--- title: "Anthropic" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/anthropic-1 source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:07:35.101Z license: CC-BY-4.0 attribution: "Anthropic — Claudary (https://claudary.paisolsolutions.com/skills/anthropic-1)" --- # Anthropic import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Anthropic LiteLLM supports all anthropic models. - `claude-opus-4-6` (`claude-opus-4-6-20260205`) - `claude-sonnet-4-6` - `claude-sonnet-4-5-20250929` - `claude-opus-4-5-20251101` - `claude-opus-4-1-20250805` - `claude-4` (`claude-opus-4-20250514`, `claude-sonnet-4-20250514`) - `claude-3.7` (`claude-3-7-sonnet-20250219`) - `claude-3.5` (`claude-3-5-sonnet-20240620`) - `claude-3` (`claude-3-haiku-20240307`, `claude-3-opus-20240229`, `claude-3-sonnet-20240229`) - `claude-2` - `claude-2.1` - `claude-instant-1.2` | Property | Details | |-------|-------| | Description | Claude is a highly performant, trustworthy, and intelligent AI platform built by Anthropic. Claude excels at tasks involving language, reasoning, analysis, coding, and more. Also available via Azure Foundry. | | Provider Route on LiteLLM | `anthropic/` (add this prefix to the model name, to route any requests to Anthropic - e.g. `anthropic/claude-3-5-sonnet-20240620`). For Azure Foundry deployments, use `azure/claude-*` (see [Azure Anthropic documentation](../providers/azure/azure_anthropic)) | | Provider Doc | [Anthropic ↗](https://docs.anthropic.com/en/docs/build-with-claude/overview), [Azure Foundry Claude ↗](https://learn.microsoft.com/en-us/azure/ai-services/foundry-models/claude) | | API Endpoint for Provider | https://api.anthropic.com (or Azure Foundry endpoint: `https://.services.ai.azure.com/anthropic`) | | Supported Endpoints | `/chat/completions`, `/v1/messages` (passthrough) | ## Supported OpenAI Parameters Check this in code, [here](../completion/input.md#translated-openai-params) ``` "stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "reasoning_effort", ``` :::info **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 4.6 and Opus 4.5 models (see [Effort Parameter](./anthropic_effort.md)) ::: ## **Structured Outputs** LiteLLM supports Anthropic's [structured outputs feature](https://platform.claude.com/docs/en/build-with-claude/structured-outputs) for Claude Sonnet 4.5 and Opus 4.1 models. When you use `response_format` with these models, LiteLLM automatically: - Adds the required `structured-outputs-2025-11-13` beta header - Transforms OpenAI's `response_format` to Anthropic's `output_format` format ### Supported Models - `sonnet-4-5` or `sonnet-4.5` (all Sonnet 4.5 variants) - `opus-4-1` or `opus-4.1` (all Opus 4.1 variants) - `opus-4-5` or `opus-4.5` (all Opus 4.5 variants) ### Example Usage ```python from litellm import completion response = completion( model="claude-sonnet-4-5-20250929", messages=[{"role": "user", "content": "What is the capital of France?"}], response_format={ "type": "json_schema", "json_schema": { "name": "capital_response", "strict": True, "schema": { "type": "object", "properties": { "country": {"type": "string"}, "capital": {"type": "string"} }, "required": ["country", "capital"], "additionalProperties": False } } } ) print(response.choices[0].message.content) # Output: {"country": "France", "capital": "Paris"} ``` 1. Setup config.yaml ```yaml model_list: - model_name: claude-sonnet-4-5 litellm_params: model: anthropic/claude-sonnet-4-5-20250929 api_key: os.environ/ANTHROPIC_API_KEY ``` 2. Start proxy ```bash litellm --config /path/to/config.yaml ``` 3. Test it! ```bash curl http://0.0.0.0:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer $LITELLM_KEY" \\ -d '{ "model": "claude-sonnet-4-5", "messages": [{"role": "user", "content": "What is the capital of France?"}], "response_format": { "type": "json_schema", "json_schema": { "name": "capital_response", "strict": true, "schema": { "type": "object", "properties": { "country": {"type": "string"}, "capital": {"type": "string"} }, "required": ["country", "capital"], "additionalProperties": false } } } }' ``` :::info When using structured outputs with supported models, LiteLLM automatically: - Converts OpenAI's `response_format` to Anthropic's `output_schema` - Adds the `anthropic-beta: structured-outputs-2025-11-13` header - Creates a tool with the schema and forces the model to use it ::: ## API Keys ```python import os os.environ["ANTHROPIC_API_KEY"] = "your-api-key" # os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL' # os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true" # [OPTIONAL] Disable automatic URL suffix appending ``` :::tip Azure Foundry Support Claude models are also available via Microsoft Azure Foundry. Use the `azure/` prefix instead of `anthropic/` and configure Azure authentication. See the [Azure Anthropic documentation](../providers/azure/azure_anthropic) for details. Example: ```python response = completion( model="azure/claude-sonnet-4-5", api_base="https://.services.ai.azure.com/anthropic", api_key="your-azure-api-key", messages=[{"role": "user", "content": "Hello!"}] ) ``` ::: ### Custom API Base When using a custom API base for Anthropic (e.g., a proxy or custom endpoint), LiteLLM automatically appends the appropriate suffix (`/v1/messages` or `/v1/complete`) to your base URL. If your custom endpoint already includes the full path or doesn't follow Anthropic's standard URL structure, you can disable this automatic suffix appending: ```python import os os.environ["ANTHROPIC_API_BASE"] = "https://my-custom-endpoint.com/custom/path" os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true" # Prevents automatic suffix ``` Without `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX`: - Base URL `https://my-proxy.com` → `https://my-proxy.com/v1/messages` - Base URL `https://my-proxy.com/api` → `https://my-proxy.com/api/v1/messages` 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://.services.ai.azure.com/anthropic", 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://.services.ai.azure.com/anthropic` ::: ## Usage ```python import os 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?"}] response = completion(model="claude-opus-4-20250514", messages=messages) print(response) ``` ## Usage - Streaming Just set `stream=True` when calling completion. ```python import os from litellm import completion # set env os.environ["ANTHROPIC_API_KEY"] = "your-api-key" messages = [{"role": "user", "content": "Hey! how's it going?"}] response = completion(model="claude-opus-4-20250514", messages=messages, stream=True) for chunk in response: print(chunk["choices"][0]["delta"]["content"]) # same as openai format ``` ## Usage with LiteLLM Proxy Here's how to call Anthropic with the LiteLLM Proxy Server ### 1. Save key in your environment ```bash export ANTHROPIC_API_KEY="your-api-key" ``` ### 2. Start the proxy ```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-4-20250514 ### MODEL NAME sent to `litellm.completion()` ### api_key: "os.environ/ANTHROPIC_API_KEY" # does os.getenv("ANTHROPIC_API_KEY") ``` ```bash litellm --config /path/to/config.yaml ``` Use this if you want to make requests to `claude-3-haiku-20240307`,`claude-3-opus-20240229`,`claude-2.1` without defining them on the config.yaml #### Required env variables ``` ANTHROPIC_API_KEY=sk-ant**** ``` ```yaml model_list: - model_name: "*" litellm_params: model: "*" ``` ```bash litellm --config /path/to/config.yaml ``` Example Request for this config.yaml **Ensure you use `anthropic/` prefix to route the request to Anthropic API** ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data ' { "model": "anthropic/claude-3-haiku-20240307", "messages": [ { "role": "user", "content": "what llm are you" } ] } ' ``` ```bash $ litellm --model claude-opus-4-20250514 # Server running on http://0.0.0.0:4000 ``` ### 3. Test it ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data ' { "model": "claude-3", "messages": [ { "role": "user", "content": "what llm are you" } ] } ' ``` ```python import openai client = openai.OpenAI( api_key="anything", base_url="http://0.0.0.0:4000" ) # request sent to model set on litellm proxy, `litellm --model` response = client.chat.completions.create(model="claude-3", messages = [ { "role": "user", "content": "this is a test request, write a short poem" } ]) print(response) ``` ```python from langchain.chat_models import ChatOpenAI from langchain.prompts.chat import ( ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) from langchain.schema import HumanMessage, SystemMessage chat = ChatOpenAI( openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy model = "claude-3", temperature=0.1 ) messages = [ SystemMessage( content="You are a helpful assistant that im using to make a test request to." ), HumanMessage( content="test from litellm. tell me why it's amazing in 1 sentence" ), ] response = chat(messages) print(response) ``` ## Supported Models `Model Name` 👉 Human-friendly name. `Function Call` 👉 How to call the model in LiteLLM. | Model Name | Function Call | |------------------|--------------------------------------------| | claude-opus-4-6 | `completion('claude-opus-4-6-20260205', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-sonnet-4-5 | `completion('claude-sonnet-4-5-20250929', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-opus-4-5 | `completion('claude-opus-4-5-20251101', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-opus-4-1 | `completion('claude-opus-4-1-20250805', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-opus-4 | `completion('claude-opus-4-20250514', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-sonnet-4 | `completion('claude-sonnet-4-20250514', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-3.7 | `completion('claude-3-7-sonnet-20250219', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-3-5-sonnet | `completion('claude-3-5-sonnet-20240620', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-3-haiku | `completion('claude-3-haiku-20240307', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-3-opus | `completion('claude-3-opus-20240229', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-3-5-sonnet-20240620 | `completion('claude-3-5-sonnet-20240620', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-3-sonnet | `completion('claude-3-sonnet-20240229', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-2.1 | `completion('claude-2.1', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-2 | `completion('claude-2', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-instant-1.2 | `completion('claude-instant-1.2', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-instant-1 | `completion('claude-instant-1', messages)` | `os.environ['ANTHROPIC_API_KEY']` | ## **Prompt Caching** Use Anthropic Prompt Caching [Relevant Anthropic API Docs](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching) :::note Here's what a sample Raw Request from LiteLLM for Anthropic Context Caching looks like: ```bash POST Request Sent from LiteLLM: curl -X POST \\ https://api.anthropic.com/v1/messages \\ -H 'accept: application/json' -H 'anthropic-version: 2023-06-01' -H 'content-type: application/json' -H 'x-api-key: sk-...' \\ -d '{'model': 'claude-3-5-sonnet-20240620', [ { "role": "user", "content": [ { "type": "text", "text": "What are the key terms and conditions in this agreement?", "cache_control": { "type": "ephemeral" } } ] }, { "role": "assistant", "content": [ { "type": "text", "text": "Certainly! The key terms and conditions are the following: the contract is 1 year long for $10/mo" } ] } ], "temperature": 0.2, "max_tokens": 10 }' ``` **Note:** Anthropic no longer requires the `anthropic-beta: prompt-caching-2024-07-31` header. Prompt caching now works automatically when you use `cache_control` in your message --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/anthropic-1) · https://claudary.paisolsolutions.com