An AI workflow chains several tools together so that the output of one becomes the input of the next. Instead of moving text by hand between a research tool, a writing tool and an image generator, the workflow runs the sequence for you and returns the finished result.
The workflows here are pre-built for jobs that genuinely take several steps: turning a topic into a researched article, converting a long video into a set of social posts, or taking a product description through copy, images and ad variants. Each one shows the steps it runs and which tools it uses before you start it.
Browse the available workflows and open the one matching your task. Each shows its steps and expected output up front.
Give the workflow its starting material — a topic, a URL, a document or a brief.
The workflow surfaces intermediate output as well as the final result, so you can see where a run went wrong and re-run just that stage.
A prompt is one call to one model. A workflow is a sequence of calls where each step is specialised and receives the previous step's output, which produces better results on multi-stage tasks than asking one model to do everything at once.
The pre-built workflows run a fixed sequence. If you need a different chain, the n8n templates library is designed for building custom automations.
Most complete in under a minute. Workflows that involve image generation or long documents take longer, because each stage waits for the previous one to finish.
Browsing workflows is open to everyone. Running one requires an account so that usage can be attributed and rate-limited fairly.
An autonomous agent that searches the web, scrapes articles, and synthesizes 10-page research reports on any topic.
Initialize a Python environment and install the necessary agentic frameworks.
Open Python / LangChainpip install langchain-anthropic langchain-community tavily-python python-dotenv
touch agent.py .envGive the agent the ability to search the web and scrape pages.
Open Tavilyfrom langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WebBaseLoader
search_tool = TavilySearchResults(max_results=5)
def scrape_webpage(url: str):
loader = WebBaseLoader(url)
return loader.load()[0].page_content
tools = [search_tool, scrape_webpage]Instruct the agent to think step-by-step and use its tools iteratively.
Open ClaudeYou are an expert Research Analyst.
Given a topic, you must:
1. Search the web for recent, highly credible sources.
2. Scrape the full content of the most relevant URLs.
3. Synthesize the findings into a comprehensive, deeply technical 10-page report using Markdown.
You have access to the following tools: {tool_names}
Use them wisely. Always cite your sources.