{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-labring--fastgpt","slug":"labring--fastgpt","name":"FastGPT","type":"platform","url":"https://fastgpt.io","page_url":"https://unfragile.ai/labring--fastgpt","categories":["app-builders","rag-knowledge","deployment-infra","automation"],"tags":["agent","claude","deepseek","llm","mcp","nextjs","openai","qwen","rag","workflow"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-labring--fastgpt__cap_0","uri":"capability://automation.workflow.visual.workflow.orchestration.with.node.based.dag.execution","name":"visual workflow orchestration with node-based dag execution","description":"FastGPT provides a drag-and-drop workflow editor that compiles visual node graphs into a directed acyclic graph (DAG) executed server-side with streaming support. The system resolves variable dependencies across nodes, supports branching logic, pause-resume semantics for interactive workflows, and child workflow composition. Each node type (AI, HTTP, dataset query, etc.) has a standardized execution interface that handles both synchronous and asynchronous operations with real-time streaming of intermediate results back to the client.","intents":["Build multi-step AI pipelines without writing backend code","Create conditional logic flows that branch based on LLM outputs","Compose reusable workflow templates as plugins for other workflows","Debug workflow execution by inspecting intermediate node outputs in real-time"],"best_for":["Non-technical product managers building AI applications","Teams prototyping complex multi-step AI systems quickly","Organizations needing visual audit trails of AI decision logic"],"limitations":["Workflow execution latency increases ~50-100ms per node due to variable resolution and streaming overhead","Complex branching logic with many conditional paths can become difficult to visualize and maintain in the UI","No built-in loop constructs — recursive patterns require child workflow composition","Pause-resume state must be persisted externally; no automatic checkpoint management"],"requires":["FastGPT backend service running (Node.js 18+)","At least one LLM provider configured (OpenAI, Anthropic, Qwen, DeepSeek, etc.)","MongoDB for workflow definition storage","Vector database for dataset nodes (Milvus, Weaviate, Qdrant, or PineCone)"],"input_types":["node configuration JSON","variable bindings (string, number, boolean, array, object)","file uploads for dataset processing nodes","HTTP request payloads"],"output_types":["streaming text responses","structured JSON from AI parsing nodes","dataset query results","HTTP response bodies","workflow execution logs with node-level tracing"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.request.routing.with.streaming.and.token.accounting","name":"multi-provider llm request routing with streaming and token accounting","description":"FastGPT abstracts LLM provider APIs (OpenAI, Anthropic, Qwen, DeepSeek, Ollama, etc.) behind a unified request interface that handles model selection, streaming response aggregation, token counting, and cost tracking. The system normalizes chat message formats across providers, manages API key rotation, implements retry logic with exponential backoff, and streams partial responses to clients in real-time. Token usage is tracked per request and aggregated for billing/analytics.","intents":["Switch between LLM providers without changing application code","Track token usage and costs across multiple model deployments","Stream LLM responses to users in real-time for better UX","Implement fallback logic if primary LLM provider fails"],"best_for":["Teams managing costs across multiple LLM providers","Applications requiring provider flexibility for compliance or latency reasons","Builders needing real-time streaming responses in chat interfaces"],"limitations":["Token counting accuracy varies by provider — OpenAI token counts may differ from actual usage by 1-5%","Streaming responses add ~100-200ms latency due to chunking and serialization overhead","No built-in load balancing across provider instances — requires external orchestration","Private/self-hosted models (Ollama) require manual endpoint configuration per deployment"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Qwen, DeepSeek, etc.)","Network connectivity to provider endpoints","MongoDB for storing model configuration and usage logs","Node.js 18+ runtime"],"input_types":["chat message arrays with role/content","system prompts","model selection parameters","temperature, max_tokens, top_p configuration"],"output_types":["streaming text chunks (Server-Sent Events)","complete response text","token usage metadata (prompt_tokens, completion_tokens, total_cost)","model metadata (context window, pricing)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_10","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.helm.charts.and.environment.configuration","name":"docker and kubernetes deployment with helm charts and environment configuration","description":"FastGPT provides Docker images and Kubernetes manifests (Helm charts) for containerized deployment, with comprehensive environment variable configuration for all components (backend, frontend, vector DB, etc.). The system includes health checks, resource limits, and scaling policies. Deployment documentation covers single-container setups, multi-replica production deployments, and cloud-specific configurations (AWS, GCP, Azure). Environment variables control feature flags, database connections, and LLM provider credentials.","intents":["Deploy FastGPT to Kubernetes clusters with Helm charts","Run FastGPT in Docker containers for local development or production","Configure all FastGPT components via environment variables","Scale FastGPT horizontally across multiple replicas"],"best_for":["DevOps teams deploying FastGPT to Kubernetes","Organizations requiring containerized deployments for compliance","Teams managing multi-environment deployments (dev, staging, prod)"],"limitations":["Helm charts require Kubernetes 1.20+; no support for older versions","Stateful components (MongoDB, vector DB) require separate management; not included in Helm charts","Environment variable configuration can become complex with many options; no built-in validation","Docker images are large (~1GB) due to dependencies; no minimal/slim variants"],"requires":["Docker runtime (Docker Desktop, containerd, etc.)","Kubernetes cluster (1.20+) for Helm deployment","Helm 3+ for chart deployment","External MongoDB and vector database instances","Container registry for storing images"],"input_types":["Docker Compose files","Helm values.yaml configuration","environment variable files (.env)","Kubernetes manifests"],"output_types":["running Docker containers","Kubernetes pods and services","deployment logs and health checks","resource usage metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_11","uri":"capability://automation.workflow.observability.and.monitoring.with.structured.logging.and.metrics.collection","name":"observability and monitoring with structured logging and metrics collection","description":"FastGPT includes an observability SDK that collects structured logs, traces, and metrics from all components (workflows, LLM calls, database operations, etc.). The system integrates with popular observability platforms (Datadog, New Relic, Prometheus) via standard protocols (OpenTelemetry). Logs include request IDs for tracing across services, structured fields for filtering/searching, and configurable log levels. Metrics cover latency, error rates, token usage, and cost tracking.","intents":["Monitor workflow execution performance and identify bottlenecks","Track LLM API costs and token usage across all applications","Debug issues by tracing requests across multiple services","Set up alerts for high error rates or latency"],"best_for":["Teams running FastGPT in production with SLO requirements","Organizations needing cost visibility across LLM usage","DevOps teams managing observability infrastructure"],"limitations":["Observability SDK adds ~5-10% overhead to request latency due to logging/tracing","Log volume can be high for high-traffic applications; requires log aggregation/retention policies","Metrics are sampled by default; no 100% accuracy for low-volume events","Integration with observability platforms requires additional configuration and credentials"],"requires":["FastGPT backend service with observability SDK enabled","Log aggregation platform (ELK, Datadog, New Relic, etc.) or local file storage","Metrics collection system (Prometheus, Datadog, etc.) or local storage","OpenTelemetry collector (optional, for advanced tracing)"],"input_types":["observability configuration (log level, sampling rate, platform credentials)","custom metrics and events from workflows"],"output_types":["structured logs with request IDs and context","metrics (latency, error rate, token count, cost)","traces showing request flow across services","dashboards and alerts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_12","uri":"capability://planning.reasoning.evaluation.and.testing.framework.for.workflows.with.metric.tracking","name":"evaluation and testing framework for workflows with metric tracking","description":"FastGPT provides a testing framework that allows users to create test cases for workflows, run them against different model configurations, and track metrics like accuracy, latency, and cost. The system supports batch testing with result comparison, A/B testing between workflow versions, and metric aggregation across test runs. Test results are stored with full execution logs for debugging. The framework integrates with the workflow editor for easy test creation and execution.","intents":["Test workflow changes before deploying to production","Compare performance of different LLM models on the same workflow","Track workflow quality metrics over time","Debug workflow failures with full execution logs"],"best_for":["Teams iterating on workflows with quality requirements","Organizations comparing LLM models for cost/performance tradeoffs","Applications requiring regression testing before deployment"],"limitations":["Test execution is synchronous; large test suites (>1000 cases) may timeout","Metric definitions are limited to built-in metrics; no custom metric support","Test result storage grows quickly; no automatic cleanup or archival","A/B testing requires manual result comparison; no statistical significance testing"],"requires":["FastGPT backend service with testing framework enabled","MongoDB for test case and result storage","LLM provider credentials for test execution"],"input_types":["test case definitions (input, expected output, metrics)","workflow versions to test","model configurations to compare"],"output_types":["test execution results with pass/fail status","metric values (accuracy, latency, cost)","execution logs with full workflow trace","comparison reports between test runs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_13","uri":"capability://automation.workflow.plugin.system.and.marketplace.for.sharing.workflows.and.tools","name":"plugin system and marketplace for sharing workflows and tools","description":"FastGPT supports publishing workflows as reusable plugins that can be shared with other users or teams via a built-in marketplace. Plugins can be simple workflows or complex tools with custom UI. The system handles plugin versioning, dependency management, and installation. Users can browse available plugins, install them with one click, and customize them for their use case. Plugin authors can monetize their work via the marketplace.","intents":["Share workflow templates with other teams or the community","Discover and install pre-built workflows for common tasks","Customize installed plugins for specific use cases","Monetize workflow expertise by selling plugins"],"best_for":["Communities building ecosystem of reusable workflows","Teams sharing internal workflow templates across departments","Developers monetizing workflow expertise"],"limitations":["Plugin marketplace is centralized; no support for private/internal marketplaces","Plugin dependencies are not automatically resolved; manual dependency management required","Plugin versioning can cause conflicts if multiple versions are installed","No built-in plugin sandboxing; malicious plugins could access sensitive data"],"requires":["FastGPT backend service with marketplace enabled","Plugin author account with publishing permissions","MongoDB for plugin metadata and version storage"],"input_types":["workflow definition for plugin","plugin metadata (name, description, version, author)","plugin configuration schema","pricing information (optional)"],"output_types":["published plugin in marketplace","plugin installation package","plugin usage statistics and reviews","revenue reports (for paid plugins)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_2","uri":"capability://memory.knowledge.rag.based.knowledge.base.retrieval.with.semantic.search.and.hybrid.ranking","name":"rag-based knowledge base retrieval with semantic search and hybrid ranking","description":"FastGPT implements a multi-stage retrieval pipeline that converts documents into embeddings, stores them in vector databases, and retrieves relevant chunks via semantic similarity search combined with BM25 keyword matching. The system supports hierarchical dataset organization, configurable chunk size and overlap, multiple embedding models, and re-ranking of results before passing to LLMs. Retrieved context is automatically injected into chat prompts with source attribution and confidence scores.","intents":["Build question-answering systems over proprietary documents without fine-tuning","Retrieve relevant context from large knowledge bases in <500ms","Combine semantic and keyword search for better recall on domain-specific queries","Track which documents were used to generate each response for audit/compliance"],"best_for":["Organizations with large document repositories (10K+ documents)","Teams building customer support chatbots with proprietary knowledge","Compliance-heavy industries requiring source attribution for AI responses"],"limitations":["Embedding quality depends on model choice — smaller models (384-dim) may miss semantic nuance vs larger models (1536-dim)","Retrieval latency scales with dataset size; >1M documents may require sharding across vector DB instances","Chunk-level retrieval can lose cross-document context — no built-in multi-hop reasoning","Re-ranking adds ~50-100ms latency per query; disabled by default for performance"],"requires":["Vector database instance (Milvus, Weaviate, Qdrant, PineCone, or Chroma)","Embedding model API or local embedding service (OpenAI, Hugging Face, etc.)","MongoDB for dataset metadata and chunk storage","Minimum 2GB RAM for local vector DB; cloud instances recommended for >100K documents"],"input_types":["PDF, DOCX, TXT, Markdown files","Web URLs for crawling","Structured data (JSON, CSV) with text fields","User queries (natural language text)"],"output_types":["ranked list of relevant document chunks with similarity scores","source document metadata (filename, URL, page number)","embedding vectors (for debugging/analysis)","retrieval metrics (recall@k, precision@k)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_3","uri":"capability://data.processing.analysis.dataset.ingestion.and.chunking.with.multi.format.support.and.incremental.updates","name":"dataset ingestion and chunking with multi-format support and incremental updates","description":"FastGPT provides a data pipeline that ingests documents in multiple formats (PDF, DOCX, TXT, Markdown, JSON, CSV), automatically chunks them with configurable size/overlap, generates embeddings, and stores chunks in vector databases with metadata. The system supports incremental updates (add/delete chunks without re-processing entire dataset), batch processing with progress tracking, and automatic format detection. Chunks are versioned and linked to source documents for traceability.","intents":["Upload 1000+ documents and automatically prepare them for RAG without manual preprocessing","Update knowledge bases incrementally without re-embedding entire datasets","Extract structured data from unstructured documents (tables, forms, etc.)","Track which source document each retrieved chunk came from"],"best_for":["Teams managing frequently-updated knowledge bases (daily/weekly document additions)","Organizations with mixed document formats (PDFs, Word docs, web content)","Applications requiring audit trails of knowledge base changes"],"limitations":["PDF extraction quality varies by document structure — scanned PDFs require OCR (not built-in)","Chunking strategy is fixed (sliding window) — no adaptive chunking based on document structure","Batch processing blocks other operations; no background job queue for large uploads (>1GB)","Incremental updates require manual chunk deletion; no automatic deduplication of re-uploaded content"],"requires":["FastGPT backend service with file upload storage (local filesystem or S3)","Vector database instance for chunk storage","Embedding model API or service","MongoDB for dataset metadata","Minimum 500MB free disk space for temporary file processing"],"input_types":["PDF files (text-based, not scanned)","DOCX, DOC files","TXT, Markdown files","JSON, CSV files with text fields","Web URLs for crawling"],"output_types":["chunked text with metadata (source, page number, chunk index)","embedding vectors stored in vector DB","ingestion logs with success/failure counts","dataset statistics (total chunks, avg chunk size, token count)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_4","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.with.tool.discovery.and.execution","name":"model context protocol (mcp) server integration with tool discovery and execution","description":"FastGPT implements MCP server support that allows workflows to discover and execute tools from external MCP servers via a standardized protocol. The system maintains a tool registry, handles tool schema validation, manages tool execution with timeout/error handling, and streams tool results back into workflow execution. Tools can be published as plugins and shared across teams. The MCP integration layer abstracts provider differences (Anthropic, custom implementations) and handles authentication/authorization.","intents":["Extend workflows with external tools (web search, database queries, API calls) via MCP servers","Publish custom tools as reusable plugins for other teams","Execute tools with automatic schema validation and error handling","Discover available tools and their capabilities without manual documentation"],"best_for":["Teams building extensible AI applications with plugin ecosystems","Organizations standardizing on MCP for tool integration across multiple products","Developers creating custom tools that need to be shared across workflows"],"limitations":["MCP server discovery requires manual registration — no automatic service discovery","Tool execution latency depends on MCP server performance; no built-in caching of tool results","Schema validation is strict — tools with dynamic/undocumented parameters may fail","No built-in retry logic for failed tool calls; requires workflow-level error handling"],"requires":["MCP server running and accessible (HTTP/stdio transport)","Tool schema definitions in JSON Schema format","Authentication credentials for MCP servers (API keys, OAuth tokens, etc.)","FastGPT backend with MCP client library"],"input_types":["MCP server endpoint configuration","tool schema JSON","tool input parameters (typed according to schema)","authentication credentials"],"output_types":["tool execution results (JSON, text, or structured data)","tool metadata (name, description, schema)","execution logs with timing and error details","tool availability status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_5","uri":"capability://text.generation.language.interactive.chat.interface.with.streaming.responses.and.variable.input.binding","name":"interactive chat interface with streaming responses and variable input binding","description":"FastGPT provides a chat UI component that streams LLM responses in real-time, supports variable input forms for workflow initialization, maintains conversation history with context management, and handles both shared (public) and authenticated chat modes. The chat system manages message state, implements pagination for long conversations, supports file uploads in chat, and provides feedback mechanisms (thumbs up/down) for response quality tracking. Responses can be streamed token-by-token or chunk-by-chunk depending on workflow configuration.","intents":["Deploy AI chatbots with real-time streaming responses without building custom UI","Collect user inputs via forms before starting workflow execution","Maintain conversation context across multiple turns","Track user feedback on responses for model improvement"],"best_for":["Teams deploying customer-facing chatbots quickly","Applications requiring real-time response streaming for better UX","Organizations needing feedback collection for model evaluation"],"limitations":["Chat history is stored in MongoDB — no built-in archival or data retention policies","Streaming responses require WebSocket or Server-Sent Events; not compatible with HTTP-only environments","Variable input forms are limited to simple types (text, number, select) — no complex nested forms","Conversation context is limited to recent messages; no automatic summarization for long conversations"],"requires":["FastGPT backend service running","MongoDB for chat history storage","WebSocket or Server-Sent Events support in client environment","Browser with modern JavaScript support (ES2020+)"],"input_types":["user text messages","file uploads (images, documents)","variable input form submissions","feedback signals (thumbs up/down, ratings)"],"output_types":["streaming text responses (token-by-token)","chat message history","conversation metadata (timestamps, user info, feedback)","file upload confirmations"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_6","uri":"capability://safety.moderation.permission.and.access.control.system.with.team.resource.hierarchies","name":"permission and access control system with team/resource hierarchies","description":"FastGPT implements a fine-grained permission system with role-based access control (RBAC) supporting team hierarchies, resource-level permissions, and permission inheritance. Users can be assigned roles (owner, editor, viewer) at team or resource level, with permissions cascading from parent to child resources. The system supports sharing resources via public links with optional expiration, and maintains audit logs of permission changes. API keys can be scoped to specific resources and operations.","intents":["Control who can view, edit, or execute workflows and knowledge bases","Share chatbots with external users via public links without exposing internal workflows","Manage team access to shared resources with granular permissions","Track who made changes to workflows and knowledge bases"],"best_for":["Multi-tenant SaaS applications with team-based access control","Organizations with compliance requirements for audit trails","Teams sharing AI applications with external partners or customers"],"limitations":["Permission checks add ~10-20ms latency per request due to database lookups","No attribute-based access control (ABAC) — only role-based; complex policies require custom code","Public link sharing has no built-in rate limiting — requires external API gateway","Audit logs are stored in MongoDB without automatic archival or retention policies"],"requires":["MongoDB for permission and audit log storage","User authentication system (OAuth, JWT, or FastGPT built-in auth)","FastGPT backend service"],"input_types":["role assignments (owner, editor, viewer)","resource IDs","user/team IDs","permission scope (read, write, execute, delete)"],"output_types":["permission check results (allowed/denied)","audit logs with timestamp, user, action, resource","public share links with metadata","permission inheritance chains"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_7","uri":"capability://tool.use.integration.application.deployment.and.api.exposure.with.chat.completion.endpoints","name":"application deployment and api exposure with chat completion endpoints","description":"FastGPT exposes deployed applications via REST APIs compatible with OpenAI's chat completion format, allowing applications to be consumed by any client supporting that standard. The system generates API keys scoped to specific applications, implements rate limiting and quota management, provides webhook support for async operations, and includes built-in API documentation. Applications can be deployed as simple chatbots or complex workflows, and the API layer handles request routing, authentication, and response formatting automatically.","intents":["Expose FastGPT applications as OpenAI-compatible APIs for integration with existing tools","Implement rate limiting and quota management for API consumers","Generate API keys with fine-grained scoping per application","Monitor API usage and performance metrics"],"best_for":["Teams integrating FastGPT applications into existing systems via APIs","SaaS platforms offering AI features to customers via standard APIs","Developers building client applications that need to call FastGPT workflows"],"limitations":["API response format is fixed to OpenAI chat completion schema — no custom response formats","Rate limiting is per-API-key; no per-user or per-IP rate limiting without external gateway","Webhook delivery is not guaranteed; no built-in retry logic or dead-letter queues","API documentation is auto-generated; no custom documentation or examples"],"requires":["FastGPT backend service with API server running","API key generation and management system","Rate limiting middleware (built-in or external)","HTTP client library for consuming APIs"],"input_types":["HTTP POST requests with chat completion format","API key in Authorization header","JSON request body with messages array"],"output_types":["HTTP 200 response with chat completion format","streaming responses (Server-Sent Events)","error responses with standard HTTP status codes","usage metadata (tokens, cost)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_8","uri":"capability://automation.workflow.workflow.versioning.and.rollback.with.change.tracking","name":"workflow versioning and rollback with change tracking","description":"FastGPT maintains version history for workflows, allowing users to view changes between versions, rollback to previous versions, and compare workflow definitions. The system tracks who made changes and when, stores complete workflow snapshots, and supports branching workflows from specific versions. Version management is integrated into the workflow editor UI with visual diff support. Rollbacks are instant and don't affect running workflow instances.","intents":["Revert workflow changes if a new version causes issues","Track who modified workflows and when for audit purposes","Compare workflow definitions to understand what changed","Create workflow variants by branching from specific versions"],"best_for":["Teams collaborating on workflows with multiple editors","Organizations with compliance requirements for change tracking","Applications requiring workflow stability and rollback capability"],"limitations":["Version storage grows with workflow complexity — large workflows with many versions consume significant MongoDB space","No automatic version cleanup; manual deletion required to manage storage","Rollback is instant but doesn't affect in-flight workflow executions — may cause inconsistency","Visual diff is limited to node-level changes; no semantic diff for parameter changes"],"requires":["MongoDB for version storage","FastGPT backend service","Workflow editor UI"],"input_types":["workflow definition JSON","version selection (version number or timestamp)","change description (optional)"],"output_types":["version history list with metadata","workflow definition snapshots","change diffs (node additions/deletions/modifications)","rollback confirmation"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-labring--fastgpt__cap_9","uri":"capability://data.processing.analysis.multi.database.backend.support.with.vector.db.abstraction","name":"multi-database backend support with vector db abstraction","description":"FastGPT abstracts vector database implementations behind a unified interface, supporting Milvus, Weaviate, Qdrant, PineCone, and Chroma. The system handles database-specific connection details, query syntax translation, and schema management transparently. Users can switch vector databases without changing application code. The platform also supports multiple relational databases (PostgreSQL, MySQL) for metadata storage, with automatic migration scripts for version upgrades.","intents":["Deploy FastGPT with different vector databases based on infrastructure preferences","Migrate between vector databases without data loss or application changes","Use cloud-hosted vector databases (PineCone) or self-hosted (Milvus) interchangeably","Scale vector storage independently from application tier"],"best_for":["Organizations with existing vector database infrastructure","Teams requiring self-hosted deployments for data residency","Applications needing to switch databases for cost or performance reasons"],"limitations":["Vector DB abstraction adds ~5-10% latency overhead due to translation layer","Database-specific features (custom indexing, filtering) are not exposed through abstraction","Migration between databases requires data re-embedding if vector formats differ","No automatic database failover; requires external orchestration"],"requires":["At least one vector database instance (Milvus, Weaviate, Qdrant, PineCone, or Chroma)","MongoDB or PostgreSQL for metadata storage","Network connectivity to database instances","Database credentials and connection strings"],"input_types":["database configuration (type, host, port, credentials)","vector dimension and similarity metric","collection/index names"],"output_types":["vector storage status and metrics","database connection health checks","migration logs and progress"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["FastGPT backend service running (Node.js 18+)","At least one LLM provider configured (OpenAI, Anthropic, Qwen, DeepSeek, etc.)","MongoDB for workflow definition storage","Vector database for dataset nodes (Milvus, Weaviate, Qdrant, or PineCone)","API keys for at least one LLM provider (OpenAI, Anthropic, Qwen, DeepSeek, etc.)","Network connectivity to provider endpoints","MongoDB for storing model configuration and usage logs","Node.js 18+ runtime","Docker runtime (Docker Desktop, containerd, etc.)","Kubernetes cluster (1.20+) for Helm deployment"],"failure_modes":["Workflow execution latency increases ~50-100ms per node due to variable resolution and streaming overhead","Complex branching logic with many conditional paths can become difficult to visualize and maintain in the UI","No built-in loop constructs — recursive patterns require child workflow composition","Pause-resume state must be persisted externally; no automatic checkpoint management","Token counting accuracy varies by provider — OpenAI token counts may differ from actual usage by 1-5%","Streaming responses add ~100-200ms latency due to chunking and serialization overhead","No built-in load balancing across provider instances — requires external orchestration","Private/self-hosted models (Ollama) require manual endpoint configuration per deployment","Helm charts require Kubernetes 1.20+; no support for older versions","Stateful components (MongoDB, vector DB) require separate management; not included in Helm charts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.40053941604128557,"quality":0.6,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"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":"2026-05-03T13:58:26.976Z","last_commit":"2026-04-30T06:44:00Z"},"community":{"stars":27891,"forks":7067,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=labring--fastgpt","compare_url":"https://unfragile.ai/compare?artifact=labring--fastgpt"}},"signature":"QUCwMdevxrUwcOdo6MJeAKALoTC31OUSdMzg3B4pHBLbGyjyMI175GyWckSKYH4VPteqsMEGgepA89kIBIxXCA==","signedAt":"2026-06-19T23:06:20.224Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/labring--fastgpt","artifact":"https://unfragile.ai/labring--fastgpt","verify":"https://unfragile.ai/api/v1/verify?slug=labring--fastgpt","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"}}