ **kwargs,
+ )
+
+ def transform_ocr_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ **kwargs,
+ ) -> OCRResponse:
+ """
+ Transform chat completion response to OCR format.
+
+ Vertex AI DeepSeek OCR returns chat completion format:
+ {
+ "id": "...",
+ "object": "chat.completion",
+ "choices": [{
+ "message": {
+ "role": "assistant",
+ "content": ""
+ }
+ }],
+ "usage": {...}
+ }
+
+ We need to extract the content and convert it to OCRResponse format.
+
+ Args:
+ model: Model name
+ raw_response: Raw HTTP response from Vertex AI
+ logging_obj: Logging object
+ **kwargs: Additional arguments
+
+ Returns:
+ OCRResponse in standard format
+ """
+ verbose_logger.debug("Vertex AI DeepSeek OCR transform_ocr_response called")
+ verbose_logger.debug(f"Raw response: {raw_response.text}")
+
+ try:
+ response_json = raw_response.json()
+
+ # Extract content from chat completion response
+ choices = response_json.get("choices", [])
+ if not choices:
+ raise ValueError("No choices in chat completion response")
+
+ message = choices[0].get("message", {})
+ content = message.get("content", "")
+
+ if not content:
+ raise ValueError("No content in chat completion response")
+
+ # Try to parse content as JSON (OCR result might be JSON string)
+ ocr_data = None
+ try:
+ # If content is a JSON string, parse it
+ if isinstance(content, str) and content.strip().startswith("{"):
+ ocr_data = json.loads(content)
+ elif isinstance(content, dict):
+ ocr_data = content
+ else:
+ # If content is markdown text, create a single page with the markdown
+ ocr_data = {
+ "pages": [
+ {
+ "index": 0,
+ "markdown": content
+ }
+ ],
+ "model": model,
+ "usage_info": response_json.get("usage", {})
+ }
+ except json.JSONDecodeError:
+ # If JSON parsing fails, treat content as markdown
+ ocr_data = {
+ "pages": [
+ {
+ "index": 0,
+ "markdown": content
+ }
+ ],
+ "model": model,
+ "usage_info": response_json.get("usage", {})
+ }
+
+ # Ensure we have the expected structure
+ if "pages" not in ocr_data:
+ # If OCR data doesn't have pages, wrap the content in a page
+ ocr_data = {
+ "pages": [
+ {
+ "index": 0,
+ "markdown": content if isinstance(content, str) else json.dumps(content)
+ }
+ ],
+ "model": ocr_data.get("model", model),
+ "usage_info": ocr_data.get("usage_info", response_json.get("usage", {}))
+ }
+
+ # Convert usage info if present
+ usage_info = None
+ if "usage_info" in ocr_data:
+ usage_dict = ocr_data["usage_info"]
+ if isinstance(usage_dict, dict):
+ usage_info = OCRUsageInfo(**usage_dict)
+
+ # Build OCRResponse
+ pages = []
+ for page_data in ocr_data.get("pages", []):
+ # Ensure page has required fields
+ if isinstance(page_data, dict):
+ page = OCRPage(
+ index=page_data.get("index", 0),
+ markdown=page_data.get("markdown", ""),
+ images=page_data.get("images"),
+ dimensions=page_data.get("dimensions")
+ )
+ pages.append(page)
+
+ if not pages:
+ # Create a default page if none exist
+ pages = [OCRPage(index=0, markdown=content if isinstance(content, str) else "")]
+
+ return OCRResponse(
+ pages=pages,
+ model=ocr_data.get("model", model),
+ document_annotation=ocr_data.get("document_annotation"),
+ usage_info=usage_info,
+ object="ocr",
+ )
+
+ except Exception as e:
+ verbose_logger.error(f"Error parsing Vertex AI DeepSeek OCR response: {e}")
+ raise e
+
+ async def async_transform_ocr_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ logging_obj: LiteLLMLoggingObj,
+ **kwargs,
+ ) -> OCRResponse:
+ """
+ Async transform chat completion response to OCR format.
+
+ Same as sync version - no async-specific logic needed.
+
+ Args:
+ model: Model name
+ raw_response: Raw HTTP response
+ logging_obj: Logging object
+ **kwargs: Additional arguments
+
+ Returns:
+ OCRResponse in standard format
+ """
+ return self.transform_ocr_response(
+ model=model,
+ raw_response=raw_response,
+ logging_obj=logging_obj,
+ **kwargs,
+ )
+
diff --git a/litellm/llms/watsonx/chat/transformation.py b/litellm/llms/watsonx/chat/transformation.py
index 917f7d89a2b..0bb96673ef6 100644
--- a/litellm/llms/watsonx/chat/transformation.py
+++ b/litellm/llms/watsonx/chat/transformation.py
@@ -6,6 +6,7 @@ Docs: https://cloud.ibm.com/apidocs/watsonx-ai#text-chat
from typing import Dict, List, Optional, Tuple, Union
+from litellm import verbose_logger
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.watsonx import (
WatsonXAIEndpoint,
@@ -150,8 +151,13 @@ class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig):
else:
hf_model = model
try:
- return hf_template_fn(model=hf_model, messages=messages)
+ result = hf_template_fn(model=hf_model, messages=messages)
+ # Return result if it's truthy (not None and not empty string)
+ # The caller will handle None/empty by falling back to default
+ if result:
+ return result
except Exception:
+ # Silently fall through to return None - caller will handle fallback
pass
elif WatsonXModelPattern.LLAMA3_INSTRUCT.value in model:
return custom_prompt(
@@ -204,11 +210,23 @@ class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig):
try:
# Use sync if cached, async if not
if hf_model in litellm.known_tokenizer_config:
- return hf_chat_template(model=hf_model, messages=messages)
+ result = hf_chat_template(model=hf_model, messages=messages)
else:
- return await ahf_chat_template(model=hf_model, messages=messages)
- except Exception:
- pass
+ result = await ahf_chat_template(model=hf_model, messages=messages)
+ # Return result if it's truthy (not None and not empty string)
+ # The caller (_aconvert_watsonx_messages_core) will handle None/empty by falling back to default
+ if result:
+ return result
+ except Exception as e:
+ # Log the exception for debugging but don't raise it
+ # The caller will fall back to default prompt factory
+ try:
+ verbose_logger.debug(
+ f"Failed to apply HuggingFace template for model {hf_model}: {e}"
+ )
+ except Exception:
+ # If logging fails, silently continue - don't break the flow
+ pass
elif WatsonXModelPattern.LLAMA3_INSTRUCT.value in model:
return custom_prompt(
role_dict={
diff --git a/litellm/main.py b/litellm/main.py
index b46208cc432..60fe3eb2dec 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -68,6 +68,7 @@ from litellm.constants import (
DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT,
)
from litellm.exceptions import LiteLLMUnknownProvider
+from litellm.llms.openai_like.json_loader import JSONProviderRegistry
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.asyncify import run_async_function
from litellm.litellm_core_utils.audio_utils.utils import (
@@ -104,10 +105,22 @@ from litellm.llms.vertex_ai.common_utils import (
from litellm.realtime_api.main import _realtime_health_check
from litellm.secret_managers.main import get_secret_bool, get_secret_str
from litellm.types.router import GenericLiteLLMParams
-from litellm.types.utils import RawRequestTypedDict, StreamingChoices
+from litellm.types.utils import (
+ ModelResponseStream,
+ RawRequestTypedDict,
+ StreamingChoices,
+)
from litellm.utils import (
+ Choices,
CustomStreamWrapper,
+ EmbeddingResponse,
+ Message,
+ ModelResponse,
ProviderConfigManager,
+ TextChoices,
+ TextCompletionResponse,
+ TextCompletionStreamWrapper,
+ TranscriptionResponse,
Usage,
_get_model_info_helper,
add_provider_specific_params_to_optional_params,
@@ -165,7 +178,8 @@ from .llms.azure_ai.anthropic.handler import AzureAnthropicChatCompletion
from .llms.azure_ai.embed import AzureAIEmbedding
from .llms.bedrock.chat import BedrockConverseLLM, BedrockLLM
from .llms.bedrock.embed.embedding import BedrockEmbedding
-from .llms.bedrock.image.image_handler import BedrockImageGeneration
+from .llms.bedrock.image_edit.handler import BedrockImageEdit
+from .llms.bedrock.image_generation.image_handler import BedrockImageGeneration
from .llms.bytez.chat.transformation import BytezChatConfig
from .llms.clarifai.chat.transformation import ClarifaiConfig
from .llms.codestral.completion.handler import CodestralTextCompletion
@@ -238,18 +252,6 @@ from .types.utils import (
all_litellm_params,
)
-from litellm.types.utils import ModelResponseStream
-from litellm.utils import (
- Choices,
- EmbeddingResponse,
- Message,
- ModelResponse,
- TextChoices,
- TextCompletionResponse,
- TextCompletionStreamWrapper,
- TranscriptionResponse,
-)
-
####### ENVIRONMENT VARIABLES ###################
openai_chat_completions = OpenAIChatCompletion()
openai_text_completions = OpenAITextCompletion()
@@ -271,6 +273,7 @@ codestral_text_completions = CodestralTextCompletion()
bedrock_converse_chat_completion = BedrockConverseLLM()
bedrock_embedding = BedrockEmbedding()
bedrock_image_generation = BedrockImageGeneration()
+bedrock_image_edit = BedrockImageEdit()
vertex_chat_completion = VertexLLM()
vertex_embedding = VertexEmbedding()
vertex_multimodal_embedding = VertexMultimodalEmbedding()
@@ -2261,6 +2264,7 @@ def completion( # type: ignore # noqa: PLR0915
or custom_llm_provider == "wandb"
or custom_llm_provider == "clarifai"
or custom_llm_provider in litellm.openai_compatible_providers
+ or JSONProviderRegistry.exists(custom_llm_provider) # JSON-configured providers
or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo
): # allow user to make an openai call with a custom base
# note: if a user sets a custom base - we should ensure this works
@@ -6467,6 +6471,35 @@ def speech( # noqa: PLR0915
api_key=api_key,
**kwargs,
)
+ elif custom_llm_provider == "aws_polly":
+ from litellm.llms.aws_polly.text_to_speech.transformation import (
+ AWSPollyTextToSpeechConfig,
+ )
+
+ # AWS Polly Text-to-Speech
+ if text_to_speech_provider_config is None:
+ text_to_speech_provider_config = AWSPollyTextToSpeechConfig()
+
+ # Cast to specific AWS Polly config type to access dispatch method
+ aws_polly_config = cast(
+ AWSPollyTextToSpeechConfig, text_to_speech_provider_config
+ )
+
+ response = aws_polly_config.dispatch_text_to_speech(
+ model=model,
+ input=input,
+ voice=voice,
+ optional_params=optional_params,
+ litellm_params_dict=litellm_params_dict,
+ logging_obj=logging_obj,
+ timeout=timeout,
+ extra_headers=extra_headers,
+ base_llm_http_handler=base_llm_http_handler,
+ aspeech=aspeech or False,
+ api_base=api_base,
+ api_key=api_key,
+ **kwargs,
+ )
if response is None:
raise Exception(
@@ -6901,6 +6934,7 @@ def _get_encoding():
global _encoding_cache
if _encoding_cache is None:
import sys
+
# Access via module to trigger __getattr__ if not cached
_encoding_cache = sys.modules[__name__].encoding
return _encoding_cache
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 99ef201acda..52c86695149 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -6570,6 +6570,18 @@
"supports_tool_choice": true,
"supports_vision": true
},
+ "gpt-4o-transcribe-diarize": {
+ "input_cost_per_audio_token": 6e-06,
+ "input_cost_per_token": 2.5e-06,
+ "litellm_provider": "openai",
+ "max_input_tokens": 16000,
+ "max_output_tokens": 2000,
+ "mode": "audio_transcription",
+ "output_cost_per_token": 1e-05,
+ "supported_endpoints": [
+ "/v1/audio/transcriptions"
+ ]
+ },
"claude-3-5-haiku-20241022": {
"cache_creation_input_token_cost": 1e-06,
"cache_creation_input_token_cost_above_1hr": 6e-06,
@@ -12342,6 +12354,7 @@
"max_videos_per_prompt": 10,
"mode": "image_generation",
"output_cost_per_image": 0.039,
+ "output_cost_per_image_token": 3e-05,
"output_cost_per_reasoning_token": 2.5e-06,
"output_cost_per_token": 2.5e-06,
"rpm": 100000,
@@ -12390,6 +12403,7 @@
"max_videos_per_prompt": 10,
"mode": "image_generation",
"output_cost_per_image": 0.039,
+ "output_cost_per_image_token": 3e-05,
"output_cost_per_reasoning_token": 3e-05,
"output_cost_per_token": 3e-05,
"rpm": 100000,
@@ -12957,6 +12971,49 @@
"supports_vision": true,
"supports_web_search": true
},
+ "vertex_ai/gemini-3-flash-preview": {
+ "cache_read_input_token_cost": 5e-08,
+ "input_cost_per_token": 5e-07,
+ "input_cost_per_audio_token": 1e-06,
+ "litellm_provider": "vertex_ai",
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_images_per_prompt": 3000,
+ "max_input_tokens": 1048576,
+ "max_output_tokens": 65535,
+ "max_pdf_size_mb": 30,
+ "max_tokens": 65535,
+ "max_video_length": 1,
+ "max_videos_per_prompt": 10,
+ "mode": "chat",
+ "output_cost_per_token": 3e-06,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+ "supported_endpoints": [
+ "/v1/chat/completions",
+ "/v1/completions",
+ "/v1/batch"
+ ],
+ "supported_modalities": [
+ "text",
+ "image",
+ "audio",
+ "video"
+ ],
+ "supported_output_modalities": [
+ "text"
+ ],
+ "supports_audio_input": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_video_input": true,
+ "supports_vision": true,
+ "supports_web_search": true
+ },
"gemini-2.5-pro-exp-03-25": {
"cache_read_input_token_cost": 3.125e-07,
"input_cost_per_token": 1.25e-06,
@@ -14080,6 +14137,7 @@
"max_videos_per_prompt": 10,
"mode": "image_generation",
"output_cost_per_image": 0.039,
+ "output_cost_per_image_token": 3e-05,
"output_cost_per_reasoning_token": 2.5e-06,
"output_cost_per_token": 2.5e-06,
"rpm": 100000,
@@ -14128,6 +14186,7 @@
"max_videos_per_prompt": 10,
"mode": "image_generation",
"output_cost_per_image": 0.039,
+ "output_cost_per_image_token": 3e-05,
"output_cost_per_reasoning_token": 3e-05,
"output_cost_per_token": 3e-05,
"rpm": 100000,
@@ -14732,6 +14791,98 @@
"supports_web_search": true,
"tpm": 800000
},
+ "gemini/gemini-3-flash-preview": {
+ "cache_read_input_token_cost": 5e-08,
+ "input_cost_per_audio_token": 1e-06,
+ "input_cost_per_token": 5e-07,
+ "litellm_provider": "gemini",
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_images_per_prompt": 3000,
+ "max_input_tokens": 1048576,
+ "max_output_tokens": 65535,
+ "max_pdf_size_mb": 30,
+ "max_tokens": 65535,
+ "max_video_length": 1,
+ "max_videos_per_prompt": 10,
+ "mode": "chat",
+ "output_cost_per_reasoning_token": 3e-06,
+ "output_cost_per_token": 3e-06,
+ "rpm": 2000,
+ "source": "https://ai.google.dev/pricing/gemini-3",
+ "supported_endpoints": [
+ "/v1/chat/completions",
+ "/v1/completions",
+ "/v1/batch"
+ ],
+ "supported_modalities": [
+ "text",
+ "image",
+ "audio",
+ "video"
+ ],
+ "supported_output_modalities": [
+ "text"
+ ],
+ "supports_audio_output": false,
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_url_context": true,
+ "supports_vision": true,
+ "supports_web_search": true,
+ "tpm": 800000
+ },
+ "gemini-3-flash-preview": {
+ "cache_read_input_token_cost": 5e-08,
+ "input_cost_per_audio_token": 1e-06,
+ "input_cost_per_token": 5e-07,
+ "litellm_provider": "vertex_ai-language-models",
+ "max_audio_length_hours": 8.4,
+ "max_audio_per_prompt": 1,
+ "max_images_per_prompt": 3000,
+ "max_input_tokens": 1048576,
+ "max_output_tokens": 65535,
+ "max_pdf_size_mb": 30,
+ "max_tokens": 65535,
+ "max_video_length": 1,
+ "max_videos_per_prompt": 10,
+ "mode": "chat",
+ "output_cost_per_reasoning_token": 3e-06,
+ "output_cost_per_token": 3e-06,
+ "source": "https://ai.google.dev/pricing/gemini-3",
+ "supported_endpoints": [
+ "/v1/chat/completions",
+ "/v1/completions",
+ "/v1/batch"
+ ],
+ "supported_modalities": [
+ "text",
+ "image",
+ "audio",
+ "video"
+ ],
+ "supported_output_modalities": [
+ "text"
+ ],
+ "supports_audio_output": false,
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_tool_choice": true,
+ "supports_url_context": true,
+ "supports_vision": true,
+ "supports_web_search": true
+ },
"gemini/gemini-2.5-pro-exp-03-25": {
"cache_read_input_token_cost": 0.0,
"input_cost_per_token": 0.0,
@@ -15213,6 +15364,34 @@
"video"
]
},
+ "gemini/veo-3.1-fast-generate-001": {
+ "litellm_provider": "gemini",
+ "max_input_tokens": 1024,
+ "max_tokens": 1024,
+ "mode": "video_generation",
+ "output_cost_per_second": 0.15,
+ "source": "https://ai.google.dev/gemini-api/docs/video",
+ "supported_modalities": [
+ "text"
+ ],
+ "supported_output_modalities": [
+ "video"
+ ]
+ },
+ "gemini/veo-3.1-generate-001": {
+ "litellm_provider": "gemini",
+ "max_input_tokens": 1024,
+ "max_tokens": 1024,
+ "mode": "video_generation",
+ "output_cost_per_second": 0.40,
+ "source": "https://ai.google.dev/gemini-api/docs/video",
+ "supported_modalities": [
+ "text"
+ ],
+ "supported_output_modalities": [
+ "video"
+ ]
+ },
"github_copilot/claude-haiku-4.5": {
"litellm_provider": "github_copilot",
"max_input_tokens": 128000,
@@ -15220,7 +15399,7 @@
"max_tokens": 16000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions"
+ "/v1/chat/completions"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -15233,7 +15412,7 @@
"max_tokens": 16000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions"
+ "/v1/chat/completions"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -15246,7 +15425,7 @@
"max_tokens": 16000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions"
+ "/v1/chat/completions"
],
"supports_vision": true
},
@@ -15257,7 +15436,7 @@
"max_tokens": 16000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions"
+ "/v1/chat/completions"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -15270,7 +15449,7 @@
"max_tokens": 16000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions"
+ "/v1/chat/completions"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -15427,8 +15606,8 @@
"max_tokens": 128000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions",
- "/responses"
+ "/v1/chat/completions",
+ "/v1/responses"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -15453,8 +15632,8 @@
"max_tokens": 64000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions",
- "/responses"
+ "/v1/chat/completions",
+ "/v1/responses"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -15468,7 +15647,7 @@
"max_tokens": 128000,
"mode": "responses",
"supported_endpoints": [
- "/responses"
+ "/v1/responses"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -15482,8 +15661,8 @@
"max_tokens": 64000,
"mode": "chat",
"supported_endpoints": [
- "/chat/completions",
- "/responses"
+ "/v1/chat/completions",
+ "/v1/responses"
],
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -16685,7 +16864,8 @@
"supported_endpoints": [
"/v1/images/generations"
],
- "supports_vision": true
+ "supports_vision": true,
+ "supports_pdf_input": true
},
"gpt-image-1.5-2025-12-16": {
"cache_read_input_image_token_cost": 2e-06,
@@ -16699,7 +16879,8 @@
"supported_endpoints": [
"/v1/images/generations"
],
- "supports_vision": true
+ "supports_vision": true,
+ "supports_pdf_input": true
},
"gpt-5": {
"cache_read_input_token_cost": 1.25e-07,
@@ -17872,75 +18053,6 @@
"supports_response_schema": true,
"supports_vision": true
},
- "groq/deepseek-r1-distill-llama-70b": {
- "input_cost_per_token": 7.5e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 128000,
- "max_output_tokens": 128000,
- "max_tokens": 128000,
- "mode": "chat",
- "output_cost_per_token": 9.9e-07,
- "supports_function_calling": true,
- "supports_reasoning": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/distil-whisper-large-v3-en": {
- "input_cost_per_second": 5.56e-06,
- "litellm_provider": "groq",
- "mode": "audio_transcription",
- "output_cost_per_second": 0.0
- },
- "groq/gemma-7b-it": {
- "deprecation_date": "2024-12-18",
- "input_cost_per_token": 7e-08,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 7e-08,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/gemma2-9b-it": {
- "input_cost_per_token": 2e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 2e-07,
- "supports_function_calling": false,
- "supports_response_schema": false,
- "supports_tool_choice": false
- },
- "groq/llama-3.1-405b-reasoning": {
- "input_cost_per_token": 5.9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 7.9e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/llama-3.1-70b-versatile": {
- "deprecation_date": "2025-01-24",
- "input_cost_per_token": 5.9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 7.9e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
"groq/llama-3.1-8b-instant": {
"input_cost_per_token": 5e-08,
"litellm_provider": "groq",
@@ -17953,97 +18065,6 @@
"supports_response_schema": false,
"supports_tool_choice": true
},
- "groq/llama-3.2-11b-text-preview": {
- "deprecation_date": "2024-10-28",
- "input_cost_per_token": 1.8e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 1.8e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/llama-3.2-11b-vision-preview": {
- "deprecation_date": "2025-04-14",
- "input_cost_per_token": 1.8e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 1.8e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true,
- "supports_vision": true
- },
- "groq/llama-3.2-1b-preview": {
- "deprecation_date": "2025-04-14",
- "input_cost_per_token": 4e-08,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 4e-08,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/llama-3.2-3b-preview": {
- "deprecation_date": "2025-04-14",
- "input_cost_per_token": 6e-08,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 6e-08,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/llama-3.2-90b-text-preview": {
- "deprecation_date": "2024-11-25",
- "input_cost_per_token": 9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 9e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/llama-3.2-90b-vision-preview": {
- "deprecation_date": "2025-04-14",
- "input_cost_per_token": 9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 9e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true,
- "supports_vision": true
- },
- "groq/llama-3.3-70b-specdec": {
- "deprecation_date": "2025-04-14",
- "input_cost_per_token": 5.9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 9.9e-07,
- "supports_tool_choice": true
- },
"groq/llama-3.3-70b-versatile": {
"input_cost_per_token": 5.9e-07,
"litellm_provider": "groq",
@@ -18056,7 +18077,7 @@
"supports_response_schema": false,
"supports_tool_choice": true
},
- "groq/llama-guard-3-8b": {
+ "groq/meta-llama/llama-guard-4-12b": {
"input_cost_per_token": 2e-07,
"litellm_provider": "groq",
"max_input_tokens": 8192,
@@ -18065,44 +18086,6 @@
"mode": "chat",
"output_cost_per_token": 2e-07
},
- "groq/llama2-70b-4096": {
- "input_cost_per_token": 7e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 4096,
- "max_output_tokens": 4096,
- "max_tokens": 4096,
- "mode": "chat",
- "output_cost_per_token": 8e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/llama3-groq-70b-8192-tool-use-preview": {
- "deprecation_date": "2025-01-06",
- "input_cost_per_token": 8.9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 8.9e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/llama3-groq-8b-8192-tool-use-preview": {
- "deprecation_date": "2025-01-06",
- "input_cost_per_token": 1.9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 8192,
- "max_output_tokens": 8192,
- "max_tokens": 8192,
- "mode": "chat",
- "output_cost_per_token": 1.9e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
"groq/meta-llama/llama-4-maverick-17b-128e-instruct": {
"input_cost_per_token": 2e-07,
"litellm_provider": "groq",
@@ -18113,7 +18096,8 @@
"output_cost_per_token": 6e-07,
"supports_function_calling": true,
"supports_response_schema": true,
- "supports_tool_choice": true
+ "supports_tool_choice": true,
+ "supports_vision": true
},
"groq/meta-llama/llama-4-scout-17b-16e-instruct": {
"input_cost_per_token": 1.1e-07,
@@ -18125,41 +18109,8 @@
"output_cost_per_token": 3.4e-07,
"supports_function_calling": true,
"supports_response_schema": true,
- "supports_tool_choice": true
- },
- "groq/mistral-saba-24b": {
- "input_cost_per_token": 7.9e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 32000,
- "max_output_tokens": 32000,
- "max_tokens": 32000,
- "mode": "chat",
- "output_cost_per_token": 7.9e-07
- },
- "groq/mixtral-8x7b-32768": {
- "deprecation_date": "2025-03-20",
- "input_cost_per_token": 2.4e-07,
- "litellm_provider": "groq",
- "max_input_tokens": 32768,
- "max_output_tokens": 32768,
- "max_tokens": 32768,
- "mode": "chat",
- "output_cost_per_token": 2.4e-07,
- "supports_function_calling": true,
- "supports_response_schema": false,
- "supports_tool_choice": true
- },
- "groq/moonshotai/kimi-k2-instruct": {
- "input_cost_per_token": 1e-06,
- "litellm_provider": "groq",
- "max_input_tokens": 131072,
- "max_output_tokens": 16384,
- "max_tokens": 131072,
- "mode": "chat",
- "output_cost_per_token": 3e-06,
- "supports_function_calling": true,
- "supports_response_schema": true,
- "supports_tool_choice": true
+ "supports_tool_choice": true,
+ "supports_vision": true
},
"groq/moonshotai/kimi-k2-instruct-0905": {
"input_cost_per_token": 1e-06,
@@ -22240,7 +22191,7 @@
"input_cost_per_token": 0,
"litellm_provider": "openrouter",
"max_input_tokens": 262144,
- "max_output_tokens": null,
+ "max_output_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 0,
@@ -22268,7 +22219,7 @@
"input_cost_per_token": 1e-07,
"litellm_provider": "openrouter",
"max_input_tokens": 131072,
- "max_output_tokens": null,
+ "max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 1e-07,
@@ -22282,7 +22233,7 @@
"input_cost_per_token": 1.5e-07,
"litellm_provider": "openrouter",
"max_input_tokens": 262144,
- "max_output_tokens": null,
+ "max_output_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 1.5e-07,
@@ -22296,7 +22247,7 @@
"input_cost_per_token": 2e-07,
"litellm_provider": "openrouter",
"max_input_tokens": 262144,
- "max_output_tokens": null,
+ "max_output_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 2e-07,
@@ -22310,7 +22261,7 @@
"input_cost_per_token": 5e-07,
"litellm_provider": "openrouter",
"max_input_tokens": 262144,
- "max_output_tokens": null,
+ "max_output_tokens": 262144,
"max_tokens": 262144,
"mode": "chat",
"output_cost_per_token": 1.5e-06,
@@ -24330,6 +24281,90 @@
"output_cost_per_image": 0.08,
"supported_endpoints": ["/v1/images/generations"]
},
+ "stability/inpaint": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/outpaint": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.004,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/erase": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/search-and-replace": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/search-and-recolor": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/remove-background": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/replace-background-and-relight": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.008,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/sketch": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/structure": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/style": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.005,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/style-transfer": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.008,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/fast": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.002,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/conservative": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.04,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
+ "stability/creative": {
+ "litellm_provider": "stability",
+ "mode": "image_edit",
+ "output_cost_per_image": 0.06,
+ "supported_endpoints": ["/v1/images/edits"]
+ },
"stability/stable-image-core": {
"litellm_provider": "stability",
"mode": "image_generation",
@@ -24357,6 +24392,84 @@
"mode": "image_generation",
"output_cost_per_image": 0.04
},
+ "stability.stable-conservative-upscale-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.40
+ },
+ "stability.stable-creative-upscale-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.60
+ },
+ "stability.stable-fast-upscale-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.03
+ },
+ "stability.stable-outpaint-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.06
+ },
+ "stability.stable-image-control-sketch-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-image-control-structure-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-image-erase-object-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-image-inpaint-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-image-remove-background-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-image-search-recolor-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-image-search-replace-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-image-style-guide-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.07
+ },
+ "stability.stable-style-transfer-v1:0": {
+ "litellm_provider": "bedrock",
+ "max_input_tokens": 77,
+ "mode": "image_edit",
+ "output_cost_per_image": 0.08
+ },
"stability.stable-image-core-v1:1": {
"litellm_provider": "bedrock",
"max_input_tokens": 77,
@@ -24396,6 +24509,16 @@
"mode": "image_generation",
"output_cost_per_pixel": 0.0
},
+ "linkup/search": {
+ "input_cost_per_query": 5.87e-03,
+ "litellm_provider": "linkup",
+ "mode": "search"
+ },
+ "linkup/search-deep": {
+ "input_cost_per_query": 58.67e-03,
+ "litellm_provider": "linkup",
+ "mode": "search"
+ },
"tavily/search": {
"input_cost_per_query": 0.008,
"litellm_provider": "tavily",
@@ -25070,6 +25193,42 @@
"/v1/audio/speech"
]
},
+ "aws_polly/standard": {
+ "input_cost_per_character": 4e-06,
+ "litellm_provider": "aws_polly",
+ "mode": "audio_speech",
+ "supported_endpoints": [
+ "/v1/audio/speech"
+ ],
+ "source": "https://aws.amazon.com/polly/pricing/"
+ },
+ "aws_polly/neural": {
+ "input_cost_per_character": 1.6e-05,
+ "litellm_provider": "aws_polly",
+ "mode": "audio_speech",
+ "supported_endpoints": [
+ "/v1/audio/speech"
+ ],
+ "source": "https://aws.amazon.com/polly/pricing/"
+ },
+ "aws_polly/long-form": {
+ "input_cost_per_character": 1e-04,
+ "litellm_provider": "aws_polly",
+ "mode": "audio_speech",
+ "supported_endpoints": [
+ "/v1/audio/speech"
+ ],
+ "source": "https://aws.amazon.com/polly/pricing/"
+ },
+ "aws_polly/generative": {
+ "input_cost_per_character": 3e-05,
+ "litellm_provider": "aws_polly",
+ "mode": "audio_speech",
+ "supported_endpoints": [
+ "/v1/audio/speech"
+ ],
+ "source": "https://aws.amazon.com/polly/pricing/"
+ },
"us.amazon.nova-lite-v1:0": {
"input_cost_per_token": 6e-08,
"litellm_provider": "bedrock_converse",
@@ -27130,6 +27289,7 @@
"max_videos_per_prompt": 10,
"mode": "image_generation",
"output_cost_per_image": 0.039,
+ "output_cost_per_image_token": 3e-05,
"output_cost_per_reasoning_token": 2.5e-06,
"output_cost_per_token": 2.5e-06,
"rpm": 100000,
@@ -27613,6 +27773,14 @@
],
"source": "https://cloud.google.com/generative-ai-app-builder/pricing"
},
+ "vertex_ai/deepseek-ai/deepseek-ocr-maas": {
+ "litellm_provider": "vertex_ai",
+ "mode": "ocr",
+ "input_cost_per_token": 3e-07,
+ "output_cost_per_token": 1.2e-06,
+ "ocr_cost_per_page": 3e-04,
+ "source": "https://cloud.google.com/vertex-ai/pricing"
+ },
"vertex_ai/openai/gpt-oss-120b-maas": {
"input_cost_per_token": 1.5e-07,
"litellm_provider": "vertex_ai-openai_models",
@@ -27781,6 +27949,34 @@
"video"
]
},
+ "vertex_ai/veo-3.1-generate-001": {
+ "litellm_provider": "vertex_ai-video-models",
+ "max_input_tokens": 1024,
+ "max_tokens": 1024,
+ "mode": "video_generation",
+ "output_cost_per_second": 0.4,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+ "supported_modalities": [
+ "text"
+ ],
+ "supported_output_modalities": [
+ "video"
+ ]
+ },
+ "vertex_ai/veo-3.1-fast-generate-001": {
+ "litellm_provider": "vertex_ai-video-models",
+ "max_input_tokens": 1024,
+ "max_tokens": 1024,
+ "mode": "video_generation",
+ "output_cost_per_second": 0.15,
+ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo",
+ "supported_modalities": [
+ "text"
+ ],
+ "supported_output_modalities": [
+ "video"
+ ]
+ },
"voyage/rerank-2": {
"input_cost_per_token": 5e-08,
"litellm_provider": "voyage",
@@ -29212,7 +29408,8 @@
"input_cost_per_token": 4.5e-07,
"output_cost_per_token": 1.8e-06,
"litellm_provider": "fireworks_ai",
- "mode": "chat"
+ "mode": "chat",
+ "supports_reasoning": true
},
"fireworks_ai/accounts/fireworks/models/flux-kontext-pro": {
"max_tokens": 4096,
@@ -30914,7 +31111,8 @@
"input_cost_per_token": 9e-07,
"output_cost_per_token": 9e-07,
"litellm_provider": "fireworks_ai",
- "mode": "chat"
+ "mode": "chat",
+ "supports_reasoning": true
},
"fireworks_ai/accounts/fireworks/models/qwen3-4b": {
"max_tokens": 40960,
@@ -30941,7 +31139,8 @@
"input_cost_per_token": 2e-07,
"output_cost_per_token": 2e-07,
"litellm_provider": "fireworks_ai",
- "mode": "chat"
+ "mode": "chat",
+ "supports_reasoning": true
},
"fireworks_ai/accounts/fireworks/models/qwen3-coder-30b-a3b-instruct": {
"max_tokens": 262144,
diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
index d6df3b76f1a..b43f4217177 100644
--- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
+++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
@@ -525,30 +525,9 @@ class MCPRequestHandler:
async def _get_allowed_mcp_servers_for_key(
user_api_key_auth: Optional[UserAPIKeyAuth] = None,
) -> List[str]:
- from litellm.proxy.auth.auth_checks import get_object_permission
- from litellm.proxy.proxy_server import (
- prisma_client,
- proxy_logging_obj,
- user_api_key_cache,
- )
-
- if user_api_key_auth is None:
- return []
-
- if user_api_key_auth.object_permission_id is None:
- return []
-
- if prisma_client is None:
- verbose_logger.debug("prisma_client is None")
- return []
-
try:
- key_object_permission = await get_object_permission(
- object_permission_id=user_api_key_auth.object_permission_id,
- prisma_client=prisma_client,
- user_api_key_cache=user_api_key_cache,
- parent_otel_span=user_api_key_auth.parent_otel_span,
- proxy_logging_obj=proxy_logging_obj,
+ key_object_permission = await MCPRequestHandler._get_key_object_permission(
+ user_api_key_auth
)
if key_object_permission is None:
return []
@@ -583,12 +562,6 @@ class MCPRequestHandler:
1. First checks if object_permission is already loaded on the team
2. If not, fetches from DB using object_permission_id if it exists
"""
- if user_api_key_auth is None:
- return []
-
- if user_api_key_auth.team_id is None:
- return []
-
try:
# Use the helper method that properly handles fetching from DB if needed
object_permissions = await MCPRequestHandler._get_team_object_permission(
diff --git a/litellm/proxy/_experimental/out/_next/static/qCeWtTTvIQuU871jPeeNA/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/BMqdCjUaq8FHE7G2pguZS/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/qCeWtTTvIQuU871jPeeNA/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/BMqdCjUaq8FHE7G2pguZS/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/qCeWtTTvIQuU871jPeeNA/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/BMqdCjUaq8FHE7G2pguZS/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/qCeWtTTvIQuU871jPeeNA/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/BMqdCjUaq8FHE7G2pguZS/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1130-8e58d6f70a0ae076.js b/litellm/proxy/_experimental/out/_next/static/chunks/1130-8e58d6f70a0ae076.js
new file mode 100644
index 00000000000..93885d70059
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/1130-8e58d6f70a0ae076.js
@@ -0,0 +1 @@
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deleted file mode 100644
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+++ /dev/null
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new file mode 100644
index 00000000000..40ab2174168
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deleted file mode 100644
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+++ /dev/null
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el;en(this,o,"m",M).call(this)}_connected(e){this.ended||(et(this,w,e,"f"),et(this,_,e?.headers.get("request-id"),"f"),en(this,u,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,m,"f")}get errored(){return en(this,v,"f")}get aborted(){return en(this,b,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,g,"f")[e]||(en(this,g,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,g,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,g,"f")[e]||(en(this,g,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,y,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,y,!0,"f"),await en(this,h,"f")}get currentMessage(){return en(this,l,"f")}async finalMessage(){return await this.done(),en(this,o,"m",S).call(this)}async finalText(){return await this.done(),en(this,o,"m",k).call(this)}_emit(e,...t){if(en(this,m,"f"))return;"end"===e&&(et(this,m,!0,"f"),en(this,f,"f").call(this));let n=en(this,g,"f")[e];if(n&&(en(this,g,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,y,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,y,"f")||n?.length||Promise.reject(e),en(this,d,"f").call(this,e),en(this,p,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,o,"m",S).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,o,"m",R).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,o,"m",C).call(this,e);if(r.controller.signal?.aborted)throw new el;en(this,o,"m",M).call(this)}[(l=new WeakMap,c=new WeakMap,u=new WeakMap,d=new WeakMap,h=new WeakMap,f=new WeakMap,p=new WeakMap,g=new WeakMap,m=new WeakMap,v=new WeakMap,b=new WeakMap,y=new WeakMap,w=new WeakMap,_=new WeakMap,x=new WeakMap,o=new WeakSet,S=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},k=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ei("stream ended without producing a content block with type=text");return e.join(" ")},R=function(){this.ended||et(this,l,void 0,"f")},C=function(e){if(this.ended)return;let t=en(this,o,"m",E).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tR(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,l,t,"f")}},M=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,l,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,l,void 0,"f"),e},E=function(e){let t=en(this,l,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tR(n)){let t=n[tx]||"";if(Object.defineProperty(n,tx,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{n.input=tk(t)}catch(n){let e=new ei(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${n}. JSON: ${t}`);en(this,x,"f").call(this,e)}}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}let tM={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tE={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tj extends tc{constructor(){super(...arguments),this.batches=new tb(this._client)}create(e,t){let{betas:n,...r}=e;r.model in tE&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tE[r.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tM[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tC.createMessage(this,e,t)}countTokens(e,t){let{betas:n,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:th([{"anthropic-beta":[...n??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tj.Batches=tb;class tO extends tc{constructor(){super(...arguments),this.models=new tm(this._client),this.messages=new tj(this._client),this.files=new tg(this._client)}}tO.Models=tm,tO.Messages=tj,tO.Files=tg;class tP extends tc{create(e,t){let{betas:n,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tA="__json_buf";function tI(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tz{constructor(){j.add(this),this.messages=[],this.receivedMessages=[],O.set(this,void 0),this.controller=new AbortController,P.set(this,void 0),A.set(this,()=>{}),I.set(this,()=>{}),z.set(this,void 0),L.set(this,()=>{}),T.set(this,()=>{}),Z.set(this,{}),N.set(this,!1),F.set(this,!1),q.set(this,!1),B.set(this,!1),$.set(this,void 0),D.set(this,void 0),W.set(this,e=>{if(et(this,F,!0,"f"),ea(e)&&(e=new el),e instanceof el)return et(this,q,!0,"f"),this._emit("abort",e);if(e instanceof ei)return this._emit("error",e);if(e instanceof Error){let t=new ei(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new ei(String(e)))}),et(this,P,new Promise((e,t)=>{et(this,A,e,"f"),et(this,I,t,"f")}),"f"),et(this,z,new Promise((e,t)=>{et(this,L,e,"f"),et(this,T,t,"f")}),"f"),en(this,P,"f").catch(()=>{}),en(this,z,"f").catch(()=>{})}get response(){return en(this,$,"f")}get request_id(){return en(this,D,"f")}async withResponse(){let e=await en(this,P,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tz;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,n){let r=new tz;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...n,headers:{...n?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},en(this,W,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,n){let r=n?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),en(this,j,"m",V).call(this);let{response:a,data:s}=await e.create({...t,stream:!0},{...n,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),s))en(this,j,"m",X).call(this,e);if(s.controller.signal?.aborted)throw new el;en(this,j,"m",J).call(this)}_connected(e){this.ended||(et(this,$,e,"f"),et(this,D,e?.headers.get("request-id"),"f"),en(this,A,"f").call(this,e),this._emit("connect"))}get ended(){return en(this,N,"f")}get errored(){return en(this,F,"f")}get aborted(){return en(this,q,"f")}abort(){this.controller.abort()}on(e,t){return(en(this,Z,"f")[e]||(en(this,Z,"f")[e]=[])).push({listener:t}),this}off(e,t){let n=en(this,Z,"f")[e];if(!n)return this;let r=n.findIndex(e=>e.listener===t);return r>=0&&n.splice(r,1),this}once(e,t){return(en(this,Z,"f")[e]||(en(this,Z,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,n)=>{et(this,B,!0,"f"),"error"!==e&&this.once("error",n),this.once(e,t)})}async done(){et(this,B,!0,"f"),await en(this,z,"f")}get currentMessage(){return en(this,O,"f")}async finalMessage(){return await this.done(),en(this,j,"m",H).call(this)}async finalText(){return await this.done(),en(this,j,"m",U).call(this)}_emit(e,...t){if(en(this,N,"f"))return;"end"===e&&(et(this,N,!0,"f"),en(this,L,"f").call(this));let n=en(this,Z,"f")[e];if(n&&(en(this,Z,"f")[e]=n.filter(e=>!e.once),n.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];en(this,B,"f")||n?.length||Promise.reject(e),en(this,I,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];en(this,B,"f")||n?.length||Promise.reject(e),en(this,I,"f").call(this,e),en(this,T,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",en(this,j,"m",H).call(this))}async _fromReadableStream(e,t){let n=t?.signal;n&&(n.aborted&&this.controller.abort(),n.addEventListener("abort",()=>this.controller.abort())),en(this,j,"m",V).call(this),this._connected(null);let r=eV.fromReadableStream(e,this.controller);for await(let e of r)en(this,j,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new el;en(this,j,"m",J).call(this)}[(O=new WeakMap,P=new WeakMap,A=new WeakMap,I=new WeakMap,z=new WeakMap,L=new WeakMap,T=new WeakMap,Z=new WeakMap,N=new WeakMap,F=new WeakMap,q=new WeakMap,B=new WeakMap,$=new WeakMap,D=new WeakMap,W=new WeakMap,j=new WeakSet,H=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},U=function(){if(0===this.receivedMessages.length)throw new ei("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new ei("stream ended without producing a content block with type=text");return e.join(" ")},V=function(){this.ended||et(this,O,void 0,"f")},X=function(e){if(this.ended)return;let t=en(this,j,"m",G).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let n=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===n.type&&this._emit("text",e.delta.text,n.text||"");break;case"citations_delta":"text"===n.type&&this._emit("citation",e.delta.citation,n.citations??[]);break;case"input_json_delta":tI(n)&&n.input&&this._emit("inputJson",e.delta.partial_json,n.input);break;case"thinking_delta":"thinking"===n.type&&this._emit("thinking",e.delta.thinking,n.thinking);break;case"signature_delta":"thinking"===n.type&&this._emit("signature",n.signature);break;default:e.delta}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":et(this,O,t,"f")}},J=function(){if(this.ended)throw new ei("stream has ended, this shouldn't happen");let e=en(this,O,"f");if(!e)throw new ei("request ended without sending any chunks");return et(this,O,void 0,"f"),e},G=function(e){let t=en(this,O,"f");if("message_start"===e.type){if(t)throw new ei(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new ei(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let n=t.content.at(e.index);switch(e.delta.type){case"text_delta":n?.type==="text"&&(n.text+=e.delta.text);break;case"citations_delta":n?.type==="text"&&(n.citations??(n.citations=[]),n.citations.push(e.delta.citation));break;case"input_json_delta":if(n&&tI(n)){let t=n[tA]||"";Object.defineProperty(n,tA,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(n.input=tk(t))}break;case"thinking_delta":n?.type==="thinking"&&(n.thinking+=e.delta.thinking);break;case"signature_delta":n?.type==="thinking"&&(n.signature=e.delta.signature);break;default:e.delta}return t}}},Symbol.asyncIterator)](){let e=[],t=[],n=!1;return this.on("streamEvent",n=>{let r=t.shift();r?r.resolve(n):e.push(n)}),this.on("end",()=>{for(let e of(n=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(n=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:n?{value:void 0,done:!0}:new Promise((e,n)=>t.push({resolve:e,reject:n})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new eV(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}class tL extends tc{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tp`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",e2,{query:e,...t})}delete(e,t){return this._client.delete(tp`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tp`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let n=await this.retrieve(e);if(!n.results_url)throw new ei(`No batch \`results_url\`; Has it finished processing? ${n.processing_status} - ${n.id}`);return this._client.get(n.results_url,{...t,headers:th([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tv.fromResponse(t.response,t.controller))}}class tT extends tc{constructor(){super(...arguments),this.batches=new tL(this._client)}create(e,t){e.model in tZ&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tZ[e.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let n=this._client._options.timeout;if(!e.stream&&null==n){let t=tM[e.model]??void 0;n=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:n??6e5,...t,stream:e.stream??!1})}stream(e,t){return tz.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tZ={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tT.Batches=tL;class tN extends tc{retrieve(e,t={},n){let{betas:r}=t??{};return this._client.get(tp`/v1/models/${e}`,{...n,headers:th([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},n?.headers])})}list(e={},t){let{betas:n,...r}=e??{};return this._client.getAPIList("/v1/models",e2,{query:r,...t,headers:th([{...n?.toString()!=null?{"anthropic-beta":n?.toString()}:void 0},t?.headers])})}}let tF=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tq{constructor({baseURL:e=tF("ANTHROPIC_BASE_URL"),apiKey:t=tF("ANTHROPIC_API_KEY")??null,authToken:n=tF("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){K.set(this,void 0);let a={apiKey:t,authToken:n,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&ez())throw new ei("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??tB.DEFAULT_TIMEOUT,this.logger=a.logger??console;let s="warn";this.logLevel=s,this.logLevel=eC(a.logLevel,"ClientOptions.logLevel",this)??eC(tF("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??s,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("undefined"!=typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),et(this,K,eD,"f"),this._options=a,this.apiKey=t,this.authToken=n}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization")||t.has("authorization")))throw Error('Could not resolve authentication method. 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new file mode 100644
index 00000000000..30b5624a75b
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/302-a78d84f204cc1081.js
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index c7cef8a881e..00000000000
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new file mode 100644
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deleted file mode 100644
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+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/6653-7001d6e6100af8cb.js b/litellm/proxy/_experimental/out/_next/static/chunks/6653-bdb4cfe11ecbcb53.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/chunks/6653-7001d6e6100af8cb.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/6653-bdb4cfe11ecbcb53.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/667-cbb542e4dc0d37bb.js b/litellm/proxy/_experimental/out/_next/static/chunks/667-cbb542e4dc0d37bb.js
deleted file mode 100644
index 2a08e17f359..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/667-cbb542e4dc0d37bb.js
+++ /dev/null
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