---
title: "Get Started"
id: get-started
slug: "/get-started"
description: "Learn how to quickly get up and running with Haystack. Build your first RAG pipeline and tool-calling Agent with step-by-step examples for multiple LLM providers."
---

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# Get Started

Have a look at this page to learn how to quickly get up and running with Haystack. It contains instructions for installing Haystack, building your first RAG pipeline, and creating a tool-calling Agent.

## Build your first RAG application

Let's build your first Retrieval Augmented Generation (RAG) pipeline and see how Haystack answers questions.

First, install the minimal form of Haystack:

```shell
pip install haystack-ai
```

In the examples below, we show how to set an API key using a Haystack [Secret](../concepts/secret-management.mdx). Choose your preferred LLM provider from the tabs below. For easier use, you can also set the API key as an environment variable.

<Tabs>
<TabItem value="openai" label="OpenAI" default>

[OpenAIChatGenerator](../pipeline-components/generators/openaichatgenerator.mdx) is included in the `haystack-ai` package.

```python
from haystack import Pipeline, Document
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.builders import ChatPromptBuilder
from haystack.utils import Secret
from haystack.dataclasses import ChatMessage

document_store = InMemoryDocumentStore()
document_store.write_documents(
    [
        Document(content="My name is Jean and I live in Paris."),
        Document(content="My name is Mark and I live in Berlin."),
        Document(content="My name is Giorgio and I live in Rome."),
    ],
)

prompt_template = [
    ChatMessage.from_system(
        """
        Given these documents, answer the question.
        Documents:
        {% for doc in documents %}
            {{ doc.content }}
        {% endfor %}
        """,
    ),
    ChatMessage.from_user("{{question}}"),
]

retriever = InMemoryBM25Retriever(document_store=document_store)
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
llm = OpenAIChatGenerator(
    api_key=Secret.from_env_var("OPENAI_API_KEY"),
    model="gpt-4o-mini",
)

rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

question = "Who lives in Paris?"
results = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    },
)

print(results["llm"]["replies"])
```

</TabItem>
<TabItem value="huggingface" label="Hugging Face">

[HuggingFaceAPIChatGenerator](../pipeline-components/generators/huggingfaceapichatgenerator.mdx) is included in the `huggingface-api-haystack` package. You can get a [free Hugging Face token](https://huggingface.co/settings/tokens) to use the Serverless Inference API.

The examples on this page use the Hugging Face API components and the SerperDev web search component, which have moved to the `huggingface-api-haystack` and `serperdev-haystack` packages. Install them to run the examples:

```shell
pip install huggingface-api-haystack serperdev-haystack
```

```python
from haystack import Pipeline, Document
from haystack_integrations.components.generators.huggingface_api import (
    HuggingFaceAPIChatGenerator,
)
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.builders import ChatPromptBuilder
from haystack.utils import Secret
from haystack.dataclasses import ChatMessage

document_store = InMemoryDocumentStore()
document_store.write_documents(
    [
        Document(content="My name is Jean and I live in Paris."),
        Document(content="My name is Mark and I live in Berlin."),
        Document(content="My name is Giorgio and I live in Rome."),
    ],
)

prompt_template = [
    ChatMessage.from_system(
        """
        Given these documents, answer the question.
        Documents:
        {% for doc in documents %}
            {{ doc.content }}
        {% endfor %}
        """,
    ),
    ChatMessage.from_user("{{question}}"),
]

retriever = InMemoryBM25Retriever(document_store=document_store)
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
llm = HuggingFaceAPIChatGenerator(
    api_type="serverless_inference_api",
    api_params={"model": "Qwen/Qwen2.5-72B-Instruct"},
    token=Secret.from_env_var("HF_API_TOKEN"),
)

rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

question = "Who lives in Paris?"
results = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    },
)

print(results["llm"]["replies"])
```

</TabItem>
<TabItem value="anthropic" label="Anthropic">

Install the [Anthropic integration](https://haystack.deepset.ai/integrations/anthropic):

```bash
pip install anthropic-haystack
```

See the [AnthropicChatGenerator](../pipeline-components/generators/anthropicchatgenerator.mdx) docs for more details.

```python
from haystack import Pipeline, Document
from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.builders import ChatPromptBuilder
from haystack.utils import Secret
from haystack.dataclasses import ChatMessage

document_store = InMemoryDocumentStore()
document_store.write_documents(
    [
        Document(content="My name is Jean and I live in Paris."),
        Document(content="My name is Mark and I live in Berlin."),
        Document(content="My name is Giorgio and I live in Rome."),
    ],
)

prompt_template = [
    ChatMessage.from_system(
        """
        Given these documents, answer the question.
        Documents:
        {% for doc in documents %}
            {{ doc.content }}
        {% endfor %}
        """,
    ),
    ChatMessage.from_user("{{question}}"),
]

retriever = InMemoryBM25Retriever(document_store=document_store)
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
llm = AnthropicChatGenerator(
    api_key=Secret.from_env_var("ANTHROPIC_API_KEY"),
    model="claude-sonnet-4-5-20250929",
)

rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

question = "Who lives in Paris?"
results = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    },
)

print(results["llm"]["replies"])
```

</TabItem>
<TabItem value="amazon-bedrock" label="Amazon Bedrock">

Install the [Amazon Bedrock integration](https://haystack.deepset.ai/integrations/amazon-bedrock):

```bash
pip install amazon-bedrock-haystack
```

See the [AmazonBedrockChatGenerator](../pipeline-components/generators/amazonbedrockchatgenerator.mdx) docs for more details.

```python
import os
from haystack import Pipeline, Document
from haystack_integrations.components.generators.amazon_bedrock import (
    AmazonBedrockChatGenerator,
)
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage

os.environ["AWS_ACCESS_KEY_ID"] = "YOUR_AWS_ACCESS_KEY_ID"
os.environ["AWS_SECRET_ACCESS_KEY"] = "YOUR_AWS_SECRET_ACCESS_KEY"
os.environ["AWS_DEFAULT_REGION"] = "YOUR_AWS_REGION"

document_store = InMemoryDocumentStore()
document_store.write_documents(
    [
        Document(content="My name is Jean and I live in Paris."),
        Document(content="My name is Mark and I live in Berlin."),
        Document(content="My name is Giorgio and I live in Rome."),
    ],
)

prompt_template = [
    ChatMessage.from_system(
        """
        Given these documents, answer the question.
        Documents:
        {% for doc in documents %}
            {{ doc.content }}
        {% endfor %}
        """,
    ),
    ChatMessage.from_user("{{question}}"),
]

retriever = InMemoryBM25Retriever(document_store=document_store)
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
llm = AmazonBedrockChatGenerator(model="anthropic.claude-3-5-sonnet-20240620-v1:0")

rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

question = "Who lives in Paris?"
results = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    },
)

print(results["llm"]["replies"])
```

</TabItem>
<TabItem value="google-gemini" label="Google Gemini">

Install the [Google Gen AI integration](https://haystack.deepset.ai/integrations/google-genai):

```bash
pip install google-genai-haystack
```

See the [GoogleGenAIChatGenerator](../pipeline-components/generators/googlegenaichatgenerator.mdx) docs for more details.

```python
from haystack import Pipeline, Document
from haystack_integrations.components.generators.google_genai import (
    GoogleGenAIChatGenerator,
)
from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.builders import ChatPromptBuilder
from haystack.utils import Secret
from haystack.dataclasses import ChatMessage

document_store = InMemoryDocumentStore()
document_store.write_documents(
    [
        Document(content="My name is Jean and I live in Paris."),
        Document(content="My name is Mark and I live in Berlin."),
        Document(content="My name is Giorgio and I live in Rome."),
    ],
)

prompt_template = [
    ChatMessage.from_system(
        """
        Given these documents, answer the question.
        Documents:
        {% for doc in documents %}
            {{ doc.content }}
        {% endfor %}
        """,
    ),
    ChatMessage.from_user("{{question}}"),
]

retriever = InMemoryBM25Retriever(document_store=document_store)
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
llm = GoogleGenAIChatGenerator(
    api_key=Secret.from_env_var("GOOGLE_API_KEY"),
    model="gemini-2.5-flash",
)

rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

question = "Who lives in Paris?"
results = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    },
)

print(results["llm"]["replies"])
```

</TabItem>
<TabItem value="more-providers" label="More Providers">

<div style={{backgroundColor: 'var(--ifm-color-emphasis-100)', padding: '1.5rem', borderRadius: '8px'}}>

Haystack supports many more model providers including **Cohere**, **Mistral**, **NVIDIA**, **Ollama**, and others—both cloud-hosted and local options.

Browse the full list of supported models and chat generators in the [Generators documentation](../pipeline-components/generators.mdx).

You can also explore all available integrations on the [Haystack Integrations](https://haystack.deepset.ai/integrations) page.

</div>

</TabItem>
</Tabs>

### Next Steps

Ready to dive deeper? Check out the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial for a step-by-step guide on building a complete RAG pipeline with your own data.

## Build your first Agent

Agents are AI systems that can use tools to gather information, perform actions, and interact with external systems. Let's build an agent that can search the web to answer questions.

This example requires a [SerperDev API key](https://serper.dev/) for web search. Set it as the `SERPERDEV_API_KEY` environment variable.

<Tabs>
<TabItem value="openai" label="OpenAI" default>

[OpenAIChatGenerator](../pipeline-components/generators/openaichatgenerator.mdx) is included in the `haystack-ai` package.

```python
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools import ComponentTool
from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch
from haystack.utils import Secret

search_tool = ComponentTool(component=SerperDevWebSearch())

agent = Agent(
    chat_generator=OpenAIChatGenerator(
        api_key=Secret.from_env_var("OPENAI_API_KEY"),
        model="gpt-4o-mini",
    ),
    tools=[search_tool],
    system_prompt="You are a helpful assistant that can search the web for information.",
)

result = agent.run(messages=[ChatMessage.from_user("What is Haystack AI?")])

print(result["last_message"].text)
```

</TabItem>
<TabItem value="huggingface" label="Hugging Face">

[HuggingFaceAPIChatGenerator](../pipeline-components/generators/huggingfaceapichatgenerator.mdx) is included in the `huggingface-api-haystack` package. You can get a [free Hugging Face token](https://huggingface.co/settings/tokens) to use the Serverless Inference API.

```python
from haystack.components.agents import Agent
from haystack_integrations.components.generators.huggingface_api import (
    HuggingFaceAPIChatGenerator,
)
from haystack.dataclasses import ChatMessage
from haystack.tools import ComponentTool
from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch
from haystack.utils import Secret

search_tool = ComponentTool(component=SerperDevWebSearch())

agent = Agent(
    chat_generator=HuggingFaceAPIChatGenerator(
        api_type="serverless_inference_api",
        api_params={"model": "Qwen/Qwen2.5-72B-Instruct"},
        token=Secret.from_env_var("HF_API_TOKEN"),
    ),
    tools=[search_tool],
    system_prompt="You are a helpful assistant that can search the web for information.",
)

result = agent.run(messages=[ChatMessage.from_user("What is Haystack AI?")])

print(result["last_message"].text)
```

</TabItem>
<TabItem value="anthropic" label="Anthropic">

Install the [Anthropic integration](https://haystack.deepset.ai/integrations/anthropic):

```bash
pip install anthropic-haystack
```

See the [AnthropicChatGenerator](../pipeline-components/generators/anthropicchatgenerator.mdx) docs for more details.

```python
from haystack.components.agents import Agent
from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools import ComponentTool
from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch
from haystack.utils import Secret

search_tool = ComponentTool(component=SerperDevWebSearch())

agent = Agent(
    chat_generator=AnthropicChatGenerator(
        api_key=Secret.from_env_var("ANTHROPIC_API_KEY"),
        model="claude-sonnet-4-5-20250929",
    ),
    tools=[search_tool],
    system_prompt="You are a helpful assistant that can search the web for information.",
)

result = agent.run(messages=[ChatMessage.from_user("What is Haystack AI?")])

print(result["last_message"].text)
```

</TabItem>
<TabItem value="amazon-bedrock" label="Amazon Bedrock">

Install the [Amazon Bedrock integration](https://haystack.deepset.ai/integrations/amazon-bedrock):

```bash
pip install amazon-bedrock-haystack
```

See the [AmazonBedrockChatGenerator](../pipeline-components/generators/amazonbedrockchatgenerator.mdx) docs for more details.

```python
import os
from haystack.components.agents import Agent
from haystack_integrations.components.generators.amazon_bedrock import (
    AmazonBedrockChatGenerator,
)
from haystack.dataclasses import ChatMessage
from haystack.tools import ComponentTool
from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch

os.environ["AWS_ACCESS_KEY_ID"] = "YOUR_AWS_ACCESS_KEY_ID"
os.environ["AWS_SECRET_ACCESS_KEY"] = "YOUR_AWS_SECRET_ACCESS_KEY"
os.environ["AWS_DEFAULT_REGION"] = "YOUR_AWS_REGION"

search_tool = ComponentTool(component=SerperDevWebSearch())

agent = Agent(
    chat_generator=AmazonBedrockChatGenerator(
        model="anthropic.claude-3-5-sonnet-20240620-v1:0",
    ),
    tools=[search_tool],
    system_prompt="You are a helpful assistant that can search the web for information.",
)

result = agent.run(messages=[ChatMessage.from_user("What is Haystack AI?")])

print(result["last_message"].text)
```

</TabItem>
<TabItem value="google-gemini" label="Google Gemini">

Install the [Google Gen AI integration](https://haystack.deepset.ai/integrations/google-genai):

```bash
pip install google-genai-haystack
```

See the [GoogleGenAIChatGenerator](../pipeline-components/generators/googlegenaichatgenerator.mdx) docs for more details.

```python
from haystack.components.agents import Agent
from haystack_integrations.components.generators.google_genai import (
    GoogleGenAIChatGenerator,
)
from haystack.dataclasses import ChatMessage
from haystack.tools import ComponentTool
from haystack_integrations.components.websearch.serperdev import SerperDevWebSearch
from haystack.utils import Secret

search_tool = ComponentTool(component=SerperDevWebSearch())

agent = Agent(
    chat_generator=GoogleGenAIChatGenerator(
        api_key=Secret.from_env_var("GOOGLE_API_KEY"),
        model="gemini-2.5-flash",
    ),
    tools=[search_tool],
    system_prompt="You are a helpful assistant that can search the web for information.",
)

result = agent.run(messages=[ChatMessage.from_user("What is Haystack AI?")])

print(result["last_message"].text)
```

</TabItem>
<TabItem value="more-providers" label="More Providers">

<div style={{backgroundColor: 'var(--ifm-color-emphasis-100)', padding: '1.5rem', borderRadius: '8px'}}>

Haystack supports many more model providers including **Cohere**, **Mistral**, **NVIDIA**, **Ollama**, and others—both cloud-hosted and local options.

Browse the full list of supported models and chat generators in the [Generators documentation](../pipeline-components/generators.mdx).

You can also explore all available integrations on the [Haystack Integrations](https://haystack.deepset.ai/integrations) page.

</div>

</TabItem>
</Tabs>

### Next Steps

For a hands-on guide on creating a tool-calling agent that can use both components and pipelines as tools, check out the [Build a Tool-Calling Agent](https://haystack.deepset.ai/tutorials/43_building_a_tool_calling_agent) tutorial.