FlowiseAI/Flowise

Features

• Agent Frameworks - Provides a framework for building autonomous agents that handle complex workflows.
• Agentic Orchestration Patterns - Implements a supervisor-worker pattern to decompose and distribute complex tasks across specialized agents.
• Agentic RAG Development - Creates intelligent retrieval systems that search, validate, and self-correct responses using autonomous agents.
• Multi-Agent Orchestration - Decomposes user requests into sub-tasks and aggregates results from specialized worker agents.
• Multi-Agent Orchestrators - Orchestrates teams of specialized AI agents that collaborate and delegate tasks to solve complex workflows.
• Agent Task Orchestration - Executes complex tasks by using an agent that autonomously selects tools based on conversation context.
• Agentic Workflow Construction - Constructs conversational systems using directed cyclic graph architectures that support iterative processes.
• Agentic Workflow Frameworks - Constructs autonomous multi-agent systems that utilize specialized workers and iterative reasoning strategies.
• Assistant Builders - Builds assistants that follow instructions and utilize external tools.
• Retrieval-Augmented Generation - Constructs retrieval-augmented generation flows with validation and self-correction.
• Retrieval Augmented Generation Engines - Indexes document stores and vector databases to provide language models with context-aware, searchable knowledge.
• Visual Workflow Orchestration - Connects specialized nodes on a visual canvas to define execution paths and conditional branching.
• Workflow Orchestration - Uses directed acyclic graphs to define execution paths and data flow for AI workflows.
• Visual Programming Environments - Provides a canvas-based interface to construct and configure modular AI components.
• Agent Management APIs - Remove an assistant permanently from the system by submitting a deletion request with its unique identifier.
• Conversational AI APIs - Using Flowise public API, you can programmatically execute many of the same tasks as you can in the GUI. This section introduces Flowise REST API. Assistants Attachments Chat Message Chatflows Document Store
• Conversational AI Backends - Provides RESTful access to managed chat sessions, persistent state, and secure credential handling for AI applications.
• Conversational Interfaces - Enables the creation of interactive assistants that automate information retrieval.
• Conversational State Managers - Tracks user interactions and persistent data across multi-turn conversations to ensure context-aware AI behavior.
• Conversational Workflow APIs - Exposes a comprehensive REST API for managing conversational agents and their associated data.
• Custom State Tracking - Defines and updates key-value pairs within shared conversation state to track application-specific information.
• Database Agents - Constructs agents that retrieve database schemas and generate valid SQL queries.
• Retrieval Agents - Connects document stores to language models for accurate information retrieval.
• Supervisor Agent Configurations - Manages task delegation using supervisor nodes with custom prompts and recursion limits.
• Tool Integrations - Connects external APIs and databases as pluggable components for autonomous agent invocation.
• Workflow State Management - Shares data across non-adjacent nodes using a persistent key-value store during workflow execution.
• Deployment Orchestration - Provides standardized containerized deployment using Docker.
• Authentication Strategies - Provides centralized authentication controls for platform and administrative access.
• Credential Encryption - Stores sensitive third-party API keys in an encrypted database for secure reuse.
• AI Application Lifecycle Management - Manages the deployment, security, and scaling of production-ready AI assistants through centralized configuration.
• Conversation State Persistence - Tracks execution steps and state snapshots across interactions using a database-backed checkpoint system.
• External Tool Integration - Performs external API calls within a workflow, including human-in-the-loop approval processes.
• Low-Code AI Orchestrators - Enables building and deploying complex language model workflows through drag-and-drop node orchestration.
• Multi-Agent Systems - Orchestrates multiple agents into scalable automation architectures.
• Worker Agent Definitions - Creates specialized worker agents that execute specific tasks using function-calling models.
• Workflow Management APIs - Enables programmatic creation of new conversational workflow configurations.
• Agent Building Frameworks - Low-code interface for creating custom LLM orchestration and agent flows.
• Agent Frameworks - Visual interface for building and managing AI agents.
• AI Agents - Drag-and-drop interface for building LLM workflows.
• AI Agents and Automation - Open-source UI visual tool for building custom LLM orchestration flows and AI agents.
• Artificial Intelligence - Visual builder for creating AI agent workflows.
• Inference and Serving - Drag-and-drop UI for building model workflows.
• Model Serving Engines - Drag-and-drop UI for building LLM workflows with LangChain.
• RAG Frameworks - Drag-and-drop interface for designing custom LLM workflows.
• Low Code Tools - Drag-and-drop UI for building LLM flows.
• State Persistence - Tracks conversation history and variables to maintain context across multi-step interactions.
• Vector Databases - Indexes document data into vector databases for searchable information.
• REST APIs - Exposes a comprehensive REST API for programmatic control over system tasks.
• Agent Delegation - Allows agents to delegate tasks to other agents based on specific capabilities.
• Assistant Management APIs - Allows programmatic configuration of assistant parameters including models and behavioral instructions.
• Chat History APIs - Allows programmatic deletion of chat history filtered by session or date.
• Conversation History APIs - Fetch all messages from a specific workflow to review conversation history, including source documents, tool usage, and agent reasoning.
• Structured Data Extraction - Extracts specific data formats from model responses by defining JSON schemas for subsequent workflow steps.
• Visual AI Workflow Builders - Connects language models, tools, and memory on a drag-and-drop canvas to build complex conversational systems.
• Workflow Branching Logic - Directs workflow paths by evaluating predefined conditions against the current conversation state.
• Database Management Utilities - Supports database data export and integrity verification for reliable recovery.
• Application Lifecycle Management - Provides visual editing, monitoring, and deployment controls for AI applications.
• Data Connectors - Connects language models to external databases for information retrieval and processing.
• Visual LLM Pipeline Designers - Provides a graphical interface for connecting language models, external tools, and data sources into structured conversational pipelines.
• Workflow Execution Interfaces - Configures entry points that accept user input while initializing runtime memory and state.
• Workflow Initialization - Establishes conversational workflows by setting default language models, memory, and initial state.
• Content Delivery APIs - Supports uploading and retrieving file attachments for specific chat sessions.
• Containerized Deployments - Provides containerized packaging to ensure environment consistency across local and cloud hosting.
• Credential Management - Stores third-party API keys in an encrypted database for secure reuse.
• REST APIs - Using Flowise public API, you can programmatically execute many of the same tasks as you can in the GUI. This section introduces Flowise REST API. Assistants Attachments Chat Message Chatflows Document Store

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