> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-update-deprecated-models.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Anthropic Claude
> Use Anthropic Claude models with Agno agents.
Claude is a family of foundational AI models by Anthropic that can be used in a variety of applications.
See their model comparisons [here](https://docs.anthropic.com/en/docs/about-claude/models#model-comparison-table).
We recommend experimenting to find the best-suited model for your use-case. Here are some general recommendations:
* `claude-sonnet-4-6` model is good for most use-cases and supports image input.
* `claude-opus-4-1-20250805` model is their best model.
* `claude-sonnet-4-6` model is their fastest model.
Anthropic has rate limits on their APIs. See the [docs](https://docs.anthropic.com/en/api/rate-limits#response-headers) for more information.
Claude API expects a `max_tokens` param to be sent with each request. Unless
set as a param, Agno will default to 8192. See the
[docs](https://docs.claude.com/en/api/messages) for more information.
## Authentication
Set your `ANTHROPIC_API_KEY` environment. You can get one [from Anthropic here](https://console.anthropic.com/settings/keys).
```bash Mac theme={null}
export ANTHROPIC_API_KEY=***
```
```bash Windows theme={null}
setx ANTHROPIC_API_KEY ***
```
## Example
Use `Claude` with your `Agent`:
```python agent.py theme={null}
from agno.agent import Agent
from agno.models.anthropic import Claude
agent = Agent(
model=Claude(id="claude-sonnet-4-6"),
markdown=True
)
# Print the response on the terminal
agent.print_response("Share a 2 sentence horror story.")
```
## Beta Features
You can use Anthropic's beta features with Agno by setting the `betas` parameter:
```python theme={null}
from agno.agent import Agent
from agno.models.anthropic import Claude
agent = Agent(
model=Claude(
betas=["context-management-2025-06-27"],
),
)
```
Read more about beta features with Agno `Claude` model [here](/models/providers/native/anthropic/usage/betas).
## Prompt caching
You can enable system prompt caching by setting `cache_system_prompt` to `True`:
```python theme={null}
from agno.agent import Agent
from agno.models.anthropic import Claude
agent = Agent(
model=Claude(
id="claude-sonnet-4-6",
cache_system_prompt=True,
),
)
```
Read more about prompt caching with Agno's `Claude` model [here](/models/providers/native/anthropic/usage/prompt-caching).
## Structured Outputs
Structured outputs are used to ensure that the model's response matches a defined schema.
This is useful to eliminate issues like missing fields or invalid values. Use it for production systems that need reliable, consistent responses in a specific format.
Agno uses Claude's native support for structured outputs. This feature is available for `claude-sonnet-4-5-20250929` and all newer models. See Anthropic's [structured outputs documentation](https://docs.anthropic.com/en/build-with-claude/structured-outputs) for more details.
```python theme={null}
from agno.agent import Agent
from agno.models.anthropic import Claude
from pydantic import BaseModel
class User(BaseModel):
name: str
age: int
email: str
agent = Agent(
model=Claude(id="claude-sonnet-4-6"),
description="Extract user information.",
output_schema=User,
)
```
Read more about structured outputs with Agno's `Claude` model:
* [Basic structured outputs](/models/providers/native/anthropic/usage/structured-output)
* [Streaming structured outputs](/models/providers/native/anthropic/usage/structured-output-stream)
* [Structured outputs with strict tools](/models/providers/native/anthropic/usage/structured-output-strict-tools)
## Params
| Parameter | Type | Default | Description |
| --------------------- | ---------------------------------------- | --------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
| `id` | `str` | `"claude-sonnet-4-6"` | The id of the Anthropic Claude model to use |
| `name` | `str` | `"Claude"` | The name of the model |
| `provider` | `str` | `"Anthropic"` | The provider of the model |
| `max_tokens` | `Optional[int]` | `4096` | Maximum number of tokens to generate in the chat completion |
| `thinking` | `Optional[Dict[str, Any]]` | `None` | Configuration for the thinking (reasoning) process (See [their docs](https://www.anthropic.com/news/visible-extended-thinking))) |
| `temperature` | `Optional[float]` | `None` | Controls randomness in the model's output |
| `stop_sequences` | `Optional[List[str]]` | `None` | A list of strings that the model should stop generating text at |
| `top_p` | `Optional[float]` | `None` | Controls diversity via nucleus sampling |
| `top_k` | `Optional[int]` | `None` | Controls diversity via top-k sampling |
| `cache_system_prompt` | `Optional[bool]` | `False` | Whether to cache the system prompt for improved performance |
| `extended_cache_time` | `Optional[bool]` | `False` | Whether to use extended cache time (1 hour instead of default) |
| `request_params` | `Optional[Dict[str, Any]]` | `None` | Additional parameters to include in the request |
| `mcp_servers` | `Optional[List[MCPServerConfiguration]]` | `None` | List of MCP (Model Context Protocol) server configurations |
| `api_key` | `Optional[str]` | `None` | The API key for authenticating with Anthropic |
| `default_headers` | `Optional[Dict[str, Any]]` | `None` | Default headers to include in all requests |
| `client_params` | `Optional[Dict[str, Any]]` | `None` | Additional parameters for client configuration |
| `client` | `Optional[AnthropicClient]` | `None` | A pre-configured instance of the Anthropic client |
| `async_client` | `Optional[AsyncAnthropicClient]` | `None` | A pre-configured instance of the async Anthropic client |
`Claude` is a subclass of the [Model](/reference/models/model) class and has access to the same params.