Build flows and components with Langflow Assistant

Langflow Assistant is an in-app virtual assistant pane accessible from the canvas toolbar.

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 build complete flows or create individual 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 for Langflow Assistant to use

• Custom component creation must be enabled and permitted for your user role:

  • LANGFLOW_ALLOW_CUSTOM_COMPONENTS must be true (default).
  • LANGFLOW_CUSTOM_COMPONENT_ADMIN_ONLY must be false (default) for non-superusers. For superusers, this option is irrelevant.

  For more information, see Restrict custom component creation to superusers.

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 canvas toolbar, 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:

   
   

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   Create a custom component URLTitleExtractor with:

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   input: text

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   output: list of {url, title, status}

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   timeout handling + per-URL error handling

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   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

   
   

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   import html

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   import re

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   from typing import Any

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   from urllib.error import HTTPError, URLError

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   from urllib.request import Request, urlopen

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   from lfx.custom import Component

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   from lfx.io import FloatInput, MessageTextInput, Output

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   from lfx.schema import DataFrame

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   class URLTitleExtractor(Component):

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   """Extract URLs from text, fetch each page, and return URL/title/status rows."""

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   display_name = "URLTitleExtractor"

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   description = "Extracts URLs from input text and fetches page titles with per-URL status handling."

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   icon = "Link"

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   inputs = [

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   MessageTextInput(

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   name="text",

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   display_name="Text",

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   info="Text that may contain one or more URLs.",

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   required=True,

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   ),

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   FloatInput(

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   name="timeout",

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   display_name="Timeout",

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   value=5.0,

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   info="HTTP request timeout in seconds for each URL.",

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   range_spec={"min": 0.1, "max": 60.0, "step": 0.1, "step_type": "float"},

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   ),

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   ]

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   outputs = [

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   Output(

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   name="dataframe",

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   display_name="DataFrame",

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   method="build_dataframe",

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   ),

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   ]

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   URL_PATTERN = re.compile(r"https?://[^\s<>\]\"'`{}|\\^]+", re.IGNORECASE)

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   TITLE_PATTERN = re.compile(r"<title\b[^>]*>(.*?)</title>", re.IGNORECASE | re.DOTALL)

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   def build_dataframe(self) -> DataFrame:

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   """Build a DataFrame containing URL, extracted title, and request status."""

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   urls = self._extract_urls(self.text)

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   rows = [self._process_url(url, self.timeout) for url in urls]

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   return DataFrame(rows)

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   def _extract_urls(self, text: str) -> list[str]:

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   """Extract unique URLs from text using a conservative regex."""

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   if not text:

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   return []

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   matches = self.URL_PATTERN.findall(text)

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   cleaned_urls: list[str] = []

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   seen: set[str] = set()

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   for url in matches:

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   cleaned = url.rstrip(".,);:!?]}")

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   if cleaned and cleaned not in seen:

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   seen.add(cleaned)

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   cleaned_urls.append(cleaned)

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   return cleaned_urls

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   def _process_url(self, url: str, timeout: float) -> dict[str, Any]:

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   """Fetch a URL and return a row with url, title, and status."""

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   request = Request(

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   url,

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   headers={

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   "User-Agent": "Mozilla/5.0 (compatible; Langflow URLTitleExtractor/1.0)"

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   },

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   )

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   try:

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   with urlopen(request, timeout=float(timeout)) as response:

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   status_code = getattr(response, "status", 200)

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   content_bytes = response.read()

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   content_type = response.headers.get_content_charset() or "utf-8"

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   html_text = content_bytes.decode(content_type, errors="replace")

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   title = self._extract_title(html_text)

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   return {"url": url, "title": title, "status": str(status_code)}

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   except HTTPError as exc:

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   title = ""

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   try:

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   body = exc.read()

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   charset = exc.headers.get_content_charset() if exc.headers else None

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   if body:

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   html_text = body.decode(charset or "utf-8", errors="replace")

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   title = self._extract_title(html_text)

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   except Exception:

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   title = ""

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   return {"url": url, "title": title, "status": str(exc.code)}

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   except TimeoutError:

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   return {"url": url, "title": "", "status": "timeout"}

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   except URLError as exc:

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   reason = getattr(exc, "reason", None)

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   if isinstance(reason, TimeoutError):

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   status = "timeout"

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   else:

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   status = "request_error"

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   return {"url": url, "title": "", "status": status}

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   except Exception:

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   return {"url": url, "title": "", "status": "request_error"}

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   def _extract_title(self, html_text: str) -> str:

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   """Extract and normalize the HTML title from a document string."""

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   if not html_text:

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   return ""

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   match = self.TITLE_PATTERN.search(html_text)

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   if not match:

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   return ""

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   title = html.unescape(match.group(1))

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   title = re.sub(r"\s+", " ", title).strip()

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   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 with url, title, and status columns.
   • Chat Output receives URLTitleExtractor's result and displays the Table back to the user or calling application.

   

6. Open the Playground, and tell Langflow to check a list of URLs. For example:

   
   

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   Check these links: https://langflow.org

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   https://github.com/langflow-ai/langflow

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   https://python.org

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   https://this-domain-should-not-resolve-12345.invalid

   
   

7. Run the flow. The output will be similar to the following, with a 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:

   
   

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   Check these links: https://langflow.org

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   https://github.com/langflow-ai/langflow

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   https://python.org

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   https://langflow.org

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   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

Build an agent flow with Langflow Assistant

warning

Building a flow reads and writes the entire Langflow graph on every turn, which can use significantly more tokens than single-component generation.

In addition to generating individual components, Langflow Assistant can create a flow from a single prompt.

Continuing from the Create a custom component with Langflow Assistant example, ask Langflow Assistant to build a simple agent flow that uses the URLTitleExtractor custom component as a tool.

1. With the URLTitleExtractor component still on the canvas, open Langflow Assistant and prompt it to build the agent flow:

   
   

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   Build a simple agent flow using the URLTitleExtractor custom component as a tool.

   
   

2. Langflow Assistant presents a diagram of the flow it is proposing. To approve, click Add to Canvas.

   Langflow Assistant adds an Agent component, connects the URLTitleExtractor to its Tools input, and connects Chat Input and Chat Output to complete the flow. Flow generation is model-driven, so the exact wiring may differ from this example.

3. To iterate, describe changes to Langflow Assistant, and then click Replace canvas to replace the entire flow currently on the canvas. For example:

   
   

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   Connect the URLTitleExtractor component's Toolset port to the Agent's Tools port.

   
   

4. Open the Playground and ask the agent to check a URL:

   
   

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   What is the title of the page at https://langflow.org?

   
   

   The agent uses the URLTitleExtractor tool to fetch the page and return the title.

5. Langflow Assistant can further iterate on the flow, or answer questions about Langflow. For example, ask Langflow Assistant How do I send an API request to chat with this flow in Python? The response includes a request sourced from the Langflow documentation that you can copy and paste into your application.

Reference canvas components in your prompts

Reference components and their fields directly in a Langflow Assistant prompt using the @ and . selectors.

In the Langflow Assistant prompt input, enter @ to list all available components, and then select a component to insert its reference. To select a specific field within a component, enter . after the component.

For example, to prompt Langflow Assistant to update the system prompt field on an Agent component, enter the following:

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Update @Agent.System Prompt to always respond in bullet points.

See also

• Build flows
• Custom components
• Trigger flows with the Langflow API