---
title: "AnthropicGenerator"
id: anthropicgenerator
slug: "/anthropicgenerator"
description: "This component enables text completions using Anthropic large language models (LLMs)."
---

# AnthropicGenerator

This component enables text completions using Anthropic large language models (LLMs).

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [PromptBuilder](../builders/promptbuilder.mdx) |
| **Mandatory init variables** | `api_key`: An Anthropic API key. Can be set with `ANTHROPIC_API_KEY` env var. |
| **Mandatory run variables** | `prompt`: A string containing the prompt for the LLM |
| **Output variables** | `replies`: A list of strings with all the replies generated by the LLM  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Anthropic](/reference/integrations-anthropic) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/anthropic |

</div>

## Overview

This integration supports Anthropic models such as `claude-3-5-sonnet-20240620`,`claude-3-opus-20240229`, `claude-3-haiku-20240307`, and similar. Although these LLMs are called chat models, the main prompt interface works with the string prompts. Check out the most recent full list in the [Anthropic documentation](https://docs.anthropic.com/en/docs/about-claude/models).

### Parameters

`AnthropicGenerator` needs an Anthropic API key to work. You can provide this key in:

- The `ANTHROPIC_API_KEY` environment variable (recommended)
- The `api_key` init parameter and Haystack [Secret](../../concepts/secret-management.mdx) API: `Secret.from_token("your-api-key-here")`

Set your preferred Anthropic model in the `model` parameter when initializing the component.

`AnthropicGenerator` requires a prompt to generate text, but you can pass any text generation parameters available in the Anthropic [Messaging API](https://docs.anthropic.com/en/api/messages) method directly to this component using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the parameters supported by the Anthropic API, see [Anthropic documentation](https://docs.anthropic.com).

Finally, the component run method requires a single string prompt to generate text.

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

## Usage

Install the `anthropic-haystack` package to use the `AnthropicGenerator`:

```shell
pip install anthropic-haystack
```

### On its own

```python
from haystack_integrations.components.generators.anthropic import AnthropicGenerator

generator = AnthropicGenerator()
print(generator.run("What's Natural Language Processing? Be brief."))
```

### In a pipeline

You can also use `AnthropicGenerator` with the Anthropic models in your pipeline.

```python
from haystack import Pipeline
from haystack.components.builders import PromptBuilder
from haystack_integrations.components.generators.anthropic import AnthropicGenerator
from haystack.utils import Secret

template = """
You are an assistant giving out valuable information to language learners.
Answer this question, be brief.

Question: {{ query }}?
"""

pipe = Pipeline()
pipe.add_component("prompt_builder", PromptBuilder(template))
pipe.add_component("llm", AnthropicGenerator(Secret.from_env_var("ANTHROPIC_API_KEY")))
pipe.connect("prompt_builder", "llm")

query = "What language is spoke in Germany?"
res = pipe.run(data={"prompt_builder": {"query": {query}}})
print(res)
```
