Nodes
Nodes are the fundamental building blocks of a flow in Lamatic. Each node serves a specific purpose, acting as a self-contained unit that processes input and produces output. This modular approach allows users to create complex flows by interconnecting various nodes.
What you’ll learn
- What Nodes are and how they fit into a Flow
- The main Node categories (Apps, AI, Data, Logic)
- How to choose the right Node for your use case
Prerequisites
- Basic understanding of Flows
How to think about Nodes
If a Flow is a “recipe”, then Nodes are the steps:
- Each node has inputs
- It performs one job
- It produces outputs that the next node can use
Node Categories
Lamatic organizes nodes into four main categories, each with a unique function in building flows:
| Category | Purpose | Examples | Method |
|---|---|---|---|
| Apps | Integrate third-party applications into workflow | Slack, PostgreSQL, Google Sheets | These nodes act as bridges, enabling data exchange and action execution between Lamatic and external services. |
| AI | Perform artificial intelligence operations | Vector operations, text generation, natural language understanding | AI nodes use machine learning models to process and analyze data, allowing for tasks such as sentiment analysis, text classification, or content generation. |
| Data | Manipulate and transform data | Indexing, chunking, filtering | Data nodes provide tools for structuring, organizing, and processing information, ensuring data is in the right format for subsequent nodes. |
| Logic | Control the flow of execution | Conditional statements, loops, custom code execution | Logic nodes allow for decision-making and branching, enabling dynamic, responsive flow based on specific conditions or criteria. |
How to choose the right Node
- Need to talk to another tool/app? Use an Apps node.
- Need an LLM or AI operation? Use an AI node.
- Need to clean/transform/store/search data? Use a Data node.
- Need conditions/branching/code? Use a Logic node.
Not sure what to pick? Start from your trigger and ask: “what’s the next step in my pipeline?” Then add the node that does only that step.
Here are the Nodes
AI
AI nodes use machine learning models to process and analyze data, allowing for tasks such as sentiment analysis, text classification, or content generation.
Generate Text Node
The Generate Text node allows users to programmatically generate text outputs by submitting prompts to selected LLMs.
Generate JSON Node
The Generate JSON Node allows users to generate structured JSON data using AI models with predefined schemas.
Generate Image Node
The Generate Image Node allows users to create images using AI models based on text prompts and configuration parameters.
Classifier Node
The Classifier Node allows users to categorize and classify data based on predefined criteria or machine learning models.
Multimodal Node
The Multimodal Node allows users to process multiple types of data (text, images, audio) using AI models in a single workflow.
RAG Node
The RAG (Retrieval-Augmented Generation) Node combines information retrieval with text generation to provide contextually relevant responses.
MCP Node
The MCP Node allows users to connect to and interact with external data sources and tools through the Model Context Protocol (MCP).
Supervisor Node
The Supervisor Node monitors and manages the execution of other nodes in a workflow, providing oversight and control capabilities.
Apps
These nodes act as bridges, enabling data exchange and action execution between Lamatic and external services.
Airtable
The Airtable integration in Lamatic automates data synchronization and manipulation with Airtable bases, enabling both trigger-based workflows and action-based operations for comprehensive Airtable integration.
AWS S3
The AWS S3 node in Lamatic automates fetching and synchronizing files from Amazon S3 buckets. It supports various file types, including text files, PDFs, Word Documents, and others stored in the bucket. This node enables regular file synchronization to support vectorization and indexing for Retrieval-Augmented Generation (RAG) flows.
Firecrawl
Firecrawl is a robust tool designed to transform websites into LLM-ready data by leveraging its **Crawler** and **Scraper** functionalities. Whether you need to map website structures or extract specific data, Firecrawl provides a seamless and customizable solution.
GitHub Action
The GitHub Action integration in Lamatic automates file monitoring and webhook triggering when files are modified in your repository. It supports various file types and provides secure integration with Lamatic Flow for automated processing and indexing.
Google Drive
The Google Drive node in Lamatic automates fetching and synchronizing files from Google Drive and supports action-based operations for creating, editing, and managing docs and folders. It supports various file types including Google Docs, Slides, Word Documents, PDFs, and text files for vectorization and indexing in RAG flows.
Google Sheets
The Google Sheets integration in Lamatic automates data extraction and synchronization from Google Spreadsheets, supporting multiple sheets and various data formats for data processing and analysis flows.
Microsoft Teams
The Microsoft Teams integration in Lamatic automates data extraction, syncing, and real-time triggers from Teams channels and messages. It enables conversational intelligence and workflow automation in Lamatic Flow.
N8N
The N8N integration in Lamatic.ai enables bidirectional integration between Lamatic and N8N platforms. This integration supports both triggering N8N flows from Lamatic and executing Lamatic flows from N8N, providing flexible automation capabilities for complex workflows.
Notion
The Notion integration in Lamatic automates content management and page operations with Notion workspaces, enabling both trigger-based workflows and action-based operations for comprehensive Notion integration.
OneDrive
The OneDrive integration in Lamatic automates document syncing and processing from Microsoft OneDrive Business accounts. It supports various file types and provides secure integration with Lamatic Flow for automated document intelligence and RAG workflows.
PostgreSQL
The PostgreSQL integration in Lamatic automates data synchronization from PostgreSQL databases. It enables scheduled fetching of rows from specified tables and supports vectorization and indexing for Retrieval-Augmented Generation (RAG) workflows.
SharePoint
The SharePoint integration in Lamatic automates document syncing and processing from Microsoft SharePoint sites. It supports various file types and provides secure integration with Lamatic Flow for automated document intelligence and RAG workflows.
Slack
The Slack integration in Lamatic.ai enables seamless integration with Slack workspaces. It supports event triggering based on Slack commands and message sending capabilities, making it ideal for automating communication flow and implementing AI-powered responses within Slack channels.
Twilio
The Twilio integration in Lamatic provides communication automation capabilities through SMS, voice calls, and WhatsApp messaging, enabling multi-channel communication within workflows.
Web Search
The Web Search integration in Lamatic.ai enables powerful web scraping and search capabilities through serper.dev. It supports multiple search types, customizable filters, and structured result retrieval for comprehensive web data access.
Webflow
The Webflow integration in Lamatic automates content management and site operations with Webflow, enabling both trigger-based workflows and action-based operations for comprehensive Webflow integration.
Data
Data nodes provide tools for structuring, organizing, and processing information, ensuring data is in the right format for subsequent nodes.
Chunking Node
The Chunking Node breaks down large text documents into smaller, manageable chunks for processing by AI models.
Extract from File Node
The Extract from File Node extracts and processes data from various file formats including PDFs, Word documents, and text files.
Hybrid Search Node
The Hybrid Search Node combines multiple search strategies to provide comprehensive and accurate search results from vector databases.
Keyword Search Node
The Keyword Search Node performs text-based searches using specific keywords and phrases to find relevant content.
Memory Add Node
The Memory Add Node stores and manages data in memory for use across workflow executions.
Memory Retrieve Node
The Memory Retrieve Node retrieves stored data from memory for use in current workflow executions.
API Node
The Lamatic.ai API Node empowers users to external REST APIs seamlessly into their flow.
VectorDB Node
The VectorDB Node performs various operations on vector databases including insertion, deletion, and management of vector data.
Vector Search Node
The Vector Search Node performs semantic search operations on vector databases to find similar content.
Vectorize Node
The Vectorize Node converts text data into vector embeddings for use in semantic search and AI applications.
Logics
Logic nodes allow for decision-making and branching, enabling dynamic, responsive flow based on specific conditions or criteria.
Branch Node
The Branch Node creates multiple execution paths in a workflow based on conditions or data routing logic.
Code Node
The Code Node allows users to execute custom JavaScript code within workflows for data processing and logic implementation.
Condition Node
The Lamatic.ai Condition Node allows users to integrate custom logic into their flow with ease.
Execute Flow Node
The Execute Flow Node allows users to call and execute other workflows from within a current workflow.
Loop Node
The Loop Node enables iterative processing of data by repeating operations on collections or arrays of data.
Variable Node
The Variable Node allows users to define and manage variables for storing and manipulating data within workflows.
Wait Node
The Wait Node introduces delays in workflow execution for timing control and rate limiting purposes.
Troubleshooting
- Can’t find a node: make sure you’re in the Flow Editor and click Add Node.
- Node requires credentials: check the node config panel for required connections (models/apps).
- Output doesn’t match expectation: run a test and inspect node outputs step-by-step.
Next steps
- Learn how to build with Nodes in the Flow Editor
- Browse common patterns in Guides