{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-modsetter--surfsense","slug":"modsetter--surfsense","name":"SurfSense","type":"webapp","url":"https://www.surfsense.com","page_url":"https://unfragile.ai/modsetter--surfsense","categories":["research-search"],"tags":["agent","agents","ai","chrome-extension","extension","fastapi","langchain","langgraph","nextjs","notebooklm","ollama","perplexity","python","rag","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-modsetter--surfsense__cap_0","uri":"capability://tool.use.integration.multi.source.document.ingestion.with.connector.abstraction","name":"multi-source document ingestion with connector abstraction","description":"SurfSense implements a pluggable connector architecture supporting 28+ data sources (Google Drive, Slack, Notion, GitHub, Jira, etc.) through a standardized OAuth integration flow and periodic indexing pipeline. Each connector implements a common interface for authentication, document fetching, and metadata extraction, with background task processing handling continuous synchronization without blocking the main application. The system abstracts away source-specific API complexity through a unified document ingestion pipeline that normalizes heterogeneous data formats into a common internal representation.","intents":["Connect multiple knowledge sources (Slack, Notion, Google Drive) to a single searchable index without manual data export","Automatically sync documents from external sources on a schedule without manual intervention","Support team-specific data connectors (GitHub for engineering teams, Jira for project management) within the same platform","Add custom data sources by implementing a connector interface without modifying core indexing logic"],"best_for":["Enterprise teams with data spread across multiple SaaS platforms (Slack, Notion, Google Workspace)","Organizations needing self-hosted alternatives to Glean or Perplexity with custom connector support","Teams building internal knowledge management systems with heterogeneous data sources"],"limitations":["Connector implementation requires understanding the source API and OAuth flow; no low-code connector builder","Periodic indexing introduces latency between source updates and searchability (configurable but not real-time)","OAuth token refresh and expiration handling adds operational complexity for connector maintenance","Large-scale connectors (e.g., 100k+ Slack messages) may require tuning of batch sizes and indexing schedules"],"requires":["Python 3.9+ for backend connector implementation","OAuth credentials for each connected data source","Database instance (PostgreSQL recommended) for storing connector state and document metadata","Background task processor (Celery or similar) for periodic indexing jobs"],"input_types":["OAuth tokens and API credentials","Connector configuration (source URLs, authentication parameters)","Document metadata from external APIs"],"output_types":["Normalized document objects with extracted text, metadata, and source attribution","Indexed embeddings stored in vector database","Connector sync logs and error tracking"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_1","uri":"capability://search.retrieval.hybrid.semantic.and.full.text.search.with.reranking","name":"hybrid semantic and full-text search with reranking","description":"SurfSense combines vector similarity search (semantic embeddings) with BM25 full-text search and applies a reranking step to produce hybrid results that balance semantic relevance with keyword matching. The system stores document chunks as embeddings in a vector database and maintains full-text indices for keyword-based retrieval, then merges results using a configurable scoring strategy. This hybrid approach enables finding documents that match both conceptual meaning and specific terminology, critical for research and knowledge work where both types of relevance matter.","intents":["Search for documents by meaning (e.g., 'how do we handle customer complaints') even if exact keywords don't match","Find documents containing specific technical terms or proper nouns that semantic search might miss","Retrieve results ranked by relevance combining both semantic similarity and keyword presence","Support research workflows requiring both conceptual exploration and precise fact-finding"],"best_for":["Research teams needing semantic understanding combined with precise keyword matching","Organizations with domain-specific terminology where keyword search alone is insufficient","Teams migrating from traditional full-text search to AI-powered search without losing precision"],"limitations":["Reranking adds latency (~100-500ms per query depending on result set size) compared to single-method search","Requires tuning of hybrid scoring weights for different use cases; no automatic optimization","Vector embeddings are language-specific; multilingual search requires separate embedding models per language","Large result sets (1000+) may require pagination and lazy reranking to maintain performance"],"requires":["Vector database (Pinecone, Weaviate, or local embedding store) for semantic search","Full-text search engine (Elasticsearch, PostgreSQL full-text, or similar) for keyword matching","Embedding model (OpenAI, local Ollama, or similar) for converting documents to vectors","Reranking model or scoring function to merge hybrid results"],"input_types":["Natural language search queries","Document chunks with embeddings and full-text indices"],"output_types":["Ranked list of document chunks with relevance scores","Source attribution and citation metadata","Search metadata (query processing time, result count)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_10","uri":"capability://automation.workflow.self.hosted.deployment.with.docker.and.manual.installation.options","name":"self-hosted deployment with docker and manual installation options","description":"SurfSense provides multiple deployment options including Docker containerization for quick setup and manual installation for custom environments. The system includes database migrations (Alembic), environment configuration templates, and comprehensive documentation for both deployment methods. This enables organizations to self-host SurfSense on their infrastructure, maintaining full control over data, security, and customization without relying on cloud services or third-party hosting.","intents":["Deploy SurfSense on internal infrastructure for data privacy and compliance requirements","Customize deployment environment (database, LLM providers, storage) without vendor constraints","Maintain full control over SurfSense data and configuration without cloud dependencies","Integrate SurfSense with existing internal tools and infrastructure"],"best_for":["Enterprise organizations with strict data residency and privacy requirements","Teams needing to customize deployment for specific infrastructure or compliance needs","Organizations wanting to avoid cloud vendor lock-in and maintain data sovereignty"],"limitations":["Self-hosting requires operational expertise (Docker, database administration, networking)","No managed service; teams are responsible for backups, updates, and security patches","Scaling requires manual infrastructure management; no auto-scaling or managed scaling","Database migrations must be managed manually; no automated upgrade path","Troubleshooting requires understanding the full stack (backend, frontend, database, vector store)"],"requires":["Docker and Docker Compose (for Docker deployment) or Python 3.9+ (for manual installation)","PostgreSQL database (or compatible) for application data","Vector database (Pinecone, Weaviate, or local alternative) for embeddings","LLM provider API keys (OpenAI, Anthropic) or local Ollama instance","Linux/Unix server or cloud VM for hosting (AWS, GCP, Azure, or on-premises)"],"input_types":["Docker Compose configuration","Environment variables and configuration files","Database connection strings and credentials"],"output_types":["Running SurfSense instance accessible via web interface","Database with initialized schema","Configured vector database and LLM providers"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_11","uri":"capability://text.generation.language.multi.language.support.and.internationalization.i18n","name":"multi-language support and internationalization (i18n)","description":"SurfSense implements internationalization (i18n) infrastructure in the frontend application, supporting multiple languages through a translation system. The system includes language selection in the UI, translated strings for all user-facing text, and support for right-to-left languages. This enables teams in different regions to use SurfSense in their native language without requiring separate deployments or code modifications.","intents":["Use SurfSense in your native language without English-only interface","Deploy SurfSense globally to teams across different regions and languages","Contribute translations for new languages without modifying application code"],"best_for":["Global organizations with teams across multiple regions and languages","Non-English speaking teams wanting to use SurfSense in their native language","Organizations expanding internationally and needing multi-language support"],"limitations":["Backend API responses are English-only; only UI is translated","LLM responses are in the language of the LLM model, not the UI language","Translation maintenance requires community contributions; no professional translation service","Right-to-left language support is basic; may have layout issues in some components","Search and indexing are language-specific; multilingual search requires separate indices"],"requires":["i18n library (e.g., next-i18next for Next.js frontend)","Translation files for each supported language","Language selection mechanism in UI"],"input_types":["User language preference","UI text strings to be translated"],"output_types":["Translated UI in selected language","Language-specific formatting (dates, numbers, currency)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_12","uri":"capability://memory.knowledge.document.mention.and.reference.tracking.in.conversations","name":"document mention and reference tracking in conversations","description":"SurfSense implements a document mention system that tracks which documents are referenced in conversations, enabling users to see which knowledge base items are actively used in discussions. When users mention documents in chat or when the RAG system retrieves documents, the system records these references with timestamps and context. This creates a knowledge graph showing relationships between conversations and documents, enabling discovery of related discussions and understanding of document usage patterns.","intents":["See which documents are referenced in conversations for knowledge discovery","Find related conversations that discuss the same documents","Understand which documents are most actively used in team discussions","Track how documents evolve through team conversations and feedback"],"best_for":["Research teams wanting to understand document usage and relationships","Organizations tracking knowledge utilization and team collaboration patterns","Teams discovering related discussions through document mentions"],"limitations":["Document mention tracking adds overhead to every conversation; may impact performance","Mention tracking is limited to explicit references; implicit document usage is not captured","No automatic relationship discovery between documents; requires manual exploration","Mention data is not used for search ranking or recommendation; purely informational"],"requires":["Document reference tracking system in conversation storage","UI for displaying document mentions and related conversations","Timestamp and context preservation for each mention"],"input_types":["Document references in conversations","Retrieved documents from RAG system","User-explicit document mentions"],"output_types":["Document mention metadata (timestamp, context, conversation ID)","Related conversation suggestions","Document usage statistics"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_2","uri":"capability://text.generation.language.rag.based.document.chat.with.citation.tracking","name":"rag-based document chat with citation tracking","description":"SurfSense implements a retrieval-augmented generation (RAG) pipeline where user queries trigger hybrid search to retrieve relevant document chunks, which are then passed as context to an LLM for response generation. The system tracks source attribution throughout the pipeline—maintaining references from retrieved chunks back to original documents—and surfaces citations in the chat interface. The chat architecture supports multi-turn conversations with thread management, allowing users to ask follow-up questions while maintaining context and citation lineage across the conversation.","intents":["Ask questions about internal documents and receive answers grounded in specific sources with citations","Have multi-turn conversations about documents without losing context or citation attribution","Verify AI responses by clicking citations to view the source documents","Build conversational interfaces over proprietary knowledge bases without exposing raw documents"],"best_for":["Teams needing conversational access to internal knowledge bases with verifiable sources","Organizations requiring audit trails showing which documents informed AI responses","Research teams using AI to synthesize information from multiple documents with proper attribution"],"limitations":["RAG quality depends on retrieval quality; poor search results lead to hallucinations or off-topic responses","Context window limits (4k-200k tokens depending on LLM) constrain the number of documents that can be included per query","Citation accuracy requires careful prompt engineering; LLMs may cite documents not actually used in reasoning","Multi-turn conversations increase token usage and latency as context grows; requires conversation pruning strategies"],"requires":["LLM with sufficient context window (8k+ tokens recommended) and function calling support","Hybrid search system (semantic + full-text) for retrieving relevant document chunks","Document chunk store with metadata linking chunks back to source documents","Chat state management system for maintaining conversation history and thread context"],"input_types":["Natural language user queries","Conversation history and thread context","Retrieved document chunks with source metadata"],"output_types":["Natural language responses with embedded citations","Citation metadata (document ID, chunk ID, relevance score)","Conversation history with turn-by-turn attribution"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_3","uri":"capability://tool.use.integration.role.based.llm.provider.selection.and.configuration","name":"role-based llm provider selection and configuration","description":"SurfSense abstracts LLM provider selection through a configuration layer that allows different roles (admin, user) to select from 100+ supported models across multiple providers (OpenAI, Anthropic, Ollama, local models, etc.). The system maintains provider-specific configurations (API keys, model parameters, rate limits) and routes requests to the appropriate provider based on user role and workspace settings. This abstraction enables organizations to enforce cost controls (e.g., cheaper models for certain users), support multiple LLM providers simultaneously, and switch providers without code changes.","intents":["Allow admins to configure which LLM providers and models are available to team members","Support cost optimization by assigning different models to different user roles or use cases","Enable local LLM deployment (Ollama) for privacy-sensitive workloads while supporting cloud providers for others","Switch between LLM providers (OpenAI to Anthropic) without code changes or user disruption"],"best_for":["Enterprise teams needing cost control and provider flexibility across multiple LLM options","Organizations with privacy requirements necessitating local LLM deployment alongside cloud providers","Teams evaluating multiple LLM providers and needing to switch without application code changes"],"limitations":["Provider-specific features (function calling, vision, etc.) may not be uniformly supported across all providers","Model parameter tuning (temperature, top_p) requires understanding provider-specific APIs; no unified parameter abstraction","Rate limiting and quota management must be configured per provider; no built-in cross-provider rate limiting","Switching providers mid-conversation may produce inconsistent responses due to model differences"],"requires":["API keys for each supported LLM provider (OpenAI, Anthropic, etc.)","Ollama instance for local model deployment (optional but recommended for privacy)","Configuration management system for storing provider credentials securely","Role-based access control system to enforce provider/model assignments"],"input_types":["Provider configuration (API keys, model names, parameters)","User role and workspace context","LLM requests with provider-specific parameters"],"output_types":["LLM responses routed through selected provider","Provider-specific metadata (token usage, model version)","Configuration audit logs showing provider selections"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_4","uri":"capability://tool.use.integration.team.collaboration.with.searchspace.isolation.and.rbac","name":"team collaboration with searchspace isolation and rbac","description":"SurfSense implements multi-tenancy through SearchSpaces—isolated workspaces where teams can manage documents, conversations, and LLM configurations independently. Each SearchSpace has its own document index, conversation history, and member list, with role-based access control (RBAC) determining what actions each user can perform (view documents, create conversations, manage connectors, etc.). The system maintains workspace isolation at the database level, ensuring data from one SearchSpace cannot leak to another, while supporting team membership management with invitations and role assignments.","intents":["Create isolated team workspaces where different teams can manage their own documents and conversations","Assign different roles (admin, editor, viewer) to team members with granular permission control","Invite team members to workspaces and manage their access without sharing credentials","Ensure data isolation between teams in a shared SurfSense instance"],"best_for":["Organizations with multiple teams needing isolated knowledge bases within a single SurfSense instance","Enterprises requiring fine-grained access control and audit trails for compliance","Teams collaborating on research where different members have different permission levels"],"limitations":["Cross-workspace search and collaboration are not supported; each SearchSpace is completely isolated","Role definitions are fixed (admin, editor, viewer); custom roles require code changes","Invitation system is email-based; no SAML/LDAP integration for enterprise SSO","Permission checks add latency to every database query; no caching layer for permission evaluation"],"requires":["Database with multi-tenancy support (PostgreSQL with row-level security recommended)","Authentication system supporting user registration and session management","Email service for sending workspace invitations","RBAC implementation with permission checks on all data access paths"],"input_types":["User identity and role within SearchSpace","Workspace configuration and member list","Permission queries (can user X perform action Y on resource Z)"],"output_types":["Filtered data based on user permissions","Workspace membership and role assignments","Audit logs of permission changes and access"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_5","uri":"capability://data.processing.analysis.document.chunking.and.embedding.pipeline.with.metadata.preservation","name":"document chunking and embedding pipeline with metadata preservation","description":"SurfSense implements a document processing pipeline that ingests raw documents, chunks them into semantically meaningful segments (respecting document structure), extracts metadata (title, author, source URL), and generates embeddings for each chunk. The system preserves metadata throughout the pipeline, maintaining links from chunks back to source documents and original content locations. This enables citation tracking, source attribution in search results, and reconstruction of document context when displaying search results.","intents":["Split large documents into searchable chunks while preserving semantic meaning and document structure","Extract and preserve document metadata (title, author, source) for proper attribution in search results","Generate embeddings for chunks to enable semantic search across the knowledge base","Track which chunk came from which source document for citation and verification purposes"],"best_for":["Organizations with large document collections (100k+ documents) needing efficient chunking and indexing","Teams requiring precise source attribution and citation tracking in search results","Research teams working with diverse document types (PDFs, web pages, Slack messages) with different structure"],"limitations":["Chunk size and overlap are fixed parameters; no adaptive chunking based on document structure","Metadata extraction is basic (title, author, URL); no support for custom metadata fields without code changes","Embedding generation is sequential; large-scale document ingestion requires batching and async processing","Chunk boundaries may split important context; no semantic-aware chunking that respects document sections"],"requires":["Embedding model (OpenAI, local Ollama, or similar) for generating chunk embeddings","Vector database for storing embeddings with metadata","Document parser supporting multiple formats (PDF, Markdown, plain text, etc.)","Metadata extraction logic for each document type"],"input_types":["Raw documents in various formats (PDF, Markdown, plain text, HTML)","Document metadata (title, author, source URL, creation date)","Chunking parameters (chunk size, overlap, separator)"],"output_types":["Document chunks with text content and metadata","Embeddings for each chunk","Chunk-to-document mapping for citation tracking"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_6","uri":"capability://text.generation.language.ai.powered.podcast.generation.from.conversations.and.documents","name":"ai-powered podcast generation from conversations and documents","description":"SurfSense includes a podcast generation capability that transforms chat conversations or document collections into structured podcast scripts with multiple speakers, dialogue, and narrative flow. The system uses LLMs to synthesize information from source materials, generate speaker personas, create dialogue between speakers, and produce audio-ready scripts. This enables teams to convert research findings or internal knowledge into consumable audio content without manual scripting or production work.","intents":["Convert research findings or document collections into podcast episodes for consumption during commutes or exercise","Generate dialogue-based explanations of complex topics with multiple speaker perspectives","Create audio summaries of long conversations or documents for quick consumption","Produce branded podcast content from internal knowledge bases for external sharing"],"best_for":["Content teams needing to repurpose research into multiple formats (text, audio, video)","Organizations building internal knowledge podcasts for employee learning","Teams wanting to make research more accessible through audio format"],"limitations":["Generated podcasts require manual review and editing; LLM-generated dialogue may be awkward or inaccurate","Audio synthesis (text-to-speech) is not included; requires external TTS service for actual audio production","Podcast generation is computationally expensive; large documents may timeout or require chunking","Speaker personas and dialogue style are template-based; limited customization without prompt engineering","No support for background music, sound effects, or production-quality audio features"],"requires":["LLM with strong narrative and dialogue generation capabilities (GPT-4 or similar)","Text-to-speech service (optional, for audio production) like ElevenLabs or Google Cloud TTS","Podcast script template and speaker persona definitions","Document or conversation context to generate podcast from"],"input_types":["Chat conversations or document collections","Podcast generation parameters (length, speaker count, tone)","Optional speaker persona definitions"],"output_types":["Podcast script in markdown or structured format","Speaker assignments and dialogue","Audio file (if TTS integration enabled)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_7","uri":"capability://tool.use.integration.browser.extension.for.contextual.document.capture.and.search","name":"browser extension for contextual document capture and search","description":"SurfSense provides a Chrome/Firefox browser extension that enables users to capture web content, highlight text, and search the knowledge base directly from any webpage. The extension communicates with the SurfSense backend through API calls, allowing users to add web pages to their knowledge base, search existing documents, and access chat conversations without leaving their browser. This extends SurfSense functionality into the user's browsing workflow, enabling seamless knowledge capture and retrieval.","intents":["Capture web pages and articles directly into the knowledge base while browsing","Search the knowledge base for relevant information without switching to the SurfSense app","Highlight text on web pages and add it to conversations or documents","Access SurfSense chat and search from any webpage without context switching"],"best_for":["Research teams that spend significant time in the browser and need quick access to knowledge base","Users wanting to capture web content into their knowledge base without manual copying","Teams using SurfSense as a browser-integrated research tool"],"limitations":["Extension requires API key configuration; no seamless SSO integration","Web page capture may fail on JavaScript-heavy sites or pages with authentication","Extension communication adds latency to search and capture operations","Limited to Chrome and Firefox; no Safari or Edge support","Highlight and annotation features are basic; no rich markup or drawing tools"],"requires":["Chrome or Firefox browser (version 90+)","SurfSense API key for authentication","Backend API endpoint accessible from browser","JavaScript enabled in browser for extension functionality"],"input_types":["Web page URLs and content","User highlights and selections","Search queries from browser context"],"output_types":["Captured web pages added to knowledge base","Search results displayed in extension popup","Chat interface accessible from extension"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_8","uri":"capability://planning.reasoning.thinking.steps.and.reasoning.transparency.in.chat.responses","name":"thinking steps and reasoning transparency in chat responses","description":"SurfSense surfaces the LLM's reasoning process by capturing and displaying 'thinking steps'—intermediate reasoning, document retrieval steps, and decision-making logic—alongside final chat responses. This transparency feature helps users understand how the AI arrived at its answer, which documents influenced the response, and where the reasoning might be uncertain. The system integrates thinking steps with citation tracking, showing users both the reasoning process and the source documents that informed each step.","intents":["Understand how the AI arrived at its answer by seeing intermediate reasoning steps","Verify AI reasoning by examining which documents influenced each step of the analysis","Identify potential reasoning errors or hallucinations by inspecting the thinking process","Build trust in AI responses by making the decision-making process transparent"],"best_for":["Research teams needing to verify AI reasoning and understand decision-making","Organizations requiring explainability for compliance or audit purposes","Users skeptical of AI and wanting to understand how answers are generated"],"limitations":["Thinking steps add significant latency (2-5x slower) due to extended token generation","LLM-generated thinking may be verbose, inaccurate, or misleading; not a guarantee of correct reasoning","Displaying thinking steps increases token usage and costs significantly","Thinking steps are only available from LLMs that support extended reasoning (GPT-4, Claude with thinking)","Users may misinterpret thinking steps as ground truth when they're just LLM-generated text"],"requires":["LLM with extended reasoning or thinking capability (GPT-4 with reasoning, Claude with extended thinking)","Chat interface capable of displaying multi-step reasoning alongside responses","Citation tracking system to link thinking steps to source documents"],"input_types":["User queries","Retrieved documents and context","LLM thinking steps and reasoning"],"output_types":["Structured thinking steps showing intermediate reasoning","Final response with citations","Mapping of thinking steps to source documents"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-modsetter--surfsense__cap_9","uri":"capability://memory.knowledge.thread.based.conversation.management.with.context.preservation","name":"thread-based conversation management with context preservation","description":"SurfSense implements a thread-based conversation system where each conversation maintains its own context, message history, and document references. The system preserves conversation state across sessions, allowing users to return to previous conversations and continue discussions with full context. Threads support branching (creating new conversations from existing ones) and archiving, enabling users to organize conversations by topic or project. The architecture maintains message ordering, timestamps, and metadata for each turn, enabling conversation replay and audit trails.","intents":["Maintain separate conversations for different research topics or projects without context mixing","Return to previous conversations and continue discussions with full context preserved","Branch conversations to explore alternative directions without losing the original thread","Archive and organize conversations for future reference and knowledge management"],"best_for":["Research teams conducting multiple parallel investigations needing separate conversation threads","Users wanting to maintain conversation history for audit and compliance purposes","Teams collaborating on research where different members need to see conversation context"],"limitations":["Large conversation histories (1000+ messages) may cause performance degradation in context loading","Branching conversations create duplicate context; no deduplication or reference-based storage","Conversation search is limited to metadata; no full-text search across conversation content","Sharing conversations requires explicit permission management; no built-in collaboration features"],"requires":["Database with efficient message storage and retrieval (PostgreSQL with proper indexing)","Session management system for maintaining user context across requests","Message ordering and timestamp tracking for conversation replay","Permission system for controlling conversation access and sharing"],"input_types":["User messages and queries","Document references and citations","Conversation metadata (title, topic, participants)"],"output_types":["Conversation history with message ordering","Thread metadata and branching information","Conversation search results and organization"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ for backend connector implementation","OAuth credentials for each connected data source","Database instance (PostgreSQL recommended) for storing connector state and document metadata","Background task processor (Celery or similar) for periodic indexing jobs","Vector database (Pinecone, Weaviate, or local embedding store) for semantic search","Full-text search engine (Elasticsearch, PostgreSQL full-text, or similar) for keyword matching","Embedding model (OpenAI, local Ollama, or similar) for converting documents to vectors","Reranking model or scoring function to merge hybrid results","Docker and Docker Compose (for Docker deployment) or Python 3.9+ (for manual installation)","PostgreSQL database (or compatible) for application data"],"failure_modes":["Connector implementation requires understanding the source API and OAuth flow; no low-code connector builder","Periodic indexing introduces latency between source updates and searchability (configurable but not real-time)","OAuth token refresh and expiration handling adds operational complexity for connector maintenance","Large-scale connectors (e.g., 100k+ Slack messages) may require tuning of batch sizes and indexing schedules","Reranking adds latency (~100-500ms per query depending on result set size) compared to single-method search","Requires tuning of hybrid scoring weights for different use cases; no automatic optimization","Vector embeddings are language-specific; multilingual search requires separate embedding models per language","Large result sets (1000+) may require pagination and lazy reranking to maintain performance","Self-hosting requires operational expertise (Docker, database administration, networking)","No managed service; teams are responsible for backups, updates, and security patches","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36595714559738013,"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":"2026-05-03T13:58:24.502Z","last_commit":"2026-05-03T13:08:01Z"},"community":{"stars":14060,"forks":1319,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=modsetter--surfsense","compare_url":"https://unfragile.ai/compare?artifact=modsetter--surfsense"}},"signature":"2dch/jqQqtr2ymnl7Mz8FxwInqU5163M1NJkO0I+mkoESXjqt99kGc7ZAwinQhQghIiE67xuXXLhU55Za/pdCQ==","signedAt":"2026-06-21T15:03:39.654Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/modsetter--surfsense","artifact":"https://unfragile.ai/modsetter--surfsense","verify":"https://unfragile.ai/api/v1/verify?slug=modsetter--surfsense","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"}}