Workflow Templates

This guide will show you how to use a workflow as a reusable tool in n8n, such as integrating an AI Agent or other specialized processes into your workflows. By the end of this example, you'll have a simple, reusable workflow that can be easily plugged into larger projects, making your automations more efficient and scalable. With this approach, you can create reusable workflows like "Scrape a Page," "Search Brave," or "Generate an Image," which you can then call whenever needed. While n8n makes it easy to build these workflows from scratch, setting them up as reusable components saves time as your automations grow in complexity. Setup Add the "Execute Workflow Trigger" node Add the node(s) to perform the desired tasks in the workflow Add a final "Set" or "Edit Fields" node at the end to ensure all external workflows return a consistent output format Details In this example, the "Execute Workflow Trigger" expects input in the following JSON format: [ { "query": { "url": "https://en.wikipedia.org/wiki/some_info" } } ] Once your external workflow is ready, you can instruct the AI Agent to use this tool by connecting it to the external workflow. Set up the schema type to "Generate from JSON Example" using this structure: { "url": "URL_TO_GET" } Finally, ensure your external workflow includes a "Set" or "Edit Fields" node at the end to define the response format. This helps keep the outputs of your reusable workflows consistent and predictable.

This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs and the Pinecone vector database, it provides accurate and context-aware responses to your questions about how to use the GitHub API. You could adapt this to any OpenAPI specification for any public or private API, thus creating a documentation chatbout that anyone in your company can use. How it works: Data Ingestion: The workflow fetches the complete GitHub API OpenAPI 3 specification directly from the GitHub repository. Chunking and Embeddings: It splits the large API spec into smaller, manageable chunks. OpenAI's embedding models then generate vector embeddings for each chunk, capturing their semantic meaning. Vector Database Storage: These embeddings, along with the corresponding text chunks, are stored in a Pinecone vector database. Chat Interface and Query Processing: The workflow provides a simple chat interface. When you ask a question, it generates an embedding for your query using the same OpenAI model. Semantic Search and Retrieval: Pinecone is queried to find the most relevant text chunks from the API spec based on the query embedding. Response Generation: The retrieved chunks and your original question are fed to OpenAI's gpt-4o-mini LLM, which generates a concise, informative, and contextually relevant answer, including code snippets when applicable. Set up steps: Create accounts: You'll need accounts with OpenAI and Pinecone. API keys: Obtain API keys for both services. Configure credentials: In your n8n environment, configure credentials for OpenAI and Pinecone using your API keys. Import the workflow: Import this workflow into your n8n instance. Pinecone Index: Ensure you have a Pinecone index named "n8n-demo" or adjust the workflow accordingly. The workflow is set up to work with this index out of the box. Setup Time: Approximately 15-20 minutes. Why use this workflow? Learn RAG in Action: This is a practical, hands-on example of how to build a RAG-powered chatbot. Adaptable Template: Easily modify this workflow to create chatbots for other APIs or knowledge bases. n8n Made Easy: See how n8n simplifies complex integrations between data sources, vector databases, and LLMs.

Webhook | Paper Summarization Who is this for? This workflow is designed for researchers, students, and professionals who frequently read academic papers and need concise summaries. It is useful for anyone who wants to quickly extract key information from research papers hosted on arXiv. What problem is this workflow solving? Academic papers are often lengthy and complex, making it time-consuming to extract essential insights. This workflow automates the process of retrieving, processing, and summarizing research papers, allowing users to focus on key findings without manually reading the entire paper. What this workflow does This workflow extracts the content of an arXiv research paper, processes its abstract and main sections, and generates a structured summary. It provides a well-organized output containing the Abstract Overview, Introduction, Results, and Conclusion, ensuring that users receive critical information in a concise format. Setup Ensure you have n8n installed and configured. Import this workflow into your n8n instance. Configure an external trigger using the Webhook node to accept paper IDs. Test the workflow by providing an arXiv paper ID. (Optional) Modify the summarization model or output format according to your preferences. How to customize this workflow to your needs Adjust the HTTPRequest node to fetch papers from other sources beyond arXiv. Modify the Summarization Chain node to refine the summary output. Enhance the Reorganize Paper Summary step by integrating additional language models. Add an email or Slack notification step to receive summaries directly. Workflow Steps Webhook receives a request with an arXiv paper ID. Send an HTTP request using "Request to Paper Page" to fetch the HTML content of the paper. Extract the abstract and sections using "Extract Contents". Split out all sections using "Split out All Sections" to process individual paragraphs. Clean up text using "Remove useless links" to remove unnecessary elements. Summarize extracted content using "Summarization Chain". Aggregate summarized content using "Aggregate summarized content". Reorganize the paper summary into structured sections using "Reorganize Paper Summary". Extract key information using "Content Extractor" to classify data into Abstract Overview, Introduction, Results, and Conclusion. Respond to the webhook with the structured summary. Note: This workflow is designed for use with arXiv research papers but can be adapted to process papers from other sources.

Unlock AI-Driven Research with Jina AI (No API Key Needed!) Following the success of Open Deep Research 1.0, we are excited to introduce an improved and fully free version: AI-Powered Research with Jina AI Deep Search. This workflow leverages Jina AI’s Deep Search API, a free and powerful AI research tool that requires no API key. It automates querying, analyzing, and formatting research reports, making AI-driven research accessible to everyone. Key Features No API Keys Required** - Start researching instantly without setup hassle. Automated Deep Search* - Uses Jina AI to fetch *relevant and high-quality information**. Structured AI Reports** - Generates clear, well-formatted research documents in markdown. Flexible and Customizable* - Modify the workflow to fit *your specific research needs**. Ideal for Researchers, Writers & Students** - Speed up your research workflow. Use Cases This workflow is particularly useful for: Researchers** - Quickly gather and summarize academic papers, online sources, and deep web content. Writers & Journalists** - Automate background research for articles, essays, and investigative reports. Students & Educators** - Generate structured reports for assignments, literature reviews, or presentations. Content Creators** - Find reliable sources for blog posts, videos, or social media content. Data Analysts** - Retrieve contextual insights from various online sources for reports and analysis. How It Works The user submits a research query via chat. The workflow sends the query to Jina AI’s Deep Search API. The AI processes and generates a well-structured research report. A code node formats the response into clean markdown. The final output is a structured, easy-to-read AI-generated report. Pre-Conditions & Requirements An n8n instance (self-hosted or cloud). No API keys needed** – Jina AI Deep Search is completely free. Basic knowledge of n8n workflow automation is recommended for customization. Customization Options This workflow is fully modular, allowing users to: Modify the query prompt to refine the research focus. Adjust the report formatting to match personal or professional needs. Expand the workflow by adding additional AI tools or data sources. Integrate it with other workflows in n8n to enhance automation. Users are free to connect it with other workflows, add custom nodes, or tweak existing configurations. Getting Started Setup Time: Less than 5 minutes Import the workflow into n8n. Run the workflow and input a research topic. Receive a fully formatted AI-generated research report. Try It Now! Start your AI-powered research with Jina AI Deep Search today! Get the workflow on n8n.io

Who is this for? This workflow template is designed for AI enthusiasts, developers, and privacy-conscious users who want to leverage the power of local large language models (LLMs) without sending data to external services. It's particularly valuable for those running Ollama locally who want intelligent routing between different specialized models. What problem is this workflow solving? When working with multiple local LLMs, each with different strengths and capabilities, it can be challenging to manually select the right model for each specific task. This workflow automatically analyzes user prompts and routes them to the most appropriate specialized Ollama model, ensuring optimal performance without requiring technical knowledge from the end user. What this workflow does This intelligent router: Analyzes incoming user prompts to determine the nature of the request Automatically selects the optimal Ollama model from your local collection based on task requirements Routes requests between specialized models for different tasks: Text-only models (qwq, llama3.2, phi4) for various reasoning and conversation tasks Code-specific models (qwen2.5-coder) for programming assistance Vision-capable models (granite3.2-vision, llama3.2-vision) for image analysis Maintains conversation memory for consistent interactions Processes everything locally for complete privacy and data security Setup Ensure you have Ollama installed and running locally Pull the required models mentioned in the workflow using Ollama CLI (e.g., ollama pull phi4) Configure the Ollama API credentials in n8n (default: http://127.0.0.1:11434) Activate the workflow and start interacting through the chat interface How to customize this workflow to your needs Add or remove models from the router's decision framework based on your specific Ollama collection Adjust the system prompts in the LLM Router to prioritize different model selection criteria Modify the decision tree logic to better suit your specific use cases Add additional preprocessing steps for specialized inputs This workflow demonstrates how n8n can be used to create sophisticated AI orchestration systems that respect user privacy by keeping everything local while still providing intelligent model selection capabilities.

Instantly access real-time decentralized exchange (DEX) insights directly in Telegram! This workflow integrates the DexScreener API with GPT-4o-powered AI and Telegram, allowing users to fetch the latest blockchain token analytics, liquidity pools, and trending tokens effortlessly. Ideal for crypto traders, DeFi analysts, and investors who need actionable market data at their fingertips. How It Works A Telegram bot listens for user queries about tokens or trading pairs. The workflow interacts with the DexScreener API (no API key required) to fetch real-time data, including: Token fundamentals (profiles, images, descriptions, and links) Trending and boosted tokens (hyped projects, potential market movers) Trading pair analytics (liquidity, price action, volumes, volatility) Order and payment activity (transaction insights, investor movements) Liquidity pool depth (market stability, capital flows) Multi-chain pair comparisons (performance tracking across networks) An AI-powered language model (GPT-4o-mini) enhances responses for better insights. The workflow logs session data to improve user interaction tracking. The requested DEX insights are sent back via Telegram in an easy-to-read format. What You Can Do with This Agent This AI-driven Telegram bot enables you to: ✅ Track trending and boosted tokens before they gain mainstream traction. ✅ Monitor real-time liquidity pools to assess token stability. ✅ Analyze active trading pairs across different blockchains. ✅ Identify transaction trends by checking paid orders for tokens. ✅ Compare market activity with detailed trading pair analysis. ✅ Receive instant insights with AI-enhanced responses for deeper understanding. Set Up Steps Create a Telegram Bot Use @BotFather on Telegram to create a bot and obtain an API token. Configure Telegram API Credentials in n8n Add your Telegram bot token under Telegram API credentials. Deploy and Test Send a query (e.g., "SOL/USDC") to your Telegram bot and receive real-time insights instantly! 🚀 Unlock powerful, real-time DEX insights directly in Telegram—no API key required! 📺 Setup Video Tutorial Watch the full setup guide on YouTube:

Find OAuth URIs with AI Llama Overview: The AI agent identifies: Authorization URI Token URI Audience Methodology: Confidence scoring is utilized to assess the trustworthiness of extracted data: Score Range: 0 < x ≤ 1 Score Granularity: 0.01 increments Model Details: Leveraging the Wayfarer Large 70b Llama 3.3 model. How it works: This template is designed to assist users in obtaining OAuth2 settings using AI-powered insights. It is ideal for developers, IT professionals, or anyone working with APIs that require OAuth2 authentication. By leveraging the AI agent, users can simplify the process of extracting and validating key details such as the authorization_url, token_url, and audience. Set up instructions: 1. Configuration Nodes Structured Output Node**: Parses the AI model's output using a predefined JSON schema. This ensures the data is structured for downstream processing. Code Node**: If the AI model’s output does not match the required format, use the Code node to re-arrange and transform the data. Example code snippets are provided below for common scenarios. 2. AI Model Prompt The prompt for the AI model includes: A detailed structure and objectives of the query. Flexibility for the model to improvise when accurate results cannot be determined. 3. Confidence Scoring The AI model assigns a confidence score (0 < x ≤ 1) to indicate the reliability of the extracted data. Scores are provided in increments of 0.01 for granularity. Adaptability Customize this template: Update the AI model prompt with details specific to your API or OAuth2 setup. Adjust the JSON schema in the Structured Output node to match the data format. Modify the Code logic to suit the application's requirements.

Description This workflow empowers you to effortlessly get answers to your n8n platform questions through an AI-powered assistant. Simply send your query, and the assistant will search documentation, forum posts, and example workflows to provide comprehensive, accurate responses tailored to your specific needs. > Note: This workflow uses community nodes (n8n-nodes-mcp.mcpClientTool) and will only work on self-hosted n8n instances. You'll need to install the required community nodes before importing this workflow. ! What does this workflow do? This workflow streamlines the information retrieval process by automatically researching n8n platform documentation, community forums, and example workflows, providing you with relevant answers to your questions. Who is this for? New n8n Users**: Quickly get answers to basic platform questions and learn how to use n8n effectively Experienced Developers**: Find solutions to specific technical issues or discover advanced workflows Teams**: Boost productivity by automating the research process for n8n platform questions Anyone** looking to leverage AI for efficient and accurate n8n platform knowledge retrieval Benefits Effortless Research**: Automate the research process across n8n documentation, forum posts, and example workflows AI-Powered Intelligence**: Leverage the power of LLMs to understand context and generate helpful responses Increased Efficiency**: Save time and resources by automating the research process Quick Solutions**: Get immediate answers to your n8n platform questions Enhanced Learning**: Discover new workflows, features, and best practices to improve your n8n experience How It Works Receive Request: The workflow starts when a chat message is received containing your n8n-related question AI Processing: The AI agent powered by OpenAI GPT-4o analyzes your question Research and Information Gathering: The system searches across multiple sources: Official n8n documentation for general knowledge and how-to guides Community forums for bug reports and specific issues Example workflow repository for relevant implementations Response Generation: The AI agent compiles the research and generates a clear, comprehensive answer Output: The workflow provides you with the relevant information and step-by-step guidance when applicable n8n Nodes Used When chat message received (Chat Trigger) OpenAI Chat Model (GPT-4o mini) N8N AI Agent n8n-assistant tools (MCP Client Tool - Community Node) n8n-assistant execute (MCP Client Tool - Community Node) Prerequisites Self-hosted n8n instance OpenAI API credentials MCP client community node installed MCP server configured to search n8n resources Setup Import the workflow JSON into your n8n instance Configure the OpenAI credentials Configure your MCP client API credentials In the n8n-assistant execute node, ensure the parameter is set to "specific" (corrected from "spesific") Test the workflow by sending a message with an n8n-related question MCP Server Connection To connect to the MCP server that powers this assistant's research capabilities, you need to use the following URL: https://smithery.ai/server/@onurpolat05/n8n-assistant This MCP server is specifically designed to search across three types of n8n resources: Official documentation for general platform information and workflow creation guidance Community forums for bug-related issues and troubleshooting Example workflow repositories for reference implementations Configure this URL in your MCP client credentials to enable the assistant to retrieve relevant information based on user queries. This workflow combines the convenience of chat with the power of AI to provide a seamless n8n platform research experience. Start getting instant answers to your n8n questions today!

Analyze exchange data, market indexes, and community sentiment from CoinMarketCap—powered by AI. This sub-agent provides access to exchange listings, token holdings, metadata, and high-level metrics like the CMC 100 Index and the Fear & Greed Index. It’s designed for use within your larger CoinMarketCap AI Analyst system or as a standalone workflow. This agent can be triggered by a supervisor or manually used with message and sessionId inputs. Supported Tools (5 Total) 🔍 Exchange Map Get CoinMarketCap IDs, names, and slugs for exchanges (used as lookup before deeper queries). 🧾 Exchange Info Metadata including launch date, social links, country, and operational status. 💰 Exchange Assets Token balances, wallet addresses, and total USD value held by a specific exchange. 📈 CoinMarketCap 100 Index Constituents and weights of the CMC 100 Index, updated live. 😱 Fear & Greed Index Market sentiment score updated daily, ranging from Extreme Fear to Extreme Greed. What You Can Do with This Agent 🔹 Map exchanges to retrieve their ID and slug 🔹 Analyze exchange holdings by token and blockchain 🔹 Pull metadata for major CEXs like Binance or Coinbase 🔹 Compare global sentiment using the Fear & Greed Index 🔹 Access index data to understand CMC’s top 100 crypto asset breakdown Example Queries You Can Use ✅ "What is the latest Fear and Greed Index reading?" ✅ "Get a list of all exchanges on CoinMarketCap." ✅ "What tokens are held by Binance?" ✅ "Retrieve metadata for Coinbase." ✅ "Show me the top assets in the CMC 100 Index." Agent Architecture AI Brain**: GPT-4o-mini Memory**: Window buffer memory using sessionId Tools**: 5 API-connected nodes Trigger**: External input via message and sessionId Setup Instructions Get a CoinMarketCap API Key Apply here: https://coinmarketcap.com/api/ Configure n8n Credentials Use HTTP Header Auth to store your CoinMarketCap API key. Optional: Trigger from a Supervisor Connect to a parent agent using Execute Workflow with message and sessionId inputs. Test Sample Prompts “Get all exchanges”, “Fetch CMC index”, “Show Binance token holdings” Sticky Notes Included Exchange & Community Guide – Explains agent purpose and component connections Usage & Examples – Walkthrough for sample use cases Error Handling & Licensing – Includes API error code reference and licensing details ✅ Final Notes This agent is part of a broader CoinMarketCap AI Analyst System. Visit my Creator profile to download all available sub-agents and supervisor flows. Understand exchange behavior and community sentiment—automated with AI and CoinMarketCap.

Meet your AI-powered crypto data analyst—fully integrated with CoinMarketCap APIs. This workflow acts as the supervisor agent for a multi-agent architecture built in n8n, connecting three powerful sub-agents to extract real-time insights from centralized and decentralized markets. It’s the ultimate tool for crypto traders, analysts, developers, and researchers who need strategic multi-source intelligence—all through Telegram. This workflow requires 3 sub-agent templates to function correctly. See below. 🔌 Required Sub-Workflows (Install First) CoinMarketCap Crypto Agent Tool → Token prices, metadata, conversions, listings CoinMarketCap Exchange & Community Agent Tool → Exchange info, token holdings, Fear & Greed index CoinMarketCap DEXScan Agent Tool → DEX trading pairs, liquidity, OHLCV data Download all from my Creator Profile: https://n8n.io/creators/don-the-gem-dealer/ What Makes This Workflow Special? This is not just another API wrapper—it’s an intelligent routing agent powered by GPT-4o-mini, capable of: Understanding complex user queries Choosing the appropriate tool workflow Structuring the API request Executing sub-workflows Formatting the output Returning insights via Telegram It connects three domains of market data: Cryptocurrencies (CEX)** Exchanges & Sentiment** DEX trading data** 🔍 What You Can Do 💰 Token Intelligence Get token metadata, price, volume, supply Compare rankings and conversions 🏦 Exchange Insights View assets held by exchanges Track the CMC 100 Index and Fear & Greed Score 🌐 DEX Market Analysis Analyze pair quotes, historical OHLCV, live trades Discover the top DEXs by volume across blockchains ✅ Example Questions to Ask “What’s the market cap of Ethereum today?” “Show liquidity and volume for SOL/USDT on Solana” “Get token holdings for Binance” “Compare BTC price on Uniswap vs Binance” “What’s the Fear & Greed index right now?” 🛠️ Setup Instructions Create Telegram Bot Use @BotFather to get your bot token. Get CoinMarketCap API Key Apply here: https://coinmarketcap.com/api/ Install Sub-Agent Templates Required: Crypto Agent Tool Exchange & Community Tool DEXScan Tool Configure Credentials in n8n Add both Telegram and CoinMarketCap keys as HTTP Header Auth. Deploy & Test Ask your Telegram bot: “Top 10 tokens by 24h volume” or “Convert 5 ETH to USD” Workflow Architecture AI Brain**: GPT-4o-mini Memory**: Windowed buffer memory via sessionId Tool Agents**: toolWorkflow() → routes requests to the appropriate sub-agent Executes real-time API queries and returns structured output Included Sticky Notes System Overview** Error Handling Guide (200, 400, 401, 429, 500)** Step-by-Step Usage Instructions** Prompt Examples + API Docs** Legal & Licensing Notes** Your crypto insights—smarter, faster, and all in one Telegram message.

Access real-time cryptocurrency prices, market rankings, metadata, and global stats—powered by GPT-4o and CoinMarketCap! This modular AI-powered agent is part of a broader CoinMarketCap multi-agent system designed for crypto analysts, traders, and developers. It uses the CoinMarketCap API and intelligently routes queries to the correct tool using AI. This agent can be used standalone or triggered by a supervisor AI agent for multi-agent orchestration. Supported API Tools (6 Total) This agent intelligently selects from the following tools to answer your crypto-related questions: 🔍 Tool Summary Crypto Map – Lookup CoinMarketCap IDs and active coins Crypto Info – Get metadata, whitepapers, and social links Crypto Listings – Ranked coins by market cap CoinMarketCap Price – Live prices, volume, and supply Global Metrics – Total market cap, BTC dominance Price Conversion – Convert between crypto and fiat What You Can Do with This Agent 🔹 Get live prices and volume for tokens (e.g., BTC, ETH, SOL) 🔹 Convert crypto → fiat or fiat → crypto instantly 🔹 Retrieve whitepapers, logos, and website links for any token 🔹 Analyze total market cap, BTC dominance, and circulating supply 🔹 Discover new tokens and track their CoinMarketCap IDs 🔹 View the top 100 coins ranked by market cap or volume Example Queries ✅ "What is the CoinMarketCap ID for PEPE?" ✅ "Show me the top 10 cryptocurrencies by market cap." ✅ "Convert 5 ETH to USD." ✅ "What’s the 24h volume for ADA?" ✅ "Get the global market cap and BTC dominance." AI Architecture AI Brain**: GPT-4o-mini Memory**: Session buffer with sessionId Agent Type**: Subworkflow AI tool Connected APIs**: 6 CoinMarketCap endpoints Trigger Mode**: Executes when called by a supervisor (via message and sessionId inputs) Setup Instructions Get a CoinMarketCap API Key Register here: https://coinmarketcap.com/api/ Configure Credentials in n8n Use HTTP Header Auth with your API key for each connected endpoint Connect This Agent to a Supervisor Workflow (Optional) Trigger this agent using Execute Workflow with inputs message and sessionId Test Prompts Try asking: “Convert 1000 DOGE to BTC” or “Top 5 coins in EUR” Included Sticky Notes Crypto Agent Guide – Agent overview, node map, and endpoint details Usage Instructions – Step-by-step usage and sample prompts Error Handling & Licensing – Troubleshooting and IP rights ✅ Final Notes This agent is part of the CoinMarketCap AI Analyst System, which includes multiple specialized agents for cryptocurrencies, exchanges, community data, and DEX insights. Visit my Creator profile to find the full suite of tools. Get smarter about crypto—analyze the market in real time with AI and CoinMarketCap.

Gain full visibility into decentralized exchanges using CoinMarketCap’s DEXScan API—powered by AI. This workflow is part of the CoinMarketCap AI Analyst system and delivers real-time and historical insights on spot trading pairs, DEX liquidity, trading activity, and OHLCV data across chains like Ethereum, Polygon, Solana, and more. Use this workflow as a sub-agent triggered by a parent supervisor workflow, or run it manually with inputs sessionId and message. 🔧 Supported Tools (8 Total) DEX Metadata → Static info (name, launch date, logo, URLs) DEX Networks List → All supported DEX chains + network metadata DEX Listings Quotes → Ranked list of DEXs with live trading volume, market share DEX Pair Quotes (Latest) → Real-time liquidity, price, and buy/sell stats DEX OHLCV Historical → Time-series data (daily/hourly/1m) DEX OHLCV Latest → Today’s price, volume, open/close for pairs DEX Trades Latest → Up to 100 recent trades for any DEX pair DEX Spot Pairs Latest → Active token pairs across DEXs + filters (volume, liquidity, volatility) Agent Architecture AI Model**: gpt-4o-mini Context Memory**: Window buffer using sessionId Trigger Input**: message, sessionId Execution**: Via Execute Workflow or parent AI supervisor Design**: Tool-based LangChain agent with CMC DEXScan endpoints 💡 Use Cases 🔹 Find top DEXs by 24h volume 🔹 Get spot pairs with highest liquidity on a specific network 🔹 Track historical OHLCV for Uniswap pairs 🔹 View latest trades for SOL/USDC pool 🔹 Analyze tax, pooled % and holders for specific pairs 🔹 Filter pairs by 24h volume, percent change, liquidity, or number of transactions ✅ Example Queries ✅ "Top 5 DEXs by 24h volume on Ethereum" ✅ "Get historical OHLCV for SOL-USDC on Solana" ✅ "Latest trades for a PancakeSwap pair" ✅ "Show all spot pairs with over $500K in liquidity on Polygon" ✅ "Retrieve metadata for Uniswap and SushiSwap" 🛠️ Setup Instructions Get a CoinMarketCap API Key Sign up at: https://coinmarketcap.com/api/ Add API Key to Credentials in n8n Use HTTP Header Auth method Trigger from Parent Workflow (Optional) Use Execute Workflow and pass message and sessionId Test Prompt Ideas Try: "Compare liquidity of Uniswap and Curve pairs on Ethereum" Sticky Notes Included DEXScan Agent Guide – Workflow architecture + supported tools Usage & API Call Examples – Prompts, test inputs, setup flow Error Codes + Licensing – 400/401/429/500 troubleshooting, IP rights ✅ Final Notes This agent is part of the CoinMarketCap AI Analyst System, which includes multiple specialized agents for cryptocurrencies, exchanges, and community data. Visit my Creator profile to find the full suite of tools. Master DEX analytics with AI—get powerful liquidity, trading, and pair insights in seconds.