Anthropic

The Anthropic plugin lets you build agentic workflows with Claude on Flyte. It provides a function_tool decorator that wraps Flyte tasks as tools that Claude can call, and a run_agent function that drives the agent conversation loop.

When Claude calls a tool, the call executes as a Flyte task with full observability, retries, and caching.

Installation

pip install flyteplugins-anthropic

Requires anthropic >= 0.40.0.

Quick start

        
    
import flyte
from flyteplugins.anthropic import function_tool, run_agent

env = flyte.TaskEnvironment(
    name="claude-agent",
    resources=flyte.Resources(cpu=1, memory="250Mi"),
    image=flyte.Image.from_uv_script(__file__, name="anthropic_agent"),
    secrets=flyte.Secret("anthropic_api_key", as_env_var="ANTHROPIC_API_KEY"),
)

@function_tool
@env.task
async def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"The weather in {city} is sunny, 72F"

@env.task
async def main(prompt: str) -> str:
    tools = [get_weather]
    return await run_agent(prompt=prompt, tools=tools)

API

`function_tool`

Converts a Flyte task, @flyte.trace-decorated function, or plain callable into a tool that Claude can invoke.

        
    
@function_tool
@env.task
async def my_tool(param: str) -> str:
    """Tool description sent to Claude."""
    ...

Can also be called with optional overrides:

        
    
@function_tool(name="custom_name", description="Custom description")
@env.task
async def my_tool(param: str) -> str:
    ...

Parameters:

Parameter	Type	Description
`func`	callable	The function to wrap
`name`	`str`	Override the tool name (defaults to the function name)
`description`	`str`	Override the tool description (defaults to the docstring)

The docstring on each @function_tool task is sent to Claude as the tool description. Write clear, concise docstrings.

`Agent`

A dataclass for bundling agent configuration:

        
    
from flyteplugins.anthropic import Agent

agent = Agent(
    name="my-agent",
    instructions="You are a helpful assistant.",
    model="claude-sonnet-4-20250514",
    tools=[get_weather],
    max_tokens=4096,
    max_iterations=10,
)

Field	Type	Default	Description
`name`	`str`	`"assistant"`	Agent name
`instructions`	`str`	`"You are a helpful assistant."`	System prompt
`model`	`str`	`"claude-sonnet-4-20250514"`	Claude model ID
`tools`	`list[FunctionTool]`	`[]`	Tools available to the agent
`max_tokens`	`int`	`4096`	Maximum tokens per response
`max_iterations`	`int`	`10`	Maximum tool-call loop iterations

`run_agent`

Runs a Claude conversation loop, dispatching tool calls to Flyte tasks until Claude returns a final response.

        
    
result = await run_agent(
    prompt="What's the weather in Tokyo?",
    tools=[get_weather],
    model="claude-sonnet-4-20250514",
)

You can also pass an Agent object:

        
    
result = await run_agent(prompt="What's the weather?", agent=agent)

Parameter	Type	Default	Description
`prompt`	`str`	required	User message
`tools`	`list[FunctionTool]`	`None`	Tools available to the agent
`agent`	`Agent`	`None`	Agent config (overrides individual params)
`model`	`str`	`"claude-sonnet-4-20250514"`	Claude model ID
`system`	`str`	`None`	System prompt
`max_tokens`	`int`	`4096`	Maximum tokens per response
`max_iterations`	`int`	`10`	Maximum iterations (prevents infinite loops)
`api_key`	`str`	`None`	API key (falls back to `ANTHROPIC_API_KEY` env var)

Secrets

Store your Anthropic API key as a Flyte secret and expose it as an environment variable:

        
    
secrets=flyte.Secret("anthropic_api_key", as_env_var="ANTHROPIC_API_KEY")

API reference

See the Anthropic API reference for full details.