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set env - [OPTIONAL] replace with your anthropic key
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Claude Code Knowledge Pack7/10/2026
Overview
This guide covers Anthropic's latest model (Claude Opus 4.5) and its advanced features now available in LiteLLM: Tool Search, Programmatic Tool Calling, Tool Input Examples, and the Effort Parameter.
| Feature | Supported Models |
|---|---|
| Tool Search | Claude Opus 4.5, Sonnet 4.5 |
| Programmatic Tool Calling | Claude Opus 4.5, Sonnet 4.5 |
| Input Examples | Claude Opus 4.5, Sonnet 4.5 |
| Effort Parameter | Claude Opus 4.5 only |
Supported Providers: Anthropic, Bedrock, Vertex AI, Azure AI.
Usage
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [{"role": "user", "content": "Hey! how's it going?"}]
## OPENAI /chat/completions API format
response = completion(model="claude-opus-4-5-20251101", messages=messages)
print(response)
1. Setup config.yaml
model_list:
- model_name: claude-4 ### RECEIVED MODEL NAME ###
litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
model: claude-opus-4-5-20251101 ### MODEL NAME sent to `litellm.completion()` ###
api_key: "os.environ/ANTHROPIC_API_KEY" # does os.getenv("ANTHROPIC_API_KEY")
2. Start the proxy
litellm --config /path/to/config.yaml
3. Test it!
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": "what llm are you"
}
]
}
'
curl --location 'http://0.0.0.0:4000/v1/messages' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data ' {
"model": "claude-4",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
Usage - Bedrock
:::info
LiteLLM uses the boto3 library to authenticate with Bedrock.
For more ways to authenticate with Bedrock, see the Bedrock documentation.
:::
from litellm import completion
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
## OPENAI /chat/completions API format
response = completion(
model="bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
1. Setup config.yaml
model_list:
- model_name: claude-4 ### RECEIVED MODEL NAME ###
litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
model: bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0 ### MODEL NAME sent to `litellm.completion()` ###
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 the proxy
litellm --config /path/to/config.yaml
3. Test it!
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": "what llm are you"
}
]
}
'
curl --location 'http://0.0.0.0:4000/v1/messages' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data ' {
"model": "claude-4",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
curl --location 'http://0.0.0.0:4000/bedrock/model/claude-4/invoke' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data ' {
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Hello, how are you?"}]
}'
curl --location 'http://0.0.0.0:4000/bedrock/model/claude-4/converse' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data ' {
"messages": [{"role": "user", "content": "Hello, how are you?"}]
}'
Usage - Vertex AI
from litellm import completion
## GET CREDENTIALS
## RUN ##
# !gcloud auth application-default login - run this to add vertex credentials to your env
## OR ##
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)
## COMPLETION CALL
response = completion(
model="vertex_ai/claude-opus-4-5@20251101",
messages=[{ "content": "Hello, how are you?","role": "user"}],
vertex_credentials=vertex_credentials_json,
vertex_project="your-project-id",
vertex_location="us-east5"
)
1. Setup config.yaml
model_list:
- model_name: claude-4 ### RECEIVED MODEL NAME ###
litellm_params:
model: vertex_ai/claude-opus-4-5@20251101
vertex_credentials: "/path/to/service_account.json"
vertex_project: "your-project-id"
vertex_location: "us-east5"
2. Start the proxy
litellm --config /path/to/config.yaml
3. Test it!
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": "what llm are you"
}
]
}
'
curl --location 'http://0.0.0.0:4000/v1/messages' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer $LITELLM_KEY' \\
--data ' {
"model": "claude-4",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
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.
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)
1. Set environment variables
2. Configure the proxy
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
litellm --config /path/to/config.yaml
4. Test the Azure Claude route
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
}'
Tool Search {#tool-search}
This lets Claude work with thousands of tools, by dynamically loading tools on-demand, instead of loading all tools into the context window upfront.
Usage Example
# Configure your API key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
# Define your tools with defer_loading
tools = [
# Tool search tool (regex variant)
{
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex"
},
# Deferred tools - loaded on-demand
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location. Returns temperature and conditions.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "Temperature unit"
}
},
"required": ["location"]
}
},
"defer_loading": True # Load on-demand
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search through files in the workspace using keywords",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string"},
"file_types": {
"type": "array",
"items": {"type": "string"}
}
},
"required": ["query"]
}
},
"defer_loading": True
},
{
"type": "function",
"function": {
"name": "query_database",
"description": "Execute SQL queries against the database",
"parameters": {
"type": "object",
"properties": {
"sql": {"type": "string"}
},
"required": ["sql"]
}
},
"defer_loading": True
}
]
# Make a request - Claude will search for and use relevant tools
response = litellm.completion(
model="anthropic/claude-opus-4-5-20251101",
messages=[{
"role": "user",
"content": "What's the weather like in San Francisco?"
}],
tools=tools
)
print("Claude's response:", response.choices[0].message.content)
print("Tool calls:", response.choices[0].message.tool_calls)
# Check tool search usage
if hasattr(response.usage, 'server_tool_use'):
print(f"Tool searches performed: {response.usage.server_tool_use.tool_search_requests}")
- Setup config.yaml
model_list:
- model_name: claude-4
litellm_params:
model: anthropic/claude-opus-4-5-20251101
api_key: os.environ/ANTHROPIC_API_KEY
- Start the proxy
litellm --config /path/to/config.yaml
- Test it!
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": "What's the weather like in San Francisco?"
}],
"tools": [
# Tool search tool (regex variant)
{
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex"
},
# Deferred tools - loaded on-demand
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location. Returns temperature and conditions.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "Temperature unit"
}
},
"required": ["location"]
}
},
"defer_loading": True # Load on-demand
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search through files in the workspace using key