{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"langflow","slug":"langflow","name":"Langflow","type":"framework","url":"https://github.com/langflow-ai/langflow","page_url":"https://unfragile.ai/langflow","categories":["frameworks-sdks","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"langflow__cap_0","uri":"capability://automation.workflow.visual.drag.and.drop.flow.composition.with.real.time.graph.validation","name":"visual drag-and-drop flow composition with real-time graph validation","description":"React 19 SPA using @xyflow/react canvas that enables users to visually compose AI workflows by dragging LangChain-backed components onto a canvas and connecting them via edges. The frontend maintains a graph state model that validates connections based on component input/output type compatibility before execution, preventing invalid topologies at design time. Connection validation occurs client-side through type introspection of component schemas, reducing round-trips to the backend.","intents":["I want to build a multi-step AI workflow without writing code","I need to see the structure of my agent pipeline visually before running it","I want immediate feedback when I connect incompatible components"],"best_for":["non-technical founders and product managers prototyping AI workflows","teams building RAG and multi-agent systems who want visual debugging","developers wanting to iterate on agent architectures without recompiling"],"limitations":["Complex conditional logic and branching requires custom component wrappers; native if/else nodes have limited expressiveness","Large graphs (100+ nodes) may experience canvas rendering performance degradation in browsers with limited GPU acceleration","Type validation is schema-based and cannot enforce runtime semantic constraints (e.g., 'this LLM must support function calling')"],"requires":["Modern browser with ES2020+ support (Chrome 90+, Firefox 88+, Safari 14+)","Backend API running (FastAPI server)","JavaScript/TypeScript knowledge not required for basic flows"],"input_types":["component drag-and-drop actions","edge connection gestures","parameter input via UI forms"],"output_types":["serialized flow JSON","executable graph representation","validation error messages"],"categories":["automation-workflow","visual-programming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_1","uri":"capability://tool.use.integration.component.registry.with.dynamic.type.system.and.input.output.schema.introspection","name":"component registry with dynamic type system and input/output schema introspection","description":"Backend component system that dynamically loads and registers LangChain components (LLMs, retrievers, memory stores, tools) into a centralized registry accessible via API. Each component exposes a schema describing its input types (via Python type hints and Pydantic models) and output types, which the frontend uses for connection validation and UI form generation. The registry supports component bundles (e.g., NVIDIA, Docling) that can be installed as plugins, extending the available components without modifying core code.","intents":["I want to use a new LLM provider without rebuilding Langflow","I need to create custom components that integrate with my proprietary tools","I want the UI to automatically generate input forms based on component parameters"],"best_for":["teams building custom AI integrations and tool connectors","enterprises needing to extend Langflow with proprietary components","developers creating reusable component libraries for their organization"],"limitations":["Component schema generation relies on Python type hints; poorly annotated legacy code requires manual schema definition","No built-in versioning for components; breaking API changes in a component can silently break existing flows","Component discovery is static at startup; adding new components requires server restart (no hot-reload)"],"requires":["Python 3.9+","Pydantic for schema definition","Understanding of LangChain component interfaces (BaseLanguageModel, BaseRetriever, etc.)","Backend server running"],"input_types":["Python class definitions with type hints","Pydantic models","LangChain base classes"],"output_types":["JSON schema for component inputs/outputs","UI form definitions","Component metadata (name, description, category)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_10","uri":"capability://data.processing.analysis.file.management.and.document.ingestion.with.multi.format.support","name":"file management and document ingestion with multi-format support","description":"Backend service for handling file uploads, storage, and document parsing. Supports multiple file formats (PDF, DOCX, PPTX, TXT, CSV, JSON, images) with format-specific parsers. Files are stored in a managed file system with metadata (upload time, user, size, format). Integrates with document loaders for RAG pipelines and supports batch file processing. Includes OCR capabilities for scanned PDFs and images.","intents":["I want to upload documents and use them in my RAG pipeline","I need to extract text from PDFs and images automatically","I want to process multiple files in batch without manual intervention"],"best_for":["teams building document-based AI applications","enterprises processing large document collections","applications requiring OCR for scanned documents"],"limitations":["OCR quality depends on document quality; poor scans may have low accuracy","File size limits are configurable but large files (>100MB) may timeout during parsing","No built-in document deduplication; duplicate uploads are stored separately","Batch processing is sequential; no parallel file processing"],"requires":["File storage backend (local filesystem or cloud storage)","Document parsing libraries (PyPDF2, python-docx, etc.)","Optional: OCR library (Tesseract, EasyOCR) for scanned documents","Python 3.9+"],"input_types":["file uploads (PDF, DOCX, PPTX, TXT, CSV, JSON, images)","batch file list","parsing configuration"],"output_types":["parsed text","document metadata","OCR results","file storage path"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_11","uri":"capability://automation.workflow.webhook.integration.for.event.driven.flow.triggering","name":"webhook integration for event-driven flow triggering","description":"Enables flows to be triggered by external webhooks, allowing external systems to invoke flows via HTTP POST. Webhooks are configured per flow with URL paths and optional authentication (API key, OAuth). When a webhook receives a request, it triggers the flow with the request payload as input and returns the flow output as the response. Supports webhook retries and event logging for debugging.","intents":["I want to trigger my flow when an external system sends a webhook","I need to integrate my flow with third-party services like Slack, GitHub, or Zapier","I want to log webhook events for debugging and auditing"],"best_for":["teams integrating Langflow with external platforms and services","event-driven architectures where flows respond to external triggers","applications requiring webhook-based automation"],"limitations":["Webhook execution is synchronous; long-running flows block the HTTP response","No built-in webhook signature verification; flows must implement their own validation","Retry logic is basic (exponential backoff); no advanced retry strategies","Webhook logs are stored in-memory; no persistent audit trail by default"],"requires":["Langflow backend running and accessible from external systems","Webhook URL (publicly accessible or via tunnel)","Optional: API key for authentication"],"input_types":["HTTP POST request","request payload (JSON, form data, etc.)","webhook configuration"],"output_types":["HTTP response (flow output)","webhook execution log","status code"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_12","uri":"capability://automation.workflow.tracing.and.observability.with.execution.timeline.and.component.level.metrics","name":"tracing and observability with execution timeline and component-level metrics","description":"Built-in tracing system that captures detailed execution information including component execution order, input/output data, timing, and errors. Traces are stored in a database and accessible via the UI, showing a timeline of component execution with drill-down capability to inspect individual component runs. Integrates with external observability platforms (LangSmith, Datadog) for centralized monitoring. Includes performance metrics (latency, token usage, cost) per component and flow.","intents":["I want to see a detailed trace of how my flow executed","I need to identify which component is causing slowdowns in my flow","I want to monitor token usage and costs across my flows"],"best_for":["teams debugging complex flows in production","enterprises monitoring AI application performance and costs","developers optimizing flow performance"],"limitations":["Trace storage grows quickly with high-volume flows; no automatic pruning or archival","Trace visualization is limited to the Langflow UI; no export to standard formats (OpenTelemetry)","Token usage tracking requires LLM API integration; not all models report token counts","Cost calculation is approximate and depends on accurate LLM pricing data"],"requires":["Langflow backend running","Database for trace storage","Optional: LangSmith or Datadog account for external observability"],"input_types":["flow execution events","component metrics","LLM API responses"],"output_types":["execution trace","timeline visualization","performance metrics","cost breakdown"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_13","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.standardized.tool.calling","name":"model context protocol (mcp) server integration for standardized tool calling","description":"Implements the Model Context Protocol (MCP) standard, allowing flows to call tools exposed by MCP servers. MCP servers define tools with standardized schemas, and Langflow components can discover and invoke these tools without custom integration code. Supports multiple MCP server connections per flow, enabling access to diverse tool ecosystems (filesystem, web, databases, etc.). MCP integration abstracts away provider-specific tool calling differences.","intents":["I want to use tools from MCP servers in my flow without custom code","I need to access filesystem, web, or database tools via a standard protocol","I want to integrate with multiple tool providers using a single standard"],"best_for":["teams building tool-integrated agents using MCP-compatible servers","enterprises standardizing on MCP for tool integration","developers wanting to avoid provider-specific tool calling code"],"limitations":["MCP server availability is required at flow execution time; no offline fallback","Tool discovery is dynamic; tool schemas may change between executions","No built-in caching of tool schemas; each flow execution may re-discover tools","MCP server errors are propagated to the flow; no automatic error recovery"],"requires":["MCP server running and accessible","MCP client library","Tool definitions in MCP format","Python 3.9+"],"input_types":["MCP server configuration (host, port, credentials)","tool invocation requests"],"output_types":["tool results","tool schema discovery","execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_14","uri":"capability://code.generation.editing.langflow.python.sdk.for.programmatic.flow.creation.and.execution","name":"langflow python sdk for programmatic flow creation and execution","description":"Python SDK that enables developers to create, configure, and execute flows programmatically without the visual UI. Flows can be defined as Python code using a fluent API, with components instantiated and connected via method calls. The SDK supports local execution (in-process) and remote execution (via HTTP API). Enables integration of Langflow flows into larger Python applications and automation scripts.","intents":["I want to create flows programmatically in Python without using the UI","I need to integrate Langflow flows into my existing Python application","I want to automate flow creation and deployment via scripts"],"best_for":["Python developers building AI applications","teams automating flow creation and deployment","applications embedding Langflow flows as components"],"limitations":["SDK API is less discoverable than the visual UI; requires documentation and examples","Type hints are not always precise; IDE autocomplete may be limited","Local execution requires all dependencies installed; no sandboxing","Remote execution adds network latency; local execution is faster but less scalable"],"requires":["Python 3.9+","Langflow SDK package","Optional: Langflow backend for remote execution"],"input_types":["Python code","component definitions","flow configuration"],"output_types":["flow object","execution result","flow JSON (for UI import)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_2","uri":"capability://automation.workflow.flow.execution.engine.with.event.streaming.and.state.management","name":"flow execution engine with event streaming and state management","description":"FastAPI backend service that executes flows as directed acyclic graphs (DAGs) by topologically sorting components and executing them in dependency order. Execution is event-driven: each component emits events (start, progress, output, error) that are streamed back to the client via Server-Sent Events (SSE) or WebSocket, enabling real-time progress visualization. The engine maintains execution state (variable bindings, intermediate outputs) in memory during a single run, with optional persistence to a database for audit trails and replay.","intents":["I want to run my flow and see real-time progress as each component executes","I need to debug a flow by inspecting intermediate outputs between components","I want to replay a failed flow with the same inputs to diagnose issues"],"best_for":["teams building production AI agents that need observability","developers debugging complex multi-step workflows","enterprises requiring audit trails and execution replay for compliance"],"limitations":["No built-in distributed execution; all components run on a single server instance, limiting horizontal scaling","State is in-memory; server crashes lose execution context (mitigated by database persistence but not atomic)","Circular dependencies and dynamic graph construction are not supported; flows must be static DAGs defined at design time","Event streaming adds ~50-100ms latency per component due to serialization and network overhead"],"requires":["Python 3.9+","FastAPI 0.100+","Database (SQLite, PostgreSQL) for optional persistence","Client supporting SSE or WebSocket for real-time updates"],"input_types":["serialized flow JSON","component input parameters","execution context (user ID, session ID)"],"output_types":["event stream (JSON events)","final flow output","execution logs and traces"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_3","uri":"capability://memory.knowledge.memory.and.message.management.with.multi.provider.chat.history.persistence","name":"memory and message management with multi-provider chat history persistence","description":"Backend service that manages conversation memory across multiple message types (human, AI, tool, system) and persists them to a database-backed message store. Supports multiple memory strategies (buffer, summary, entity-based) that can be configured per flow. Messages are stored with metadata (timestamp, component source, execution ID) enabling retrieval and replay. The system integrates with LangChain's memory abstractions, allowing flows to maintain context across multiple invocations.","intents":["I want my chatbot to remember previous messages in a conversation","I need to retrieve chat history for a specific user or session","I want to implement a memory strategy that summarizes old messages to save tokens"],"best_for":["teams building conversational AI agents with multi-turn interactions","applications requiring persistent chat history for user experience","enterprises needing to audit and replay conversations for compliance"],"limitations":["Memory strategies are pre-defined (buffer, summary, entity); custom memory logic requires component development","No built-in multi-user isolation at the memory level; flows must implement their own session/user filtering","Summary-based memory uses LLM calls which add latency and cost; no configurable summarization strategy","Message retrieval is linear scan; large message histories (10k+ messages) may have slow query performance"],"requires":["Python 3.9+","Database (SQLite, PostgreSQL) for message persistence","LangChain memory components","LLM API key if using summary-based memory"],"input_types":["message text","message type (human, AI, tool, system)","session/user ID","metadata (timestamp, source)"],"output_types":["message history (list of messages)","summarized context","memory state for component input"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_4","uri":"capability://memory.knowledge.rag.pipeline.composition.with.vector.store.and.retriever.integration","name":"rag pipeline composition with vector store and retriever integration","description":"Pre-built flow patterns and components that enable rapid assembly of retrieval-augmented generation (RAG) pipelines. Includes document loaders (PDF, web, file), text splitters, embedding models, vector store connectors (Pinecone, Weaviate, Chroma, FAISS), and retriever components. Flows can chain document ingestion → embedding → storage → retrieval → LLM generation in a visual canvas. The system handles chunking strategy configuration, embedding model selection, and vector store initialization without code.","intents":["I want to build a Q&A system over my documents without writing Python","I need to ingest PDFs, split them, embed them, and store them in a vector database","I want to retrieve relevant documents and pass them to an LLM for answer generation"],"best_for":["product teams building document-based Q&A applications","enterprises implementing knowledge base search systems","developers prototyping RAG systems before production implementation"],"limitations":["Chunking strategies are limited to fixed-size, sliding-window, and semantic splitting; no custom chunking logic without component development","Vector store connectors require API keys and external service setup; no local-only option except FAISS","Retrieval is basic similarity search; no advanced ranking, reranking, or hybrid search (keyword + semantic) without custom components","No built-in handling of document metadata filtering; all retrieval is purely semantic similarity"],"requires":["Document files (PDF, TXT, DOCX, etc.) or web URLs","Embedding model API key (OpenAI, Hugging Face, etc.) or local embedding model","Vector store account (Pinecone, Weaviate, etc.) or local FAISS setup","LLM API key for generation step"],"input_types":["document files or URLs","text query","chunking parameters (chunk size, overlap)","embedding model selection"],"output_types":["retrieved document chunks","LLM-generated answer","retrieval scores and metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_5","uri":"capability://planning.reasoning.multi.agent.workflow.orchestration.with.tool.calling.and.agent.state.management","name":"multi-agent workflow orchestration with tool calling and agent state management","description":"Framework for composing multi-agent systems where agents can call tools, delegate to other agents, and maintain shared state. Agents are implemented as components that wrap LangChain's AgentExecutor, with tool calling orchestrated via function-calling APIs (OpenAI, Anthropic, Ollama). The system manages agent state (memory, context, intermediate results) and enables agents to communicate via message passing. Flows can define agent hierarchies (supervisor agents delegating to worker agents) or peer-to-peer agent networks.","intents":["I want to build a system where multiple AI agents collaborate to solve a problem","I need agents to call tools and APIs as part of their reasoning","I want to implement a supervisor agent that delegates tasks to specialized worker agents"],"best_for":["teams building complex AI systems with multiple specialized agents","enterprises implementing agentic workflows for business processes","developers prototyping multi-agent reasoning systems"],"limitations":["Agent communication is message-based; no built-in shared memory or knowledge graph for agents to reference","Tool calling requires LLM support for function calling; models without this capability cannot be used as agents","No built-in deadlock detection or timeout management; agents can enter infinite loops if tool calls are misconfigured","Agent state is not automatically synchronized; flows must explicitly manage state passing between agents"],"requires":["LLM with function-calling support (GPT-4, Claude 3+, Llama 2 with Ollama, etc.)","Tool definitions with JSON schemas","Python 3.9+","LangChain AgentExecutor and tool abstractions"],"input_types":["agent configuration (model, tools, system prompt)","initial task or query","tool definitions (name, description, schema)"],"output_types":["agent reasoning trace","tool calls and results","final agent output","execution logs"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_6","uri":"capability://code.generation.editing.custom.component.development.with.python.class.wrapping.and.schema.auto.generation","name":"custom component development with python class wrapping and schema auto-generation","description":"Developer API for creating custom components by writing Python classes that inherit from a Langflow base class and expose inputs/outputs via type-annotated methods. The framework automatically generates JSON schemas from Python type hints and Pydantic models, eliminating manual schema definition. Custom components are registered in the component registry and appear in the visual canvas alongside built-in components. Supports component bundles for packaging and distributing custom components.","intents":["I want to wrap my proprietary Python code as a reusable component in Langflow","I need to create a custom tool that integrates with my internal APIs","I want to package my components as a bundle and share them with my team"],"best_for":["teams building custom integrations and proprietary tools","enterprises extending Langflow with domain-specific components","developers creating reusable component libraries"],"limitations":["Component development requires Python knowledge; no visual component builder","Type hints must be precise; complex types (Union, Optional) may require custom schema definition","Components are synchronous; async components require wrapper code","No built-in testing framework; developers must write their own component tests"],"requires":["Python 3.9+","Understanding of Langflow component base classes","Type hints and Pydantic for schema generation","Langflow development environment or SDK"],"input_types":["Python class definition","type-annotated input parameters","Pydantic models"],"output_types":["component code","JSON schema","component bundle"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_7","uri":"capability://automation.workflow.api.endpoint.generation.and.deployment.with.flow.versioning","name":"api endpoint generation and deployment with flow versioning","description":"Automatically generates REST API endpoints from flows, exposing them as HTTP POST endpoints that accept flow inputs and return outputs. Each flow can be deployed as a versioned API with automatic OpenAPI schema generation. The system supports multiple deployment targets (local, Docker, cloud platforms) and manages flow versioning, allowing multiple versions of a flow to coexist. Deployments API provides programmatic control over flow lifecycle (create, update, delete, activate).","intents":["I want to expose my flow as a REST API without writing API code","I need to deploy multiple versions of a flow and route traffic between them","I want to generate OpenAPI documentation for my flow automatically"],"best_for":["teams deploying AI workflows as microservices","developers building production APIs from Langflow flows","enterprises managing multiple versions of AI workflows"],"limitations":["API endpoints are synchronous; long-running flows block the HTTP connection (mitigated by async execution but not fully async)","No built-in rate limiting or authentication beyond basic API key support; production deployments require additional security layers","OpenAPI schema generation is automatic but may not capture all nuances of complex flows","Versioning is manual; no automatic version management or rollback strategies"],"requires":["Langflow backend running","Flow definition","HTTP client for testing endpoints","Optional: Docker for containerized deployment"],"input_types":["flow definition","version metadata","deployment configuration"],"output_types":["REST API endpoint","OpenAPI schema","deployment status","API response (flow output)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_8","uri":"capability://automation.workflow.playground.and.interactive.testing.with.parameter.override.and.output.inspection","name":"playground and interactive testing with parameter override and output inspection","description":"Built-in web UI for testing flows interactively without deploying them. Users can override component parameters, execute the flow, and inspect intermediate outputs and execution logs in real-time. The playground supports chat-like interfaces for conversational flows and displays execution traces showing which components ran, their inputs/outputs, and timing. Supports batch testing with multiple input sets.","intents":["I want to test my flow before deploying it to production","I need to debug a flow by seeing what each component outputs","I want to try different parameter values and see how they affect the output"],"best_for":["developers iterating on flow logic","non-technical users testing flows without code","teams debugging production issues by replaying flows"],"limitations":["Playground is single-user; no collaboration or shared testing sessions","No built-in performance profiling; execution timing is approximate","Batch testing is limited to sequential execution; no parallel test runs","Parameter overrides are temporary; no saved test configurations"],"requires":["Langflow backend running","Web browser","Flow definition"],"input_types":["flow parameters","component input overrides","test input sets"],"output_types":["flow output","execution trace","component logs","timing information"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__cap_9","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":"Enables conversational flows to accept voice input via speech-to-text (STT) and return voice output via text-to-speech (TTS). Integrates with multiple STT/TTS providers (OpenAI Whisper, Google Cloud Speech, Azure Speech Services, ElevenLabs). Voice mode is configured per flow and works with the chat interface, allowing users to speak queries and hear responses. Supports audio streaming for low-latency voice interactions.","intents":["I want to build a voice-enabled chatbot that users can speak to","I need to convert user speech to text and AI responses back to speech","I want to support hands-free interaction with my AI agent"],"best_for":["teams building voice-first AI applications","accessibility-focused applications requiring voice interfaces","mobile and IoT applications where typing is impractical"],"limitations":["STT/TTS adds latency (500ms-2s per round trip); real-time conversation may feel sluggish","Requires API keys for STT/TTS providers; no local-only option except with self-hosted models","Voice quality depends on provider; some providers have regional limitations","No built-in speaker identification or multi-user voice support"],"requires":["Microphone and speaker hardware","STT/TTS provider API key (OpenAI, Google, Azure, ElevenLabs, etc.)","Modern browser with Web Audio API support","Langflow backend running"],"input_types":["audio stream (WAV, MP3, etc.)","voice configuration (STT/TTS provider, language, voice)"],"output_types":["transcribed text","audio stream (speech output)","transcript with timing"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langflow__headline","uri":"capability://rag.knowledge.visual.framework.for.building.multi.agent.and.rag.applications","name":"visual framework for building multi-agent and rag applications","description":"Langflow is a visual framework that allows users to create multi-agent and retrieval-augmented generation (RAG) applications using a drag-and-drop interface, making it accessible for developers to build complex workflows without extensive coding.","intents":["best visual framework for RAG applications","multi-agent framework for AI workflows","drag-and-drop tool for building AI applications","RAG framework for developers","best tools for creating multi-agent systems"],"best_for":["developers looking for a visual interface","teams needing to build complex AI workflows"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Modern browser with ES2020+ support (Chrome 90+, Firefox 88+, Safari 14+)","Backend API running (FastAPI server)","JavaScript/TypeScript knowledge not required for basic flows","Python 3.9+","Pydantic for schema definition","Understanding of LangChain component interfaces (BaseLanguageModel, BaseRetriever, etc.)","Backend server running","File storage backend (local filesystem or cloud storage)","Document parsing libraries (PyPDF2, python-docx, etc.)","Optional: OCR library (Tesseract, EasyOCR) for scanned documents"],"failure_modes":["Complex conditional logic and branching requires custom component wrappers; native if/else nodes have limited expressiveness","Large graphs (100+ nodes) may experience canvas rendering performance degradation in browsers with limited GPU acceleration","Type validation is schema-based and cannot enforce runtime semantic constraints (e.g., 'this LLM must support function calling')","Component schema generation relies on Python type hints; poorly annotated legacy code requires manual schema definition","No built-in versioning for components; breaking API changes in a component can silently break existing flows","Component discovery is static at startup; adding new components requires server restart (no hot-reload)","OCR quality depends on document quality; poor scans may have low accuracy","File size limits are configurable but large files (>100MB) may timeout during parsing","No built-in document deduplication; duplicate uploads are stored separately","Batch processing is sequential; no parallel file processing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langflow","compare_url":"https://unfragile.ai/compare?artifact=langflow"}},"signature":"I6b0H9t06mFWkgSru7vepP0VVxHmp7YHQU1ozO88Ru9OM6Zrn54OqKPp71rGO1Rko5NJupfDGKSFkPLSiaxlAA==","signedAt":"2026-06-20T02:20:54.855Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langflow","artifact":"https://unfragile.ai/langflow","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}