{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"flowise-chatflow-templates","slug":"flowise-chatflow-templates","name":"Flowise Chatflow Templates","type":"framework","url":"https://github.com/FlowiseAI/Flowise","page_url":"https://unfragile.ai/flowise-chatflow-templates","categories":["app-builders","rag-knowledge","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"flowise-chatflow-templates__cap_0","uri":"capability://automation.workflow.visual.drag.and.drop.chatflow.composition.with.node.based.graph.execution","name":"visual drag-and-drop chatflow composition with node-based graph execution","description":"Enables users to construct conversational AI workflows by dragging components onto a canvas and connecting them via edges, which are then serialized into a directed acyclic graph (DAG) and executed by traversing nodes in dependency order. The system uses a component plugin registry (NodesPool) to dynamically load 100+ pre-built node types (LLMs, memory, tools, retrievers) and executes the graph by resolving variable dependencies across nodes, streaming outputs back to the UI in real-time.","intents":["Build a RAG chatbot without writing code by connecting document loaders, vector stores, and LLM nodes","Create multi-step agent workflows that chain tool calls and reasoning steps visually","Prototype conversational AI applications rapidly without backend development","Compose complex chains combining memory, retrieval, and function calling in a single canvas"],"best_for":["Non-technical founders and product managers prototyping LLM applications","Teams building internal chatbots and document Q&A systems without dedicated ML engineers","Developers wanting to visualize and debug LLM workflows before deploying to production"],"limitations":["Graph execution is single-threaded per chatflow instance — parallel node execution not supported, limiting throughput for wide DAGs","Variable resolution system requires explicit node connections; implicit data flow or broadcast patterns not natively supported","Canvas UI performance degrades with >50 nodes due to DOM rendering overhead in React-based UI","No built-in version control for flows — export/import via JSON only, no git-style diffing or branching"],"requires":["Node.js 18+ for server runtime","Docker (optional, for containerized deployment)","At least one LLM API key (OpenAI, Anthropic, Ollama, etc.)","Modern browser supporting WebSockets for real-time streaming"],"input_types":["node configuration objects (JSON)","text prompts and user messages","document files (PDF, DOCX, TXT)","API credentials and connection strings"],"output_types":["streamed text responses","structured JSON from output parsers","chat history and conversation logs","exported flow definitions (JSON)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.model.registry.with.unified.chat.interface","name":"multi-provider llm model registry with unified chat interface","description":"Maintains a centralized model registry that abstracts over 50+ LLM providers (OpenAI, Anthropic, Ollama, HuggingFace, Azure, etc.) through a unified chat model interface. Each provider is implemented as a plugin with credential management, parameter mapping, and streaming support. The system resolves model selection at runtime based on node configuration, handles API key rotation via encrypted credential storage, and normalizes streaming responses across providers with different output formats.","intents":["Switch between LLM providers (e.g., OpenAI to Anthropic) without rebuilding the flow","Use local Ollama models for privacy-sensitive applications while keeping the same flow structure","Manage API credentials securely without exposing keys in flow definitions","Compare model outputs across providers by duplicating nodes with different model selections"],"best_for":["Teams evaluating multiple LLM providers for cost and latency tradeoffs","Organizations with multi-cloud or hybrid deployments requiring provider flexibility","Developers building applications that need fallback LLM providers for reliability"],"limitations":["Provider-specific parameters (e.g., OpenAI's top_p vs Anthropic's temperature) are not automatically mapped — users must manually configure per-provider settings","Streaming response normalization adds ~50-100ms latency due to format conversion between providers","No built-in load balancing or request routing across multiple provider instances","Credential rotation requires manual updates in the UI; no automatic token refresh for expiring credentials"],"requires":["API key or connection string for at least one LLM provider","Network access to provider endpoints (or local Ollama instance for on-prem models)","Flowise server with credential encryption enabled (requires DATABASE_URL)"],"input_types":["model name (string identifier)","provider-specific parameters (temperature, max_tokens, etc.)","API credentials (keys, endpoints, authentication tokens)","chat messages (user input, system prompts)"],"output_types":["streamed text tokens","complete chat responses","usage metrics (tokens consumed, cost estimates)","error messages with provider-specific failure reasons"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_10","uri":"capability://data.processing.analysis.document.loader.and.web.scraper.integration.with.format.support","name":"document loader and web scraper integration with format support","description":"Includes pre-built document loader nodes that support 20+ file formats (PDF, DOCX, XLSX, TXT, Markdown, JSON, CSV, HTML, web URLs) and automatically extract text content. The system handles format-specific parsing (PDF text extraction, DOCX table extraction, HTML DOM traversal) and provides chunking strategies (fixed size, recursive, semantic) to split documents into manageable pieces for embedding. Web scrapers support crawling websites with configurable depth and filtering rules. Loaded documents are automatically passed to embedding and vector store nodes for RAG pipelines.","intents":["Upload PDF documents and automatically extract text for RAG without manual preprocessing","Crawl websites and index their content for semantic search","Load structured data (CSV, JSON) and convert to embeddings for similarity search","Implement custom chunking strategies to optimize retrieval quality"],"best_for":["Teams building document Q&A systems without dedicated data engineering","Applications requiring web scraping and indexing for knowledge bases","Developers prototyping RAG systems with diverse document sources"],"limitations":["PDF text extraction quality varies by PDF type (scanned images, complex layouts) — OCR is not built-in","Web scraping is limited to public websites — no support for authenticated content or JavaScript-rendered pages","Chunking strategies are generic — no semantic-aware chunking based on document structure","Large documents (>100MB) may cause memory issues during parsing","No built-in deduplication — duplicate documents are indexed separately, wasting storage and affecting retrieval"],"requires":["Document files in supported formats or web URLs","Sufficient disk space for temporary file storage during parsing","For web scraping: network access to target websites"],"input_types":["document files (PDF, DOCX, XLSX, TXT, etc.)","web URLs for scraping","chunking parameters (chunk size, overlap, strategy)","document metadata (title, author, source)"],"output_types":["extracted text content","document chunks with metadata","embeddings (via downstream embedding nodes)","indexed documents in vector store"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_11","uri":"capability://data.processing.analysis.embedding.model.abstraction.with.multi.provider.support","name":"embedding model abstraction with multi-provider support","description":"Abstracts embedding models across 10+ providers (OpenAI, HuggingFace, Ollama, Cohere, Azure, etc.) through a unified embedding interface. Each provider is implemented as a plugin with its own API client, parameter mapping, and caching logic. The system supports batch embedding (multiple documents at once) and caches embeddings to avoid re-computing for identical inputs. Embedding models are selected at the node level, allowing different document sets to use different embedders in the same flow.","intents":["Switch embedding models (e.g., OpenAI to open-source) without rebuilding the RAG pipeline","Use local embedding models (via Ollama) for privacy-sensitive applications","Optimize embedding costs by choosing cheaper models for non-critical document sets","Compare embedding quality across providers by running parallel embedding nodes"],"best_for":["Teams evaluating embedding models for cost and quality tradeoffs","Organizations with privacy requirements requiring local or self-hosted embeddings","Developers building multi-model RAG systems for comparison and optimization"],"limitations":["Embedding model switching requires re-indexing all documents — no automatic migration between models","Batch embedding is limited by provider API limits — large batches may be rejected","Caching is in-memory only — embeddings are not persisted across server restarts","No built-in embedding quality metrics — users must manually evaluate embedding quality","Different embedding models produce incompatible vectors — cannot mix models in the same vector store"],"requires":["API key or connection string for at least one embedding provider","For local embeddings: Ollama instance with embedding model loaded","Sufficient memory for caching embeddings (typically 1-2MB per 1000 embeddings)"],"input_types":["text documents or chunks","embedding model name and provider","batch size and caching preferences"],"output_types":["embedding vectors (float arrays)","embedding metadata (model, dimension, provider)","cached embeddings (in-memory)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_12","uri":"capability://text.generation.language.prompt.template.management.with.variable.interpolation.and.conditioning","name":"prompt template management with variable interpolation and conditioning","description":"Provides prompt template nodes that support variable interpolation (e.g., {user_input}, {context}), conditional logic (if/else based on variables), and dynamic prompt construction. Templates are stored as text with special syntax for variables and conditions, and are compiled at runtime to inject actual values from the flow context. The system supports prompt versioning, testing, and optimization through A/B testing nodes that compare different prompt variants.","intents":["Create reusable prompt templates with variable placeholders for different use cases","Implement conditional prompts that change based on user input or flow state","Test and optimize prompts by comparing variants in A/B tests","Version control prompts and track changes over time"],"best_for":["Teams managing multiple prompts across different flows and wanting to reuse templates","Developers optimizing LLM behavior through prompt engineering and A/B testing","Organizations requiring prompt governance and version control"],"limitations":["Prompt syntax is custom and not standardized — no IDE support or syntax highlighting","Conditional logic is limited to simple if/else — complex branching requires multiple template nodes","No built-in prompt evaluation metrics — users must manually assess prompt quality","Template versioning is manual — no automatic version tracking or rollback","Variable interpolation is string-based — no type checking or validation of variable values"],"requires":["Prompt template text with variable placeholders","Variables defined in the flow context","Optional: A/B testing setup for prompt comparison"],"input_types":["prompt template text","variables and their values","conditional logic rules","prompt variants for A/B testing"],"output_types":["compiled prompt with variables interpolated","A/B test results and metrics","prompt version history"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_13","uri":"capability://automation.workflow.observability.and.execution.tracing.with.detailed.logging","name":"observability and execution tracing with detailed logging","description":"Provides comprehensive observability into flow execution through detailed logging, execution traces, and performance metrics. Each node execution is logged with input/output, latency, token usage, and error information. The system supports structured logging (JSON format) that can be exported to external logging systems (ELK, Datadog, etc.). Execution traces show the full DAG traversal with timing information, enabling bottleneck identification and optimization. Token usage is tracked per node and aggregated for cost analysis.","intents":["Debug flow execution by inspecting node inputs/outputs and execution order","Identify performance bottlenecks by analyzing node latencies and token usage","Monitor cost by tracking token consumption per flow and per user","Export logs to external systems for centralized monitoring and alerting"],"best_for":["Teams operating Flowise in production and needing visibility into execution","Developers debugging complex flows with multiple nodes and dependencies","Organizations tracking LLM costs and optimizing token usage"],"limitations":["Logging adds overhead — detailed logging can increase execution latency by 5-10%","Logs are stored in the database by default — large-scale deployments may need external log storage","Token usage tracking is approximate — actual usage may vary based on LLM provider","No built-in alerting — users must implement custom alerting based on logs","Trace visualization is limited to text logs — no graphical trace viewer"],"requires":["DATABASE_URL for storing logs (or external logging system)","Optional: ELK, Datadog, or other logging backend for centralized logs"],"input_types":["flow execution events (node start, node end, error)","node inputs and outputs","performance metrics (latency, token count)"],"output_types":["structured logs (JSON format)","execution traces with timing","performance metrics and cost analysis","error logs and stack traces"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_2","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.pipeline.with.multi.backend.vector.store.support","name":"retrieval-augmented generation (rag) pipeline with multi-backend vector store support","description":"Provides pre-built RAG nodes that orchestrate document ingestion, embedding, and retrieval across 15+ vector store backends (Pinecone, Weaviate, Milvus, Supabase, local in-memory, etc.). The pipeline includes document loaders for 20+ file formats (PDF, DOCX, web pages), chunking strategies (recursive, semantic), and retrievers that support hybrid search (keyword + semantic), metadata filtering, and re-ranking. The system manages vector store connections via credentials, handles embedding model selection (OpenAI, HuggingFace, local), and streams retrieved documents to downstream LLM nodes.","intents":["Build a document Q&A system by uploading PDFs and connecting them to an LLM without writing ETL code","Switch vector stores (e.g., Pinecone to self-hosted Milvus) by changing a single node parameter","Implement hybrid search combining keyword matching and semantic similarity for better retrieval quality","Create multi-document RAG flows where different document sets feed into the same LLM for comparison"],"best_for":["Teams building internal knowledge bases and document search systems","Organizations migrating from keyword search to semantic search without re-architecting","Developers prototyping RAG applications before committing to a specific vector store vendor"],"limitations":["Document chunking is static (fixed size or recursive split) — no adaptive chunking based on document structure or semantic boundaries","Embedding model selection is global per flow — cannot use different embedders for different document sets in the same flow","Vector store indexing is synchronous and blocking — large document uploads (>1GB) will freeze the UI until indexing completes","No built-in deduplication or update handling — re-uploading the same document creates duplicate embeddings unless manually purged","Retrieval quality depends heavily on chunk size and embedding model; no built-in evaluation or optimization tools"],"requires":["Vector store account and API credentials (or local instance for Milvus, Weaviate, etc.)","Embedding model API key (OpenAI, HuggingFace, or local model via Ollama)","Document files in supported formats (PDF, DOCX, TXT, Markdown, JSON, CSV, web URLs)","Sufficient storage quota in vector store for embeddings (typically 1-2KB per chunk)"],"input_types":["document files (PDF, DOCX, TXT, web URLs)","chunking parameters (chunk size, overlap, strategy)","embedding model configuration","vector store credentials and index name","user query (text string)"],"output_types":["retrieved document chunks (text + metadata)","similarity scores and re-ranking scores","document source references (file name, page number, URL)","augmented context passed to LLM nodes"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_3","uri":"capability://memory.knowledge.conversational.memory.management.with.multiple.backend.strategies","name":"conversational memory management with multiple backend strategies","description":"Provides memory nodes that persist conversation history across multiple backend strategies (in-memory, database, vector store, Redis) with configurable retention policies. The system supports different memory types (buffer, summary, entity-based) that integrate with the variable resolution system to inject historical context into LLM prompts. Memory is scoped per conversation session (via session ID) and can be cleared, summarized, or pruned based on token count or time-to-live (TTL) policies.","intents":["Maintain conversation context across multiple turns without manually managing chat history","Implement memory summarization to keep token usage under control in long conversations","Switch memory backends (e.g., in-memory to Redis) for scaling from single-user to multi-user deployments","Clear conversation history after a session ends to comply with data retention policies"],"best_for":["Teams building multi-turn conversational agents that need persistent context","Applications with strict data retention or privacy requirements (GDPR, HIPAA)","Developers scaling from prototype (in-memory) to production (Redis/database) deployments"],"limitations":["Memory summarization uses the same LLM as the main conversation, consuming additional tokens and adding latency (typically 2-5 seconds per summary)","No built-in memory search or retrieval — all historical context is injected into the prompt, which can exceed token limits in very long conversations","Entity-based memory requires manual entity extraction configuration; no automatic entity recognition","Memory is scoped per conversation session — no cross-session learning or knowledge accumulation across users","TTL-based pruning is not real-time; expired records are only cleaned up on next access"],"requires":["For database memory: DATABASE_URL environment variable pointing to PostgreSQL or SQLite","For Redis memory: Redis instance and REDIS_URL configuration","For vector store memory: Vector store credentials and embedding model","Session ID passed in chat API requests to scope memory per conversation"],"input_types":["chat messages (user input, assistant responses)","memory type configuration (buffer, summary, entity)","retention policy (max tokens, TTL, max messages)","session ID (string identifier for conversation scope)"],"output_types":["formatted memory context (text string injected into prompts)","summarized conversation history","extracted entities and relationships","memory metadata (creation time, token count, last accessed)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_4","uri":"capability://tool.use.integration.tool.calling.and.function.execution.with.sandboxed.custom.code","name":"tool calling and function execution with sandboxed custom code","description":"Enables agents to call external tools and custom functions through a schema-based function registry. Tools are defined as nodes with input/output schemas that are passed to the LLM as function definitions. The system supports native tool calling for OpenAI and Anthropic APIs, and implements a fallback mechanism for other providers using prompt-based function calling. Custom code execution is sandboxed using Node.js VM2 or similar isolation to prevent malicious code from accessing the host system. Tool results are automatically parsed and injected back into the agent loop.","intents":["Enable an agent to call APIs, databases, or custom functions based on LLM reasoning","Define custom tools visually by specifying input/output schemas without writing code","Execute user-provided code safely in an isolated sandbox without exposing the host system","Implement multi-step agent loops where tools are called iteratively until a goal is reached"],"best_for":["Teams building autonomous agents that need to interact with external systems","Applications requiring dynamic tool composition based on user input","Developers who want to add custom business logic to LLM workflows without modifying core code"],"limitations":["Sandboxed code execution adds 100-200ms overhead per function call due to VM isolation","Custom code is limited to synchronous operations — async/await and promises are not fully supported in sandbox","Tool schemas must be manually defined; no automatic schema inference from function signatures","Prompt-based function calling (for non-OpenAI/Anthropic models) is less reliable than native tool calling and may fail on complex schemas","No built-in rate limiting or timeout enforcement for tool calls — runaway functions can block the agent loop"],"requires":["For native tool calling: OpenAI or Anthropic API with function calling support","For custom code: Node.js runtime with VM2 or similar sandbox library","Tool schemas defined as JSON Schema objects","External APIs or databases accessible from the Flowise server"],"input_types":["tool schema definitions (JSON Schema format)","tool implementation code (JavaScript/TypeScript for custom tools)","tool parameters (resolved from LLM output or user input)","LLM reasoning and tool selection"],"output_types":["tool execution results (JSON or text)","error messages and exception handling","tool call logs and execution traces","formatted results injected back into agent loop"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_5","uri":"capability://planning.reasoning.agent.orchestration.with.sequential.and.agentic.execution.modes","name":"agent orchestration with sequential and agentic execution modes","description":"Supports two execution patterns: sequential chains (deterministic step-by-step execution) and agentic loops (LLM-driven reasoning with tool calling and reflection). Agentic flows use a ReAct-style loop where the LLM reasons about the task, selects tools to call, observes results, and iterates until a stopping condition is met. The system manages agent state (current goal, tool history, reasoning trace) and provides hooks for custom stopping criteria, tool selection strategies, and output formatting. Execution is tracked with full observability (logs, traces, token counts) for debugging and optimization.","intents":["Build autonomous agents that reason about tasks and decide which tools to call without explicit step definitions","Implement multi-step workflows with conditional branching based on intermediate results","Debug agent behavior by inspecting reasoning traces, tool calls, and decision points","Optimize agent performance by analyzing token usage, latency, and tool call patterns"],"best_for":["Teams building autonomous agents for complex tasks (research, planning, problem-solving)","Applications requiring adaptive workflows that adjust based on intermediate results","Developers who want to experiment with different agent architectures (ReAct, Chain-of-Thought, etc.)"],"limitations":["Agentic loops are non-deterministic and can be unpredictable — same input may produce different tool call sequences","Agent execution time is unbounded — no built-in timeout or max-iteration limits, risking runaway loops","Stopping criteria are difficult to define precisely — agents may terminate prematurely or continue indefinitely","Token usage is hard to predict in agentic flows due to iterative reasoning and tool calling","No built-in agent evaluation or success metrics — users must manually assess whether agent achieved its goal"],"requires":["LLM with strong reasoning capabilities (GPT-4, Claude 3+, or equivalent)","Tool definitions with clear input/output schemas for the agent to reason about","Stopping criteria configuration (max iterations, success condition, timeout)","Observability setup (logging, tracing) for debugging agent behavior"],"input_types":["agent goal or task description (text)","available tools and their schemas","stopping criteria and constraints","initial context and system prompts"],"output_types":["final agent response or task result","reasoning trace (thought process, tool calls, observations)","execution logs and performance metrics","tool call history and intermediate results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_6","uri":"capability://automation.workflow.flow.export.import.and.marketplace.template.distribution","name":"flow export, import, and marketplace template distribution","description":"Enables users to export chatflows and agentflows as JSON definitions that capture the entire graph structure, node configurations, and variable bindings. Exported flows can be imported into other Flowise instances, shared via the built-in marketplace, or version-controlled in git. The system includes a marketplace where users can publish templates (with descriptions, tags, ratings) that others can discover and import with one click. Marketplace templates are validated for security (no hardcoded credentials) and compatibility (required LLM providers, vector stores) before publication.","intents":["Share a chatflow with team members or the community without requiring code access","Version control flow definitions in git and track changes across iterations","Discover and reuse pre-built templates for common use cases (customer support, document Q&A, research assistant)","Publish a flow as a marketplace template to build reputation and help others"],"best_for":["Teams collaborating on flow development and needing to share definitions","Organizations building internal template libraries for standardized use cases","Community contributors wanting to share flows and build reputation in the Flowise ecosystem"],"limitations":["Exported flows contain node configurations but not actual data (documents, conversation history) — users must re-upload documents after importing","Credentials are not exported for security reasons — users must re-enter API keys after importing a flow","No built-in diff or merge tools for flows — git version control shows raw JSON diffs which are hard to read","Marketplace templates are not automatically updated — users must manually re-import to get template updates","No dependency resolution — importing a template doesn't automatically install required LLM providers or vector stores"],"requires":["Flowise instance with export/import enabled (default)","For marketplace: Flowise account and marketplace access (may require authentication)","For git version control: git repository and JSON diff tools"],"input_types":["flow definition (JSON exported from Flowise)","marketplace template metadata (name, description, tags, category)","template validation rules (required providers, compatibility checks)"],"output_types":["exported flow JSON file","marketplace template listing (with ratings, downloads, reviews)","imported flow ready for use in canvas","git-compatible JSON diffs for version control"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_7","uri":"capability://text.generation.language.real.time.streaming.chat.interface.with.websocket.support","name":"real-time streaming chat interface with websocket support","description":"Provides a built-in chat UI that streams LLM responses token-by-token via WebSockets, enabling real-time progressive rendering of responses. The interface supports markdown rendering, code syntax highlighting, and custom message formatting. Streaming is implemented at the execution engine level, where each node can emit partial results that are immediately sent to the client without waiting for the full response. The system handles connection management, reconnection logic, and message ordering to ensure consistent chat history.","intents":["Display LLM responses as they are generated for better user experience and perceived latency","Build chat interfaces that feel responsive and interactive even with slow LLM providers","Embed the chat UI in external applications via iframe or SDK","Monitor real-time execution traces and debugging information during flow execution"],"best_for":["Teams building customer-facing chatbot applications requiring responsive UX","Developers embedding Flowise chat in websites or applications","Users wanting to debug flow execution in real-time by observing streaming outputs"],"limitations":["WebSocket connections are stateful and require sticky sessions in load-balanced deployments","Streaming adds complexity to error handling — partial responses may be sent before errors occur","Browser compatibility issues with older browsers that don't support WebSockets","No built-in rate limiting on streaming — clients can request unlimited streams, potentially overwhelming the server","Streaming responses cannot be easily cached or replayed because they are sent incrementally"],"requires":["WebSocket support in the browser (all modern browsers)","Flowise server with WebSocket endpoint enabled (default)","Network connectivity between client and server (no offline support)"],"input_types":["user messages (text input)","chat session ID (for conversation scoping)","flow parameters and variables"],"output_types":["streamed text tokens (real-time)","complete chat responses","execution traces and debugging info","chat history (persisted in database)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_8","uri":"capability://safety.moderation.multi.tenancy.and.role.based.access.control.rbac","name":"multi-tenancy and role-based access control (rbac)","description":"Supports multi-tenant deployments where multiple organizations or users can share a single Flowise instance with isolated data and access controls. Each tenant has its own flows, credentials, documents, and conversation history. The system implements role-based access control (admin, editor, viewer) with fine-grained permissions for creating, editing, deleting, and sharing flows. Credentials are encrypted per-tenant and never shared across tenants. The database schema includes tenant isolation at the row level, ensuring data privacy and compliance.","intents":["Deploy Flowise as a SaaS platform serving multiple customers with complete data isolation","Implement team collaboration with different permission levels (admin, editor, viewer)","Ensure compliance with data privacy regulations (GDPR, HIPAA) by isolating tenant data","Share flows with specific team members while restricting access to others"],"best_for":["SaaS providers building Flowise-based platforms for multiple customers","Enterprise teams requiring role-based access control and audit trails","Organizations with strict data privacy requirements (healthcare, finance)"],"limitations":["Multi-tenancy adds complexity to database queries — all queries must include tenant_id filters, risking data leakage if filters are missed","Credential encryption is per-tenant but uses the same encryption key for all tenants — key compromise affects all tenants","No built-in audit logging for compliance — users must implement custom audit trails for regulatory requirements","Role-based permissions are coarse-grained (admin/editor/viewer) — no custom permission definitions","Tenant isolation is at the database row level, not the schema level — shared database increases blast radius of SQL injection attacks"],"requires":["DATABASE_URL pointing to a shared database (PostgreSQL recommended for multi-tenancy)","Tenant identification mechanism (subdomain, header, or user claim)","Encryption key for credential storage (ENCRYPTION_KEY environment variable)","User authentication system (OIDC, JWT, or custom)"],"input_types":["tenant ID (string identifier)","user ID and role (admin, editor, viewer)","flow definitions and configurations","credentials and API keys"],"output_types":["tenant-isolated flows and configurations","encrypted credentials (never exposed to other tenants)","access control decisions (allow/deny)","audit logs (if implemented)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flowise-chatflow-templates__cap_9","uri":"capability://automation.workflow.queue.based.asynchronous.execution.with.worker.pool.scaling","name":"queue-based asynchronous execution with worker pool scaling","description":"Provides a queue mode where chatflow and agentflow execution is decoupled from the HTTP request/response cycle. When a user submits a message, it is enqueued (in Redis or database) and processed by a pool of worker processes. Workers execute flows in parallel, with results stored in a database and delivered to clients via polling or WebSocket subscriptions. 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