This workflow follows the Agent → Chainllm recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "IyhH1KHtXidKNSIA",
"name": "\ud83d\udc0bDeepSeek V3 Chat & R1 Reasoning Quick Start",
"tags": [],
"nodes": [
{
"id": "54c59cae-fbd0-4f0d-b633-6304e6c66d89",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-840,
-740
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "ef85680e-569f-4e74-a1b4-aae9923a0dcb",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"onError": "continueErrorOutput",
"position": [
-320,
40
],
"parameters": {
"agent": "conversationalAgent",
"options": {
"systemMessage": "You are a helpful assistant."
}
},
"retryOnFail": true,
"typeVersion": 1.7,
"alwaysOutputData": true
},
{
"id": "07a8c74c-768e-4b38-854f-251f2fe5b7bf",
"name": "DeepSeek",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-360,
220
],
"parameters": {
"model": "=deepseek-reasoner",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "a6d58a8c-2d16-4c91-adde-acac98868150",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
-220,
220
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "401a5932-9f3e-4b17-a531-3a19a6a7788a",
"name": "Basic LLM Chain2",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-320,
-800
],
"parameters": {
"messages": {
"messageValues": [
{
"message": "You are a helpful assistant."
}
]
}
},
"typeVersion": 1.5
},
{
"id": "215dda87-faf7-4206-bbc3-b6a6b1eb98de",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-440,
-460
],
"parameters": {
"color": 5,
"width": 420,
"height": 340,
"content": "## DeepSeek using HTTP Request\n### DeepSeek Reasoner R1\nhttps://api-docs.deepseek.com/\nRaw Body"
},
"typeVersion": 1
},
{
"id": "6457c0f7-ad02-4ad3-a4a0-9a7a6e8f0f7f",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-440,
-900
],
"parameters": {
"color": 4,
"width": 580,
"height": 400,
"content": "## DeepSeek with Ollama Local Model"
},
"typeVersion": 1
},
{
"id": "2ac8b41f-b27d-4074-abcc-430a8f5928e8",
"name": "Ollama DeepSeek",
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"position": [
-320,
-640
],
"parameters": {
"model": "deepseek-r1:14b",
"options": {
"format": "default",
"numCtx": 16384,
"temperature": 0.6
}
},
"credentials": {
"ollamaApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "37a94fc0-eff3-4226-8633-fb170e5dcff2",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-440,
-80
],
"parameters": {
"color": 3,
"width": 600,
"height": 460,
"content": "## DeepSeek Conversational Agent w/Memory\n"
},
"typeVersion": 1
},
{
"id": "52b484bb-1693-4188-ba55-643c40f10dfc",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
20,
-460
],
"parameters": {
"color": 6,
"width": 420,
"height": 340,
"content": "## DeepSeek using HTTP Request\n### DeepSeek Chat V3\nhttps://api-docs.deepseek.com/\nJSON Body"
},
"typeVersion": 1
},
{
"id": "ec46acef-60f6-4d34-b636-3654125f5897",
"name": "DeepSeek JSON Body",
"type": "n8n-nodes-base.httpRequest",
"position": [
160,
-320
],
"parameters": {
"url": "https://api.deepseek.com/chat/completions",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"deepseek-chat\",\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"{{ $json.chatInput }}\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Hello!\"\n }\n ],\n \"stream\": false\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth"
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "e5295120-57f9-4e02-8b73-f00e4d6baa48",
"name": "DeepSeek Raw Body",
"type": "n8n-nodes-base.httpRequest",
"position": [
-300,
-320
],
"parameters": {
"url": "https://api.deepseek.com/chat/completions",
"body": "={\n \"model\": \"deepseek-reasoner\",\n \"messages\": [\n {\"role\": \"user\", \"content\": \"{{ $json.chatInput.trim() }}\"}\n ],\n \"stream\": false\n }",
"method": "POST",
"options": {},
"sendBody": true,
"contentType": "raw",
"authentication": "genericCredentialType",
"rawContentType": "application/json",
"genericAuthType": "httpHeaderAuth"
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "571dc713-ce54-4330-8bdd-94e057ecd223",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1060,
-460
],
"parameters": {
"color": 7,
"width": 580,
"height": 840,
"content": "# Your First DeepSeek API Call\n\nThe DeepSeek API uses an API format compatible with OpenAI. By modifying the configuration, you can use the OpenAI SDK or softwares compatible with the OpenAI API to access the DeepSeek API.\n\nhttps://api-docs.deepseek.com/\n\n## Configuration Parameters\n\n| Parameter | Value |\n|-----------|--------|\n| base_url | https://api.deepseek.com |\n| api_key | https://platform.deepseek.com/api_keys |\n\n\n\n## Important Notes\n\n- To be compatible with OpenAI, you can also use `https://api.deepseek.com/v1` as the base_url. Note that the v1 here has NO relationship with the model's version.\n\n- The deepseek-chat model has been upgraded to DeepSeek-V3. The API remains unchanged. You can invoke DeepSeek-V3 by specifying `model='deepseek-chat'`.\n\n- deepseek-reasoner is the latest reasoning model, DeepSeek-R1, released by DeepSeek. You can invoke DeepSeek-R1 by specifying `model='deepseek-reasoner'`."
},
"typeVersion": 1
},
{
"id": "f0ac3f32-218e-4488-b67f-7b7f7e8be130",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1060,
-900
],
"parameters": {
"color": 2,
"width": 580,
"height": 400,
"content": "## Four Examples for Connecting to DeepSeek\nhttps://api-docs.deepseek.com/\nhttps://platform.deepseek.com/api_keys"
},
"typeVersion": 1
},
{
"id": "91642d68-ab5d-4f61-abaf-8cb7cb991c29",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-180,
-640
],
"parameters": {
"color": 7,
"width": 300,
"height": 120,
"content": "### Ollama Local\nhttps://ollama.com/\nhttps://ollama.com/library/deepseek-r1"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "e354040e-7898-4ff9-91a2-b6d36030dac8",
"connections": {
"AI Agent": {
"main": [
[]
]
},
"DeepSeek": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Ollama DeepSeek": {
"ai_languageModel": [
[
{
"node": "Basic LLM Chain2",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Basic LLM Chain2",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
httpHeaderAuthollamaApiopenAiApi
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How this works
This quick-start workflow enables seamless interaction with DeepSeek V3's advanced chat and R1 reasoning capabilities, delivering intelligent responses to your queries without the hassle of manual API setups. It's ideal for developers, researchers, or AI enthusiasts eager to prototype conversational agents that leverage cutting-edge language models for tasks like problem-solving or creative brainstorming. The core step involves the AI Agent node routing inputs through the lmChatOpenAi integration for DeepSeek processing, enhanced by memoryBufferWindow to maintain context across conversations, ensuring coherent and context-aware replies.
Use this workflow when you need a lightweight, ready-to-run setup for experimenting with DeepSeek's reasoning in real-time chat scenarios, such as building a simple Q&A bot. Avoid it for production-scale applications requiring custom authentication or heavy data processing, as it's optimised for quick testing rather than robust error handling. Common variations include swapping lmChatOpenAi for lmChatOllama to run models locally, or extending the chainLlm node for multi-step reasoning chains in more complex queries.
About this workflow
🐋DeepSeek V3 Chat & R1 Reasoning Quick Start. Uses chatTrigger, agent, lmChatOpenAi, memoryBufferWindow. Chat trigger; 15 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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