Jez
I'm passionate about combining traditional web development with AI and automation to create impactful online solutions. As Founder & CEO of Jezweb, an award-winning Newcastle, Australia agency, I lead a 30+ person team delivering high-quality web design, development, and hosting services to over 3000 clients.
Templates by Jez
Discover business leads with Gemini, Brave Search and web scraping
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Uncover new business leads with this AI-Powered Prospect Discovery Agent! This n8n workflow acts as a specialized intelligent assistant that, given a business type and location, uses multiple search strategies to identify a list of potential prospect companies and their websites. Stop manually trawling through search results! This agent automates the initial phase of lead generation by: Understanding your target business profile (type, location, keywords). Strategically using web search tools (Brave Search, Google Gemini Search) to find relevant businesses. Performing quick validations to confirm relevance. Returning a clean, structured JSON list of prospect names and their website URLs. How it Works: The workflow is built around an AI agent powered by Google Gemini. This agent is equipped with tools like: Brave Web Search: For broad initial sourcing of potential business candidates. Google Gemini Search: For advanced, context-aware discovery and finding businesses mentioned in various online sources. Brave Local Search (Selective): For quick verification of local presence or finding website URLs for identified names. Jina AI Web Page Scraper (Very Selective): For extremely rapid relevance checks on uncertain websites by scanning page content for keywords. The agent's system prompt guides it to use these tools efficiently to build a list of prospects without getting bogged down in deep research on any single one at this discovery stage. Use Cases: Lead Generation: Automatically generate lists of potential clients based on industry and location. Market Research: Identify key players or types of businesses in a specific geographical area. Sales Development: Provide SDRs with initial lists of companies to research further. Called as a Sub-Workflow: Designed to be easily integrated as a "tool" into more complex orchestrating AI agents (e.g., a BNI Pitch Planner that first needs to identify who to target). Setup: Import the workflow. Configure Credentials: You'll need n8n credentials for: Google Gemini (for the Chat model and the Gemini Search/Vertex AI Search tool). Brave Search (e.g., via Smithery MCP, or adapt if you have direct API access). Jina AI (for the web scraper). Assign these to the respective nodes. Review System Prompt: The prospectdiscoveryagent node contains a detailed system prompt. You can fine-tune this to adjust its search strategies or the strictness of its matching. Inputs: This workflow is triggered by an "Execute Workflow Trigger" node (prospectdiscoveryworkflow). It expects the following inputs: business_type (string): e.g., "artisan bakery" location_query (string): e.g., "Portland, Oregon" desirednumprospects (number): e.g., 5 additional_keywords (string, optional): e.g., "organic, gluten-free" To Use (as a Sub-Workflow/Tool): This workflow is typically called by another n8n workflow (e.g., using a "Tool Workflow" node from the Langchain nodes). The calling workflow would provide the inputs listed above. The "Prospect Discovery" workflow will then execute and its final node (the prospectdiscoveryagent) will output a JSON array of found prospects, like: json [ { "business_name": "Rose Petal Bakery", "website_url": "https://rosepetalbakerypdx.com" }, { "business_name": "The Daily Bread Artisans", "website_url": "https://dailybreadpdx.com" } ] If no prospects are found, it returns an empty array []. This template provides a powerful and focused tool for automating the initial stages of prospect identification.
By JezDocumentation Lookup AI Agent using Context7 and Gemini
This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n. This workflow demonstrates how to build and expose a sophisticated n8n AI Agent as a single, callable tool using the Multi-Agent Collaboration Protocol (MCP). It allows external clients or other AI systems to easily query software library documentation via Context7, without needing to manage the underlying tool orchestration or complex conversational logic. Core Idea: Instead of building complex agentic loops on the client-side (e.g., in Python, a VS Code extension, or another AI development environment), this workflow offloads the entire agent's reasoning and tool-use process to n8n. The client simply sends a natural language query (like "How do I use Flexbox in Tailwind CSS?") to an SSE endpoint, and the n8n agent handles the rest. Key Features & How It Works: Public MCP Endpoint: The main workflow uses the Context7 MCP Server Trigger node to create an SSE endpoint. This makes the agent accessible to any MCP-compatible client. The path for the endpoint is kept long and random for basic 'security by obscurity'. Tool Workflow as an Interface: A Tool Workflow node (named callcontext7ai_agent in this example) is connected to the MCP Server Trigger. This node defines the single "tool" that external clients will see and call. Dedicated AI Agent Sub-Workflow: The callcontext7ai_agent tool invokes a separate sub-workflow which contains the actual AI logic. This sub-workflow starts with a Context7 Workflow Start node to receive the user's query. A Context7 AI Agent node (using Google Gemini in this example) is the brain, equipped with: A system prompt to guide its behavior. Simple Memory to retain context for each execution (using {{ $execution.id }} as the session key). Two specialized Context7 MCP client tools: context7-resolve-library-id: To convert library names (e.g., 'Next.js') into Context7-specific IDs. context7-get-library-docs: To fetch documentation using the resolved ID, with options for specific topics and token limits. Seamless Tool Use: The AI Agent autonomously decides when and how to use the resolve-library-id and get-library-docs tools based on the user's query, handling the multi-step process internally. Benefits of This Approach: Simplified Client Integration: Clients interact with a single, powerful tool, sending a simple query. Reduced Client-Side Token Consumption: The detailed prompts, tool descriptions, and conversational turns are managed server-side by n8n, saving tokens on the client (especially useful if the client is another LLM). Centralized Agent Management: Update your agent's capabilities, tools, or LLM model within n8n without any changes needed on the client side. Modularity for Agentic Systems: Perfect for building complex, multi-agent systems where this n8n workflow can act as a specialized "expert" agent callable by others (e.g., from environments like Smithery). Cost-Effective: By using a potentially less expensive model (like Gemini Flash) for the agent's orchestration and leveraging the free tier or efficient pricing of services like Context7, you can build powerful solutions economically. Use Cases: Providing an intelligent documentation lookup service for coding assistants or IDE extensions. Creating specialized AI "micro-agents" that can be consumed by larger AI applications. Building internal knowledge base query systems accessible via a simple API-like interface. Setup: Ensure you have the necessary n8n credentials for Google Gemini (or your chosen LLM) and the Context7 MCP client tools. The Path in the Context7 MCP Server Trigger node should be unique and secure. Clients connect to the "Production URL" (SSE endpoint) provided by the trigger node. This workflow is a great example of how n8n can serve as a powerful backend for building and deploying modular AI agents. I've made a video to try and explain this a bit too https://www.youtube.com/watch?v=dudvmyp7Pyg
By JezProcess documents & build semantic search with OpenAI, Gemini & Qdrant
🎯 Overview This n8n workflow automates the process of ingesting documents from multiple sources (Google Drive and web forms) into a Qdrant vector database for semantic search capabilities. It handles batch processing, document analysis, embedding generation, and vector storage - all while maintaining proper error handling and execution tracking. 🚀 Key Features Dual Input Sources: Accepts files from both Google Drive folders and web form uploads Batch Processing: Processes files one at a time to prevent memory issues and ensure reliability AI-Powered Analysis: Uses Google Gemini to extract metadata and understand document context Vector Embeddings: Generates OpenAI embeddings for semantic search capabilities Automated Cleanup: Optionally deletes processed files from Google Drive (configurable) Loop Processing: Handles multiple files efficiently with Split In Batches nodes Interactive Chat Interface: Built-in chatbot for testing semantic search queries against indexed documents 📋 Use Cases Knowledge Base Creation: Build searchable document repositories for organizations Document Compliance: Process and index legal/regulatory documents (like Fair Work documents) Content Management: Automatically categorize and store uploaded documents Research Libraries: Create semantic search capabilities for research papers or reports Customer Support: Enable instant answers to policy and documentation questions via chat interface 🔧 Workflow Components Input Methods Google Drive Integration Monitors a specific folder for new files Processes existing files in batch mode Supports automatic file conversion to PDF Web Form Upload Public-facing form for document submission Accepts PDF, DOCX, DOC, and CSV files Processes multiple file uploads in a single submission Processing Pipeline File Splitting: Separates multiple uploads into individual items Document Analysis: Google Gemini extracts document understanding Text Extraction: Converts documents to plain text Embedding Generation: Creates vector embeddings via OpenAI Vector Storage: Inserts documents with embeddings into Qdrant Loop Control: Manages batch processing with proper state handling Key Nodes Split In Batches: Processes files one at a time with reset: false to maintain state Google Gemini: Analyzes documents for context and metadata Langchain Vector Store: Handles Qdrant insertion with embeddings HTTP Request: Direct API calls for custom operations Chat Interface: Interactive chatbot for testing vector search queries 🛠️ Technical Implementation Batch Processing Logic The workflow uses a clever looping mechanism: Split In Batches with batchSize: 1 ensures single-file processing reset: false maintains loop state across iterations Loop continues until all files are processed Error Handling All nodes include continueOnFail options where appropriate Execution logs are preserved for debugging File deletion only occurs after successful insertion Data Flow Form Upload → Split Files → Batch Loop → Analyze → Insert → Loop Back Google Drive → List Files → Batch Loop → Download → Analyze → Insert → Delete → Loop Back 📊 Performance Considerations Processing Time: ~20-30 seconds per file Batch Size: Set to 1 for reliability (configurable) Memory Usage: Optimized for files under 10MB API Costs: Uses OpenAI embeddings (text-embedding-3-large model) 🔐 Required Credentials Google Drive OAuth2: For file access and management OpenAI API: For embedding generation Qdrant API: For vector database operations Google Gemini API: For document analysis 💡 Implementation Tips Start Small: Test with a few files before processing large batches Monitor Costs: Track OpenAI API usage for embedding generation Backup First: Consider archiving instead of deleting processed files Check Collections: Ensure Qdrant collection exists before running 🎨 Customization Options Change Embedding Model: Switch to text-embedding-3-small for cost savings Adjust Chunk Size: Modify text splitting parameters for different document types Add Metadata: Extend the Gemini prompt to extract specific fields Archive vs Delete: Replace delete operation with move to "processed" folder 📈 Real-World Application This workflow was developed to process business documents and legal agreements, making them searchable through semantic queries. It's particularly useful for organizations dealing with large volumes of regulatory documentation that need to be quickly accessible and searchable. Chat Interface Testing The integrated chatbot interface allows users to: Query processed documents using natural language Test semantic search capabilities in real-time Verify document indexing and retrieval accuracy Ask questions about specific topics (e.g., "What are the pay rates for junior employees?") Get instant AI-powered responses based on the indexed content 🌟 Benefits Automation: Eliminates manual document processing Scalability: Handles individual files or bulk uploads Intelligence: AI-powered understanding of document content Flexibility: Multiple input sources and processing options Reliability: Robust error handling and state management 👨💻 About the Creator Jeremy Dawes is the CEO of Jezweb, specializing in AI and automation deployment solutions. This workflow represents practical, production-ready automation that solves real business challenges while maintaining simplicity and reliability. 📝 Notes The workflow intelligently handles the n8n form upload pattern where multiple files create a single item with multiple binary properties (Files0, Files1, etc.) The Split In Batches pattern with reset: false is crucial for proper loop execution Direct API integration provides more control than pure Langchain implementations 🔗 Resources Qdrant Documentation OpenAI Embeddings n8n Documentation Jezweb - AI & Automation Solutions --- This workflow demonstrates practical automation that bridges document management with modern AI capabilities, creating intelligent document processing systems that scale with your needs.
By JezAi-powered local event finder with multi-tool search
Summary This n8n workflow implements an AI-powered "Local Event Finder" agent. It takes user criteria (like event type, city, date, and interests), uses a suite of search tools (Brave Web Search, Brave Local Search, Google Gemini Search) and a web scraper (Jina AI) to find relevant events, and returns formatted details. The entire agent is exposed as a single, easy-to-use MCP (Multi-Capability Peer) tool, making it simple to integrate into other workflows or applications. This template cleverly combines the MCP server endpoint and the AI agent logic into a single n8n workflow file for ease of import and management. Key Features Intelligent Multi-Tool Search: Dynamically utilizes web search, precise local search, and advanced Gemini semantic search to find events. Detailed Information via Web Scraping: Employs Jina AI to extract comprehensive details directly from event web pages. Simplified MCP Tool Exposure: Makes the complex event-finding logic available as a single, callable tool for other MCP-compatible clients (e.g., Roo Code, Cline, other n8n workflows). Customizable AI Behavior: The core AI agent's behavior, tool usage strategy, and output formatting can be tailored by modifying its System Prompt. Modular Design: Uses distinct nodes for LLM, memory, and each external tool, allowing for easier modification or extension. Benefits Simplifies Client-Side Integration: Offloads the complexity of event searching and data extraction from client applications. Provides Richer Event Data: Goes beyond simple search links to extract and format key event details. Flexible & Adaptable: Can be adjusted to various event search needs and can incorporate new tools or data sources. Efficient Processing: Leverages specialized tools for different aspects of the search process. Nodes Used MCP Trigger Tool Workflow Execute Workflow Trigger AI Agent Google Gemini Chat Model (ChatGoogleGenerativeAI) Simple Memory (Window Buffer Memory) MCP Client (for Brave Search tools via Smithery) Google Gemini Search Tool Jina AI Tool Prerequisites An active n8n instance. Google AI API Key: For the Gemini LLM (Google Gemini Chat Model node) and the Google Gemini Search Tool. Ensure your key is enabled for these services. Jina AI API Key: For the jinaaiwebpagescraper node. A free tier is often available. Access to a Brave Search MCP Provider (Optional but Recommended): This template uses MCP Client nodes configured for Brave Search via a provider like Smithery. You'll need an account/API key for your chosen Brave Search MCP provider to configure the smithery brave search credential. Alternatively, you could adapt these to call Brave Search API directly if you manage your own access, or replace them with other search tools. Setup Instructions Import Workflow: Download the JSON file for this template and import it into your n8n instance. Configure Credentials: Google Gemini LLM: Locate the Google Gemini Chat Model node. Select or create a "Google Gemini API" credential (named Google Gemini Context7 in the template) using your Google AI API Key. Google Gemini Search Tool: Locate the googlegeminievent_search node. Select or create a "Gemini API" credential (named Gemini Credentials account in the template) using your Google AI API Key (ensure it's enabled for Search/Vertex AI). Jina AI Web Scraper: Locate the jinaaiwebpagescraper node. Select or create a "Jina AI API" credential (named Jina AI account in the template) using your Jina AI API Key. Brave Search (via MCP): You'll need an MCP Client HTTP API credential to connect to your Brave Search MCP provider (e.g., Smithery). Create a new "MCP Client HTTP API" credential in n8n. Name it, for example, smithery brave search. Configure it with the Base URL and any required authentication (e.g., API key in headers) for your Brave Search MCP provider. Locate the bravewebsearch and bravelocalsearch MCP Client nodes in the workflow. Assign the smithery brave search (or your named credential) to both of these nodes. Activate Workflow: Ensure the workflow is active. Note MCP Trigger Path: Locate the localeventfinder (MCP Trigger) node. The Path field (e.g., 0ca88864-ec0a-4c27-a7ec-e28c5a900697) combined with your n8n webhook base URL forms the endpoint for client calls. Example Endpoint: YOURN8NINSTANCE_URL/webhooks/PATH-TO-MCP-SERVER Customization AI Behavior: Modify the "System Message" parameter within the eventfinderagent node to change the AI's persona, its strategy for using tools, or the desired output format. LLM Model: Swap the Google Gemini Chat Model node with another compatible LLM node (e.g., OpenAI Chat Model) if desired. You'll need to adjust credentials and potentially the system prompt. Tools: Add, remove, or replace tool nodes (e.g., use a different search provider, add a weather API tool) and update the eventfinderagent's system prompt and tool configuration accordingly. Scraping Depth: Be mindful of the jinaaiwebpagescraper's usage due to potential timeouts. The system prompt already guides the LLM on this, but you can adjust its usage instructions.
By Jez