{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-langflow-ai--langflow","slug":"langflow-ai--langflow","name":"langflow","type":"workflow","url":"http://www.langflow.org","page_url":"https://unfragile.ai/langflow-ai--langflow","categories":["app-builders"],"tags":["agents","chatgpt","generative-ai","large-language-models","multiagent","react-flow"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-langflow-ai--langflow__cap_0","uri":"capability://automation.workflow.visual.flow.graph.authoring.with.drag.and.drop.component.composition","name":"visual flow graph authoring with drag-and-drop component composition","description":"Langflow provides a React 19 SPA frontend using @xyflow/react (formerly React Flow) for visual canvas-based workflow design. Users drag component nodes onto a canvas, connect them via edges, and configure parameters through a GenericNode component abstraction that dynamically renders UI based on component input type schemas. The frontend maintains state via a Redux-like store and validates connections before execution, preventing invalid graph topologies.","intents":["I want to visually compose LLM workflows without writing code","I need to connect multiple AI components (LLMs, retrievers, tools) in a DAG without syntax errors","I want to see the structure of my agent workflow as a graph and iterate quickly"],"best_for":["non-technical founders and business users prototyping AI workflows","teams building RAG pipelines who prefer visual composition over code","developers wanting rapid iteration on agent architectures"],"limitations":["Complex conditional logic requires custom component wrapping — no native if/else branching in canvas","Large graphs (100+ nodes) may experience canvas performance degradation due to React re-renders","Connection validation is client-side only — some invalid topologies only caught at execution time"],"requires":["Modern browser with ES2020+ support (Chrome 90+, Firefox 88+, Safari 14+)","Backend API running (FastAPI server)","Node.js 18+ for development builds"],"input_types":["component definitions (JSON schema)","flow state (node positions, connections, parameters)"],"output_types":["serialized flow graph (JSON)","execution-ready DAG structure"],"categories":["automation-workflow","visual-programming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_1","uri":"capability://tool.use.integration.component.registry.and.dynamic.component.loading","name":"component registry and dynamic component loading","description":"Langflow maintains a centralized component registry that dynamically loads component definitions from Python modules at runtime. Components are discovered via a Component Lifecycle system that introspects Python classes, extracts input/output type metadata, and registers them in a schema-based registry. The registry supports component bundles (e.g., Docling, NVIDIA) that can be installed as optional packages, and components are loaded on-demand during flow execution via a Component Loading service that instantiates and validates them.","intents":["I want to extend Langflow with custom components without modifying core code","I need to organize components into reusable bundles (e.g., document processing, model inference)","I want the system to auto-discover and register my custom components at startup"],"best_for":["teams building domain-specific component libraries (e.g., medical NLP, finance)","developers extending Langflow with proprietary integrations","organizations wanting to version and distribute component bundles across teams"],"limitations":["Component discovery is Python-only — no support for external binaries or non-Python plugins","Type introspection relies on Python type hints — components with dynamic types require manual schema definition","No built-in versioning for components — bundle version conflicts must be managed externally"],"requires":["Python 3.9+","Component class inheriting from Langflow base component class","Type hints on component input/output methods"],"input_types":["Python component class definitions","Component bundle packages (pip-installable)"],"output_types":["component schema (JSON)","instantiated component objects","component metadata (name, description, inputs, outputs)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_10","uri":"capability://code.generation.editing.custom.component.development.with.python.sdk.and.type.system","name":"custom component development with python sdk and type system","description":"Langflow provides a Python SDK (langflow.custom) that allows developers to create custom components by subclassing a base component class and defining input/output methods with type hints. The SDK handles type introspection, schema generation, and component registration automatically. Custom components can access the component context (flow ID, execution metadata) and integrate with Langflow's logging and error handling. The Python SDK supports both synchronous and asynchronous component execution. Components are packaged as Python modules and can be distributed via pip.","intents":["I want to create custom components for domain-specific logic (e.g., medical NLP, financial analysis)","I need to wrap existing Python libraries as Langflow components without modifying them","I want to build reusable component libraries that my team can share"],"best_for":["developers building domain-specific component libraries","teams wanting to encapsulate proprietary logic in components","organizations standardizing on Langflow for workflow orchestration"],"limitations":["Custom components must be Python-only — no support for other languages without language-specific SDKs","Type introspection relies on Python type hints — dynamic types require manual schema definition","Component testing requires Langflow backend running — no standalone component testing framework","Component versioning is manual — no built-in version management or dependency resolution"],"requires":["Python 3.9+","Langflow Python SDK (pip install langflow)","Type hints on component methods"],"input_types":["Python component class definition","Input method with type hints"],"output_types":["Component instance","Component schema (JSON)","Output from component execution"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_11","uri":"capability://automation.workflow.tracing.and.observability.with.execution.logs.and.debugging","name":"tracing and observability with execution logs and debugging","description":"Langflow includes a tracing and observability system that logs all execution events (node start, completion, error, input/output) and makes them available for debugging. Execution traces are stored in the database and can be queried via the UI or API. The system integrates with external observability platforms (LangSmith, Datadog, New Relic) via standard logging and tracing protocols. Traces include detailed information about component execution (duration, memory usage, errors) and can be used to identify performance bottlenecks and debug failures.","intents":["I want to debug why my workflow failed or produced unexpected results","I need to monitor workflow performance and identify slow components","I want to integrate Langflow execution traces with my observability stack (Datadog, New Relic)"],"best_for":["teams operating AI workflows in production and needing observability","developers debugging complex multi-step workflows","organizations with existing observability infrastructure wanting to integrate Langflow"],"limitations":["Trace storage grows unbounded — no automatic pruning or archival of old traces","Tracing adds overhead (~5-10% latency) to workflow execution","Integration with external observability platforms requires manual configuration","Trace queries are limited to simple filters — no advanced analytics or aggregations"],"requires":["Langflow backend with tracing enabled","Database storage for traces","Optional: external observability platform credentials"],"input_types":["execution events (generated automatically during flow execution)"],"output_types":["execution traces (JSON)","logs (text)","performance metrics (duration, memory usage)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_12","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.standardized.tool.communication","name":"model context protocol (mcp) integration for standardized tool communication","description":"Langflow supports the Model Context Protocol (MCP), a standardized protocol for LLMs to communicate with external tools and data sources. MCP allows Langflow to integrate with any MCP-compatible server (e.g., Anthropic's MCP servers for file systems, databases, APIs) without custom integration code. The system handles MCP protocol negotiation, tool discovery, and execution. Tools exposed via MCP are automatically registered in the function registry and available to agents.","intents":["I want to use standardized MCP tools (file systems, databases, APIs) in my workflows","I need to integrate with Anthropic's MCP ecosystem without custom code","I want my agents to have access to a wide range of tools via a standard protocol"],"best_for":["teams building agents that need access to diverse tools and data sources","developers wanting to leverage Anthropic's MCP ecosystem","organizations standardizing on MCP for tool communication"],"limitations":["MCP support is limited to Anthropic-compatible implementations — OpenAI and other providers don't support MCP natively","MCP server availability and reliability depend on external providers","No built-in caching of MCP tool results — repeated calls to the same tool are not cached"],"requires":["MCP-compatible server running","MCP client library (Anthropic SDK or compatible)","LLM with MCP support (Claude 3+)"],"input_types":["MCP server configuration (URL, authentication)"],"output_types":["MCP tools (registered in function registry)","tool execution results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_13","uri":"capability://automation.workflow.data.serialization.and.flow.persistence.with.filesystem.sync","name":"data serialization and flow persistence with filesystem sync","description":"Langflow persists flows to a database and optionally syncs them to the filesystem as JSON files. The serialization system converts the visual DAG into a JSON representation that includes node definitions, connections, and parameter values. Flows can be exported as JSON files and imported into other Langflow instances. The filesystem sync feature allows flows to be version-controlled via Git, enabling collaborative development and CI/CD integration. The system handles schema migrations when the flow format changes between versions.","intents":["I want to version control my workflows in Git and collaborate with teammates","I need to export my workflows and import them into other Langflow instances","I want to back up my workflows and restore them if something goes wrong"],"best_for":["teams using Git for version control and wanting to include workflows","organizations migrating between Langflow instances","developers building CI/CD pipelines around Langflow workflows"],"limitations":["Filesystem sync is one-way (database → filesystem) — changes in JSON files are not automatically synced back","Schema migrations are manual — breaking changes require manual flow updates","Large flows (100+ nodes) produce large JSON files that are difficult to diff in Git","No built-in conflict resolution for concurrent edits — last write wins"],"requires":["Langflow backend with database configured","Filesystem write permissions for sync","Git (optional, for version control)"],"input_types":["flow definition (JSON)","flow metadata (name, description, version)"],"output_types":["serialized flow (JSON file)","flow metadata (JSON)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_14","uri":"capability://text.generation.language.chat.interface.with.session.management.and.conversation.ui","name":"chat interface with session management and conversation ui","description":"Langflow provides a built-in chat interface that allows users to interact with deployed workflows conversationally. The chat UI handles message rendering, input validation, and session management. Sessions are identified by unique IDs and can span multiple conversations. The interface supports rich message types (text, images, files, code blocks) and integrates with the memory system to load conversation history automatically. The chat interface is customizable via CSS and supports theming.","intents":["I want users to interact with my workflow through a chat interface without building custom UI","I need to manage multiple chat sessions and track conversation history","I want to customize the chat UI to match my brand"],"best_for":["teams deploying chatbots and conversational AI applications","developers wanting a quick way to expose workflows to end users","organizations needing a white-label chat interface"],"limitations":["Chat UI is limited to text and basic rich media — no support for complex interactive components","Customization is CSS-only — no programmatic UI component customization","Session management is simple (ID-based) — no built-in user authentication or multi-user support","Message history is loaded from memory backend — no pagination or lazy loading for large conversations"],"requires":["Langflow backend running","Chat interface enabled in flow configuration","Browser with JavaScript enabled"],"input_types":["user message (text)","file uploads (optional)"],"output_types":["assistant response (text, images, code blocks)","conversation history"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_2","uri":"capability://automation.workflow.flow.execution.engine.with.graph.processing.and.event.streaming","name":"flow execution engine with graph processing and event streaming","description":"Langflow's backend executes flows via a Flow Execution Engine that converts the visual DAG into a topologically-sorted execution plan. The engine processes nodes in dependency order, passing outputs from upstream nodes as inputs to downstream nodes. Execution is event-driven — the engine streams execution events (node start, completion, error) back to the frontend via WebSocket or Server-Sent Events, enabling real-time progress visualization. The engine supports both synchronous and asynchronous component execution, with built-in error handling and retry logic.","intents":["I want to execute my visual workflow and see real-time progress as each component runs","I need the system to handle component failures gracefully and retry failed nodes","I want to debug workflows by inspecting intermediate outputs between components"],"best_for":["teams building production AI agents that need reliable execution and observability","developers debugging complex multi-step workflows","applications requiring real-time feedback on long-running operations (e.g., document processing)"],"limitations":["No built-in distributed execution — all nodes run on a single backend instance (horizontal scaling requires external orchestration)","Circular dependencies are not detected until runtime — DAG validation is incomplete","Event streaming adds ~50-100ms latency per node due to serialization and network overhead"],"requires":["FastAPI backend running","WebSocket or SSE support in client","Components must be pickleable for async execution"],"input_types":["serialized flow graph (JSON)","node input values (any JSON-serializable type)"],"output_types":["execution events (JSON)","node outputs (any JSON-serializable type)","execution logs and error traces"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_3","uri":"capability://memory.knowledge.rag.pipeline.composition.with.vector.store.and.retrieval.integration","name":"rag pipeline composition with vector store and retrieval integration","description":"Langflow includes pre-built RAG pattern components that integrate with vector stores (Pinecone, Weaviate, Chroma, FAISS) and document loaders (PDF, web, file uploads). The RAG pattern combines document ingestion, embedding generation, vector storage, and retrieval-augmented generation in a composable workflow. Users can drag RAG components onto the canvas, configure embedding models and vector stores, and the system handles chunking, embedding, and retrieval orchestration. The architecture supports both in-memory (FAISS) and cloud-hosted vector stores.","intents":["I want to build a chatbot that answers questions about my documents without fine-tuning","I need to ingest PDFs, web pages, or files, embed them, and retrieve relevant context for LLM prompts","I want to experiment with different embedding models and vector stores without rewriting code"],"best_for":["teams building document-based Q&A systems (customer support, knowledge base search)","developers prototyping RAG applications before production deployment","non-technical users wanting to build semantic search without ML expertise"],"limitations":["No built-in hybrid search (keyword + semantic) — requires custom component wrapping","Chunking strategy is fixed (configurable chunk size/overlap) — no adaptive chunking based on document structure","Vector store credentials must be stored in environment variables — no built-in secret management UI","Retrieval is stateless — no support for re-ranking or filtering based on metadata without custom components"],"requires":["Vector store API key (Pinecone, Weaviate, etc.) or local FAISS setup","Embedding model API key (OpenAI, Hugging Face, etc.)","Document files in supported formats (PDF, TXT, DOCX, web URLs)"],"input_types":["documents (PDF, text, web URLs)","query text","embedding model configuration"],"output_types":["retrieved document chunks (text)","relevance scores","LLM-generated answers"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_4","uri":"capability://planning.reasoning.multi.agent.workflow.orchestration.with.tool.calling.and.function.registry","name":"multi-agent workflow orchestration with tool calling and function registry","description":"Langflow supports multi-agent workflows through an agent component abstraction that wraps LLM reasoning loops with tool-calling capabilities. Agents are configured with a set of tools (functions) that they can invoke, and the system implements a function registry that maps tool names to executable Python functions. The agent component handles the reasoning loop (LLM generates tool calls → execute tools → feed results back to LLM) and supports both ReAct-style agents and custom reasoning patterns. Tools are registered via a schema-based function registry that supports OpenAI, Anthropic, and Ollama function-calling APIs.","intents":["I want to build an agent that can use tools (APIs, databases, calculators) to solve problems","I need multiple agents to collaborate on a task by delegating to each other","I want to define custom tools and have the agent learn to use them automatically"],"best_for":["teams building autonomous agents for customer service, research, or task automation","developers prototyping multi-agent systems before production deployment","organizations wanting to expose internal APIs to LLMs safely via tool calling"],"limitations":["Tool execution is synchronous — long-running tools block the agent loop (no async tool execution)","No built-in tool validation — agents can call tools with invalid arguments, requiring error handling in tool implementations","Agent reasoning is opaque — limited visibility into why the agent chose a particular tool or action","Multi-agent coordination requires manual message passing — no built-in agent communication protocol"],"requires":["LLM with function-calling support (GPT-4, Claude 3, Ollama with function calling)","Tool functions defined as Python callables with type hints","Function schema compatible with OpenAI or Anthropic function-calling format"],"input_types":["user query (text)","tool definitions (Python functions or JSON schemas)","agent configuration (model, temperature, max iterations)"],"output_types":["agent response (text)","tool call history (JSON)","execution trace (intermediate steps)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_5","uri":"capability://automation.workflow.flow.versioning.and.deployment.with.api.endpoints","name":"flow versioning and deployment with api endpoints","description":"Langflow provides flow versioning capabilities that track changes to workflows over time, allowing users to revert to previous versions or run multiple versions in parallel. The Deployments API exposes flows as HTTP endpoints that can be called programmatically, with support for input/output validation and authentication. Each deployment generates a unique API endpoint with configurable authentication (API keys, OAuth), and the system tracks deployment history and can roll back to previous flow versions. Flows can be deployed to cloud platforms (AWS, GCP, Azure) via Docker containers.","intents":["I want to version my workflows and roll back to a previous version if something breaks","I need to expose my workflow as an API that other applications can call","I want to deploy my workflow to production with authentication and monitoring"],"best_for":["teams deploying AI workflows to production with version control requirements","developers building APIs on top of Langflow workflows","organizations needing to track workflow changes and audit who modified what"],"limitations":["Versioning is flow-level only — no component-level versioning or dependency tracking","Deployment API requires manual configuration of authentication — no built-in RBAC or fine-grained permissions","No built-in rate limiting or quota management on API endpoints","Rollback is manual — no automatic rollback on deployment failure"],"requires":["Langflow backend running","Docker for containerized deployment","Cloud credentials (AWS, GCP, Azure) for cloud deployment"],"input_types":["flow definition (JSON)","deployment configuration (API key, authentication method)"],"output_types":["API endpoint URL","deployment metadata (version, timestamp, status)","API response (flow output)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_6","uri":"capability://memory.knowledge.memory.and.message.management.for.stateful.conversations","name":"memory and message management for stateful conversations","description":"Langflow includes a memory system that persists conversation history and context across multiple interactions. The system supports different memory backends (in-memory, database, Redis) and memory types (buffer, summary, entity-based). Messages are stored with metadata (timestamp, user ID, flow version) and can be retrieved based on filters. The memory service integrates with the chat interface, automatically loading conversation history and passing it to the LLM as context. Memory can be cleared, exported, or analyzed for debugging.","intents":["I want my chatbot to remember previous messages in a conversation","I need to persist conversation history for audit and debugging purposes","I want to implement different memory strategies (e.g., keep last N messages vs. summarize old messages)"],"best_for":["teams building conversational AI applications with multi-turn interactions","developers needing conversation history for debugging and monitoring","applications requiring compliance with data retention policies"],"limitations":["Memory backends are not interchangeable at runtime — switching backends requires restart","No built-in memory compression — conversation history grows unbounded without manual pruning","Entity-based memory requires manual entity extraction — no automatic entity recognition","Cross-conversation memory (e.g., learning from past conversations) requires custom implementation"],"requires":["Memory backend configured (in-memory, database, Redis)","Chat interface or API client sending messages","User ID or session identifier for conversation tracking"],"input_types":["user message (text)","conversation ID or session ID"],"output_types":["conversation history (list of messages)","memory summary (if using summary memory type)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_7","uri":"capability://text.generation.language.voice.mode.with.speech.to.text.and.text.to.speech.integration","name":"voice mode with speech-to-text and text-to-speech integration","description":"Langflow includes a voice mode capability that enables voice-based interactions with workflows. The system integrates speech-to-text (STT) and text-to-speech (TTS) providers (OpenAI Whisper, Google Cloud Speech, Azure Speech Services) to convert user voice input to text, process it through the workflow, and convert the response back to speech. Voice mode is accessible through the chat interface and supports real-time streaming of audio input and output. The architecture handles audio encoding/decoding and manages the voice conversation state.","intents":["I want users to interact with my AI workflow using voice instead of typing","I need to build a voice assistant that can understand and respond to spoken queries","I want to support hands-free interaction for accessibility or mobile use cases"],"best_for":["teams building voice assistants and conversational AI applications","developers creating accessibility-focused applications","organizations building mobile-first AI experiences"],"limitations":["Voice mode requires STT/TTS API keys — no offline voice processing","Audio streaming adds latency (typically 1-3 seconds) compared to text input","Language support depends on STT/TTS provider — not all languages are supported equally","Voice quality depends on microphone and network conditions — no built-in audio enhancement"],"requires":["STT/TTS provider API key (OpenAI, Google Cloud, Azure)","Browser with Web Audio API support (Chrome, Firefox, Safari)","Microphone and speaker hardware"],"input_types":["audio stream (WAV, MP3, or provider-specific format)"],"output_types":["text transcription (from STT)","audio response (from TTS)","workflow output (text or structured data)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_8","uri":"capability://data.processing.analysis.file.management.and.document.ingestion.with.format.conversion","name":"file management and document ingestion with format conversion","description":"Langflow provides a file management system that handles document upload, storage, and format conversion. The system supports multiple file formats (PDF, DOCX, TXT, CSV, JSON, images) and includes document loaders that parse files and extract text or structured data. Files are stored in a managed filesystem or cloud storage (S3, GCS), and the system tracks file metadata (size, type, upload timestamp). Document loaders can be chained together to handle complex extraction tasks (e.g., PDF → text → chunks). The Docling bundle provides advanced document parsing with layout preservation.","intents":["I want to upload documents and extract text or structured data from them","I need to process multiple file formats (PDF, Word, Excel) in a single workflow","I want to preserve document layout and structure when extracting content"],"best_for":["teams building document processing pipelines (invoice extraction, contract analysis)","developers creating knowledge base systems from diverse document sources","organizations needing to ingest and analyze unstructured documents at scale"],"limitations":["File size limits depend on storage backend — no built-in streaming for very large files (>1GB)","Format conversion quality varies by format — complex PDFs with images may lose formatting","No built-in OCR for scanned documents — requires external OCR service integration","Metadata extraction is format-specific — no unified metadata schema across file types"],"requires":["File storage backend (local filesystem or cloud storage)","Document loader components for supported formats","Sufficient disk/cloud storage quota"],"input_types":["file uploads (PDF, DOCX, TXT, CSV, JSON, images)","file paths (local or cloud URLs)"],"output_types":["extracted text (string)","structured data (JSON)","document chunks (for RAG)","file metadata (size, type, upload time)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-langflow-ai--langflow__cap_9","uri":"capability://tool.use.integration.webhook.integration.and.event.driven.workflow.triggering","name":"webhook integration and event-driven workflow triggering","description":"Langflow supports webhook integration that allows external systems to trigger workflow execution via HTTP POST requests. Webhooks are configured with URL paths, authentication methods, and input/output schemas. When a webhook receives a request, it validates the payload against the schema and triggers the associated flow with the request data as inputs. The system supports both synchronous (wait for response) and asynchronous (queue for later execution) webhook handling. Webhook execution is logged and can be monitored via the Langflow UI.","intents":["I want external systems (Slack, Zapier, custom apps) to trigger my workflows","I need to integrate my workflow with event-driven architectures (e.g., respond to database changes)","I want to expose my workflow as a webhook endpoint that third-party services can call"],"best_for":["teams building event-driven AI systems that respond to external triggers","developers integrating Langflow with no-code platforms (Zapier, Make, IFTTT)","organizations automating workflows based on external events (email, Slack messages, database changes)"],"limitations":["Webhook authentication is limited to API keys and basic auth — no OAuth or JWT support","Asynchronous webhook execution has no built-in retry logic — failed executions are not automatically retried","Webhook payload size is limited by the backend (typically 10-100MB) — large payloads may fail","No built-in rate limiting — webhooks can be abused to overload the system"],"requires":["Langflow backend running with webhook endpoints exposed","External system capable of making HTTP POST requests","Webhook URL and authentication credentials"],"input_types":["HTTP POST request body (JSON or form data)","HTTP headers (for authentication and metadata)"],"output_types":["HTTP response (JSON or text)","workflow execution result","execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Modern browser with ES2020+ support (Chrome 90+, Firefox 88+, Safari 14+)","Backend API running (FastAPI server)","Node.js 18+ for development builds","Python 3.9+","Component class inheriting from Langflow base component class","Type hints on component input/output methods","Langflow Python SDK (pip install langflow)","Type hints on component methods","Langflow backend with tracing enabled","Database storage for traces"],"failure_modes":["Complex conditional logic requires custom component wrapping — no native if/else branching in canvas","Large graphs (100+ nodes) may experience canvas performance degradation due to React re-renders","Connection validation is client-side only — some invalid topologies only caught at execution time","Component discovery is Python-only — no support for external binaries or non-Python plugins","Type introspection relies on Python type hints — components with dynamic types require manual schema definition","No built-in versioning for components — bundle version conflicts must be managed externally","Custom components must be Python-only — no support for other languages without language-specific SDKs","Type introspection relies on Python type hints — dynamic types require manual schema definition","Component testing requires Langflow backend running — no standalone component testing framework","Component versioning is manual — no built-in version management or dependency resolution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4846784904434807,"quality":0.35,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.2,"quality":0.25,"ecosystem":0.1,"match_graph":0.4,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:21.997Z","last_commit":"2026-05-03T01:49:31Z"},"community":{"stars":147650,"forks":8900,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langflow-ai--langflow","compare_url":"https://unfragile.ai/compare?artifact=langflow-ai--langflow"}},"signature":"SuJxk5oglYClikMVBC9ddw8+Cp/vvX//DRwe8sL5I5NSNStvHzee7oVOs6Xe1m3P62O6OW73KLi9znVv8WpUDg==","signedAt":"2026-06-21T00:17:51.555Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langflow-ai--langflow","artifact":"https://unfragile.ai/langflow-ai--langflow","verify":"https://unfragile.ai/api/v1/verify?slug=langflow-ai--langflow","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"}}