[Anthropic](https://docs.anthropic.com/en/home) is an AI safety and research company focused on building reliable, interpretable, and steerable AI systems. Included in their offerings is the Claude AI family of models, which are known for their conversational abilities, careful reasoning, and capacity to follow complex instructions. The Strands Agents SDK implements an Anthropic provider, allowing users to run agents against Claude models directly.

## Installation

Anthropic is configured as an optional dependency in Strands Agents. To install, run:

(( tab "Python" ))
```bash
pip install 'strands-agents[anthropic]' strands-agents-tools
```
(( /tab "Python" ))

(( tab "TypeScript" ))
```bash
npm install @strands-agents/sdk @anthropic-ai/sdk
```
(( /tab "TypeScript" ))

## Usage

After installing dependencies, you can import and initialize the Strands Agents’ Anthropic provider as follows:

(( tab "Python" ))
```python
from strands import Agent
from strands.models.anthropic import AnthropicModel
from strands_tools import calculator

model = AnthropicModel(
    client_args={
        "api_key": "<KEY>",
    },
    # **model_config
    max_tokens=1028,
    model_id="claude-sonnet-4-6",
    params={
        "temperature": 0.7,
    }
)

agent = Agent(model=model, tools=[calculator])
response = agent("What is 2+2")
print(response)
```
(( /tab "Python" ))

(( tab "TypeScript" ))
```typescript
import { Agent } from '@strands-agents/sdk'
import { AnthropicModel } from '@strands-agents/sdk/models/anthropic'

const model = new AnthropicModel({
  apiKey: process.env.ANTHROPIC_API_KEY || '<KEY>',
  modelId: 'claude-sonnet-4-6',
  maxTokens: 1028,
  params: {
    temperature: 0.7,
  },
})

const agent = new Agent({ model })
const response = await agent.invoke('What is 2+2')
console.log(response)
```
(( /tab "TypeScript" ))

## Configuration

### Client Configuration

(( tab "Python" ))
The `client_args` configure the underlying Anthropic client. For a complete list of available arguments, please refer to the [Anthropic Python SDK docs](https://platform.claude.com/docs/en/api/sdks/python).
(( /tab "Python" ))

(( tab "TypeScript" ))
The `clientConfig` configures the underlying Anthropic client. You can also pass a pre-configured `client` instance directly (see [Custom Client](#custom-client)). For a complete list of available options, please refer to the [Anthropic TypeScript SDK docs](https://platform.claude.com/docs/en/api/sdks/typescript).
(( /tab "TypeScript" ))

### Model Configuration

(( tab "Python" ))
The `model_config` configures the underlying model selected for inference. The supported configurations are:

| Parameter | Description | Example | Options |
| --- | --- | --- | --- |
| `max_tokens` | Maximum number of tokens to generate before stopping | `1028` | [reference](https://platform.claude.com/docs/en/api/messages/create#create.max_tokens) |
| `model_id` | ID of a model to use | `claude-sonnet-4-6` | [reference](https://platform.claude.com/docs/en/api/messages/create#create.model) |
| `params` | Additional pass-through parameters | `{"metadata": {"user_id": "u1"}}` | [reference](https://platform.claude.com/docs/en/api/messages/create) |
| `cache_config` | Enables [prompt caching](#prompt-caching) on the system prompt and the conversation | `CacheConfig(ttl="1h")` | [reference](https://docs.claude.com/en/docs/build-with-claude/prompt-caching) |
| `cache_tools` | Caches the tool definitions | `"default"` or `CacheToolsConfig(ttl="1h")` | [reference](https://docs.claude.com/en/docs/build-with-claude/prompt-caching) |
(( /tab "Python" ))

(( tab "TypeScript" ))
| Parameter | Description | Example | Options |
| --- | --- | --- | --- |
| `modelId` | ID of a model to use | `'claude-sonnet-4-6'` | [reference](https://platform.claude.com/docs/en/api/messages/create#create.model) |
| `maxTokens` | Maximum tokens to generate | `1028` | [reference](https://platform.claude.com/docs/en/api/messages/create#create.max_tokens) |
| `stopSequences` | Sequences that stop generation | `['END']` | [reference](https://platform.claude.com/docs/en/api/messages/create#create.stop_sequences) |
| `params` | Additional pass-through parameters | `{ metadata: { user_id: 'u1' } }` | [reference](https://platform.claude.com/docs/en/api/messages/create) |
| `cacheConfig` | Enables [prompt caching](#prompt-caching) on the tool definitions, the system prompt, and the conversation | `{ ttl: '1h' }` | [reference](https://docs.claude.com/en/docs/build-with-claude/prompt-caching) |
(( /tab "TypeScript" ))

## Troubleshooting

(( tab "Python" ))
### Module Not Found

If you encounter the error `ModuleNotFoundError: No module named 'anthropic'`, this means you haven’t installed the `anthropic` dependency in your environment. To fix, run `pip install 'strands-agents[anthropic]'`.
(( /tab "Python" ))

(( tab "TypeScript" ))
### Import Errors

If you encounter import errors for `@anthropic-ai/sdk`, ensure the package is installed: `npm install @anthropic-ai/sdk`.
(( /tab "TypeScript" ))

## Advanced Features

### Custom Client

You can pass a pre-configured Anthropic client directly to `AnthropicModel`. You are responsible for managing the client’s lifecycle.

(( tab "Python" ))
The Python SDK does not currently support passing a pre-configured client. Use `client_args` to configure the client at initialization.
(( /tab "Python" ))

(( tab "TypeScript" ))
```typescript
import Anthropic from '@anthropic-ai/sdk'
import { Agent } from '@strands-agents/sdk'
import { AnthropicModel } from '@strands-agents/sdk/models/anthropic'

const client = new Anthropic({ apiKey: '<KEY>' })

const model = new AnthropicModel({
  client,
  modelId: 'claude-sonnet-4-6',
  maxTokens: 1028,
})

const agent = new Agent({ model })
const response = await agent.invoke('What is 2+2')
console.log(response)
```
(( /tab "TypeScript" ))

### Structured Output

Anthropic models support structured output through tool use. Pass a schema to the agent, and Strands generates a tool from it that the model calls to return validated, type-safe data.

(( tab "Python" ))
Define a Pydantic model and pass it to [`agent.structured_output()`](/docs/api/python/strands.agent.agent#Agent.structured_output):

```python
from pydantic import BaseModel, Field
from strands import Agent
from strands.models.anthropic import AnthropicModel

class MovieReview(BaseModel):
    """Analyze a movie review."""
    title: str = Field(description="Movie title")
    rating: int = Field(description="Rating from 1-10", ge=1, le=10)
    genre: str = Field(description="Primary genre")
    sentiment: str = Field(description="Overall sentiment: positive, negative, or neutral")
    summary: str = Field(description="Brief summary of the review")

model = AnthropicModel(
    client_args={"api_key": "<KEY>"},
    max_tokens=1028,
    model_id="claude-sonnet-4-6",
)

agent = Agent(model=model)

result = agent.structured_output(
    MovieReview,
    """
    Just watched "The Matrix" - what an incredible sci-fi masterpiece!
    The groundbreaking visual effects and philosophical themes make this
    a must-watch. Keanu Reeves delivers a solid performance. 9/10!
    """
)

print(f"Movie: {result.title}")
print(f"Rating: {result.rating}/10")
print(f"Genre: {result.genre}")
print(f"Sentiment: {result.sentiment}")
```
(( /tab "Python" ))

(( tab "TypeScript" ))
Define a Zod schema and pass it as `structuredOutputSchema`. Validated output is on `result.structuredOutput`:

```typescript
import { Agent } from '@strands-agents/sdk'
import { AnthropicModel } from '@strands-agents/sdk/models/anthropic'
import { z } from 'zod'

const MovieReview = z.object({
  title: z.string().describe('Movie title'),
  rating: z.number().min(1).max(10).describe('Rating from 1-10'),
  genre: z.string().describe('Primary genre'),
  sentiment: z.enum(['positive', 'negative', 'neutral']).describe('Overall sentiment'),
  summary: z.string().describe('Brief summary of the review'),
})

const model = new AnthropicModel({
  apiKey: '<KEY>',
  modelId: 'claude-sonnet-4-6',
  maxTokens: 1028,
})

const agent = new Agent({ model, structuredOutputSchema: MovieReview })

const result = await agent.invoke(
  `Just watched "The Matrix" - what an incredible sci-fi masterpiece!
   The groundbreaking visual effects and philosophical themes make this
   a must-watch. Keanu Reeves delivers a solid performance. 9/10!`
)

const review = result.structuredOutput as z.infer<typeof MovieReview>
console.log(`Movie: ${review.title}`)
console.log(`Rating: ${review.rating}/10`)
console.log(`Genre: ${review.genre}`)
console.log(`Sentiment: ${review.sentiment}`)
```
(( /tab "TypeScript" ))

For schema patterns, error handling, and per-invocation overrides, see [Structured Output](/docs/user-guide/concepts/agents/structured-output/index.md).

### Prompt Caching

[Prompt caching](https://docs.claude.com/en/docs/build-with-claude/prompt-caching) lets Claude reuse an already-processed prefix of your prompt instead of reprocessing it on every call. The mechanism, the cost model, and the cache metrics match [Amazon Bedrock](/docs/user-guide/concepts/model-providers/amazon-bedrock/index.md#caching); this section covers what differs here.

Caching is off by default. Set `cache_config``cacheConfig` to cache the system prompt and add a cache point to the last user message, which caches everything before it:

(( tab "Python" ))
```python
from strands import Agent
from strands.models.anthropic import AnthropicModel
from strands.models import CacheConfig, CacheToolsConfig

model = AnthropicModel(
    model_id="claude-sonnet-4-6",
    max_tokens=1028,
    cache_config=CacheConfig(ttl="1h"),
    cache_tools=CacheToolsConfig(ttl="1h"),
)

agent = Agent(model=model)
result = agent("Summarize the attached report.")
print(result.metrics.accumulated_usage)

# Typical output:
# {'inputTokens': 12, ..., 'cacheReadInputTokens': 2505}
```
(( /tab "Python" ))

(( tab "TypeScript" ))
```typescript
import { Agent } from '@strands-agents/sdk'
import { AnthropicModel } from '@strands-agents/sdk/models/anthropic'

const model = new AnthropicModel({
  modelId: 'claude-sonnet-4-6',
  maxTokens: 1028,
  cacheConfig: { ttl: '1h' },
})

const agent = new Agent({ model })
const result = await agent.invoke('Summarize the attached report.')
console.log(result.metrics?.accumulatedUsage)

// Typical output:
// { inputTokens: 12, ..., cacheReadInputTokens: 2505 }
```
(( /tab "TypeScript" ))

Cache activity is reported in the usage metrics, in the same fields Bedrock uses: see [cache metrics](/docs/user-guide/concepts/model-providers/amazon-bedrock/index.md#cache-metrics).

#### Choosing what to cache

(( tab "Python" ))
`cache_config` caches the system prompt and the conversation; set `system_prompt_ttl=False` to leave the system prompt uncached, or a TTL string (`system_prompt_ttl="1h"`) to give it its own duration. `cache_tools` caches the tool definitions and works on its own, so tool definitions can be cached without caching the conversation. Passing a plain string (`"default"`) only switches it on: the value is not a TTL. Use `CacheToolsConfig(ttl=...)` to set one.

`strategy` is ignored by this provider.
(( /tab "Python" ))

(( tab "TypeScript" ))
One `cacheConfig` covers the tool definitions, the system prompt, and the conversation, with `toolsTTL`, `systemPromptTTL`, and `messagesTTL` controlling them independently (set one to `false` to leave that part uncached). The shape and the per-field behavior are the same as on [Amazon Bedrock](/docs/user-guide/concepts/model-providers/amazon-bedrock/index.md#tool-caching), so a configuration survives a provider switch unchanged.

`strategy` is ignored by this provider.
(( /tab "TypeScript" ))

#### Placing cache points by hand

Placement works as it does on [Amazon Bedrock](/docs/user-guide/concepts/model-providers/amazon-bedrock/index.md#messages-caching): a cache point you put in the last user message is kept where you put it, automatic placement is suspended for that message, and points in earlier messages are removed. Place your point *ahead* of content that is rebuilt on every call, otherwise it lands inside the cached prefix and every request writes an entry that none ever reads.

Two constraints are specific to this API:

-   **Only some block types accept a cache point.** Text, image, tool use, tool result, and document blocks do; a reasoning block does not. A point with only a reasoning block, or a media block sourced by location, ahead of it cannot be honored, so automatic placement applies instead.
-   **Maximum of four cache points per request**, shared across the tool definitions, the system prompt, and the messages. Automatic placement uses one per part it caches, so hand-placing several of your own alongside it can exceed the limit, and the API rejects the request.

A prompt below the model’s minimum cacheable length is not cached: if both cache metrics stay at zero, that is the likely cause. See [cache limitations](https://docs.claude.com/en/docs/build-with-claude/prompt-caching#cache-limitations) for per-model thresholds.

### Token Counting

Token counting is used by context management strategies to estimate input tokens before each model call.

(( tab "Python" ))
The Anthropic provider can use the native `messages.count_tokens()` API, which provides exact token counts including system prompts, messages, and tool specifications.

You can enable native token counting with:

```python
model = AnthropicModel(
    model_id="claude-sonnet-4-6",
    use_native_token_count=True,
)
```

When disabled (or if the API call fails), falls back to estimation with a character-based heuristic (characters ÷ 4 for text, characters ÷ 2 for JSON).
(( /tab "Python" ))

(( tab "TypeScript" ))
The Anthropic provider can use the native `messages.countTokens()` API, which provides exact token counts including system prompts, messages, and tool specifications.

You can enable native token counting with:

```typescript
const model = new AnthropicModel({
  modelId: 'claude-sonnet-4-6',
  useNativeTokenCount: true,
})
```

When disabled (or if the API call fails), falls back to estimation with a character-based heuristic (characters ÷ 4 for text, characters ÷ 2 for JSON).
(( /tab "TypeScript" ))

## References

-   [Python API](/docs/api/python/strands.models.model)
-   [Anthropic](https://platform.claude.com/docs/en/home)

## Related pages

- [Result Caching](/docs/user-guide/evals-sdk/how-to/result_caching/index.md) (1 shared tag)
- [LiteLLM](/docs/user-guide/concepts/model-providers/litellm/index.md) (1 shared tag)
- [Structured Output](/docs/user-guide/concepts/agents/structured-output/index.md) (1 shared tag)
- [OpenAI](/docs/user-guide/concepts/model-providers/openai/index.md) (1 shared tag)
- [Writer](/docs/user-guide/concepts/model-providers/writer/index.md) (1 shared tag)
- [Amazon Bedrock](/docs/user-guide/concepts/model-providers/amazon-bedrock/index.md) (1 shared tag)


## Implementation

### Python

- [harness-sdk/strands-py/src/strands/models/anthropic.py](https://github.com/strands-agents/harness-sdk/blob/main/strands-py/src/strands/models/anthropic.py)

### TypeScript

- [harness-sdk/strands-ts/src/models/anthropic.ts](https://github.com/strands-agents/harness-sdk/blob/main/strands-ts/src/models/anthropic.ts)