Anthropic

Install

To use AnthropicModel models, you need to either install pydantic-ai, or install pydantic-ai-slim with the anthropic optional group:

pip

Terminal

pip install "pydantic-ai-slim[anthropic]"

uv

Terminal

uv add "pydantic-ai-slim[anthropic]"

Configuration

To use Anthropic through their API, go to console.anthropic.com/settings/keys to generate an API key.

AnthropicModelName contains a list of available Anthropic models.

Environment variable

Once you have the API key, you can set it as an environment variable:

Terminal

export ANTHROPIC_API_KEY='your-api-key'

You can then use AnthropicModel by name:

from pydantic_ai import Agent

agent = Agent('anthropic:claude-sonnet-4-6')
...

Or initialise the model directly with just the model name:

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModel

model = AnthropicModel('claude-sonnet-4-5')
agent = Agent(model)
...

provider argument

You can provide a custom Provider via the provider argument:

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.providers.anthropic import AnthropicProvider

model = AnthropicModel(
    'claude-sonnet-4-5', provider=AnthropicProvider(api_key='your-api-key')
)
agent = Agent(model)
...

Custom HTTP Client

You can customize the AnthropicProvider with a custom httpx.AsyncClient:

from httpx import AsyncClient

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.providers.anthropic import AnthropicProvider

custom_http_client = AsyncClient(timeout=30)
model = AnthropicModel(
    'claude-sonnet-4-5',
    provider=AnthropicProvider(api_key='your-api-key', http_client=custom_http_client),
)
agent = Agent(model)
...

Cloud Platform Integrations

You can use Anthropic models through cloud platforms by passing a custom client to AnthropicProvider.

AWS Bedrock

To use Claude models via AWS Bedrock, follow the Anthropic documentation on how to set up an AsyncAnthropicBedrock client and then pass it to AnthropicProvider:

from anthropic import AsyncAnthropicBedrock

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.providers.anthropic import AnthropicProvider

bedrock_client = AsyncAnthropicBedrock()  # Uses AWS credentials from environment
provider = AnthropicProvider(anthropic_client=bedrock_client)
model = AnthropicModel('us.anthropic.claude-sonnet-4-5-20250929-v1:0', provider=provider)
agent = Agent(model)
...

Bedrock vs BedrockConverseModel

This approach uses Anthropic’s SDK with AWS Bedrock credentials. For an alternative using AWS SDK (boto3) directly, see BedrockConverseModel.

Google Vertex AI

To use Claude models via Google Cloud Vertex AI, follow the Anthropic documentation on how to set up an AsyncAnthropicVertex client and then pass it to AnthropicProvider:

from anthropic import AsyncAnthropicVertex

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.providers.anthropic import AnthropicProvider

vertex_client = AsyncAnthropicVertex(region='us-east5', project_id='your-project-id')
provider = AnthropicProvider(anthropic_client=vertex_client)
model = AnthropicModel('claude-sonnet-4-5', provider=provider)
agent = Agent(model)
...

Vertex vs GoogleModel

This approach uses Anthropic’s SDK with Vertex AI credentials. For an alternative using Google’s Vertex AI SDK directly, see GoogleModel.

Microsoft Foundry

To use Claude models via Microsoft Foundry, follow the Anthropic documentation on how to set up an AsyncAnthropicFoundry client and then pass it to AnthropicProvider:

from anthropic import AsyncAnthropicFoundry

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.providers.anthropic import AnthropicProvider

foundry_client = AsyncAnthropicFoundry(
    api_key='your-foundry-api-key',  # Or set ANTHROPIC_FOUNDRY_API_KEY
    resource='your-resource-name',
)
provider = AnthropicProvider(anthropic_client=foundry_client)
model = AnthropicModel('claude-sonnet-4-5', provider=provider)
agent = Agent(model)
...

See Anthropic’s Microsoft Foundry documentation for setup instructions including Entra ID authentication.

Prompt Caching

Anthropic supports prompt caching to reduce costs by caching parts of your prompts. Pydantic AI provides four ways to use prompt caching:

1. Cache User Messages with CachePoint: Insert a CachePoint marker in your user messages to cache everything before it
2. Cache System Instructions: Set AnthropicModelSettings.anthropic_cache_instructions to True (uses 5m TTL by default) or specify '5m' / '1h' directly. When you have both static and dynamic instructions, the cache point is automatically placed after the last static instruction, so dynamic instructions can change without invalidating the static cache.
3. Cache Tool Definitions: Set AnthropicModelSettings.anthropic_cache_tool_definitions to True (uses 5m TTL by default) or specify '5m' / '1h' directly
4. Cache All Messages: Set AnthropicModelSettings.anthropic_cache_messages to True to automatically cache all messages

Example 1: Automatic Message Caching

Use anthropic_cache_messages to automatically cache all messages up to and including the newest user message:

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModelSettings

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='You are a helpful assistant.',
    model_settings=AnthropicModelSettings(
        anthropic_cache_messages=True,  # Automatically caches the last message
    ),
)

# The last message is automatically cached - no need for manual CachePoint
result1 = agent.run_sync('What is the capital of France?')

# Subsequent calls with similar conversation benefit from cache
result2 = agent.run_sync('What is the capital of Germany?')
print(f'Cache write: {result1.usage().cache_write_tokens}')
print(f'Cache read: {result2.usage().cache_read_tokens}')

Example 2: Comprehensive Caching Strategy

Combine multiple cache settings for maximum savings:

from pydantic_ai import Agent, RunContext
from pydantic_ai.models.anthropic import AnthropicModelSettings

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='Detailed instructions...',
    model_settings=AnthropicModelSettings(
        anthropic_cache_instructions=True,      # Cache system instructions
        anthropic_cache_tool_definitions='1h',  # Cache tool definitions with 1h TTL
        anthropic_cache_messages=True,          # Also cache the last message
    ),
)

@agent.tool
def search_docs(ctx: RunContext, query: str) -> str:
    """Search documentation."""
    return f'Results for {query}'


result = agent.run_sync('Search for Python best practices')
print(result.output)

Smart Instruction Caching

When you use anthropic_cache_instructions with both static and dynamic instructions, Pydantic AI automatically places the cache boundary at the optimal point. Static instructions (from Agent(instructions=...)) are sorted before dynamic instructions (from @agent.instructions functions or toolsets), and the cache point is placed after the last static instruction block.

This means your stable, static instructions are cached efficiently, while dynamic instructions (which may change between requests) remain outside the cache boundary and don’t cause cache invalidation.

from datetime import date

from pydantic_ai import Agent, RunContext
from pydantic_ai.models.anthropic import AnthropicModelSettings

agent = Agent(
  'anthropic:claude-sonnet-4-6',
  deps_type=str,
  instructions='You are a helpful customer service agent. Follow company policy.',  # (1)
  model_settings=AnthropicModelSettings(
      anthropic_cache_instructions=True,  # (2)
  ),
)


@agent.instructions
def dynamic_context(ctx: RunContext[str]) -> str:  # (3)
  return f"Customer name: {ctx.deps}. Today's date: {date.today()}."


result = agent.run_sync('What is your return policy?', deps='Alice')
print(result.output)

Static instructions are cached across requests.

Enables smart cache placement at the static/dynamic boundary.

Dynamic instructions change per-request and are not cached.

Example 3: Fine-Grained Control with CachePoint

Use manual CachePoint markers to control cache locations precisely:

from pydantic_ai import Agent, CachePoint

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='Instructions...',
)

# Manually control cache points for specific content blocks
result = agent.run_sync([
    'Long context from documentation...',
    CachePoint(),  # Cache everything up to this point
    'First question'
])
print(result.output)

Accessing Cache Usage Statistics

Access cache usage statistics via result.usage():

from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModelSettings

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='Instructions...',
    model_settings=AnthropicModelSettings(
        anthropic_cache_instructions=True  # Default 5m TTL
    ),
)

result = agent.run_sync('Your question')
usage = result.usage()
print(f'Cache write tokens: {usage.cache_write_tokens}')
print(f'Cache read tokens: {usage.cache_read_tokens}')

Cache Point Limits

Anthropic enforces a maximum of 4 cache points per request. Pydantic AI automatically manages this limit to ensure your requests always comply without errors.

How Cache Points Are Allocated

Cache points can be placed in three locations:

1. System Prompt: Via anthropic_cache_instructions setting (adds cache point to last system prompt block)
2. Tool Definitions: Via anthropic_cache_tool_definitions setting (adds cache point to last tool definition)
3. Messages: Via CachePoint markers or anthropic_cache_messages setting (adds cache points to message content)

Each setting uses at most 1 cache point, but you can combine them.

Example: Using All 3 Cache Point Sources

Define an agent with all cache settings enabled:

from pydantic_ai import Agent, CachePoint
from pydantic_ai.models.anthropic import AnthropicModelSettings

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='Detailed instructions...',
    model_settings=AnthropicModelSettings(
        anthropic_cache_instructions=True,      # 1 cache point
        anthropic_cache_tool_definitions=True,  # 1 cache point
        anthropic_cache_messages=True,          # 1 cache point
    ),
)

@agent.tool_plain
def my_tool() -> str:
    return 'result'


# This uses 3 cache points (instructions + tools + last message)
# You can add 1 more CachePoint marker before hitting the limit
result = agent.run_sync([
    'Context', CachePoint(),  # 4th cache point - OK
    'Question'
])
print(result.output)
usage = result.usage()
print(f'Cache write tokens: {usage.cache_write_tokens}')
print(f'Cache read tokens: {usage.cache_read_tokens}')

Automatic Cache Point Limiting

When cache points from all sources (settings + CachePoint markers) exceed 4, Pydantic AI automatically removes excess cache points from older message content (keeping the most recent ones).

Define an agent with 2 cache points from settings:

from pydantic_ai import Agent, CachePoint
from pydantic_ai.models.anthropic import AnthropicModelSettings

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='Instructions...',
    model_settings=AnthropicModelSettings(
        anthropic_cache_instructions=True,      # 1 cache point
        anthropic_cache_tool_definitions=True,  # 1 cache point
    ),
)

@agent.tool_plain
def search() -> str:
    return 'data'

# Already using 2 cache points (instructions + tools)
# Can add 2 more CachePoint markers (4 total limit)
result = agent.run_sync([
    'Context 1', CachePoint(),  # Oldest - will be removed
    'Context 2', CachePoint(),  # Will be kept (3rd point)
    'Context 3', CachePoint(),  # Will be kept (4th point)
    'Question'
])
# Final cache points: instructions + tools + Context 2 + Context 3 = 4
print(result.output)
usage = result.usage()
print(f'Cache write tokens: {usage.cache_write_tokens}')
print(f'Cache read tokens: {usage.cache_read_tokens}')

Key Points:

• System and tool cache points are always preserved
• The cache point created by anthropic_cache_messages is always preserved (as it’s the newest message cache point)
• Additional CachePoint markers in messages are removed from oldest to newest when the limit is exceeded
• This ensures critical caching (instructions/tools) is maintained while still benefiting from message-level caching