{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mintplex-labs--anything-llm","slug":"mintplex-labs--anything-llm","name":"anything-llm","type":"product","url":"https://anythingllm.com","page_url":"https://unfragile.ai/mintplex-labs--anything-llm","categories":["app-builders"],"tags":["ai-agents","custom-ai-agents","deepseek","kimi","llama3","llm","lmstudio","local-llm","localai","mcp","mcp-servers","moonshot","multimodal","no-code","ollama","qwen3","rag","vector-database","web-scraping"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-mintplex-labs--anything-llm__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.runtime.configuration","name":"multi-provider llm abstraction with runtime configuration","description":"Abstracts 40+ LLM providers (OpenAI, Anthropic, Ollama, LocalAI, DeepSeek, Kimi, Qwen, LM Studio, Moonshot) through a unified provider interface using getLLMProvider() factory pattern that loads provider classes from server/utils/AiProviders/* at runtime. Supports both cloud and local models with dynamic model discovery and per-workspace provider switching without server restart via the updateENV() system, enabling users to swap providers by updating environment variables that are read on each request.","intents":["I want to use multiple LLM providers across different workspaces without restarting the server","I need to switch from cloud LLMs to local models (Ollama, LM Studio) for privacy without code changes","I want to discover available models from a provider and select them dynamically","I need to support both proprietary and open-source models in the same application"],"best_for":["teams building multi-tenant SaaS platforms with LLM flexibility","enterprises requiring on-premises LLM deployment with cloud fallback","developers building privacy-first applications that support local inference"],"limitations":["Provider-specific features (function calling, vision) require custom adapter code per provider","Model discovery latency varies by provider (cloud providers ~200-500ms, local ~50ms)","No built-in provider failover — requires external orchestration for high availability","Token counting differs per provider, affecting cost estimation accuracy"],"requires":["Node.js 18+","API keys for cloud providers (OpenAI, Anthropic, etc.) OR local LLM server running (Ollama, LocalAI, LM Studio)","Environment variables configured for each provider (LLM_PROVIDER, OPENAI_API_KEY, etc.)"],"input_types":["text prompts","structured messages with role/content","multimodal input (text + images for vision-capable providers)"],"output_types":["text responses","streaming text chunks","structured JSON (when provider supports function calling)"],"categories":["tool-use-integration","llm-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_1","uri":"capability://memory.knowledge.document.aware.rag.with.configurable.vector.databases","name":"document-aware rag with configurable vector databases","description":"Implements a full retrieval-augmented generation pipeline using getVectorDbClass() factory to support 10+ vector databases (Pinecone, Weaviate, Qdrant, Milvus, Chroma, LanceDB, etc.) with pluggable embedding engines (local and cloud-based). Documents are chunked using configurable text splitting strategies, embedded via selected provider, stored in the chosen vector database, and retrieved via similarity search with optional reranking. The system maintains document-to-chunk mappings and metadata for source attribution, enabling users to cite retrieved passages.","intents":["I want to upload documents and chat with them using semantic search without managing vector database infrastructure","I need to choose between local (Chroma, LanceDB) and cloud (Pinecone, Weaviate) vector storage based on privacy requirements","I want to configure chunk size and overlap to optimize retrieval quality for my domain","I need to track which documents contributed to each response for compliance and transparency"],"best_for":["enterprises with sensitive documents requiring on-premises vector storage (Chroma, LanceDB)","teams building knowledge bases that need semantic search over large document collections","organizations needing document attribution and audit trails for regulatory compliance"],"limitations":["Chunking strategy is text-based; does not preserve document structure (tables, code blocks) — requires preprocessing for structured documents","Reranking adds 100-300ms latency per query depending on reranker model","Vector database selection is global per workspace — cannot mix databases for different document types","Embedding dimension mismatch between providers requires re-embedding entire corpus when switching embedding engines"],"requires":["Vector database instance (local: Chroma/LanceDB, or cloud: Pinecone/Weaviate/Qdrant API key)","Embedding provider configured (local: sentence-transformers, or cloud: OpenAI/Cohere)","Document upload via UI or collector service","Minimum 512MB RAM for local vector databases"],"input_types":["PDF files","text documents (TXT, MD)","web URLs (via collector service)","structured data (CSV, JSON)"],"output_types":["retrieved document chunks with similarity scores","augmented LLM responses with source citations","metadata (document name, chunk index, embedding vector)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_10","uri":"capability://data.processing.analysis.configurable.embedding.engines.with.local.and.cloud.providers","name":"configurable embedding engines with local and cloud providers","description":"Supports pluggable embedding engines (Embedding Engines in DeepWiki) with both local options (sentence-transformers, local models via Ollama) and cloud providers (OpenAI, Cohere, HuggingFace). Embeddings are generated during document ingestion and stored in the vector database. Users can switch embedding providers at the workspace level, though switching requires re-embedding the entire document corpus. The system includes native embedding engines that run locally without external API calls, enabling privacy-first deployments.","intents":["I want to use local embeddings for privacy without sending documents to cloud APIs","I need to switch embedding providers without losing my document index","I want to use domain-specific embedding models for better retrieval quality","I need to understand embedding costs and optimize for cost vs quality"],"best_for":["organizations with strict data privacy requirements","teams optimizing for retrieval quality in specific domains","applications where embedding costs are a significant expense"],"limitations":["Switching embedding providers requires re-embedding entire corpus — can take hours for large collections","Local embeddings have lower quality than cloud models (e.g., OpenAI embeddings) but are faster and cheaper","Embedding dimension varies by provider (384 for sentence-transformers, 1536 for OpenAI) — incompatible across providers","No built-in embedding quality metrics — requires manual evaluation of retrieval results"],"requires":["Embedding provider configured (local: Python environment for sentence-transformers, or cloud: API key)","Vector database that supports the embedding dimension","Sufficient storage for embeddings (typically 1-2GB per 100k documents)"],"input_types":["text chunks from documents","user queries (for similarity search)"],"output_types":["embedding vectors (384-3072 dimensions depending on provider)","similarity scores (for ranking retrieved chunks)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_11","uri":"capability://memory.knowledge.thread.based.conversation.management.with.message.history","name":"thread-based conversation management with message history","description":"Implements thread-based conversation management (Thread System in DeepWiki) where each conversation is stored as a thread with associated messages, metadata, and context. Threads are scoped to workspaces and can be resumed, archived, or deleted. Message history is persisted in the database and retrieved for context assembly in subsequent messages. The system supports both single-turn and multi-turn conversations with automatic context management.","intents":["I want to maintain conversation history across multiple messages","I need to resume conversations from where they left off","I want to organize conversations by thread for better UX","I need to export conversation history for analysis or compliance"],"best_for":["applications requiring persistent conversation state","customer support systems needing conversation history","research tools tracking multi-turn interactions"],"limitations":["Long conversations fill the context window — requires summarization for conversations >10 turns","No built-in conversation summarization — requires external summarization step","Thread storage grows unbounded — requires periodic cleanup or archival","No built-in conversation search — requires external search index for finding past conversations"],"requires":["Database with thread and message tables","Message schema with role (user/assistant), content, and metadata fields"],"input_types":["user messages","assistant responses"],"output_types":["thread records","message history","conversation metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_12","uri":"capability://data.processing.analysis.data.connector.service.for.external.data.source.integration","name":"data connector service for external data source integration","description":"Provides a data connector service (Data Connectors in DeepWiki) that enables ingestion from external data sources (databases, APIs, cloud storage) without manual document upload. Connectors can be scheduled to periodically sync data, enabling dynamic knowledge bases that stay up-to-date with source systems. Supported connectors include web URLs, APIs, databases, and cloud storage services. Connectors handle authentication, data transformation, and incremental updates.","intents":["I want to automatically sync documents from cloud storage (Google Drive, Dropbox) without manual upload","I need to ingest data from databases or APIs on a schedule","I want to keep my knowledge base up-to-date with live data sources","I need to handle incremental updates without re-processing entire datasets"],"best_for":["organizations with dynamic data sources that change frequently","teams building knowledge bases from multiple external systems","applications requiring real-time or near-real-time knowledge updates"],"limitations":["Connector setup requires authentication credentials — adds security management complexity","Incremental updates are connector-specific — not all connectors support delta sync","Scheduling is basic (fixed intervals) — no cron-like flexibility","No built-in error handling or retry logic — failed syncs require manual intervention"],"requires":["Data connector service running","Authentication credentials for external data sources","Database for storing connector configurations and sync history"],"input_types":["connector configuration (source type, credentials, schedule)","external data (from APIs, databases, cloud storage)"],"output_types":["ingested documents","sync history and logs","error reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_13","uri":"capability://automation.workflow.frontend.settings.interface.with.real.time.configuration.updates","name":"frontend settings interface with real-time configuration updates","description":"Provides a React-based frontend settings interface (Frontend Settings Interface in DeepWiki) that allows users to configure LLM providers, vector databases, embedding engines, and workspace settings without touching configuration files. Settings are validated and persisted to the database, with changes taking effect immediately via the updateENV() system. The interface includes provider-specific configuration forms, model selection dropdowns, and real-time validation feedback.","intents":["I want to change LLM providers without restarting the server","I need a user-friendly interface to configure embedding and vector database settings","I want to validate settings before applying them to avoid breaking the system","I need to see available models and select them from a dropdown"],"best_for":["non-technical users managing AnythingLLM configuration","teams needing to switch providers without DevOps intervention","organizations requiring audit trails of configuration changes"],"limitations":["Complex provider-specific settings may not have UI forms — requires manual environment variable configuration","Real-time validation is limited to format checking — doesn't test actual provider connectivity","No configuration rollback — requires manual revert if settings break the system","Settings are stored in database — requires database backup for disaster recovery"],"requires":["React frontend running","Backend API for persisting settings","Database for storing configuration"],"input_types":["form inputs (text, dropdowns, toggles)","provider credentials (API keys)"],"output_types":["persisted settings","validation feedback","configuration status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_2","uri":"capability://text.generation.language.streaming.chat.with.context.assembly.and.rag.integration","name":"streaming chat with context assembly and rag integration","description":"Implements a streaming chat engine (Chat Architecture Overview in DeepWiki) that assembles context by retrieving relevant document chunks from the vector database, constructing a prompt with retrieved context, and streaming responses from the selected LLM provider via Server-Sent Events (SSE). The context assembly process includes similarity search, optional reranking, and token-aware context truncation to fit within the LLM's context window. Supports multi-turn conversations with thread-based message history stored in the database.","intents":["I want to chat with documents and see streamed responses in real-time without waiting for full completion","I need the LLM to cite which documents it used to answer my question","I want to maintain conversation history across multiple messages with document context","I need to control how much context is included to manage token costs and latency"],"best_for":["web application builders implementing real-time chat interfaces","teams building customer support chatbots with knowledge base integration","developers creating research assistants that need to cite sources"],"limitations":["Context assembly adds 200-500ms latency before streaming begins (vector search + reranking)","Token counting is approximate — may truncate context unexpectedly if estimate is off","No built-in conversation summarization — context window fills with history in long conversations","Streaming requires WebSocket or SSE support — not compatible with simple REST polling"],"requires":["Vector database with documents already embedded and indexed","LLM provider configured with streaming support (most providers support this)","Frontend capable of handling Server-Sent Events (SSE) or WebSocket streams","Database for storing conversation threads and message history"],"input_types":["text messages","multimodal messages (text + images for vision-capable LLMs)"],"output_types":["streamed text responses","message metadata (timestamp, tokens used, retrieved documents)","conversation thread records"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_3","uri":"capability://automation.workflow.multi.tenant.workspace.isolation.with.per.workspace.configuration","name":"multi-tenant workspace isolation with per-workspace configuration","description":"Implements workspace-level data and configuration isolation (Workspace Model and Configuration in DeepWiki) where each workspace has its own documents, vector database connection, LLM provider selection, embedding engine, and chat threads. Workspaces are stored in the database with configuration metadata, and all API requests are scoped to a workspace ID. This enables multiple teams or projects to coexist in a single AnythingLLM instance with completely isolated data and settings, supporting both single-tenant and multi-tenant deployments.","intents":["I want to run a single AnythingLLM instance for multiple teams without data leakage","I need each team to choose their own LLM provider and vector database","I want to isolate document collections by project or department","I need per-workspace API keys and access control for security"],"best_for":["SaaS platforms offering white-label RAG capabilities","enterprises with multiple departments needing isolated knowledge bases","managed service providers deploying AnythingLLM for multiple customers"],"limitations":["No cross-workspace search or context sharing — each workspace is completely isolated","Workspace switching requires re-authentication if using workspace-level API keys","Database schema requires workspace_id foreign key on all data tables — adds query complexity","No built-in workspace quotas — requires external rate limiting for multi-tenant cost control"],"requires":["Database with workspace table and workspace_id foreign keys on documents, embeddings, threads","Authentication system that maps users to workspaces","API middleware that validates workspace access before processing requests"],"input_types":["workspace configuration (LLM provider, vector DB, embedding engine)","documents uploaded to specific workspace","chat messages scoped to workspace"],"output_types":["workspace metadata","workspace-scoped chat responses","workspace-specific document collections"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_4","uri":"capability://data.processing.analysis.document.collection.and.ingestion.via.collector.service","name":"document collection and ingestion via collector service","description":"Provides a dedicated collector service (Collector Service in DeepWiki) that handles document upload, format detection, parsing, and chunking before vectorization. Supports multiple input formats (PDF, TXT, MD, CSV, JSON, web URLs) with format-specific parsers. The collector service can run as a separate process or be embedded, enabling asynchronous document processing without blocking the main API. Documents are chunked using configurable text splitting strategies (recursive character splitting, token-based splitting) and metadata is extracted for source attribution.","intents":["I want to upload documents in multiple formats without writing custom parsers","I need to ingest documents asynchronously without blocking chat operations","I want to scrape web content and add it to my knowledge base","I need to extract and preserve document metadata (title, author, date) for filtering and attribution"],"best_for":["teams building document management systems with semantic search","organizations ingesting diverse document types (PDFs, web pages, structured data)","applications requiring asynchronous document processing at scale"],"limitations":["PDF parsing quality varies by PDF type (scanned images, complex layouts) — may require OCR preprocessing","Web scraping requires handling robots.txt and rate limiting — not suitable for large-scale crawling","Chunking is text-based — does not preserve document structure (tables, code blocks, images)","No built-in duplicate detection — same document uploaded twice creates duplicate embeddings"],"requires":["Collector service running (separate Node.js process or embedded)","Document storage (local filesystem or cloud storage like S3)","Vector database connection for storing embeddings","Embedding provider configured"],"input_types":["PDF files","text files (TXT, MD, RST)","structured data (CSV, JSON, JSONL)","web URLs","email content (via integration)"],"output_types":["chunked text with metadata","embeddings stored in vector database","document records with source attribution"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_5","uri":"capability://planning.reasoning.agent.builder.with.flow.based.task.decomposition","name":"agent builder with flow-based task decomposition","description":"Provides a visual agent builder (Agent Builder and Flows in DeepWiki) that enables no-code creation of multi-step agents using flow diagrams. Agents decompose complex tasks into sequential steps, each step can invoke different LLM providers, call external tools/APIs, or perform conditional logic. The system supports agent persistence, execution history tracking, and integration with the chat interface for interactive agent execution. Agents can be embedded as chat widgets for end-user interaction.","intents":["I want to create multi-step workflows without writing code (e.g., research → summarize → email)","I need agents that can call external APIs and tools based on LLM decisions","I want to test agent flows and debug step-by-step execution","I need to embed agents as chat widgets in external applications"],"best_for":["non-technical users building automation workflows","teams creating customer-facing AI agents without engineering resources","organizations needing visual workflow design for compliance and auditability"],"limitations":["Flow-based design has limited expressiveness compared to code — complex conditional logic requires workarounds","Agent execution is synchronous per step — no built-in parallelization across branches","Error handling is basic — requires manual retry logic in flow design","Debugging is limited to execution history — no step-by-step breakpoints or variable inspection"],"requires":["React frontend for visual agent builder","Database for storing agent definitions and execution history","Tool/API integrations configured (e.g., web search, email, Slack)","LLM provider configured for agent reasoning steps"],"input_types":["flow diagram (nodes and edges)","tool/API configurations","user input (chat messages triggering agent execution)"],"output_types":["agent execution results","execution history with step-by-step logs","structured output (JSON) from final step"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_6","uri":"capability://text.generation.language.text.to.speech.and.messaging.platform.integration","name":"text-to-speech and messaging platform integration","description":"Integrates text-to-speech (TTS) capabilities (Text-to-Speech and Telegram Integration in DeepWiki) allowing chat responses to be converted to audio and delivered via messaging platforms like Telegram. Supports multiple TTS providers and voice options, enabling voice-based interaction with the RAG system. Telegram integration allows users to interact with agents via chat messages, with responses delivered as text or audio.","intents":["I want users to hear chat responses as audio instead of reading text","I need to expose my RAG agent to Telegram users without building a custom bot","I want to support voice-first interactions for accessibility","I need to deliver agent responses via messaging platforms for better UX"],"best_for":["accessibility-focused applications serving users with visual impairments","mobile-first applications where audio is preferred over text","teams building Telegram bots with AI capabilities"],"limitations":["TTS latency adds 1-3 seconds per response depending on text length and provider","Audio quality varies by TTS provider — some sound robotic or unnatural","Telegram integration requires bot token and webhook setup — adds operational complexity","No support for voice input (speech-to-text) — only text-to-speech output"],"requires":["TTS provider API key (e.g., Google Cloud TTS, Azure Speech Services, ElevenLabs)","Telegram bot token and webhook configuration","Audio storage (local or cloud) for caching TTS outputs"],"input_types":["text responses from LLM","Telegram messages (text input)"],"output_types":["audio files (MP3, WAV, OGG)","Telegram messages (text or audio)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_7","uri":"capability://tool.use.integration.embedded.chat.widget.for.external.applications","name":"embedded chat widget for external applications","description":"Provides embeddable chat widgets (Embedded Chat Widgets in DeepWiki) that can be deployed on external websites or applications, allowing end-users to interact with RAG agents without accessing the main AnythingLLM interface. Widgets are configured with workspace and agent selection, styling options, and can be embedded via iframe or script tag. Supports both synchronous and asynchronous message handling with streaming responses.","intents":["I want to add a chat interface to my website without building custom UI","I need to expose my RAG agent to customers without giving them access to AnythingLLM","I want to customize the chat widget appearance to match my brand","I need to track user interactions and feedback from embedded chats"],"best_for":["SaaS companies adding AI chat to their product","websites needing customer support chatbots","organizations deploying white-label AI solutions"],"limitations":["Widget styling is limited to CSS customization — complex UI changes require forking the widget code","Cross-origin requests require CORS configuration — may have security implications","Widget performance depends on host page performance — slow pages degrade chat UX","No built-in analytics — requires custom instrumentation to track user interactions"],"requires":["AnythingLLM instance running and accessible from the web","Workspace and agent configured","Host website with CORS headers configured","API key for widget authentication"],"input_types":["user messages (text, optionally multimodal)","widget configuration (workspace, agent, styling)"],"output_types":["streamed chat responses","widget interaction events (for analytics)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_8","uri":"capability://tool.use.integration.developer.api.with.openai.compatible.endpoints","name":"developer api with openai-compatible endpoints","description":"Exposes a comprehensive REST API (Developer API in DeepWiki) with workspace, document, and admin endpoints, plus OpenAI-compatible chat completion endpoints for drop-in compatibility with existing OpenAI client libraries. The API supports authentication via API keys, request validation, and returns structured JSON responses. OpenAI-compatible endpoints allow developers to use AnythingLLM as a drop-in replacement for OpenAI's API, enabling easy migration from cloud LLMs to local/private deployments.","intents":["I want to programmatically upload documents and manage workspaces via API","I need to integrate AnythingLLM into my application without using the web UI","I want to use existing OpenAI client libraries with AnythingLLM without code changes","I need to build custom integrations with external tools and platforms"],"best_for":["developers building applications on top of AnythingLLM","teams migrating from OpenAI API to local/private deployments","organizations integrating AnythingLLM with existing workflows"],"limitations":["OpenAI-compatible endpoints don't support all OpenAI features (e.g., fine-tuning, embeddings API)","API rate limiting is not built-in — requires external rate limiter for production","Authentication is API-key based — no OAuth or SAML support","API versioning is not explicit — breaking changes may occur between releases"],"requires":["AnythingLLM instance running and accessible","API key generated in the admin panel","HTTP client library (curl, Python requests, JavaScript fetch, etc.)"],"input_types":["JSON request bodies","multipart form data (for document uploads)","query parameters (for filtering, pagination)"],"output_types":["JSON responses","streaming responses (for chat endpoints)","file downloads (for document exports)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mintplex-labs--anything-llm__cap_9","uri":"capability://automation.workflow.system.administration.with.multi.user.management.and.audit.logging","name":"system administration with multi-user management and audit logging","description":"Provides administrative controls (System Administration in DeepWiki) for managing users, API keys, workspace assignments, and system settings. Includes event logging and telemetry (Event Logging and Telemetry in DeepWiki) that tracks user actions, API calls, and system events for audit trails and compliance. Multi-user management allows admins to create users, assign them to workspaces, and control their permissions. API key management enables per-user or per-application API keys with granular scope control.","intents":["I need to manage multiple users and control which workspaces they can access","I want to audit who accessed which documents and when for compliance","I need to generate API keys for external applications with specific permissions","I want to track system health and usage metrics"],"best_for":["enterprises deploying AnythingLLM with multiple users","organizations with compliance requirements (SOC 2, HIPAA, GDPR)","teams managing shared AnythingLLM instances across departments"],"limitations":["Role-based access control (RBAC) is basic — only admin/user roles, no custom roles","Audit logs are stored in the database — no built-in export to external SIEM systems","No single sign-on (SSO) support — requires manual user creation","API key rotation is manual — no automatic expiration or rotation policies"],"requires":["Database with user, api_key, and audit_log tables","Admin user account with system administration privileges","Authentication middleware for protecting admin endpoints"],"input_types":["user creation/update requests","API key generation requests","workspace assignment requests"],"output_types":["user records","API keys","audit logs","usage metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","API keys for cloud providers (OpenAI, Anthropic, etc.) OR local LLM server running (Ollama, LocalAI, LM Studio)","Environment variables configured for each provider (LLM_PROVIDER, OPENAI_API_KEY, etc.)","Vector database instance (local: Chroma/LanceDB, or cloud: Pinecone/Weaviate/Qdrant API key)","Embedding provider configured (local: sentence-transformers, or cloud: OpenAI/Cohere)","Document upload via UI or collector service","Minimum 512MB RAM for local vector databases","Embedding provider configured (local: Python environment for sentence-transformers, or cloud: API key)","Vector database that supports the embedding dimension","Sufficient storage for embeddings (typically 1-2GB per 100k documents)"],"failure_modes":["Provider-specific features (function calling, vision) require custom adapter code per provider","Model discovery latency varies by provider (cloud providers ~200-500ms, local ~50ms)","No built-in provider failover — requires external orchestration for high availability","Token counting differs per provider, affecting cost estimation accuracy","Chunking strategy is text-based; does not preserve document structure (tables, code blocks) — requires preprocessing for structured documents","Reranking adds 100-300ms latency per query depending on reranker model","Vector database selection is global per workspace — cannot mix databases for different document types","Embedding dimension mismatch between providers requires re-embedding entire corpus when switching embedding engines","Switching embedding providers requires re-embedding entire corpus — can take hours for large collections","Local embeddings have lower quality than cloud models (e.g., OpenAI embeddings) but are faster and cheaper","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4387489226408984,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:57:01.479Z","last_commit":"2026-05-02T06:05:14Z"},"community":{"stars":59449,"forks":6418,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mintplex-labs--anything-llm","compare_url":"https://unfragile.ai/compare?artifact=mintplex-labs--anything-llm"}},"signature":"qwYkSns6RzNE4rQmUcnL+P8RIjb9NBxryrXmGHX0B1qWYZqw4CNgfu3WGOhM0y/VCZbOwwD12YcDjJggwAC/DA==","signedAt":"2026-06-20T18:47:07.809Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mintplex-labs--anything-llm","artifact":"https://unfragile.ai/mintplex-labs--anything-llm","verify":"https://unfragile.ai/api/v1/verify?slug=mintplex-labs--anything-llm","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"}}