Node categories
Text operations
Process and analyze text content
Document operations
Work with documents and files
Image operations
Analyze visual content
Data operations
Transform and enrich data
Presets. The text, document, image, and data operation nodes each come with presets — ready-made instruction templates for common tasks. Selecting a preset prefills the node’s Instructions field (and, where useful, an example response schema), which you can then edit. The presets available depend on the node type — for example, Document Understanding offers Content Summarization, Key Information Extraction, Topic Analysis, and Sentiment Analysis, and Document Extraction offers Metadata Generation, Named Entity Recognition, and Table Extraction.
Intent Classification
Classify user messages and automatically route the workflow to the matching branch — combining AI classification and conditional branching in a single node.Configuration options
- User message — Input text to classify (supports
${}references) - Intents — Up to 10 natural language intent descriptions, each becoming an output branch
- Use conversation memory — Include conversation history for context-aware classification
- Include Reason for Selection — Add LLM explanation for the chosen intent
- Fallback branch — Automatic “If No Intent Matches” path
Intent Classification
Learn more about configuring intents, output format, and routing behavior
Context Retrieval
Perform RAG (Retrieval-Augmented Generation) searches against a Knowledge Base and return relevant chunks — without calling an LLM.Configuration options
- Source — Knowledge Base or Memory (Memory only available in chat-driven workflows)
- Knowledge Base — Select which Knowledge Base to query (when source is Knowledge Base)
- User Query — the search query, supports process variable expressions
- Search type — Hybrid (default), Semantic, or Keywords
- Max Number of Chunks — how many chunks to return (1-10)
- Min Relevance Score — minimum relevance threshold (0-100%)
- Metadata Filters — structured filters with typed operators and AND/OR grouping to refine results by chunk metadata
- Use advance metadata filters — toggle for expression-based filtering
- Use Re-rank — re-rank retrieved chunks before returning
Output format
The node outputs an array of retrieved chunks, each containing:Context Retrieval
Learn more about configuring Context Retrieval nodes
Custom Agent
Create custom agents with advanced capabilities powered by Model Context Protocol (MCP) tools, optional Knowledge Base retrieval, and direct chat reply in Chat Driven workflows.Configuration options
- Instructions (mandatory) — The agent’s role, tasks, and expected input/output. Sent as the System message and cached by the LLM between executions, so
${variable}references are not allowed in this field. - Background → Use conversation memory — Include conversation history in the LLM call (Chat Driven workflows only)
- Background → Knowledge Base — Attach one or more knowledge bases for RAG retrieval; includes Search Type (vector / keyword / hybrid) and Re-rank options. When several knowledge bases are attached, the agent searches all of them, with one retriever per knowledge base (available starting with 5.9.2).
- Background → Context — Free-text field for the per-execution prompt; supports
${variable}references. Sent as the User message and not cached. - Background → Use only referenced values as input — Send only values referenced in the prompt to the LLM; narrows context and reduces token usage
- Tools → MCP Servers — Attach MCP tools the agent can call
- Tools → Built-in tools → Use Web Search Tool — Toggle ON to let the agent look up recent information from the public web. Off by default.
- Tools → Built-in tools → Use built-in tools — Toggle ON to give the agent deterministic helper tools for exact math, value comparison, list sorting, and list filtering, so these operations run as code instead of relying on the model. Off by default. (SaaS · 5.10)
- Personal Information Guard — Toggle PII redaction on the node’s input and/or output (see Personal Information Guard)
- Response → Send as Chat Reply — In Chat Driven workflows, deliver the response directly to the Chat component as Markdown
- Response → Include Task for Prompt Suggestions — In Chat Driven workflows, generate AI follow-up prompts shown in the Chat component after the response
- Response Key — Key where the node output is stored in the workflow data
- Response Schema — Expected JSON structure of the LLM response (hidden when Send as Chat Reply is ON). Supports
${variable}placeholders resolved at runtime against workflow data, with substituted values coerced to the schema-declared types (SaaS · 5.10).
Web Search. When Use Web Search Tool is on, the agent can look up up-to-date information from the public web and use it in its response. There are no extra settings to configure — the agent decides when to search based on its instructions and the user’s request. The toggle is off by default.
Custom Agent in Chat Driven workflows
Full reference for Use conversation memory, Send as Chat Reply, Prompt Suggestions, and other Chat Driven specifics
Speech to Text
Transcribe audio to text within integration workflows.Configuration options
- File Source — Document Plugin, S3, or Chat Input (chat-driven workflows)
- File Path — Path to the audio file (supports
${}references); becomes a test-file dropdown when Use Test File is on - Use Test File — Upload a sample audio file for development testing
- Response Key — Key under which the transcript and metadata are stored
Speech to Text
Learn more about configuring transcription and chat voice integration
Understanding nodes
Understanding nodes analyze content to extract meaning and intent.Configuration options
- Classification labels - Define categories for classification
- Confidence threshold - Minimum score for results
- Multi-label - Allow multiple classifications per input
AI Document Operations
Process documents to extract data, generate reports, or understand content.Extract Data from File
Learn more about configuring extraction strategies, image extraction, and signature detection
Configuration options
- Document type - Specify expected document format
- Schema definition - Define expected output structure
- Field mapping - Map extracted fields to data model
- Confidence threshold - Minimum score for extractions
AI Image Operations
Analyze visual content to generate captions, extract details, or identify objects.Configuration options
- Detection confidence - Minimum threshold for detections
- Region of interest - Focus on specific image areas
- Output format - Structured data or annotations
Structured-output validation and run diagnostics
Available starting with FlowX.AI 5.9.2
Combining nodes
Nodes can be connected in workflows to create complex processing pipelines:Best practices
Start simple
Start simple
Begin with a single node and add complexity incrementally. Test each addition before moving on.
Use validation nodes
Use validation nodes
Add validation steps after extraction to ensure data quality before processing continues.
Handle errors
Handle errors
Include fallback paths for when nodes fail or return low-confidence results.
Monitor performance
Monitor performance
Track execution times and accuracy metrics to identify bottlenecks and improvement opportunities.
Related resources
Agent Builder overview
Get started with Agent Builder
Use cases
See real-world examples
Chat-driven workflows
Multi-turn chat with Custom Agent node changes for chat replies and memory