Research Assistant Agents

This n8n workflow template creates an intelligent data analysis chatbot that can answer questions about data stored in Google Sheets using OpenAI's GPT-5 Mini model. The system automatically analyzes your spreadsheet data and provides insights through natural language conversations. What This Workflow Does Chat Interface**: Provides a conversational interface for asking questions about your data Smart Data Analysis**: Uses AI to understand column structures and data relationships Google Sheets Integration**: Connects directly to your Google Sheets data Memory Buffer**: Maintains conversation context for follow-up questions Automated Column Detection**: Automatically identifies and describes your data columns 🚀 Try It Out! 1. Set Up OpenAI Connection Get Your API Key Visit the OpenAI API Keys page. Go to OpenAI Billing. Add funds to your billing account. Copy your API key into your OpenAI credentials in n8n (or your chosen platform). 2. Prepare Your Google Sheet Connect Your Data in Google Sheets Data must follow this format: Sample Marketing Data First row** contains column names. Data should be in rows 2–100. Log in using OAuth, then select your workbook and sheet. 3. Ask Questions of Your Data You can ask natural language questions to analyze your marketing data, such as: Total spend** across all campaigns. Spend for Paid Search only**. Month-over-month changes** in ad spend. Top-performing campaigns** by conversion rate. Cost per lead** for each channel. 📬 Need Help or Want to Customize This? 📧 [email protected] 🔗 LinkedIn 🔗 n8n Automation Experts

🤖🥗 Telegram Nutrition AI Assistant (Alternative to Cal AI App) > AI-powered nutrition assistant for Telegram — log meals, set goals, and get personalized daily reports with Google Sheets integration. 📋 Description This n8n template creates a Telegram-based Nutrition AI Assistant 🥑🔥 designed as an open-source alternative to the Cal AI mobile app. It allows users to interact with an AI agent via text, voice, or images to track meals, calculate macros, and monitor nutrition goals directly from Telegram. The system integrates Google Sheets as the database, handling both user profiles and meal logs, while leveraging Gemini AI for natural conversation, food recognition, and daily progress reports. ✨ Key Features 💬 Multi-input support: Text, voice messages (transcribed), and food images (AI analysis). 📊 Macro calculation: Automatic estimation of calories, proteins, carbs, and fats. 📝 User-friendly registration: Simple onboarding without storing personal health data (no weight/height required). 🎯 Goal tracking: Users can set and update calorie and protein targets. 📈 Daily reports: Personalized progress messages with visual progress bars. 🗂 Google Sheets integration: Profile table for user targets. Meals table for food logs. 🔄 Advanced n8n nodes: Includes use of Merge, Subworkflow, and Code nodes for data processing and report generation. 💡 Acknowledgment Inspired by the Cal AI concept 💡 — this template demonstrates how to reproduce its main functionality with n8n, Telegram, and AI agents as a flexible, open-source automation workflow. 🏷 Tags telegram ai-assistant nutrition meal-tracking google-sheets food-logging voice-transcription image-analysis daily-reports n8n-template merge-node subworkflow-node code-node telegram-trigger google-gemini 💼 Use Case Use this template if you want to: 🥗 Log meals using text, images, or voice messages. 📊 Track nutrition goals (calories, proteins) with daily progress updates. 🤖 Provide a chat-based nutrition assistant without building a full app. 🗂 Store structured nutrition data in Google Sheets for easy access and analysis. 💬 Example User Interactions 📸 User sends a photo of a meal → AI analyzes the food and logs calories/macros. 🎤 User sends a voice message → AI transcribes and logs the meal. ⌨️ User types “report” → AI returns a daily nutrition summary with progress bars. 🥅 User says “update my protein goal” → AI updates profile in Google Sheets. 🔑 Required Credentials Telegram Bot API (Bot Token) Google Sheets API credentials AI Provider API (Google Gemini or compatible LLM) ⚙️ Setup Instructions 🗂 Create two Google Sheets tables: Profile: User_ID, Name, Calories_target, Protein_target Meals: User_ID, Date, Meal_description, Calories, Proteins, Carbs, Fats 🔌 Configure the Telegram Trigger with your bot token. 🤖 Connect your AI provider credentials (Gemini recommended). 📑 Connect Google Sheets with your credentials. ▶️ Deploy the workflow in n8n. 🎯 Start interacting with your nutrition assistant via Telegram. 📌 Extra Notes 🟩 Green section: Handles Telegram trigger and user check. 🟥 Red section: Registers new users and sets goals. 🟦 Blue section: Processes text, voice, and images. 🟨 Yellow section: Generates nutrition reports. 🟪 Purple section: Main AI agent controlling tools and logic. 💡 Need Assistance? If you’d like help customizing or extending this workflow, feel free to reach out: 📧 Email: [email protected] 🔗 LinkedIn: John Alejandro Silva Rodríguez

This n8n template automates crypto market analysis by combining multi-timeframe candlestick data with real-time news sentiment to generate actionable AI-backed Buy / Sell / Hold signals for any cryptocurrency. Built around the Kaizen principle of continuous improvement, it delivers sharper, more reliable insights with every run. Use cases Automatically analyze crypto market trends using 15m, 1h, and 1d candlestick data. Aggregate global crypto news sentiment to reinforce price-action confidence. Generate AI-powered Buy / Sell / Hold recommendations for traders. Build your own Telegram-based crypto trading assistant or analytics bot. Create backends for crypto dashboards, portfolio advisors, or auto-alert systems. Expand trading strategies using AI reasoning instead of manual analysis. Good to know This workflow merges both technical analysis (candlesticks) and fundamental sentiment (news), then passes them through an AI model (Gemini) to produce clean, easy-to-understand trading signals. It works seamlessly on n8n Cloud and self-hosted setups, with configuration taking about 5 minutes. Requirements n8n Cloud or self-hosted instance Crypto price data API key Crypto news API key AI model API key (Google Gemini) Telegram Bot Token (via @BotFather) Customising this workflow Replace Telegram with Slack, Discord, WhatsApp Cloud API, or Notion for alerts. Switch AI models (Gemini, OpenAI, Claude) to change tone or reasoning style. Modify candlestick intervals (15m, 1h, 1d) or add more timeframes. Attach TradingView webhooks for live alerts or automated triggers. Add portfolio tracking, price-level alerts, or risk scoring for advanced users. Expand the template to support forex, stocks, indices, or commodities with minimal edits.

This n8n template automates intraday trading insights by combining candlestick pattern analysis and news sentiment aggregation to suggest actionable Buy / Sell / Hold decisions across international stock markets. Use cases Automatically analyze stock trends using live candlestick data. Aggregate real-time news sentiment to strengthen trading confidence. Generate AI-backed Buy / Sell / Hold recommendations for traders. Build an automated Telegram trading assistant or analytics bot. Create a backend for AI-powered portfolio advisors or trading dashboards. Good to know This workflow integrates both market and news APIs, processes data intelligently, and leverages an Gemini AI for trading recommendations. It runs smoothly on both n8n Cloud and self-hosted instances, and setup typically takes 10–15 minutes. Requirements n8n Cloud or self-hosted instance TwelveData API key (for fetching OHLC & candlestick data) → twelvedata.com NewsAPI.org key (for aggregating relevant stock news) → newsapi.org AI model API key (Google Gemini or OpenAI) for sentiment reasoning Telegram Bot Token (via @BotFather) for command input & output Customising this workflow Replace Telegram with Slack, Discord, or Notion for alternate alerts. Integrate TradingView or Alpaca API for executing mock trades. Modify the candlestick intervals (1m, 15m, 1h) or patterns as per strategy. Add portfolio tracking or alert thresholds for advanced users. Expand to cover crypto, forex, or commodities with minimal edits.

Setup & Instructions — fluidX: Create Session, Analyze & Notify Goal: This workflow demonstrates the full fluidX THE EYE integration — starting a live session, inviting both the customer (via SMS) and the service agent (via email), and then accessing the media (photos and videos) created during the session. Captured images are automatically analyzed with AI, uploaded to an external storage (such as Google Drive), and a media summary for the session is generated at the end. The agent receives an email with a link to join the live session. The customer receives an SMS with a link to start sharing their camera. Once both are connected, the agent can view the live feed, and the system automatically stores uploaded images and videos in Google Drive. When the session ends, the workflow collects all media and creates a complete AI-powered session summary (stored and updated in Google Drive). Below is an example screenshot from the customer’s phone: Prerequisites Developer account:* https://live.fluidx.digital (activate the *TEST plan**, €0) API docs (Swagger):** fluidX.digital API 🔐 Required Credentials 1️⃣ fluidX API key (HTTP Header Auth) • Credential name in n8n: fluidx API key • Header name: x-api-key • Header value: YOUR_API_KEY 2️⃣ SMTP account (for outbound email) • Credential name in n8n: SMTP account • Configure host, port, username, and password according to your provider • Enable TLS/SSL as required 3️⃣ Google Drive account • Used to store photos, videos, and automatically update the session summary files. 4️⃣ OpenAI API (for AI analysis & summary) •Used in the Analyze Images (AI) and Generate Summary parts of the workflow. • Credential type: OpenAI • Credential name (suggested): OpenAI account • API Key: your OpenAI API key • Model: e.g. gpt-4.1, gpt-4o, or similar (choose in the OpenAI node settings) ⚙️ Configuration (in the “Set Config” node) BASE_URL: https://live.fluidx.digital company / project / billingcode / sku: adjust as needed emailAgent: set before running (empty in template) phoneNumberUser: set before running (empty in template) Flow Overview Form Trigger → Create Session → Set Session Vars → Send SMS (User) → Send Email (Agent) → Monitor Media → Analyze Images (AI) → Upload Files to Google Drive → Generate Summary → Update Summary File The workflow starts automatically when a Form submission is received. Users enter the customer’s phone number and agent’s email, and the system creates a new fluidX THE EYE session. As media is uploaded during the session, the workflow automatically retrieves, stores, analyzes, and summarizes it — providing a complete end-to-end automation example for remote inspection, support, or field-service use cases. Notes Do not store real personal data inside the template. Manage API keys and secrets via n8n Credentials or environment variables. Log out of https://live.fluidx.digital in the agent’s browser before testing, to ensure a clean invite flow and session creation.

Production-ready multi-agent AI system for financial signal analysis. Demonstrates CrewAI orchestration, RAG with ChromaDB, MLOps pipeline, FastAPI, Docker, and Kubernetes deployment. Full-stack portfolio project showcasing real-world AI system architecture.

Autonomous Multi-Agent Arbitrage Engine powered by CrewAI and Llama 3.3 (Groq). Features a 4-agent sequential workflow for real-time procurement research, TCO analysis, negotiation scripting, and market-exit ROI strategy. Built with Streamlit for rapid enterprise deployment.

An autonomous multi-agent system built with CrewAI and Gemini 2.0 Flash to automate financial market research. Features specialized agents for real-time data analysis and technical report writing.

A financial analysis framework utilizing crewAI to orchestrate multi-agent systems. Demonstrates sequential, parallel, and hierarchical task delegation among autonomous AI agents.Features specialized LLM agents collaborating to perform deep market research and financial analysis. Features specialized LLM agents collaborating to perform deep mar

Open‑Box Deals Aggregator is a Dify tool powered by the TinyFish Web Agent. It searches multiple major retailers for open‑box, refurbished, and clearance deals in real time, then returns **clean, structured results** (e.g., product name, price, condition, and URL) that you can use inside your Dify workflows. Use it to: - Compare deals across retailers quickly - Build price‑tracking or deal‑alert workflows - Power shopping, sourcing, or research assistants with agent‑ready outputs

Build a smart chatbot that answers questions based on your own knowledge base.

Upload a file and get a clean and organized summary.

DeepResearch uses loop variables and agent nodes to iteratively explore the web, identify knowledge gaps, and synthesize findings into a structured report. The workflow leverages tools like Tavily Search to collect relevant web results and Deepseek Reasoner to analyze and summarize them, delivering comprehensive answers to complex questions.

搜索主题，反复执行搜索并生成完整报告。

Get a clean, AI-powered summary of the latest technology stories from The New York Times.

Paste a company website URL and this workflow will generate a basic structured market research report.

Author：Tianwei Wu A daily digest of the top-xranked Hugging Face research papers, written for non-researchers. Each paper is explained in plain English, highlighting its core idea, its real contribution, and who it is actually useful for. The goal is to help you quickly decide which of today’s most important papers are worth reading, without jargon, hype, or unnecessary detail.

Just enter what you want to search, and it will repeatedly perform searches and create reports.

Basic Workflow Template. A chatbot with a knowledge base.

I am a YouTube Channel Data Analysis Copilot, I am here to provide expert data analysis tailored to your needs.

This workflow extracts and analyzes the balance sheet, income statement, and cash flow statement from PDF financial reports. It uses TextIn document parsing to structure the document, sends only table previews to an LLM for table ID selection, then extracts the selected tables and generates a financial analysis report.

Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial Click here to view the YouTube Tutorial How it works Build a chatbot that answers based on documents you provide it (Retrieval Augmented Generation). You can upload as many PDF files as you want to the Qdrant database. The chatbot will use its retrieval tool to fetch the chunks and use them to answer questions. Installation Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. How to use it First run the "Data Ingestion" part and upload as many PDF files as you want Run the Chatbot and start asking questions about the documents you uploaded

How it works This template is a complete, hands-on tutorial that lets you build and interact with your very first AI Agent. Think of an AI Agent as a standard AI chatbot with superpowers. The agent doesn't just talk; it can use tools to perform actions and find information in real-time. This workflow is designed to show you exactly how that works. The Chat Interface (Chat Trigger): This is your window to the agent. It's a fully styled, public-facing chat window where you can have a conversation. The Brain (AI Agent Node): This is the core of the operation. It takes your message, understands your intent, and intelligently decides which "superpower" (or tool) it needs to use to answer your request. The agent's personality and instructions are defined in its extensive system prompt. The Tools (Tool Nodes): These are the agent's superpowers. We've included a variety of useful and fun tools to showcase its capabilities: Get a random joke. Search Wikipedia for a summary of any topic. Calculate a future date. Generate a secure password. Calculate a monthly loan payment. Fetch the latest articles from the n8n blog. The Memory (Memory Node): This gives the agent a short-term memory, allowing it to remember the last few messages in your conversation for better context. When you send a message, the agent's brain analyzes it, picks the right tool for the job, executes it, and then formulates a helpful response based on the tool's output. Set up steps Setup time: ~3 minutes This template is nearly ready to go out of the box. You just need to provide the AI's "brain." Configure Credentials: This workflow requires an API key for an AI model. Make sure you have credentials set up in your n8n instance for either Google AI (Gemini) or OpenAI. Choose Your AI Brain (LLM): By default, the workflow uses the Google Gemini node. If you have Google AI credentials, you're all set! If you prefer to use OpenAI, simply disable the Gemini node and enable the OpenAI node. You only need one active LLM node. Make sure it is connected to the Agent parent node. Explore the Tools: Take a moment to look at the different tool nodes connected to the Your First AI Agent node. This is where the agent gets its abilities! You can add, remove, or modify these to create your own custom agent. Activate and Test! Activate the workflow. Open the public URL for the Example Chat Window node (you can copy it from the node's panel). Start chatting! Try asking it things like: "Tell me a joke." "What is n8n?" "Generate a 16-character password for me." "What are the latest posts on the n8n blog?" "What is the monthly payment for a $300,000 loan at 5% interest over 30 years?"

This workflow generates an automated information digest. A scheduled trigger runs at a predefined time and uses Google Search to retrieve relevant content from the web. The retrieved results are analyzed and summarized by a language model, which extracts key insights and formats them into a concise report. The final digest is then delivered via email. This workflow is suitable for monitoring industry trends, tracking specific topics, or creating automated daily briefings without manual searching.

This workflow acts as an automated cybersecurity analyst. It triggers upon receiving an incident report via webhook, classifies the type of threat, performs a real-time web search for the latest intelligence on the identified malware or vulnerability, and uses an LLM to generate a comprehensive technical report with mitigation recommendations. This tool is ideal for SOC teams needing to rapidly analyze incoming security alerts without manual research.

How It Works This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, the official n8n documentation—and then build a chatbot to ask it questions. Think of it like this: instead of a general-knowledge AI, you're building an expert librarian. 🔧 Workflow Overview The workflow is split into two main parts: Part 1: Indexing the Knowledge (📚 Building the Library) This is a one-time process you run manually. The workflow will: Automatically scrape all pages of the n8n documentation. Break them down into small, digestible chunks. Use an AI model to create a numerical representation (an embedding) for each chunk. Store these embeddings in n8n's built-in Simple Vector Store. > This is like a librarian reading every book and creating a hyper-detailed index card for every paragraph. > ⚠️ Important: This in-memory knowledge base is temporary. It will be erased if you restart your n8n instance. You'll need to run the indexing process again in that case. Part 2: The AI Agent (🧠 The Expert Librarian) This is the chat interface. When you ask a question: The AI agent doesn't guess the answer. It searches the knowledge base to find the most relevant “index cards” (chunks). It feeds those chunks to a language model (Gemini) with strict instructions: > “Answer the user's question using ONLY this information.” This ensures answers are accurate, factual, and grounded in your documents. 🚀 Setup Steps > Total setup time: ~2 minutes > Indexing time: ~15–20 minutes This template uses n8n’s built-in tools, so no external database is needed. 1. Configure OpenAI Credentials You’ll need an OpenAI API key (for GPT models). In your n8n workflow: Go to any of the three OpenAI nodes (e.g., OpenAI Chat Model). Click the Credential dropdown → + Create New Credential. Enter your OpenAI API key and save. 2. Apply Credentials to All Nodes Your new credential is now saved. Go to the other two OpenAI nodes (e.g., OpenAI Embeddings) and select the newly created credential from the dropdown. 3. Build the Knowledge Base Find the Start Indexing manual trigger node (top-left of the workflow). Click the Execute Workflow button to start indexing. > ⚠️ Be patient: This takes 15–20 minutes to scrape and process the full documentation. > You only need to do this once per n8n session. 4. Chat With Your Expert Agent After indexing completes, activate the entire workflow (toggle at the top). Open the RAG Chatbot chat trigger node (bottom-left). Copy its Public URL. Open it in a new tab and ask questions about n8n! Example questions: "How does the IF node work?" "What is a sub-workflow?" 👤 Credits All credits go to Lucas Peyrin 🔗 lucaspeyrin on n8n.io

🐍 ☁️ - Enables autonomous data exploration on .csv-based datasets, providing intelligent insights with minimal effort.

📇 🏠 🍎 🪟 🐧 - Agent Skill Ninja for MCP: Search, install, and manage AI agent skills (SKILL.md files) from GitHub repositories. Features workspace analysis for personalized recommendations and supports 140+ pre-indexed skills.

Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.

Automatically update Clawdbot and all installed skills once daily. Runs via cron, checks for updates, applies them, and messages the user with a summary of what changed.

Ad intelligence and app analytics assistant for searching ad creatives, analyzing apps, rankings, downloads, revenue, and market insights. Use for 广告素材, 竞品分析...

Analyze stocks and cryptocurrencies using Yahoo Finance data. Supports portfolio management, watchlists with alerts, dividend analysis, 8-dimension stock scoring, viral trend detection (Hot Scanner), and rumor/early signal detection. Use for stock analysis, portfolio tracking, earnings reactions, crypto monitoring, trending stocks, or finding rumors before they hit mainstream.

🤖 Discover and connect with similar companies using the CrewAI Anthropic Similar Company Finder, a powerful tool for enhancing business insights.

Multi-agent writing style clone using CrewAI with style analysis, RAG, and quality evaluation

CrewAI multi-agent research assistant with FastAPI, Streamlit, Redis memory, Tavily search, and markdown reports

Multi agent financial analysis system using CrewAI, FastAPI, and GPT-4o-mini. Features technical analysis and adversarial risk auditing.