AI-Powered Financial Analyst Chatbot
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An intelligent n8n workflow that powers an AI financial analyst chatbot. It leverages advanced LLMs, maintains conversational memory, and integrates with specialized tools for real-time market insights and technical analysis, all initiated via a simple chat message.
About This Workflow
This sophisticated n8n workflow creates a dynamic AI Financial Analyst chatbot designed to streamline financial research and market analysis. By combining LangChain AI agents with various powerful Large Language Models (LLMs) like Sonnet and Perplexity Sonar, it can process natural language queries, conduct technical analysis using TradingView, perform web searches, and even interact with email. The chatbot maintains conversational context with Window Buffer Memory, ensuring coherent and personalized interactions for detailed financial inquiries. It's an indispensable tool for traders, analysts, and financial professionals seeking instant, intelligent insights.
Key Features
- Interactive AI Chat Interface: Initiate complex financial queries through a user-friendly chat trigger.
- Contextual Conversation Memory: Utilizes Window Buffer Memory to maintain chat history, enabling multi-turn, coherent discussions.
- Multi-Model LLM Integration: Leverages diverse LLMs (Sonnet, Perplexity Sonar) for enhanced analytical capabilities and varied perspectives.
- Advanced Tooling for Finance: Integrates specialized tools for Technical Analysis (TradingView charts), web research (Perplexity Tool), and email communication (Gmail Tool).
- Automated Data Extraction & Analysis: Extracts analyst questions and management responses, and processes data from external sources like charts for comprehensive insights.
How To Use
- Set up Chat Trigger: Configure the "When chat message received" node to connect to your preferred chat platform (e.g., Slack, Telegram, custom webhook).
- Integrate LLM Credentials: Provide API keys for your chosen Large Language Models (e.g., OpenRouter for Sonnet, Perplexity) in the respective
lmChatOpenRouternodes. - Configure Memory & Agent: Ensure the "Window Buffer Memory" is correctly linked to the "Technical Analyst Leader" agent to maintain conversational context.
- Connect External Tools: Set up credentials and specific parameters for external services like TradingView (for
httpRequestnodes), Perplexity (forhttpRequestandtoolWorkflow), and Gmail. - Define Agent Capabilities: Customize the "Technical Analyst Leader" agent with specific prompts and instructions to guide its decision-making and tool usage for financial analysis.
- Test & Refine: Run test messages through the chat trigger and monitor the workflow execution to fine-tune responses and ensure accurate data retrieval and analysis.
Apps Used
Workflow JSON
{
"id": "d5c9c577-15e5-4bd2-bc8a-e353a5da9277",
"name": "AI-Powered Financial Analyst Chatbot",
"nodes": 7,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: d5c9c577-15e5...
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