AI agent that can scrape webpages
n8n-workflow-2006.n8n.io
Β· n8n.io
!n8nReAct.webp βοΈπ οΈππ€π¦Ύ This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: - url β an address of the page to fetch - method = full / simplified - maxlimit - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: 1. An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: 2. Extract HTML BODY; content 3. Remove all unnecessary tags to recuce the page size 4. Further eliminate external URLs and IMG scr values (based on the method query parameter) 5. Remaining HTML is converted to Markdown
n8n-workflow-2006.n8n.io via a single DNS TXT record to add the
verified by owner badge, embed an Agenstry badge on your README, and earn back the missing conformance points listed below.
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C
Conformance score: 60/100
Catalogue listing, not an A2A agent: a workflow template listed by n8n.io. Scored on the 2 criteria that apply (9 of 15 points, scaled to 100); the A2A card criteria do not apply.
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Activity (audit trail)
last 24h Β· 0 invocations Public aggregate Β· no PII recordedNothing observed in the last 7 days β no invocations, no lookups, no listing impressions. Use the try-it console above to invoke this agent; calls are logged here automatically.
Endpoints
| Agent card | https://n8n.io/workflows/2006 |
| Provider | https://n8n.io |
| Docs | https://n8n.io/workflows/2006 |
Health Β· last 0 probes
Who's calling this agent 30d
1 interactions captured (impressions + lookups + A2A calls)
web
1
Public view: top 3 per dimension. Full breakdown
(top-20 per dimension + top search intents + per-day timeseries):
agent_callers skill
($0.05/call, $0 on
Enterprise).
Per-caller-identity drill-down stays private to the agent owner on the
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JSON snapshot for vendor-review files. Add ?sign=true for a JWS-signed envelope verifiable against
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Raw agent card JSON
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"url": "https://n8n.io/blog/how-a-membership-development-manager-automates-his-work-and-investments/",
"icon": "\ud83d\udcc8",
"label": "How a Membership Development Manager automates his work and investments"
},
{
"url": "https://n8n.io/blog/a-low-code-bitcoin-ticker-built-with-questdb-and-n8n-io/",
"icon": "\ud83d\udcc8",
"label": "A low-code bitcoin ticker built with QuestDB and n8n.io"
},
{
"url": "https://n8n.io/blog/how-to-set-up-a-ci-cd-pipeline-with-no-code/",
"icon": "\ud83c\udfa1",
"label": "How to set up a no-code CI/CD pipeline with GitHub and TravisCI"
},
{
"url": "https://n8n.io/blog/benefits-of-automation-and-n8n-an-interview-with-hubspots-hugh-durkin/",
"icon": "\ud83c\udf96",
"label": "Benefits of automation and n8n: An interview with HubSpot's Hugh Durkin"
},
{
"url": "https://n8n.io/blog/how-goomer-automated-their-operations-with-over-200-n8n-workflows/",
"icon": "\ud83d\udef5",
"label": "How Goomer automated their operations with over 200 n8n workflows"
},
{
"url": "https://n8n.io/blog/aws-workflow-automation/",
"label": "7 no-code workflow automations for Amazon Web Services"
}
],
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.set/"
}
]
},
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Data Transformation"
]
}
}
},
"group": "[\"input\"]",
"defaults": {
"name": "Edit Fields"
},
"iconData": {
"icon": "pen",
"type": "icon"
},
"displayName": "Edit Fields (Set)",
"typeVersion": 3,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 564,
"icon": "file:markdown.svg",
"name": "n8n-nodes-base.markdown",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.markdown/"
}
]
},
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Data Transformation"
]
}
}
},
"group": "[\"output\"]",
"defaults": {
"name": "Markdown"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iNDAiIGhlaWdodD0iNDAiIHZpZXdCb3g9IjAgMCA0MCA0MCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPHBhdGggZD0iTTM3LjExNjQgMzIuNjE4N0gyLjg4MzU3QzIuNTA0OSAzMi42MTg3IDIuMTI5OTMgMzIuNTQ0MSAxLjc4MDA4IDMyLjM5OTJDMS40MzAyMyAzMi4yNTQzIDEuMTEyMzQgMzIuMDQxOSAwLjg0NDU3OSAzMS43NzQxQzAuNTc2ODE1IDMxLjUwNjQgMC4zNjQ0MTIgMzEuMTg4NSAwLjIxOTQ5OSAzMC44Mzg2QzAuMDc0NTg1OCAzMC40ODg4IDAgMzAuMTEzOCAwIDI5LjczNTFWMTAuODgzNkMwIDEwLjExODggMC4zMDM4MDQgOS4zODUzNSAwLjg0NDU3OSA4Ljg0NDU4QzEuMzg1MzUgOC4zMDM4IDIuMTE4OCA4IDIuODgzNTcgOEgzNy4xMTY0QzM3LjQ5NTEgOCAzNy44NzAxIDguMDc0NTkgMzguMjE5OSA4LjIxOTVDMzguNTY5OCA4LjM2NDQxIDM4Ljg4NzcgOC41NzY4MSAzOS4xNTU0IDguODQ0NThDMzkuNDIzMiA5LjExMjM0IDM5LjYzNTYgOS40MzAyMyAzOS43ODA1IDkuNzgwMDhDMzkuOTI1NCAxMC4xMjk5IDQwIDEwLjUwNDkgNDAgMTAuODgzNlYyOS43MzE4QzQwLjAwMDIgMzAuMTEwNiAzOS45MjU4IDMwLjQ4NTggMzkuNzgxIDMwLjgzNThDMzkuNjM2MiAzMS4xODU5IDM5LjQyMzggMzEuNTAzOSAzOS4xNTYgMzEuNzcxOUMzOC44ODgyIDMyLjAzOTggMzguNTcwMyAzMi4yNTI0IDM4LjIyMDMgMzIuMzk3NEMzNy44NzAzIDMyLjU0MjQgMzcuNDk1MiAzMi42MTg3IDM3LjExNjQgMzIuNjE4N1pNOS42MTQxMyAyNi44NDgyVjE5LjM0NzZMMTMuNDYxMSAyNC4xNTYzTDE3LjMwNjQgMTkuMzQ3NlYyNi44NDgySDIxLjE1MzRWMTMuNzcyMUgxNy4zMDY0TDEzLjQ2MTEgMTguNTgwOUw5LjYxNDEzIDEzLjc3MjFINS43NjcxNVYyNi44NTE2TDkuNjE0MTMgMjYuODQ4MlpNMzUuMzg2MyAyMC4zMDk0SDMxLjUzOTNWMTMuNzcwNUgyNy42OTRWMjAuMzA5NEgyMy44NDdMMjkuNjE1OCAyNy4wNDE2TDM1LjM4NjMgMjAuMzA5NFoiIGZpbGw9IiMzODM4MzkiLz4KPC9zdmc+Cg=="
},
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"name": "Core Nodes"
}
]
},
{
"id": 565,
"icon": "fa:sticky-note",
"name": "n8n-nodes-base.stickyNote",
"codex": {
"data": {
"alias": [
"Comments",
"Notes",
"Sticky"
],
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Helpers"
]
}
}
},
"group": "[\"input\"]",
"defaults": {
"name": "Sticky Note",
"color": "#FFD233"
},
"iconData": {
"icon": "sticky-note",
"type": "icon"
},
"displayName": "Sticky Note",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 837,
"icon": "fa:sign-out-alt",
"name": "n8n-nodes-base.executeWorkflowTrigger",
"codex": {
"data": {
"resources": {
"generic": [],
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executeworkflowtrigger/"
}
]
},
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Helpers"
]
}
}
},
"group": "[\"trigger\"]",
"defaults": {
"name": "When Executed by Another Workflow",
"color": "#ff6d5a"
},
"iconData": {
"icon": "sign-out-alt",
"type": "icon"
},
"displayName": "Execute Workflow Trigger",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 1119,
"icon": "fa:robot",
"name": "@n8n/n8n-nodes-langchain.agent",
"codex": {
"data": {
"alias": [
"LangChain",
"Chat",
"Conversational",
"Plan and Execute",
"ReAct",
"Tools"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Agents",
"Root Nodes"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "AI Agent",
"color": "#404040"
},
"iconData": {
"icon": "robot",
"type": "icon"
},
"displayName": "AI Agent",
"typeVersion": 3,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1153,
"icon": "file:openAiLight.svg",
"name": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatopenai/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Language Models",
"Root Nodes"
],
"Language Models": [
"Chat Models (Recommended)"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "OpenAI Chat Model"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "OpenAI Chat Model",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1205,
"icon": "fa:network-wired",
"name": "@n8n/n8n-nodes-langchain.toolWorkflow",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolworkflow/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Tools"
],
"Tools": [
"Recommended Tools"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Call n8n Workflow Tool"
},
"iconData": {
"icon": "network-wired",
"type": "icon"
},
"displayName": "Call n8n Workflow Tool",
"typeVersion": 2,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1207,
"icon": "fa:comments",
"name": "@n8n/n8n-nodes-langchain.manualChatTrigger",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.chattrigger/"
}
]
},
"categories": [
"Core Nodes",
"Langchain"
],
"subcategories": {
"Core Nodes": [
"Other Trigger Nodes"
]
}
}
},
"group": "[\"trigger\"]",
"defaults": {
"name": "When chat message received",
"color": "#909298"
},
"iconData": {
"icon": "comments",
"type": "icon"
},
"displayName": "Manual Chat Trigger",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
},
{
"id": 26,
"name": "Langchain"
}
]
}
]
},
"detail": {
"description": "!n8nReAct.webp \u2699\ufe0f\ud83d\udee0\ufe0f\ud83d\ude80\ud83e\udd16\ud83e\uddbe This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: - url \u2013 an address of the page to fetch - method = full / simplified - maxlimit - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: 1. An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: 2. Extract HTML BODY; content 3. Remove all unnecessary tags to recuce the page size 4. Further eliminate external URLs and IMG scr values (based on the method query parameter) 5. Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure 6. The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: - You can isolate the HTTP Request part into a separate workflow. - Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later."
}
}