Using Opus 4.1 with LlamaIndex¶

In this notebook we are going to exploit Claude Opus 4.1 by Anthropic advanced coding capabilities to create a cute website, and we're going to do it within LlamaIndex!

Build an LLM-based assistant with Opus 4.1¶

1. Install needed dependencies

In [ ]:

! pip install -q llama-index-llms-anthropic get-code-from-markdown

! pip install -q llama-index-llms-anthropic get-code-from-markdown

Let's just define a helper function to help us fetch the code from Markdown:

In [ ]:

from get_code_from_markdown import get_code_from_markdown


def fetch_code_from_markdown(markdown: str) -> str:
    return get_code_from_markdown(markdown, language="html")

from get_code_from_markdown import get_code_from_markdown def fetch_code_from_markdown(markdown: str) -> str: return get_code_from_markdown(markdown, language="html")

Let's now initialize our LLM:

In [ ]:

import os
import getpass

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

import os import getpass os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

In [ ]:

from llama_index.llms.anthropic import Anthropic

llm = Anthropic(model="claude-opus-4-1-20250805", max_tokens=12000)

from llama_index.llms.anthropic import Anthropic llm = Anthropic(model="claude-opus-4-1-20250805", max_tokens=12000)

In [ ]:

res = llm.complete(
    "Can you build a llama-themed static HTML page, with cute little bouncing animations and blue/white/indigo as theme colors?"
)

res = llm.complete( "Can you build a llama-themed static HTML page, with cute little bouncing animations and blue/white/indigo as theme colors?" )

Let's now get the code and write it to an HTML file!

In [ ]:

html_code = fetch_code_from_markdown(res.text)

with open("index.html", "w") as f:
    for block in html_code:
        f.write(block)

html_code = fetch_code_from_markdown(res.text) with open("index.html", "w") as f: for block in html_code: f.write(block)

You can now download index.html and take a look at the results :)

Build an agent with Opus 4.1¶

We can also build a simple calculator agent using Claude Opus 4.1

In [ ]:

from llama_index.core.agent.workflow import FunctionAgent


def multiply(a: int, b: int) -> int:
    """Multiply two integers and return an integer"""
    return a * b


def add(a: int, b: int) -> int:
    """Sum two integers and return an integer"""
    return a + b


agent = FunctionAgent(
    name="CalculatorAgent",
    description="Useful to perform basic arithmetic operations",
    system_prompt="You are a calculator agent, you should perform arithmetic operations using the tools available to you.",
    tools=[multiply, add],
    llm=llm,
)

from llama_index.core.agent.workflow import FunctionAgent def multiply(a: int, b: int) -> int: """Multiply two integers and return an integer""" return a * b def add(a: int, b: int) -> int: """Sum two integers and return an integer""" return a + b agent = FunctionAgent( name="CalculatorAgent", description="Useful to perform basic arithmetic operations", system_prompt="You are a calculator agent, you should perform arithmetic operations using the tools available to you.", tools=[multiply, add], llm=llm, )

Let's now run the agent through and get the result for a multiplication:

In [ ]:

from llama_index.core.agent.workflow import ToolCall, ToolCallResult

handler = agent.run("What is 60 multiplied by 95?")

async for event in handler.stream_events():
    if isinstance(event, ToolCallResult):
        print(
            f"Result from calling tool {event.tool_name}:\n\n{event.tool_output}"
        )
    if isinstance(event, ToolCall):
        print(
            f"Calling tool {event.tool_name} with arguments:\n\n{event.tool_kwargs}"
        )

response = await handler

print("Final response")
print(response)

from llama_index.core.agent.workflow import ToolCall, ToolCallResult handler = agent.run("What is 60 multiplied by 95?") async for event in handler.stream_events(): if isinstance(event, ToolCallResult): print( f"Result from calling tool {event.tool_name}:\n\n{event.tool_output}" ) if isinstance(event, ToolCall): print( f"Calling tool {event.tool_name} with arguments:\n\n{event.tool_kwargs}" ) response = await handler print("Final response") print(response)

Calling tool multiply with arguments:

{'a': 60, 'b': 95}
Result from calling tool multiply:

5700
Final response
60 multiplied by 95 equals 5,700.

Let's also run it with a sum!

In [ ]:

from llama_index.core.agent.workflow import ToolCall, ToolCallResult

handler = agent.run("What is 1234 plus 5678?")

async for event in handler.stream_events():
    if isinstance(event, ToolCallResult):
        print(
            f"Result from calling tool {event.tool_name}:\n\n{event.tool_output}"
        )
    if isinstance(event, ToolCall):
        print(
            f"Calling tool {event.tool_name} with arguments:\n\n{event.tool_kwargs}"
        )

response = await handler

print("Final response")
print(response)

from llama_index.core.agent.workflow import ToolCall, ToolCallResult handler = agent.run("What is 1234 plus 5678?") async for event in handler.stream_events(): if isinstance(event, ToolCallResult): print( f"Result from calling tool {event.tool_name}:\n\n{event.tool_output}" ) if isinstance(event, ToolCall): print( f"Calling tool {event.tool_name} with arguments:\n\n{event.tool_kwargs}" ) response = await handler print("Final response") print(response)

Calling tool add with arguments:

{'a': 1234, 'b': 5678}
Result from calling tool add:

6912
Final response
1234 plus 5678 equals 6912.

If you want more content around Anthropic, make sure to check out our general example notebook