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AI-Powered Crypto Analysis Using OpenRouter, Gemini, and SerpAPI
https://n8nworkflows.xyz/workflows/ai-powered-crypto-analysis-using-openrouter--gemini--and-serpapi-2906
# AI-Powered Crypto Analysis Using OpenRouter, Gemini, and SerpAPI
### 1. Workflow Overview
This workflow automates cryptocurrency market analysis by leveraging AI-powered agents and real-time data sources. It is designed to receive a user-input crypto symbol, fetch corresponding candlestick charts at daily and 5-minute intervals, analyze these charts using advanced AI models, and enrich the analysis with the latest crypto news. The final insights combine technical and fundamental analysis, delivered through a chat interface.
The workflow is logically divided into the following blocks:
- **1.1 Input Reception:** Captures user input of a cryptocurrency symbol in a strict format.
- **1.2 Data Retrieval:** Fetches daily and 5-minute candlestick charts for the specified crypto symbol.
- **1.3 AI Analysis:** Uses two AI agents powered by Google Gemini 2.0 Flash via OpenRouter to analyze the charts at different timeframes.
- **1.4 Memory Buffer:** Stores intermediate AI analysis results to maintain context between agents.
- **1.5 Fundamental Data Retrieval:** Obtains the latest cryptocurrency news via SerpAPI to supplement technical analysis.
- **1.6 Output Delivery:** Presents the combined analysis and news insights back to the user in a chat window.
---
### 2. Block-by-Block Analysis
#### 2.1 Input Reception
- **Overview:**
This block captures the cryptocurrency symbol input from the user in the format EXCHANGE:SYMBOL (e.g., BINANCE:BTCUSDT), ensuring the workflow targets the correct market data.
- **Nodes Involved:**
- Provide Crypto Symbol
- crypto_symbol (Set Node)
- **Node Details:**
- **Provide Crypto Symbol**
- Type: Chat Trigger (LangChain Chat Trigger)
- Role: Entry point that waits for user input of the crypto symbol.
- Configuration: Default chat trigger settings; expects user to input symbol in required format.
- Inputs: Webhook trigger from chat interface
- Outputs: Passes data to `crypto_symbol` node
- Edge Cases: Input format errors (missing colon, invalid exchange or symbol), no input received
- Version: 1.1
- **crypto_symbol**
- Type: Set Node
- Role: Stores and formats the cryptocurrency symbol for downstream HTTP requests.
- Configuration: Extracts and sets the symbol value from the chat trigger output.
- Inputs: From `Provide Crypto Symbol`
- Outputs: Passes formatted symbol to `1d_Chart` node
- Edge Cases: Empty or malformed symbol values causing API request failures
- Version: 3.4
---
#### 2.2 Data Retrieval
- **Overview:**
This block fetches candlestick chart images for the specified cryptocurrency at two timeframes: daily (1d) and 5-minute (5m) intervals, providing both macro and micro market perspectives.
- **Nodes Involved:**
- 1d_Chart (HTTP Request)
- 5m_Chart (HTTP Request)
- **Node Details:**
- **1d_Chart**
- Type: HTTP Request
- Role: Retrieves the daily candlestick chart image for the crypto symbol.
- Configuration: Uses Chart Img API with the symbol parameter from `crypto_symbol`.
- Inputs: From `crypto_symbol`
- Outputs: Passes chart image data to `Long Term Agent`
- Edge Cases: API rate limits, invalid symbol causing 404 or empty response, network timeouts
- Version: 4.2
- Notes: "Get day candlestick chart."
- **5m_Chart**
- Type: HTTP Request
- Role: Retrieves the 5-minute interval candlestick chart image for the crypto symbol.
- Configuration: Uses Chart Img API similarly, but with 5-minute interval parameter.
- Inputs: From `Long Term Agent` (after daily analysis)
- Outputs: Passes chart image data to `AI Agent`
- Edge Cases: Same as `1d_Chart`
- Version: 4.2
- Notes: "Get 5 min candlestick chart."
---
#### 2.3 AI Analysis
- **Overview:**
Two AI agents analyze the candlestick charts: the first agent interprets the daily chart for long-term trends, and the second agent analyzes the 5-minute chart in context with the daily analysis for short-term predictions.
- **Nodes Involved:**
- Long Term Agent (LangChain Agent)
- AI Agent (LangChain Agent)
- OpenRouter Chat Model (Language Model)
- **Node Details:**
- **OpenRouter Chat Model**
- Type: Language Model (LangChain OpenRouter Chat)
- Role: Provides the AI language model interface using Google Gemini 2.0 Flash via OpenRouter.
- Configuration: Connected as the language model for both AI agents. Requires OpenRouter API credentials.
- Inputs: None directly; used as a resource by agents
- Outputs: Feeds AI responses to agents
- Edge Cases: API key invalid or expired, daily usage limits reached, network errors
- Version: 1
- **Long Term Agent**
- Type: LangChain Agent
- Role: Analyzes the daily candlestick chart to detect long-term market trends and signals.
- Configuration: Uses OpenRouter Chat Model as language model and Window Buffer Memory for context.
- Inputs: Receives daily chart image from `1d_Chart`
- Outputs: Passes control and data to `5m_Chart` for next step
- Edge Cases: AI model misinterpretation, memory buffer failures, API errors
- Version: 1.7
- **AI Agent**
- Type: LangChain Agent
- Role: Performs advanced analysis combining 5-minute chart data with long-term analysis to generate refined market predictions.
- Configuration: Uses OpenRouter Chat Model and Window Buffer Memory for context sharing.
- Inputs: Receives 5-minute chart from `5m_Chart` and memory from `Window Buffer Memory`
- Outputs: Passes analysis results to `SerpAPI` for news enrichment
- Edge Cases: Same as Long Term Agent, plus potential data synchronization issues
- Version: 1.7
---
#### 2.4 Memory Buffer
- **Overview:**
This block temporarily stores intermediate AI analysis results, enabling context sharing between the Long Term Agent and AI Agent for coherent multi-step reasoning.
- **Nodes Involved:**
- Window Buffer Memory (LangChain Memory Buffer Window)
- **Node Details:**
- **Window Buffer Memory**
- Type: Memory Buffer (LangChain Memory Buffer Window)
- Role: Stores conversation and analysis context between AI agents.
- Configuration: Connected as memory source for both `Long Term Agent` and `AI Agent`.
- Inputs: Receives AI outputs from agents
- Outputs: Provides memory context back to agents
- Edge Cases: Memory overflow, data corruption, synchronization delays
- Version: 1.3
---
#### 2.5 Fundamental Data Retrieval
- **Overview:**
This block fetches the latest cryptocurrency news using SerpAPI to provide fundamental market context, complementing the technical analysis.
- **Nodes Involved:**
- SerpAPI (LangChain Tool SerpApi)
- **Node Details:**
- **SerpAPI**
- Type: LangChain Tool (SerpApi)
- Role: Queries SerpAPI for recent crypto-related news articles.
- Configuration: Uses SerpAPI credentials; query likely based on crypto symbol or general crypto news.
- Inputs: Receives AI analysis context from `AI Agent` via ai_tool connection
- Outputs: Passes news data to final chat output (not explicitly shown but implied)
- Edge Cases: API quota limits, invalid API key, no news results, network errors
- Version: 1
---
#### 2.6 Output Delivery
- **Overview:**
The final combined insights from technical AI analysis and fundamental news are delivered back to the user through the chat interface.
- **Nodes Involved:**
- Provide Crypto Symbol (entry node also serves as chat output)
- **Node Details:**
- **Provide Crypto Symbol** (Chat Trigger)
- Role: Besides input, it acts as the chat interface to deliver final AI-powered insights back to the user.
- Configuration: Outputs the final message after all processing is complete.
- Inputs: Receives final combined data (implied via workflow connections)
- Edge Cases: Message formatting errors, chat session timeouts
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
|-----------------------|----------------------------------|---------------------------------------|-------------------------|-------------------------|---------------------------------------------------------------------------------------------|
| Provide Crypto Symbol | LangChain Chat Trigger | User input and final chat output | Webhook (external) | crypto_symbol | |
| crypto_symbol | Set Node | Store and format crypto symbol | Provide Crypto Symbol | 1d_Chart | Store value of cryptocurrency. |
| 1d_Chart | HTTP Request | Fetch daily candlestick chart | crypto_symbol | Long Term Agent | Get day candlestick chart. |
| Long Term Agent | LangChain Agent | Analyze daily chart for long-term trends | 1d_Chart | 5m_Chart | |
| 5m_Chart | HTTP Request | Fetch 5-minute candlestick chart | Long Term Agent | AI Agent | Get 5 min candlestick chart. |
| AI Agent | LangChain Agent | Analyze 5-minute chart with context | 5m_Chart, Window Buffer Memory | SerpAPI | |
| Window Buffer Memory | LangChain Memory Buffer Window | Store intermediate AI analysis context | AI Agents (Long Term Agent, AI Agent) | Long Term Agent, AI Agent | |
| SerpAPI | LangChain Tool SerpApi | Retrieve latest crypto news | AI Agent (ai_tool) | AI Agent (implied output) | |
| OpenRouter Chat Model | LangChain LM Chat OpenRouter | Provide AI language model (Google Gemini 2.0 Flash) | None (resource node) | Long Term Agent, AI Agent | |
| Sticky Note | Sticky Note | Visual notes | None | None | |
| Sticky Note1 | Sticky Note | Visual notes | None | None | |
| Sticky Note2 | Sticky Note | Visual notes | None | None | |
---
### 4. Reproducing the Workflow from Scratch
1. **Create Chat Trigger Node**
- Name: Provide Crypto Symbol
- Type: LangChain Chat Trigger
- Configuration: Default settings to receive user input via chat webhook.
- Purpose: Entry point for user to input crypto symbol in format EXCHANGE:SYMBOL.
2. **Create Set Node**
- Name: crypto_symbol
- Type: Set Node
- Configuration: Extract and store the crypto symbol from the chat trigger input.
- Connect: Output of `Provide Crypto Symbol` → Input of `crypto_symbol`.
3. **Create HTTP Request Node for Daily Chart**
- Name: 1d_Chart
- Type: HTTP Request
- Configuration:
- Method: GET
- URL: Chart Img API endpoint for daily candlestick chart
- Query Parameters: symbol from `crypto_symbol` node, interval set to daily
- Authentication: Use Chart Img API credentials
- Connect: Output of `crypto_symbol` → Input of `1d_Chart`.
4. **Create LangChain Agent Node for Long-Term Analysis**
- Name: Long Term Agent
- Type: LangChain Agent
- Configuration:
- Language Model: OpenRouter Chat Model (configured separately)
- Memory: Window Buffer Memory (configured separately)
- Purpose: Analyze daily chart image for long-term trends
- Connect: Output of `1d_Chart` → Input of `Long Term Agent`.
5. **Create HTTP Request Node for 5-Minute Chart**
- Name: 5m_Chart
- Type: HTTP Request
- Configuration:
- Method: GET
- URL: Chart Img API endpoint for 5-minute candlestick chart
- Query Parameters: symbol from `crypto_symbol` or passed from `Long Term Agent`, interval set to 5 minutes
- Authentication: Use Chart Img API credentials
- Connect: Output of `Long Term Agent` → Input of `5m_Chart`.
6. **Create LangChain Agent Node for Short-Term Analysis**
- Name: AI Agent
- Type: LangChain Agent
- Configuration:
- Language Model: OpenRouter Chat Model
- Memory: Window Buffer Memory
- Purpose: Analyze 5-minute chart combined with long-term context
- Connect: Output of `5m_Chart` → Input of `AI Agent`.
7. **Create LangChain Memory Buffer Node**
- Name: Window Buffer Memory
- Type: LangChain Memory Buffer Window
- Configuration: Default settings to store conversation context between AI agents.
- Connect:
- Memory input/output connected to both `Long Term Agent` and `AI Agent`.
8. **Create LangChain Language Model Node**
- Name: OpenRouter Chat Model
- Type: LangChain LM Chat OpenRouter
- Configuration:
- API Key: OpenRouter API key (Google Gemini 2.0 Flash)
- Model: Gemini 2.0 Flash
- Connect: Used as language model resource for both AI agents.
9. **Create LangChain Tool Node for News Retrieval**
- Name: SerpAPI
- Type: LangChain Tool SerpApi
- Configuration:
- API Key: SerpAPI key
- Query: Crypto-related news, possibly parameterized by symbol or general crypto news
- Connect: Output of `AI Agent` (ai_tool connection) → Input of `SerpAPI`.
10. **Connect Final Output**
- The final combined analysis and news results should be routed back to the chat interface for user delivery.
- Connect output of `SerpAPI` or `AI Agent` back to `Provide Crypto Symbol` node's chat output (or create a dedicated chat output node if preferred).
11. **Credential Setup**
- Add credentials for:
- OpenRouter (Google Gemini 2.0 Flash)
- SerpAPI
- Chart Img API
12. **Test Workflow**
- Trigger the chat input with a valid crypto symbol (e.g., BINANCE:BTCUSDT).
- Verify each step completes successfully and final insights are delivered.
---
### 5. General Notes & Resources
| Note Content | Context or Link |
|----------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
| Video demonstration of the workflow is available at: [YouTube Video](https://www.youtube.com/watch?v=XW03ztGgbg0) | Video tutorial linked in workflow description. |
| OpenRouter API key required for Google Gemini 2.0 Flash model: https://openrouter.ai/ | Credential setup for AI language model. |
| SerpAPI key required for news retrieval: https://serpapi.com/ | Credential setup for fundamental data retrieval. |
| Chart Img API key required for candlestick chart images: https://chart-img.com/ | Credential setup for market data visualization. |
| Important: Not all LLM models support image analysis; Google Gemini 2.0 Flash via OpenRouter is recommended. | Model selection note. |
| Usage limits apply for free tiers of OpenRouter and SerpAPI; exceeding limits will halt workflow. | Operational limitation warning. |
| This workflow is for educational and research purposes only; it does not provide financial advice. | Legal disclaimer. |
---
This structured documentation provides a complete understanding of the AI-Powered Crypto Analysis workflow, enabling users and developers to reproduce, modify, and troubleshoot the automation effectively.