{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"flowise","slug":"flowise","name":"Flowise","type":"framework","url":"https://github.com/FlowiseAI/Flowise","page_url":"https://unfragile.ai/flowise","categories":["app-builders","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"flowise__cap_0","uri":"capability://automation.workflow.visual.node.based.chatflow.composition.with.drag.and.drop.canvas","name":"visual node-based chatflow composition with drag-and-drop canvas","description":"Provides a React-based canvas UI where users drag LLM components (models, chains, tools, memory) onto a graph and connect them via edges. The system uses a node registry (NodesPool) that loads pre-built component definitions, validates connections via TypeScript schema validation, and serializes the graph structure to JSON for persistence. Execution traverses the DAG at runtime, resolving variable dependencies and streaming outputs back to the UI via WebSocket.","intents":["Build multi-step LLM chains without writing code","Visually prototype agent workflows before deployment","Connect disparate AI components (models, retrievers, tools) in a single interface","Iterate on prompt flows with real-time feedback"],"best_for":["Non-technical product managers prototyping chatbot flows","Teams migrating from hardcoded LangChain chains to visual workflows","Rapid prototyping teams that need to iterate on agent logic without code deployment"],"limitations":["Complex conditional logic requires custom code nodes; pure visual composition limited to sequential/parallel flows","Canvas performance degrades with >50 nodes due to React re-render overhead","No built-in version control for flows; requires external Git integration for collaboration","Variable scoping across nested subflows not fully supported; global context only"],"requires":["Node.js 18+","React 18+ (UI layer)","Database (SQLite, PostgreSQL, or MySQL) for flow persistence","At least one LLM API key (OpenAI, Anthropic, etc.)"],"input_types":["node definitions (JSON schema)","connection metadata (source node, target node, socket IDs)","user-configured node parameters"],"output_types":["serialized flow graph (JSON)","execution logs","streamed chat responses"],"categories":["automation-workflow","ui-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_1","uri":"capability://tool.use.integration.multi.model.llm.provider.abstraction.with.credential.management","name":"multi-model llm provider abstraction with credential management","description":"Implements a model registry that abstracts over OpenAI, Anthropic, Ollama, HuggingFace, and other LLM providers through a unified interface. Credentials are encrypted and stored per-user in the database; at runtime, the system instantiates the correct provider client based on node configuration and routes API calls through a credential resolver that injects secrets without exposing them in flow definitions. Supports both chat and embedding models with provider-specific parameter mapping.","intents":["Switch between LLM providers without rebuilding flows","Securely manage API keys without hardcoding in flow definitions","Use multiple models in a single flow (e.g., GPT-4 for reasoning, GPT-3.5 for cost-sensitive steps)","Support on-premise LLMs (Ollama) alongside cloud providers"],"best_for":["Teams evaluating multiple LLM providers in production","Enterprises requiring credential isolation per user/tenant","Cost-optimization workflows that route requests to cheaper models conditionally"],"limitations":["No built-in fallback mechanism if primary provider is down; requires custom error handling nodes","Provider-specific parameters (temperature, top_p, etc.) must be manually mapped; no auto-conversion","Credential rotation requires manual re-entry; no automated secret refresh","Rate limiting and quota management delegated to provider SDKs; no centralized throttling"],"requires":["Valid API keys for at least one LLM provider","Database with encryption support (AES-256 for credential storage)","Environment variables for provider endpoints (optional for self-hosted models)"],"input_types":["provider name (string)","model identifier (string)","credential object (API key, endpoint URL, etc.)","model parameters (temperature, max_tokens, etc.)"],"output_types":["LLM response (text)","token usage metadata","provider-specific metadata (finish_reason, etc.)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_10","uri":"capability://automation.workflow.queue.based.asynchronous.execution.with.worker.pool.scaling","name":"queue-based asynchronous execution with worker pool scaling","description":"Implements a queue-based execution model where flows are submitted as jobs to a message queue (Redis, Bull, etc.) and processed by a pool of worker processes. This decouples flow submission from execution, enabling asynchronous processing and horizontal scaling. The system tracks job status (pending, running, completed, failed), stores results in the database, and provides webhooks for job completion notifications. Workers are stateless and can be scaled up/down based on queue depth.","intents":["Execute long-running flows without blocking the API","Scale flow execution across multiple machines","Handle traffic spikes by queuing jobs and processing them asynchronously","Monitor job status and retry failed executions"],"best_for":["High-throughput applications processing many flows concurrently","Batch processing workflows (e.g., processing 1000 documents overnight)","Applications requiring horizontal scaling of flow execution"],"limitations":["Asynchronous execution adds latency; results are not immediately available","Queue persistence requires external service (Redis, RabbitMQ); adds operational complexity","Job retries are basic; no exponential backoff or circuit breaker patterns","No built-in job prioritization; all jobs processed in FIFO order","Worker failures can cause job loss if not properly persisted"],"requires":["Message queue service (Redis, RabbitMQ, Bull, etc.)","Worker processes (Node.js or Python)","Database for job status and results","Docker or container orchestration for worker scaling"],"input_types":["flow execution request (flow ID, input variables)","job configuration (priority, timeout, retry count)"],"output_types":["job ID (for status tracking)","job status (pending, running, completed, failed)","job result (stored in database)","webhook notification (on completion)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_11","uri":"capability://safety.moderation.multi.tenant.flow.isolation.with.user.scoped.credentials.and.data","name":"multi-tenant flow isolation with user-scoped credentials and data","description":"Implements multi-tenancy at the database and credential level, where each user has isolated flows, credentials, and chat history. Flows are scoped to users via foreign keys; credentials are encrypted per-user and never shared across tenants. The system enforces access control at the API level, preventing users from accessing other users' flows or credentials. Supports both single-tenant (self-hosted) and multi-tenant (SaaS) deployments with configurable isolation levels.","intents":["Build SaaS platforms where each customer has isolated flows and data","Prevent credential leakage between users","Audit user actions (flow creation, execution, credential access)","Support white-label deployments with customer-specific branding"],"best_for":["SaaS platforms offering Flowise as a service","Enterprises requiring strict data isolation between departments","Managed service providers hosting flows for multiple customers"],"limitations":["Multi-tenancy adds database complexity; queries must filter by user ID","Credential isolation prevents sharing; teams cannot share credentials across users","No built-in billing or usage tracking; requires external integration","Cross-tenant analytics not supported; each tenant sees only their own data","Tenant deletion requires cascading deletes; no soft-delete or archival"],"requires":["Database with support for foreign keys and row-level security (PostgreSQL recommended)","User authentication system (OAuth, SAML, or custom)","Encryption for credential storage (AES-256)"],"input_types":["user ID (from authentication)","flow definition (scoped to user)","credential (encrypted and stored per-user)"],"output_types":["user-scoped flows (only accessible to that user)","user-scoped credentials (never exposed to other users)","audit logs (user actions)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_12","uri":"capability://data.processing.analysis.document.ingestion.and.web.scraping.with.multiple.source.connectors","name":"document ingestion and web scraping with multiple source connectors","description":"Provides document loader nodes that ingest data from multiple sources: local files (PDF, DOCX, TXT), web pages (via web scraper), databases (SQL queries), and APIs. Each loader parses the source format, extracts text, and outputs chunks ready for embedding. Loaders support metadata extraction (title, author, URL) and can be chained with text splitters for further processing. Web scrapers handle pagination and JavaScript-rendered content (via Playwright).","intents":["Ingest documents from diverse sources (files, web, databases) into RAG pipelines","Extract text and metadata from PDFs and web pages","Scrape web content for knowledge base construction","Load structured data from databases and APIs"],"best_for":["Building knowledge bases from heterogeneous sources","Automating document ingestion pipelines","Creating RAG systems over web content or proprietary documents"],"limitations":["PDF parsing quality varies; complex layouts or scanned PDFs may fail","Web scraping is brittle; page structure changes break scrapers","No built-in deduplication; duplicate documents from multiple sources may be ingested","Large files (>100MB) may timeout during processing","Metadata extraction is basic; custom metadata fields not supported"],"requires":["Document source (file path, URL, database connection, API endpoint)","For web scraping: Playwright or similar browser automation","For PDFs: PDF parsing library (pdfjs, pypdf, etc.)","For databases: SQL connection string and query"],"input_types":["source type (file, web, database, API)","source location (path, URL, connection string)","loader configuration (format, encoding, metadata fields)"],"output_types":["extracted text (string)","metadata (title, author, URL, etc.)","chunks (if connected to text splitter)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_13","uri":"capability://text.generation.language.streaming.response.output.with.real.time.token.by.token.delivery","name":"streaming response output with real-time token-by-token delivery","description":"Implements streaming execution where LLM responses are sent to the client token-by-token as they are generated, rather than waiting for the complete response. The system uses Server-Sent Events (SSE) or WebSocket to push tokens to the client in real-time, providing a ChatGPT-like experience. Streaming is transparent to the flow definition; users don't need to configure anything—it's automatic for LLM nodes. Supports both text streaming and structured output streaming (JSON).","intents":["Provide real-time feedback to users as LLM generates responses","Reduce perceived latency by showing partial responses immediately","Enable interactive experiences where users see reasoning steps as they happen","Support long-form content generation without waiting for completion"],"best_for":["Chat interfaces where real-time feedback improves UX","Long-form content generation (articles, code) where streaming reduces wait time","Interactive agents where users see reasoning steps in real-time"],"limitations":["Streaming requires persistent connection; not compatible with stateless HTTP","Partial responses may be incomplete or grammatically incorrect; clients must handle incomplete tokens","Error handling is complex; errors mid-stream cannot be recovered cleanly","Streaming disables certain optimizations (batching, caching); may increase latency for short responses","Browser compatibility: older browsers may not support SSE or WebSocket"],"requires":["LLM with streaming support (OpenAI, Anthropic, etc.)","Client with SSE or WebSocket support","Network connection stable enough for persistent streaming"],"input_types":["LLM prompt (text)","streaming configuration (chunk size, timeout)"],"output_types":["streamed tokens (sent incrementally)","complete response (after streaming ends)","metadata (token count, finish reason)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_14","uri":"capability://text.generation.language.prompt.templating.and.variable.interpolation.with.dynamic.context.injection","name":"prompt templating and variable interpolation with dynamic context injection","description":"Implements a prompt templating system where users define prompts with variable placeholders (e.g., `{context}`, `{user_input}`) that are dynamically filled at execution time. Variables can come from upstream nodes, user input, or flow-level context. The system supports conditional prompts (if-else logic) and prompt chaining (output of one prompt feeds into another). Supports both simple string interpolation and complex template languages (Handlebars, Jinja2).","intents":["Define reusable prompt templates that adapt to different inputs","Inject context from upstream nodes into prompts dynamically","Create conditional prompts that change based on intermediate results","Chain multiple prompts for complex reasoning workflows"],"best_for":["Teams building prompt-heavy applications (content generation, summarization)","Workflows requiring dynamic prompt construction based on data","Iterative prompt optimization where templates are frequently updated"],"limitations":["Template syntax errors are not caught until execution; no compile-time validation","Complex template logic can become hard to maintain; no IDE support","Variable scoping is global; no local variables or namespacing","No built-in prompt versioning; template changes affect all flows immediately"],"requires":["Prompt template with variable placeholders","Variable definitions (name, type, source)","Template language support (Handlebars, Jinja2, or simple string interpolation)"],"input_types":["prompt template (string with placeholders)","variable values (from upstream nodes or user input)","conditional logic (if-else expressions)"],"output_types":["interpolated prompt (with variables filled in)","LLM response (if prompt is sent to LLM)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_2","uri":"capability://memory.knowledge.conversational.memory.and.context.management.across.chat.sessions","name":"conversational memory and context management across chat sessions","description":"Manages chat history and context through a memory abstraction layer that supports multiple backends (buffer memory, summary memory, entity memory). The system persists conversation history to the database, retrieves relevant context based on message count or summarization, and injects it into the LLM prompt at execution time. Supports both stateless (per-request context) and stateful (session-based) memory modes, with configurable window sizes and summarization strategies.","intents":["Maintain conversation context across multiple user messages","Summarize long conversations to fit within token limits","Extract and track entities across conversation turns","Reset or clear chat history on demand"],"best_for":["Multi-turn chatbot applications requiring conversation continuity","Customer support agents that need to reference previous interactions","Long-running conversations where token limits require context compression"],"limitations":["Summary-based memory loses fine-grained details; may miss important context from early turns","No built-in semantic deduplication; redundant context can inflate token usage","Memory window size is fixed per flow; no dynamic adjustment based on token budget","Cross-conversation context (learning from past sessions) not supported; each session isolated"],"requires":["Database for storing conversation history","LLM with summarization capability (if using summary memory)","Session identifier (user ID or conversation ID)"],"input_types":["user message (text)","session ID (string)","memory configuration (type, window size, summarization strategy)"],"output_types":["injected context (text prepended to prompt)","conversation history (array of message objects)","summary (if using summary memory)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_3","uri":"capability://tool.use.integration.tool.calling.and.function.execution.with.schema.based.routing","name":"tool calling and function execution with schema-based routing","description":"Implements a tool registry where users define tools (API calls, database queries, custom functions) as nodes with JSON schema specifications. At runtime, the LLM generates tool calls based on the schema, the system routes calls to the correct tool handler, executes the function (with optional sandboxing for custom code), and returns results back to the LLM for further reasoning. Supports both LangChain tool bindings and custom function nodes with parameter validation.","intents":["Enable LLMs to call external APIs and databases as part of agent reasoning","Define custom business logic that agents can invoke (e.g., database lookups, payment processing)","Validate tool parameters before execution to prevent malformed requests","Chain tool outputs into subsequent steps or LLM calls"],"best_for":["Agentic workflows that require external data access (APIs, databases, web search)","Teams building domain-specific tools that LLMs should invoke autonomously","Applications requiring audit trails of tool invocations and results"],"limitations":["Custom code execution runs in a sandboxed environment with limited library access; complex dependencies not supported","Tool schema must be manually defined; no automatic schema generation from function signatures","No built-in retry logic for failed tool calls; requires explicit error handling nodes","Tool execution is synchronous; long-running operations (>30s) may timeout"],"requires":["Tool definition with JSON schema (parameters, return type)","For API tools: endpoint URL and authentication credentials","For custom code: JavaScript/Python code that conforms to sandbox restrictions","LLM with function calling support (OpenAI, Anthropic, etc.)"],"input_types":["tool schema (JSON schema object)","tool name (string)","function implementation (code string or API endpoint)","LLM-generated tool call (name + parameters)"],"output_types":["tool execution result (JSON or text)","error message (if execution fails)","execution metadata (latency, token usage)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_4","uri":"capability://memory.knowledge.rag.pipeline.composition.with.vector.store.integration","name":"rag pipeline composition with vector store integration","description":"Provides nodes for document loading, chunking, embedding, and vector store operations (Pinecone, Weaviate, Supabase, Milvus, etc.). Users compose RAG flows by connecting a document loader → text splitter → embedding model → vector store node. At runtime, documents are chunked, embedded using the configured embedding model, and stored in the vector database. Retrieval nodes query the vector store with semantic similarity, returning top-k results that are injected into the LLM prompt. Supports both in-memory and persistent vector stores.","intents":["Build semantic search over custom documents without writing embedding code","Create knowledge-grounded chatbots that retrieve relevant context before generating responses","Ingest and index documents from multiple sources (PDFs, web pages, databases)","Switch between vector store providers without modifying retrieval logic"],"best_for":["Teams building document Q&A systems (customer support, knowledge bases)","Applications requiring semantic search over large document collections","Enterprises needing to ground LLM responses in proprietary data"],"limitations":["Chunking strategy is fixed (character-based or token-based); no semantic chunking","No built-in deduplication; duplicate documents may be indexed multiple times","Vector store sync is one-way (ingestion only); no automatic updates when source documents change","Retrieval quality depends on embedding model; no automatic tuning of similarity threshold or top-k"],"requires":["Vector store account and API key (Pinecone, Weaviate, etc.) or local instance","Embedding model API key (OpenAI, HuggingFace, etc.)","Document source (PDF files, web URLs, database connection)","Text splitter configuration (chunk size, overlap)"],"input_types":["documents (PDF, text, web pages)","embedding model name (string)","vector store configuration (endpoint, API key, index name)","retrieval query (text)"],"output_types":["embedded documents (stored in vector store)","retrieved documents (top-k results with similarity scores)","augmented prompt (original query + retrieved context)"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_5","uri":"capability://planning.reasoning.agent.loop.execution.with.tool.use.reasoning.and.step.by.step.planning","name":"agent loop execution with tool-use reasoning and step-by-step planning","description":"Implements agentic execution patterns (ReAct, Plan-and-Execute) where the LLM reasons about available tools, decides which to call, executes them, and iterates until a final answer is reached. The system manages the agent loop by maintaining state across iterations, tracking tool calls and results, and enforcing max-step limits to prevent infinite loops. Supports both synchronous agents (single-turn reasoning) and multi-turn agents (conversation-based reasoning). Execution is observable via step-by-step logs showing LLM thoughts, tool calls, and results.","intents":["Build autonomous agents that reason about which tools to use and when","Create multi-step workflows where agents decompose tasks and execute sub-tasks","Debug agent behavior by inspecting step-by-step reasoning and tool calls","Implement complex business logic that requires iterative refinement (e.g., research agents, planning agents)"],"best_for":["Teams building autonomous agents for complex tasks (research, planning, problem-solving)","Applications requiring explainable AI where reasoning steps must be visible","Iterative workflows where agents refine outputs based on intermediate results"],"limitations":["Agent loops are non-deterministic; same input may produce different tool sequences due to LLM sampling","Max-step limits prevent infinite loops but may truncate valid reasoning; no adaptive step budgeting","Tool hallucination (LLM inventing tools that don't exist) requires explicit validation; not prevented by default","Agent state is not persisted across sessions; long-running agents cannot resume from checkpoints"],"requires":["LLM with strong reasoning capability (GPT-4, Claude 3+, etc.)","Tool definitions with clear descriptions and parameters","Max-step configuration (typically 10-20 steps)","Error handling for tool failures (retry logic, fallback tools)"],"input_types":["user query or task (text)","available tools (tool registry)","agent configuration (model, max steps, temperature)","system prompt (optional, for agent behavior tuning)"],"output_types":["final answer (text)","step-by-step execution log (array of thought/action/observation tuples)","tool call history (which tools were invoked and with what parameters)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_6","uri":"capability://code.generation.editing.custom.code.execution.with.javascript.python.sandbox","name":"custom code execution with javascript/python sandbox","description":"Allows users to define custom logic nodes that execute arbitrary JavaScript or Python code in a sandboxed environment. The sandbox restricts access to dangerous libraries (file system, network) while allowing data transformation, calculation, and conditional logic. Code nodes receive inputs from upstream nodes, execute the user-defined function, and pass outputs to downstream nodes. Execution is isolated per-invocation, preventing state leakage between runs.","intents":["Implement custom business logic that isn't available as pre-built nodes","Transform data between nodes (e.g., parsing JSON, formatting text)","Add conditional branching based on intermediate results","Integrate proprietary algorithms or domain-specific computations"],"best_for":["Teams with domain-specific logic that can't be expressed visually","Rapid prototyping where custom nodes are faster than waiting for pre-built components","Hybrid workflows mixing visual composition with imperative code"],"limitations":["Sandbox restricts library imports; complex dependencies (ML models, specialized libraries) not available","Code execution timeout (typically 30s); long-running computations will fail","No persistent state between invocations; each code node execution is isolated","Debugging is limited to console logs; no interactive debugger or breakpoints","Security: user-provided code could still exploit sandbox vulnerabilities; requires code review"],"requires":["JavaScript or Python runtime (Node.js or Python 3.9+)","Code that conforms to sandbox restrictions (no file I/O, limited imports)","Input/output type definitions (for validation and documentation)"],"input_types":["code string (JavaScript or Python)","input variables (from upstream nodes)","execution context (flow variables, secrets)"],"output_types":["computed result (any JSON-serializable type)","error message (if execution fails)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_7","uri":"capability://automation.workflow.flow.export.import.and.marketplace.template.sharing","name":"flow export, import, and marketplace template sharing","description":"Enables users to export flows as JSON files that capture the complete graph structure, node configurations, and connections. Exported flows can be imported by other users, restoring the graph and all node settings. The system includes a marketplace where users can publish flows as templates, making them discoverable and reusable by the community. Import validates flow schema, handles version compatibility, and allows users to override credentials before importing.","intents":["Share flow templates with team members or the community","Version control flows by exporting to Git","Backup flows before making changes","Discover and reuse community-built flows for common tasks"],"best_for":["Teams collaborating on flow development","Organizations building internal flow libraries","Community members sharing reusable templates"],"limitations":["Exported flows contain node configurations but not execution history or performance metrics","Credentials are not exported (for security); users must re-enter API keys after import","No built-in version control; multiple versions must be manually managed","Marketplace has no quality gates; users must validate imported flows before production use","Breaking changes in node definitions can make old flows incompatible; no automatic migration"],"requires":["Flow definition (JSON)","For import: valid node definitions matching the flow schema","For marketplace: user account and publishing permissions"],"input_types":["flow JSON (exported flow definition)","marketplace metadata (name, description, tags)"],"output_types":["flow JSON file (for export)","imported flow (restored in canvas)","marketplace listing (for published templates)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_8","uri":"capability://tool.use.integration.embeddable.chatbot.widget.for.web.integration","name":"embeddable chatbot widget for web integration","description":"Generates a standalone JavaScript widget (iframe or embedded component) that can be embedded on external websites to expose a chatflow as a user-facing chatbot. The widget communicates with the Flowise backend via REST API or WebSocket, sends user messages, and displays streamed responses. Supports customization (colors, fonts, branding) via configuration parameters. The widget handles session management, message history, and typing indicators without requiring backend changes.","intents":["Deploy chatbots on websites without custom frontend development","Expose internal flows as customer-facing chat interfaces","Customize chatbot appearance to match brand guidelines","Track user interactions and feedback"],"best_for":["Non-technical teams deploying chatbots on websites","Customer support teams adding AI assistants to support pages","Product teams A/B testing chatbot UX with minimal engineering effort"],"limitations":["Widget is iframe-based; limited styling customization due to iframe sandbox restrictions","No built-in analytics; user interactions not automatically logged","Widget size and position are fixed; no responsive layout for mobile","CORS restrictions may require backend configuration for cross-origin requests","Session management is stateless; user context not persisted across page reloads without explicit session ID"],"requires":["Flowise instance running and accessible from the web","Chatflow ID (from Flowise UI)","Embedding code snippet (HTML/JavaScript)","Optional: custom CSS for styling"],"input_types":["chatflow ID (string)","widget configuration (colors, fonts, position)","user message (text)"],"output_types":["embedded widget (HTML/JavaScript)","chat response (streamed text)","session metadata (session ID, message history)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__cap_9","uri":"capability://tool.use.integration.rest.api.generation.for.flows.with.automatic.endpoint.creation","name":"rest api generation for flows with automatic endpoint creation","description":"Automatically generates REST API endpoints for each chatflow, allowing external applications to invoke flows programmatically. Each endpoint accepts POST requests with input variables, executes the flow, and returns the result as JSON. The system generates OpenAPI/Swagger documentation for all endpoints, enabling API discovery and client generation. Endpoints support authentication (API keys), rate limiting, and request/response logging.","intents":["Integrate flows into external applications via REST API","Build backend services that invoke flows without embedding Flowise","Enable third-party integrations (Zapier, Make, etc.) to trigger flows","Document flow APIs for other developers"],"best_for":["Teams integrating Flowise flows into larger applications","API-first architectures where flows are backend services","Organizations exposing flows to external partners via API"],"limitations":["API endpoints are synchronous; long-running flows may timeout (typically 30-60s)","No built-in pagination for large result sets; responses must fit in memory","Rate limiting is per-endpoint; no global rate limiting across all flows","Authentication is API-key based; no OAuth or JWT support","Request/response logging is basic; no detailed audit trails"],"requires":["Flowise instance with API server running","Chatflow ID","API key (for authentication)","HTTP client (curl, Python requests, etc.)"],"input_types":["HTTP POST request with JSON body (input variables)","API key (header or query parameter)"],"output_types":["JSON response (flow output)","HTTP status code (200, 400, 500, etc.)","OpenAPI/Swagger documentation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise__headline","uri":"capability://automation.workflow.visual.ai.workflow.builder","name":"visual ai workflow builder","description":"Flowise is a low-code platform that allows users to visually create AI workflows and chatbots using a drag-and-drop interface, connecting various AI models and tools without writing code.","intents":["best visual AI workflow builder","low-code platform for AI chatbots","drag-and-drop AI workflow tool","create AI agent workflows visually","no-code chatbot development tool"],"best_for":["developers looking for low-code solutions","teams needing rapid prototyping"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","React 18+ (UI layer)","Database (SQLite, PostgreSQL, or MySQL) for flow persistence","At least one LLM API key (OpenAI, Anthropic, etc.)","Valid API keys for at least one LLM provider","Database with encryption support (AES-256 for credential storage)","Environment variables for provider endpoints (optional for self-hosted models)","Message queue service (Redis, RabbitMQ, Bull, etc.)","Worker processes (Node.js or Python)","Database for job status and results"],"failure_modes":["Complex conditional logic requires custom code nodes; pure visual composition limited to sequential/parallel flows","Canvas performance degrades with >50 nodes due to React re-render overhead","No built-in version control for flows; requires external Git integration for collaboration","Variable scoping across nested subflows not fully supported; global context only","No built-in fallback mechanism if primary provider is down; requires custom error handling nodes","Provider-specific parameters (temperature, top_p, etc.) must be manually mapped; no auto-conversion","Credential rotation requires manual re-entry; no automated secret refresh","Rate limiting and quota management delegated to provider SDKs; no centralized throttling","Asynchronous execution adds latency; results are not immediately available","Queue persistence requires external service (Redis, RabbitMQ); adds operational complexity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=flowise","compare_url":"https://unfragile.ai/compare?artifact=flowise"}},"signature":"g/Fhw+3vAOAP1ZeiceDtlNw2MPbBOcaYfWVlLV7xUHFrBV/i1HNH6cbFEeCyDSlkn31p65GvR3uZJnnGENDuAQ==","signedAt":"2026-06-23T14:23:31.835Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/flowise","artifact":"https://unfragile.ai/flowise","verify":"https://unfragile.ai/api/v1/verify?slug=flowise","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}