---
title: Build components with Langflow Assistant
slug: /langflow-assistant
---
import Icon from "@site/src/components/icon";
**Langflow Assistant** is an in-app virtual assistant pane within the **Playground**.
It can answer questions about the application and help you get more out of Langflow.
**Langflow Assistant** understands the structure of the Langflow graph, so it can create components from natural language prompts.
Behind the scenes, **Langflow Assistant** runs a built-in Langflow flow on your Langflow server each time you send a message.
This flow is distinct from the flow that is open in the canvas, and has its own language model.
The language model in **Langflow Assistant** only has the currently opened flow in your workspace for context.
To give **Langflow Assistant** context for a different flow, switch to that flow in your workspace, and open **Langflow Assistant**.
## Prerequisites
* Connect an LLM provider in the [Global model providers page](/components-models) for **Langflow Assistant** to use
## Create a custom component with Langflow Assistant
In this example, you'll prompt **Langflow Assistant** to create a custom component that validates and normalizes a list of URLs.
You'll then iterate on the code based on the results in the **Playground**.
1. In the **Playground**, click the **Langflow** icon.
The **Langflow Assistant** pane opens.
2. Optionally, ask `What can you help me with?` for a list of **Langflow Assistant's** capabilities.
3. Prompt **Langflow Assistant** to generate a custom component. For example:
```
Create a custom component URLTitleExtractor with:
input: text
output: list of {url, title, status}
timeout handling + per-URL error handling
clean docstring and typed methods.”
```
4. **Langflow Assistant** generates component code from your prompt. Because generation is model-driven, your code may differ from the example below.
Python component code
```
import html
import re
from typing import Any
from urllib.error import HTTPError, URLError
from urllib.request import Request, urlopen
from lfx.custom import Component
from lfx.io import FloatInput, MessageTextInput, Output
from lfx.schema import DataFrame
class URLTitleExtractor(Component):
"""Extract URLs from text, fetch each page, and return URL/title/status rows."""
display_name = "URLTitleExtractor"
description = "Extracts URLs from input text and fetches page titles with per-URL status handling."
icon = "Link"
inputs = [
MessageTextInput(
name="text",
display_name="Text",
info="Text that may contain one or more URLs.",
required=True,
),
FloatInput(
name="timeout",
display_name="Timeout",
value=5.0,
info="HTTP request timeout in seconds for each URL.",
range_spec={"min": 0.1, "max": 60.0, "step": 0.1, "step_type": "float"},
),
]
outputs = [
Output(
name="dataframe",
display_name="DataFrame",
method="build_dataframe",
),
]
URL_PATTERN = re.compile(r"https?://[^\s<>\]\"'`{}|\\^]+", re.IGNORECASE)
TITLE_PATTERN = re.compile(r"]*>(.*?)", re.IGNORECASE | re.DOTALL)
def build_dataframe(self) -> DataFrame:
"""Build a DataFrame containing URL, extracted title, and request status."""
urls = self._extract_urls(self.text)
rows = [self._process_url(url, self.timeout) for url in urls]
return DataFrame(rows)
def _extract_urls(self, text: str) -> list[str]:
"""Extract unique URLs from text using a conservative regex."""
if not text:
return []
matches = self.URL_PATTERN.findall(text)
cleaned_urls: list[str] = []
seen: set[str] = set()
for url in matches:
cleaned = url.rstrip(".,);:!?]}")
if cleaned and cleaned not in seen:
seen.add(cleaned)
cleaned_urls.append(cleaned)
return cleaned_urls
def _process_url(self, url: str, timeout: float) -> dict[str, Any]:
"""Fetch a URL and return a row with url, title, and status."""
request = Request(
url,
headers={
"User-Agent": "Mozilla/5.0 (compatible; Langflow URLTitleExtractor/1.0)"
},
)
try:
with urlopen(request, timeout=float(timeout)) as response:
status_code = getattr(response, "status", 200)
content_bytes = response.read()
content_type = response.headers.get_content_charset() or "utf-8"
html_text = content_bytes.decode(content_type, errors="replace")
title = self._extract_title(html_text)
return {"url": url, "title": title, "status": str(status_code)}
except HTTPError as exc:
title = ""
try:
body = exc.read()
charset = exc.headers.get_content_charset() if exc.headers else None
if body:
html_text = body.decode(charset or "utf-8", errors="replace")
title = self._extract_title(html_text)
except Exception:
title = ""
return {"url": url, "title": title, "status": str(exc.code)}
except TimeoutError:
return {"url": url, "title": "", "status": "timeout"}
except URLError as exc:
reason = getattr(exc, "reason", None)
if isinstance(reason, TimeoutError):
status = "timeout"
else:
status = "request_error"
return {"url": url, "title": "", "status": status}
except Exception:
return {"url": url, "title": "", "status": "request_error"}
def _extract_title(self, html_text: str) -> str:
"""Extract and normalize the HTML title from a document string."""
if not html_text:
return ""
match = self.TITLE_PATTERN.search(html_text)
if not match:
return ""
title = html.unescape(match.group(1))
title = re.sub(r"\s+", " ", title).strip()
return title
```
To inspect the code, click **View Code**.
To add the component to the canvas, click **Add to Canvas**.
5. Connect the component to **Chat Input** and **Chat Output** components.
At this point your flow has three connected components:
* **Chat Input** sends the text that contains URLs into the **URLTitleExtractor** so the flow runs when you chat in the **Playground** or send a prompt from an app.
* **URLTitleExtractor** reads that text, finds URLs, fetches each page, and outputs a [Table](/data-types#table) with `url`, `title`, and `status` columns.
* **Chat Output** receives **URLTitleExtractor**'s result and displays the [Table](/data-types#table) back to the user or calling application.
6. Open the **Playground**, and tell Langflow to check a list of URLs.
For example:
```
Check these links: https://langflow.org
https://github.com/langflow-ai/langflow
https://python.org
https://this-domain-should-not-resolve-12345.invalid
```
7. Run the flow.
The output will be similar to the following, with a [Table](/data-types#table) with one row per URL, including page titles and status codes.
| url | title | status |
| --- | --- | --- |
| https://langflow.org | Langflow \| Low-code AI builder for agentic and RAG applications | 200 |
| https://github.com/langflow-ai/langflow | GitHub - langflow-ai/langflow: Langflow is a powerful tool for building and deploying AI-powered agents and workflows. - GitHub | 200 |
| https://python.org | Welcome to Python.org | 200 |
| https://this-domain-should-not-resolve-12345.invalid | | |
8. To iterate further, tell **Langflow Assistant** what you want.
For example, prompt it to `Update URLTitleExtractor to add max_urls (default 5) and skip duplicates.`.
**Langflow Assistant** generates an updated **URLTitleExtractor** component (again, the exact code may differ from a previous run).
9. Replace the old component with the new component.
Set **max_urls** to `3`.
In the **Playground**, enter a list that includes a duplicate URL and the invalid URL from before:
```
Check these links: https://langflow.org
https://github.com/langflow-ai/langflow
https://python.org
https://langflow.org
https://this-domain-should-not-resolve-12345.invalid
```
10. Run the flow.
The output will be similar to the following, with the duplicate `https://langflow.org` removed and only 3 valid URLs displayed.
| url | title | status |
| --- | --- | --- |
| https://langflow.org | Langflow \| Low-code AI builder for agentic and RAG applications | 200 |
| https://github.com/langflow-ai/langflow | GitHub - langflow-ai/langflow: Langflow is a powerful tool for building and deploying AI-powered agents and workflows. - GitHub | 200 |
| https://python.org | Welcome to Python.org | 200 |