{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-flowiseai--flowise","slug":"flowiseai--flowise","name":"Flowise","type":"product","url":"https://flowiseai.com","page_url":"https://unfragile.ai/flowiseai--flowise","categories":["app-builders"],"tags":["agentic-ai","agentic-workflow","agents","artificial-intelligence","chatbot","chatgpt","javascript","langchain","large-language-models","low-code","multiagent-systems","no-code","openai","rag","react","typescript","workflow-automation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-flowiseai--flowise__cap_0","uri":"capability://automation.workflow.visual.node.graph.workflow.composition.with.drag.and.drop.canvas","name":"visual node-graph workflow composition with drag-and-drop canvas","description":"Flowise provides a React-based canvas UI that renders a directed acyclic graph (DAG) of interconnected nodes representing AI components (models, tools, retrievers, memory). Users drag nodes onto the canvas, configure their properties via side panels, and connect edges to define data flow. The canvas maintains node state, validates connections, and serializes the entire workflow graph to JSON for persistence and execution. This eliminates the need to write orchestration code manually.","intents":["I want to build a multi-step AI workflow without writing Python or JavaScript orchestration code","I need to visually see how data flows between LLM calls, tool invocations, and memory components","I want to rapidly prototype agent architectures and iterate on node configurations"],"best_for":["non-technical founders and product managers prototyping AI applications","teams building internal AI tools who want to avoid boilerplate orchestration code","researchers experimenting with different agent topologies and tool combinations"],"limitations":["Complex conditional logic and branching require custom code nodes; pure visual composition has limited expressiveness for multi-path workflows","Canvas performance degrades with >100 nodes due to React re-render overhead and edge rendering","No built-in version control for workflows; export/import via JSON is manual"],"requires":["Node.js 18+","Modern browser with WebGL support for smooth canvas rendering","Flowise server running locally or remotely"],"input_types":["node configuration objects (JSON)","edge definitions (source node ID, target node ID, connection type)"],"output_types":["serialized workflow graph (JSON)","executable chatflow or agentflow definition"],"categories":["automation-workflow","ui-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--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":"Flowise abstracts over multiple LLM providers (OpenAI, Anthropic, Ollama, HuggingFace, etc.) through a unified Model Registry that maps provider-specific APIs to a common interface. Credentials are encrypted and stored per-user in the database; at runtime, the system resolves provider credentials from environment variables or the credential store, instantiates the appropriate chat model class, and handles provider-specific configuration (temperature, max_tokens, system prompts). This allows users to swap LLM providers in the UI without code changes.","intents":["I want to test my workflow with different LLM providers (GPT-4, Claude, local Ollama) without rebuilding","I need to securely manage API keys for multiple providers without hardcoding them","I want to use a cheaper model in production but test with a more capable model during development"],"best_for":["teams evaluating multiple LLM providers for cost/performance tradeoffs","enterprises requiring multi-provider redundancy or compliance with specific model vendors","developers building SaaS platforms where end-users bring their own API keys"],"limitations":["Provider-specific features (vision, function calling, streaming) require custom node implementations; abstraction layer does not auto-map all capabilities","Credential rotation and expiration are not built-in; manual updates required","Rate limiting and quota management are delegated to provider SDKs; no unified throttling layer"],"requires":["API key or credentials for at least one LLM provider (OpenAI, Anthropic, etc.)","Database access to store encrypted credentials","Environment variables or credential store configured for the chosen provider"],"input_types":["provider name (string: 'openai', 'anthropic', 'ollama')","model identifier (string: 'gpt-4', 'claude-3-opus', etc.)","credential object (API key, endpoint URL, etc.)"],"output_types":["instantiated chat model object (LangChain BaseChatModel subclass)","streaming or non-streaming LLM responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_10","uri":"capability://data.processing.analysis.document.loader.and.web.scraper.integration.for.knowledge.ingestion","name":"document loader and web scraper integration for knowledge ingestion","description":"Flowise provides pre-built Document Loader nodes that ingest data from various sources: PDF files, web pages, CSV/JSON files, text documents, and more. Each loader handles format-specific parsing (PDF extraction, HTML scraping, CSV parsing) and outputs standardized document objects with content and metadata. Users connect a loader to a Vector Store node to index documents for RAG. The system supports both file uploads and URL-based loading, and loaders can be chained to process multiple sources in a single workflow.","intents":["I want to ingest my company's PDF documentation into a searchable knowledge base","I need to scrape content from multiple web pages and index them for semantic search","I want to load data from CSV or JSON files and make it queryable via a chatbot"],"best_for":["teams building knowledge base chatbots from existing documentation","enterprises ingesting large document repositories","applications requiring multi-source data integration"],"limitations":["PDF parsing is basic; complex layouts, images, and scanned PDFs may not extract correctly","Web scraping is limited to simple HTML; JavaScript-rendered content requires additional configuration","No built-in deduplication; loading the same document multiple times creates duplicate embeddings","Large files are not streamed; entire documents are loaded into memory, which can cause OOM errors for very large files"],"requires":["Document source (file upload, URL, or file path)","Appropriate file format (PDF, HTML, CSV, JSON, TXT, etc.)","Vector store and embedding model configured downstream"],"input_types":["file path or URL (string)","file content (binary or text)","loader configuration (e.g., chunk size, metadata extraction rules)"],"output_types":["document objects (array of {content: string, metadata: object})","extracted text (string)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_11","uri":"capability://text.generation.language.prompt.template.composition.with.variable.interpolation.and.formatting","name":"prompt template composition with variable interpolation and formatting","description":"Flowise provides Prompt Template nodes that allow users to define LLM prompts with variable placeholders. Users write prompt text with {variable_name} syntax, and the system interpolates values from upstream nodes at execution time. Templates support conditional formatting (if-else logic), loops, and custom formatting functions. This enables dynamic prompt generation based on workflow state without hardcoding prompts. Prompt templates are versioned and can be reused across multiple workflows.","intents":["I want to create a prompt that dynamically changes based on the user's input or context","I need to manage multiple prompt variations (e.g., different system prompts for different use cases) without duplicating workflows","I want to test different prompt formulations and compare their effectiveness"],"best_for":["teams experimenting with prompt engineering and A/B testing","applications requiring dynamic prompts based on user context or data","enterprises managing multiple prompt variations for different domains"],"limitations":["Prompt template syntax is limited; complex logic requires custom code nodes","No built-in prompt versioning or A/B testing framework; users must manually manage variations","Variable interpolation is simple string substitution; no type checking or validation","No integration with prompt optimization tools or frameworks (e.g., DSPy, PromptOptimizer)"],"requires":["Prompt template text with {variable_name} placeholders","Upstream nodes providing variable values"],"input_types":["prompt template string (with {variable} placeholders)","variable values (any JSON-serializable type)"],"output_types":["interpolated prompt string (ready for LLM)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_12","uri":"capability://data.processing.analysis.output.parsing.and.structured.data.extraction.from.llm.responses","name":"output parsing and structured data extraction from llm responses","description":"Flowise provides Output Parser nodes that convert unstructured LLM responses into structured data (JSON, CSV, etc.). Users define an output schema (e.g., JSON Schema) and the parser attempts to extract and validate the response against that schema. If parsing fails, the system can retry with a corrected prompt or return an error. This enables workflows to reliably extract structured data from LLM outputs for downstream processing. Parsers support multiple formats: JSON, CSV, key-value pairs, and custom regex patterns.","intents":["I want to extract structured data (e.g., name, email, phone) from an LLM response","I need to validate that the LLM output matches a specific schema before passing it to downstream systems","I want to convert LLM responses into a format that my database or API expects"],"best_for":["workflows that need to extract structured data from LLM responses","systems integrating LLM outputs with databases or APIs","applications requiring reliable data extraction without manual post-processing"],"limitations":["Parsing reliability depends on LLM capability and prompt clarity; weaker models may produce unparseable output","No built-in retry logic with prompt refinement; failed parsing returns an error","Schema validation is basic; complex nested structures or conditional fields are not well-supported","No feedback loop to improve parsing; users must manually adjust prompts or schemas"],"requires":["Output schema definition (JSON Schema, regex pattern, etc.)","LLM response (string)"],"input_types":["LLM response (string)","output schema (JSON Schema or pattern)"],"output_types":["parsed data (JSON object, CSV, etc.)","parsing error (if validation fails)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_13","uri":"capability://memory.knowledge.caching.and.response.memoization.for.repeated.queries","name":"caching and response memoization for repeated queries","description":"Flowise implements caching at multiple levels to reduce redundant LLM calls and improve performance. Semantic caching stores LLM responses keyed by input embeddings, so similar queries return cached results without calling the LLM. Exact-match caching stores responses for identical inputs. The system also caches embeddings and vector store queries. Users can enable/disable caching per node, and cache TTL is configurable. This reduces API costs and latency for repeated or similar queries.","intents":["I want to reduce LLM API costs by caching responses for repeated queries","I need to speed up responses for similar user inputs without calling the LLM again","I want to cache embeddings and vector store queries to reduce latency"],"best_for":["applications with repetitive user queries (e.g., FAQ chatbots)","cost-sensitive deployments where LLM API costs are a concern","systems requiring low-latency responses for cached queries"],"limitations":["Semantic caching requires embedding similarity computation, which adds latency; not beneficial for one-off queries","Cache invalidation is manual; no automatic refresh when underlying data changes","Cache storage requires additional infrastructure (Redis, database); adds operational complexity","Cached responses may become stale if the LLM's knowledge or behavior changes"],"requires":["Cache backend (Redis, database, or in-memory)","Caching enabled on relevant nodes (LLM, embedding, vector store)"],"input_types":["query (string)","cache key (string or embedding vector)"],"output_types":["cached response (if hit) or fresh response (if miss)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_14","uri":"capability://safety.moderation.content.moderation.and.safety.filtering.for.llm.outputs","name":"content moderation and safety filtering for llm outputs","description":"Flowise provides Moderation nodes that filter LLM outputs for harmful content (hate speech, violence, sexual content, etc.). The system integrates with moderation APIs (OpenAI Moderation, Azure Content Moderator, etc.) and allows users to define custom moderation rules. If output is flagged as unsafe, the system can reject it, return a sanitized response, or escalate to a human reviewer. This enables workflows to enforce safety policies without manual review.","intents":["I want to ensure my chatbot doesn't output harmful or inappropriate content","I need to comply with content policies and avoid liability for user-generated or LLM-generated harmful content","I want to flag potentially unsafe outputs for human review"],"best_for":["consumer-facing chatbot applications with safety requirements","enterprises with strict content policies","systems handling sensitive topics where moderation is critical"],"limitations":["Moderation APIs are not perfect; false positives and false negatives occur","Moderation adds latency and API costs; not suitable for real-time, low-latency applications","Custom moderation rules are limited to simple pattern matching; complex contextual moderation requires custom code","No appeal or override mechanism; users cannot contest moderation decisions"],"requires":["Moderation API key (OpenAI, Azure, etc.) or custom moderation service","Moderation rules or thresholds configured"],"input_types":["LLM response (string)","moderation policy (rules or thresholds)"],"output_types":["moderation result (safe/unsafe flag)","moderation scores (per category: hate, violence, sexual, etc.)","sanitized response (if applicable)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_15","uri":"capability://planning.reasoning.evaluation.and.testing.framework.for.workflow.quality.assessment","name":"evaluation and testing framework for workflow quality assessment","description":"Flowise provides an Evaluation System that allows users to test workflows against predefined test cases and metrics. Users define test inputs, expected outputs, and evaluation criteria (e.g., semantic similarity, exact match, custom scoring functions). The system runs workflows against test cases, compares outputs to expectations, and generates reports showing pass/fail rates and performance metrics. This enables continuous testing and quality assurance for workflows without manual testing.","intents":["I want to test my workflow against a set of test cases to ensure it works correctly","I need to measure the quality of my chatbot's responses (e.g., relevance, accuracy)","I want to detect regressions when I update my workflow"],"best_for":["teams building production chatbots and agents","workflows requiring quality assurance and regression testing","applications where response quality is critical"],"limitations":["Evaluation metrics are limited to basic scoring (exact match, similarity); complex quality assessment requires custom code","No integration with external evaluation frameworks (e.g., RAGAS, LLM-as-judge); users must implement custom metrics","Test case management is manual; no built-in test generation or data augmentation","Evaluation results are not automatically tracked over time; no trend analysis or performance dashboards"],"requires":["Test cases with inputs and expected outputs","Evaluation metrics or scoring functions"],"input_types":["test case (input, expected output)","evaluation metric (function or predefined metric)"],"output_types":["evaluation result (pass/fail, score)","evaluation report (summary of all test cases)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_2","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.pipeline.with.multi.backend.vector.stores","name":"retrieval-augmented generation (rag) pipeline with multi-backend vector stores","description":"Flowise provides pre-built RAG nodes that orchestrate document loading, chunking, embedding, and retrieval. Users connect a Document Loader node (web scraper, PDF parser, etc.) to a Vector Store node (Pinecone, Weaviate, Chroma, FAISS, etc.), which embeds documents using a selected embedding model and stores them. At query time, a Retriever node converts user input to embeddings and performs similarity search, returning relevant documents to feed into an LLM prompt. The system abstracts over multiple vector store backends and embedding models, allowing users to swap storage without workflow changes.","intents":["I want to build a chatbot that answers questions about my company's documentation without fine-tuning","I need to ingest PDFs, web pages, and CSV files into a searchable knowledge base","I want to experiment with different vector stores (Pinecone, local FAISS) without rewriting retrieval logic"],"best_for":["customer support teams building internal knowledge base chatbots","enterprises with large document repositories needing semantic search","teams prototyping RAG systems and comparing vector store backends"],"limitations":["Chunking strategy is fixed (configurable chunk size/overlap) but does not support semantic or hierarchical chunking; complex document structures may require preprocessing","No built-in re-ranking or hybrid search (BM25 + semantic); retrieval quality depends entirely on embedding model and chunk size","Vector store synchronization is one-way (load → embed → store); no change detection or incremental updates for modified documents","Embedding model is fixed per workflow; cannot dynamically select embeddings based on query type"],"requires":["Vector store account or local instance (Pinecone API key, Weaviate endpoint, etc.)","Document source (web URL, PDF file, CSV, etc.)","Embedding model API key (OpenAI, HuggingFace, etc.) or local embedding service"],"input_types":["document source (URL, file path, text content)","chunk size and overlap parameters (integers)","vector store credentials and configuration"],"output_types":["retrieved document chunks (text with metadata)","similarity scores (float 0-1)","formatted context for LLM prompt"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_3","uri":"capability://planning.reasoning.agentic.reasoning.with.tool.calling.and.multi.step.planning","name":"agentic reasoning with tool calling and multi-step planning","description":"Flowise implements agent execution via a ReAct (Reasoning + Acting) pattern where an LLM iteratively decides which tools to invoke based on a user query. The system maintains an Agent node that wraps an LLM with a tool registry; at each step, the LLM generates a thought and action (tool name + arguments), the system executes the tool, and the result is fed back to the LLM for the next iteration. Tools are registered via a Tool node that defines input schema, execution logic, and output format. The agent continues until it reaches a stopping condition (max iterations, tool returns 'final answer', etc.). This enables complex multi-step reasoning without explicit workflow branching.","intents":["I want to build an agent that can search the web, read documents, and call APIs to answer complex questions","I need the agent to reason about which tools to use and in what order without hardcoding the sequence","I want to add custom tools (database queries, internal APIs) without modifying the agent logic"],"best_for":["teams building research assistants or data analysis agents","enterprises automating multi-step business processes (e.g., ticket resolution, data enrichment)","developers prototyping agentic AI systems and iterating on tool sets"],"limitations":["Agent reasoning quality depends heavily on LLM capability and prompt engineering; weaker models may fail to select appropriate tools or get stuck in loops","No built-in cost control; agents can make unbounded tool calls, leading to high API costs or rate limit hits","Tool execution is synchronous; long-running tools (API calls, database queries) block the agent loop and may timeout","No native support for hierarchical or multi-agent coordination; complex workflows require custom orchestration"],"requires":["LLM with function calling support (OpenAI, Anthropic, etc.)","Tool definitions with input schema (JSON Schema format)","Tool execution environment (local functions, API endpoints, etc.)"],"input_types":["user query (string)","tool registry (array of tool definitions with schema)","agent configuration (max iterations, stopping conditions, etc.)"],"output_types":["final answer (string)","tool call history (array of {tool_name, input, output})","reasoning trace (thought process at each step)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_4","uri":"capability://memory.knowledge.conversational.memory.management.with.multiple.backend.strategies","name":"conversational memory management with multiple backend strategies","description":"Flowise provides memory nodes that persist conversation history across turns using different backend strategies: in-memory (fast but ephemeral), database (persistent across sessions), or vector store (semantic memory for long-context retrieval). Users connect a Memory node to an Agent or Chat node, and the system automatically appends user messages and LLM responses to the memory store. At each turn, the memory is retrieved (optionally filtered by recency or relevance) and injected into the LLM prompt as context. This enables multi-turn conversations without manual context management.","intents":["I want my chatbot to remember previous messages in a conversation and use them to answer follow-up questions","I need to store conversation history persistently so users can resume chats across sessions","I want to use semantic memory to retrieve relevant past interactions for long-running conversations"],"best_for":["teams building customer support chatbots with multi-turn conversations","applications requiring persistent user sessions across deployments","systems managing long-context conversations where full history exceeds token limits"],"limitations":["In-memory storage is lost on server restart; no automatic persistence without explicit database configuration","Memory retrieval is not intelligent; the system appends all recent messages or top-K semantic matches without filtering for relevance to current query","No built-in memory summarization; long conversations can exceed LLM token limits, requiring manual pruning or external summarization","Multi-user memory isolation requires explicit user ID tracking; shared memory stores can leak information between users if not configured correctly"],"requires":["Memory backend configuration (in-memory, database, vector store)","Database connection string (if using persistent storage)","User session identifier (for multi-user isolation)"],"input_types":["user message (string)","LLM response (string)","memory backend type (enum: 'buffer', 'database', 'vector')"],"output_types":["retrieved memory context (string or array of messages)","formatted conversation history for LLM prompt"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_5","uri":"capability://code.generation.editing.custom.code.execution.with.sandboxed.function.nodes","name":"custom code execution with sandboxed function nodes","description":"Flowise allows users to define custom logic via Function nodes that execute arbitrary JavaScript/TypeScript code in a sandboxed environment. Users write code that receives input variables from upstream nodes, performs custom transformations or API calls, and returns output to downstream nodes. The system validates function signatures, provides access to a limited set of safe APIs (HTTP requests, JSON parsing, etc.), and isolates execution to prevent code injection or resource exhaustion. This enables workflows to incorporate logic that cannot be expressed via pre-built nodes.","intents":["I need to transform data between nodes in a way that pre-built nodes don't support","I want to call a custom internal API or perform business logic that's specific to my domain","I need to parse or format data (e.g., extract fields from JSON, build a prompt dynamically)"],"best_for":["developers building domain-specific workflows that require custom logic","teams integrating Flowise with proprietary systems or APIs","power users who need flexibility beyond pre-built nodes but want to stay in the visual builder"],"limitations":["Sandbox security is limited; code execution is not fully isolated and could potentially access sensitive environment variables or make unintended API calls","No built-in debugging or error handling UI; errors in custom code are logged but not easily debugged from the canvas","Code is stored as plain text in the workflow JSON; no version control or code review process","Performance is limited by Node.js event loop; long-running or CPU-intensive code blocks the entire workflow"],"requires":["JavaScript/TypeScript knowledge","Understanding of input/output variable names and types","Access to safe APIs (HTTP client, JSON utilities, etc.)"],"input_types":["JavaScript/TypeScript code (string)","input variables from upstream nodes (any JSON-serializable type)"],"output_types":["custom output (any JSON-serializable type)","error messages (string)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_6","uri":"capability://automation.workflow.queue.based.distributed.execution.with.worker.pool.architecture","name":"queue-based distributed execution with worker pool architecture","description":"Flowise supports a Queue Mode where workflow execution is decoupled from the main server. When a user triggers a workflow, the request is enqueued (Redis or database-backed) and picked up by worker processes that execute the workflow in parallel. This enables horizontal scaling: multiple workers can process workflows concurrently, and the main server remains responsive to UI requests. Workers pull jobs from the queue, execute the workflow graph, and write results back to the database. The system tracks job status (pending, running, completed, failed) and provides APIs to query results.","intents":["I want to scale my Flowise deployment to handle multiple concurrent workflow executions","I need to run long-running workflows without blocking the UI server","I want to distribute workflow execution across multiple machines for fault tolerance"],"best_for":["teams deploying Flowise in production with high concurrency requirements","enterprises running long-running agent workflows that cannot block the main server","systems requiring horizontal scaling and fault tolerance"],"limitations":["Queue setup requires Redis or database configuration; adds operational complexity","Job status tracking and result retrieval require polling or webhooks; no real-time bidirectional updates","Worker failure handling is basic; no automatic retry logic or dead-letter queue for failed jobs","Debugging distributed execution is harder than single-process mode; logs are scattered across worker processes"],"requires":["Redis instance or database for job queue","Worker process(es) running with access to the same database and queue","Environment variables configured for queue backend (REDIS_URL, DATABASE_URL, etc.)"],"input_types":["workflow execution request (chatflow ID, user input, variables)","job configuration (timeout, retry policy, etc.)"],"output_types":["job ID (string)","job status (enum: pending, running, completed, failed)","workflow execution result (string or structured data)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_7","uri":"capability://text.generation.language.streaming.response.generation.with.real.time.token.output","name":"streaming response generation with real-time token output","description":"Flowise implements streaming for LLM responses, allowing tokens to be sent to the client as they are generated rather than waiting for the full response. The system uses Server-Sent Events (SSE) or WebSocket connections to push tokens in real-time, and the UI displays them incrementally. This applies to both chat responses and agent reasoning steps. Streaming is transparent to the workflow definition; users enable it via configuration, and the execution engine handles buffering and flushing tokens to the client.","intents":["I want my chatbot to show responses as they are being generated, not wait for the full response","I need to display agent reasoning steps in real-time as the agent thinks through a problem","I want to reduce perceived latency by showing partial results immediately"],"best_for":["consumer-facing chatbot applications where perceived latency matters","research or analysis tools where users want to see reasoning steps in real-time","applications with long-running workflows where partial results are valuable"],"limitations":["Streaming adds complexity to error handling; errors mid-stream cannot be fully recovered","Token-by-token streaming increases network overhead compared to single response; not ideal for low-bandwidth connections","Streaming is not compatible with some LLM providers or models that do not support streaming APIs","UI must handle partial/incomplete responses gracefully; malformed JSON or incomplete tokens can break parsing"],"requires":["LLM provider with streaming API support (OpenAI, Anthropic, etc.)","Client-side WebSocket or SSE support","Workflow configured to enable streaming"],"input_types":["streaming enabled flag (boolean)","LLM response stream (async iterator of tokens)"],"output_types":["streamed tokens (string, sent incrementally)","complete response (string, after streaming ends)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_8","uri":"capability://automation.workflow.multi.tenant.workflow.isolation.with.user.scoped.credentials.and.data","name":"multi-tenant workflow isolation with user-scoped credentials and data","description":"Flowise supports multi-tenancy where multiple users can create and execute workflows independently. Each user's workflows, credentials, and conversation history are isolated via user ID checks at the database and API levels. Credentials are encrypted and stored per-user, so one user's API keys cannot be accessed by another. Workflows are scoped to the user who created them, and execution results are stored separately per user. This enables Flowise to be deployed as a shared platform where multiple teams or customers can use the same instance without data leakage.","intents":["I want to deploy Flowise as a multi-user SaaS platform where each customer has isolated workflows and credentials","I need to ensure that one user's API keys and conversation history are not visible to other users","I want to manage user access and permissions without running separate Flowise instances"],"best_for":["SaaS platforms built on top of Flowise","enterprises deploying Flowise for multiple teams with data isolation requirements","managed Flowise services offering multi-tenant deployments"],"limitations":["Multi-tenancy is not enforced at the database level; relies on application-level checks that could be bypassed if not carefully implemented","No built-in role-based access control (RBAC); all users have equal permissions within their tenant","Shared resources (vector stores, LLM providers) are not isolated; one user's queries can affect another's performance","Audit logging for multi-tenant access is not built-in; requires external logging infrastructure"],"requires":["User authentication system (JWT, OAuth, etc.)","User ID propagation in API requests and database queries","Database schema with user_id foreign keys on all relevant tables"],"input_types":["user ID (string or UUID)","workflow definition (JSON)","credentials (encrypted)"],"output_types":["user-scoped workflow execution result","user-scoped conversation history"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-flowiseai--flowise__cap_9","uri":"capability://automation.workflow.workflow.import.export.with.template.marketplace","name":"workflow import/export with template marketplace","description":"Flowise allows users to export workflows as JSON files and import them into other Flowise instances. The system also provides a Marketplace where pre-built workflow templates are shared and discoverable. Users can browse templates, import them with one click, and customize them for their use case. Exported workflows include node definitions, connections, and configuration but not credentials (which must be re-entered on import for security). 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