---
title: "FirecrawlWebSearch"
id: firecrawlwebsearch
slug: "/firecrawlwebsearch"
description: "Search engine using the Firecrawl API."
---

# FirecrawlWebSearch

Search the web and extract content using the Firecrawl API.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`ChatPromptBuilder`](../builders/chatpromptbuilder.mdx) or right at the beginning of an indexing pipeline. |
| **Mandatory init variables** | `api_key`: The Firecrawl API key. Can be set with the `FIRECRAWL_API_KEY` env var. |
| **Mandatory run variables** | `query`: A string with your search query. |
| **Output variables** | `documents`: A list of Haystack Documents containing the scraped content and metadata. <br /> <br />`links`: A list of strings of resulting URLs. |
| **API reference** | [Firecrawl Search API](/reference/integrations-firecrawl) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/firecrawl/src/haystack_integrations/components/websearch/firecrawl/firecrawl_websearch.py |

</div>

## Overview

When you give `FirecrawlWebSearch` a query, it uses the Firecrawl Search API to search the web, crawl the resulting pages, and return the structured text as a list of Haystack `Document` objects. It also returns a list of the underlying URLs.

Because Firecrawl actively scrapes and structures the content of the pages it finds into LLM-friendly formats, you generally don't need an additional component like `LinkContentFetcher` to read the web pages. `FirecrawlWebSearch` handles the retrieval and scraping all in one step.

`FirecrawlWebSearch` requires a [Firecrawl](https://firecrawl.dev) API key to work. By default, it looks for a `FIRECRAWL_API_KEY` environment variable. Alternatively, you can pass an `api_key` directly during initialization.

## Usage

### On its own

Here is a quick example of how `FirecrawlWebSearch` searches the web based on a query, scrapes the resulting web pages, and returns a list of Documents containing the page content.

```python
from haystack_integrations.components.websearch.firecrawl import FirecrawlWebSearch
from haystack.utils import Secret

web_search = FirecrawlWebSearch(
    api_key=Secret.from_env_var("FIRECRAWL_API_KEY"),
    top_k=5,
    search_params={"scrape_options": {"formats": ["markdown"]}},
)
query = "What is Haystack by deepset?"

response = web_search.run(query=query)

for doc in response["documents"]:
    print(doc.content)
```

### In a pipeline

Here is an example of a Retrieval-Augmented Generation (RAG) pipeline where using `FirecrawlWebSearch` to look up an answer. Because Firecrawl returns the actual text of the scraped pages, you can pass its `documents` output directly into the `ChatPromptBuilder` to give the LLM the necessary context.

```python
from haystack import Pipeline
from haystack.utils import Secret
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack_integrations.components.websearch.firecrawl import FirecrawlWebSearch
from haystack.dataclasses import ChatMessage

web_search = FirecrawlWebSearch(
    api_key=Secret.from_env_var("FIRECRAWL_API_KEY"),
    top_k=2,
    search_params={"scrape_options": {"formats": ["markdown"]}},
)

prompt_template = [
    ChatMessage.from_system("You are a helpful assistant."),
    ChatMessage.from_user(
        "Given the information below:\n"
        "{% for document in documents %}{{ document.content }}\n{% endfor %}\n"
        "Answer the following question: {{ query }}.\nAnswer:",
    ),
]

prompt_builder = ChatPromptBuilder(
    template=prompt_template,
    required_variables={"query", "documents"},
)

llm = OpenAIChatGenerator(
    api_key=Secret.from_env_var("OPENAI_API_KEY"),
    model="gpt-5-nano",
)

pipe = Pipeline()
pipe.add_component("search", web_search)
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", llm)

pipe.connect("search.documents", "prompt_builder.documents")
pipe.connect("prompt_builder.prompt", "llm.messages")

query = "What is Haystack by deepset?"

result = pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})

print(result["llm"]["replies"][0].content)
```
