This article provides a complete, practical walkthrough of the Http Stickynote Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   **Title**  
   Getting Started with n8n and DeepSeek: Building an AI Chatbot with Reasoning and Memory
   
   **Meta Description**  
   Learn how to set up a powerful conversational AI using n8n, DeepSeek’s chat and reasoning models, and Ollama’s local LLM support. This hands-on guide walks through an efficient workflow connecting APIs and memory to build a smart chatbot.
   
   **Keywords**  
   n8n workflow, DeepSeek Reasoner, DeepSeek Chat V3, Ollama DeepSeek R1, LLM with memory, conversational AI, local language models, AI chat bot integration, Langchain agent, OpenAI-compatible API, n8n deepseek integration
   
   **Third-Party APIs and Services Used**  
   
   1. DeepSeek API (https://api.deepseek.com)
   2. Ollama (https://ollama.com)
   3. Langchain (via n8n nodes)
   4. OpenAI-compatible REST endpoints through DeepSeek
   5. HTTP Header Authentication (via n8n credential system)
   
   ---
   
   **Article:**
   
   # Getting Started with n8n and DeepSeek: Building an AI Chatbot with Reasoning and Memory
   
   As AI-driven tools become more accessible and modular, building advanced automation workflows that include intelligent agents, LLMs, and user interaction becomes easier. This article breaks down a production-ready n8n workflow to set up an interactive chatbot using DeepSeek and Ollama—a combination that enables serverless reasoning, local language models, and persistent memory.
   
   Let’s explore this step-by-step guide and see how each piece plays a role in delivering an intelligent assistant through seamless integrations.
   
   ## Overview of the Workflow
   
   This workflow, created in n8n, is titled **“🐋DeepSeek V3 Chat & R1 Reasoning Quick Start”**—appropriately named for its versatility in connecting to both DeepSeek's remote and Ollama’s local inference engines.
   
   At its core, the workflow does the following:
   
   - Listens for a new chat message.
   - Routes it to different LLMs depending on configuration.
   - Supports both DeepSeek’s cloud models and Ollama for local model inference.
   - Maintains conversational memory.
   - Returns intelligent responses based on the input query.
   
   Let’s break the workflow down into its essential parts.
   
   ## 1. Chat Message Trigger
   
   The automation kicks off with a node titled **“When chat message received”**, which acts as a webhook trigger in real-time. It listens for webhook data (e.g., messages from a chatbot front end) and parses it into a standard session format.
   
   The input structure allows for personalized sessions (through a session ID) and thoughtful prompts (through `chatInput`).
   
   ✉️ Example Input:
   
   ```json
   {
     "action": "sendMessage",
     "chatInput": "provide 10 sentences that end in the word apple.",
     "sessionId": "68cb82d504c14f5eb80bdf2478bd39bb"
   }
   ```
   
   ## 2. Language Models and Chains
   
   Incoming messages are routed into a basic Language Model chain, called **Basic LLM Chain2**, which sets a system message — “You are a helpful assistant.” This sets context for the downstream model to stay helpful and focused.
   
   ### Supported Model Paths
   
   - 🌐 **Cloud Reasoning via DeepSeek Reasoner (R1):**
     - Model: `deepseek-reasoner`
     - Triggered by: DeepSeek Raw HTTP Request
     - Known for: Complex logical reasoning tasks and step-by-step answers
   
   - ☁️ **Chat Capabilities via DeepSeek Chat V3:**
     - Model: `deepseek-chat`
     - Accessed through: DeepSeek JSON-formatted HTTP node
     - Known for: General conversational prompts and chat-style tasks
   
   - 🖥️ **Local Reasoning via Ollama DeepSeek R1:**
     - Model: `deepseek-r1:14b`
     - Triggered via: Langchain Ollama integration in n8n
     - Best used for: On-device processing and offline environments
   
   ## 3. AI Agent with Memory Buffer
   
   To enhance conversational continuity, the workflow integrates a **Langchain AI Agent** connected with **Window Buffer Memory**. This setup ensures that the chatbot can:
   
   - Remember previous messages for a session.
   - Offer contextually relevant replies.
   - Handle multi-turn conversations like a human assistant.
   
   Whether you’re troubleshooting, engaging in deep Q&A, or storytelling, the memory integration boosts personalization significantly.
   
   ## 4. DeepSeek HTTP Integrations
   
   Two types of HTTP request nodes are used for connection to the DeepSeek API:
   
   ### a. Raw Body (Reasoning)
   A direct connection to "https://api.deepseek.com/chat/completions" using raw JSON allows you to initiate prompts with the DeepSeek Reasoner model. It's optimized for tasks requiring deeper logic capability.
   
   ### b. JSON Body (Chat)
   This variant allows interaction with DeepSeek V3 using JSON payloads. It's suitable for fast, human-like chat responses and integrates seamlessly with tools mimicking OpenAI Chat format.
   
   📌 Note: DeepSeek follows OpenAI-compatible API conventions, so tools like OpenAI SDKs can be used with just a base URL swap (`https://api.deepseek.com`). Also, specifying `deepseek-chat` seamlessly harnesses the latest V3 upgrade.
   
   ## 5. Sticky Note Documentation
   
   The creator of this workflow thoughtfully included color-coded sticky notes within n8n to serve as:
   
   - Documentation for setting up DeepSeek and Ollama.
   - Links to endpoints, models, and authentication methods.
   - Summary of four different connection techniques to DeepSeek Learning models.
   
   ## Why Use Ollama?
   
   Ollama empowers users to run large language models locally. This means you don’t have to:
   
   - Rely on API limits or subscriptions.
   - Send data over the internet (good for privacy).
   - Wait for latency-prone requests from remote servers.
   
   In this workflow, Ollama is configured to run a DeepSeek-compatible model (`deepseek-r1:14b`) with flexible options like context window and temperature control.
   
   ## Final Thoughts
   
   This n8n workflow elegantly merges the power of cloud-based AI from DeepSeek with local language models via Ollama. Combined with Langchain’s agent interface and memory persistence, it provides a solid foundation for anyone looking to build custom AI chatbots, reasoning tools, automation assistants, and more—whether for development or customer onboarding.
   
   If you're exploring how to bring intelligent conversation to your applications without overwhelming complexity, this setup offers everything you need to get started.
   
   🎯 Pro Tip: Use this as a boilerplate to add more functionality—integrate databases, voice support, Slack connectors, or CRM tools to supercharge this intelligent agent.
   
   ---
   
   Happy automating!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.