Giselle’s AI Agents Can Be Built by Combining “Nodes”
Giselle is a platform that allows you to build powerful AI agents through an intuitive node-based interface. By connecting “nodes” with various functions, like assembling building blocks, you can visually design complex AI workflows. This makes it easy for anyone, even without specialized knowledge, to achieve automation with AI agents.Node Categories
Giselle organizes nodes into four main categories, accessible from the toolbar at the bottom of the canvas:| Category | Hotkey | Description |
|---|---|---|
| App | A | Define the entry and exit points of your workflow |
| Model | M | Select AI models for content generation |
| Context | C | Add source data and retrieval capabilities |
| Integration | I | Connect with external services |
App
The App category contains nodes that define the boundaries of your workflow.Start Node & End Node
Start Node and End Node are the essential boundary nodes that define the entry and exit points of any workflow. They work as a pair to establish the complete flow of your app, from receiving user input to delivering the final output. For more details, see the Start Node & End Node documentation.Model
The Model category provides access to AI models for generating content.Generator Node
The Generator Node can create both text and images using advanced AI models. By configuring prompts and conditions, you can generate content tailored to your needs. For text, this includes applications such as document creation, blog writing, and research report generation, leveraging Giselle’s LLM API for high-quality, natural-sounding text. For images, you can generate visuals from text prompts, enabling creative content creation and workflows that utilize visual data. Web Search Capability: When using models that support web search functionality, the Generator Node can access real-time information from the internet. This enables the creation of content based on the latest data and current events, making your AI workflows more dynamic and up-to-date. For more details, see the Generator Node documentation.Context
The Context category contains nodes for providing data sources and retrieval capabilities to your workflows. It is divided into two groups: Source and Retrieval.Source
Source nodes provide input data for your AI workflows.Text Node
Text Node is used to hold text data. It can be used to record prompts, instructions, annotations, and more. It helps manage and organize text data within the workflow, streamlining input to generation nodes and data usage in other nodes. For more details, see the Text Node documentation.File Node
File Node is used to handle file data. You can upload PDF files, images, and text files to use as input for AI models. It is useful for various tasks that involve utilizing file data, such as data-based analysis and report generation. For more details, see the File Node documentation.Web Page Node
The Web Page Node is used to fetch and hold content from web pages. You can input one or more URLs, and Giselle will attempt to retrieve the content from these pages. The fetched content is processed and made available primarily in Markdown format, enabling tasks like summarization, analysis, or content generation based on information from the web. For more details, see the Web Page Node documentation.Document Vector Store Node
The Document Vector Store Node creates a searchable knowledge base from your uploaded documents (PDFs, text files, and markdown). It uses advanced embedding models to convert your document content into vector embeddings, enabling semantic search within your workflows. For more details, see the Document Vector Store Node documentation.GitHub Vector Store Node
The GitHub Vector Store Node creates a searchable knowledge base from the contents of a GitHub repository. It vectorizes code, issues, and pull requests, enabling semantic search within your workflows. This is especially useful for building Retrieval-Augmented Generation (RAG) applications. For more details, see the GitHub Vector Store Node documentation.Retrieval
Retrieval nodes enable searching through vector stores to find relevant information.Vector Query Node
The Vector Query Node is designed to execute search queries against connected vector store data sources (GitHub Vector Store Node or Document Vector Store Node). It retrieves the most relevant information (text chunks) from your vectorized content, which can then be used as dynamic context for AI generation. For more details, see the Vector Query Node documentation.Integration
The Integration category contains nodes for connecting with external services. It is divided into two groups: Trigger and Action.Trigger
Trigger nodes start workflows based on external events.Trigger Node
Trigger Node is the starting point for running workflows automatically. Currently, the GitHub Trigger Node enables workflows to be automatically triggered by GitHub webhooks. When the Giselle GitHub App is installed in a repository, this node can listen for specific events (like issue comments or pull requests) and start the workflow in response. For more details, see the Trigger Node documentation.Action
Action nodes perform operations on external services.Action Node
Action Node is a node that can call external services. Currently, it primarily supports integrations with GitHub, allowing workflows to perform actions within your repositories such as creating issues or posting comments. For more details, see the Action Node documentation.Tips for Using Nodes
Here are some helpful tips to enhance your experience with nodes in Giselle.Node Duplication
To enhance workflow efficiency, Giselle supports node duplication. This feature allows you to quickly create a copy of an existing node within the workflow designer. How to Duplicate a Node:- Right-Click Context Menu: Right-click on any node in the workflow designer to open the context menu, then select “Duplicate Node.”
- Keyboard Shortcut: Select the node you wish to duplicate and use the keyboard shortcut
Cmd + D(on macOS) orCtrl + D(on Windows/Linux).
- The duplicated node will appear at an offset position from the original node.
- Connections from the original node are preserved in the duplicate where applicable.
- For File nodes, the duplicated node will correctly reference newly copied files.
- Cloned nodes will start in an “unconfigured” state, allowing you to customize their settings independently from the original node.
- If the duplication process encounters an issue, an error message will be displayed.