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Merge remote-tracking branch 'origin' into litellm_non_root_docker_logo_fix

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yuneng-jiang 2025-12-06 20:00:33 -08:00
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7
.circleci/config.yml
View file

@ -3496,8 +3496,13 @@ jobs:
command: |
npx playwright test e2e_ui_tests/ --reporter=html --output=test-results
no_output_timeout: 120m
- store_test_results:
- store_artifacts:
path: test-results
destination: playwright-results
- store_artifacts:
path: playwright-report
destination: playwright-report
test_nonroot_image:
machine:

15
Dockerfile
View file

@ -1,8 +1,8 @@
# Base image for building
ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev
ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base
# Runtime image
ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev
ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base
# Builder stage
FROM $LITELLM_BUILD_IMAGE AS builder
@ -12,11 +12,9 @@ WORKDIR /app
USER root
# Install build dependencies
RUN apk add --no-cache gcc python3-dev openssl openssl-dev
RUN apk add --no-cache bash gcc py3-pip python3 python3-dev openssl openssl-dev
RUN pip install --upgrade pip>=24.3.1 && \
pip install build
RUN python -m pip install build
# Copy the current directory contents into the container at /app
COPY . .
@ -48,10 +46,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
USER root
# Install runtime dependencies
RUN apk add --no-cache openssl tzdata nodejs npm
# Upgrade pip to fix CVE-2025-8869
RUN pip install --upgrade pip>=24.3.1
RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip
WORKDIR /app
# Copy the current directory contents into the container at /app

4
README.md
View file

@ -274,8 +274,6 @@ echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
# password generator to get a random hash for litellm salt key
echo 'LITELLM_SALT_KEY="sk-1234"' >> .env
source .env
# Start
docker compose up
```
@ -348,7 +346,7 @@ curl 'http://0.0.0.0:4000/key/generate' \
| [Fireworks AI (`fireworks_ai`)](https://docs.litellm.ai/docs/providers/fireworks_ai) | ✅ | ✅ | ✅ | | | | | | | |
| [FriendliAI (`friendliai`)](https://docs.litellm.ai/docs/providers/friendliai) | ✅ | ✅ | ✅ | | | | | | | |
| [Galadriel (`galadriel`)](https://docs.litellm.ai/docs/providers/galadriel) | ✅ | ✅ | ✅ | | | | | | | |
| [GitHub Copilot (`github_copilot`)](https://docs.litellm.ai/docs/providers/github_copilot) | ✅ | ✅ | ✅ | | | | | | | |
| [GitHub Copilot (`github_copilot`)](https://docs.litellm.ai/docs/providers/github_copilot) | ✅ | ✅ | ✅ | ✅ | | | | | | |
| [GitHub Models (`github`)](https://docs.litellm.ai/docs/providers/github) | ✅ | ✅ | ✅ | | | | | | | |
| [Google - PaLM](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | | | | | | | |
| [Google - Vertex AI (`vertex_ai`)](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | |

2
ci_cd/security_scans.sh
View file

@ -76,6 +76,8 @@ run_grype_scans() {
"GHSA-4xh5-x5gv-qwph"
"CVE-2025-8291" # no fix available as of Oct 11, 2025
"GHSA-5j98-mcp5-4vw2"
"CVE-2025-13836" # Python 3.13 HTTP response reading OOM/DoS - no fix available in base image
"CVE-2025-12084" # Python 3.13 xml.dom.minidom quadratic algorithm - no fix available in base image
)
# Build JSON array of allowlisted CVE IDs for jq

87
cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
View file

@ -404,6 +404,93 @@ This release has a known issue...
- **New Providers** - Provider name, supported endpoints, description
- **New LLM API Endpoints** (optional) - Endpoint, method, description, documentation link
- Only include major new provider integrations, not minor provider updates
- **IMPORTANT**: When adding new providers, also update `provider_endpoints_support.json` in the repository root (see Section 13)
### 12. Section Header Counts
**Always include counts in section headers for:**
- **New Providers** - Add count in parentheses: `### New Providers (X new providers)`
- **New LLM API Endpoints** - Add count in parentheses: `### New LLM API Endpoints (X new endpoints)`
- **New Model Support** - Add count in parentheses: `#### New Model Support (X new models)`
**Format:**
```markdown
### New Providers (4 new providers)
| Provider | Supported LiteLLM Endpoints | Description |
| -------- | --------------------------- | ----------- |
...
### New LLM API Endpoints (2 new endpoints)
| Endpoint | Method | Description | Documentation |
| -------- | ------ | ----------- | ------------- |
...
#### New Model Support (32 new models)
| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
...
```
**Counting Rules:**
- Count each row in the table (excluding the header row)
- For models, count each model entry in the pricing table
- For providers, count each new provider added
- For endpoints, count each new API endpoint added
### 13. Update provider_endpoints_support.json
**When adding new providers or endpoints, you MUST also update `provider_endpoints_support.json` in the repository root.**
This file tracks which endpoints are supported by each LiteLLM provider and is used to generate documentation.
**Required Steps:**
1. For each new provider added to the release notes, add a corresponding entry to `provider_endpoints_support.json`
2. For each new endpoint type added, update the schema comment and add the endpoint to relevant providers
**Provider Entry Format:**
```json
"provider_slug": {
"display_name": "Provider Name (`provider_slug`)",
"url": "https://docs.litellm.ai/docs/providers/provider_slug",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": true
}
}
```
**Available Endpoint Types:**
- `chat_completions` - `/chat/completions` endpoint
- `messages` - `/messages` endpoint (Anthropic format)
- `responses` - `/responses` endpoint (OpenAI/Anthropic unified)
- `embeddings` - `/embeddings` endpoint
- `image_generations` - `/image/generations` endpoint
- `audio_transcriptions` - `/audio/transcriptions` endpoint
- `audio_speech` - `/audio/speech` endpoint
- `moderations` - `/moderations` endpoint
- `batches` - `/batches` endpoint
- `rerank` - `/rerank` endpoint
- `ocr` - `/ocr` endpoint
- `search` - `/search` endpoint
- `vector_stores` - `/vector_stores` endpoint
- `a2a` - `/a2a/{agent}/message/send` endpoint (A2A Protocol)
**Checklist:**
- [ ] All new providers from release notes are added to `provider_endpoints_support.json`
- [ ] Endpoint support flags accurately reflect provider capabilities
- [ ] Documentation URL points to correct provider docs page
## Example Command Workflow

540
cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py Normal file
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@ -0,0 +1,540 @@
#!/usr/bin/env python3
"""
Mock Bedrock Guardrail API Server
This is a FastAPI server that mimics the AWS Bedrock Guardrail API for testing purposes.
It follows the same API spec as the real Bedrock guardrail endpoint.
Usage:
python mock_bedrock_guardrail_server.py
The server will start on http://localhost:8080
"""
import os
import re
from typing import Any, Dict, List, Literal, Optional
from fastapi import Depends, FastAPI, Header, HTTPException, status
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field
# ============================================================================
# Request/Response Models (matching Bedrock API spec)
# ============================================================================
class BedrockTextContent(BaseModel):
text: str
class BedrockContentItem(BaseModel):
text: BedrockTextContent
class BedrockRequest(BaseModel):
source: Literal["INPUT", "OUTPUT"]
content: List[BedrockContentItem] = Field(default_factory=list)
class BedrockGuardrailOutput(BaseModel):
text: Optional[str] = None
class TopicPolicyItem(BaseModel):
name: str
type: str
action: Literal["BLOCKED", "NONE"]
class TopicPolicy(BaseModel):
topics: List[TopicPolicyItem] = Field(default_factory=list)
class ContentFilterItem(BaseModel):
type: str
confidence: str
action: Literal["BLOCKED", "NONE"]
class ContentPolicy(BaseModel):
filters: List[ContentFilterItem] = Field(default_factory=list)
class CustomWord(BaseModel):
match: str
action: Literal["BLOCKED", "NONE"]
class WordPolicy(BaseModel):
customWords: List[CustomWord] = Field(default_factory=list)
managedWordLists: List[Dict[str, Any]] = Field(default_factory=list)
class PiiEntity(BaseModel):
type: str
match: str
action: Literal["BLOCKED", "ANONYMIZED", "NONE"]
class RegexMatch(BaseModel):
name: str
match: str
regex: str
action: Literal["BLOCKED", "ANONYMIZED", "NONE"]
class SensitiveInformationPolicy(BaseModel):
piiEntities: List[PiiEntity] = Field(default_factory=list)
regexes: List[RegexMatch] = Field(default_factory=list)
class ContextualGroundingFilter(BaseModel):
type: str
threshold: float
score: float
action: Literal["BLOCKED", "NONE"]
class ContextualGroundingPolicy(BaseModel):
filters: List[ContextualGroundingFilter] = Field(default_factory=list)
class Assessment(BaseModel):
topicPolicy: Optional[TopicPolicy] = None
contentPolicy: Optional[ContentPolicy] = None
wordPolicy: Optional[WordPolicy] = None
sensitiveInformationPolicy: Optional[SensitiveInformationPolicy] = None
contextualGroundingPolicy: Optional[ContextualGroundingPolicy] = None
class BedrockGuardrailResponse(BaseModel):
usage: Dict[str, int] = Field(
default_factory=lambda: {"topicPolicyUnits": 1, "contentPolicyUnits": 1}
)
action: Literal["NONE", "GUARDRAIL_INTERVENED"] = "NONE"
outputs: List[BedrockGuardrailOutput] = Field(default_factory=list)
assessments: List[Assessment] = Field(default_factory=list)
# ============================================================================
# Mock Guardrail Configuration
# ============================================================================
class GuardrailConfig(BaseModel):
"""Configuration for mock guardrail behavior"""
blocked_words: List[str] = Field(
default_factory=lambda: ["offensive", "inappropriate", "badword"]
)
blocked_topics: List[str] = Field(default_factory=lambda: ["violence", "illegal"])
pii_patterns: Dict[str, str] = Field(
default_factory=lambda: {
"EMAIL": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
"PHONE": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b",
"SSN": r"\b\d{3}-\d{2}-\d{4}\b",
"CREDIT_CARD": r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b",
}
)
anonymize_pii: bool = True # If True, ANONYMIZE PII; if False, BLOCK it
bearer_token: str = "mock-bedrock-token-12345"
# Global config
GUARDRAIL_CONFIG = GuardrailConfig()
# ============================================================================
# FastAPI App Setup
# ============================================================================
app = FastAPI(
title="Mock Bedrock Guardrail API",
description="Mock server mimicking AWS Bedrock Guardrail API",
version="1.0.0",
)
# ============================================================================
# Authentication
# ============================================================================
async def verify_bearer_token(authorization: Optional[str] = Header(None)) -> str:
"""
Verify the Bearer token from the Authorization header.
Args:
authorization: The Authorization header value
Returns:
The token if valid
Raises:
HTTPException: If token is missing or invalid
"""
if authorization is None:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Missing Authorization header",
headers={"WWW-Authenticate": "Bearer"},
)
# Check if it's a Bearer token
parts = authorization.split()
print(f"parts: {parts}")
if len(parts) != 2 or parts[0].lower() != "bearer":
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid Authorization header format. Expected: Bearer <token>",
headers={"WWW-Authenticate": "Bearer"},
)
token = parts[1]
# Verify token
if token != GUARDRAIL_CONFIG.bearer_token:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Invalid bearer token",
)
return token
# ============================================================================
# Guardrail Logic
# ============================================================================
def check_blocked_words(text: str) -> Optional[WordPolicy]:
"""Check if text contains blocked words"""
found_words = []
text_lower = text.lower()
for word in GUARDRAIL_CONFIG.blocked_words:
if word.lower() in text_lower:
found_words.append(CustomWord(match=word, action="BLOCKED"))
if found_words:
return WordPolicy(customWords=found_words)
return None
def check_blocked_topics(text: str) -> Optional[TopicPolicy]:
"""Check if text contains blocked topics"""
found_topics = []
text_lower = text.lower()
for topic in GUARDRAIL_CONFIG.blocked_topics:
if topic.lower() in text_lower:
found_topics.append(
TopicPolicyItem(name=topic, type=topic.upper(), action="BLOCKED")
)
if found_topics:
return TopicPolicy(topics=found_topics)
return None
def check_pii(text: str) -> tuple[Optional[SensitiveInformationPolicy], str]:
"""
Check for PII in text and return policy + anonymized text
Returns:
Tuple of (SensitiveInformationPolicy or None, anonymized_text)
"""
pii_entities = []
anonymized_text = text
action = "ANONYMIZED" if GUARDRAIL_CONFIG.anonymize_pii else "BLOCKED"
for pii_type, pattern in GUARDRAIL_CONFIG.pii_patterns.items():
try:
# Compile the regex pattern with a timeout to prevent ReDoS attacks
compiled_pattern = re.compile(pattern)
matches = compiled_pattern.finditer(text)
for match in matches:
matched_text = match.group()
pii_entities.append(
PiiEntity(type=pii_type, match=matched_text, action=action)
)
# Anonymize the text if configured
if GUARDRAIL_CONFIG.anonymize_pii:
anonymized_text = anonymized_text.replace(
matched_text, f"[{pii_type}_REDACTED]"
)
except re.error:
# Invalid regex pattern - skip it and log a warning
print(f"Warning: Invalid regex pattern for PII type {pii_type}: {pattern}")
continue
if pii_entities:
return SensitiveInformationPolicy(piiEntities=pii_entities), anonymized_text
return None, text
def process_guardrail_request(
request: BedrockRequest,
) -> tuple[BedrockGuardrailResponse, List[str]]:
"""
Process a guardrail request and return the response.
Returns:
Tuple of (response, list of output texts)
"""
all_text_content = []
output_texts = []
# Extract all text from content items
for content_item in request.content:
if content_item.text and content_item.text.text:
all_text_content.append(content_item.text.text)
# Combine all text for analysis
combined_text = " ".join(all_text_content)
# Initialize response
response = BedrockGuardrailResponse()
assessment = Assessment()
has_intervention = False
# Check for blocked words
word_policy = check_blocked_words(combined_text)
if word_policy:
assessment.wordPolicy = word_policy
has_intervention = True
# Check for blocked topics
topic_policy = check_blocked_topics(combined_text)
if topic_policy:
assessment.topicPolicy = topic_policy
has_intervention = True
# Check for PII
for text in all_text_content:
pii_policy, anonymized_text = check_pii(text)
if pii_policy:
assessment.sensitiveInformationPolicy = pii_policy
if GUARDRAIL_CONFIG.anonymize_pii:
# If anonymizing, we don't block, we modify the text
output_texts.append(anonymized_text)
has_intervention = True
else:
# If not anonymizing PII, we block it
output_texts.append(text)
has_intervention = True
else:
output_texts.append(text)
# Build response
if has_intervention:
response.action = "GUARDRAIL_INTERVENED"
# Only add assessment if there were interventions
response.assessments = [assessment]
# Add outputs (modified or original text)
response.outputs = [BedrockGuardrailOutput(text=txt) for txt in output_texts]
return response, output_texts
# ============================================================================
# API Endpoints
# ============================================================================
@app.get("/")
async def root():
"""Health check endpoint"""
return {
"service": "Mock Bedrock Guardrail API",
"status": "running",
"endpoint_format": "/guardrail/{guardrailIdentifier}/version/{guardrailVersion}/apply",
}
@app.get("/health")
async def health():
"""Health check endpoint"""
return {"status": "healthy"}
"""
LiteLLM exposes a basic guardrail API with the text extracted from the request and sent to the guardrail API, as well as the received request body for any further processing.
This works across all LiteLLM endpoints (completion, anthropic /v1/messages, responses api, image generation, embedding, etc.)
This makes it easy to support your own guardrail API without having to make a PR to LiteLLM.
LiteLLM supports passing any provider specific params from LiteLLM config.yaml to the guardrail API.
Example:
```yaml
guardrails:
- guardrail_name: "bedrock-content-guard"
litellm_params:
guardrail: generic_guardrail_api
mode: "pre_call"
api_key: os.environ/GUARDRAIL_API_KEY
api_base: os.environ/GUARDRAIL_API_BASE
additional_provider_specific_params:
api_version: os.environ/GUARDRAIL_API_VERSION # additional provider specific params
```
This is a beta API. Please help us improve it.
"""
class LitellmBasicGuardrailRequest(BaseModel):
texts: List[str]
images: Optional[List[str]] = None
tools: Optional[List[dict]] = None
tool_calls: Optional[List[dict]] = None
request_data: Dict[str, Any] = Field(default_factory=dict)
additional_provider_specific_params: Dict[str, Any] = Field(default_factory=dict)
input_type: Literal["request", "response"]
litellm_call_id: Optional[str] = None
litellm_trace_id: Optional[str] = None
structured_messages: Optional[List[Dict[str, Any]]] = None
class LitellmBasicGuardrailResponse(BaseModel):
action: Literal[
"BLOCKED", "NONE", "GUARDRAIL_INTERVENED"
] # BLOCKED = litellm will raise an error, NONE = litellm will continue, GUARDRAIL_INTERVENED = litellm will continue, but the text was modified by the guardrail
blocked_reason: Optional[str] = None # only if action is BLOCKED, otherwise None
texts: Optional[List[str]] = None
images: Optional[List[str]] = None
@app.post(
"/beta/litellm_basic_guardrail_api",
response_model=LitellmBasicGuardrailResponse,
)
async def beta_litellm_basic_guardrail_api(
request: LitellmBasicGuardrailRequest,
) -> LitellmBasicGuardrailResponse:
"""
Apply guardrail to input or output content.
This endpoint mimics the AWS Bedrock ApplyGuardrail API.
Args:
request: The guardrail request containing content to analyze
token: Bearer token (verified by dependency)
Returns:
LitellmBasicGuardrailResponse with analysis results
"""
print(f"request: {request}")
if any("ishaan" in text.lower() for text in request.texts):
return LitellmBasicGuardrailResponse(
action="BLOCKED", blocked_reason="Ishaan is not allowed"
)
elif any("pii_value" in text for text in request.texts):
return LitellmBasicGuardrailResponse(
action="GUARDRAIL_INTERVENED",
texts=[
text.replace("pii_value", "pii_value_redacted")
for text in request.texts
],
)
return LitellmBasicGuardrailResponse(action="NONE")
@app.post("/config/update")
async def update_config(
config: GuardrailConfig, token: str = Depends(verify_bearer_token)
):
"""
Update the guardrail configuration.
This is a testing endpoint to modify the mock guardrail behavior.
Args:
config: New guardrail configuration
token: Bearer token (verified by dependency)
Returns:
Updated configuration
"""
global GUARDRAIL_CONFIG
GUARDRAIL_CONFIG = config
return {"status": "updated", "config": GUARDRAIL_CONFIG}
@app.get("/config")
async def get_config(token: str = Depends(verify_bearer_token)):
"""
Get the current guardrail configuration.
Args:
token: Bearer token (verified by dependency)
Returns:
Current configuration
"""
return GUARDRAIL_CONFIG
# ============================================================================
# Error Handlers
# ============================================================================
@app.exception_handler(HTTPException)
async def http_exception_handler(request, exc: HTTPException):
"""Custom error handler for HTTP exceptions"""
return JSONResponse(
status_code=exc.status_code,
content={"error": exc.detail},
headers=exc.headers,
)
# ============================================================================
# Main
# ============================================================================
if __name__ == "__main__":
import uvicorn
# Get configuration from environment
host = os.getenv("MOCK_BEDROCK_HOST", "0.0.0.0")
port = int(os.getenv("MOCK_BEDROCK_PORT", "8080"))
bearer_token = os.getenv("MOCK_BEDROCK_TOKEN", "mock-bedrock-token-12345")
# Update config with environment token
GUARDRAIL_CONFIG.bearer_token = bearer_token
print("=" * 80)
print("Mock Bedrock Guardrail API Server")
print("=" * 80)
print(f"Server starting on: http://{host}:{port}")
print(f"Bearer Token: {bearer_token}")
print(f"Endpoint: POST /guardrail/{{id}}/version/{{version}}/apply")
print("=" * 80)
print("\nExample curl command:")
print(
f"""
curl -X POST "http://{host}:{port}/guardrail/test-guardrail/version/1/apply" \\
-H "Authorization: Bearer {bearer_token}" \\
-H "Content-Type: application/json" \\
-d '{{
"source": "INPUT",
"content": [
{{
"text": {{
"text": "Hello, my email is test@example.com"
}}
}}
]
}}'
"""
)
print("=" * 80)
uvicorn.run(app, host=host, port=port)

4
deploy/charts/litellm-helm/Chart.yaml
View file

@ -18,7 +18,7 @@ type: application
# This is the chart version. This version number should be incremented each time you make changes
# to the chart and its templates, including the app version.
# Versions are expected to follow Semantic Versioning (https://semver.org/)
version: 0.4.8
version: 0.4.10
# This is the version number of the application being deployed. This version number should be
# incremented each time you make changes to the application. Versions are not expected to
@ -33,5 +33,5 @@ dependencies:
condition: db.deployStandalone
- name: redis
version: ">=18.0.0"
repository: oci://registry-1.docker.io/bitnamicharts
repository: oci://registry-1.docker.io/bitnamicharts
condition: redis.enabled

125
deploy/charts/litellm-helm/README.md
View file

@ -10,46 +10,48 @@
- Helm 3.8.0+
If `db.deployStandalone` is used:
- PV provisioner support in the underlying infrastructure
If `db.useStackgresOperator` is used (not yet implemented):
- The Stackgres Operator must already be installed in the Kubernetes Cluster. This chart will **not** install the operator if it is missing.
- The Stackgres Operator must already be installed in the Kubernetes Cluster. This chart will **not** install the operator if it is missing.
## Parameters
### LiteLLM Proxy Deployment Settings
| Name | Description | Value |
| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- |
| `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` |
| `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A |
| `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A |
| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A |
| `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` |
| `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` |
| `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` |
| `image.pullPolicy` | LiteLLM Proxy image pull policy | `IfNotPresent` |
| `image.tag` | Overrides the image tag whose default the latest version of LiteLLM at the time this chart was published. | `""` |
| `imagePullSecrets` | Registry credentials for the LiteLLM and initContainer images. | `[]` |
| `serviceAccount.create` | Whether or not to create a Kubernetes Service Account for this deployment. The default is `false` because LiteLLM has no need to access the Kubernetes API. | `false` |
| `service.type` | Kubernetes Service type (e.g. `LoadBalancer`, `ClusterIP`, etc.) | `ClusterIP` |
| `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` |
| `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` |
| `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A |
| `proxyConfigMap.create` | When `true`, render a ConfigMap from `.Values.proxy_config` and mount it. | `true` |
| `proxyConfigMap.name` | When `create=false`, name of the existing ConfigMap to mount. | `""` |
| `proxyConfigMap.key` | Key in the ConfigMap that contains the proxy config file. | `"config.yaml"` |
| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. Rendered into the ConfigMap’s `config.yaml` only when `proxyConfigMap.create=true`. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | `N/A` |
| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy.
| `pdb.enabled` | Enable a PodDisruptionBudget for the LiteLLM proxy Deployment | `false` |
| `pdb.minAvailable` | Minimum number/percentage of pods that must be available during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` |
| `pdb.maxUnavailable` | Maximum number/percentage of pods that can be unavailable during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` |
| `pdb.annotations` | Extra metadata annotations to add to the PDB | `{}` |
| `pdb.labels` | Extra metadata labels to add to the PDB | `{}` |
| Name | Description | Value |
| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- |
| `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` |
| `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A |
| `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A |
| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A |
| `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` |
| `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` |
| `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` |
| `image.pullPolicy` | LiteLLM Proxy image pull policy | `IfNotPresent` |
| `image.tag` | Overrides the image tag whose default the latest version of LiteLLM at the time this chart was published. | `""` |
| `imagePullSecrets` | Registry credentials for the LiteLLM and initContainer images. | `[]` |
| `serviceAccount.create` | Whether or not to create a Kubernetes Service Account for this deployment. The default is `false` because LiteLLM has no need to access the Kubernetes API. | `false` |
| `service.type` | Kubernetes Service type (e.g. `LoadBalancer`, `ClusterIP`, etc.) | `ClusterIP` |
| `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` |
| `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` |
| `ingress.labels` | Additional labels for the Ingress resource | `{}` |
| `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A |
| `proxyConfigMap.create` | When `true`, render a ConfigMap from `.Values.proxy_config` and mount it. | `true` |
| `proxyConfigMap.name` | When `create=false`, name of the existing ConfigMap to mount. | `""` |
| `proxyConfigMap.key` | Key in the ConfigMap that contains the proxy config file. | `"config.yaml"` |
| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. Rendered into the ConfigMap’s `config.yaml` only when `proxyConfigMap.create=true`. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | `N/A` |
| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy. |
| `pdb.enabled` | Enable a PodDisruptionBudget for the LiteLLM proxy Deployment | `false` |
| `pdb.minAvailable` | Minimum number/percentage of pods that must be available during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` |
| `pdb.maxUnavailable` | Maximum number/percentage of pods that can be unavailable during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` |
| `pdb.annotations` | Extra metadata annotations to add to the PDB | `{}` |
| `pdb.labels` | Extra metadata labels to add to the PDB | `{}` |
#### Example `proxy_config` ConfigMap from values (default):
```
proxyConfigMap:
create: true
@ -67,7 +69,6 @@ proxy_config:
#### Example using existing `proxyConfigMap` instead of creating it:
```
proxyConfigMap:
create: false
@ -77,8 +78,7 @@ proxyConfigMap:
# proxy_config is ignored in this mode
```
#### Example `environmentSecrets` Secret
#### Example `environmentSecrets` Secret
```
apiVersion: v1
@ -91,21 +91,23 @@ type: Opaque
```
### Database Settings
| Name | Description | Value |
| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- |
| `db.useExisting` | Use an existing Postgres database. A Kubernetes Secret object must exist that contains credentials for connecting to the database. An example secret object definition is provided below. | `false` |
| `db.endpoint` | If `db.useExisting` is `true`, this is the IP, Hostname or Service Name of the Postgres server to connect to. | `localhost` |
| `db.database` | If `db.useExisting` is `true`, the name of the existing database to connect to. | `litellm` |
| `db.url` | If `db.useExisting` is `true`, the connection url of the existing database to connect to can be overwritten with this value. | `postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_HOST)/$(DATABASE_NAME)` |
| `db.secret.name` | If `db.useExisting` is `true`, the name of the Kubernetes Secret that contains credentials. | `postgres` |
| `db.secret.usernameKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the username for authenticating with the Postgres instance. | `username` |
| `db.secret.passwordKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the password associates with the above user. | `password` |
| `db.useStackgresOperator` | Not yet implemented. | `false` |
| `db.deployStandalone` | Deploy a standalone, single instance deployment of Postgres, using the Bitnami postgresql chart. This is useful for getting started but doesn't provide HA or (by default) data backups. | `true` |
| `postgresql.*` | If `db.deployStandalone` is `true`, configuration passed to the Bitnami postgresql chart. See the [Bitnami Documentation](https://github.com/bitnami/charts/tree/main/bitnami/postgresql) for full configuration details. See [values.yaml](./values.yaml) for the default configuration. | See [values.yaml](./values.yaml) |
| `postgresql.auth.*` | If `db.deployStandalone` is `true`, care should be taken to ensure the default `password` and `postgres-password` values are **NOT** used. | `NoTaGrEaTpAsSwOrD` |
| Name | Description | Value |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
| `db.useExisting` | Use an existing Postgres database. A Kubernetes Secret object must exist that contains credentials for connecting to the database. An example secret object definition is provided below. | `false` |
| `db.endpoint` | If `db.useExisting` is `true`, this is the IP, Hostname or Service Name of the Postgres server to connect to. | `localhost` |
| `db.database` | If `db.useExisting` is `true`, the name of the existing database to connect to. | `litellm` |
| `db.url` | If `db.useExisting` is `true`, the connection url of the existing database to connect to can be overwritten with this value. | `postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_HOST)/$(DATABASE_NAME)` |
| `db.secret.name` | If `db.useExisting` is `true`, the name of the Kubernetes Secret that contains credentials. | `postgres` |
| `db.secret.usernameKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the username for authenticating with the Postgres instance. | `username` |
| `db.secret.passwordKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the password associates with the above user. | `password` |
| `db.useStackgresOperator` | Not yet implemented. | `false` |
| `db.deployStandalone` | Deploy a standalone, single instance deployment of Postgres, using the Bitnami postgresql chart. This is useful for getting started but doesn't provide HA or (by default) data backups. | `true` |
| `postgresql.*` | If `db.deployStandalone` is `true`, configuration passed to the Bitnami postgresql chart. See the [Bitnami Documentation](https://github.com/bitnami/charts/tree/main/bitnami/postgresql) for full configuration details. See [values.yaml](./values.yaml) for the default configuration. | See [values.yaml](./values.yaml) |
| `postgresql.auth.*` | If `db.deployStandalone` is `true`, care should be taken to ensure the default `password` and `postgres-password` values are **NOT** used. | `NoTaGrEaTpAsSwOrD` |
#### Example Postgres `db.useExisting` Secret
```yaml
apiVersion: v1
kind: Secret
@ -143,7 +145,7 @@ metadata:
name: litellm-env-secret
type: Opaque
data:
SOME_PASSWORD: cDZbUGVXeU5e0ZW # base64 encoded
SOME_PASSWORD: cDZbUGVXeU5e0ZW # base64 encoded
ANOTHER_PASSWORD: AAZbUGVXeU5e0ZB # base64 encoded
```
@ -153,23 +155,23 @@ Source: [GitHub Gist from troyharvey](https://gist.github.com/troyharvey/4506472
The migration job supports both ArgoCD and Helm hooks to ensure database migrations run at the appropriate time during deployments.
| Name | Description | Value |
| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- |
| `migrationJob.enabled` | Enable or disable the schema migration Job | `true` |
| `migrationJob.backoffLimit` | Backoff limit for Job restarts | `4` |
| `migrationJob.ttlSecondsAfterFinished` | TTL for completed migration jobs | `120` |
| `migrationJob.annotations` | Additional annotations for the migration job pod | `{}` |
| `migrationJob.extraContainers` | Additional containers to run alongside the migration job | `[]` |
| `migrationJob.hooks.argocd.enabled` | Enable ArgoCD hooks for the migration job (uses PreSync hook with BeforeHookCreation delete policy) | `true` |
| `migrationJob.hooks.helm.enabled` | Enable Helm hooks for the migration job (uses pre-install,pre-upgrade hooks with before-hook-creation delete policy) | `false` |
| `migrationJob.hooks.helm.weight` | Helm hook execution order (lower weights executed first). Optional - defaults to "1" if not specified. | N/A |
| Name | Description | Value |
| -------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | ------- |
| `migrationJob.enabled` | Enable or disable the schema migration Job | `true` |
| `migrationJob.backoffLimit` | Backoff limit for Job restarts | `4` |
| `migrationJob.ttlSecondsAfterFinished` | TTL for completed migration jobs | `120` |
| `migrationJob.annotations` | Additional annotations for the migration job pod | `{}` |
| `migrationJob.extraContainers` | Additional containers to run alongside the migration job | `[]` |
| `migrationJob.hooks.argocd.enabled` | Enable ArgoCD hooks for the migration job (uses PreSync hook with BeforeHookCreation delete policy) | `true` |
| `migrationJob.hooks.helm.enabled` | Enable Helm hooks for the migration job (uses pre-install,pre-upgrade hooks with before-hook-creation delete policy) | `false` |
| `migrationJob.hooks.helm.weight` | Helm hook execution order (lower weights executed first). Optional - defaults to "1" if not specified. | N/A |
## Accessing the Admin UI
When browsing to the URL published per the settings in `ingress.*`, you will
be prompted for **Admin Configuration**. The **Proxy Endpoint** is the internal
be prompted for **Admin Configuration**. The **Proxy Endpoint** is the internal
(from the `litellm` pod's perspective) URL published by the `<RELEASE>-litellm`
Kubernetes Service. If the deployment uses the default settings for this
Kubernetes Service. If the deployment uses the default settings for this
service, the **Proxy Endpoint** should be set to `http://<RELEASE>-litellm:4000`.
The **Proxy Key** is the value specified for `masterkey` or, if a `masterkey`
@ -181,7 +183,8 @@ kubectl -n litellm get secret <RELEASE>-litellm-masterkey -o jsonpath="{.data.ma
```
## Admin UI Limitations
At the time of writing, the Admin UI is unable to add models. This is because
At the time of writing, the Admin UI is unable to add models. This is because
it would need to update the `config.yaml` file which is a exposed ConfigMap, and
therefore, read-only. This is a limitation of this helm chart, not the Admin UI
therefore, read-only. This is a limitation of this helm chart, not the Admin UI
itself.

10
deploy/charts/litellm-helm/templates/deployment.yaml
View file

@ -6,6 +6,9 @@ metadata:
name: {{ include "litellm.fullname" . }}
labels:
{{- include "litellm.labels" . | nindent 4 }}
{{- if .Values.deploymentLabels }}
{{- toYaml .Values.deploymentLabels | nindent 4 }}
{{- end }}
spec:
{{- if not .Values.autoscaling.enabled }}
replicas: {{ .Values.replicaCount }}
@ -126,6 +129,12 @@ spec:
- configMapRef:
name: {{ . }}
{{- end }}
{{- if .Values.command }}
command: {{ toYaml .Values.command | nindent 12 }}
{{- end }}
{{- if .Values.args }}
args: {{ toYaml .Values.args | nindent 12 }}
{{- else }}
args:
- --config
- /etc/litellm/config.yaml
@ -133,6 +142,7 @@ spec:
- --num_workers
- {{ .Values.numWorkers | quote }}
{{- end }}
{{- end }}
ports:
- name: http
containerPort: {{ .Values.service.port }}

3
deploy/charts/litellm-helm/templates/ingress.yaml
View file

@ -18,6 +18,9 @@ metadata:
name: {{ $fullName }}
labels:
{{- include "litellm.labels" . | nindent 4 }}
{{- with .Values.ingress.labels }}
{{- toYaml . | nindent 4 }}
{{- end }}
{{- with .Values.ingress.annotations }}
annotations:
{{- toYaml . | nindent 4 }}

68
deploy/charts/litellm-helm/tests/deployment_command_args_labels_tests.yaml Normal file
View file

@ -0,0 +1,68 @@
suite: test deployment command, args, and deploymentLabels
templates:
- deployment.yaml
- configmap-litellm.yaml
tests:
- it: should override args when custom args specified
template: deployment.yaml
set:
args:
- --custom-arg1
- value1
- --custom-arg2
asserts:
- equal:
path: spec.template.spec.containers[0].args
value:
- --custom-arg1
- value1
- --custom-arg2
- it: should set custom command when specified
template: deployment.yaml
set:
command:
- /bin/sh
- -c
asserts:
- equal:
path: spec.template.spec.containers[0].command
value:
- /bin/sh
- -c
- it: should set custom command and args together
template: deployment.yaml
set:
command:
- python
- -u
args:
- my_script.py
- --verbose
asserts:
- equal:
path: spec.template.spec.containers[0].command
value:
- python
- -u
- equal:
path: spec.template.spec.containers[0].args
value:
- my_script.py
- --verbose
- it: should add deploymentLabels to deployment metadata
template: deployment.yaml
set:
deploymentLabels:
environment: production
team: platform
version: v1.2.3
asserts:
- equal:
path: metadata.labels.environment
value: production
- equal:
path: metadata.labels.team
value: platform
- equal:
path: metadata.labels.version
value: v1.2.3

45
deploy/charts/litellm-helm/tests/ingress_tests.yaml Normal file
View file

@ -0,0 +1,45 @@
suite: Ingress Configuration Tests
templates:
- ingress.yaml
tests:
- it: should not create Ingress by default
asserts:
- hasDocuments:
count: 0
- it: should create Ingress when enabled
set:
ingress.enabled: true
asserts:
- hasDocuments:
count: 1
- isKind:
of: Ingress
- it: should add custom labels
set:
ingress.enabled: true
ingress.labels:
custom-label: "true"
another-label: "value"
asserts:
- isKind:
of: Ingress
- equal:
path: metadata.labels.custom-label
value: "true"
- equal:
path: metadata.labels.another-label
value: "value"
- it: should add annotations
set:
ingress.enabled: true
ingress.annotations:
kubernetes.io/ingress.class: "nginx"
asserts:
- isKind:
of: Ingress
- equal:
path: metadata.annotations["kubernetes.io/ingress.class"]
value: "nginx"

51
deploy/charts/litellm-helm/values.yaml
View file

@ -30,12 +30,14 @@ serviceAccount:
# annotations for litellm deployment
deploymentAnnotations: {}
deploymentLabels: {}
# annotations for litellm pods
podAnnotations: {}
podLabels: {}
terminationGracePeriodSeconds: 90
topologySpreadConstraints: []
topologySpreadConstraints:
[]
# - maxSkew: 1
# topologyKey: kubernetes.io/hostname
# whenUnsatisfiable: DoNotSchedule
@ -46,7 +48,8 @@ topologySpreadConstraints: []
# At the time of writing, the litellm docker image requires write access to the
# filesystem on startup so that prisma can install some dependencies.
podSecurityContext: {}
securityContext: {}
securityContext:
{}
# capabilities:
# drop:
# - ALL
@ -57,13 +60,15 @@ securityContext: {}
# A list of Kubernetes Secret objects that will be exported to the LiteLLM proxy
# pod as environment variables. These secrets can then be referenced in the
# configuration file (or "litellm" ConfigMap) with `os.environ/<Env Var Name>`
environmentSecrets: []
environmentSecrets:
[]
# - litellm-env-secret
# A list of Kubernetes ConfigMap objects that will be exported to the LiteLLM proxy
# pod as environment variables. The ConfigMap kv-pairs can then be referenced in the
# configuration file (or "litellm" ConfigMap) with `os.environ/<Env Var Name>`
environmentConfigMaps: []
environmentConfigMaps:
[]
# - litellm-env-configmap
service:
@ -82,7 +87,9 @@ separateHealthPort: 8081
ingress:
enabled: false
className: "nginx"
annotations: {}
labels: {}
annotations:
{}
# kubernetes.io/ingress.class: nginx
# kubernetes.io/tls-acme: "true"
hosts:
@ -129,7 +136,8 @@ proxy_config:
general_settings:
master_key: os.environ/PROXY_MASTER_KEY
resources: {}
resources:
{}
# We usually recommend not to specify default resources and to leave this as a conscious
# choice for the user. This also increases chances charts run on environments with little
# resources, such as Minikube. If you do want to specify resources, uncomment the following
@ -231,7 +239,7 @@ migrationJob:
# cpu: 100m
# memory: 100Mi
extraContainers: []
# Hook configuration
hooks:
argocd:
@ -240,30 +248,35 @@ migrationJob:
enabled: false
# Additional environment variables to be added to the deployment as a map of key-value pairs
envVars: {
# USE_DDTRACE: "true"
}
envVars: {}
# USE_DDTRACE: "true"
# Additional environment variables to be added to the deployment as a list of k8s env vars
extraEnvVars: {
# - name: EXTRA_ENV_VAR
# value: EXTRA_ENV_VAR_VALUE
}
extraEnvVars: {}
# if you want to override the container command, you can do so here
command: {}
# if you want to override the container args, you can do so here
args: {}
# - name: EXTRA_ENV_VAR
# value: EXTRA_ENV_VAR_VALUE
# Pod Disruption Budget
pdb:
enabled: false
# Set exactly one of the following. If both are set, minAvailable takes precedence.
minAvailable: null # e.g. "50%" or 1
maxUnavailable: null # e.g. 1 or "20%"
minAvailable: null # e.g. "50%" or 1
maxUnavailable: null # e.g. 1 or "20%"
annotations: {}
labels: {}
serviceMonitor:
enabled: false
labels: {}
labels:
{}
# test: test
annotations: {}
annotations:
{}
# kubernetes.io/test: test
interval: 15s
scrapeTimeout: 10s
@ -273,4 +286,4 @@ serviceMonitor:
# action: replace
namespaceSelector:
matchNames: []
# - test-namespace
# - test-namespace

16
docker/Dockerfile.database
View file

@ -1,8 +1,8 @@
# Base image for building
ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev
ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base
# Runtime image
ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev
ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base
# Builder stage
FROM $LITELLM_BUILD_IMAGE AS builder
@ -13,13 +13,15 @@ USER root
# Install build dependencies
RUN apk add --no-cache \
build-base \
bash \
gcc \
py3-pip \
python3 \
python3-dev \
openssl \
openssl-dev
RUN pip install --upgrade pip && \
pip install build
RUN python -m pip install build
# Copy the current directory contents into the container at /app
COPY . .
@ -46,7 +48,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
USER root
# Install runtime dependencies
RUN apk add --no-cache openssl
RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip
WORKDIR /app
# Copy the current directory contents into the container at /app

41
docker/Dockerfile.non_root
View file

@ -1,6 +1,6 @@
# Base images
ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev
ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev
ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base
ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base
# -----------------
# Builder Stage
@ -10,7 +10,20 @@ WORKDIR /app
# Install build dependencies including Node.js for UI build
USER root
RUN apk add --no-cache build-base bash nodejs npm \
RUN for i in 1 2 3; do \
apk add --no-cache \
python3 \
py3-pip \
clang \
llvm \
lld \
gcc \
linux-headers \
build-base \
bash \
nodejs \
npm && break || sleep 5; \
done \
&& pip install --no-cache-dir --upgrade pip build
# Copy project files
@ -26,7 +39,7 @@ RUN npm install -g npm@latest && npm cache clean --force
RUN cd /app/ui/litellm-dashboard && \
if [ -f "/app/enterprise/enterprise_ui/enterprise_colors.json" ]; then \
cp /app/enterprise/enterprise_ui/enterprise_colors.json ./ui_colors.json; \
cp /app/enterprise/enterprise_ui/enterprise_colors.json ./ui_colors.json; \
fi
RUN cd /app/ui/litellm-dashboard && rm -f package-lock.json
@ -40,11 +53,11 @@ RUN mkdir -p /tmp/litellm_assets && cp /app/litellm/proxy/logo.jpg /tmp/litellm_
RUN cd /tmp/litellm_ui && \
for html_file in *.html; do \
if [ "$html_file" != "index.html" ] && [ -f "$html_file" ]; then \
folder_name="${html_file%.html}" && \
mkdir -p "$folder_name" && \
mv "$html_file" "$folder_name/index.html"; \
fi; \
if [ "$html_file" != "index.html" ] && [ -f "$html_file" ]; then \
folder_name="${html_file%.html}" && \
mkdir -p "$folder_name" && \
mv "$html_file" "$folder_name/index.html"; \
fi; \
done
RUN cd /app/ui/litellm-dashboard && rm -rf ./out
@ -62,8 +75,12 @@ WORKDIR /app
# Install runtime dependencies
USER root
RUN apk upgrade --no-cache && \
apk add --no-cache bash libstdc++ ca-certificates openssl supervisor
RUN for i in 1 2 3; do \
apk upgrade --no-cache && break || sleep 5; \
done \
&& for i in 1 2 3; do \
apk add --no-cache python3 py3-pip bash openssl tzdata nodejs npm supervisor && break || sleep 5; \
done
# Copy only necessary artifacts from builder stage for runtime
COPY . .
@ -82,7 +99,7 @@ RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ \
# Remove test files and keys from dependencies
RUN find /usr/lib -type f -path "*/tornado/test/*" -delete && \
find /usr/lib -type d -path "*/tornado/test" -delete
find /usr/lib -type d -path "*/tornado/test" -delete
# Install semantic_router and aurelio-sdk using script
RUN chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh

8
docker/build_from_pip/Dockerfile.build_from_pip
View file

@ -1,14 +1,16 @@
FROM cgr.dev/chainguard/python:latest-dev
FROM python:3.13-alpine
USER root
WORKDIR /app
ENV HOME=/home/litellm
ENV PATH="${HOME}/venv/bin:$PATH"
# Install runtime dependencies
# Note: Using Python 3.13 for compatibility with ddtrace and other packages
# rust and cargo are required for building ddtrace from source
# musl-dev and libffi-dev are needed for some Python packages on Alpine
RUN apk update && \
apk add --no-cache gcc python3-dev openssl openssl-dev
apk add --no-cache gcc musl-dev libffi-dev openssl openssl-dev rust cargo
RUN python -m venv ${HOME}/venv
RUN ${HOME}/venv/bin/pip install --no-cache-dir --upgrade pip

378
docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md
View file

@ -33,7 +33,7 @@ This guide covers Anthropic's latest model (Claude Opus 4.5) and its advanced fe
| Input Examples | Claude Opus 4.5, Sonnet 4.5 |
| Effort Parameter | Claude Opus 4.5 only |
Supported Providers: [Anthropic](../../docs/providers/anthropic), [Bedrock](../../docs/providers/bedrock), [Vertex AI](../../docs/providers/vertex_partner#vertex-ai---anthropic-claude).
Supported Providers: [Anthropic](../../docs/providers/anthropic), [Bedrock](../../docs/providers/bedrock), [Vertex AI](../../docs/providers/vertex_partner#vertex-ai---anthropic-claude), [Azure AI](../../docs/providers/azure_ai).
## Usage
@ -327,6 +327,81 @@ curl --location 'http://0.0.0.0:4000/v1/messages' \
</TabItem>
</Tabs>
## Usage - Azure Anthropic (Azure Foundry Claude)
LiteLLM funnels Azure Claude deployments through the `azure_ai/` provider so Claude Opus models on Azure Foundry keep working with Tool Search, Effort, streaming, and the rest of the advanced feature set. Point `AZURE_AI_API_BASE` to `https://<resource>.services.ai.azure.com/anthropic` (LiteLLM appends `/v1/messages` automatically) and authenticate with `AZURE_AI_API_KEY` or an Azure AD token.
<Tabs>
<TabItem value="sdk" label="LiteLLM Python SDK">
```python
import os
from litellm import completion
# Configure Azure credentials
os.environ["AZURE_AI_API_KEY"] = "your-azure-ai-api-key"
os.environ["AZURE_AI_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic"
response = completion(
model="azure_ai/claude-opus-4-1",
messages=[{"role": "user", "content": "Explain how Azure Anthropic hosts Claude Opus differently from the public Anthropic API."}],
max_tokens=1200,
temperature=0.7,
stream=True,
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Set environment variables**
```bash
export AZURE_AI_API_KEY="your-azure-ai-api-key"
export AZURE_AI_API_BASE="https://my-resource.services.ai.azure.com/anthropic"
```
**2. Configure the proxy**
```yaml
model_list:
- model_name: claude-4-azure
litellm_params:
model: azure_ai/claude-opus-4-1
api_key: os.environ/AZURE_AI_API_KEY
api_base: os.environ/AZURE_AI_API_BASE
```
**3. Start LiteLLM**
```bash
litellm --config /path/to/config.yaml
```
**4. Test the Azure Claude route**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-4-azure",
"messages": [
{
"role": "user",
"content": "How do I use Claude Opus 4 via Azure Anthropic in LiteLLM?"
}
],
"max_tokens": 1024
}'
```
</TabItem>
</Tabs>
## Tool Search {#tool-search}
@ -897,14 +972,13 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
## Effort Parameter: Control Token Usage {#effort-parameter}
Controls aspects like how much effort the model puts into its response, via `output_config={"effort": ..}`.
Control how much effort Claude puts into its response using the `reasoning_effort` parameter. This allows you to trade off between response thoroughness and token efficiency.
:::info
Soon, we will map OpenAI's `reasoning_effort` parameter to this.
LiteLLM automatically maps `reasoning_effort` to Anthropic's `output_config` format and adds the required `effort-2025-11-24` beta header for Claude Opus 4.5.
:::
Potential Values for `effort` parameter: `"high"`, `"medium"`, `"low"`.
Potential values for `reasoning_effort` parameter: `"high"`, `"medium"`, `"low"`.
### Usage Example
@ -920,7 +994,7 @@ message = "Analyze the trade-offs between microservices and monolithic architect
response_high = litellm.completion(
model="anthropic/claude-opus-4-5-20251101",
messages=[{"role": "user", "content": message}],
output_config={"effort": "high"}
reasoning_effort="high"
)
print("High effort response:")
@ -931,7 +1005,7 @@ print(f"Tokens used: {response_high.usage.completion_tokens}\n")
response_medium = litellm.completion(
model="anthropic/claude-opus-4-5-20251101",
messages=[{"role": "user", "content": message}],
output_config={"effort": "medium"}
reasoning_effort="medium"
)
print("Medium effort response:")
@ -942,7 +1016,7 @@ print(f"Tokens used: {response_medium.usage.completion_tokens}\n")
response_low = litellm.completion(
model="anthropic/claude-opus-4-5-20251101",
messages=[{"role": "user", "content": message}],
output_config={"effort": "low"}
reasoning_effort="low"
)
print("Low effort response:")
@ -987,295 +1061,9 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
"role": "user",
"content": "Analyze the trade-offs between microservices and monolithic architectures"
}],
"output_config": {
"effort": "high"
}
"reasoning_effort": "high"
}
'
```
</TabItem>
</Tabs>
## Cost Tracking: Monitor Tool Search Usage {#cost-tracking}
### Understanding Tool Search Costs
Tool search operations are tracked separately in the usage object, allowing you to monitor and optimize costs.
It is available in the `usage` object, under `server_tool_use.tool_search_requests`.
Anthropic charges $0.0001 per tool search request.
### Tracking Example
<Tabs>
<TabItem value="sdk" label="LiteLLM Python SDK">
```python
import litellm
tools = [
{
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex"
},
# ... 100 deferred tools
]
response = litellm.completion(
model="anthropic/claude-sonnet-4-5-20250929",
messages=[{
"role": "user",
"content": "Find and use the weather tool for San Francisco"
}],
tools=tools
)
# Standard token usage
print("Token Usage:")
print(f" Input tokens: {response.usage.prompt_tokens}")
print(f" Output tokens: {response.usage.completion_tokens}")
print(f" Total tokens: {response.usage.total_tokens}")
# Tool search specific usage
if hasattr(response.usage, 'server_tool_use') and response.usage.server_tool_use:
print(f"\nTool Search Usage:")
print(f" Search requests: {response.usage.server_tool_use.tool_search_requests}")
# Calculate cost (example pricing)
input_cost = response.usage.prompt_tokens * 0.000003 # $3 per 1M tokens
output_cost = response.usage.completion_tokens * 0.000015 # $15 per 1M tokens
search_cost = response.usage.server_tool_use.tool_search_requests * 0.0001 # Example
total_cost = input_cost + output_cost + search_cost
print(f"\nCost Breakdown:")
print(f" Input tokens: ${input_cost:.6f}")
print(f" Output tokens: ${output_cost:.6f}")
print(f" Tool searches: ${search_cost:.6f}")
print(f" Total: ${total_cost:.6f}")
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">
1. Setup config.yaml
```yaml
model_list:
- model_name: claude-4
litellm_params:
model: anthropic/claude-opus-4-5-20251101
api_key: os.environ/ANTHROPIC_API_KEY
```
2. Start the proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"model": "claude-4",
"messages": [{
"role": "user",
"content": "Find and use the weather tool for San Francisco"
}],
"tools": [
{
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex"
},
# ... 100 deferred tools
]
}
'
```
Expected Response:
```json
{
...,
"usage": {
...,
"server_tool_use": {
"tool_search_requests": 1
}
}
}
```
</TabItem>
</Tabs>
### Cost Optimization Tips
1. **Keep frequently used tools non-deferred** (3-5 tools)
2. **Use tool search for large catalogs** (10+ tools)
3. **Monitor search requests** to identify optimization opportunities
4. **Combine with effort parameter** for maximum efficiency
---
## Combining Features {#combining-features}
### The Power of Integration
These features work together seamlessly. Here's a real-world example combining all of them:
<Tabs>
<TabItem value="sdk" label="LiteLLM Python SDK">
```python
import litellm
import json
# Large tool catalog with search, programmatic calling, and examples
tools = [
# Enable tool search
{
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex"
},
# Enable programmatic calling
{
"type": "code_execution_20250825",
"name": "code_execution"
},
# Database tool with all features
{
"type": "function",
"function": {
"name": "query_database",
"description": "Execute SQL queries against the analytics database. Returns JSON array of results.",
"parameters": {
"type": "object",
"properties": {
"sql": {
"type": "string",
"description": "SQL SELECT statement"
},
"limit": {
"type": "integer",
"description": "Maximum rows to return"
}
},
"required": ["sql"]
}
},
"defer_loading": True, # Tool search
"allowed_callers": ["code_execution_20250825"], # Programmatic calling
"input_examples": [ # Input examples
{
"sql": "SELECT region, SUM(revenue) as total FROM sales GROUP BY region",
"limit": 100
}
]
},
# ... 50 more tools with defer_loading
]
# Make request with effort control
response = litellm.completion(
model="anthropic/claude-opus-4-5-20251101",
messages=[{
"role": "user",
"content": "Analyze sales by region for the last quarter and identify top performers"
}],
tools=tools,
output_config={"effort": "medium"} # Balanced efficiency
)
# Track comprehensive usage
print("Complete Usage Metrics:")
print(f" Input tokens: {response.usage.prompt_tokens}")
print(f" Output tokens: {response.usage.completion_tokens}")
print(f" Total tokens: {response.usage.total_tokens}")
if hasattr(response.usage, 'server_tool_use') and response.usage.server_tool_use:
print(f" Tool searches: {response.usage.server_tool_use.tool_search_requests}")
print(f"\nResponse: {response.choices[0].message.content}")
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">
1. Setup config.yaml
```yaml
model_list:
- model_name: claude-4
litellm_params:
model: anthropic/claude-opus-4-5-20251101
api_key: os.environ/ANTHROPIC_API_KEY
```
2. Start the proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"model": "claude-4",
"messages": [{
"role": "user",
"content": "Analyze sales by region for the last quarter and identify top performers"
}],
"tools": [
{
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex"
},
# ... 100 deferred tools
],
"output_config": {
"effort": "medium"
}
}
'
```
Expected Response:
```json
{
...,
"usage": {
...,
"server_tool_use": {
"tool_search_requests": 1
}
}
}
```
</TabItem>
</Tabs>
### Real-World Benefits
This combination enables:
1. **Massive scale** - Handle 1000+ tools efficiently
2. **Low latency** - Programmatic calling reduces round trips
3. **High accuracy** - Input examples ensure correct tool usage
4. **Cost control** - Effort parameter optimizes token spend
5. **Full visibility** - Track all usage metrics

232
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@ -0,0 +1,232 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Image from '@theme/IdealImage';
# Agent Gateway (A2A Protocol) - Overview
Add A2A Agents on LiteLLM AI Gateway, Invoke agents in A2A Protocol, track request/response logs in LiteLLM Logs. Manage which Teams, Keys can access which Agents onboarded.
<Image
img={require('../img/a2a_gateway.png')}
style={{width: '80%', display: 'block', margin: '0', borderRadius: '8px'}}
/>
<br />
<br />
| Feature | Supported |
|---------|-----------|
| Logging | ✅ |
| Load Balancing | ✅ |
| Streaming | ✅ |
:::tip
LiteLLM follows the [A2A (Agent-to-Agent) Protocol](https://github.com/google/A2A) for invoking agents.
:::
## Adding your Agent
You can add A2A-compatible agents through the LiteLLM Admin UI.
1. Navigate to the **Agents** tab
2. Click **Add Agent**
3. Enter the agent name (e.g., `ij-local`) and the URL of your A2A agent
<Image
img={require('../img/add_agent_1.png')}
style={{width: '80%', display: 'block', margin: '0'}}
/>
The URL should be the invocation URL for your A2A agent (e.g., `http://localhost:10001`).
## Invoking your Agents
Use the [A2A Python SDK](https://pypi.org/project/a2a/) to invoke agents through LiteLLM.
This example shows how to:
1. **List available agents** - Query `/v1/agents` to see which agents your key can access
2. **Select an agent** - Pick an agent from the list
3. **Invoke via A2A** - Use the A2A protocol to send messages to the agent
```python showLineNumbers title="invoke_a2a_agent.py"
from uuid import uuid4
import httpx
import asyncio
from a2a.client import A2ACardResolver, A2AClient
from a2a.types import MessageSendParams, SendMessageRequest
# === CONFIGURE THESE ===
LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL
LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key
# =======================
async def main():
headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"}
async with httpx.AsyncClient(headers=headers) as client:
# Step 1: List available agents
response = await client.get(f"{LITELLM_BASE_URL}/v1/agents")
agents = response.json()
print("Available agents:")
for agent in agents:
print(f" - {agent['agent_name']} (ID: {agent['agent_id']})")
if not agents:
print("No agents available for this key")
return
# Step 2: Select an agent and invoke it
selected_agent = agents[0]
agent_id = selected_agent["agent_id"]
agent_name = selected_agent["agent_name"]
print(f"\nInvoking: {agent_name}")
# Step 3: Use A2A protocol to invoke the agent
base_url = f"{LITELLM_BASE_URL}/a2a/{agent_id}"
resolver = A2ACardResolver(httpx_client=client, base_url=base_url)
agent_card = await resolver.get_agent_card()
a2a_client = A2AClient(httpx_client=client, agent_card=agent_card)
request = SendMessageRequest(
id=str(uuid4()),
params=MessageSendParams(
message={
"role": "user",
"parts": [{"kind": "text", "text": "Hello, what can you do?"}],
"messageId": uuid4().hex,
}
),
)
response = await a2a_client.send_message(request)
print(f"Response: {response.model_dump(mode='json', exclude_none=True, indent=4)}")
if __name__ == "__main__":
asyncio.run(main())
```
### Streaming Responses
For streaming responses, use `send_message_streaming`:
```python showLineNumbers title="invoke_a2a_agent_streaming.py"
from uuid import uuid4
import httpx
import asyncio
from a2a.client import A2ACardResolver, A2AClient
from a2a.types import MessageSendParams, SendStreamingMessageRequest
# === CONFIGURE THESE ===
LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL
LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key
LITELLM_AGENT_NAME = "ij-local" # Agent name registered in LiteLLM
# =======================
async def main():
base_url = f"{LITELLM_BASE_URL}/a2a/{LITELLM_AGENT_NAME}"
headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"}
async with httpx.AsyncClient(headers=headers) as httpx_client:
# Resolve agent card and create 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)
# Send a streaming message
request = SendStreamingMessageRequest(
id=str(uuid4()),
params=MessageSendParams(
message={
"role": "user",
"parts": [{"kind": "text", "text": "Hello, what can you do?"}],
"messageId": uuid4().hex,
}
),
)
# Stream the response
async for chunk in client.send_message_streaming(request):
print(chunk.model_dump(mode="json", exclude_none=True))
if __name__ == "__main__":
asyncio.run(main())
```
## Tracking Agent Logs
After invoking an agent, you can view the request logs in the LiteLLM **Logs** tab.
The logs show:
- **Request/Response content** sent to and received from the agent
- **User, Key, Team** information for tracking who made the request
- **Latency and cost** metrics
<Image
img={require('../img/agent2.png')}
style={{width: '100%', display: 'block', margin: '2rem auto'}}
/>
## API Reference
### Endpoint
```
POST /a2a/{agent_name}/message/send
```
### Authentication
Include your LiteLLM Virtual Key in the `Authorization` header:
```
Authorization: Bearer sk-your-litellm-key
```
### Request Format
LiteLLM follows the [A2A JSON-RPC 2.0 specification](https://github.com/google/A2A):
```json title="Request Body"
{
"jsonrpc": "2.0",
"id": "unique-request-id",
"method": "message/send",
"params": {
"message": {
"role": "user",
"parts": [{"kind": "text", "text": "Your message here"}],
"messageId": "unique-message-id"
}
}
}
```
### Response Format
```json title="Response"
{
"jsonrpc": "2.0",
"id": "unique-request-id",
"result": {
"kind": "task",
"id": "task-id",
"contextId": "context-id",
"status": {"state": "completed", "timestamp": "2025-01-01T00:00:00Z"},
"artifacts": [
{
"artifactId": "artifact-id",
"name": "response",
"parts": [{"kind": "text", "text": "Agent response here"}]
}
]
}
}
```
## Agent Registry
Want to create a central registry so your team can discover what agents are available within your company?
Use the [AI Hub](./proxy/ai_hub) to make agents public and discoverable across your organization. This allows developers to browse available agents without needing to rebuild them.

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@ -0,0 +1,259 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Image from '@theme/IdealImage';
# Agent Permission Management
Control which A2A agents can be accessed by specific keys or teams in LiteLLM.
## Overview
Agent Permission Management lets you restrict which agents a LiteLLM Virtual Key or Team can access. This is useful for:
- **Multi-tenant environments**: Give different teams access to different agents
- **Security**: Prevent keys from invoking agents they shouldn't have access to
- **Compliance**: Enforce access policies for sensitive agent workflows
When permissions are configured:
- `GET /v1/agents` only returns agents the key/team can access
- `POST /a2a/{agent_id}` (Invoking an agent) returns `403 Forbidden` if access is denied
## Setting Permissions on a Key
This example shows how to create a key with agent permissions and test access.
### 1. Get Your Agent ID
<Tabs>
<TabItem value="ui" label="UI">
1. Go to **Agents** in the sidebar
2. Click into the agent you want
3. Copy the **Agent ID**
<Image
img={require('../img/agent_id.png')}
style={{width: '80%', display: 'block', margin: '0', borderRadius: '8px'}}
/>
</TabItem>
<TabItem value="api" label="API">
```bash title="List all agents" showLineNumbers
curl "http://localhost:4000/v1/agents" \
-H "Authorization: Bearer sk-master-key"
```
Response:
```json title="Response" showLineNumbers
{
"agents": [
{"agent_id": "agent-123", "name": "Support Agent"},
{"agent_id": "agent-456", "name": "Sales Agent"}
]
}
```
</TabItem>
</Tabs>
### 2. Create a Key with Agent Permissions
<Tabs>
<TabItem value="ui" label="UI">
1. Go to **Keys** → **Create Key**
2. Expand **Agent Settings**
3. Select the agents you want to allow
<Image
img={require('../img/agent_key.png')}
style={{width: '80%', display: 'block', margin: '0', borderRadius: '8px'}}
/>
</TabItem>
<TabItem value="api" label="API">
```bash title="Create key with agent permissions" showLineNumbers
curl -X POST "http://localhost:4000/key/generate" \
-H "Authorization: Bearer sk-master-key" \
-H "Content-Type: application/json" \
-d '{
"object_permission": {
"agents": ["agent-123"]
}
}'
```
</TabItem>
</Tabs>
### 3. Test Access
**Allowed agent (succeeds):**
```bash title="Invoke allowed agent" showLineNumbers
curl -X POST "http://localhost:4000/a2a/agent-123" \
-H "Authorization: Bearer sk-your-new-key" \
-H "Content-Type: application/json" \
-d '{"message": {"role": "user", "parts": [{"type": "text", "text": "Hello"}]}}'
```
**Blocked agent (fails with 403):**
```bash title="Invoke blocked agent" showLineNumbers
curl -X POST "http://localhost:4000/a2a/agent-456" \
-H "Authorization: Bearer sk-your-new-key" \
-H "Content-Type: application/json" \
-d '{"message": {"role": "user", "parts": [{"type": "text", "text": "Hello"}]}}'
```
Response:
```json title="403 Forbidden Response" showLineNumbers
{
"error": {
"message": "Access denied to agent: agent-456",
"code": 403
}
}
```
## Setting Permissions on a Team
Restrict all keys belonging to a team to only access specific agents.
### 1. Create a Team with Agent Permissions
<Tabs>
<TabItem value="ui" label="UI">
1. Go to **Teams** → **Create Team**
2. Expand **Agent Settings**
3. Select the agents you want to allow for this team
<Image
img={require('../img/agent_key.png')}
style={{width: '80%', display: 'block', margin: '0', borderRadius: '8px'}}
/>
</TabItem>
<TabItem value="api" label="API">
```bash title="Create team with agent permissions" showLineNumbers
curl -X POST "http://localhost:4000/team/new" \
-H "Authorization: Bearer sk-master-key" \
-H "Content-Type: application/json" \
-d '{
"team_alias": "support-team",
"object_permission": {
"agents": ["agent-123"]
}
}'
```
Response:
```json title="Response" showLineNumbers
{
"team_id": "team-abc-123",
"team_alias": "support-team"
}
```
</TabItem>
</Tabs>
### 2. Create a Key for the Team
<Tabs>
<TabItem value="ui" label="UI">
1. Go to **Keys** → **Create Key**
2. Select the **Team** from the dropdown
<Image
img={require('../img/agent_team.png')}
style={{width: '80%', display: 'block', margin: '0', borderRadius: '8px'}}
/>
</TabItem>
<TabItem value="api" label="API">
```bash title="Create key for team" showLineNumbers
curl -X POST "http://localhost:4000/key/generate" \
-H "Authorization: Bearer sk-master-key" \
-H "Content-Type: application/json" \
-d '{
"team_id": "team-abc-123"
}'
```
</TabItem>
</Tabs>
### 3. Test Access
The key inherits agent permissions from the team.
**Allowed agent (succeeds):**
```bash title="Invoke allowed agent" showLineNumbers
curl -X POST "http://localhost:4000/a2a/agent-123" \
-H "Authorization: Bearer sk-team-key" \
-H "Content-Type: application/json" \
-d '{"message": {"role": "user", "parts": [{"type": "text", "text": "Hello"}]}}'
```
**Blocked agent (fails with 403):**
```bash title="Invoke blocked agent" showLineNumbers
curl -X POST "http://localhost:4000/a2a/agent-456" \
-H "Authorization: Bearer sk-team-key" \
-H "Content-Type: application/json" \
-d '{"message": {"role": "user", "parts": [{"type": "text", "text": "Hello"}]}}'
```
## How It Works
```mermaid
flowchart TD
A[Request to invoke agent] --> B{LiteLLM Virtual Key has agent restrictions?}
B -->|Yes| C{LiteLLM Team has agent restrictions?}
B -->|No| D{LiteLLM Team has agent restrictions?}
C -->|Yes| E[Use intersection of key + team permissions]
C -->|No| F[Use key permissions only]
D -->|Yes| G[Inherit team permissions]
D -->|No| H[Allow ALL agents]
E --> I{Agent in allowed list?}
F --> I
G --> I
H --> J[Allow request]
I -->|Yes| J
I -->|No| K[Return 403 Forbidden]
```
| Key Permissions | Team Permissions | Result | Notes |
|-----------------|------------------|--------|-------|
| None | None | Key can access **all** agents | Open access by default when no restrictions are set |
| `["agent-1", "agent-2"]` | None | Key can access `agent-1` and `agent-2` | Key uses its own permissions |
| None | `["agent-1", "agent-3"]` | Key can access `agent-1` and `agent-3` | Key inherits team's permissions |
| `["agent-1", "agent-2"]` | `["agent-1", "agent-3"]` | Key can access `agent-1` only | Intersection of both lists (most restrictive wins) |
## Viewing Permissions
<Tabs>
<TabItem value="ui" label="UI">
1. Go to **Keys** or **Teams**
2. Click into the key/team you want to view
3. Agent permissions are displayed in the info view
</TabItem>
<TabItem value="api" label="API">
```bash title="Get key info" showLineNumbers
curl "http://localhost:4000/key/info?key=sk-your-key" \
-H "Authorization: Bearer sk-master-key"
```
</TabItem>
</Tabs>

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@ -0,0 +1,373 @@
# [BETA] Generic Guardrail API - Integrate Without a PR
## The Problem
As a guardrail provider, integrating with LiteLLM traditionally requires:
- Making a PR to the LiteLLM repository
- Waiting for review and merge
- Maintaining provider-specific code in LiteLLM's codebase
- Updating the integration for changes to your API
## The Solution
The **Generic Guardrail API** lets you integrate with LiteLLM **instantly** by implementing a simple API endpoint. No PR required.
### Key Benefits
1. **No PR Needed** - Deploy and integrate immediately
2. **Universal Support** - Works across ALL LiteLLM endpoints (chat, embeddings, image generation, etc.)
3. **Simple Contract** - One endpoint, three response types
4. **Multi-Modal Support** - Handle both text and images in requests/responses
5. **Custom Parameters** - Pass provider-specific params via config
6. **Full Control** - You own and maintain your guardrail API
## Supported Endpoints
The Generic Guardrail API works with the following LiteLLM endpoints:
- `/v1/chat/completions` - OpenAI Chat Completions
- `/v1/completions` - OpenAI Text Completions
- `/v1/responses` - OpenAI Responses API
- `/v1/images/generations` - OpenAI Image Generation
- `/v1/audio/transcriptions` - OpenAI Audio Transcriptions
- `/v1/audio/speech` - OpenAI Text-to-Speech
- `/v1/messages` - Anthropic Messages
- `/v1/rerank` - Cohere Rerank
- Pass-through endpoints
## How It Works
1. LiteLLM extracts text and images from any request (chat messages, embeddings, image prompts, etc.)
2. Sends extracted content + metadata to your API endpoint
3. Your API responds with: `BLOCKED`, `NONE`, or `GUARDRAIL_INTERVENED`
4. LiteLLM enforces the decision and applies any modifications
## API Contract
### Endpoint
Implement `POST /beta/litellm_basic_guardrail_api`
### Request Format
```json
{
"texts": ["extracted text from the request"], // array of text strings
"images": ["base64_encoded_image_data"], // optional array of images
"tools": [ // tool calls sent to the LLM (in the OpenAI Chat Completions spec)
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
}
}
}
}
],
"tool_calls": [ // tool calls received from the LLM (in the OpenAI Chat Completions spec)
{
"id": "call_abc123",
"type": "function",
"function": {
"name": "get_weather",
"arguments": "{\"location\": \"San Francisco\"}"
}
}
],
"structured_messages": [ // optional, full messages in OpenAI format (for chat endpoints)
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Hello"}
],
"request_data": {
"user_api_key_hash": "hash of the litellm virtual key used",
"user_api_key_alias": "alias of the litellm virtual key used",
"user_api_key_user_id": "user id associated with the litellm virtual key used",
"user_api_key_user_email": "user email associated with the litellm virtual key used",
"user_api_key_team_id": "team id associated with the litellm virtual key used",
"user_api_key_team_alias": "team alias associated with the litellm virtual key used",
"user_api_key_end_user_id": "end user id associated with the litellm virtual key used",
"user_api_key_org_id": "org id associated with the litellm virtual key used"
},
"input_type": "request", // "request" or "response"
"litellm_call_id": "unique_call_id", // the call id of the individual LLM call
"litellm_trace_id": "trace_id", // the trace id of the LLM call - useful if there are multiple LLM calls for the same conversation
"additional_provider_specific_params": {
// your custom params from config
}
}
```
### Response Format
```json
{
"action": "BLOCKED" | "NONE" | "GUARDRAIL_INTERVENED",
"blocked_reason": "why content was blocked", // required if action=BLOCKED
"texts": ["modified text"], // optional array of modified text strings
"images": ["modified_base64_image"] // optional array of modified images
}
```
**Actions:**
- `BLOCKED` - LiteLLM raises error and blocks request
- `NONE` - Request proceeds unchanged
- `GUARDRAIL_INTERVENED` - Request proceeds with modified texts/images (provide `texts` and/or `images` fields)
## Parameters
### `tools` Parameter
The `tools` parameter provides information about available function/tool definitions in the request.
**Format:** OpenAI `ChatCompletionToolParam` format (see [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools))
**Example:**
```json
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"]
}
},
"required": ["location"]
}
}
}
```
**Availability:**
- **Input only:** Tools are only passed for `input_type="request"` (pre-call guardrails). Output/response guardrails do not currently receive tool definitions.
- **Supported endpoints:** The `tools` parameter is supported on: `/v1/chat/completions`, `/v1/responses`, and `/v1/messages`. Other endpoints do not have tool support.
**Use cases:**
- Enforce tool permission policies (e.g., only allow certain users/teams to access specific tools)
- Validate tool schemas before sending to LLM
- Log tool usage for audit purposes
- Block sensitive tools based on user context
### `tool_calls` Parameter
The `tool_calls` parameter contains actual function/tool invocations being made in the request or response.
**Format:** OpenAI `ChatCompletionMessageToolCall` format (see [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/object#chat/object-tool_calls))
**Example:**
```json
{
"id": "call_abc123",
"type": "function",
"function": {
"name": "get_weather",
"arguments": "{\"location\": \"San Francisco\", \"unit\": \"celsius\"}"
}
}
```
**Key Difference from `tools`:**
- **`tools`** = Tool definitions/schemas (what tools are *available*)
- **`tool_calls`** = Tool invocations/executions (what tools are *being called* with what arguments)
**Availability:**
- **Both input and output:** Tool calls can be present in both `input_type="request"` (assistant messages requesting tool calls) and `input_type="response"` (LLM responses with tool calls).
- **Supported endpoints:** The `tool_calls` parameter is supported on: `/v1/chat/completions`, `/v1/responses`, and `/v1/messages`.
**Use cases:**
- Validate tool call arguments before execution
- Redact sensitive data from tool call arguments (e.g., PII)
- Log tool invocations for audit/debugging
- Block tool calls with dangerous parameters
- Modify tool call arguments (e.g., enforce constraints, sanitize inputs)
- Monitor tool usage patterns across users/teams
### `structured_messages` Parameter
The `structured_messages` parameter provides the full input in OpenAI chat completion spec format, useful for distinguishing between system and user messages.
**Format:** Array of OpenAI chat completion messages (see [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages))
**Example:**
```json
[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Hello"}
]
```
**Availability:**
- **Supported endpoints:** `/v1/chat/completions`, `/v1/messages`, `/v1/responses`
- **Input only:** Only passed for `input_type="request"` (pre-call guardrails)
**Use cases:**
- Apply different policies for system vs user messages
- Enforce role-based content restrictions
- Log structured conversation context
## LiteLLM Configuration
Add to `config.yaml`:
```yaml
litellm_settings:
guardrails:
- guardrail_name: "my-guardrail"
litellm_params:
guardrail: generic_guardrail_api
mode: pre_call # or post_call, during_call
api_base: https://your-guardrail-api.com
api_key: os.environ/YOUR_GUARDRAIL_API_KEY # optional
additional_provider_specific_params:
# your custom parameters
threshold: 0.8
language: "en"
```
## Usage
Users apply your guardrail by name:
```python
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "hello"}],
guardrails=["my-guardrail"]
)
```
Or with dynamic parameters:
```python
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "hello"}],
guardrails=[{
"my-guardrail": {
"extra_body": {
"custom_threshold": 0.9
}
}
}]
)
```
## Implementation Example
See [mock_bedrock_guardrail_server.py](https://github.com/BerriAI/litellm/blob/main/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py) for a complete reference implementation.
**Minimal FastAPI example:**
```python
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
app = FastAPI()
class GuardrailRequest(BaseModel):
texts: List[str]
images: Optional[List[str]] = None
tools: Optional[List[Dict[str, Any]]] = None # OpenAI ChatCompletionToolParam format (tool definitions)
tool_calls: Optional[List[Dict[str, Any]]] = None # OpenAI ChatCompletionMessageToolCall format (tool invocations)
structured_messages: Optional[List[Dict[str, Any]]] = None # OpenAI messages format (for chat endpoints)
request_data: Dict[str, Any]
input_type: str # "request" or "response"
litellm_call_id: Optional[str] = None
litellm_trace_id: Optional[str] = None
additional_provider_specific_params: Dict[str, Any]
class GuardrailResponse(BaseModel):
action: str # BLOCKED, NONE, or GUARDRAIL_INTERVENED
blocked_reason: Optional[str] = None
texts: Optional[List[str]] = None
images: Optional[List[str]] = None
@app.post("/beta/litellm_basic_guardrail_api")
async def apply_guardrail(request: GuardrailRequest):
# Your guardrail logic here
# Example: Check text content
for text in request.texts:
if "badword" in text.lower():
return GuardrailResponse(
action="BLOCKED",
blocked_reason="Content contains prohibited terms"
)
# Example: Check tool definitions (if present in request)
if request.tools:
for tool in request.tools:
if tool.get("type") == "function":
function_name = tool.get("function", {}).get("name", "")
# Block sensitive tool definitions
if function_name in ["delete_data", "access_admin_panel"]:
return GuardrailResponse(
action="BLOCKED",
blocked_reason=f"Tool '{function_name}' is not allowed"
)
# Example: Check tool calls (if present in request or response)
if request.tool_calls:
for tool_call in request.tool_calls:
if tool_call.get("type") == "function":
function_name = tool_call.get("function", {}).get("name", "")
arguments_str = tool_call.get("function", {}).get("arguments", "{}")
# Parse arguments and validate
import json
try:
arguments = json.loads(arguments_str)
# Block dangerous arguments
if "file_path" in arguments and ".." in str(arguments["file_path"]):
return GuardrailResponse(
action="BLOCKED",
blocked_reason="Tool call contains path traversal attempt"
)
except json.JSONDecodeError:
pass
# Example: Check structured messages (if present in request)
if request.structured_messages:
for message in request.structured_messages:
if message.get("role") == "system":
# Apply stricter policies to system messages
if "admin" in message.get("content", "").lower():
return GuardrailResponse(
action="BLOCKED",
blocked_reason="System message contains restricted terms"
)
return GuardrailResponse(action="NONE")
```
## When to Use This
✅ **Use Generic Guardrail API when:**
- You want instant integration without waiting for PRs
- You maintain your own guardrail service
- You need full control over updates and features
- You want to support all LiteLLM endpoints automatically
❌ **Make a PR when:**
- You want deeper integration with LiteLLM internals
- Your guardrail requires complex LiteLLM-specific logic
- You want to be featured as a built-in provider
## Questions?
This is a **beta API**. We're actively improving it based on feedback. Open an issue or PR if you need additional capabilities.

3
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@ -13,7 +13,7 @@ import TabItem from '@theme/TabItem';
| Fallbacks | ✅ | Works between supported models |
| Loadbalancing | ✅ | Works between supported models |
| Guardrails | ✅ | Applies to output transcribed text (non-streaming only) |
| Supported Providers | `openai`, `azure`, `vertex_ai`, `gemini`, `deepgram`, `groq`, `fireworks_ai` | |
| Supported Providers | `openai`, `azure`, `vertex_ai`, `gemini`, `deepgram`, `groq`, `fireworks_ai`, `ovhcloud` | |
## Quick Start
@ -126,6 +126,7 @@ transcript = client.audio.transcriptions.create(
- [Fireworks AI](./providers/fireworks_ai.md#audio-transcription)
- [Groq](./providers/groq.md#speech-to-text---whisper)
- [Deepgram](./providers/deepgram.md)
- [OVHcloud AI Endpoints](./providers/ovhcloud.md)
---

1
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@ -21,6 +21,7 @@ LiteLLM integrates with vector stores, allowing your models to access your organ
- [Azure Vector Stores](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/file-search?tabs=python#vector-stores) (Cannot be directly queried. Only available for calling in Assistants messages. We will be adding Azure AI Search Vector Store API support soon.)
- [Vertex AI RAG API](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-overview)
- [Gemini File Search](https://ai.google.dev/gemini-api/docs/file-search)
- [RAGFlow Datasets](/docs/providers/ragflow_vector_store.md) (Dataset management only, search not supported)
## Quick Start

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@ -371,6 +371,22 @@ model_list:
web_search_options: {} # Enables web search with default settings
```
### Advanced
You can configure LiteLLM's router to optionally drop models that do not support WebSearch, for example
```yaml
- model_name: gpt-4.1
litellm_params:
model: openai/gpt-4.1
- model_name: gpt-4.1
litellm_params:
model: azure/gpt-4.1
api_base: "x.openai.azure.com/"
api_version: 2025-03-01-preview
model_info:
supports_web_search: False <---- KEY CHANGE!
```
In this example, LiteLLM will still route LLM requests to both deployments, but for WebSearch, will solely route to OpenAI.
</TabItem>
<TabItem value="custom" label="Custom Search Context">

130
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@ -0,0 +1,130 @@
# Adding OpenAI-Compatible Providers
For simple OpenAI-compatible providers (like Hyperbolic, Nscale, etc.), you can add support by editing a single JSON file.
## Quick Start
1. Edit `litellm/llms/openai_like/providers.json`
2. Add your provider configuration
3. Test with: `litellm.completion(model="your_provider/model-name", ...)`
## Basic Configuration
For a fully OpenAI-compatible provider:
```json
{
"your_provider": {
"base_url": "https://api.yourprovider.com/v1",
"api_key_env": "YOUR_PROVIDER_API_KEY"
}
}
```
That's it! The provider is now available.
## Configuration Options
### Required Fields
- `base_url` - API endpoint (e.g., `https://api.provider.com/v1`)
- `api_key_env` - Environment variable name for API key (e.g., `PROVIDER_API_KEY`)
### Optional Fields
- `api_base_env` - Environment variable to override `base_url`
- `base_class` - Use `"openai_gpt"` (default) or `"openai_like"`
- `param_mappings` - Map OpenAI parameter names to provider-specific names
- `constraints` - Parameter value constraints (min/max)
- `special_handling` - Special behaviors like content format conversion
## Examples
### Simple Provider (Fully Compatible)
```json
{
"hyperbolic": {
"base_url": "https://api.hyperbolic.xyz/v1",
"api_key_env": "HYPERBOLIC_API_KEY"
}
}
```
### Provider with Parameter Mapping
```json
{
"publicai": {
"base_url": "https://api.publicai.co/v1",
"api_key_env": "PUBLICAI_API_KEY",
"param_mappings": {
"max_completion_tokens": "max_tokens"
}
}
}
```
### Provider with Constraints
```json
{
"custom_provider": {
"base_url": "https://api.custom.com/v1",
"api_key_env": "CUSTOM_API_KEY",
"constraints": {
"temperature_max": 1.0,
"temperature_min": 0.0
}
}
}
```
## Usage
```python
import litellm
import os
# Set your API key
os.environ["YOUR_PROVIDER_API_KEY"] = "your-key-here"
# Use the provider
response = litellm.completion(
model="your_provider/model-name",
messages=[{"role": "user", "content": "Hello"}],
)
```
## When to Use Python Instead
Use a Python config class if you need:
- Custom authentication flows (OAuth, JWT, etc.)
- Complex request/response transformations
- Provider-specific streaming logic
- Advanced tool calling modifications
For these cases, create a config class in `litellm/llms/your_provider/chat/transformation.py` that inherits from `OpenAIGPTConfig` or `OpenAILikeChatConfig`.
## Testing
Test your provider:
```bash
# Quick test
python -c "
import litellm
import os
os.environ['PROVIDER_API_KEY'] = 'your-key'
response = litellm.completion(
model='provider/model-name',
messages=[{'role': 'user', 'content': 'test'}]
)
print(response.choices[0].message.content)
"
```
## Reference
See existing providers in `litellm/llms/openai_like/providers.json` for examples.

2
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@ -263,6 +263,8 @@ print(response)
| Model Name | Function Call |
|----------------------|---------------------------------------------|
| Amazon Nova Multimodal Embeddings | `embedding(model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", input=input)` | [Nova Docs](../providers/bedrock_embedding#amazon-nova-multimodal-embeddings) |
| Amazon Nova (Async) | `embedding(model="bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0", input=input, input_type="text", output_s3_uri="s3://bucket/")` | [Nova Async Docs](../providers/bedrock_embedding#asynchronous-embeddings-with-segmentation) |
| Titan Embeddings - G1 | `embedding(model="amazon.titan-embed-text-v1", input=input)` |
| Cohere Embeddings - English | `embedding(model="cohere.embed-english-v3", input=input)` |
| Cohere Embeddings - Multilingual | `embedding(model="cohere.embed-multilingual-v3", input=input)` |

13
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@ -301,6 +301,17 @@ content = await litellm.afile_content(
print("file content=", content)
```
**Get File Content (Bedrock)**
```python
# For Bedrock batch output files stored in S3
content = await litellm.afile_content(
file_id="s3://bucket-name/path/to/file.jsonl", # S3 URI or unified file ID
custom_llm_provider="bedrock",
aws_region_name="us-west-2"
)
print("file content=", content.text)
```
</TabItem>
</Tabs>
@ -313,4 +324,6 @@ print("file content=", content)
### [Vertex AI](./providers/vertex#batch-apis)
### [Bedrock](./providers/bedrock_batches#4-retrieve-batch-results)
## [Swagger API Reference](https://litellm-api.up.railway.app/#/files)

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@ -1,108 +0,0 @@
# Getting Started
import QuickStart from '../src/components/QuickStart.js'
LiteLLM simplifies LLM API calls by mapping them all to the [OpenAI ChatCompletion format](https://platform.openai.com/docs/api-reference/chat).
## basic usage
By default we provide a free $10 community-key to try all providers supported on LiteLLM.
```python
from litellm import completion
## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["COHERE_API_KEY"] = "your-api-key"
messages = [{ "content": "Hello, how are you?","role": "user"}]
# openai call
response = completion(model="gpt-3.5-turbo", messages=messages)
# cohere call
response = completion("command-nightly", messages)
```
**Need a dedicated key?**
Email us @ krrish@berri.ai
Next Steps 👉 [Call all supported models - e.g. Claude-2, Llama2-70b, etc.](./proxy_api.md#supported-models)
More details 👉
- [Completion() function details](./completion/)
- [Overview of supported models / providers on LiteLLM](./providers/)
- [Search all models / providers](https://models.litellm.ai/)
- [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main)
## streaming
Same example from before. Just pass in `stream=True` in the completion args.
```python
from litellm import completion
## set ENV variables
os.environ["OPENAI_API_KEY"] = "openai key"
os.environ["COHERE_API_KEY"] = "cohere key"
messages = [{ "content": "Hello, how are you?","role": "user"}]
# openai call
response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
# cohere call
response = completion("command-nightly", messages, stream=True)
print(response)
```
More details 👉
- [streaming + async](./completion/stream.md)
- [tutorial for streaming Llama2 on TogetherAI](./tutorials/TogetherAI_liteLLM.md)
## exception handling
LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM.
```python
from openai.error import OpenAIError
from litellm import completion
os.environ["ANTHROPIC_API_KEY"] = "bad-key"
try:
# some code
completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}])
except OpenAIError as e:
print(e)
```
## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
LiteLLM exposes pre defined callbacks to send data to MLflow, Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack
```python
from litellm import completion
## set env variables for logging tools (API key set up is not required when using MLflow)
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" # get your public key at https://app.lunary.ai/settings
os.environ["HELICONE_API_KEY"] = "your-helicone-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["OPENAI_API_KEY"]
# set callbacks
litellm.success_callback = ["lunary", "mlflow", "langfuse", "helicone"] # log input/output to MLflow, langfuse, lunary, helicone
#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
```
More details 👉
- [exception mapping](./exception_mapping.md)
- [retries + model fallbacks for completion()](./completion/reliable_completions.md)
- [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md)

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@ -71,17 +71,19 @@ DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source. use to different
Send logs through a local DataDog agent (useful for containerized environments):
```shell
DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent
DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518)
DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth)
DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source
LITELLM_DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent
LITELLM_DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518)
DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth)
DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source
```
When `DD_AGENT_HOST` is set, logs are sent to the agent instead of directly to DataDog API. This is useful for:
When `LITELLM_DD_AGENT_HOST` is set, logs are sent to the agent instead of directly to DataDog API. This is useful for:
- Centralized log shipping in containerized environments
- Reducing direct API calls from multiple services
- Leveraging agent-side processing and filtering
**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.
**Step 3**: Start the proxy, make a test request
Start proxy
@ -191,8 +193,8 @@ LiteLLM supports customizing the following Datadog environment variables
|---------------------|-------------|---------------|----------|
| `DD_API_KEY` | Your Datadog API key for authentication (required for direct API, optional for agent) | None | Conditional* |
| `DD_SITE` | Your Datadog site (e.g., "us5.datadoghq.com") (required for direct API) | None | Conditional* |
| `DD_AGENT_HOST` | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API | None | ❌ No |
| `DD_AGENT_PORT` | Port of DataDog agent for log intake | "10518" | ❌ No |
| `LITELLM_DD_AGENT_HOST` | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API | None | ❌ No |
| `LITELLM_DD_AGENT_PORT` | Port of DataDog agent for log intake | "10518" | ❌ No |
| `DD_ENV` | Environment tag for your logs (e.g., "production", "staging") | "unknown" | ❌ No |
| `DD_SERVICE` | Service name for your logs | "litellm-server" | ❌ No |
| `DD_SOURCE` | Source name for your logs | "litellm" | ❌ No |
@ -201,5 +203,5 @@ LiteLLM supports customizing the following Datadog environment variables
| `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No |
\* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required
\* **Optional when using DataDog Agent**: Set `DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required
\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required

79
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@ -6,7 +6,7 @@ Open source tracing and evaluation platform
:::tip
This is community maintained, Please make an issue if you run into a bug
This is community maintained. Please make an issue if you run into a bug:
https://github.com/BerriAI/litellm
:::
@ -31,19 +31,16 @@ litellm.callbacks = ["arize_phoenix"]
import litellm
import os
os.environ["PHOENIX_API_KEY"] = "" # Necessary only using Phoenix Cloud
os.environ["PHOENIX_COLLECTOR_HTTP_ENDPOINT"] = "" # The URL of your Phoenix OSS instance e.g. http://localhost:6006/v1/traces
os.environ["PHOENIX_PROJECT_NAME"]="litellm" # OPTIONAL: you can configure project names, otherwise traces would go to "default" project
# Set env variables
os.environ["PHOENIX_API_KEY"] = "d0*****" # Set the Phoenix API key here. It is necessary only when using Phoenix Cloud.
os.environ["PHOENIX_COLLECTOR_HTTP_ENDPOINT"] = "https://app.phoenix.arize.com/s/<space-name>/v1/traces" # Set the URL of your Phoenix OSS instance, otherwise tracer would use https://app.phoenix.arize.com/v1/traces for Phoenix Cloud.
os.environ["PHOENIX_PROJECT_NAME"] = "litellm" # Configure the project name, otherwise traces would go to "default" project.
os.environ['OPENAI_API_KEY'] = "fake-key" # Set the OpenAI API key here.
# This defaults to https://app.phoenix.arize.com/v1/traces for Phoenix Cloud
# LLM API Keys
os.environ['OPENAI_API_KEY']=""
# set arize as a callback, litellm will send the data to arize
# Set arize_phoenix as a callback & LiteLLM will send the data to Phoenix.
litellm.callbacks = ["arize_phoenix"]
# openai call
# OpenAI call
response = litellm.completion(
model="gpt-3.5-turbo",
messages=[
@ -52,8 +49,9 @@ response = litellm.completion(
)
```
### Using with LiteLLM Proxy
## Using with LiteLLM Proxy
1. Setup config.yaml
```yaml
model_list:
@ -66,12 +64,63 @@ model_list:
litellm_settings:
callbacks: ["arize_phoenix"]
general_settings:
master_key: "sk-1234"
environment_variables:
PHOENIX_API_KEY: "d0*****"
PHOENIX_COLLECTOR_ENDPOINT: "https://app.phoenix.arize.com/v1/traces" # OPTIONAL, for setting the GRPC endpoint
PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/v1/traces" # OPTIONAL, for setting the HTTP endpoint
PHOENIX_COLLECTOR_ENDPOINT: "https://app.phoenix.arize.com/s/<space-name>/v1/traces" # OPTIONAL - For setting the gRPC endpoint
PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/s/<space-name>/v1/traces" # OPTIONAL - For setting the HTTP endpoint
```
2. Start the proxy
```bash
litellm --config config.yaml
```
3. Test it!
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{ "model": "gpt-4o", "messages": [{"role": "user", "content": "Hi 👋 - i'm openai"}]}'
```
## Supported Phoenix Endpoints
Phoenix now supports multiple deployment types. The correct endpoint depends on which version of Phoenix Cloud you are using.
**Phoenix Cloud (With Spaces - New Version)**
Use this if your Phoenix URL contains `/s/<space-name>` path.
```bash
https://app.phoenix.arize.com/s/<space-name>/v1/traces
```
**Phoenix Cloud (Legacy - Deprecated)**
Use this only if your deployment still shows the `/legacy` pattern.
```bash
https://app.phoenix.arize.com/legacy/v1/traces
```
**Phoenix Cloud (Without Spaces - Old Version)**
Use this if your Phoenix Cloud URL does not contain `/s/<space-name>` or `/legacy` path.
```bash
https://app.phoenix.arize.com/v1/traces
```
**Self-Hosted Phoenix (Local Instance)**
Use this when running Phoenix on your machine or a private server.
```bash
http://localhost:6006/v1/traces
```
Depending on which Phoenix Cloud version or deployment you are using, you should set the corresponding endpoint in `PHOENIX_COLLECTOR_HTTP_ENDPOINT` or `PHOENIX_COLLECTOR_ENDPOINT`.
## Support & Talk to Founders
- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)

10
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@ -0,0 +1,10 @@
# Agent Lightning
[Agent Lightning](https://github.com/microsoft/agent-lightning) is Microsoft's open-source framework for training and optimizing AI agents with Reinforcement Learning, Automatic Prompt Optimization, and Supervised Fine-tuning — with almost zero code changes.
It works with any agent framework including LangChain, OpenAI Agents SDK, AutoGen, and CrewAI. Agent Lightning uses LiteLLM Proxy under the hood to route LLM requests and collect traces that power its training algorithms.
- [GitHub](https://github.com/microsoft/agent-lightning)
- [Docs](https://microsoft.github.io/agent-lightning/)
- [arXiv Paper](https://arxiv.org/abs/2508.03680)

21
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@ -0,0 +1,21 @@
# Google ADK (Agent Development Kit)
[Google ADK](https://github.com/google/adk-python) is an open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents. While optimized for Gemini, ADK is model-agnostic and supports LiteLLM for using 100+ providers.
```python
from google.adk.agents.llm_agent import Agent
from google.adk.models.lite_llm import LiteLlm
root_agent = Agent(
model=LiteLlm(model="openai/gpt-4o"), # Or any LiteLLM-supported model
name="my_agent",
description="An agent using LiteLLM",
instruction="You are a helpful assistant.",
tools=[your_tools],
)
```
- [GitHub](https://github.com/google/adk-python)
- [Documentation](https://google.github.io/adk-docs)
- [LiteLLM Samples](https://github.com/google/adk-python/tree/main/contributing/samples/hello_world_litellm)

8
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@ -0,0 +1,8 @@
# Microsoft GraphRAG
GraphRAG is a data pipeline and transformation suite that extracts meaningful, structured data from unstructured text using the power of LLMs. It uses a graph-based approach to RAG (Retrieval-Augmented Generation) that leverages knowledge graphs to improve reasoning over private datasets.
- [Github](https://github.com/microsoft/graphrag)
- [Docs](https://microsoft.github.io/graphrag/)
- [Paper](https://arxiv.org/pdf/2404.16130)

24
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@ -0,0 +1,24 @@
# Harbor
[Harbor](https://github.com/laude-institute/harbor) is a framework from the creators of Terminal-Bench for evaluating and optimizing agents and language models. It uses LiteLLM to call 100+ LLM providers.
```bash
# Install
pip install harbor
# Run a benchmark with any LiteLLM-supported model
harbor run --dataset terminal-bench@2.0 \
--agent claude-code \
--model anthropic/claude-opus-4-1 \
--n-concurrent 4
```
Key features:
- Evaluate agents like Claude Code, OpenHands, Codex CLI
- Build and share benchmarks and environments
- Run experiments in parallel across cloud providers (Daytona, Modal)
- Generate rollouts for RL optimization
- [GitHub](https://github.com/laude-institute/harbor)
- [Documentation](https://harborframework.com/docs)

6
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@ -2,6 +2,12 @@
title: "Integrate as a Model Provider"
---
## Quick Start for OpenAI-Compatible Providers
If your API is OpenAI-compatible, you can add support by editing a single JSON file. See [Adding OpenAI-Compatible Providers](/docs/contributing/adding_openai_compatible_providers) for the simple approach.
---
This guide focuses on how to setup the classes and configuration necessary to act as a chat provider.
Please see this guide first and look at the existing code in the codebase to understand how to act as a different provider, e.g. handling embeddings or image-generation.

291
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@ -0,0 +1,291 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Amazon Nova
| Property | Details |
|-------|-------|
| Description | Amazon Nova is a family of foundation models built by Amazon that deliver frontier intelligence and industry-leading price performance. |
| Provider Route on LiteLLM | `amazon_nova/` |
| Provider Doc | [Amazon Nova ↗](https://docs.aws.amazon.com/nova/latest/userguide/what-is-nova.html) |
| Supported OpenAI Endpoints | `/chat/completions`, `v1/responses` |
| Other Supported Endpoints | `v1/messages`, `/generateContent` |
## Authentication
Amazon Nova uses API key authentication. You can obtain your API key from the [Amazon Nova developer console ↗](https://nova.amazon.com/dev/documentation).
```bash
export AMAZON_NOVA_API_KEY="your-api-key"
```
## Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import os
from litellm import completion
# Set your API key
os.environ["AMAZON_NOVA_API_KEY"] = "your-api-key"
response = completion(
model="amazon_nova/nova-micro-v1",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Hello, how are you?"}
]
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
### 1. Setup config.yaml
```yaml
model_list:
- model_name: amazon-nova-micro
litellm_params:
model: amazon_nova/nova-micro-v1
api_key: os.environ/AMAZON_NOVA_API_KEY
```
### 2. Start the proxy
```bash
litellm --config /path/to/config.yaml
```
### 3. Test it
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "amazon-nova-micro",
"messages": [
{
"role": "user",
"content": "Hello, how are you?"
}
]
}'
```
</TabItem>
</Tabs>
## Supported Models
| Model Name | Usage | Context Window |
|------------|-------|----------------|
| Nova Micro | `completion(model="amazon_nova/nova-micro-v1", messages=messages)` | 128K tokens |
| Nova Lite | `completion(model="amazon_nova/nova-lite-v1", messages=messages)` | 300K tokens |
| Nova Pro | `completion(model="amazon_nova/nova-pro-v1", messages=messages)` | 300K tokens |
| Nova Premier | `completion(model="amazon_nova/nova-premier-v1", messages=messages)` | 1M tokens |
## Usage - Streaming
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import os
from litellm import completion
os.environ["AMAZON_NOVA_API_KEY"] = "your-api-key"
response = completion(
model="amazon_nova/nova-micro-v1",
messages=[
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Tell me about machine learning"}
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "amazon-nova-micro",
"messages": [
{
"role": "user",
"content": "Tell me about machine learning"
}
],
"stream": true
}'
```
</TabItem>
</Tabs>
## Usage - Function Calling / Tool Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import os
from litellm import completion
os.environ["AMAZON_NOVA_API_KEY"] = "your-api-key"
tools = [
{
"type": "function",
"function": {
"name": "getCurrentWeather",
"description": "Get the current weather in a given city",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
response = completion(
model="amazon_nova/nova-micro-v1",
messages=[
{"role": "user", "content": "What's the weather like in San Francisco?"}
],
tools=tools
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "amazon-nova-micro",
"messages": [
{
"role": "user",
"content": "What'\''s the weather like in San Francisco?"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "getCurrentWeather",
"description": "Get the current weather in a given city",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
}'
```
</TabItem>
</Tabs>
## Set temperature, top_p, etc.
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import os
from litellm import completion
os.environ["AMAZON_NOVA_API_KEY"] = "your-api-key"
response = completion(
model="amazon_nova/nova-pro-v1",
messages=[
{"role": "user", "content": "Write a creative story"}
],
temperature=0.8,
max_tokens=500,
top_p=0.9
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
**Set on yaml**
```yaml
model_list:
- model_name: amazon-nova-pro
litellm_params:
model: amazon_nova/nova-pro-v1
temperature: 0.8
max_tokens: 500
top_p: 0.9
```
**Set on request**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "amazon-nova-pro",
"messages": [
{
"role": "user",
"content": "Write a creative story"
}
],
"temperature": 0.8,
"max_tokens": 500,
"top_p": 0.9
}'
```
</TabItem>
</Tabs>
## Model Comparison
| Model | Best For | Speed | Cost | Context |
|-------|----------|-------|------|---------|
| **Nova Micro** | Simple tasks, high throughput | Fastest | Lowest | 128K |
| **Nova Lite** | Balanced performance | Fast | Low | 300K |
| **Nova Pro** | Complex reasoning | Medium | Medium | 300K |
| **Nova Premier** | Most advanced tasks | Slower | Higher | 1M |
## Error Handling
Common error codes and their meanings:
- `401 Unauthorized`: Invalid API key
- `429 Too Many Requests`: Rate limit exceeded
- `400 Bad Request`: Invalid request format
- `500 Internal Server Error`: Service temporarily unavailable

28
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@ -41,7 +41,8 @@ Check this in code, [here](../completion/input.md#translated-openai-params)
"extra_headers",
"parallel_tool_calls",
"response_format",
"user"
"user",
"reasoning_effort",
```
:::info
@ -49,6 +50,7 @@ Check this in code, [here](../completion/input.md#translated-openai-params)
**Notes:**
- Anthropic API fails requests when `max_tokens` are not passed. Due to this litellm passes `max_tokens=4096` when no `max_tokens` are passed.
- `response_format` is fully supported for Claude Sonnet 4.5 and Opus 4.1 models (see [Structured Outputs](#structured-outputs) section)
- `reasoning_effort` is automatically mapped to `output_config={"effort": ...}` for Claude Opus 4.5 models (see [Effort Parameter](./anthropic_effort.md))
:::
@ -199,6 +201,30 @@ Without `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX`:
With `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX=true`:
- Base URL `https://my-proxy.com/custom/path` → `https://my-proxy.com/custom/path` (unchanged)
### Azure AI Foundry (Alternative Method)
:::tip Recommended Method
For full Azure support including Azure AD authentication, use the dedicated [Azure Anthropic provider](./azure/azure_anthropic) with `azure_ai/` prefix.
:::
As an alternative, you can use the `anthropic/` provider directly with your Azure endpoint since Azure exposes Claude using Anthropic's native API.
```python
from litellm import completion
response = completion(
model="anthropic/claude-sonnet-4-5",
api_base="https://<your-resource>.services.ai.azure.com/anthropic",
api_key="<your-azure-api-key>",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response)
```
:::info
**Finding your Azure endpoint:** Go to Azure AI Foundry → Your deployment → Overview. Your base URL will be `https://<resource-name>.services.ai.azure.com/anthropic`
:::
## Usage
```python

27
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@ -9,7 +9,10 @@ Control how many tokens Claude uses when responding with the `effort` parameter,
The `effort` parameter allows you to control how eager Claude is about spending tokens when responding to requests. This gives you the ability to trade off between response thoroughness and token efficiency, all with a single model.
**Note**: The effort parameter is currently in beta and only supported by Claude Opus 4.5. You must include the beta header `effort-2025-11-24` when using this feature (LiteLLM automatically adds this header when `output_config` with `effort` is detected).
**Note**: The effort parameter is currently in beta and only supported by Claude Opus 4.5. LiteLLM automatically adds the `effort-2025-11-24` beta header when:
- `reasoning_effort` parameter is provided (for Claude Opus 4.5 only)
For Claude Opus 4.5, `reasoning_effort="medium"`—both are automatically mapped to the correct format.
## How Effort Works
@ -52,9 +55,7 @@ response = litellm.completion(
"role": "user",
"content": "Analyze the trade-offs between microservices and monolithic architectures"
}],
output_config={
"effort": "medium"
}
reasoning_effort="medium" # Automatically mapped to output_config for Opus 4.5
)
print(response.choices[0].message.content)
@ -217,11 +218,14 @@ response = litellm.completion(
The effort parameter is supported across all Anthropic-compatible providers:
- **Standard Anthropic**: ✅ Supported (Claude Opus 4.5)
- **Azure Anthropic**: ✅ Supported (Claude Opus 4.5)
- **Vertex AI Anthropic**: ✅ Supported (Claude Opus 4.5)
- **Standard Anthropic API**: ✅ Supported (Claude Opus 4.5)
- **Azure Anthropic / Microsoft Foundry**: ✅ Supported (Claude Opus 4.5)
- **Amazon Bedrock**: ✅ Supported (Claude Opus 4.5)
- **Google Cloud Vertex AI**: ✅ Supported (Claude Opus 4.5)
LiteLLM automatically handles the beta header injection for all providers.
LiteLLM automatically handles:
- Beta header injection (`effort-2025-11-24`) for all providers
- Parameter mapping: `reasoning_effort` → `output_config={"effort": ...}` for Claude Opus 4.5
## Usage and Pricing
@ -242,9 +246,12 @@ print(f"Total tokens: {response.usage.total_tokens}")
### Beta header not being added
LiteLLM automatically adds the `effort-2025-11-24` beta header when `output_config` with `effort` is detected. If you're not seeing the header:
LiteLLM automatically adds the `effort-2025-11-24` beta header when:
- `reasoning_effort` parameter is provided (for Claude Opus 4.5 only)
1. Ensure you're using `output_config` with an `effort` field
If you're not seeing the header:
1. Ensure you're using `reasoning_effort` parameter
2. Verify the model is Claude Opus 4.5
3. Check that LiteLLM version supports this feature

17
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@ -3,7 +3,11 @@
Programmatic tool calling allows Claude to write code that calls your tools programmatically within a code execution container, rather than requiring round trips through the model for each tool invocation. This reduces latency for multi-tool workflows and decreases token consumption by allowing Claude to filter or process data before it reaches the model's context window.
:::info
Programmatic tool calling is currently in public beta. LiteLLM automatically adds the required `advanced-tool-use-2025-11-20` beta header when it detects tools with the `allowed_callers` field.
Programmatic tool calling is currently in public beta. LiteLLM automatically detects tools with the `allowed_callers` field and adds the appropriate beta header based on your provider:
- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20`
- **Amazon Bedrock**: `advanced-tool-use-2025-11-20`
- **Google Cloud Vertex AI**: Not supported
This feature requires the code execution tool to be enabled.
:::
@ -380,13 +384,14 @@ For example, calling 10 tools directly uses ~10x the tokens of calling them prog
## Provider Support
LiteLLM supports programmatic tool calling across all Anthropic-compatible providers:
LiteLLM supports programmatic tool calling across the following Anthropic-compatible providers:
- **Standard Anthropic API** (`anthropic/claude-sonnet-4-5-20250929`)
- **Azure Anthropic** (`azure/claude-sonnet-4-5-20250929`)
- **Vertex AI Anthropic** (`vertex_ai/claude-sonnet-4-5-20250929`)
- **Standard Anthropic API** (`anthropic/claude-sonnet-4-5-20250929`) ✅
- **Azure Anthropic / Microsoft Foundry** (`azure/claude-sonnet-4-5-20250929`) ✅
- **Amazon Bedrock** (`bedrock/invoke/anthropic.claude-sonnet-4-5-20250929-v1:0`) ✅
- **Google Cloud Vertex AI** (`vertex_ai/claude-sonnet-4-5-20250929`) ❌ Not supported
The beta header is automatically added when LiteLLM detects tools with `allowed_callers` field.
The beta header (`advanced-tool-use-2025-11-20`) is automatically added when LiteLLM detects tools with the `allowed_callers` field.
## Limitations

19
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@ -3,7 +3,13 @@
Provide concrete examples of valid tool inputs to help Claude understand how to use your tools more effectively. This is particularly useful for complex tools with nested objects, optional parameters, or format-sensitive inputs.
:::info
Tool input examples is a beta feature. LiteLLM automatically adds the required `advanced-tool-use-2025-11-20` beta header when it detects tools with the `input_examples` field.
Tool input examples is a beta feature. LiteLLM automatically detects tools with the `input_examples` field and adds the appropriate beta header based on your provider:
- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20`
- **Amazon Bedrock**: `advanced-tool-use-2025-11-20` (Claude Opus 4.5 only)
- **Google Cloud Vertex AI**: Not supported
You don't need to manually specify beta headers—LiteLLM handles this automatically.
:::
## When to Use Input Examples
@ -378,13 +384,14 @@ Input examples work seamlessly with other Anthropic tool features:
## Provider Support
LiteLLM supports input examples across all Anthropic-compatible providers:
LiteLLM supports input examples across the following Anthropic-compatible providers:
- **Standard Anthropic API** (`anthropic/claude-sonnet-4-5-20250929`)
- **Azure Anthropic** (`azure/claude-sonnet-4-5-20250929`)
- **Vertex AI Anthropic** (`vertex_ai/claude-sonnet-4-5-20250929`)
- **Standard Anthropic API** (`anthropic/claude-sonnet-4-5-20250929`) ✅
- **Azure Anthropic / Microsoft Foundry** (`azure/claude-sonnet-4-5-20250929`) ✅
- **Amazon Bedrock** (`bedrock/invoke/anthropic.claude-opus-4-5-20251101-v1:0`) ✅ (Opus 4.5 only)
- **Google Cloud Vertex AI** (`vertex_ai/claude-sonnet-4-5-20250929`) ❌ Not supported
The beta header is automatically added when LiteLLM detects tools with `input_examples` field.
The beta header (`advanced-tool-use-2025-11-20`) is automatically added when LiteLLM detects tools with the `input_examples` field.
## Troubleshooting

17
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@ -290,7 +290,13 @@ response = client.chat.completions.create(
### Beta Header
LiteLLM automatically adds the `advanced-tool-use-2025-11-20` beta header when tool search tools are detected. You don't need to manually specify it.
LiteLLM automatically detects tool search tools and adds the appropriate beta header based on your provider:
- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20`
- **Google Cloud Vertex AI**: `tool-search-tool-2025-10-19`
- **Amazon Bedrock** (Invoke API, Opus 4.5 only): `tool-search-tool-2025-10-19`
You don't need to manually specify beta headers—LiteLLM handles this automatically.
### Deferred Loading
@ -387,9 +393,18 @@ If Claude references a tool that isn't in your deferred tools list, you'll get a
- Not compatible with tool use examples
- Requires Claude Opus 4.5 or Sonnet 4.5
- On Bedrock, only available via invoke API (not converse API)
- On Bedrock, only supported for Claude Opus 4.5 (not Sonnet 4.5)
- BM25 variant (`tool_search_tool_bm25_20251119`) is not supported on Bedrock
- Maximum 10,000 tools in catalog
- Returns 3-5 most relevant tools per search
### Bedrock-Specific Notes
When using Bedrock's Invoke API:
- The regex variant (`tool_search_tool_regex_20251119`) is automatically normalized to `tool_search_tool_regex`
- The BM25 variant (`tool_search_tool_bm25_20251119`) is automatically filtered out as it's not supported
- Tool search is only available for Claude Opus 4.5 models
## Additional Resources
- [Anthropic Tool Search Documentation](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/tool-search)

18
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@ -16,7 +16,7 @@ Azure Foundry supports the following Claude models:
| Property | Details |
|-------|-------|
| Description | Claude models deployed via Microsoft Azure Foundry. Uses the same API as Anthropic's Messages API but with Azure authentication. |
| Provider Route on LiteLLM | `azure/` (add this prefix to Claude model names - e.g. `azure/claude-sonnet-4-5`) |
| Provider Route on LiteLLM | `azure_ai/` (add this prefix to Claude model names - e.g. `azure_ai/claude-sonnet-4-5`) |
| Provider Doc | [Azure Foundry Claude Models ↗](https://learn.microsoft.com/en-us/azure/ai-services/foundry-models/claude) |
| API Endpoint | `https://<resource-name>.services.ai.azure.com/anthropic/v1/messages` |
| Supported Endpoints | `/chat/completions`, `/anthropic/v1/messages`|
@ -68,7 +68,7 @@ os.environ["AZURE_API_BASE"] = "https://<resource-name>.services.ai.azure.com/an
# Make a completion request
response = completion(
model="azure/claude-sonnet-4-5",
model="azure_ai/claude-sonnet-4-5",
messages=[
{"role": "user", "content": "What are 3 things to visit in Seattle?"}
],
@ -85,7 +85,7 @@ print(response)
import litellm
response = litellm.completion(
model="azure/claude-sonnet-4-5",
model="azure_ai/claude-sonnet-4-5",
api_base="https://<resource-name>.services.ai.azure.com/anthropic",
api_key="your-azure-api-key",
messages=[
@ -101,7 +101,7 @@ response = litellm.completion(
import litellm
response = litellm.completion(
model="azure/claude-sonnet-4-5",
model="azure_ai/claude-sonnet-4-5",
api_base="https://<resource-name>.services.ai.azure.com/anthropic",
azure_ad_token="your-azure-ad-token",
messages=[
@ -117,7 +117,7 @@ response = litellm.completion(
from litellm import completion
response = completion(
model="azure/claude-sonnet-4-5",
model="azure_ai/claude-sonnet-4-5",
messages=[
{"role": "user", "content": "Write a short story"}
],
@ -136,7 +136,7 @@ for chunk in response:
from litellm import completion
response = completion(
model="azure/claude-sonnet-4-5",
model="azure_ai/claude-sonnet-4-5",
messages=[
{"role": "user", "content": "What's the weather in Seattle?"}
],
@ -181,7 +181,7 @@ export AZURE_API_BASE="https://<resource-name>.services.ai.azure.com/anthropic"
model_list:
- model_name: claude-sonnet-4-5
litellm_params:
model: azure/claude-sonnet-4-5
model: azure_ai/claude-sonnet-4-5
api_base: https://<resource-name>.services.ai.azure.com/anthropic
api_key: os.environ/AZURE_API_KEY
```
@ -331,7 +331,7 @@ os.environ["AZURE_API_BASE"] = "https://my-resource.services.ai.azure.com/anthro
# Make a request
response = completion(
model="azure/claude-sonnet-4-5",
model="azure_ai/claude-sonnet-4-5",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain quantum computing in simple terms."}
@ -358,7 +358,7 @@ Or pass it directly:
```python
response = completion(
model="azure/claude-sonnet-4-5",
model="azure_ai/claude-sonnet-4-5",
api_base="https://<resource-name>.services.ai.azure.com/anthropic",
# ...
)

79
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@ -312,6 +312,82 @@ LiteLLM supports **ALL** azure ai models. Here's a few examples:
| mistral-large-latest | `completion(model="azure_ai/mistral-large-latest", messages)` |
| AI21-Jamba-Instruct | `completion(model="azure_ai/ai21-jamba-instruct", messages)` |
## Usage - Azure Anthropic (Azure Foundry Claude)
LiteLLM funnels Azure Claude deployments through the `azure_ai/` provider so Claude Opus models on Azure Foundry keep working with Tool Search, Effort, streaming, and the rest of the advanced feature set. Point `AZURE_AI_API_BASE` to `https://<resource>.services.ai.azure.com/anthropic` (LiteLLM appends `/v1/messages` automatically) and authenticate with `AZURE_AI_API_KEY` or an Azure AD token.
<Tabs>
<TabItem value="sdk" label="LiteLLM Python SDK">
```python
import os
from litellm import completion
# Configure Azure credentials
os.environ["AZURE_AI_API_KEY"] = "your-azure-ai-api-key"
os.environ["AZURE_AI_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic"
response = completion(
model="azure_ai/claude-opus-4-1",
messages=[{"role": "user", "content": "Explain how Azure Anthropic hosts Claude Opus differently from the public Anthropic API."}],
max_tokens=1200,
temperature=0.7,
stream=True,
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Set environment variables**
```bash
export AZURE_AI_API_KEY="your-azure-ai-api-key"
export AZURE_AI_API_BASE="https://my-resource.services.ai.azure.com/anthropic"
```
**2. Configure the proxy**
```yaml
model_list:
- model_name: claude-4-azure
litellm_params:
model: azure_ai/claude-opus-4-1
api_key: os.environ/AZURE_AI_API_KEY
api_base: os.environ/AZURE_AI_API_BASE
```
**3. Start LiteLLM**
```bash
litellm --config /path/to/config.yaml
```
**4. Test the Azure Claude route**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
"model": "claude-4-azure",
"messages": [
{
"role": "user",
"content": "How do I use Claude Opus 4 via Azure Anthropic in LiteLLM?"
}
],
"max_tokens": 1024
}'
```
</TabItem>
</Tabs>
## Rerank Endpoint
@ -397,4 +473,5 @@ curl http://0.0.0.0:4000/rerank \
```
</TabItem>
</Tabs>
</Tabs>

143
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@ -7,7 +7,7 @@ ALL Bedrock models (Anthropic, Meta, Deepseek, Mistral, Amazon, etc.) are Suppor
| Property | Details |
|-------|-------|
| Description | Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs). |
| Provider Route on LiteLLM | `bedrock/`, [`bedrock/converse/`](#set-converse--invoke-route), [`bedrock/invoke/`](#set-invoke-route), [`bedrock/converse_like/`](#calling-via-internal-proxy), [`bedrock/llama/`](#deepseek-not-r1), [`bedrock/deepseek_r1/`](#deepseek-r1), [`bedrock/qwen3/`](#qwen3-imported-models), [`bedrock/openai/`](./bedrock_imported.md#openai-compatible-imported-models-qwen-25-vl-etc) |
| Provider Route on LiteLLM | `bedrock/`, [`bedrock/converse/`](#set-converse--invoke-route), [`bedrock/invoke/`](#set-invoke-route), [`bedrock/converse_like/`](#calling-via-internal-proxy), [`bedrock/llama/`](#deepseek-not-r1), [`bedrock/deepseek_r1/`](#deepseek-r1), [`bedrock/qwen3/`](#qwen3-imported-models), [`bedrock/qwen2/`](./bedrock_imported.md#qwen2-imported-models), [`bedrock/openai/`](./bedrock_imported.md#openai-compatible-imported-models-qwen-25-vl-etc) |
| Provider Doc | [Amazon Bedrock ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) |
| Supported OpenAI Endpoints | `/chat/completions`, `/completions`, `/embeddings`, `/images/generations` |
| Rerank Endpoint | `/rerank` |
@ -43,6 +43,8 @@ export AWS_BEARER_TOKEN_BEDROCK="your-api-key"
Option 2: use the api_key parameter to pass in API key for completion, embedding, image_generation API calls.
<Tabs>
<TabItem value="sdk" label="SDK">
```python
response = completion(
model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
@ -50,7 +52,17 @@ response = completion(
api_key="your-api-key"
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
model_list:
- model_name: bedrock-claude-3-sonnet
litellm_params:
model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
api_key: os.environ/AWS_BEARER_TOKEN_BEDROCK
```
</TabItem>
</Tabs>
## Usage
@ -1683,6 +1695,131 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
</TabItem>
</Tabs>
## TwelveLabs Pegasus - Video Understanding
TwelveLabs Pegasus 1.2 is a video understanding model that can analyze and describe video content. LiteLLM supports this model through Bedrock's `/invoke` endpoint.
| Property | Details |
|----------|---------|
| Provider Route | `bedrock/us.twelvelabs.pegasus-1-2-v1:0`, `bedrock/eu.twelvelabs.pegasus-1-2-v1:0` |
| Provider Documentation | [TwelveLabs Pegasus Docs ↗](https://docs.twelvelabs.io/docs/models/pegasus) |
| Supported Parameters | `max_tokens`, `temperature`, `response_format` |
| Media Input | S3 URI or base64-encoded video |
### Supported Features
- **Video Analysis**: Analyze video content from S3 or base64 input
- **Structured Output**: Support for JSON schema response format
- **S3 Integration**: Support for S3 video URLs with bucket owner specification
### Usage with S3 Video
<Tabs>
<TabItem value="sdk" label="SDK">
```python title="TwelveLabs Pegasus SDK Usage" showLineNumbers
from litellm import completion
import os
# Set AWS credentials
os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key"
os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-key"
os.environ["AWS_REGION_NAME"] = "us-east-1"
response = completion(
model="bedrock/us.twelvelabs.pegasus-1-2-v1:0",
messages=[{"role": "user", "content": "Describe what happens in this video."}],
mediaSource={
"s3Location": {
"uri": "s3://your-bucket/video.mp4",
"bucketOwner": "123456789012", # 12-digit AWS account ID
}
},
temperature=0.2
)
print(response.choices[0].message.content)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml title="config.yaml" showLineNumbers
model_list:
- model_name: pegasus-video
litellm_params:
model: bedrock/us.twelvelabs.pegasus-1-2-v1:0
aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
aws_region_name: os.environ/AWS_REGION_NAME
```
**2. Start proxy**
```bash title="Start LiteLLM Proxy" showLineNumbers
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash title="Test Pegasus via Proxy" showLineNumbers
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "pegasus-video",
"messages": [
{
"role": "user",
"content": "Describe what happens in this video."
}
],
"mediaSource": {
"s3Location": {
"uri": "s3://your-bucket/video.mp4",
"bucketOwner": "123456789012"
}
},
"temperature": 0.2
}'
```
</TabItem>
</Tabs>
### Usage with Base64 Video
You can also pass video content directly as base64:
```python title="Base64 Video Input" showLineNumbers
from litellm import completion
import base64
# Read video file and encode to base64
with open("video.mp4", "rb") as video_file:
video_base64 = base64.b64encode(video_file.read()).decode("utf-8")
response = completion(
model="bedrock/us.twelvelabs.pegasus-1-2-v1:0",
messages=[{"role": "user", "content": "What is happening in this video?"}],
mediaSource={
"base64String": video_base64
},
temperature=0.2,
)
print(response.choices[0].message.content)
```
### Important Notes
- **Response Format**: The model supports structured output via `response_format` with JSON schema
## Provisioned throughput models
To use provisioned throughput Bedrock models pass
- `model=bedrock/<base-model>`, example `model=bedrock/anthropic.claude-v2`. Set `model` to any of the [Supported AWS models](#supported-aws-bedrock-models)
@ -1743,6 +1880,8 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re
| Meta Llama 2 Chat 70b | `completion(model='bedrock/meta.llama2-70b-chat-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
| Mistral 7B Instruct | `completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
| Mixtral 8x7B Instruct | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
| TwelveLabs Pegasus 1.2 (US) | `completion(model='bedrock/us.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
| TwelveLabs Pegasus 1.2 (EU) | `completion(model='bedrock/eu.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` |
## Bedrock Embedding

91
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@ -172,6 +172,97 @@ curl http://localhost:4000/v1/batches \
</TabItem>
</Tabs>
### 4. Retrieve batch results
Once the batch job is completed, download the results from S3:
<Tabs>
<TabItem value="python" label="Python">
```python showLineNumbers title="bedrock_batch.py"
...
# Wait for batch completion (check status periodically)
batch_status = client.batches.retrieve(batch_id=batch.id)
if batch_status.status == "completed":
# Download the output file
result = client.files.content(
file_id=batch_status.output_file_id,
extra_headers={"custom-llm-provider": "bedrock"}
)
# Save or process the results
with open("batch_output.jsonl", "wb") as f:
f.write(result.content)
# Parse JSONL results
for line in result.text.strip().split('\n'):
record = json.loads(line)
print(f"Record ID: {record['recordId']}")
print(f"Output: {record.get('modelOutput', {})}")
```
</TabItem>
<TabItem value="curl" label="Curl">
```bash showLineNumbers title="Download Batch Results"
# First retrieve batch to get output_file_id
curl http://localhost:4000/v1/batches/batch_abc123 \
-H "Authorization: Bearer sk-1234"
# Then download the output file
curl http://localhost:4000/v1/files/{output_file_id}/content \
-H "Authorization: Bearer sk-1234" \
-H "custom-llm-provider: bedrock" \
-o batch_output.jsonl
```
</TabItem>
<TabItem value="litellm-direct" label="LiteLLM Direct">
```python showLineNumbers title="bedrock_batch.py"
import litellm
from litellm import file_content
# Download using litellm directly (bypasses proxy managed files)
result = file_content(
file_id=batch_status.output_file_id, # Can be S3 URI or unified file ID
custom_llm_provider="bedrock",
aws_region_name="us-west-2",
)
# Process results
print(result.text)
```
</TabItem>
</Tabs>
**Output Format:**
The batch output file is in JSONL format with each line containing:
```json
{
"recordId": "request-1",
"modelInput": {
"messages": [...],
"max_tokens": 1000
},
"modelOutput": {
"content": [...],
"id": "msg_abc123",
"model": "claude-3-5-sonnet-20240620-v1:0",
"role": "assistant",
"stop_reason": "end_turn",
"usage": {
"input_tokens": 15,
"output_tokens": 10
}
}
}
```
## FAQ
### Where are my files written?

51
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@ -4,7 +4,8 @@
| Provider | LiteLLM Route | AWS Documentation | Cost Tracking |
|----------|---------------|-------------------|---------------|
| Amazon Titan | `bedrock/amazon.*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | ✅ |
| Amazon Titan | `bedrock/amazon.titan-*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | ✅ |
| Amazon Nova | `bedrock/amazon.nova-*` | [Amazon Nova Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/nova-embed.html) | ✅ |
| Cohere | `bedrock/cohere.*` | [Cohere Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-embed.html) | ✅ |
| TwelveLabs | `bedrock/us.twelvelabs.*` | [TwelveLabs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-twelvelabs.html) | ✅ |
@ -16,6 +17,7 @@ LiteLLM supports AWS Bedrock's async-invoke feature for embedding models that re
| Provider | Async Invoke Route | Use Case |
|----------|-------------------|----------|
| Amazon Nova | `bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0` | Multimodal embeddings with segmentation for long text, video, and audio |
| TwelveLabs Marengo | `bedrock/async_invoke/us.twelvelabs.marengo-embed-2-7-v1:0` | Video, audio, image, and text embeddings |
### Required Parameters
@ -116,7 +118,7 @@ def check_async_job_status(invocation_arn, aws_region_name="us-east-1"):
"""Check the status of an async invoke job using LiteLLM batch API"""
try:
response = retrieve_batch(
batch_id=invocation_arn,
batch_id=invocation_arn, # Pass the invocation ARN here
custom_llm_provider="bedrock",
aws_region_name=aws_region_name
)
@ -128,11 +130,47 @@ def check_async_job_status(invocation_arn, aws_region_name="us-east-1"):
# Check status
status = check_async_job_status(invocation_arn, "us-east-1")
if status:
print(f"Job Status: {status.status}")
print(f"Output Location: {status.output_file_id}")
print(f"Job Status: {status.status}") # "in_progress", "completed", or "failed"
print(f"Output Location: {status.metadata['output_file_id']}") # S3 URI where results are stored
```
**Note:** The actual embedding results are stored in S3. The `output_file_id` from the batch status can be used to locate the results file in your S3 bucket.
#### Polling Until Complete
Here's a complete example of polling for job completion:
```python
def wait_for_async_job(invocation_arn, aws_region_name="us-east-1", max_wait=3600):
"""Poll job status until completion"""
start_time = time.time()
while True:
status = retrieve_batch(
batch_id=invocation_arn,
custom_llm_provider="bedrock",
aws_region_name=aws_region_name,
)
if status.status == "completed":
print("✅ Job completed!")
return status
elif status.status == "failed":
error_msg = status.metadata.get('failure_message', 'Unknown error')
raise Exception(f"❌ Job failed: {error_msg}")
else:
elapsed = time.time() - start_time
if elapsed > max_wait:
raise TimeoutError(f"Job timed out after {max_wait} seconds")
print(f"⏳ Job still processing... (elapsed: {elapsed:.0f}s)")
time.sleep(10) # Wait 10 seconds before checking again
# Wait for completion
completed_status = wait_for_async_job(invocation_arn)
output_s3_uri = completed_status.metadata['output_file_id']
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.
### Error Handling
@ -179,7 +217,7 @@ except Exception as e:
### Limitations
- Async-invoke is currently only supported for TwelveLabs Marengo models
- Async-invoke is supported for TwelveLabs Marengo and Amazon Nova models
- Results are stored in S3 and must be retrieved separately using the output file ID
- Job status checking requires using LiteLLM's `retrieve_batch()` function
- No built-in polling mechanism in LiteLLM (must implement your own status checking loop)
@ -259,6 +297,7 @@ print(response)
| Model Name | Usage | Supported Additional OpenAI params |
|----------------------|---------------------------------------------|-----|
| **Amazon Nova Multimodal Embeddings** | `embedding(model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", input=input)` | Supports multimodal input (text, image, video, audio), multiple purposes, dimensions (256, 384, 1024, 3072) |
| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) |
| Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53)
| Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) |

65
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@ -203,6 +203,71 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
</TabItem>
</Tabs>
### Qwen2 Imported Models
| Property | Details |
|----------|---------|
| Provider Route | `bedrock/qwen2/{model_arn}` |
| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html) |
| Note | Qwen2 and Qwen3 architectures are mostly similar. The main difference is in the response format: Qwen2 uses "text" field while Qwen3 uses "generation" field. |
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
response = completion(
model="bedrock/qwen2/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen2-model", # bedrock/qwen2/{your-model-arn}
messages=[{"role": "user", "content": "Tell me a joke"}],
max_tokens=100,
temperature=0.7
)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
**1. Add to config**
```yaml
model_list:
- model_name: Qwen2-72B
litellm_params:
model: bedrock/qwen2/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen2-model
```
**2. Start proxy**
```bash
litellm --config /path/to/config.yaml
# RUNNING at http://0.0.0.0:4000
```
**3. Test it!**
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"model": "Qwen2-72B", # 👈 the 'model_name' in config
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
</TabItem>
</Tabs>
### OpenAI-Compatible Imported Models (Qwen 2.5 VL, etc.)
Use this route for Bedrock imported models that follow the **OpenAI Chat Completions API spec**. This includes models like Qwen 2.5 VL that accept OpenAI-formatted messages with support for vision (images), tool calling, and other OpenAI features.

31
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@ -2006,3 +2006,34 @@ curl -L -X POST 'http://localhost:4000/v1/chat/completions' \
</TabItem>
</Tabs>
### Image Generation Pricing
Gemini image generation models (like `gemini-3-pro-image-preview`) return `image_tokens` in the response usage. These tokens are priced differently from text tokens:
| Token Type | Price per 1M tokens | Price per token |
|------------|---------------------|-----------------|
| Text output | $12 | $0.000012 |
| Image output | $120 | $0.00012 |
The number of image tokens depends on the output resolution:
| Resolution | Tokens per image | Cost per image |
|------------|------------------|----------------|
| 1K-2K (1024x1024 to 2048x2048) | 1,120 | $0.134 |
| 4K (4096x4096) | 2,000 | $0.24 |
LiteLLM automatically calculates costs using `output_cost_per_image_token` from the model pricing configuration.
**Example response usage:**
```json
{
"completion_tokens_details": {
"reasoning_tokens": 225,
"text_tokens": 0,
"image_tokens": 1120
}
}
```
For more details, see [Google's Gemini pricing documentation](https://ai.google.dev/gemini-api/docs/pricing).

42
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@ -15,7 +15,7 @@ https://docs.github.com/en/copilot
|-------|-------|
| Description | GitHub Copilot Chat API provides access to GitHub's AI-powered coding assistant. |
| Provider Route on LiteLLM | `github_copilot/` |
| Supported Endpoints | `/chat/completions` |
| Supported Endpoints | `/chat/completions`, `/embeddings` |
| API Reference | [GitHub Copilot docs](https://docs.github.com/en/copilot) |
## Authentication
@ -62,6 +62,34 @@ for chunk in stream:
print(chunk.choices[0].delta.content, end="")
```
### Responses
For GPT Codex models, only responses API is supported.
```python showLineNumbers title="GitHub Copilot Responses"
import litellm
response = await litellm.aresponses(
model="github_copilot/gpt-5.1-codex",
input="Write a Python hello world",
max_output_tokens=500
)
print(response)
```
### Embedding
```python showLineNumbers title="GitHub Copilot Embedding"
import litellm
response = litellm.embedding(
model="github_copilot/text-embedding-3-small",
input=["good morning from litellm"]
)
print(response)
```
## Usage - LiteLLM Proxy
Add the following to your LiteLLM Proxy configuration file:
@ -71,6 +99,16 @@ model_list:
- model_name: github_copilot/gpt-4
litellm_params:
model: github_copilot/gpt-4
- model_name: github_copilot/gpt-5.1-codex
model_info:
mode: responses
litellm_params:
model: github_copilot/gpt-5.1-codex
- model_name: github_copilot/text-embedding-ada-002
model_info:
mode: embedding
litellm_params:
model: github_copilot/text-embedding-ada-002
```
Start your LiteLLM Proxy server:
@ -180,7 +218,7 @@ extra_headers = {
"editor-version": "vscode/1.85.1", # Editor version
"editor-plugin-version": "copilot/1.155.0", # Plugin version
"Copilot-Integration-Id": "vscode-chat", # Integration ID
"user-agent": "GithubCopilot/1.155.0" # User agent
"user-agent": "GithubCopilot/1.155.0" # User agent
}
```

9
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@ -191,6 +191,7 @@ os.environ["OPENAI_BASE_URL"] = "https://your_host/v1" # OPTIONAL
| gpt-5.1 | `response = completion(model="gpt-5.1", messages=messages)` |
| gpt-5.1-codex | `response = completion(model="gpt-5.1-codex", messages=messages)` |
| gpt-5.1-codex-mini | `response = completion(model="gpt-5.1-codex-mini", messages=messages)` |
| gpt-5.1-codex-max | `response = completion(model="gpt-5.1-codex-max", messages=messages)` |
| gpt-4.1 | `response = completion(model="gpt-4.1", messages=messages)` |
| gpt-4.1-mini | `response = completion(model="gpt-4.1-mini", messages=messages)` |
| gpt-4.1-nano | `response = completion(model="gpt-4.1-nano", messages=messages)` |
@ -427,7 +428,7 @@ Expected Response:
### Advanced: Using `reasoning_effort` with `summary` field
By default, `reasoning_effort` accepts a string value (`"none"`, `"minimal"`, `"low"`, `"medium"`, `"high"`) and only sets the effort level without including a reasoning summary.
By default, `reasoning_effort` accepts a string value (`"none"`, `"minimal"`, `"low"`, `"medium"`, `"high"`, `"xhigh"`—`"xhigh"` is only supported on `gpt-5.1-codex-max`) and only sets the effort level without including a reasoning summary.
To opt-in to the `summary` feature, you can pass `reasoning_effort` as a dictionary. **Note:** The `summary` field requires your OpenAI organization to have verification status. Using `summary` without verification will result in a 400 error from OpenAI.
@ -494,10 +495,12 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
| `gpt-5-codex` | `adaptive` | `low`, `medium`, `high` (no `minimal`) |
| `gpt-5.1-codex` | `adaptive` | `low`, `medium`, `high` (no `minimal`) |
| `gpt-5.1-codex-mini` | `adaptive` | `low`, `medium`, `high` (no `minimal`) |
| `gpt-5.1-codex-max` | `adaptive` | `low`, `medium`, `high`, `xhigh` (no `minimal`) |
| `gpt-5-pro` | `high` | `high` only |
**Note:**
- GPT-5.1 introduced a new `reasoning_effort="none"` setting for faster, lower-latency responses. This replaces the `"minimal"` setting from GPT-5.
- `gpt-5.1-codex-max` is the only model that supports `reasoning_effort="xhigh"`. All other models will reject this value.
- `gpt-5-pro` only accepts `reasoning_effort="high"`. Other values will return an error.
- When `reasoning_effort` is not set (None), OpenAI defaults to the value shown in the "Default" column.
@ -509,7 +512,7 @@ The `verbosity` parameter controls the length and detail of responses from GPT-5
**Supported models:** `gpt-5`, `gpt-5.1`, `gpt-5-mini`, `gpt-5-nano`, `gpt-5-pro`
**Note:** GPT-5-Codex models (`gpt-5-codex`, `gpt-5.1-codex`, `gpt-5.1-codex-mini`) do **not** support the `verbosity` parameter.
**Note:** GPT-5-Codex models (`gpt-5-codex`, `gpt-5.1-codex`, `gpt-5.1-codex-mini`, `gpt-5.1-codex-max`) do **not** support the `verbosity` parameter.
**Use cases:**
- **`"low"`**: Best for concise answers or simple code generation (e.g., SQL queries)
@ -988,4 +991,4 @@ response = completion(
LiteLLM supports OpenAI's video generation models including Sora.
For detailed documentation on video generation, see [OpenAI Video Generation →](./openai/video_generation.md)
For detailed documentation on video generation, see [OpenAI Video Generation →](./openai/video_generation.md)

4
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@ -11,7 +11,7 @@ Selecting `openai` as the provider routes your request to an OpenAI-compatible e
This library **requires** an API key for all requests, either through the `api_key` parameter
or the `OPENAI_API_KEY` environment variable.
If you don’t want to provide a fake API key in each request, consider using a provider that directly matches your
If you don't want to provide a fake API key in each request, consider using a provider that directly matches your
OpenAI-compatible endpoint, such as [`hosted_vllm`](/docs/providers/vllm) or [`llamafile`](/docs/providers/llamafile).
:::
@ -150,4 +150,4 @@ model_list:
api_base: http://my-custom-base
api_key: ""
supports_system_message: False # 👈 KEY CHANGE
```
```

15
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View file

@ -311,6 +311,21 @@ response = embedding(
print(response.data)
```
### Audio Transcription
```python
from litellm import transcription
audio_file = open("path/to/your/audio.wav", "rb")
response = transcription(
model="ovhcloud/whisper-large-v3-turbo",
file=audio_file
)
print(response.text)
```
## Usage with LiteLLM Proxy Server
Here's how to call a OVHCloud AI Endpoints model with the LiteLLM Proxy Server

244
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@ -0,0 +1,244 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# RAGFlow
Litellm supports Ragflow's chat completions APIs
## Supported Features
- ✅ Chat completions
- ✅ Streaming responses
- ✅ Both chat and agent endpoints
- ✅ Multiple credential sources (params, env vars, litellm_params)
- ✅ OpenAI-compatible API format
## API Key
```python
# env variable
os.environ['RAGFLOW_API_KEY']
```
## API Base
```python
# env variable
os.environ['RAGFLOW_API_BASE']
```
## Overview
RAGFlow provides OpenAI-compatible APIs with unique path structures that include chat and agent IDs:
- **Chat endpoint**: `/api/v1/chats_openai/{chat_id}/chat/completions`
- **Agent endpoint**: `/api/v1/agents_openai/{agent_id}/chat/completions`
The model name format embeds the endpoint type and ID:
- Chat: `ragflow/chat/{chat_id}/{model_name}`
- Agent: `ragflow/agent/{agent_id}/{model_name}`
## Sample Usage - Chat Endpoint
```python
from litellm import completion
import os
os.environ['RAGFLOW_API_KEY'] = "your-ragflow-api-key"
os.environ['RAGFLOW_API_BASE'] = "http://localhost:9380" # or your hosted URL
response = completion(
model="ragflow/chat/my-chat-id/gpt-4o-mini",
messages=[{"role": "user", "content": "How does the deep doc understanding work?"}]
)
print(response)
```
## Sample Usage - Agent Endpoint
```python
from litellm import completion
import os
os.environ['RAGFLOW_API_KEY'] = "your-ragflow-api-key"
os.environ['RAGFLOW_API_BASE'] = "http://localhost:9380" # or your hosted URL
response = completion(
model="ragflow/agent/my-agent-id/gpt-4o-mini",
messages=[{"role": "user", "content": "What are the key features?"}]
)
print(response)
```
## Sample Usage - With Parameters
You can also pass `api_key` and `api_base` directly as parameters:
```python
from litellm import completion
response = completion(
model="ragflow/chat/my-chat-id/gpt-4o-mini",
messages=[{"role": "user", "content": "Hello!"}],
api_key="your-ragflow-api-key",
api_base="http://localhost:9380"
)
print(response)
```
## Sample Usage - Streaming
```python
from litellm import completion
import os
os.environ['RAGFLOW_API_KEY'] = "your-ragflow-api-key"
os.environ['RAGFLOW_API_BASE'] = "http://localhost:9380"
response = completion(
model="ragflow/agent/my-agent-id/gpt-4o-mini",
messages=[{"role": "user", "content": "Explain RAGFlow"}],
stream=True
)
for chunk in response:
print(chunk)
```
## Model Name Format
The model name must follow one of these formats:
### Chat Endpoint
```
ragflow/chat/{chat_id}/{model_name}
```
Example: `ragflow/chat/my-chat-id/gpt-4o-mini`
### Agent Endpoint
```
ragflow/agent/{agent_id}/{model_name}
```
Example: `ragflow/agent/my-agent-id/gpt-4o-mini`
Where:
- `{chat_id}` or `{agent_id}` is the ID of your chat or agent in RAGFlow
- `{model_name}` is the actual model name (e.g., `gpt-4o-mini`, `gpt-4o`, etc.)
## Configuration Sources
LiteLLM supports multiple ways to provide credentials, checked in this order:
1. **Function parameters**: `api_key="..."`, `api_base="..."`
2. **litellm_params**: `litellm_params={"api_key": "...", "api_base": "..."}`
3. **Environment variables**: `RAGFLOW_API_KEY`, `RAGFLOW_API_BASE`
4. **Global litellm settings**: `litellm.api_key`, `litellm.api_base`
## Usage - LiteLLM Proxy Server
### 1. Save key in your environment
```bash
export RAGFLOW_API_KEY="your-ragflow-api-key"
export RAGFLOW_API_BASE="http://localhost:9380"
```
### 2. Start the proxy
<Tabs>
<TabItem value="config" label="config.yaml">
```yaml
model_list:
- model_name: ragflow-chat-gpt4
litellm_params:
model: ragflow/chat/my-chat-id/gpt-4o-mini
api_key: os.environ/RAGFLOW_API_KEY
api_base: os.environ/RAGFLOW_API_BASE
- model_name: ragflow-agent-gpt4
litellm_params:
model: ragflow/agent/my-agent-id/gpt-4o-mini
api_key: os.environ/RAGFLOW_API_KEY
api_base: os.environ/RAGFLOW_API_BASE
```
</TabItem>
<TabItem value="cli" label="CLI">
```bash
$ litellm --config /path/to/config.yaml
# Server running on http://0.0.0.0:4000
```
</TabItem>
</Tabs>
### 3. Test it
<Tabs>
<TabItem value="Curl" label="Curl Request">
```bash
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-d '{
"model": "ragflow-chat-gpt4",
"messages": [
{"role": "user", "content": "How does RAGFlow work?"}
]
}'
```
</TabItem>
<TabItem value="Python" label="Python SDK">
```python
from openai import OpenAI
client = OpenAI(
api_key="sk-1234", # Your LiteLLM proxy key
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(
model="ragflow-chat-gpt4",
messages=[
{"role": "user", "content": "How does RAGFlow work?"}
]
)
print(response)
```
</TabItem>
</Tabs>
## API Base URL Handling
The `api_base` parameter can be provided with or without `/v1` suffix. LiteLLM will automatically handle it:
- `http://localhost:9380` → `http://localhost:9380/api/v1/chats_openai/{chat_id}/chat/completions`
- `http://localhost:9380/v1` → `http://localhost:9380/api/v1/chats_openai/{chat_id}/chat/completions`
- `http://localhost:9380/api/v1` → `http://localhost:9380/api/v1/chats_openai/{chat_id}/chat/completions`
All three formats will work correctly.
## Error Handling
If you encounter errors:
1. **Invalid model format**: Ensure your model name follows `ragflow/{chat|agent}/{id}/{model_name}` format
2. **Missing api_base**: Provide `api_base` via parameter, environment variable, or litellm_params
3. **Connection errors**: Verify your RAGFlow server is running and accessible at the provided `api_base`
:::info
For more information about passing provider-specific parameters, [go here](../completion/provider_specific_params.md)
:::

349
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@ -0,0 +1,349 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Image from '@theme/IdealImage';
# RAGFlow Vector Stores
Litellm support creation and management of datasets for document processing and knowledge base management in Ragflow.
| Property | Details |
|----------|---------|
| Description | RAGFlow datasets enable document processing, chunking, and knowledge base management for RAG applications. |
| Provider Route on LiteLLM | `ragflow` in the litellm vector_store_registry |
| Provider Doc | [RAGFlow API Documentation ↗](https://ragflow.io/docs) |
| Supported Operations | Dataset Management (Create, List, Update, Delete) |
| Search/Retrieval | ❌ Not supported (management only) |
## Quick Start
### LiteLLM Python SDK
```python showLineNumbers title="Example using LiteLLM Python SDK"
import os
import litellm
# Set RAGFlow credentials
os.environ["RAGFLOW_API_KEY"] = "your-ragflow-api-key"
os.environ["RAGFLOW_API_BASE"] = "http://localhost:9380" # Optional, defaults to localhost:9380
# Create a RAGFlow dataset
response = litellm.vector_stores.create(
name="my-dataset",
custom_llm_provider="ragflow",
metadata={
"description": "My knowledge base dataset",
"embedding_model": "BAAI/bge-large-zh-v1.5@BAAI",
"chunk_method": "naive"
}
)
print(f"Created dataset ID: {response.id}")
print(f"Dataset name: {response.name}")
```
### LiteLLM Proxy
#### 1. Configure your vector_store_registry
<Tabs>
<TabItem value="config-yaml" label="config.yaml">
```yaml
model_list:
- model_name: gpt-4o-mini
litellm_params:
model: gpt-4o-mini
api_key: os.environ/OPENAI_API_KEY
vector_store_registry:
- vector_store_name: "ragflow-knowledge-base"
litellm_params:
vector_store_id: "your-dataset-id"
custom_llm_provider: "ragflow"
api_key: os.environ/RAGFLOW_API_KEY
api_base: os.environ/RAGFLOW_API_BASE # Optional
vector_store_description: "RAGFlow dataset for knowledge base"
vector_store_metadata:
source: "Company documentation"
```
</TabItem>
<TabItem value="litellm-ui" label="LiteLLM UI">
On the LiteLLM UI, Navigate to Experimental > Vector Stores > Create Vector Store. On this page you can create a vector store with a name, vector store id and credentials.
<Image
img={require('../../img/kb_2.png')}
style={{width: '50%'}}
/>
</TabItem>
</Tabs>
#### 2. Create a dataset via Proxy
<Tabs>
<TabItem value="curl" label="Curl">
```bash
curl http://localhost:4000/v1/vector_stores \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-d '{
"name": "my-ragflow-dataset",
"custom_llm_provider": "ragflow",
"metadata": {
"description": "Test dataset",
"chunk_method": "naive"
}
}'
```
</TabItem>
<TabItem value="openai-sdk" label="OpenAI Python SDK">
```python
from openai import OpenAI
# Initialize client with your LiteLLM proxy URL
client = OpenAI(
base_url="http://localhost:4000",
api_key="your-litellm-api-key"
)
# Create a RAGFlow dataset
response = client.vector_stores.create(
name="my-ragflow-dataset",
custom_llm_provider="ragflow",
metadata={
"description": "Test dataset",
"chunk_method": "naive"
}
)
print(f"Created dataset: {response.id}")
```
</TabItem>
</Tabs>
## Configuration
### Environment Variables
RAGFlow vector stores support configuration via environment variables:
- `RAGFLOW_API_KEY` - Your RAGFlow API key (required)
- `RAGFLOW_API_BASE` - RAGFlow API base URL (optional, defaults to `http://localhost:9380`)
### Parameters
You can also pass these via `litellm_params`:
- `api_key` - RAGFlow API key (overrides `RAGFLOW_API_KEY` env var)
- `api_base` - RAGFlow API base URL (overrides `RAGFLOW_API_BASE` env var)
## Dataset Creation Options
### Basic Dataset Creation
```python
response = litellm.vector_stores.create(
name="basic-dataset",
custom_llm_provider="ragflow"
)
```
### Dataset with Chunk Method
RAGFlow supports various chunk methods for different document types:
<Tabs>
<TabItem value="naive" label="Naive (General)">
```python
response = litellm.vector_stores.create(
name="general-dataset",
custom_llm_provider="ragflow",
metadata={
"chunk_method": "naive",
"parser_config": {
"chunk_token_num": 512,
"delimiter": "\n",
"html4excel": False,
"layout_recognize": "DeepDOC"
}
}
)
```
</TabItem>
<TabItem value="book" label="Book">
```python
response = litellm.vector_stores.create(
name="book-dataset",
custom_llm_provider="ragflow",
metadata={
"chunk_method": "book",
"parser_config": {
"raptor": {
"use_raptor": False
}
}
}
)
```
</TabItem>
<TabItem value="qa" label="Q&A">
```python
response = litellm.vector_stores.create(
name="qa-dataset",
custom_llm_provider="ragflow",
metadata={
"chunk_method": "qa",
"parser_config": {
"raptor": {
"use_raptor": False
}
}
}
)
```
</TabItem>
<TabItem value="paper" label="Paper">
```python
response = litellm.vector_stores.create(
name="paper-dataset",
custom_llm_provider="ragflow",
metadata={
"chunk_method": "paper",
"parser_config": {
"raptor": {
"use_raptor": False
}
}
}
)
```
</TabItem>
</Tabs>
### Dataset with Ingestion Pipeline
Instead of using a chunk method, you can use an ingestion pipeline:
```python
response = litellm.vector_stores.create(
name="pipeline-dataset",
custom_llm_provider="ragflow",
metadata={
"parse_type": 2, # Number of parsers in your pipeline
"pipeline_id": "d0bebe30ae2211f0970942010a8e0005" # 32-character hex ID
}
)
```
**Note**: `chunk_method` and `pipeline_id` are mutually exclusive. Use one or the other.
### Advanced Parser Configuration
```python
response = litellm.vector_stores.create(
name="advanced-dataset",
custom_llm_provider="ragflow",
metadata={
"chunk_method": "naive",
"description": "Advanced dataset with custom parser config",
"embedding_model": "BAAI/bge-large-zh-v1.5@BAAI",
"permission": "me", # or "team"
"parser_config": {
"chunk_token_num": 1024,
"delimiter": "\n!?;。；！？",
"html4excel": True,
"layout_recognize": "DeepDOC",
"auto_keywords": 5,
"auto_questions": 3,
"task_page_size": 12,
"raptor": {
"use_raptor": True
},
"graphrag": {
"use_graphrag": False
}
}
}
)
```
## Supported Chunk Methods
RAGFlow supports the following chunk methods:
- `naive` - General purpose (default)
- `book` - For book documents
- `email` - For email documents
- `laws` - For legal documents
- `manual` - Manual chunking
- `one` - Single chunk
- `paper` - For academic papers
- `picture` - For image documents
- `presentation` - For presentation documents
- `qa` - Q&A format
- `table` - For table documents
- `tag` - Tag-based chunking
## RAGFlow-Specific Parameters
All RAGFlow-specific parameters should be passed via the `metadata` field:
| Parameter | Type | Description |
|-----------|------|-------------|
| `avatar` | string | Base64 encoding of the avatar (max 65535 chars) |
| `description` | string | Brief description of the dataset (max 65535 chars) |
| `embedding_model` | string | Embedding model name (e.g., "BAAI/bge-large-zh-v1.5@BAAI") |
| `permission` | string | Access permission: "me" (default) or "team" |
| `chunk_method` | string | Chunking method (see supported methods above) |
| `parser_config` | object | Parser configuration (varies by chunk_method) |
| `parse_type` | int | Number of parsers in pipeline (required with pipeline_id) |
| `pipeline_id` | string | 32-character hex pipeline ID (required with parse_type) |
## Error Handling
RAGFlow returns error responses in the following format:
```json
{
"code": 101,
"message": "Dataset name 'my-dataset' already exists"
}
```
LiteLLM automatically maps these to appropriate exceptions:
- `code != 0` → Raises exception with the error message
- Missing required fields → Raises `ValueError`
- Mutually exclusive parameters → Raises `ValueError`
## Limitations
- **Search/Retrieval**: RAGFlow vector stores support dataset management only. Search operations are not supported and will raise `NotImplementedError`.
- **List/Update/Delete**: These operations are not yet implemented through the standard vector store API. Use RAGFlow's native API endpoints directly.
## Further Reading
Vector Stores:
- [Vector Store Creation](../vector_stores/create.md)
- [Using Vector Stores with Completions](../completion/knowledgebase.md)
- [Vector Store Registry](../completion/knowledgebase.md#vectorstoreregistry)

399
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@ -1604,6 +1604,56 @@ litellm.vertex_location = "us-central1 # Your Location
| gemini-2.5-flash-preview-09-2025 | `completion('gemini-2.5-flash-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-preview-09-2025', messages)` |
| gemini-2.5-flash-lite-preview-09-2025 | `completion('gemini-2.5-flash-lite-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-lite-preview-09-2025', messages)` |
## Private Service Connect (PSC) Endpoints
LiteLLM supports Vertex AI models deployed to Private Service Connect (PSC) endpoints, allowing you to use custom `api_base` URLs for private deployments.
### Usage
```python
from litellm import completion
# Use PSC endpoint with custom api_base
response = completion(
model="vertex_ai/1234567890", # Numeric endpoint ID
messages=[{"role": "user", "content": "Hello!"}],
api_base="http://10.96.32.8", # Your PSC endpoint
vertex_project="my-project-id",
vertex_location="us-central1",
use_psc_endpoint_format=True
)
```
**Key Features:**
- Supports both numeric endpoint IDs and custom model names
- Works with both completion and embedding endpoints
- Automatically constructs full PSC URL: `{api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint}`
- Compatible with streaming requests
### Configuration
Add PSC endpoints to your `config.yaml`:
```yaml
model_list:
- model_name: psc-gemini
litellm_params:
model: vertex_ai/1234567890 # Numeric endpoint ID
api_base: "http://10.96.32.8" # Your PSC endpoint
vertex_project: "my-project-id"
vertex_location: "us-central1"
vertex_credentials: "/path/to/service_account.json"
use_psc_endpoint_format: True
- model_name: psc-embedding
litellm_params:
model: vertex_ai/text-embedding-004
api_base: "http://10.96.32.8" # Your PSC endpoint
vertex_project: "my-project-id"
vertex_location: "us-central1"
vertex_credentials: "/path/to/service_account.json"
use_psc_endpoint_format: True
```
## Fine-tuned Models
You can call fine-tuned Vertex AI Gemini models through LiteLLM
@ -2550,355 +2600,6 @@ print(response)
</TabItem>
</Tabs>
## **Gemini TTS (Text-to-Speech) Audio Output**
:::info
LiteLLM supports Gemini TTS models on Vertex AI that can generate audio responses using the OpenAI-compatible `audio` parameter format.
:::
### Supported Models
LiteLLM supports Gemini TTS models with audio capabilities on Vertex AI (e.g. `vertex_ai/gemini-2.5-flash-preview-tts` and `vertex_ai/gemini-2.5-pro-preview-tts`). For the complete list of available TTS models and voices, see the [official Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation).
### Limitations
:::warning
**Important Limitations**:
- Gemini TTS models only support the `pcm16` audio format
- **Streaming support has not been added** to TTS models yet
- The `modalities` parameter must be set to `['audio']` for TTS requests
:::
### Quick Start
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import json
## GET CREDENTIALS
file_path = 'path/to/vertex_ai_service_account.json'
# Load the JSON file
with open(file_path, 'r') as file:
vertex_credentials = json.load(file)
# Convert to JSON string
vertex_credentials_json = json.dumps(vertex_credentials)
response = completion(
model="vertex_ai/gemini-2.5-flash-preview-tts",
messages=[{"role": "user", "content": "Say hello in a friendly voice"}],
modalities=["audio"], # Required for TTS models
audio={
"voice": "Kore",
"format": "pcm16" # Required: must be "pcm16"
},
vertex_credentials=vertex_credentials_json
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: gemini-tts-flash
litellm_params:
model: vertex_ai/gemini-2.5-flash-preview-tts
vertex_project: "your-project-id"
vertex_location: "us-central1"
vertex_credentials: "/path/to/service_account.json"
- model_name: gemini-tts-pro
litellm_params:
model: vertex_ai/gemini-2.5-pro-preview-tts
vertex_project: "your-project-id"
vertex_location: "us-central1"
vertex_credentials: "/path/to/service_account.json"
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Make TTS request
```bash
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
-d '{
"model": "gemini-tts-flash",
"messages": [{"role": "user", "content": "Say hello in a friendly voice"}],
"modalities": ["audio"],
"audio": {
"voice": "Kore",
"format": "pcm16"
}
}'
```
</TabItem>
</Tabs>
### Advanced Usage
You can combine TTS with other Gemini features:
```python
response = completion(
model="vertex_ai/gemini-2.5-pro-preview-tts",
messages=[
{"role": "system", "content": "You are a helpful assistant that speaks clearly."},
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
modalities=["audio"],
audio={
"voice": "Charon",
"format": "pcm16"
},
temperature=0.7,
max_tokens=150,
vertex_credentials=vertex_credentials_json
)
```
For more information about Gemini's TTS capabilities and available voices, see the [official Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation).
## **Text to Speech APIs**
:::info
LiteLLM supports calling [Vertex AI Text to Speech API](https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech) in the OpenAI text to speech API format
:::
### Usage - Basic
<Tabs>
<TabItem value="sdk" label="SDK">
Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param
**Sync Usage**
```python
speech_file_path = Path(__file__).parent / "speech_vertex.mp3"
response = litellm.speech(
model="vertex_ai/",
input="hello what llm guardrail do you have",
)
response.stream_to_file(speech_file_path)
```
**Async Usage**
```python
speech_file_path = Path(__file__).parent / "speech_vertex.mp3"
response = litellm.aspeech(
model="vertex_ai/",
input="hello what llm guardrail do you have",
)
response.stream_to_file(speech_file_path)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
1. Add model to config.yaml
```yaml
model_list:
- model_name: vertex-tts
litellm_params:
model: vertex_ai/ # Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param
vertex_project: "adroit-crow-413218"
vertex_location: "us-central1"
vertex_credentials: adroit-crow-413218-a956eef1a2a8.json
litellm_settings:
drop_params: True
```
2. Start Proxy
```
$ litellm --config /path/to/config.yaml
```
3. Make Request use OpenAI Python SDK
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# see supported values for "voice" on vertex here:
# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech
response = client.audio.speech.create(
model = "vertex-tts",
input="the quick brown fox jumped over the lazy dogs",
voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'}
)
print("response from proxy", response)
```
</TabItem>
</Tabs>
### Usage - `ssml` as input
Pass your `ssml` as input to the `input` param, if it contains `<speak>`, it will be automatically detected and passed as `ssml` to the Vertex AI API
If you need to force your `input` to be passed as `ssml`, set `use_ssml=True`
<Tabs>
<TabItem value="sdk" label="SDK">
Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param
```python
speech_file_path = Path(__file__).parent / "speech_vertex.mp3"
ssml = """
<speak>
<p>Hello, world!</p>
<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
</speak>
"""
response = litellm.speech(
input=ssml,
model="vertex_ai/test",
voice={
"languageCode": "en-UK",
"name": "en-UK-Studio-O",
},
audioConfig={
"audioEncoding": "LINEAR22",
"speakingRate": "10",
},
)
response.stream_to_file(speech_file_path)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
ssml = """
<speak>
<p>Hello, world!</p>
<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
</speak>
"""
# see supported values for "voice" on vertex here:
# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech
response = client.audio.speech.create(
model = "vertex-tts",
input=ssml,
voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'},
)
print("response from proxy", response)
```
</TabItem>
</Tabs>
### Forcing SSML Usage
You can force the use of SSML by setting the `use_ssml` parameter to `True`. This is useful when you want to ensure that your input is treated as SSML, even if it doesn't contain the `<speak>` tags.
Here are examples of how to force SSML usage:
<Tabs>
<TabItem value="sdk" label="SDK">
Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param
```python
speech_file_path = Path(__file__).parent / "speech_vertex.mp3"
ssml = """
<speak>
<p>Hello, world!</p>
<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
</speak>
"""
response = litellm.speech(
input=ssml,
use_ssml=True,
model="vertex_ai/test",
voice={
"languageCode": "en-UK",
"name": "en-UK-Studio-O",
},
audioConfig={
"audioEncoding": "LINEAR22",
"speakingRate": "10",
},
)
response.stream_to_file(speech_file_path)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
ssml = """
<speak>
<p>Hello, world!</p>
<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
</speak>
"""
# see supported values for "voice" on vertex here:
# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech
response = client.audio.speech.create(
model = "vertex-tts",
input=ssml, # pass as None since OpenAI SDK requires this param
voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'},
extra_body={"use_ssml": True},
)
print("response from proxy", response)
```
</TabItem>
</Tabs>
## **Fine Tuning APIs**

587
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@ -0,0 +1,587 @@
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Vertex AI Embedding
## Usage - Embedding
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import litellm
from litellm import embedding
litellm.vertex_project = "hardy-device-38811" # Your Project ID
litellm.vertex_location = "us-central1" # proj location
response = embedding(
model="vertex_ai/textembedding-gecko",
input=["good morning from litellm"],
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY">
1. Add model to config.yaml
```yaml
model_list:
- model_name: snowflake-arctic-embed-m-long-1731622468876
litellm_params:
model: vertex_ai/<your-model-id>
vertex_project: "adroit-crow-413218"
vertex_location: "us-central1"
vertex_credentials: adroit-crow-413218-a956eef1a2a8.json
litellm_settings:
drop_params: True
```
2. Start Proxy
```
$ litellm --config /path/to/config.yaml
```
3. Make Request using OpenAI Python SDK, Langchain Python SDK
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.embeddings.create(
model="snowflake-arctic-embed-m-long-1731622468876",
input = ["good morning from litellm", "this is another item"],
)
print(response)
```
</TabItem>
</Tabs>
#### Supported Embedding Models
All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a0249f630a6792d49dffc2c5d9b7/model_prices_and_context_window.json#L835) are supported
| Model Name | Function Call |
|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| text-embedding-004 | `embedding(model="vertex_ai/text-embedding-004", input)` |
| text-multilingual-embedding-002 | `embedding(model="vertex_ai/text-multilingual-embedding-002", input)` |
| textembedding-gecko | `embedding(model="vertex_ai/textembedding-gecko", input)` |
| textembedding-gecko-multilingual | `embedding(model="vertex_ai/textembedding-gecko-multilingual", input)` |
| textembedding-gecko-multilingual@001 | `embedding(model="vertex_ai/textembedding-gecko-multilingual@001", input)` |
| textembedding-gecko@001 | `embedding(model="vertex_ai/textembedding-gecko@001", input)` |
| textembedding-gecko@003 | `embedding(model="vertex_ai/textembedding-gecko@003", input)` |
| text-embedding-preview-0409 | `embedding(model="vertex_ai/text-embedding-preview-0409", input)` |
| text-multilingual-embedding-preview-0409 | `embedding(model="vertex_ai/text-multilingual-embedding-preview-0409", input)` |
| Fine-tuned OR Custom Embedding models | `embedding(model="vertex_ai/<your-model-id>", input)` |
### Supported OpenAI (Unified) Params
| [param](../embedding/supported_embedding.md#input-params-for-litellmembedding) | type | [vertex equivalent](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api) |
|-------|-------------|--------------------|
| `input` | **string or List[string]** | `instances` |
| `dimensions` | **int** | `output_dimensionality` |
| `input_type` | **Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]** | `task_type` |
#### Usage with OpenAI (Unified) Params
<Tabs>
<TabItem value="sdk" label="SDK">
```python
response = litellm.embedding(
model="vertex_ai/text-embedding-004",
input=["good morning from litellm", "gm"]
input_type = "RETRIEVAL_DOCUMENT",
dimensions=1,
)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY">
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.embeddings.create(
model="text-embedding-004",
input = ["good morning from litellm", "gm"],
dimensions=1,
extra_body = {
"input_type": "RETRIEVAL_QUERY",
}
)
print(response)
```
</TabItem>
</Tabs>
### Supported Vertex Specific Params
| param | type |
|-------|-------------|
| `auto_truncate` | **bool** |
| `task_type` | **Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]** |
| `title` | **str** |
#### Usage with Vertex Specific Params (Use `task_type` and `title`)
You can pass any vertex specific params to the embedding model. Just pass them to the embedding function like this:
[Relevant Vertex AI doc with all embedding params](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#request_body)
<Tabs>
<TabItem value="sdk" label="SDK">
```python
response = litellm.embedding(
model="vertex_ai/text-embedding-004",
input=["good morning from litellm", "gm"]
task_type = "RETRIEVAL_DOCUMENT",
title = "test",
dimensions=1,
auto_truncate=True,
)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY">
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.embeddings.create(
model="text-embedding-004",
input = ["good morning from litellm", "gm"],
dimensions=1,
extra_body = {
"task_type": "RETRIEVAL_QUERY",
"auto_truncate": True,
"title": "test",
}
)
print(response)
```
</TabItem>
</Tabs>
## **BGE Embeddings**
Use BGE (Baidu General Embedding) models deployed on Vertex AI.
### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
```python showLineNumbers title="Using BGE on Vertex AI"
import litellm
response = litellm.embedding(
model="vertex_ai/bge/<your-endpoint-id>",
input=["Hello", "World"],
vertex_project="your-project-id",
vertex_location="your-location"
)
print(response)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY">
1. Add model to config.yaml
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: bge-embedding
litellm_params:
model: vertex_ai/bge/<your-endpoint-id>
vertex_project: "your-project-id"
vertex_location: "us-central1"
vertex_credentials: your-credentials.json
litellm_settings:
drop_params: True
```
2. Start Proxy
```bash
$ litellm --config /path/to/config.yaml
```
3. Make Request using OpenAI Python SDK
```python showLineNumbers title="Making requests to BGE"
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.embeddings.create(
model="bge-embedding",
input=["good morning from litellm", "this is another item"]
)
print(response)
```
Using a Private Service Connect (PSC) endpoint
```yaml showLineNumbers title="config.yaml (PSC)"
model_list:
- model_name: bge-small-en-v1.5
litellm_params:
model: vertex_ai/bge/1234567890
api_base: http://10.96.32.8 # Your PSC IP
vertex_project: my-project-id #optional
vertex_location: us-central1 #optional
```
</TabItem>
</Tabs>
## **Multi-Modal Embeddings**
Known Limitations:
- Only supports 1 image / video / image per request
- Only supports GCS or base64 encoded images / videos
### Usage
<Tabs>
<TabItem value="sdk" label="SDK">
Using GCS Images
```python
response = await litellm.aembedding(
model="vertex_ai/multimodalembedding@001",
input="gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png" # will be sent as a gcs image
)
```
Using base 64 encoded images
```python
response = await litellm.aembedding(
model="vertex_ai/multimodalembedding@001",
input="data:image/jpeg;base64,..." # will be sent as a base64 encoded image
)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
1. Add model to config.yaml
```yaml
model_list:
- model_name: multimodalembedding@001
litellm_params:
model: vertex_ai/multimodalembedding@001
vertex_project: "adroit-crow-413218"
vertex_location: "us-central1"
vertex_credentials: adroit-crow-413218-a956eef1a2a8.json
litellm_settings:
drop_params: True
```
2. Start Proxy
```
$ litellm --config /path/to/config.yaml
```
3. Make Request use OpenAI Python SDK, Langchain Python SDK
<Tabs>
<TabItem value="OpenAI SDK" label="OpenAI SDK">
Requests with GCS Image / Video URI
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# # request sent to model set on litellm proxy, `litellm --model`
response = client.embeddings.create(
model="multimodalembedding@001",
input = "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png",
)
print(response)
```
Requests with base64 encoded images
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# # request sent to model set on litellm proxy, `litellm --model`
response = client.embeddings.create(
model="multimodalembedding@001",
input = "data:image/jpeg;base64,...",
)
print(response)
```
</TabItem>
<TabItem value="langchain" label="Langchain">
Requests with GCS Image / Video URI
```python
from langchain_openai import OpenAIEmbeddings
embeddings_models = "multimodalembedding@001"
embeddings = OpenAIEmbeddings(
model="multimodalembedding@001",
base_url="http://0.0.0.0:4000",
api_key="sk-1234", # type: ignore
)
query_result = embeddings.embed_query(
"gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
)
print(query_result)
```
Requests with base64 encoded images
```python
from langchain_openai import OpenAIEmbeddings
embeddings_models = "multimodalembedding@001"
embeddings = OpenAIEmbeddings(
model="multimodalembedding@001",
base_url="http://0.0.0.0:4000",
api_key="sk-1234", # type: ignore
)
query_result = embeddings.embed_query(
"data:image/jpeg;base64,..."
)
print(query_result)
```
</TabItem>
</Tabs>
</TabItem>
<TabItem value="proxy-vtx" label="LiteLLM PROXY (Vertex SDK)">
1. Add model to config.yaml
```yaml
default_vertex_config:
vertex_project: "adroit-crow-413218"
vertex_location: "us-central1"
vertex_credentials: adroit-crow-413218-a956eef1a2a8.json
```
2. Start Proxy
```
$ litellm --config /path/to/config.yaml
```
3. Make Request use OpenAI Python SDK
```python
import vertexai
from vertexai.vision_models import Image, MultiModalEmbeddingModel, Video
from vertexai.vision_models import VideoSegmentConfig
from google.auth.credentials import Credentials
LITELLM_PROXY_API_KEY = "sk-1234"
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"
import datetime
class CredentialsWrapper(Credentials):
def __init__(self, token=None):
super().__init__()
self.token = token
self.expiry = None # or set to a future date if needed
def refresh(self, request):
pass
def apply(self, headers, token=None):
headers['Authorization'] = f'Bearer {self.token}'
@property
def expired(self):
return False # Always consider the token as non-expired
@property
def valid(self):
return True # Always consider the credentials as valid
credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
vertexai.init(
project="adroit-crow-413218",
location="us-central1",
api_endpoint=LITELLM_PROXY_BASE,
credentials = credentials,
api_transport="rest",
)
model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")
image = Image.load_from_file(
"gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
)
embeddings = model.get_embeddings(
image=image,
contextual_text="Colosseum",
dimension=1408,
)
print(f"Image Embedding: {embeddings.image_embedding}")
print(f"Text Embedding: {embeddings.text_embedding}")
```
</TabItem>
</Tabs>
### Text + Image + Video Embeddings
<Tabs>
<TabItem value="sdk" label="SDK">
Text + Image
```python
response = await litellm.aembedding(
model="vertex_ai/multimodalembedding@001",
input=["hey", "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"] # will be sent as a gcs image
)
```
Text + Video
```python
response = await litellm.aembedding(
model="vertex_ai/multimodalembedding@001",
input=["hey", "gs://my-bucket/embeddings/supermarket-video.mp4"] # will be sent as a gcs image
)
```
Image + Video
```python
response = await litellm.aembedding(
model="vertex_ai/multimodalembedding@001",
input=["gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png", "gs://my-bucket/embeddings/supermarket-video.mp4"] # will be sent as a gcs image
)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM PROXY (Unified Endpoint)">
1. Add model to config.yaml
```yaml
model_list:
- model_name: multimodalembedding@001
litellm_params:
model: vertex_ai/multimodalembedding@001
vertex_project: "adroit-crow-413218"
vertex_location: "us-central1"
vertex_credentials: adroit-crow-413218-a956eef1a2a8.json
litellm_settings:
drop_params: True
```
2. Start Proxy
```
$ litellm --config /path/to/config.yaml
```
3. Make Request use OpenAI Python SDK, Langchain Python SDK
Text + Image
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# # request sent to model set on litellm proxy, `litellm --model`
response = client.embeddings.create(
model="multimodalembedding@001",
input = ["hey", "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"],
)
print(response)
```
Text + Video
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# # request sent to model set on litellm proxy, `litellm --model`
response = client.embeddings.create(
model="multimodalembedding@001",
input = ["hey", "gs://my-bucket/embeddings/supermarket-video.mp4"],
)
print(response)
```
Image + Video
```python
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# # request sent to model set on litellm proxy, `litellm --model`
response = client.embeddings.create(
model="multimodalembedding@001",
input = ["gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png", "gs://my-bucket/embeddings/supermarket-video.mp4"],
)
print(response)
```
</TabItem>
</Tabs>

423
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@ -0,0 +1,423 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Vertex AI Text to Speech
| Property | Details |
|-------|-------|
| Description | Google Cloud Text-to-Speech with Chirp3 HD voices and Gemini TTS |
| Provider Route on LiteLLM | `vertex_ai/chirp` (Chirp), `vertex_ai/gemini-*-tts` (Gemini) |
## Chirp3 HD Voices
Google Cloud Text-to-Speech API with high-quality Chirp3 HD voices.
### Quick Start
#### LiteLLM Python SDK
```python showLineNumbers title="Chirp3 Quick Start"
from litellm import speech
from pathlib import Path
speech_file_path = Path(__file__).parent / "speech.mp3"
response = speech(
model="vertex_ai/chirp",
voice="alloy", # OpenAI voice name - automatically mapped
input="Hello, this is Vertex AI Text to Speech",
vertex_project="your-project-id",
vertex_location="us-central1",
)
response.stream_to_file(speech_file_path)
```
#### LiteLLM AI Gateway
**1. Setup config.yaml**
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: vertex-tts
litellm_params:
model: vertex_ai/chirp
vertex_project: "your-project-id"
vertex_location: "us-central1"
vertex_credentials: "/path/to/service_account.json"
```
**2. Start the proxy**
```bash title="Start LiteLLM Proxy"
litellm --config /path/to/config.yaml
```
**3. Make requests**
<Tabs>
<TabItem value="curl" label="curl">
```bash showLineNumbers title="Chirp3 Quick Start"
curl http://0.0.0.0:4000/v1/audio/speech \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d '{
"model": "vertex-tts",
"voice": "alloy",
"input": "Hello, this is Vertex AI Text to Speech"
}' \
--output speech.mp3
```
</TabItem>
<TabItem value="openai-sdk" label="OpenAI Python SDK">
```python showLineNumbers title="Chirp3 Quick Start"
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.audio.speech.create(
model="vertex-tts",
voice="alloy",
input="Hello, this is Vertex AI Text to Speech",
)
response.stream_to_file("speech.mp3")
```
</TabItem>
</Tabs>
### Voice Mapping
LiteLLM maps OpenAI voice names to Google Cloud voices. You can use either OpenAI voices or Google Cloud voices directly.
| OpenAI Voice | Google Cloud Voice |
|-------------|-------------------|
| `alloy` | en-US-Studio-O |
| `echo` | en-US-Studio-M |
| `fable` | en-GB-Studio-B |
| `onyx` | en-US-Wavenet-D |
| `nova` | en-US-Studio-O |
| `shimmer` | en-US-Wavenet-F |
### Using Google Cloud Voices Directly
#### LiteLLM Python SDK
```python showLineNumbers title="Chirp3 HD Voice"
from litellm import speech
# Pass Chirp3 HD voice name directly
response = speech(
model="vertex_ai/chirp",
voice="en-US-Chirp3-HD-Charon",
input="Hello with a Chirp3 HD voice",
vertex_project="your-project-id",
)
response.stream_to_file("speech.mp3")
```
```python showLineNumbers title="Voice as Dict (Multilingual)"
from litellm import speech
# Pass as dict for full control over language and voice
response = speech(
model="vertex_ai/chirp",
voice={
"languageCode": "de-DE",
"name": "de-DE-Chirp3-HD-Charon",
},
input="Hallo, dies ist ein Test",
vertex_project="your-project-id",
)
response.stream_to_file("speech.mp3")
```
#### LiteLLM AI Gateway
<Tabs>
<TabItem value="curl" label="curl">
```bash showLineNumbers title="Chirp3 HD Voice"
curl http://0.0.0.0:4000/v1/audio/speech \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d '{
"model": "vertex-tts",
"voice": "en-US-Chirp3-HD-Charon",
"input": "Hello with a Chirp3 HD voice"
}' \
--output speech.mp3
```
```bash showLineNumbers title="Voice as Dict (Multilingual)"
curl http://0.0.0.0:4000/v1/audio/speech \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d '{
"model": "vertex-tts",
"voice": {"languageCode": "de-DE", "name": "de-DE-Chirp3-HD-Charon"},
"input": "Hallo, dies ist ein Test"
}' \
--output speech.mp3
```
</TabItem>
<TabItem value="openai-sdk" label="OpenAI Python SDK">
```python showLineNumbers title="Chirp3 HD Voice"
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.audio.speech.create(
model="vertex-tts",
voice="en-US-Chirp3-HD-Charon",
input="Hello with a Chirp3 HD voice",
)
response.stream_to_file("speech.mp3")
```
```python showLineNumbers title="Voice as Dict (Multilingual)"
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.audio.speech.create(
model="vertex-tts",
voice={"languageCode": "de-DE", "name": "de-DE-Chirp3-HD-Charon"},
input="Hallo, dies ist ein Test",
)
response.stream_to_file("speech.mp3")
```
</TabItem>
</Tabs>
Browse available voices: [Google Cloud Text-to-Speech Console](https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech)
### Passing Raw SSML
LiteLLM auto-detects SSML when your input contains `<speak>` tags and passes it through unchanged.
#### LiteLLM Python SDK
```python showLineNumbers title="SSML Input"
from litellm import speech
ssml = """
<speak>
<p>Hello, world!</p>
<p>This is a test of the <break strength="medium" /> text-to-speech API.</p>
</speak>
"""
response = speech(
model="vertex_ai/chirp",
voice="en-US-Studio-O",
input=ssml, # Auto-detected as SSML
vertex_project="your-project-id",
)
response.stream_to_file("speech.mp3")
```
```python showLineNumbers title="Force SSML Mode"
from litellm import speech
# Force SSML mode with use_ssml=True
response = speech(
model="vertex_ai/chirp",
voice="en-US-Studio-O",
input="<speak><prosody rate='slow'>Speaking slowly</prosody></speak>",
use_ssml=True,
vertex_project="your-project-id",
)
response.stream_to_file("speech.mp3")
```
#### LiteLLM AI Gateway
<Tabs>
<TabItem value="curl" label="curl">
```bash showLineNumbers title="SSML Input"
curl http://0.0.0.0:4000/v1/audio/speech \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d '{
"model": "vertex-tts",
"voice": "en-US-Studio-O",
"input": "<speak><p>Hello!</p><break time=\"500ms\"/><p>How are you?</p></speak>"
}' \
--output speech.mp3
```
</TabItem>
<TabItem value="openai-sdk" label="OpenAI Python SDK">
```python showLineNumbers title="SSML Input"
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
ssml = """<speak><p>Hello!</p><break time="500ms"/><p>How are you?</p></speak>"""
response = client.audio.speech.create(
model="vertex-tts",
voice="en-US-Studio-O",
input=ssml,
)
response.stream_to_file("speech.mp3")
```
</TabItem>
</Tabs>
### Supported Parameters
| Parameter | Description | Values |
|-----------|-------------|--------|
| `voice` | Voice selection | OpenAI voice, Google Cloud voice name, or dict |
| `input` | Text to convert | Plain text or SSML |
| `speed` | Speaking rate | 0.25 to 4.0 (default: 1.0) |
| `response_format` | Audio format | `mp3`, `opus`, `wav`, `pcm`, `flac` |
| `use_ssml` | Force SSML mode | `True` / `False` |
### Async Usage
```python showLineNumbers title="Async Speech Generation"
import asyncio
from litellm import aspeech
async def main():
response = await aspeech(
model="vertex_ai/chirp",
voice="alloy",
input="Hello from async",
vertex_project="your-project-id",
)
response.stream_to_file("speech.mp3")
asyncio.run(main())
```
---
## Gemini TTS
Gemini models with audio output capabilities using the chat completions API.
:::warning
**Limitations:**
- Only supports `pcm16` audio format
- Streaming not yet supported
- Must set `modalities: ["audio"]`
:::
### Quick Start
#### LiteLLM Python SDK
```python showLineNumbers title="Gemini TTS Quick Start"
from litellm import completion
import json
# Load credentials
with open('path/to/service_account.json', 'r') as file:
vertex_credentials = json.dumps(json.load(file))
response = completion(
model="vertex_ai/gemini-2.5-flash-preview-tts",
messages=[{"role": "user", "content": "Say hello in a friendly voice"}],
modalities=["audio"],
audio={
"voice": "Kore",
"format": "pcm16"
},
vertex_credentials=vertex_credentials
)
print(response)
```
#### LiteLLM AI Gateway
**1. Setup config.yaml**
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: gemini-tts
litellm_params:
model: vertex_ai/gemini-2.5-flash-preview-tts
vertex_project: "your-project-id"
vertex_location: "us-central1"
vertex_credentials: "/path/to/service_account.json"
```
**2. Start the proxy**
```bash title="Start LiteLLM Proxy"
litellm --config /path/to/config.yaml
```
**3. Make requests**
<Tabs>
<TabItem value="curl" label="curl">
```bash showLineNumbers title="Gemini TTS Request"
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-d '{
"model": "gemini-tts",
"messages": [{"role": "user", "content": "Say hello in a friendly voice"}],
"modalities": ["audio"],
"audio": {"voice": "Kore", "format": "pcm16"}
}'
```
</TabItem>
<TabItem value="openai-sdk" label="OpenAI Python SDK">
```python showLineNumbers title="Gemini TTS Request"
import openai
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
response = client.chat.completions.create(
model="gemini-tts",
messages=[{"role": "user", "content": "Say hello in a friendly voice"}],
modalities=["audio"],
audio={"voice": "Kore", "format": "pcm16"},
)
print(response)
```
</TabItem>
</Tabs>
### Supported Models
- `vertex_ai/gemini-2.5-flash-preview-tts`
- `vertex_ai/gemini-2.5-pro-preview-tts`
See [Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation) for available voices.
### Advanced Usage
```python showLineNumbers title="Gemini TTS with System Prompt"
from litellm import completion
response = completion(
model="vertex_ai/gemini-2.5-pro-preview-tts",
messages=[
{"role": "system", "content": "You are a helpful assistant that speaks clearly."},
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
modalities=["audio"],
audio={"voice": "Charon", "format": "pcm16"},
temperature=0.7,
max_tokens=150,
vertex_credentials=vertex_credentials
)
```

53
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View file

@ -175,3 +175,56 @@ For all available models, see [watsonx.ai documentation](https://dataplatform.cl
For all available embedding models, see [watsonx.ai embedding documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx).
## Advanced
### Using Zen API Key
You can use a Zen API key for long-term authentication instead of generating IAM tokens. Pass it either as an environment variable or as a parameter:
```python
import os
from litellm import completion
# Option 1: Set as environment variable
os.environ["WATSONX_ZENAPIKEY"] = "your-zen-api-key"
response = completion(
model="watsonx/ibm/granite-13b-chat-v2",
messages=[{"content": "What is your favorite color?", "role": "user"}],
project_id="your-project-id"
)
# Option 2: Pass as parameter
response = completion(
model="watsonx/ibm/granite-13b-chat-v2",
messages=[{"content": "What is your favorite color?", "role": "user"}],
zen_api_key="your-zen-api-key",
project_id="your-project-id"
)
```
**Using with LiteLLM Proxy via OpenAI client:**
```python
import openai
client = openai.OpenAI(
api_key="sk-1234", # LiteLLM proxy key
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(
model="watsonx/ibm/granite-3-3-8b-instruct",
messages=[{"role": "user", "content": "What is your favorite color?"}],
max_tokens=2048,
extra_body={
"project_id": "your-project-id",
"zen_api_key": "your-zen-api-key"
}
)
```
See [IBM documentation](https://www.ibm.com/docs/en/watsonx/w-and-w/2.2.0?topic=keys-generating-zenapikey-authorization-tokens) for more information on generating Zen API keys.

135
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@ -0,0 +1,135 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Z.AI (Zhipu AI)
https://z.ai/
**We support Z.AI GLM text/chat models, just set `zai/` as a prefix when sending completion requests**
## API Key
```python
# env variable
os.environ['ZAI_API_KEY']
```
## Sample Usage
```python
from litellm import completion
import os
os.environ['ZAI_API_KEY'] = ""
response = completion(
model="zai/glm-4.6",
messages=[
{"role": "user", "content": "hello from litellm"}
],
)
print(response)
```
## Sample Usage - Streaming
```python
from litellm import completion
import os
os.environ['ZAI_API_KEY'] = ""
response = completion(
model="zai/glm-4.6",
messages=[
{"role": "user", "content": "hello from litellm"}
],
stream=True
)
for chunk in response:
print(chunk)
```
## Supported Models
We support ALL Z.AI GLM models, just set `zai/` as a prefix when sending completion requests.
| Model Name | Function Call | Notes |
|------------|---------------|-------|
| glm-4.6 | `completion(model="zai/glm-4.6", messages)` | Latest flagship model, 200K context |
| glm-4.5 | `completion(model="zai/glm-4.5", messages)` | 128K context |
| glm-4.5v | `completion(model="zai/glm-4.5v", messages)` | Vision model |
| glm-4.5-x | `completion(model="zai/glm-4.5-x", messages)` | Premium tier |
| glm-4.5-air | `completion(model="zai/glm-4.5-air", messages)` | Lightweight |
| glm-4.5-airx | `completion(model="zai/glm-4.5-airx", messages)` | Fast lightweight |
| glm-4-32b-0414-128k | `completion(model="zai/glm-4-32b-0414-128k", messages)` | 32B parameter model |
| glm-4.5-flash | `completion(model="zai/glm-4.5-flash", messages)` | **FREE tier** |
## Model Pricing
| Model | Input ($/1M tokens) | Output ($/1M tokens) | Context Window |
|-------|---------------------|----------------------|----------------|
| glm-4.6 | $0.60 | $2.20 | 200K |
| glm-4.5 | $0.60 | $2.20 | 128K |
| glm-4.5v | $0.60 | $1.80 | 128K |
| glm-4.5-x | $2.20 | $8.90 | 128K |
| glm-4.5-air | $0.20 | $1.10 | 128K |
| glm-4.5-airx | $1.10 | $4.50 | 128K |
| glm-4-32b-0414-128k | $0.10 | $0.10 | 128K |
| glm-4.5-flash | **FREE** | **FREE** | 128K |
## Using with LiteLLM Proxy
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import completion
import os
os.environ['ZAI_API_KEY'] = ""
response = completion(
model="zai/glm-4.6",
messages=[{"role": "user", "content": "Hello, how are you?"}],
)
print(response.choices[0].message.content)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: glm-4.6
litellm_params:
model: zai/glm-4.6
api_key: os.environ/ZAI_API_KEY
- model_name: glm-4.5-flash # Free tier
litellm_params:
model: zai/glm-4.5-flash
api_key: os.environ/ZAI_API_KEY
```
2. Run proxy
```bash
litellm --config config.yaml
```
3. Test it!
```bash
curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
"model": "glm-4.6",
"messages": [
{
"role": "user",
"content": "Hello, how are you?"
}
]
}'
```
</TabItem>
</Tabs>

24
docs/my-website/docs/proxy/config_settings.md
View file

@ -29,7 +29,8 @@ litellm_settings:
request_timeout: 10 # (int) llm requesttimeout in seconds. Raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout
force_ipv4: boolean # If true, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6 + Anthropic API
set_verbose: boolean # sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION
# Debugging - see debugging docs for more options
# Use `--debug` or `--detailed_debug` CLI flags, or set LITELLM_LOG env var to "INFO", "DEBUG", or "ERROR"
json_logs: boolean # if true, logs will be in json format
# Fallbacks, reliability
@ -113,7 +114,7 @@ general_settings:
# Database Settings
database_url: string
database_connection_pool_limit: 0 # default 100
database_connection_pool_limit: 0 # default 10
database_connection_timeout: 0 # default 60s
allow_requests_on_db_unavailable: boolean # if true, will allow requests that can not connect to the DB to verify Virtual Key to still work
@ -171,7 +172,7 @@ router_settings:
| redact_user_api_key_info | boolean | If true, redacts information about the user api key from logs [Proxy Logging](logging#redacting-userapikeyinfo) |
| mcp_aliases | object | Maps friendly aliases to MCP server names for easier tool access. Only the first alias for each server is used. [MCP Aliases](../mcp#mcp-aliases) |
| langfuse_default_tags | array of strings | Default tags for Langfuse Logging. Use this if you want to control which LiteLLM-specific fields are logged as tags by the LiteLLM proxy. By default LiteLLM Proxy logs no LiteLLM-specific fields as tags. [Further docs](./logging#litellm-specific-tags-on-langfuse---cache_hit-cache_key) |
| set_verbose | boolean | If true, sets litellm.set_verbose=True to view verbose debug logs. DO NOT LEAVE THIS ON IN PRODUCTION |
| set_verbose | boolean | [DEPRECATED - see debugging docs](./debugging) Use `--debug` or `--detailed_debug` CLI flags, or set `LITELLM_LOG` env var to "INFO", "DEBUG", or "ERROR" instead. |
| json_logs | boolean | If true, logs will be in json format. If you need to store the logs as JSON, just set the `litellm.json_logs = True`. We currently just log the raw POST request from litellm as a JSON [Further docs](./debugging) |
| default_fallbacks | array of strings | List of fallback models to use if a specific model group is misconfigured / bad. [Further docs](./reliability#default-fallbacks) |
| request_timeout | integer | The timeout for requests in seconds. If not set, the default value is `6000 seconds`. [For reference OpenAI Python SDK defaults to `600 seconds`.](https://github.com/openai/openai-python/blob/main/src/openai/_constants.py) |
@ -234,7 +235,7 @@ router_settings:
| max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. |
| proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. **Default is 597 seconds** |
| proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. **Default is 605 seconds** |
| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 30 seconds** |
| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 10 seconds** |
| proxy_batch_polling_interval | int | Time (in seconds) to wait before polling a batch, to check if it's completed. **Default is 6000 seconds (1 hour)** |
| alerting_args | dict | Args for Slack Alerting [Doc on Slack Alerting](./alerting.md) |
| custom_key_generate | str | Custom function for key generation [Doc on custom key generation](./virtual_keys.md#custom--key-generate) |
@ -333,7 +334,7 @@ router_settings:
| caching_groups | Optional[List[tuple]] | List of model groups for caching across model groups. Defaults to None. - e.g. caching_groups=[("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")]|
| alerting_config | AlertingConfig | [SDK-only arg] Slack alerting configuration. Defaults to None. [Further Docs](../routing.md#alerting-) |
| assistants_config | AssistantsConfig | Set on proxy via `assistant_settings`. [Further docs](../assistants.md) |
| set_verbose | boolean | [DEPRECATED PARAM - see debug docs](./debugging.md) If true, sets the logging level to verbose. |
| set_verbose | boolean | [DEPRECATED PARAM - see debug docs](./debugging) If true, sets the logging level to verbose. |
| retry_after | int | Time to wait before retrying a request in seconds. Defaults to 0. If `x-retry-after` is received from LLM API, this value is overridden. |
| provider_budget_config | ProviderBudgetConfig | Provider budget configuration. Use this to set llm_provider budget limits. example $100/day to OpenAI, $100/day to Azure, etc. Defaults to None. [Further Docs](./provider_budget_routing.md) |
| enable_pre_call_checks | boolean | If true, checks if a call is within the model's context window before making the call. [More information here](reliability) |
@ -359,6 +360,7 @@ router_settings:
| AISPEND_ACCOUNT_ID | Account ID for AI Spend
| AISPEND_API_KEY | API Key for AI Spend
| AIOHTTP_CONNECTOR_LIMIT | Connection limit for aiohttp connector. When set to 0, no limit is applied. **Default is 0**
| AIOHTTP_CONNECTOR_LIMIT_PER_HOST | Connection limit per host for aiohttp connector. When set to 0, no limit is applied. **Default is 0**
| AIOHTTP_KEEPALIVE_TIMEOUT | Keep-alive timeout for aiohttp connections in seconds. **Default is 120**
| AIOHTTP_TRUST_ENV | Flag to enable aiohttp trust environment. When this is set to True, aiohttp will respect HTTP(S)_PROXY env vars. **Default is False**
| AIOHTTP_TTL_DNS_CACHE | DNS cache time-to-live for aiohttp in seconds. **Default is 300**
@ -377,6 +379,8 @@ router_settings:
| ATHINA_API_KEY | API key for Athina service
| ATHINA_BASE_URL | Base URL for Athina service (defaults to `https://log.athina.ai`)
| AUTH_STRATEGY | Strategy used for authentication (e.g., OAuth, API key)
| AUTO_REDIRECT_UI_LOGIN_TO_SSO | Flag to enable automatic redirect of UI login page to SSO when SSO is configured. Default is **true**
| AUDIO_SPEECH_CHUNK_SIZE | Chunk size for audio speech processing. Default is 1024
| ANTHROPIC_API_KEY | API key for Anthropic service
| ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com
| AWS_ACCESS_KEY_ID | Access Key ID for AWS services
@ -439,6 +443,7 @@ router_settings:
| CYBERARK_CLIENT_CERT | Path to client certificate for CyberArk authentication
| CYBERARK_CLIENT_KEY | Path to client key for CyberArk authentication
| CYBERARK_USERNAME | Username for CyberArk authentication
| CYBERARK_SSL_VERIFY | Flag to enable or disable SSL certificate verification for CyberArk. Default is True
| CONFIDENT_API_KEY | API key for DeepEval integration
| CUSTOM_TIKTOKEN_CACHE_DIR | Custom directory for Tiktoken cache
| CONFIDENT_API_KEY | API key for Confident AI (Deepeval) Logging service
@ -653,6 +658,8 @@ router_settings:
| LITERAL_API_URL | API URL for Literal service
| LITERAL_BATCH_SIZE | Batch size for Literal operations
| LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX | Disable automatic URL suffix appending for Anthropic API base URLs. When set to `true`, prevents LiteLLM from automatically adding `/v1/messages` or `/v1/complete` to custom Anthropic API endpoints
| LITELLM_DD_AGENT_HOST | Hostname or IP of DataDog agent for LiteLLM-specific logging. When set, logs are sent to agent instead of direct API
| LITELLM_DD_AGENT_PORT | Port of DataDog agent for LiteLLM-specific log intake. Default is 10518
| LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI
| LITELLM_DROP_PARAMS | Parameters to drop in LiteLLM requests
| LITELLM_MODIFY_PARAMS | Parameters to modify in LiteLLM requests
@ -763,7 +770,7 @@ router_settings:
| PROMPTLAYER_API_KEY | API key for PromptLayer integration
| PROXY_ADMIN_ID | Admin identifier for proxy server
| PROXY_BASE_URL | Base URL for proxy service
| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 30
| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 10
| PROXY_BATCH_POLLING_INTERVAL | Time in seconds to wait before polling a batch, to check if it's completed. Default is 6000s (1 hour)
| PROXY_BUDGET_RESCHEDULER_MAX_TIME | Maximum time in seconds to wait before checking database for budget resets. Default is 605
| PROXY_BUDGET_RESCHEDULER_MIN_TIME | Minimum time in seconds to wait before checking database for budget resets. Default is 597
@ -797,7 +804,7 @@ router_settings:
| SEND_USER_API_KEY_ALIAS | Flag to send user API key alias to Zscaler AI Guard. Default is False
| SEND_USER_API_KEY_TEAM_ID | Flag to send user API key team ID to Zscaler AI Guard. Default is False
| SEND_USER_API_KEY_USER_ID | Flag to send user API key user ID to Zscaler AI Guard. Default is False
| SET_VERBOSE | Flag to enable verbose logging
| SET_VERBOSE | [DEPRECATED] Use `LITELLM_LOG` instead with values "INFO", "DEBUG", or "ERROR". See [debugging docs](./debugging)
| SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD | Minimum number of requests to consider "reasonable traffic" for single-deployment cooldown logic. Default is 1000
| SLACK_DAILY_REPORT_FREQUENCY | Frequency of daily Slack reports (e.g., daily, weekly)
| SLACK_WEBHOOK_URL | Webhook URL for Slack integration
@ -839,6 +846,9 @@ router_settings:
| UPSTREAM_LANGFUSE_SECRET_KEY | Secret key for upstream Langfuse authentication
| USE_AWS_KMS | Flag to enable AWS Key Management Service for encryption
| USE_PRISMA_MIGRATE | Flag to use prisma migrate instead of prisma db push. Recommended for production environments.
| WANDB_API_KEY | API key for Weights & Biases (W&B) logging integration
| WANDB_HOST | Host URL for Weights & Biases (W&B) service
| WANDB_PROJECT_ID | Project ID for Weights & Biases (W&B) logging integration
| WEBHOOK_URL | URL for receiving webhooks from external services
| SPEND_LOG_RUN_LOOPS | Constant for setting how many runs of 1000 batch deletes should spend_log_cleanup task run
| SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000

2
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@ -576,7 +576,7 @@ custom_tokenizer:
```yaml
general_settings:
database_connection_pool_limit: 100 # sets connection pool for prisma client to postgres db at 100
database_connection_pool_limit: 10 # sets connection pool for prisma client to postgres db (default: 10, recommended: 10-20)
database_connection_timeout: 60 # sets a 60s timeout for any connection call to the db
```

108
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@ -0,0 +1,108 @@
---
id: cursor
title: /cursor/chat/completions - Cursor Endpoint
description: Accept Responses API input from Cursor and return OpenAI Chat Completions output
---
LiteLLM provides a Cursor-specific endpoint to make Cursor IDE work seamlessly with the LiteLLM Proxy when using BYOK + custom `base_url`.
- Accepts Requests in OpenAI Responses API input format (Cursor sends this)
- Returns Responses in OpenAI Chat Completions format (Cursor expects this)
- Supports streaming and non‑streaming
## Endpoint
- Path: `/cursor/chat/completions`
- Auth: Standard LiteLLM Proxy auth (`Authorization: Bearer <key>`)
- Behavior: Internally routes to LiteLLM `/responses` flow and transforms output to Chat Completions
## Why this exists
When setting up Cursor with BYOK against a custom `base_url`, Cursor sends requests to the Chat Completions endpoint but in the OpenAI Responses API input shape. Without translation, Cursor won’t display streamed output. This endpoint bridges the formats:
- Input: Responses API (`input`, tool calls, etc.)
- Output: Chat Completions (`choices`, `delta`, `finish_reason`, etc.)
## Usage
### Non-streaming
```bash
curl -X POST https://litellm-internal/cursor/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-d '{
"model": "gpt-4o",
"input": [{"role": "user", "content": "Hello"}]
}'
```
Example response (shape):
```json
{
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1733333333,
"model": "gpt-4o",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help you?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 8,
"total_tokens": 18
}
}
```
### Streaming
```bash
curl -N -X POST https://litellm-internal/cursor/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-d '{
"model": "gpt-4o",
"input": [{"role": "user", "content": "Hello"}],
"stream": true
}'
```
- Server-Sent Events (SSE)
- Emits `chat.completion.chunk` deltas (`choices[].delta`) and ends with `data: [DONE]`
## Configuration
### Base URL Setup
**Important**: When configuring Cursor IDE to use this endpoint, you must include `/cursor` in the base URL.
Cursor automatically appends `/chat/completions` to the base URL you provide. To ensure requests go to `/cursor/chat/completions`, configure your base URL in Cursor as:
```
Base URL: https://litellm-internal/cursor
```
This way, when Cursor appends `/chat/completions`, the full path becomes `/cursor/chat/completions`, which is the correct endpoint.
**Example**: If your LiteLLM Proxy is running at `https://litellm-internal`, set the base URL in Cursor to `https://litellm-internal/cursor` (not just `https://litellm-internal`).
### General Setup
No special configuration is required beyond your normal LiteLLM Proxy setup. Ensure that:
- Your `config.yaml` includes the models you want to call via this endpoint
- Your Cursor project uses your LiteLLM Proxy `base_url` (with `/cursor` included) and a valid API key
## Notes
- This endpoint is intended specifically for Cursor’s request/response expectations. Other clients should continue to use `/v1/chat/completions` or `/v1/responses` as appropriate.

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@ -0,0 +1,110 @@
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Customer Usage
Track and visualize end-user spend directly in the dashboard. Monitor customer-level usage analytics, spend logs, and activity metrics to understand how your customers are using your LLM services.
This feature is **available in v1.80.8-stable and above**.
## Overview
Customer Usage enables you to track spend and usage for individual customers (end users) by passing an ID in your API requests. This allows you to:
- Track spend per customer automatically
- View customer-level usage analytics in the Admin UI
- Filter spend logs and activity metrics by customer ID
- Set budgets and rate limits per customer
- Monitor customer usage patterns and trends
<Image img={require('../../img/customer_usage.png')} />
## How to Track Spend
Track customer spend by including a `user` field in your API requests. The customer ID will be automatically tracked and associated with all spend from that request.
### Example using cURL
Make a `/chat/completions` call with the `user` field containing your customer ID:
```bash showLineNumbers title="Track spend with customer ID"
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-1234' \ # 👈 YOUR PROXY KEY
--data '{
"model": "gpt-3.5-turbo",
"user": "customer-123", # 👈 CUSTOMER ID
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'
```
The customer ID (`customer-123`) will be automatically upserted into the database with the new spend. If the customer ID already exists, spend will be incremented.
### Example using OpenWebUI
See the [Open WebUI tutorial](../tutorials/openweb_ui.md) for detailed instructions on connecting Open WebUI to LiteLLM and tracking customer usage.
## How to View Spend
### View Spend in Admin UI
Navigate to the Customer Usage tab in the Admin UI to view customer-level spend analytics:
#### 1. Access Customer Usage
Go to the Usage page in the Admin UI (`PROXY_BASE_URL/ui/?login=success&page=new_usage`) and click on the **Customer Usage** tab.
<Image img={require('../../img/customer_usage_ui_navigation.png')} />
#### 2. View Customer Analytics
The Customer Usage dashboard provides:
- **Total spend per customer**: View aggregated spend across all customers
- **Daily spend trends**: See how customer spend changes over time
- **Model usage breakdown**: Understand which models each customer uses
- **Activity metrics**: Track requests, tokens, and success rates per customer
<Image img={require('../../img/customer_usage_analytics.png')} />
#### 3. Filter by Customer
Use the customer filter dropdown to view spend for specific customers:
- Select one or more customer IDs from the dropdown
- View filtered analytics, spend logs, and activity metrics
- Compare spend across different customers
<Image img={require('../../img/customer_usage_filter.png')} />
## Use Cases
### Customer Billing
Track spend per customer to accurately bill your end users:
- Monitor individual customer usage
- Generate invoices based on actual spend
- Set spending limits per customer
### Usage Analytics
Understand how different customers use your service:
- Identify high-value customers
- Analyze usage patterns
- Optimize resource allocation
---
## Related Features
- [Customers / End-User Budgets](./customers.md) - Set budgets and rate limits for customers
- [Cost Tracking](./cost_tracking.md) - Comprehensive cost tracking and analytics
- [Billing](./billing.md) - Bill customers based on their usage

4
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@ -26,8 +26,6 @@ echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
# password generator to get a random hash for litellm salt key
echo 'LITELLM_SALT_KEY="sk-1234"' >> .env
source .env
# Start
docker compose up
```
@ -1072,4 +1070,4 @@ A: We explored MySQL but that was hard to maintain and led to bugs for customers
**Q: If there is Postgres downtime, how does LiteLLM react? Does it fail-open or is there API downtime?**
A: You can gracefully handle DB unavailability if it's on your VPC. See our production guide for more details: [Gracefully Handle DB Unavailability](https://docs.litellm.ai/docs/proxy/prod#6-if-running-litellm-on-vpc-gracefully-handle-db-unavailability)
A: You can gracefully handle DB unavailability if it's on your VPC. See our production guide for more details: [Gracefully Handle DB Unavailability](https://docs.litellm.ai/docs/proxy/prod#6-if-running-litellm-on-vpc-gracefully-handle-db-unavailability)

2
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@ -52,8 +52,6 @@ echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
# password generator to get a random hash for litellm salt key
echo 'LITELLM_SALT_KEY="sk-1234"' >> .env
source .env
# Start
docker compose up
```

39
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@ -175,7 +175,37 @@ general_settings:
litellm --config /path/to/config.yaml
```
#### 2. Create Keys with Priority Levels
### Set priority on either a team or a key
Priority can be set at either the **team level** or **key level**. Team-level priority takes precedence over key-level priority.
**Option A: Set Priority on Team (Recommended)**
All keys within a team will inherit the team's priority. This is useful when you want all keys for a specific environment or project to have the same priority.
```bash
curl -X POST 'http://0.0.0.0:4000/team/new' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"team_alias": "production-team",
"metadata": {"priority": "prod"}
}'
```
Create a key for this team:
```bash
curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"team_id": "team-id-from-previous-response"
}'
```
**Option B: Set Priority on Individual Keys**
Set priority directly on the key. This is useful when you need fine-grained control per key.
**Production Key:**
```bash
@ -205,7 +235,7 @@ curl -X POST 'http://0.0.0.0:4000/key/generate' \
-d '{}'
```
**Expected Response for both:**
**Expected Response:**
```json
{
"key": "sk-...",
@ -214,6 +244,11 @@ curl -X POST 'http://0.0.0.0:4000/key/generate' \
}
```
**Priority Resolution Order:**
1. If key belongs to a team with `metadata.priority` set → use team priority
2. Else if key has `metadata.priority` set → use key priority
3. Else → use `default_priority` from config
#### 3. Test Priority Allocation
**Test Production Key (should get 9 RPM):**

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@ -0,0 +1,90 @@
# Diagnosing Errors - Provider vs Gateway
Having trouble diagnosing if an error is from the **LLM Provider** (OpenAI, Anthropic, etc.) or from the **LiteLLM AI Gateway** itself? Here's how to tell.
## Quick Rule
**If the error contains `<Provider>Exception`, it's from the provider.**
| Error Contains | Error Source |
|----------------|--------------|
| `AnthropicException` | Anthropic |
| `OpenAIException` | OpenAI |
| `AzureException` | Azure |
| `BedrockException` | AWS Bedrock |
| `VertexAIException` | Google Vertex AI |
| No provider name | LiteLLM AI Gateway |
## Examples
### Provider Error (from AWS Bedrock)
```
{
"error": {
"message": "litellm.BadRequestError: BedrockException - {\"message\":\"The model returned the following errors: messages.1.content.0.type: Expected `thinking` or `redacted_thinking`, but found `text`.\"}",
"type": "invalid_request_error",
"param": null,
"code": "400"
}
}
```
This error is from **AWS Bedrock** (notice `BedrockException`). The Bedrock API is rejecting the request due to invalid message format - this is not a LiteLLM issue.
### Provider Error (from OpenAI)
```
{
"error": {
"message": "litellm.AuthenticationError: OpenAIException - Incorrect API key provided: <my-key>. You can find your API key at https://platform.openai.com/account/api-keys.",
"type": "invalid_request_error",
"param": null,
"code": "invalid_api_key"
}
}
```
This error is from **OpenAI** (notice `OpenAIException`). The OpenAI API key configured in LiteLLM is invalid.
### Provider Error (from Anthropic)
```
{
"error": {
"message": "litellm.InternalServerError: AnthropicException - Overloaded. Handle with `litellm.InternalServerError`.",
"type": "internal_server_error",
"param": null,
"code": "500"
}
}
```
This error is from **Anthropic** (notice `AnthropicException`). The Anthropic API is overloaded - this is not a LiteLLM issue.
### Gateway Error (from LiteLLM)
```
{
"error": {
"message": "Invalid API Key. Please check your LiteLLM API key.",
"type": "auth_error",
"param": null,
"code": "401"
}
}
```
This error is from the **LiteLLM AI Gateway** (no provider name). Your LiteLLM virtual key is invalid.
## What to do?
| Error Source | Action |
|--------------|--------|
| Provider Error | Check the provider's status page, adjust rate limits, or retry later |
| Gateway Error | Check your LiteLLM configuration, API keys, or [open an issue](https://github.com/BerriAI/litellm/issues) |
## See Also
- [Debugging](/docs/proxy/debugging) - Enable debug logs to see detailed request/response info
- [Exception Mapping](/docs/exception_mapping) - Full list of LiteLLM exception types

22
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@ -188,6 +188,28 @@ My email is [EMAIL] and my phone number is [PHONE_NUMBER]
This helps protect sensitive information while still allowing the model to understand the context of the request.
## Experimental: Only Send Latest User Message
When you're chaining long conversations through Bedrock guardrails, you can opt into a lighter, experimental behavior by setting `experimental_use_latest_role_message_only: true` in the guardrail's `litellm_params`. When enabled, LiteLLM only sends the most recent `user` message (or assistant output during post-call checks) to Bedrock, which:
- prevents unintended blocks on older system/dev messages
- keeps Bedrock payloads smaller, reducing latency and cost
- applies to proxy hooks (`pre_call`, `during_call`) and the `/guardrails/apply_guardrail` testing endpoint
```yaml showLineNumbers title="litellm proxy config.yaml"
guardrails:
- guardrail_name: "bedrock-pre-guard"
litellm_params:
guardrail: bedrock
mode: "pre_call"
guardrailIdentifier: wf0hkdb5x07f
guardrailVersion: "DRAFT"
aws_region_name: os.environ/AWS_REGION
experimental_use_latest_role_message_only: true # NEW
```
> ⚠️ This flag is currently experimental and defaults to `false` to preserve the legacy behavior (entire message history). We'll be listening to user feedback to decide if this becomes the default or rolls out more broadly.
## Disabling Exceptions on Bedrock BLOCK
By default, when Bedrock guardrails block content, LiteLLM raises an HTTP 400 exception. However, you can disable this behavior by setting `disable_exception_on_block: true`. This is particularly useful when integrating with **OpenWebUI**, where exceptions can interrupt the chat flow and break the user experience.

4
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@ -35,7 +35,7 @@ guardrails:
guardrail: lasso
mode: "pre_call"
api_key: os.environ/LASSO_API_KEY
api_base: "https://server.lasso.security"
api_base: "https://server.lasso.security/gateway/v3"
- guardrail_name: "lasso-post-guard"
litellm_params:
guardrail: lasso
@ -228,7 +228,7 @@ Expected response:
## PII Masking with Lasso
Lasso supports automatic PII detection and masking using the `/gateway/v1/classifix` endpoint. When enabled, sensitive information like emails, phone numbers, and other PII will be automatically masked with appropriate placeholders.
Lasso supports automatic PII detection and masking using the `/classifix` endpoint. When enabled, sensitive information like emails, phone numbers, and other PII will be automatically masked with appropriate placeholders.
### Enabling PII Masking

42
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@ -1,4 +1,3 @@
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
@ -7,9 +6,34 @@ import TabItem from '@theme/TabItem';
LiteLLM provides the LiteLLM Tool Permission Guardrail that lets you control which **tool calls** a model is allowed to invoke, using configurable allow/deny rules. This offers fine-grained, provider-agnostic control over tool execution (e.g., OpenAI Chat Completions `tool_calls`, Anthropic Messages `tool_use`, MCP tools).
## Quick Start
### 1. Define Guardrails on your LiteLLM config.yaml
Define your guardrails under the `guardrails` section
### LiteLLM UI
#### Step 1: Select Tool Permission Guardrail
Open the LiteLLM Dashboard, click **Add New Guardrail**, and choose **LiteLLM Tool Permission Guardrail**. This loads the rule builder UI.
#### Step 2: Define Regex Rules
1. Click **Add Rule**.
2. Enter a unique Rule ID.
3. Provide a regex for the tool name (e.g., `^mcp__github_.*$`).
4. Optionally add a regex for tool type (e.g., `^function$`).
5. Pick **Allow** or **Deny**.
#### Step 3: Restrict Tool Arguments (Optional)
Select **+ Restrict tool arguments** to attach regex validations to nested paths (dot + `[]` notation). This enforces that sensitive parameters (such as `arguments.to[]`) conform to pre-approved formats.
#### Step 4: Choose Defaults & Actions
- Set the fallback decision (`default_action`) for tools that do not hit any rule.
- Decide how disallowed tools behave: **Block** halts the request, **Rewrite** strips forbidden tools and returns an error message inside the response.
- Customize `violation_message_template` if you want branded error copy.
- Save the guardrail.
### LiteLLM Config.yaml Setup
```yaml
guardrails:
- guardrail_name: "tool-permission-guardrail"
@ -21,16 +45,17 @@ guardrails:
tool_name: "Bash"
decision: "allow"
- id: "allow_github_mcp"
tool_name: "mcp__github_*"
tool_name: "^mcp__github_.*$"
decision: "allow"
- id: "allow_aws_documentation"
tool_name: "mcp__aws-documentation_*_documentation"
tool_name: "^mcp__aws-documentation_.*_documentation$"
decision: "allow"
- id: "deny_read_commands"
tool_name: "Read"
decision: "Deny"
decision: "deny"
- id: "mail-domain"
tool_name: "send_email"
tool_name: "^send_email$"
tool_type: "^function$"
decision: "allow"
allowed_param_patterns:
"to[]": "^.+@berri\\.ai$"
@ -44,7 +69,8 @@ guardrails:
```yaml
- id: "unique_rule_id" # Unique identifier for the rule
tool_name: "pattern" # Tool name or pattern to match
tool_name: "^regex$" # Regex for tool name (optional, at least one of name/type required)
tool_type: "^function$" # Regex for tool type (optional)
decision: "allow" # "allow" or "deny"
allowed_param_patterns: # Optional - regex map for argument paths (dot + [] notation)
"path.to[].field": "^regex$"

710
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@ -0,0 +1,710 @@
import Image from '@theme/IdealImage';
# Multi-Tenant Architecture with LiteLLM
## Overview
LiteLLM provides a centralized solution that scales across multiple tenants, enabling organizations to:
- **Centrally manage** LLM access for multiple tenants (organizations, teams, departments)
- **Isolate spend and usage** across different organizational units
- **Delegate administration** without compromising security
- **Track costs** at granular levels (organization → team → user → key)
- **Scale seamlessly** as new teams and users are added
:::info Open Source vs. Enterprise
- **Teams + Virtual Keys**: ✅ Available in open source
- **Organizations + Org Admins**: ✨ Enterprise feature ([Get a 7 day trial](https://www.litellm.ai/#trial))
You can implement multi-tenancy using **Teams** alone in the open source version, or add **Organizations** on top for additional hierarchy in the enterprise version.
:::
## The Multi-Tenant Challenge
Organizations with multi-tenant architectures face several challenges when deploying LLM solutions:
1. **Centralized vs. Decentralized**: Need a single unified gateway while maintaining tenant isolation
2. **Cost Attribution**: Tracking spend across different business units, departments, or customers
3. **Access Control**: Different teams need different models, budgets, and rate limits
4. **Delegation**: Team leads should manage their teams without platform-wide admin access
5. **Scalability**: Solution must scale from 10 to 10,000+ users without architectural changes
## How LiteLLM Solves Multi-Tenancy
<Image img={require('../../img/litellm_user_heirarchy.png')} style={{ width: '100%', maxWidth: '4000px' }} />
LiteLLM implements a hierarchical multi-tenant architecture with four levels:
### 1. Organizations (Top-Level Tenants) ✨ Enterprise Feature
**Organizations** represent the highest level of tenant isolation - typically different business units, departments, or customers.
- Each organization has its own:
- Budget limits
- Allowed models
- Admin users (org admins)
- Teams
- Spend tracking
**Use Cases:**
- **Enterprise Departments**: Separate organizations for Engineering, Marketing, Sales
- **Multi-Customer SaaS**: Each customer is an organization with full isolation
- **Geographic Regions**: EMEA, APAC, Americas as separate organizations
**Key Features:**
- Organizations cannot see each other's data
- Each organization can have multiple teams
- Organization admins manage teams within their organization only
- Spend and usage tracked at organization level
[API Reference for Organizations](https://litellm-api.up.railway.app/#/organization%20management)
---
### 2. Teams (Mid-Level Grouping) ✅ Open Source
**Teams** can work independently or sit within organizations, representing logical groupings of users working together.
:::tip
Teams are available in **open source** and can be used as your primary multi-tenant boundary without needing Organizations. Organizations provide an additional layer of hierarchy for enterprise deployments.
:::
- Each team has:
- Team-specific budgets and rate limits
- Team admins who manage members
- Service account keys for shared resources
- Model access controls
- Granular team member permissions
**Use Cases:**
- **Project Teams**: ML Research team, Product team, Data Science team
- **Customer Sub-Groups**: Different divisions within a customer organization
- **Environment Separation**: Development, Staging, Production teams
**Key Features:**
- Teams inherit organization constraints (can't exceed org budget/models)
- Team admins can manage their team without affecting others
- Service account keys survive team member changes
- Per-team spend tracking and billing
[API Reference for Teams](https://litellm-api.up.railway.app/#/team%20management)
---
### 3. Users (Individual Members) ✅ Open Source
**Users** are individuals who belong to teams and create/use API keys.
- Each user can:
- Belong to multiple teams
- Have their own budget limits
- Create personal API keys
- Track individual spend
**User Types:**
- **Internal Users**: Employees, developers, data scientists
- **Team Admins**: Lead their teams, manage members
- **Org Admins**: Manage multiple teams within their organization
- **Proxy Admins**: Platform-wide administrators
**Key Features:**
- User spend tracked individually
- Users can be on multiple teams simultaneously
- Role-based permissions control what users can do
- User keys deleted when user is removed
[API Reference for Users](https://litellm-api.up.railway.app/#/user%20management)
---
### 4. Virtual Keys (Authentication Layer) ✅ Open Source
**Virtual Keys** are the API keys used to authenticate requests and track spend.
Each key can be one of three types:
| Key Type | Configuration | Use Case | Spend Tracking | Lifecycle |
|----------|---------------|----------|----------------|-----------|
| **User-only** | `user_id` only | Developer personal keys | User level | Deleted with user |
| **Team Service Account** | `team_id` only | Production apps, CI/CD | Team level | Survives member changes |
| **User + Team** | Both `user_id` and `team_id` | User within team context | User AND Team | Deleted with user |
**Example Scenarios:**
- Use **user-only keys** for developers testing locally
- Use **team service account keys** for your production application that shouldn't break when employees leave
- Use **user + team keys** when you want individual accountability within a team budget
[API Reference for Keys](https://litellm-api.up.railway.app/#/key%20management)
---
## Role-Based Access Control (RBAC)
LiteLLM provides granular RBAC across the hierarchy:
### Global Proxy Roles (Platform-Wide)
| Role | Scope | Permissions |
|------|-------|-------------|
| **Proxy Admin** | Entire platform | Create orgs, teams, users. View all spend. Full control. |
| **Proxy Admin Viewer** | Entire platform | View-only access to all data. Cannot make changes. |
| **Internal User** | Own resources | Create/delete own keys. View own spend. |
### Organization/Team Roles (Scoped)
| Role | Scope | Permissions |
|------|-------|-------------|
| **Org Admin** ✨ | Specific organization | Create teams, add users, view org spend within their org only. |
| **Team Admin** ✨ | Specific team | Manage team members, budgets, keys within their team only. |
✨ = Premium Feature
### Team Member Permissions
Team admins can configure granular permissions for regular team members:
**Read-only** (default):
```json
["/key/info", "/key/health"]
```
**Allow key creation**:
```json
["/key/info", "/key/health", "/key/generate", "/key/update"]
```
**Full key management**:
```json
["/key/info", "/key/health", "/key/generate", "/key/update", "/key/delete", "/key/regenerate", "/key/block", "/key/unblock"]
```
[Learn more about RBAC](./access_control)
---
## Spend Tracking & Cost Attribution
LiteLLM provides multi-level spend tracking that flows through the hierarchy:
### Hierarchical Spend Flow
```
Organization Spend
├── Team 1 Spend
│ ├── User A Spend
│ │ ├── Key 1 Spend
│ │ └── Key 2 Spend
│ └── Service Account Spend
│ └── Key 3 Spend
└── Team 2 Spend
└── User B Spend
└── Key 4 Spend
```
### Budget Enforcement
Budgets can be set at every level with inheritance:
1. **Organization Budget**: `$10,000/month`
- Team 1: `$6,000/month` (within org limit)
- User A: `$3,000/month` (within team limit)
- User B: `$3,000/month` (within team limit)
- Team 2: `$4,000/month` (within org limit)
**Enforcement Rules:**
- Team budgets cannot exceed organization budget
- User budgets cannot exceed team budget
- Requests blocked when any level exceeds budget
- Real-time tracking prevents overruns
[Learn more about Budgets](./team_budgets)
---
## Common Multi-Tenant Patterns
### Pattern 1: Enterprise Departments
**Scenario**: Large enterprise with multiple departments needing centralized LLM access
**Enterprise Setup** (with Organizations):
```
Platform (LiteLLM Instance)
├── Engineering Organization ✨
│ ├── Backend Team
│ ├── Frontend Team
│ └── ML Team
├── Marketing Organization ✨
│ ├── Content Team
│ └── Analytics Team
└── Sales Organization ✨
├── Sales Ops Team
└── Customer Success Team
```
**Open Source Alternative** (Teams only):
```
Platform (LiteLLM Instance)
├── Engineering Backend Team
├── Engineering Frontend Team
├── Engineering ML Team
├── Marketing Content Team
├── Marketing Analytics Team
├── Sales Ops Team
└── Customer Success Team
```
**Benefits:**
- Each department/team manages their own budget
- Department leads (org/team admins) control their teams
- Centralized billing and model access
- Cross-department cost visibility for finance
---
### Pattern 2: Multi-Customer SaaS
**Scenario**: SaaS provider offering LLM-powered features to multiple customers
**Enterprise Setup** (with Organizations):
```
Platform (LiteLLM Instance)
├── Customer A Organization ✨
│ ├── Production Team (Service Accounts)
│ ├── Development Team
│ └── QA Team
├── Customer B Organization ✨
│ ├── Production Team (Service Accounts)
│ └── Development Team
└── Customer C Organization ✨
└── Production Team (Service Accounts)
```
**Open Source Alternative** (Teams only):
```
Platform (LiteLLM Instance)
├── Customer A Production Team (Service Accounts)
├── Customer A Development Team
├── Customer A QA Team
├── Customer B Production Team (Service Accounts)
├── Customer B Development Team
└── Customer C Production Team (Service Accounts)
```
**Benefits:**
- Complete isolation between customers/teams
- Per-customer/team billing and usage tracking
- Customer/team admins can self-serve
- Production service account keys survive employee turnover
---
### Pattern 3: Environment Separation
**Scenario**: Single organization with multiple environments
```
Platform (LiteLLM Instance)
└── Company Organization
├── Production Team
│ └── Service Account Keys (strict rate limits)
├── Staging Team
│ └── Service Account Keys (moderate limits)
└── Development Team
└── User Keys (generous limits for testing)
```
**Benefits:**
- Separate budgets for each environment
- Different model access (production vs. development)
- Prevent development usage from affecting production budget
- Easy cost attribution by environment
---
## Delegation & Self-Service
One of LiteLLM's key advantages is delegated administration:
### Without LiteLLM
```
Every team → Requests platform admin → Admin makes changes
```
❌ Bottleneck on platform team
❌ Slow onboarding
❌ Poor scalability
### With LiteLLM
```
Proxy Admin → Creates org + org admin
Org Admin → Creates teams + team admins
Team Admin → Manages their team independently
```
✅ Decentralized management
✅ Fast onboarding
✅ Scales to thousands of users
### Self-Service Capabilities
**Team Admins Can:**
- Add/remove team members
- Create API keys for team members
- Update team budgets (within org limits)
- Configure team member permissions
- View team usage and spend
**Org Admins Can:**
- Create new teams within their organization
- Assign team admins
- View organization-wide spend
- Manage users across their teams
**Platform Admins Can:**
- Create organizations
- Assign org admins
- Set organization-level policies
- View platform-wide analytics
---
## Scalability
LiteLLM's architecture scales from small teams to enterprise deployments:
### Small Team (10-100 users)
- Single organization
- Few teams (5-10)
- Proxy admins manage everything
### Mid-Size (100-1,000 users)
- Multiple organizations
- Many teams (50+)
- Org admins delegate to team admins
### Enterprise (1,000+ users)
- Many organizations (departments/regions)
- Hundreds of teams
- Fully delegated admin structure
- Centralized observability and billing
**Key Scalability Features:**
- No architectural changes needed as you grow
- Database-backed (PostgreSQL) for reliability
- Horizontal scaling support
- Efficient spend tracking and logging
---
## Security & Isolation
### Tenant Isolation
Each tenant (organization) is isolated:
- ✅ Cannot view other organizations' data
- ✅ Cannot access other organizations' keys
- ✅ Cannot exceed their budget limits
- ✅ Cannot access models not in their allowed list
### Authentication Security
- Master key for platform admins
- Virtual keys with scoped permissions
- SSO integration support
- JWT authentication
- IP allowlisting
### Audit & Compliance
- All API calls logged with user/team/org context
- Spend tracking for chargeback/showback
- Admin actions audited
- Integration with observability tools
[Learn more about Security](../data_security)
---
## Getting Started
:::info Enterprise vs. Open Source Setup
The steps below show the **full enterprise hierarchy** with Organizations.
For **open source**, skip Steps 1-2 and start directly with **Step 3** (creating teams). Teams can function as your top-level tenant boundary without Organizations.
:::
### Step 1: Set Up Organizations ✨ Enterprise
Create your first organization:
```bash
curl --location 'http://0.0.0.0:4000/organization/new' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"organization_alias": "engineering_department",
"models": ["gpt-4", "gpt-4o", "claude-3-5-sonnet"],
"max_budget": 10000
}'
```
### Step 2: Add an Organization Admin ✨ Enterprise
```bash
curl -X POST 'http://0.0.0.0:4000/organization/member_add' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"organization_id": "org-123",
"member": {
"role": "org_admin",
"user_id": "admin@company.com"
}
}'
```
### Step 3: Create Teams ✅ Open Source
**For Enterprise:** Organization admin creates team within their organization
**For Open Source:** Proxy admin creates team directly (no `organization_id` needed)
```bash
# Enterprise: Org admin creates team in their organization
curl --location 'http://0.0.0.0:4000/team/new' \
--header 'Authorization: Bearer sk-org-admin-key' \
--header 'Content-Type: application/json' \
--data '{
"team_alias": "ml_team",
"organization_id": "org-123",
"max_budget": 5000
}'
# Open Source: Proxy admin creates team directly
curl --location 'http://0.0.0.0:4000/team/new' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"team_alias": "ml_team",
"max_budget": 5000
}'
```
### Step 4: Add Team Admin
```bash
curl -X POST 'http://0.0.0.0:4000/team/member_add' \
-H 'Authorization: Bearer sk-org-admin-key' \
-H 'Content-Type: application/json' \
-d '{
"team_id": "team-456",
"member": {
"role": "admin",
"user_id": "team-lead@company.com"
}
}'
```
### Step 5: Team Admin Manages Their Team
```bash
# Team admin adds members
curl -X POST 'http://0.0.0.0:4000/team/member_add' \
-H 'Authorization: Bearer sk-team-admin-key' \
-H 'Content-Type: application/json' \
-d '{
"team_id": "team-456",
"member": {
"role": "user",
"user_id": "developer@company.com"
}
}'
# Team admin creates keys for members
curl --location 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-team-admin-key' \
--header 'Content-Type: application/json' \
--data '{
"user_id": "developer@company.com",
"team_id": "team-456"
}'
```
---
## Use Case Examples
### Example 1: Chargeback Model
**Goal**: Each business unit pays for their own LLM usage
**Setup:**
1. Create organization per business unit
2. Set budgets based on allocated budgets
3. Track spend per organization
4. Generate monthly reports for finance
**Result**: Finance can charge back costs to respective departments with accurate attribution.
---
### Example 2: Customer-Facing AI Product
**Goal**: Provide LLM capabilities to customers with isolation and cost tracking
**Setup:**
1. Create organization per customer
2. Use service account keys for production workloads
3. Track spend per customer organization
4. Set rate limits per customer tier
**Result**: Bill customers accurately, prevent noisy neighbors, maintain isolation.
---
### Example 3: Development vs. Production
**Goal**: Separate development and production environments with different policies
**Setup:**
1. Create "Development" and "Production" teams
2. Development: Generous budgets, all models, user keys
3. Production: Strict budgets, approved models only, service account keys
4. Different rate limits per environment
**Result**: Developers can experiment freely without impacting production budget or reliability.
---
## Best Practices
### 1. Organization Design
- ✅ Map organizations to cost centers or customers
- ✅ Set realistic budgets with buffer for growth
- ✅ Assign 1-2 org admins per organization
- ❌ Don't create too many organizations (adds management overhead)
### 2. Team Structure
- ✅ Keep teams aligned with actual working groups
- ✅ Use service account keys for production
- ✅ Give team admins enough permissions to self-serve
- ❌ Don't create single-user teams (use user-only keys instead)
### 3. Key Management
- ✅ Use descriptive key names
- ✅ Rotate keys regularly
- ✅ Delete unused keys
- ✅ Use appropriate key type for use case
- ❌ Don't share keys across users/teams
### 4. Budget Management
- ✅ Set budgets at multiple levels (org → team → user)
- ✅ Monitor spend regularly
- ✅ Alert before budget exhaustion
- ❌ Don't set budgets too tight (may block legitimate usage)
### 5. Delegation
- ✅ Assign org admins for large organizations
- ✅ Assign team admins for active teams
- ✅ Configure team member permissions appropriately
- ❌ Don't make everyone a proxy admin
---
## Monitoring & Observability
LiteLLM provides comprehensive monitoring:
- **Spend Tracking**: Real-time spend by org/team/user/key
- **Usage Analytics**: Request counts, token usage, model usage
- **Admin UI**: Visual dashboard for all metrics
- **Logging**: Detailed logs with tenant context
- **Alerting**: Budget alerts, rate limit alerts, error alerts
[Learn more about Logging](./logging)
---
## Comparison with Other Approaches
| Approach | Pros | Cons | LiteLLM Advantage |
|----------|------|------|-------------------|
| **Separate instances per tenant** | Strong isolation | High operational overhead, cost inefficient | Single instance, same isolation, 90% cost reduction |
| **Single shared pool** | Simple setup | No cost attribution, no access control | Full attribution, granular access control |
| **API key prefixes** | Basic separation | Manual tracking, no hierarchy, no RBAC | Automatic tracking, hierarchical, full RBAC |
| **External auth layer** | Flexible | Complex integration, no built-in budgets | Native integration, built-in budgets |
---
## FAQ
**Q: Can users belong to multiple teams?**
A: Yes, users can be members of multiple teams and have different keys for each team.
**Q: What happens when a user leaves?**
A: User-specific keys are deleted, but team service account keys remain active.
**Q: Can team budgets exceed organization budget?**
A: No, the system enforces that team budgets cannot exceed their organization's budget.
**Q: How granular is the cost tracking?**
A: Every API call is tracked with organization, team, user, and key context.
**Q: Can I have teams without organizations?**
A: Yes! Teams work independently in **open source** without needing Organizations. Organizations are an **enterprise feature** that adds an additional hierarchy layer on top of teams.
**Q: Is there a limit to hierarchy depth?**
A: The hierarchy is: Organization → Team → User → Key (4 levels). This covers most use cases.
**Q: How do I migrate from flat structure to hierarchical?**
A: You can gradually create organizations and teams, then move existing users/keys into them.
---
## Related Documentation
- [User Management Hierarchy](./user_management_heirarchy) - Visual hierarchy overview
- [Access Control (RBAC)](./access_control) - Detailed role permissions
- [Team Budgets](./team_budgets) - Budget management guide
- [Virtual Keys](./virtual_keys) - API key management
- [Admin UI](./ui) - Visual dashboard for management
---
## Summary
LiteLLM solves multi-tenant architecture challenges through:
1. **Hierarchical Structure**: Organizations → Teams → Users → Keys
2. **Granular RBAC**: Platform-wide and tenant-scoped roles
3. **Cost Attribution**: Spend tracking at every level
4. **Delegation**: Org admins and team admins self-manage
5. **Isolation**: Strong tenant boundaries
6. **Scalability**: Handles 10 to 10,000+ users with same architecture
### Open Source vs. Enterprise
**Open Source** (Teams + Users + Keys):
- ✅ Teams as primary tenant boundary
- ✅ Team admins manage their teams
- ✅ Virtual keys with team/user tracking
- ✅ Budget and rate limits per team
- ✅ Spend tracking and logging
**Enterprise** (Adds Organizations layer):
- ✨ Organizations for top-level tenant isolation
- ✨ Organization admins manage multiple teams
- ✨ Organization-level budgets and model access
- ✨ Hierarchical delegation and reporting
This makes LiteLLM ideal for:
- ✅ Enterprises with multiple departments
- ✅ SaaS providers with multiple customers
- ✅ Organizations needing cost chargeback/showback
- ✅ Teams requiring self-service LLM access
- ✅ Any multi-tenant LLM deployment
[Start with LiteLLM Proxy →](./quick_start)

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@ -275,6 +275,20 @@ In this video, we'll add the Azure OpenAI Assistants API as a pass through endpo
- Check LiteLLM proxy logs for error details
- Verify the target API's expected request format
### Allowing Team JWTs to use pass-through routes
If you are using pass-through provider routes (e.g., `/anthropic/*`) and want your JWT team tokens to access these routes, add `mapped_pass_through_routes` to the `team_allowed_routes` in `litellm_jwtauth` or explicitly add the relevant route(s).
Example (`proxy_server_config.yaml`):
```yaml
general_settings:
enable_jwt_auth: True
litellm_jwtauth:
team_ids_jwt_field: "team_ids"
team_allowed_routes: ["openai_routes","info_routes","mapped_pass_through_routes"]
```
### Getting Help
[Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)

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@ -338,6 +338,58 @@ general_settings:
team_allowed_routes: ["/v1/chat/completions"] # 👈 Set accepted routes
```
### Allowing other provider routes for Teams
To enable team JWT tokens to access Anthropic-style endpoints such as `/v1/messages`, update `team_allowed_routes` in your `litellm_jwtauth` configuration. `team_allowed_routes` supports the following values:
- Named route groups from `LiteLLMRoutes` (e.g., `openai_routes`, `anthropic_routes`, `info_routes`, `mapped_pass_through_routes`).
Below is a quick reference for the route groups you can use and example representative routes from each group. If you need the exhaustive list, see the `LiteLLMRoutes` enum in `litellm/proxy/_types.py` for the authoritative list.
| Route Group | What it contains | Representative routes |
|-------------|------------------|-----------------------|
| `openai_routes` | OpenAI-compatible REST endpoints (chat, completion, embeddings, images, responses, models, etc.) | `/v1/chat/completions`, `/v1/completions`, `/v1/embeddings`, `/v1/images/generations`, `/v1/models` |
| `anthropic_routes` | Anthropic-style endpoints (`/v1/messages` and related) | `/v1/messages`, `/v1/messages/count_tokens`, `/v1/skills` |
| `mapped_pass_through_routes` | Provider-specific pass-through route prefixes (e.g., Anthropic when proxied via `/anthropic`). Use with `mapped_pass_through_routes` for provider wildcard mapping | `/anthropic/*`, `/vertex-ai/*`, `/bedrock/*` |
| `passthrough_routes_wildcard` | Wildcard mapping for providers (e.g., `/anthropic/*`) - precomputed wildcard list used by the proxy | `/anthropic/*`, `/vllm/*` |
| `google_routes` | Google-specific (e.g., Vertex / Batching endpoints) | `/v1beta/models/{model_name}:generateContent` |
| `mcp_routes` | Internal MCP management endpoints | `/mcp/tools`, `/mcp/tools/call` |
| `info_routes` | Read-only & info endpoints used by the UI | `/key/info`, `/team/info`, `/v1/models` |
| `management_routes` | Admin-only management endpoints (create/update/delete user/team/model) | `/team/new`, `/key/generate`, `/model/new` |
| `spend_tracking_routes` | Budget/spend related endpoints | `/spend/logs`, `/spend/keys` |
| `public_routes` | Public and unauthenticated endpoints | `/`, `/routes`, `/.well-known/litellm-ui-config` |
Note: `llm_api_routes` is the union of OpenAI, Anthropic, Google, pass-through and other LLM routes (`openai_routes + anthropic_routes + google_routes + mapped_pass_through_routes + passthrough_routes_wildcard + apply_guardrail_routes + mcp_routes + litellm_native_routes`).
Defaults (what the proxy uses if you don't override them in `litellm_jwtauth`):
- `admin_jwt_scope`: `litellm_proxy_admin`
- `admin_allowed_routes` (default): `management_routes`, `spend_tracking_routes`, `global_spend_tracking_routes`, `info_routes`
- `team_allowed_routes` (default): `openai_routes`, `info_routes`
- `public_allowed_routes` (default): `public_routes`
Example: Allow team JWTs to call Anthropic `/v1/messages` (either by route group or by explicit route string):
```yaml
general_settings:
enable_jwt_auth: True
litellm_jwtauth:
team_ids_jwt_field: "team_ids"
team_allowed_routes: ["openai_routes", "info_routes", "anthropic_routes"]
```
Or selectively allow the exact Anthropic message endpoint only:
```yaml
general_settings:
enable_jwt_auth: True
litellm_jwtauth:
team_ids_jwt_field: "team_ids"
team_allowed_routes: ["/v1/messages", "info_routes"]
```
### Caching Public Keys
Control how long public keys are cached for (in seconds).
@ -407,6 +459,72 @@ general_settings:
user_id_upsert: true # 👈 upserts the user to db, if valid email but not in db
```
## OIDC UserInfo Endpoint
Use this when your JWT/access token doesn't contain user-identifying information. LiteLLM will call your identity provider's UserInfo endpoint to fetch user details.
### When to Use
- Your JWT is opaque (not self-contained) or lacks user claims
- You need to fetch fresh user information from your identity provider
- Your access tokens don't include email, roles, or other identifying data
### Configuration
```yaml title="config.yaml" showLineNumbers
general_settings:
enable_jwt_auth: True
litellm_jwtauth:
# Enable OIDC UserInfo endpoint
oidc_userinfo_enabled: true
oidc_userinfo_endpoint: "https://your-idp.com/oauth2/userinfo"
oidc_userinfo_cache_ttl: 300 # Cache for 5 minutes (default: 300)
# Map fields from UserInfo response
user_id_jwt_field: "sub"
user_email_jwt_field: "email"
user_roles_jwt_field: "roles"
```
### Flow Diagram
```mermaid
sequenceDiagram
participant Client
participant LiteLLM
participant IdP as Identity Provider
Client->>LiteLLM: Request with Bearer token
Note over LiteLLM: Check cache for UserInfo
LiteLLM->>IdP: GET /userinfo (if not cached)<br/>Authorization: Bearer {token}
IdP-->>LiteLLM: User data (sub, email, roles)
Note over LiteLLM: Cache response (TTL: 5min)<br/>Extract user_id, email, roles<br/>Perform RBAC checks
LiteLLM-->>Client: Authorized/Denied
```
### Example: Azure AD
```yaml title="config.yaml" showLineNumbers
litellm_jwtauth:
oidc_userinfo_enabled: true
oidc_userinfo_endpoint: "https://graph.microsoft.com/oidc/userinfo"
user_id_jwt_field: "sub"
user_email_jwt_field: "email"
```
### Example: Keycloak
```yaml title="config.yaml" showLineNumbers
litellm_jwtauth:
oidc_userinfo_enabled: true
oidc_userinfo_endpoint: "https://keycloak.example.com/realms/your-realm/protocol/openid-connect/userinfo"
user_id_jwt_field: "sub"
user_roles_jwt_field: "resource_access.your-client.roles"
```
## [BETA] Control Access with OIDC Roles
Allow JWT tokens with supported roles to access the proxy.

79
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@ -81,6 +81,85 @@ for event in stream:
f.write(image_bytes)
```
#### Image Generation (Non-streaming)
Image generation is supported for models that generate images. Generated images are returned in the `output` array with `type: "image_generation_call"`.
**Gemini (Google AI Studio):**
```python showLineNumbers title="Gemini Image Generation"
import litellm
import base64
# Gemini image generation models don't require tools parameter
response = litellm.responses(
model="gemini/gemini-2.5-flash-image",
input="Generate a cute cat playing with yarn"
)
# Access generated images from output
for item in response.output:
if item.type == "image_generation_call":
# item.result contains pure base64 (no data: prefix)
image_bytes = base64.b64decode(item.result)
# Save the image
with open(f"generated_{item.id}.png", "wb") as f:
f.write(image_bytes)
print(f"Image saved: generated_{response.output[0].id}.png")
```
**OpenAI:**
```python showLineNumbers title="OpenAI Image Generation"
import litellm
import base64
# OpenAI models require tools parameter for image generation
response = litellm.responses(
model="openai/gpt-4o",
input="Generate a futuristic city at sunset",
tools=[{"type": "image_generation"}]
)
# Access generated images from output
for item in response.output:
if item.type == "image_generation_call":
image_bytes = base64.b64decode(item.result)
with open(f"generated_{item.id}.png", "wb") as f:
f.write(image_bytes)
```
**Response Format:**
When image generation is successful, the response contains:
```json
{
"id": "resp_abc123",
"status": "completed",
"output": [
{
"type": "image_generation_call",
"id": "resp_abc123_img_0",
"status": "completed",
"result": "iVBORw0KGgo..." // Pure base64 string (no data: prefix)
}
]
}
```
**Supported Models:**
| Provider | Models | Requires `tools` Parameter |
|----------|--------|---------------------------|
| Google AI Studio | `gemini/gemini-2.5-flash-image` | ❌ No |
| Vertex AI | `vertex_ai/gemini-2.5-flash-image-preview` | ❌ No |
| OpenAI | `gpt-4o`, `gpt-4o-mini`, `gpt-4.1`, `gpt-4.1-mini`, `gpt-4.1-nano`, `o3` | ✅ Yes |
| AWS Bedrock | Stability AI, Amazon Nova Canvas models | Model-specific |
| Fal AI | Various image generation models | Check model docs |
**Note:** The `result` field contains pure base64-encoded image data without the `data:image/png;base64,` prefix. You must decode it with `base64.b64decode()` before saving.
#### GET a Response
```python showLineNumbers title="Get Response by ID"
import litellm

19
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View file

@ -41,6 +41,7 @@ CYBERARK_CLIENT_KEY="path/to/client.key"
# OPTIONAL
CYBERARK_REFRESH_INTERVAL="300" # defaults to 300 seconds (5 minutes), frequency of token refresh
CYBERARK_SSL_VERIFY="true" # defaults to true, set to "false" to disable SSL verification (for self-signed certificates)
```
**Step 2.** Add to proxy config.yaml
@ -172,6 +173,24 @@ If these commands work successfully against your CyberArk instance, then CyberAr
- The `CYBERARK_API_BASE` URL is accessible from your LiteLLM instance
- Your API key or certificates have the necessary permissions in CyberArk
### SSL Certificate Errors
If you encounter SSL certificate verification errors like:
```
RuntimeError: Could not authenticate to CyberArk Conjur: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain
```
This typically occurs when your CyberArk Conjur instance uses a self-signed certificate. You can disable SSL verification by setting:
```bash
CYBERARK_SSL_VERIFY="false"
```
:::warning
Disabling SSL verification is insecure and should only be used for testing or development environments with self-signed certificates. For production, configure your certificate chain properly or use certificate-based authentication with `CYBERARK_CLIENT_CERT` and `CYBERARK_CLIENT_KEY`.
:::
## Video Walkthrough
This video walks through using CyberArk Conjur as a secret manager with LiteLLM. We create a virtual key in the LiteLLM Admin UI and verify it exists in CyberArk. Then we rotate the secret key and verify it exists in CyberArk.

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@ -0,0 +1,226 @@
---
sidebar_label: "Cursor IDE"
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Cursor IDE Integration with LiteLLM
This tutorial shows you how to integrate Cursor IDE with LiteLLM Proxy, allowing you to use any LiteLLM-supported model through Cursor's interface with BYOK (Bring Your Own Key) and custom base URL.
## Benefits of using Cursor with LiteLLM
When you use Cursor IDE with LiteLLM you get the following benefits:
**Developer Benefits:**
- Universal Model Access: Use any LiteLLM supported model (Anthropic, OpenAI, Vertex AI, Bedrock, etc.) through the Cursor IDE interface.
- Higher Rate Limits & Reliability: Load balance across multiple models and providers to avoid hitting individual provider limits, with fallbacks to ensure you get responses even if one provider fails.
- Streaming Support: Full streaming support with proper response transformation for Cursor's expected format.
**Proxy Admin Benefits:**
- Centralized Management: Control access to all models through a single LiteLLM proxy instance without giving your developers API Keys to each provider.
- Budget Controls: Set spending limits and track costs across all Cursor usage.
- Request Logging: Track all requests made through Cursor for debugging and monitoring.
## Prerequisites
Before you begin, ensure you have:
- Cursor IDE installed
- A running LiteLLM Proxy instance with **HTTPS enabled** (HTTP is not supported)
- A valid LiteLLM Proxy API key
- An HTTPS domain for your LiteLLM Proxy (required by Cursor)
## Quick Start Guide
### Step 1: Install LiteLLM
Install LiteLLM with proxy support:
```bash
pip install litellm[proxy]
```
### Step 2: Configure LiteLLM Proxy
Create a `config.yaml` file with your model configurations:
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: gpt-4o
litellm_params:
model: gpt-4o
api_key: os.environ/OPENAI_API_KEY
- model_name: claude-3-5-sonnet
litellm_params:
model: anthropic/claude-3-5-sonnet-20241022
api_key: os.environ/ANTHROPIC_API_KEY
general_settings:
master_key: sk-1234567890 # Change this to a secure key
```
### Step 3: Start LiteLLM Proxy
Start the proxy server with HTTPS enabled:
```bash
litellm --config config.yaml --port 4000
```
:::warning HTTPS Required
**Important**: Cursor IDE requires HTTPS connections. HTTP (`http://`) will not work. You must:
- Deploy your LiteLLM Proxy with HTTPS enabled
- Use a valid SSL certificate
- Access the proxy via an HTTPS domain (e.g., `https://your-proxy-domain.com`)
For local development, you'll need to set up HTTPS (e.g., using a reverse proxy like nginx with SSL, or deploying to a cloud service with HTTPS).
:::
### Step 4: Configure Cursor IDE
Configure Cursor IDE to use your LiteLLM proxy with the `/cursor/chat/completions` endpoint:
1. Open Cursor IDE
2. Go to **Settings** → **Features** → **AI**
3. Enable **"Use Custom API"** or **"Bring Your Own Key"**
4. Set the following:
- **Base URL**: `https://your-proxy-domain.com/cursor` (⚠️ **Important**: Must use HTTPS and include `/cursor`)
- **API Key**: Your LiteLLM Proxy API key (e.g., `sk-1234567890`)
:::warning HTTPS Required
Cursor IDE **requires HTTPS** connections. HTTP (`http://`) will not work. You must:
- Use an HTTPS URL for your base URL (e.g., `https://your-proxy-domain.com/cursor`)
- Ensure your LiteLLM Proxy is accessible via HTTPS
- Have a valid SSL certificate configured
:::
**Example Configuration:**
```
Base URL: https://your-proxy-domain.com/cursor
API Key: sk-1234567890
```
Replace `your-proxy-domain.com` with your actual HTTPS domain where LiteLLM Proxy is running.
:::info Why `/cursor` in the base URL?
Cursor automatically appends `/chat/completions` to the base URL you provide. By setting the base URL to `https://your-proxy-domain.com/cursor`, Cursor will send requests to `/cursor/chat/completions`, which is the special endpoint that handles Cursor's Responses API input format and transforms it to Chat Completions output format.
If you set the base URL to just `https://your-proxy-domain.com`, Cursor would send requests to `/chat/completions`, which won't work correctly with Cursor's request format.
:::
### Step 5: Test the Integration
1. Restart Cursor IDE to apply the settings
2. Open a code file and try using Cursor's AI features (completions, chat, etc.)
3. Your requests will now be routed through LiteLLM Proxy
You can verify it's working by:
- Checking the LiteLLM Proxy logs for incoming requests
- Using Cursor's chat feature and seeing responses stream correctly
- Checking your LiteLLM dashboard for request logs and cost tracking
## How It Works
The `/cursor/chat/completions` endpoint is specifically designed to handle Cursor's unique request format:
1. **Input**: Cursor sends requests in OpenAI Responses API format (with `input` field)
2. **Processing**: LiteLLM processes the request through its internal `/responses` flow
3. **Output**: The response is transformed to OpenAI Chat Completions format (with `choices` field) that Cursor expects
This transformation happens automatically for both streaming and non-streaming responses.
## Advanced Configuration
### Using Different Models
You can configure Cursor to use different models by updating your `config.yaml`:
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: gpt-4o
litellm_params:
model: gpt-4o
api_key: os.environ/OPENAI_API_KEY
- model_name: claude-3-5-sonnet
litellm_params:
model: anthropic/claude-3-5-sonnet-20241022
api_key: os.environ/ANTHROPIC_API_KEY
- model_name: gemini-pro
litellm_params:
model: gemini/gemini-1.5-pro
api_key: os.environ/GEMINI_API_KEY
```
Then in Cursor, you can specify which model to use in your requests.
### Rate Limiting and Budgets
Set up rate limits and budgets in your `config.yaml`:
```yaml showLineNumbers title="config.yaml"
general_settings:
master_key: sk-1234567890
litellm_settings:
# Set max budget per user
max_budget: 100.0
# Set rate limits
rate_limit: 100 # requests per minute
```
### Request Logging
All requests from Cursor will be logged by LiteLLM Proxy. You can:
- View logs in the LiteLLM Admin UI
- Export logs to your preferred logging service
- Track costs per user/team
## Troubleshooting
### Cursor shows no output
- **Check base URL**: Ensure it uses HTTPS and includes `/cursor` (e.g., `https://your-proxy-domain.com/cursor`, not `http://` or without `/cursor`)
- **Verify HTTPS**: Cursor requires HTTPS - HTTP connections will not work
- **Check API key**: Verify your LiteLLM Proxy API key is correct
- **Check proxy logs**: Look for errors in the LiteLLM Proxy logs
### Requests failing
- **Verify HTTPS is enabled**: Cursor requires HTTPS connections. Ensure your LiteLLM Proxy is accessible via HTTPS with a valid SSL certificate
- **Verify proxy is running**: Check that LiteLLM Proxy is accessible at your HTTPS base URL
- **Check SSL certificate**: Ensure your SSL certificate is valid and not expired
- **Check model configuration**: Ensure the model you're trying to use is configured in `config.yaml`
- **Check API keys**: Verify provider API keys are set correctly in environment variables
### HTTP not working
If you're trying to use HTTP (`http://`) and it's not working:
- **This is expected**: Cursor IDE requires HTTPS connections
- **Solution**: Deploy your LiteLLM Proxy with HTTPS enabled (use a reverse proxy like nginx, or deploy to a cloud service that provides HTTPS)
### Streaming not working
The `/cursor/chat/completions` endpoint automatically handles streaming. If streaming isn't working:
- Check that your model supports streaming
- Verify the proxy logs for any transformation errors
- Ensure Cursor IDE is up to date
## Related Documentation
- [Cursor Endpoint Documentation](/docs/proxy/cursor) - Detailed endpoint documentation
- [LiteLLM Proxy Setup](/docs/proxy/quick_start) - General proxy setup guide
- [Model Configuration](/docs/proxy/configs) - How to configure models

1
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@ -14,6 +14,7 @@ Create a vector store which can be used to store and search document chunks for
| End-user Tracking | ✅ | |
| Support LLM Providers (OpenAI `/vector_stores` API) | **OpenAI** | Full vector stores API support across providers |
| Support LLM Providers (Passthrough API) | [**Azure AI**](/docs/providers/azure_ai/azure_ai_vector_stores_passthrough) | Full vector stores API support across providers |
| Support LLM Providers (Dataset Management) | [**RAGFlow**](/docs/providers/ragflow_vector_store.md) | Dataset creation and management (search not supported) |
## Usage

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6
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@ -14619,9 +14619,9 @@
}
},
"node_modules/mdast-util-to-hast": {
"version": "13.2.0",
"resolved": "https://registry.npmjs.org/mdast-util-to-hast/-/mdast-util-to-hast-13.2.0.tgz",
"integrity": "sha512-QGYKEuUsYT9ykKBCMOEDLsU5JRObWQusAolFMeko/tYPufNkRffBAQjIE+99jbA87xv6FgmjLtwjh9wBWajwAA==",
"version": "13.2.1",
"resolved": "https://registry.npmjs.org/mdast-util-to-hast/-/mdast-util-to-hast-13.2.1.tgz",
"integrity": "sha512-cctsq2wp5vTsLIcaymblUriiTcZd0CwWtCbLvrOzYCDZoWyMNV8sZ7krj09FSnsiJi3WVsHLM4k6Dq/yaPyCXA==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",

5
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@ -61,6 +61,7 @@
"mermaid": ">=11.10.0",
"gray-matter": "4.0.3",
"glob": ">=11.1.0",
"node-forge": ">=1.3.2"
"node-forge": ">=1.3.2",
"mdast-util-to-hast": ">=13.2.1"
}
}
}

4
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@ -1,5 +1,5 @@
---
title: "[PREVIEW] v1.80.5.rc.2 - Gemini 3.0 Support"
title: "v1.80.5-stable - Gemini 3.0 Support"
slug: "v1-80-5"
date: 2025-11-22T10:00:00
authors:
@ -27,7 +27,7 @@ import TabItem from '@theme/TabItem';
docker run \
-e STORE_MODEL_IN_DB=True \
-p 4000:4000 \
ghcr.io/berriai/litellm:v1.80.5.rc.2
ghcr.io/berriai/litellm:v1.80.5-stable
```
</TabItem>

607
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@ -0,0 +1,607 @@
---
title: "[Preview] v1.80.8.rc.1 - Introducing A2A Agent Gateway"
slug: "v1-80-8"
date: 2025-12-06T10:00:00
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
<Tabs>
<TabItem value="docker" label="Docker">
``` showLineNumbers title="docker run litellm"
docker run \
-e STORE_MODEL_IN_DB=True \
-p 4000:4000 \
ghcr.io/berriai/litellm:v1.80.8.rc.1
```
</TabItem>
<TabItem value="pip" label="Pip">
``` showLineNumbers title="pip install litellm"
pip install litellm==1.80.8
```
</TabItem>
</Tabs>
---
## Key Highlights
- **Agent Gateway (A2A)** - [Invoke agents through the AI Gateway with request/response logging and access controls](../../docs/a2a)
- **Guardrails API v2** - [Generic Guardrail API with streaming support, structured messages, and tool call checks](../../docs/adding_provider/generic_guardrail_api)
- **Customer (End User) Usage UI** - [Track and visualize end-user spend directly in the dashboard](../../docs/proxy/customer_usage)
- **vLLM Batch + Files API** - [Support for batch and files API with vLLM deployments](../../docs/batches)
- **Dynamic Rate Limiting on Teams** - [Enable dynamic rate limits and priority reservation on team-level](../../docs/proxy/team_budgets)
- **Google Cloud Chirp3 HD** - [New text-to-speech provider with Chirp3 HD voices](../../docs/text_to_speech)
---
### Agent Gateway (A2A)
<Image
img={require('../../img/a2a_gateway.png')}
style={{width: '100%', display: 'block', margin: '2rem auto'}}
/>
<br/>
This release introduces **A2A Agent Gateway** for LiteLLM, allowing you to invoke and manage A2A agents with the same controls you have for LLM APIs.
As a **LiteLLM Gateway Admin**, you can now do the following:
- **Request/Response Logging** - Every agent invocation is logged to the Logs page with full request and response tracking.
- **Access Control** - Control which Team/Key can access which agents.
As a developer, you can continue using the A2A SDK, all you need to do is point you `A2AClient` to the LiteLLM proxy URL and your API key.
**Works with the A2A SDK:**
```python
from a2a.client import A2AClient
client = A2AClient(
base_url="http://localhost:4000", # Your LiteLLM proxy
api_key="sk-1234" # LiteLLM API key
)
response = client.send_message(
agent_id="my-agent",
message="What's the status of my order?"
)
```
Get started with Agent Gateway here: [Agent Gateway Documentation](../../docs/a2a)
---
### Customer (End User) Usage UI
<Image
img={require('../../img/customer_usage.png')}
style={{width: '100%', display: 'block', margin: '2rem auto'}}
/>
Users can now filter usage statistics by customers, providing the same granular filtering capabilities available for teams and organizations.
**Details:**
- Filter usage analytics, spend logs, and activity metrics by customer ID
- View customer-level breakdowns alongside existing team and user-level filters
- Consistent filtering experience across all usage and analytics views
---
## New Providers and Endpoints
### New Providers (5 new providers)
| Provider | Supported LiteLLM Endpoints | Description |
| -------- | ------------------- | ----------- |
| **[Z.AI (Zhipu AI)](../../docs/providers/zai)** | `/v1/chat/completions`, `/v1/responses`, `/v1/messages` | Built-in support for Zhipu AI GLM models |
| **[RAGFlow](../../docs/providers/ragflow)** | `/v1/chat/completions`, `/v1/responses`, `/v1/messages`, `/v1/vector_stores` | RAG-based chat completions with vector store support |
| **[PublicAI](../../docs/providers/publicai)** | `/v1/chat/completions`, `/v1/responses`, `/v1/messages` | OpenAI-compatible provider via JSON config |
| **[Google Cloud Chirp3 HD](../../docs/text_to_speech)** | `/v1/audio/speech`, `/v1/audio/speech/stream` | Text-to-speech with Google Cloud Chirp3 HD voices |
### New LLM API Endpoints (2 new endpoints)
| Endpoint | Method | Description | Documentation |
| -------- | ------ | ----------- | ------------- |
| `/v1/agents/invoke` | POST | Invoke A2A agents through the AI Gateway | [Agent Gateway](../../docs/a2a) |
| `/cursor/chat/completions` | POST | Cursor BYOK endpoint - accepts Responses API input, returns Chat Completions output | [Cursor Integration](../../docs/tutorials/cursor_integration) |
---
## New Models / Updated Models
#### New Model Support (33 new models)
| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
| OpenAI | `gpt-5.1-codex-max` | 400K | $1.25 | $10.00 | Reasoning, vision, PDF input, responses API |
| Azure | `azure/gpt-5.1-codex-max` | 400K | $1.25 | $10.00 | Reasoning, vision, PDF input, responses API |
| Anthropic | `claude-opus-4-5` | 200K | $5.00 | $25.00 | Computer use, reasoning, vision |
| Bedrock | `global.anthropic.claude-opus-4-5-20251101-v1:0` | 200K | $5.00 | $25.00 | Computer use, reasoning, vision |
| Bedrock | `amazon.nova-2-lite-v1:0` | 1M | $0.30 | $2.50 | Reasoning, vision, video, PDF input |
| Bedrock | `amazon.titan-image-generator-v2:0` | - | - | $0.008/image | Image generation |
| Fireworks | `fireworks_ai/deepseek-v3p2` | 164K | $1.20 | $1.20 | Function calling, response schema |
| Fireworks | `fireworks_ai/kimi-k2-instruct-0905` | 262K | $0.60 | $2.50 | Function calling, response schema |
| DeepSeek | `deepseek/deepseek-v3.2` | 164K | $0.28 | $0.40 | Reasoning, function calling |
| Mistral | `mistral/mistral-large-3` | 256K | $0.50 | $1.50 | Function calling, vision |
| Azure AI | `azure_ai/mistral-large-3` | 256K | $0.50 | $1.50 | Function calling, vision |
| Moonshot | `moonshot/kimi-k2-0905-preview` | 262K | $0.60 | $2.50 | Function calling, web search |
| Moonshot | `moonshot/kimi-k2-turbo-preview` | 262K | $1.15 | $8.00 | Function calling, web search |
| Moonshot | `moonshot/kimi-k2-thinking-turbo` | 262K | $1.15 | $8.00 | Function calling, web search |
| OpenRouter | `openrouter/deepseek/deepseek-v3.2` | 164K | $0.28 | $0.40 | Reasoning, function calling |
| Databricks | `databricks/databricks-claude-haiku-4-5` | 200K | $1.00 | $5.00 | Reasoning, function calling |
| Databricks | `databricks/databricks-claude-opus-4` | 200K | $15.00 | $75.00 | Reasoning, function calling |
| Databricks | `databricks/databricks-claude-opus-4-1` | 200K | $15.00 | $75.00 | Reasoning, function calling |
| Databricks | `databricks/databricks-claude-opus-4-5` | 200K | $5.00 | $25.00 | Reasoning, function calling |
| Databricks | `databricks/databricks-claude-sonnet-4` | 200K | $3.00 | $15.00 | Reasoning, function calling |
| Databricks | `databricks/databricks-claude-sonnet-4-1` | 200K | $3.00 | $15.00 | Reasoning, function calling |
| Databricks | `databricks/databricks-gemini-2-5-flash` | 1M | $0.30 | $2.50 | Function calling |
| Databricks | `databricks/databricks-gemini-2-5-pro` | 1M | $1.25 | $10.00 | Function calling |
| Databricks | `databricks/databricks-gpt-5` | 400K | $1.25 | $10.00 | Function calling |
| Databricks | `databricks/databricks-gpt-5-1` | 400K | $1.25 | $10.00 | Function calling |
| Databricks | `databricks/databricks-gpt-5-mini` | 400K | $0.25 | $2.00 | Function calling |
| Databricks | `databricks/databricks-gpt-5-nano` | 400K | $0.05 | $0.40 | Function calling |
| Vertex AI | `vertex_ai/chirp` | - | $30.00/1M chars | - | Text-to-speech (Chirp3 HD) |
| Z.AI | `zai/glm-4.6` | 200K | $0.60 | $2.20 | Function calling |
| Z.AI | `zai/glm-4.5` | 128K | $0.60 | $2.20 | Function calling |
| Z.AI | `zai/glm-4.5v` | 128K | $0.60 | $1.80 | Function calling, vision |
| Z.AI | `zai/glm-4.5-flash` | 128K | Free | Free | Function calling |
| Vertex AI | `vertex_ai/bge-large-en-v1.5` | - | - | - | BGE Embeddings |
#### Features
- **[OpenAI](../../docs/providers/openai)**
- Add `gpt-5.1-codex-max` model pricing and configuration - [PR #17541](https://github.com/BerriAI/litellm/pull/17541)
- Add xhigh reasoning effort for gpt-5.1-codex-max - [PR #17585](https://github.com/BerriAI/litellm/pull/17585)
- Add clear error message for empty LLM endpoint responses - [PR #17445](https://github.com/BerriAI/litellm/pull/17445)
- **[Azure OpenAI](../../docs/providers/azure/azure)**
- Allow reasoning_effort='none' for Azure gpt-5.1 models - [PR #17311](https://github.com/BerriAI/litellm/pull/17311)
- **[Anthropic](../../docs/providers/anthropic)**
- Add `claude-opus-4-5` alias to pricing data - [PR #17313](https://github.com/BerriAI/litellm/pull/17313)
- Parse `<budget:thinking>` blocks for opus 4.5 - [PR #17534](https://github.com/BerriAI/litellm/pull/17534)
- Update new Anthropic features as reviewed - [PR #17142](https://github.com/BerriAI/litellm/pull/17142)
- Skip empty text blocks in Anthropic system messages - [PR #17442](https://github.com/BerriAI/litellm/pull/17442)
- **[Bedrock](../../docs/providers/bedrock)**
- Add Nova embedding support - [PR #17253](https://github.com/BerriAI/litellm/pull/17253)
- Add support for Bedrock Qwen 2 imported model - [PR #17461](https://github.com/BerriAI/litellm/pull/17461)
- Bedrock OpenAI model support - [PR #17368](https://github.com/BerriAI/litellm/pull/17368)
- Add support for file content download for Bedrock batches - [PR #17470](https://github.com/BerriAI/litellm/pull/17470)
- Make streaming chunk size configurable in Bedrock API - [PR #17357](https://github.com/BerriAI/litellm/pull/17357)
- Add experimental latest-user filtering for Bedrock - [PR #17282](https://github.com/BerriAI/litellm/pull/17282)
- Handle Cohere v4 embed response dictionary format - [PR #17220](https://github.com/BerriAI/litellm/pull/17220)
- Remove not compatible beta header from Bedrock - [PR #17301](https://github.com/BerriAI/litellm/pull/17301)
- Add model price and details for Global Opus 4.5 Bedrock endpoint - [PR #17380](https://github.com/BerriAI/litellm/pull/17380)
- **[Gemini (Google AI Studio + Vertex AI)](../../docs/providers/gemini)**
- Add better handling in image generation for Gemini models - [PR #17292](https://github.com/BerriAI/litellm/pull/17292)
- Fix reasoning_content showing duplicate content in streaming responses - [PR #17266](https://github.com/BerriAI/litellm/pull/17266)
- Handle partial JSON chunks after first valid chunk - [PR #17496](https://github.com/BerriAI/litellm/pull/17496)
- Fix Gemini 3 last chunk thinking block - [PR #17403](https://github.com/BerriAI/litellm/pull/17403)
- Fix Gemini image_tokens treated as text tokens in cost calculation - [PR #17554](https://github.com/BerriAI/litellm/pull/17554)
- Make sure that media resolution is only for Gemini 3 model - [PR #17137](https://github.com/BerriAI/litellm/pull/17137)
- **[Vertex AI](../../docs/providers/vertex)**
- Add Google Cloud Chirp3 HD support on /speech - [PR #17391](https://github.com/BerriAI/litellm/pull/17391)
- Add BGE Embeddings support - [PR #17362](https://github.com/BerriAI/litellm/pull/17362)
- Handle global location for Vertex AI image generation endpoint - [PR #17255](https://github.com/BerriAI/litellm/pull/17255)
- Add Google Private API Endpoint to Vertex AI fields - [PR #17382](https://github.com/BerriAI/litellm/pull/17382)
- **[Z.AI (Zhipu AI)](../../docs/providers/zai)**
- Add Z.AI as built-in provider - [PR #17307](https://github.com/BerriAI/litellm/pull/17307)
- **[GitHub Copilot](../../docs/providers/github_copilot)**
- Add Embedding API support - [PR #17278](https://github.com/BerriAI/litellm/pull/17278)
- Preserve encrypted_content in reasoning items for multi-turn conversations - [PR #17130](https://github.com/BerriAI/litellm/pull/17130)
- **[Databricks](../../docs/providers/databricks)**
- Update Databricks model pricing and add new models - [PR #17277](https://github.com/BerriAI/litellm/pull/17277)
- **[OVHcloud](../../docs/providers/ovhcloud)**
- Add support of audio transcription for OVHcloud - [PR #17305](https://github.com/BerriAI/litellm/pull/17305)
- **[Mistral](../../docs/providers/mistral)**
- Add Mistral Large 3 model support - [PR #17547](https://github.com/BerriAI/litellm/pull/17547)
- **[Moonshot](../../docs/providers/moonshot)**
- Fix missing Moonshot turbo models and fix incorrect pricing - [PR #17432](https://github.com/BerriAI/litellm/pull/17432)
- **[Together AI](../../docs/providers/togetherai)**
- Add context window exception mapping for Together AI - [PR #17284](https://github.com/BerriAI/litellm/pull/17284)
- **[WatsonX](../../docs/providers/watsonx/index)**
- Allow passing zen_api_key dynamically - [PR #16655](https://github.com/BerriAI/litellm/pull/16655)
- Fix Watsonx Audio Transcription API - [PR #17326](https://github.com/BerriAI/litellm/pull/17326)
- Fix audio transcriptions, don't force content type in request headers - [PR #17546](https://github.com/BerriAI/litellm/pull/17546)
- **[Fireworks AI](../../docs/providers/fireworks_ai)**
- Add new model `fireworks_ai/kimi-k2-instruct-0905` - [PR #17328](https://github.com/BerriAI/litellm/pull/17328)
- Add `fireworks/deepseek-v3p2` - [PR #17395](https://github.com/BerriAI/litellm/pull/17395)
- **[DeepSeek](../../docs/providers/deepseek)**
- Support Deepseek 3.2 with Reasoning - [PR #17384](https://github.com/BerriAI/litellm/pull/17384)
- **[Nova Lite 2](../../docs/providers/bedrock)**
- Add Nova Lite 2 reasoning support with reasoningConfig - [PR #17371](https://github.com/BerriAI/litellm/pull/17371)
- **[Ollama](../../docs/providers/ollama)**
- Fix auth not working with ollama.com - [PR #17191](https://github.com/BerriAI/litellm/pull/17191)
- **[Groq](../../docs/providers/groq)**
- Fix supports_response_schema before using json_tool_call workaround - [PR #17438](https://github.com/BerriAI/litellm/pull/17438)
- **[vLLM](../../docs/providers/vllm)**
- Fix empty response + vLLM streaming - [PR #17516](https://github.com/BerriAI/litellm/pull/17516)
- **[Azure AI](../../docs/providers/azure_ai)**
- Migrate Anthropic provider to Azure AI - [PR #17202](https://github.com/BerriAI/litellm/pull/17202)
- Fix GA path for Azure OpenAI realtime models - [PR #17260](https://github.com/BerriAI/litellm/pull/17260)
- **[Bedrock TwelveLabs](../../docs/providers/bedrock#twelvelabs-pegasus---video-understanding)**
- Add support for TwelveLabs Pegasus video understanding - [PR #17193](https://github.com/BerriAI/litellm/pull/17193)
### Bug Fixes
- **[Bedrock](../../docs/providers/bedrock)**
- Fix extra_headers in messages API bedrock invoke - [PR #17271](https://github.com/BerriAI/litellm/pull/17271)
- Fix Bedrock models in model map - [PR #17419](https://github.com/BerriAI/litellm/pull/17419)
- Make Bedrock converse messages respect modify_params as expected - [PR #17427](https://github.com/BerriAI/litellm/pull/17427)
- Fix Anthropic beta headers for Bedrock imported Qwen models - [PR #17467](https://github.com/BerriAI/litellm/pull/17467)
- Preserve usage from JSON response for OpenAI provider in Bedrock - [PR #17589](https://github.com/BerriAI/litellm/pull/17589)
- **[SambaNova](../../docs/providers/sambanova)**
- Fix acompletion throws error with SambaNova models - [PR #17217](https://github.com/BerriAI/litellm/pull/17217)
- **General**
- Fix AttributeError when metadata is null in request body - [PR #17306](https://github.com/BerriAI/litellm/pull/17306)
- Fix 500 error for malformed request - [PR #17291](https://github.com/BerriAI/litellm/pull/17291)
- Respect custom LLM provider in header - [PR #17290](https://github.com/BerriAI/litellm/pull/17290)
- Replace deprecated .dict() with .model_dump() in streaming_handler - [PR #17359](https://github.com/BerriAI/litellm/pull/17359)
---
## LLM API Endpoints
#### Features
- **[Responses API](../../docs/response_api)**
- Add cost tracking for responses API - [PR #17258](https://github.com/BerriAI/litellm/pull/17258)
- Map output_tokens_details of responses API to completion_tokens_details - [PR #17458](https://github.com/BerriAI/litellm/pull/17458)
- Add image generation support for Responses API - [PR #16586](https://github.com/BerriAI/litellm/pull/16586)
- **[Batch API](../../docs/batches)**
- Add vLLM batch+files API support - [PR #15823](https://github.com/BerriAI/litellm/pull/15823)
- Fix optional parameter default value - [PR #17434](https://github.com/BerriAI/litellm/pull/17434)
- Add status parameter as optional for FileObject - [PR #17431](https://github.com/BerriAI/litellm/pull/17431)
- **[Video Generation API](../../docs/videos)**
- Add passthrough cost tracking for Veo - [PR #17296](https://github.com/BerriAI/litellm/pull/17296)
- **[OCR API](../../docs/ocr)**
- Add missing OCR and aOCR to CallTypes enum - [PR #17435](https://github.com/BerriAI/litellm/pull/17435)
- **General**
- Support routing to only websearch supported deployments - [PR #17500](https://github.com/BerriAI/litellm/pull/17500)
#### Bugs
- **General**
- Fix streaming error validation - [PR #17242](https://github.com/BerriAI/litellm/pull/17242)
- Add length validation for empty tool_calls in delta - [PR #17523](https://github.com/BerriAI/litellm/pull/17523)
---
## Management Endpoints / UI
#### Features
- **New Login Page**
- New Login Page UI - [PR #17443](https://github.com/BerriAI/litellm/pull/17443)
- Refactor /login route - [PR #17379](https://github.com/BerriAI/litellm/pull/17379)
- Add auto_redirect_to_sso to UI Config - [PR #17399](https://github.com/BerriAI/litellm/pull/17399)
- Add Auto Redirect to SSO to New Login Page - [PR #17451](https://github.com/BerriAI/litellm/pull/17451)
- **Customer (End User) Usage**
- Customer (end user) Usage feature - [PR #17498](https://github.com/BerriAI/litellm/pull/17498)
- Customer Usage UI - [PR #17506](https://github.com/BerriAI/litellm/pull/17506)
- Add Info Banner for Customer Usage - [PR #17598](https://github.com/BerriAI/litellm/pull/17598)
- **Virtual Keys**
- Standardize API Key vs Virtual Key in UI - [PR #17325](https://github.com/BerriAI/litellm/pull/17325)
- Add User Alias Column to Internal User Table - [PR #17321](https://github.com/BerriAI/litellm/pull/17321)
- Delete Credential Enhancements - [PR #17317](https://github.com/BerriAI/litellm/pull/17317)
- **Models + Endpoints**
- Show all credential values on Edit Credential Modal - [PR #17397](https://github.com/BerriAI/litellm/pull/17397)
- Change Edit Team Models Shown to Match Create Team - [PR #17394](https://github.com/BerriAI/litellm/pull/17394)
- Support Images in Compare UI - [PR #17562](https://github.com/BerriAI/litellm/pull/17562)
- **Callbacks**
- Show all callbacks on UI - [PR #16335](https://github.com/BerriAI/litellm/pull/16335)
- Credentials to use React Query - [PR #17465](https://github.com/BerriAI/litellm/pull/17465)
- **Management Routes**
- Allow admin viewer to access global tag usage - [PR #17501](https://github.com/BerriAI/litellm/pull/17501)
- Allow wildcard routes for nonproxy admin (SCIM) - [PR #17178](https://github.com/BerriAI/litellm/pull/17178)
- Return 404 when a user is not found on /user/info - [PR #16850](https://github.com/BerriAI/litellm/pull/16850)
- **OCI Configuration**
- Enable Oracle Cloud Infrastructure configuration via UI - [PR #17159](https://github.com/BerriAI/litellm/pull/17159)
#### Bugs
- **UI Fixes**
- Fix Request and Response Panel JSONViewer - [PR #17233](https://github.com/BerriAI/litellm/pull/17233)
- Adding Button Loading States to Edit Settings - [PR #17236](https://github.com/BerriAI/litellm/pull/17236)
- Fix Various Text, button state, and test changes - [PR #17237](https://github.com/BerriAI/litellm/pull/17237)
- Fix Fallbacks Immediately Deleting before API resolves - [PR #17238](https://github.com/BerriAI/litellm/pull/17238)
- Remove Feature Flags - [PR #17240](https://github.com/BerriAI/litellm/pull/17240)
- Fix metadata tags and model name display in UI for Azure passthrough - [PR #17258](https://github.com/BerriAI/litellm/pull/17258)
- Change labeling around Vertex Fields - [PR #17383](https://github.com/BerriAI/litellm/pull/17383)
- Remove second scrollbar when sidebar is expanded + tooltip z index - [PR #17436](https://github.com/BerriAI/litellm/pull/17436)
- Fix Select in Edit Membership Modal - [PR #17524](https://github.com/BerriAI/litellm/pull/17524)
- Change useAuthorized Hook to redirect to new Login Page - [PR #17553](https://github.com/BerriAI/litellm/pull/17553)
- **SSO**
- Fix the generic SSO provider - [PR #17227](https://github.com/BerriAI/litellm/pull/17227)
- Clear SSO integration for all users - [PR #17287](https://github.com/BerriAI/litellm/pull/17287)
- Fix SSO users not added to Entra synced team - [PR #17331](https://github.com/BerriAI/litellm/pull/17331)
- **Auth / JWT**
- JWT Auth - Allow using regular OIDC flow with user info endpoints - [PR #17324](https://github.com/BerriAI/litellm/pull/17324)
- Fix litellm user auth not passing issue - [PR #17342](https://github.com/BerriAI/litellm/pull/17342)
- Add other routes in JWT auth - [PR #17345](https://github.com/BerriAI/litellm/pull/17345)
- Fix new org team validate against org - [PR #17333](https://github.com/BerriAI/litellm/pull/17333)
- Fix litellm_enterprise ensure imported routes exist - [PR #17337](https://github.com/BerriAI/litellm/pull/17337)
- Use organization.members instead of deprecated organization field - [PR #17557](https://github.com/BerriAI/litellm/pull/17557)
- **Organizations/Teams**
- Fix organization max budget not enforced - [PR #17334](https://github.com/BerriAI/litellm/pull/17334)
- Fix budget update to allow null max_budget - [PR #17545](https://github.com/BerriAI/litellm/pull/17545)
---
## AI Integrations (2 new integrations)
### Logging (1 new integration)
#### New Integration
- **[Weave](../../docs/proxy/logging)**
- Basic Weave OTEL integration - [PR #17439](https://github.com/BerriAI/litellm/pull/17439)
#### Improvements & Fixes
- **[DataDog](../../docs/proxy/logging#datadog)**
- Fix Datadog callback regression when ddtrace is installed - [PR #17393](https://github.com/BerriAI/litellm/pull/17393)
- **[Arize Phoenix](../../docs/observability/arize_integration)**
- Fix clean arize-phoenix traces - [PR #16611](https://github.com/BerriAI/litellm/pull/16611)
- **[MLflow](../../docs/proxy/logging#mlflow)**
- Fix MLflow streaming spans for Anthropic passthrough - [PR #17288](https://github.com/BerriAI/litellm/pull/17288)
- **[Langfuse](../../docs/proxy/logging#langfuse)**
- Fix Langfuse logger test mock setup - [PR #17591](https://github.com/BerriAI/litellm/pull/17591)
- **General**
- Improve PII anonymization handling in logging callbacks - [PR #17207](https://github.com/BerriAI/litellm/pull/17207)
### Guardrails (1 new integration)
#### New Integration
- **[Generic Guardrail API](../../docs/adding_provider/generic_guardrail_api)**
- Generic Guardrail API - allows guardrail providers to add INSTANT support for LiteLLM w/out PR to repo - [PR #17175](https://github.com/BerriAI/litellm/pull/17175)
- Guardrails API V2 - user api key metadata, session id, specify input type (request/response), image support - [PR #17338](https://github.com/BerriAI/litellm/pull/17338)
- Guardrails API - add streaming support - [PR #17400](https://github.com/BerriAI/litellm/pull/17400)
- Guardrails API - support tool call checks on OpenAI `/chat/completions`, OpenAI `/responses`, Anthropic `/v1/messages` - [PR #17459](https://github.com/BerriAI/litellm/pull/17459)
- Guardrails API - new `structured_messages` param - [PR #17518](https://github.com/BerriAI/litellm/pull/17518)
- Correctly map a v1/messages call to the anthropic unified guardrail - [PR #17424](https://github.com/BerriAI/litellm/pull/17424)
- Support during_call event type for unified guardrails - [PR #17514](https://github.com/BerriAI/litellm/pull/17514)
#### Improvements & Fixes
- **[Noma Guardrail](../../docs/proxy/guardrails/noma_security)**
- Refactor Noma guardrail to use shared Responses transformation and include system instructions - [PR #17315](https://github.com/BerriAI/litellm/pull/17315)
- **[Presidio](../../docs/proxy/guardrails/pii_masking_v2)**
- Handle empty content and error dict responses in guardrails - [PR #17489](https://github.com/BerriAI/litellm/pull/17489)
- Fix Presidio guardrail test TypeError and license base64 decoding error - [PR #17538](https://github.com/BerriAI/litellm/pull/17538)
- **[Tool Permissions](../../docs/proxy/guardrails/tool_permission)**
- Add regex-based tool_name/tool_type matching for tool-permission - [PR #17164](https://github.com/BerriAI/litellm/pull/17164)
- Add images for tool permission guardrail documentation - [PR #17322](https://github.com/BerriAI/litellm/pull/17322)
- **[AIM Guardrails](../../docs/proxy/guardrails/aim_security)**
- Fix AIM guardrail tests - [PR #17499](https://github.com/BerriAI/litellm/pull/17499)
- **[Bedrock Guardrails](../../docs/proxy/guardrails/bedrock)**
- Fix Bedrock Guardrail indent and import - [PR #17378](https://github.com/BerriAI/litellm/pull/17378)
- **General Guardrails**
- Mask all matching keywords in content filter - [PR #17521](https://github.com/BerriAI/litellm/pull/17521)
- Ensure guardrail metadata is preserved in request_data - [PR #17593](https://github.com/BerriAI/litellm/pull/17593)
- Fix apply_guardrail method and improve test isolation - [PR #17555](https://github.com/BerriAI/litellm/pull/17555)
### Secret Managers
- **[CyberArk](../../docs/secret_managers/cyberark)**
- Allow setting SSL verify to false - [PR #17433](https://github.com/BerriAI/litellm/pull/17433)
- **General**
- Make email and secret manager operations independent in key management hooks - [PR #17551](https://github.com/BerriAI/litellm/pull/17551)
---
## Spend Tracking, Budgets and Rate Limiting
- **Rate Limiting**
- Parallel Request Limiter with /messages - [PR #17426](https://github.com/BerriAI/litellm/pull/17426)
- Allow using dynamic rate limit/priority reservation on teams - [PR #17061](https://github.com/BerriAI/litellm/pull/17061)
- Dynamic Rate Limiter - Fix token count increases/decreases by 1 instead of actual count + Redis TTL - [PR #17558](https://github.com/BerriAI/litellm/pull/17558)
- **Spend Logs**
- Deprecate `spend/logs` & add `spend/logs/v2` - [PR #17167](https://github.com/BerriAI/litellm/pull/17167)
- Optimize SpendLogs queries to use timestamp filtering for index usage - [PR #17504](https://github.com/BerriAI/litellm/pull/17504)
- **Enforce User Param**
- Enforce support of enforce_user_param to OpenAI post endpoints - [PR #17407](https://github.com/BerriAI/litellm/pull/17407)
---
## MCP Gateway
- **MCP Configuration**
- Remove URL format validation for MCP server endpoints - [PR #17270](https://github.com/BerriAI/litellm/pull/17270)
- Add stack trace to MCP error message - [PR #17269](https://github.com/BerriAI/litellm/pull/17269)
- **MCP Tool Results**
- Preserve tool metadata in CallToolResult - [PR #17561](https://github.com/BerriAI/litellm/pull/17561)
---
## Agent Gateway (A2A)
- **Agent Invocation**
- Allow invoking agents through AI Gateway - [PR #17440](https://github.com/BerriAI/litellm/pull/17440)
- Allow tracking request/response in "Logs" Page - [PR #17449](https://github.com/BerriAI/litellm/pull/17449)
- **Agent Access Control**
- Enforce Allowed agents by key, team + add agent access groups on backend - [PR #17502](https://github.com/BerriAI/litellm/pull/17502)
- **Agent Gateway UI**
- Allow testing agents on UI - [PR #17455](https://github.com/BerriAI/litellm/pull/17455)
- Set allowed agents by key, team - [PR #17511](https://github.com/BerriAI/litellm/pull/17511)
---
## Performance / Loadbalancing / Reliability improvements
- **Audio/Speech Performance**
- Fix `/audio/speech` performance by using `shared_sessions` - [PR #16739](https://github.com/BerriAI/litellm/pull/16739)
- **Memory Optimization**
- Prevent memory leak in aiohttp connection pooling - [PR #17388](https://github.com/BerriAI/litellm/pull/17388)
- Lazy-load utils to reduce memory + import time - [PR #17171](https://github.com/BerriAI/litellm/pull/17171)
- **Database**
- Update default database connection number - [PR #17353](https://github.com/BerriAI/litellm/pull/17353)
- Update default proxy_batch_write_at number - [PR #17355](https://github.com/BerriAI/litellm/pull/17355)
- Add background health checks to db - [PR #17528](https://github.com/BerriAI/litellm/pull/17528)
- **Proxy Caching**
- Fix proxy caching between requests in aiohttp transport - [PR #17122](https://github.com/BerriAI/litellm/pull/17122)
- **Session Management**
- Fix session consistency, move Lasso API version away from source code - [PR #17316](https://github.com/BerriAI/litellm/pull/17316)
- Conditionally pass enable_cleanup_closed to aiohttp TCPConnector - [PR #17367](https://github.com/BerriAI/litellm/pull/17367)
- **Vector Store**
- Fix vector store configuration synchronization failure - [PR #17525](https://github.com/BerriAI/litellm/pull/17525)
---
## Documentation Updates
- **Provider Documentation**
- Add Azure AI Foundry documentation for Claude models - [PR #17104](https://github.com/BerriAI/litellm/pull/17104)
- Document responses and embedding API for GitHub Copilot - [PR #17456](https://github.com/BerriAI/litellm/pull/17456)
- Add gpt-5.1-codex-max to OpenAI provider documentation - [PR #17602](https://github.com/BerriAI/litellm/pull/17602)
- Update Instructions For Phoenix Integration - [PR #17373](https://github.com/BerriAI/litellm/pull/17373)
- **Guides**
- Add guide on how to debug gateway error vs provider error - [PR #17387](https://github.com/BerriAI/litellm/pull/17387)
- Agent Gateway documentation - [PR #17454](https://github.com/BerriAI/litellm/pull/17454)
- A2A Permission management documentation - [PR #17515](https://github.com/BerriAI/litellm/pull/17515)
- Update docs to link agent hub - [PR #17462](https://github.com/BerriAI/litellm/pull/17462)
- **Projects**
- Add Google ADK and Harbor to projects - [PR #17352](https://github.com/BerriAI/litellm/pull/17352)
- Add Microsoft Agent Lightning to projects - [PR #17422](https://github.com/BerriAI/litellm/pull/17422)
- **Cleanup**
- Cleanup: Remove orphan docs pages and Docusaurus template files - [PR #17356](https://github.com/BerriAI/litellm/pull/17356)
- Remove `source .env` from docs - [PR #17466](https://github.com/BerriAI/litellm/pull/17466)
---
## Infrastructure / CI/CD
- **Helm Chart**
- Add ingress-only labels - [PR #17348](https://github.com/BerriAI/litellm/pull/17348)
- **Docker**
- Add retry logic to apk package installation in Dockerfile.non_root - [PR #17596](https://github.com/BerriAI/litellm/pull/17596)
- Chainguard fixes - [PR #17406](https://github.com/BerriAI/litellm/pull/17406)
- **OpenAPI Schema**
- Refactor add_schema_to_components to move definitions to components/schemas - [PR #17389](https://github.com/BerriAI/litellm/pull/17389)
- **Security**
- Fix security vulnerability: update mdast-util-to-hast to 13.2.1 - [PR #17601](https://github.com/BerriAI/litellm/pull/17601)
- Bump jws from 3.2.2 to 3.2.3 - [PR #17494](https://github.com/BerriAI/litellm/pull/17494)
---
## New Contributors
* @weichiet made their first contribution in [PR #17242](https://github.com/BerriAI/litellm/pull/17242)
* @AndyForest made their first contribution in [PR #17220](https://github.com/BerriAI/litellm/pull/17220)
* @omkar806 made their first contribution in [PR #17217](https://github.com/BerriAI/litellm/pull/17217)
* @v0rtex20k made their first contribution in [PR #17178](https://github.com/BerriAI/litellm/pull/17178)
* @hxomer made their first contribution in [PR #17207](https://github.com/BerriAI/litellm/pull/17207)
* @orgersh92 made their first contribution in [PR #17316](https://github.com/BerriAI/litellm/pull/17316)
* @dannykopping made their first contribution in [PR #17313](https://github.com/BerriAI/litellm/pull/17313)
* @rioiart made their first contribution in [PR #17333](https://github.com/BerriAI/litellm/pull/17333)
* @codgician made their first contribution in [PR #17278](https://github.com/BerriAI/litellm/pull/17278)
* @epistoteles made their first contribution in [PR #17277](https://github.com/BerriAI/litellm/pull/17277)
* @kothamah made their first contribution in [PR #17368](https://github.com/BerriAI/litellm/pull/17368)
* @flozonn made their first contribution in [PR #17371](https://github.com/BerriAI/litellm/pull/17371)
* @richardmcsong made their first contribution in [PR #17389](https://github.com/BerriAI/litellm/pull/17389)
* @matt-greathouse made their first contribution in [PR #17384](https://github.com/BerriAI/litellm/pull/17384)
* @mossbanay made their first contribution in [PR #17380](https://github.com/BerriAI/litellm/pull/17380)
* @mhielpos-asapp made their first contribution in [PR #17376](https://github.com/BerriAI/litellm/pull/17376)
* @Joilence made their first contribution in [PR #17367](https://github.com/BerriAI/litellm/pull/17367)
* @deepaktammali made their first contribution in [PR #17357](https://github.com/BerriAI/litellm/pull/17357)
* @axiomofjoy made their first contribution in [PR #16611](https://github.com/BerriAI/litellm/pull/16611)
* @DevajMody made their first contribution in [PR #17445](https://github.com/BerriAI/litellm/pull/17445)
* @andrewtruong made their first contribution in [PR #17439](https://github.com/BerriAI/litellm/pull/17439)
* @AnasAbdelR made their first contribution in [PR #17490](https://github.com/BerriAI/litellm/pull/17490)
* @dominicfeliton made their first contribution in [PR #17516](https://github.com/BerriAI/litellm/pull/17516)
* @kristianmitk made their first contribution in [PR #17504](https://github.com/BerriAI/litellm/pull/17504)
* @rgshr made their first contribution in [PR #17130](https://github.com/BerriAI/litellm/pull/17130)
* @dominicfallows made their first contribution in [PR #17489](https://github.com/BerriAI/litellm/pull/17489)
* @irfansofyana made their first contribution in [PR #17467](https://github.com/BerriAI/litellm/pull/17467)
* @GusBricker made their first contribution in [PR #17191](https://github.com/BerriAI/litellm/pull/17191)
* @OlivverX made their first contribution in [PR #17255](https://github.com/BerriAI/litellm/pull/17255)
* @withsmilo made their first contribution in [PR #17585](https://github.com/BerriAI/litellm/pull/17585)
---
## Full Changelog
**[View complete changelog on GitHub](https://github.com/BerriAI/litellm/compare/v1.80.7-nightly...v1.80.8)**

39
docs/my-website/sidebars.js
View file

@ -45,6 +45,7 @@ const sidebars = {
type: "category",
"label": "Contributing to Guardrails",
items: [
"adding_provider/generic_guardrail_api",
"adding_provider/simple_guardrail_tutorial",
"adding_provider/adding_guardrail_support",
]
@ -104,6 +105,7 @@ const sidebars = {
items: [
"tutorials/claude_responses_api",
"tutorials/cost_tracking_coding",
"tutorials/cursor_integration",
"tutorials/github_copilot_integration",
"tutorials/litellm_gemini_cli",
"tutorials/litellm_qwen_code_cli",
@ -128,6 +130,16 @@ const sidebars = {
},
items: [
"proxy/docker_quick_start",
{
type: "link",
label: "A2A Agent Gateway",
href: "https://docs.litellm.ai/docs/a2a",
},
{
type: "link",
label: "MCP Gateway",
href: "https://docs.litellm.ai/docs/mcp",
},
{
"type": "category",
"label": "Config.yaml",
@ -140,6 +152,7 @@ const sidebars = {
"proxy/quick_start",
"proxy/cli",
"proxy/debugging",
"proxy/error_diagnosis",
"proxy/deploy",
"proxy/health",
"proxy/master_key_rotations",
@ -183,6 +196,7 @@ const sidebars = {
label: "Architecture",
items: [
"proxy/architecture",
"proxy/multi_tenant_architecture",
"proxy/control_plane_and_data_plane",
"proxy/db_deadlocks",
"proxy/db_info",
@ -221,6 +235,7 @@ const sidebars = {
"proxy/team_budgets",
"proxy/tag_budgets",
"proxy/customers",
"proxy/customer_usage",
"proxy/dynamic_rate_limit",
"proxy/rate_limit_tiers",
"proxy/temporary_budget_increase",
@ -314,6 +329,14 @@ const sidebars = {
slug: "/supported_endpoints",
},
items: [
{
type: "category",
label: "/a2a - A2A Agent Gateway",
items: [
"a2a",
"a2a_agent_permissions",
],
},
"assistants",
{
type: "category",
@ -469,6 +492,11 @@ const sidebars = {
id: "provider_registration/index",
label: "Integrate as a Model Provider",
},
{
type: "doc",
id: "contributing/adding_openai_compatible_providers",
label: "Add OpenAI-Compatible Provider (JSON)",
},
{
type: "doc",
id: "provider_registration/add_model_pricing",
@ -518,7 +546,9 @@ const sidebars = {
"providers/vertex_ai/videos",
"providers/vertex_partner",
"providers/vertex_self_deployed",
"providers/vertex_embedding",
"providers/vertex_image",
"providers/vertex_speech",
"providers/vertex_batch",
"providers/vertex_ocr",
]
@ -624,6 +654,7 @@ const sidebars = {
"providers/petals",
"providers/publicai",
"providers/predibase",
"providers/ragflow",
"providers/recraft",
"providers/replicate",
{
@ -655,6 +686,7 @@ const sidebars = {
},
"providers/xai",
"providers/xinference",
"providers/zai",
],
},
{
@ -790,6 +822,7 @@ const sidebars = {
type: "category",
label: "Adding Providers",
items: [
"contributing/adding_openai_compatible_providers",
"adding_provider/directory_structure",
"adding_provider/new_rerank_provider",
]
@ -816,10 +849,14 @@ const sidebars = {
"Learn how to deploy + call models from different providers on LiteLLM",
slug: "/project",
},
items: [
items: [
"projects/smolagents",
"projects/mini-swe-agent",
"projects/openai-agents",
"projects/Google ADK",
"projects/Agent Lightning",
"projects/Harbor",
"projects/GraphRAG",
"projects/Docq.AI",
"projects/PDL",
"projects/OpenInterpreter",

47
docs/my-website/src/pages/intro.md
View file

@ -1,47 +0,0 @@
---
sidebar_position: 1
---
# Tutorial Intro
Let's discover **Docusaurus in less than 5 minutes**.
## Getting Started
Get started by **creating a new site**.
Or **try Docusaurus immediately** with **[docusaurus.new](https://docusaurus.new)**.
### What you'll need
- [Node.js](https://nodejs.org/en/download/) version 16.14 or above:
- When installing Node.js, you are recommended to check all checkboxes related to dependencies.
## Generate a new site
Generate a new Docusaurus site using the **classic template**.
The classic template will automatically be added to your project after you run the command:
```bash
npm init docusaurus@latest my-website classic
```
You can type this command into Command Prompt, Powershell, Terminal, or any other integrated terminal of your code editor.
The command also installs all necessary dependencies you need to run Docusaurus.
## Start your site
Run the development server:
```bash
cd my-website
npm run start
```
The `cd` command changes the directory you're working with. In order to work with your newly created Docusaurus site, you'll need to navigate the terminal there.
The `npm run start` command builds your website locally and serves it through a development server, ready for you to view at http://localhost:3000/.
Open `docs/intro.md` (this page) and edit some lines: the site **reloads automatically** and displays your changes.

8
docs/my-website/src/pages/tutorial-basics/_category_.json
View file

@ -1,8 +0,0 @@
{
"label": "Tutorial - Basics",
"position": 2,
"link": {
"type": "generated-index",
"description": "5 minutes to learn the most important Docusaurus concepts."
}
}

23
docs/my-website/src/pages/tutorial-basics/congratulations.md
View file

@ -1,23 +0,0 @@
---
sidebar_position: 6
---
# Congratulations!
You have just learned the **basics of Docusaurus** and made some changes to the **initial template**.
Docusaurus has **much more to offer**!
Have **5 more minutes**? Take a look at **[versioning](../tutorial-extras/manage-docs-versions.md)** and **[i18n](../tutorial-extras/translate-your-site.md)**.
Anything **unclear** or **buggy** in this tutorial? [Please report it!](https://github.com/facebook/docusaurus/discussions/4610)
## What's next?
- Read the [official documentation](https://docusaurus.io/)
- Modify your site configuration with [`docusaurus.config.js`](https://docusaurus.io/docs/api/docusaurus-config)
- Add navbar and footer items with [`themeConfig`](https://docusaurus.io/docs/api/themes/configuration)
- Add a custom [Design and Layout](https://docusaurus.io/docs/styling-layout)
- Add a [search bar](https://docusaurus.io/docs/search)
- Find inspirations in the [Docusaurus showcase](https://docusaurus.io/showcase)
- Get involved in the [Docusaurus Community](https://docusaurus.io/community/support)

34
docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md
View file

@ -1,34 +0,0 @@
---
sidebar_position: 3
---
# Create a Blog Post
Docusaurus creates a **page for each blog post**, but also a **blog index page**, a **tag system**, an **RSS** feed...
## Create your first Post
Create a file at `blog/2021-02-28-greetings.md`:
```md title="blog/2021-02-28-greetings.md"
---
slug: greetings
title: Greetings!
authors:
- name: Joel Marcey
title: Co-creator of Docusaurus 1
url: https://github.com/JoelMarcey
image_url: https://github.com/JoelMarcey.png
- name: Sébastien Lorber
title: Docusaurus maintainer
url: https://sebastienlorber.com
image_url: https://github.com/slorber.png
tags: [greetings]
---
Congratulations, you have made your first post!
Feel free to play around and edit this post as much you like.
```
A new blog post is now available at [http://localhost:3000/blog/greetings](http://localhost:3000/blog/greetings).

57
docs/my-website/src/pages/tutorial-basics/create-a-document.md
View file

@ -1,57 +0,0 @@
---
sidebar_position: 2
---
# Create a Document
Documents are **groups of pages** connected through:
- a **sidebar**
- **previous/next navigation**
- **versioning**
## Create your first Doc
Create a Markdown file at `docs/hello.md`:
```md title="docs/hello.md"
# Hello
This is my **first Docusaurus document**!
```
A new document is now available at [http://localhost:3000/docs/hello](http://localhost:3000/docs/hello).
## Configure the Sidebar
Docusaurus automatically **creates a sidebar** from the `docs` folder.
Add metadata to customize the sidebar label and position:
```md title="docs/hello.md" {1-4}
---
sidebar_label: 'Hi!'
sidebar_position: 3
---
# Hello
This is my **first Docusaurus document**!
```
It is also possible to create your sidebar explicitly in `sidebars.js`:
```js title="sidebars.js"
module.exports = {
tutorialSidebar: [
'intro',
// highlight-next-line
'hello',
{
type: 'category',
label: 'Tutorial',
items: ['tutorial-basics/create-a-document'],
},
],
};
```

43
docs/my-website/src/pages/tutorial-basics/create-a-page.md
View file

@ -1,43 +0,0 @@
---
sidebar_position: 1
---
# Create a Page
Add **Markdown or React** files to `src/pages` to create a **standalone page**:
- `src/pages/index.js` → `localhost:3000/`
- `src/pages/foo.md` → `localhost:3000/foo`
- `src/pages/foo/bar.js` → `localhost:3000/foo/bar`
## Create your first React Page
Create a file at `src/pages/my-react-page.js`:
```jsx title="src/pages/my-react-page.js"
import React from 'react';
import Layout from '@theme/Layout';
export default function MyReactPage() {
return (
<Layout>
<h1>My React page</h1>
<p>This is a React page</p>
</Layout>
);
}
```
A new page is now available at [http://localhost:3000/my-react-page](http://localhost:3000/my-react-page).
## Create your first Markdown Page
Create a file at `src/pages/my-markdown-page.md`:
```mdx title="src/pages/my-markdown-page.md"
# My Markdown page
This is a Markdown page
```
A new page is now available at [http://localhost:3000/my-markdown-page](http://localhost:3000/my-markdown-page).

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