{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-casibase--casibase","slug":"casibase--casibase","name":"casibase","type":"mcp","url":"https://casibase.org","page_url":"https://unfragile.ai/casibase--casibase","categories":["mcp-servers"],"tags":["a2a","agent","agi","casibase","chatbot","chatgpt","claude","gemini","gpt","huggingface","knowledge-base","langchain","llama","llm","manus","mcp","model-context-protocol","multi-agent","openai","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-casibase--casibase__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.chat.with.unified.interface","name":"multi-provider llm chat with unified interface","description":"Abstracts 30+ AI model providers (OpenAI, Claude, Gemini, Llama, Ollama, HuggingFace) behind a single chat API using a pluggable provider registry pattern. Routes chat requests to configured providers via standardized adapter interfaces, handling model-specific parameter mapping, streaming responses, and error fallback. Implemented via provider.go model with provider-specific controller logic that normalizes request/response formats across heterogeneous APIs.","intents":["I want to let users chat with multiple LLM providers without rewriting integration code for each","I need to switch between Claude and OpenAI at runtime based on cost or availability","I want to support both cloud APIs (OpenAI) and local models (Ollama) in the same application"],"best_for":["teams building multi-model AI applications","enterprises with provider lock-in concerns","developers prototyping with multiple LLM backends"],"limitations":["Provider-specific features (vision, function calling) require custom adapter code per provider","Response latency varies by provider; no built-in load balancing across providers","Token counting and cost estimation must be implemented per-provider"],"requires":["API keys for desired providers (OpenAI, Anthropic, etc.)","Go 1.16+","Configured provider entries in Casibase database"],"input_types":["text prompts","chat message history (JSON)","provider configuration (model name, parameters)"],"output_types":["streaming text responses","structured JSON (usage stats, model metadata)","error messages with provider-specific codes"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_1","uri":"capability://memory.knowledge.rag.augmented.chat.with.vector.embeddings.and.semantic.search","name":"rag-augmented chat with vector embeddings and semantic search","description":"Implements a retrieval-augmented generation pipeline that embeds documents into vector space using configurable embedding providers, stores vectors in a knowledge base (Store entity), and retrieves semantically similar documents during chat to augment LLM context. The system uses vector.go to manage embeddings, store.go for knowledge base configuration, and integrates with the AI answer generation pipeline to inject retrieved context into prompts before sending to LLMs.","intents":["I want my chatbot to answer questions based on proprietary documents without fine-tuning","I need to retrieve relevant knowledge base articles and inject them into chat context automatically","I want to support semantic search across PDFs, markdown, and web content"],"best_for":["customer support teams building knowledge-base chatbots","enterprises with large document repositories","teams building domain-specific AI assistants"],"limitations":["Embedding quality depends on provider choice; no built-in re-ranking of retrieved documents","Vector storage is provider-dependent (no native vector DB abstraction); requires external vector store configuration","Context window limits may truncate retrieved documents; no automatic chunking strategy optimization","Semantic search may retrieve irrelevant documents if embedding model is misaligned with domain"],"requires":["Embedding provider configured (OpenAI, HuggingFace, etc.)","Knowledge base (Store) with documents uploaded","Vector storage backend (configured via provider system)","Minimum 512MB RAM for in-memory vector caching"],"input_types":["PDF, markdown, text documents","chat queries (text)","store/knowledge base configuration"],"output_types":["augmented prompts with retrieved context","chat responses with source citations","vector embeddings (float arrays)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_10","uri":"capability://automation.workflow.email.notification.system.for.chat.and.workflow.events","name":"email notification system for chat and workflow events","description":"Provides email notifications for chat events (new messages, mentions), workflow completions, and system alerts. Integrated with the message lifecycle (message.go) and background task system (main.go), allowing notifications to be triggered based on configurable rules. Email provider is abstracted through the provider system, supporting multiple SMTP backends and email service providers.","intents":["I want users to receive email notifications when they're mentioned in a chat","I need to notify admins when workflows complete or fail","I want to send digest emails summarizing chat activity"],"best_for":["teams requiring asynchronous notifications","enterprises with email-based communication workflows","organizations needing audit trails of notifications"],"limitations":["Email delivery is asynchronous; no guarantee of delivery or read receipts","Email templates are basic; no rich HTML formatting or dynamic content","Notification rules are coarse-grained; no fine-grained filtering","Email provider configuration is manual; no auto-discovery of SMTP settings"],"requires":["Email provider configured (SMTP, SendGrid, etc.)","User email addresses populated in database","Background task system running (for async delivery)","Email templates configured"],"input_types":["notification triggers (chat events, workflow completions)","email templates (text/HTML)","recipient lists (user emails)"],"output_types":["sent emails","delivery status (sent, bounced, etc.)","notification logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_11","uri":"capability://data.processing.analysis.medical.domain.features.with.ehr.integration.and.hipaa.compliance","name":"medical domain features with ehr integration and hipaa compliance","description":"Implements specialized features for medical applications including electronic health record (EHR) integration, HIPAA-compliant data handling, and medical document parsing. Medical records are stored with enhanced encryption, access control is audit-logged, and sensitive data is masked in logs. Integrated with the knowledge base system for medical document indexing and the security scanning system for compliance validation.","intents":["I want to build a medical chatbot that can access patient records securely","I need to ensure HIPAA compliance for all data handling and access","I want to parse medical documents (lab reports, prescriptions) and make them searchable"],"best_for":["healthcare organizations building AI-powered clinical tools","medical practices requiring HIPAA-compliant knowledge bases","teams integrating with existing EHR systems"],"limitations":["HIPAA compliance is partial; full compliance requires additional infrastructure (audit logging, encryption at rest)","EHR integration is limited to common formats (HL7, FHIR); proprietary EHR formats require custom adapters","Medical document parsing is basic; complex medical records may not parse correctly","No built-in de-identification; sensitive data must be manually masked"],"requires":["HIPAA-compliant deployment environment (encrypted storage, secure network)","EHR system with API access","Medical document parser libraries","Audit logging infrastructure","Encryption keys for sensitive data"],"input_types":["EHR data (HL7, FHIR format)","medical documents (PDFs, lab reports)","patient records (structured data)"],"output_types":["indexed medical documents","patient data summaries","audit logs of data access","de-identified data for analysis"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_12","uri":"capability://automation.workflow.kubernetes.application.deployment.and.orchestration","name":"kubernetes application deployment and orchestration","description":"Provides integration with Kubernetes for deploying Casibase and managing containerized AI workloads. Includes Helm charts, deployment manifests, and orchestration logic for scaling chat services, managing provider connections, and handling stateful components (databases, vector stores). Deployment configuration is managed through the application configuration system (conf/app.conf) with environment-based overrides for different Kubernetes clusters.","intents":["I want to deploy Casibase on Kubernetes with automatic scaling","I need to manage multiple Casibase instances across different environments (dev, staging, prod)","I want to orchestrate AI workloads (embeddings, chat) as Kubernetes services"],"best_for":["teams deploying Casibase at scale on Kubernetes","enterprises with existing Kubernetes infrastructure","organizations requiring multi-region deployments"],"limitations":["Stateful components (chat history, vector store) require persistent volumes; no built-in distributed state management","Horizontal scaling of chat services requires sticky sessions or shared state","Provider credentials must be managed via Kubernetes secrets; no built-in secret rotation","Database migrations are manual; no automatic schema versioning in Kubernetes"],"requires":["Kubernetes 1.20+","Helm 3.0+","Persistent volume provisioner (for databases, vector stores)","Container registry for Casibase images","Kubernetes secrets for provider credentials"],"input_types":["Helm values (configuration overrides)","Kubernetes manifests (deployments, services)","environment variables (cluster-specific config)"],"output_types":["deployed Casibase pods","Kubernetes services (chat API, admin UI)","persistent volumes for state","deployment logs and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_13","uri":"capability://text.generation.language.internationalization.i18n.and.multi.language.support","name":"internationalization (i18n) and multi-language support","description":"Implements comprehensive internationalization using a JSON-based locale system (web/src/locales/en/data.json, web/src/locales/zh/data.json) supporting multiple languages. All UI strings are externalized to locale files, allowing language switching without code changes. Backend supports locale-aware responses (timestamps, number formatting) and the frontend dynamically loads locale data based on user preference.","intents":["I want to deploy Casibase to users in different countries with localized UI","I need to support multiple languages without maintaining separate codebases","I want to add new languages without code changes"],"best_for":["teams building global AI applications","organizations serving multi-language user bases","enterprises expanding to new markets"],"limitations":["Locale files are manually maintained; no automatic translation or translation management UI","Right-to-left (RTL) language support is not built-in; requires custom CSS","Backend locale support is basic; no locale-aware formatting for dates, currencies","Pluralization rules are not language-aware; English pluralization is hardcoded"],"requires":["Locale JSON files for each supported language","Frontend locale loader implementation","User preference storage (browser localStorage or database)","Translation contributors for each language"],"input_types":["locale JSON files (key-value pairs)","user language preference (browser language, user setting)"],"output_types":["localized UI strings","locale-aware formatted data (dates, numbers)","language-specific responses"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_14","uri":"capability://image.visual.graph.visualization.and.knowledge.graph.exploration","name":"graph visualization and knowledge graph exploration","description":"Implements graph visualization capabilities (graph visualization system in web/src/App.js) for exploring relationships between documents, entities, and concepts in the knowledge base. Supports interactive graph rendering, node/edge filtering, and traversal. Integrated with the knowledge base system to automatically extract and visualize entity relationships from indexed documents.","intents":["I want to visualize relationships between documents in my knowledge base","I need to explore entity connections (people, organizations, concepts) interactively","I want to identify knowledge gaps by visualizing missing connections"],"best_for":["teams building knowledge discovery tools","organizations with complex knowledge bases","researchers exploring document relationships"],"limitations":["Entity extraction is basic; no advanced NLP for relationship detection","Graph rendering performance degrades with >1000 nodes; no built-in clustering","Graph layout algorithms are basic; no force-directed or hierarchical layouts","No graph query language; exploration is limited to UI interactions"],"requires":["Graph visualization library (D3.js, Cytoscape, etc.)","Entity extraction model or service","Knowledge base with indexed documents","Browser with WebGL support for large graphs"],"input_types":["knowledge base documents","entity extraction results","graph layout preferences"],"output_types":["interactive graph visualization","node/edge metadata","traversal paths between entities"],"categories":["image-visual","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_15","uri":"capability://data.processing.analysis.articles.workflows.and.usage.analytics","name":"articles, workflows, and usage analytics","description":"Provides content management for articles and workflows, with built-in analytics tracking user interactions, chat usage, and knowledge base access patterns. Analytics data is collected via event tracking in the frontend and backend, aggregated in the database, and visualized in dashboards. Supports custom metrics and event definitions for domain-specific analytics.","intents":["I want to track which knowledge base articles are most frequently accessed","I need to understand user behavior and chat patterns to improve the system","I want to measure the effectiveness of my knowledge base in reducing support tickets"],"best_for":["teams optimizing knowledge base content","organizations measuring AI chatbot ROI","enterprises tracking user engagement metrics"],"limitations":["Analytics collection is basic; no advanced cohort analysis or funnel tracking","Data retention is configurable but not automatic; manual cleanup required","Privacy controls are limited; no built-in PII filtering in analytics","Real-time analytics are not supported; data is aggregated periodically"],"requires":["Analytics event tracking enabled in frontend and backend","Database storage for analytics data","Analytics dashboard UI (custom or third-party)","Data retention policy configured"],"input_types":["user interaction events (clicks, searches, chat messages)","system events (API calls, errors)","custom event definitions"],"output_types":["aggregated analytics data (JSON)","usage reports (CSV, charts)","custom metrics and KPIs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_2","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.integration.and.agent.to.agent.communication","name":"mcp (model context protocol) server integration and agent-to-agent communication","description":"Implements Model Context Protocol support enabling Casibase to act as an MCP server and client, allowing external tools and agents to be registered as context providers. The system uses a schema-based function registry (via provider system) to expose capabilities to connected clients, handle tool invocation requests, and manage bidirectional agent-to-agent (A2A) communication. MCP integration is managed through the provider ecosystem, allowing tools to be dynamically registered and invoked during chat.","intents":["I want to expose my knowledge base and chat capabilities to external AI agents via MCP","I need to integrate external tools (APIs, databases) as context providers for my chatbot","I want to enable multi-agent workflows where agents can call each other's capabilities"],"best_for":["teams building agent ecosystems with standardized tool interfaces","enterprises integrating Casibase with external AI systems","developers creating composable AI workflows"],"limitations":["MCP protocol support is limited to schema-based function calling; no streaming context providers","Agent-to-agent communication requires explicit trust/authentication setup between agents","No built-in retry logic or circuit breakers for failed tool invocations","Tool discovery is manual; no automatic capability advertisement to unknown agents"],"requires":["MCP client library compatible with Casibase version","Tool schemas defined in JSON Schema format","Network connectivity between agent endpoints","Authentication credentials for inter-agent communication"],"input_types":["MCP protocol messages (JSON-RPC)","tool schemas (JSON Schema)","function invocation requests"],"output_types":["MCP protocol responses","tool execution results","context updates for connected agents"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_3","uri":"capability://safety.moderation.enterprise.user.management.with.sso.and.role.based.access.control","name":"enterprise user management with sso and role-based access control","description":"Provides built-in user management integrated with Casdoor (external identity provider) for Single-Sign-On, role-based access control (RBAC), and permission management across knowledge bases, chats, and admin functions. Implemented via authz_filter.go and auto_signin_filter.go middleware that intercepts requests, validates user identity and permissions, and enforces access policies. Supports multi-tenant scenarios with per-user knowledge base visibility and chat history isolation.","intents":["I need to manage multiple users with different access levels to knowledge bases and chat features","I want to integrate Casibase with our corporate SSO (LDAP, OAuth2) without managing passwords","I need to audit who accessed what knowledge and when"],"best_for":["enterprises deploying Casibase for internal use","teams requiring compliance with access control policies","organizations with existing Casdoor or OAuth2 infrastructure"],"limitations":["SSO integration requires Casdoor setup; no built-in LDAP/SAML support","RBAC is coarse-grained (role-based); no fine-grained attribute-based access control (ABAC)","Audit logging is basic; no detailed activity tracking per user action","Permission inheritance across knowledge base hierarchies is not supported"],"requires":["Casdoor instance configured and accessible","OAuth2 client credentials for Casdoor","User roles defined in Casibase database","HTTPS for secure cookie/token transmission"],"input_types":["user credentials (via SSO provider)","role assignments (admin configuration)","access control policies (JSON)"],"output_types":["authenticated session tokens","user profile data","permission check results (allow/deny)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_4","uri":"capability://data.processing.analysis.file.based.knowledge.base.ingestion.with.automatic.vector.indexing","name":"file-based knowledge base ingestion with automatic vector indexing","description":"Supports uploading diverse document types (PDF, markdown, text, web content) into a knowledge base (Store entity), automatically parsing and chunking documents, generating vector embeddings via configured embedding providers, and storing vectors for semantic search. The system uses file_cache.go and local_file_system.go for storage abstraction, integrates with document parsers (initialized in main.go), and manages the Store entity lifecycle including file tree permissions and access control.","intents":["I want to upload a folder of PDFs and make them searchable via semantic search","I need to ingest web content and markdown documentation into my knowledge base automatically","I want to control who can access which documents in the knowledge base"],"best_for":["teams building document-based chatbots","enterprises migrating legacy knowledge bases to AI-powered search","support teams creating searchable FAQ systems"],"limitations":["Document parsing is provider-dependent; complex layouts (tables, multi-column) may not parse correctly","Chunking strategy is fixed; no configurable chunk size or overlap parameters","Large file uploads (>100MB) may timeout; no built-in resumable upload support","File tree permissions are coarse-grained; no per-document access control"],"requires":["Storage backend configured (local filesystem, S3, OSS, etc.)","Embedding provider configured","Document parser libraries installed (PDF, markdown support)","Sufficient disk space for document cache"],"input_types":["PDF files","markdown documents","plain text files","web URLs (for content scraping)"],"output_types":["indexed documents in knowledge base","vector embeddings stored in vector DB","file tree structure with permissions"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_5","uri":"capability://text.generation.language.real.time.streaming.chat.responses.with.provider.agnostic.streaming","name":"real-time streaming chat responses with provider-agnostic streaming","description":"Implements streaming responses from LLMs using provider-agnostic adapters that normalize streaming APIs across different providers (OpenAI, Claude, Ollama, etc.). The chat pipeline uses server-sent events (SSE) or WebSocket to stream tokens to the frontend in real-time, with provider adapters handling format conversion (e.g., OpenAI's delta format to generic token stream). Message lifecycle is managed via message.go model with transactional consistency and retry logic for failed streams.","intents":["I want users to see chat responses appear token-by-token instead of waiting for full completion","I need to stream responses from different LLM providers without changing frontend code","I want to cancel long-running streams and handle network interruptions gracefully"],"best_for":["teams building real-time chat UIs","applications with high latency requirements (mobile, slow networks)","multi-provider systems needing consistent streaming behavior"],"limitations":["Streaming adds complexity to error handling; partial responses may be incomplete if stream is interrupted","Provider-specific streaming formats require custom adapter code; no automatic format detection","WebSocket connections are stateful; scaling requires sticky sessions or shared state","Token counting during streaming is approximate; final token count may differ from estimate"],"requires":["Frontend support for SSE or WebSocket","Provider streaming API support (most modern LLM APIs support this)","Network stability for long-lived connections","Message transaction retry logic enabled"],"input_types":["chat prompts (text)","streaming configuration (timeout, buffer size)"],"output_types":["streamed tokens (text chunks)","metadata (model, usage stats) at stream end","error messages if stream fails"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_6","uri":"capability://tool.use.integration.admin.ui.for.provider.and.knowledge.base.configuration","name":"admin ui for provider and knowledge base configuration","description":"Provides a React-based admin dashboard (ProviderEditPage.js, ProviderListPage.js, StoreBackend.js) for managing provider credentials, knowledge bases, embeddings, and system configuration without code changes. The UI communicates with backend controllers (provider.go, store.go, vector.go) via REST APIs, allowing admins to add/remove providers, configure embedding settings, upload documents, and manage access control. Configuration is persisted in the database and hot-reloaded without server restart.","intents":["I want non-technical admins to configure LLM providers and knowledge bases via UI","I need to manage multiple knowledge bases and their permissions from a central dashboard","I want to test provider configurations before deploying to production"],"best_for":["non-technical administrators managing Casibase deployments","teams requiring UI-driven configuration management","enterprises with frequent provider/knowledge base changes"],"limitations":["Admin UI is tightly coupled to backend API structure; schema changes require UI updates","No built-in configuration versioning or rollback; changes are immediate","Provider credential validation is basic; no dry-run or test connection feature","Bulk operations (import multiple documents) are not supported via UI"],"requires":["React 16.8+ (frontend framework)","Backend API endpoints accessible from admin UI","User with admin role to access configuration pages","Modern browser with JavaScript enabled"],"input_types":["provider credentials (API keys, endpoints)","knowledge base metadata (name, description)","file uploads (documents)","configuration parameters (JSON)"],"output_types":["provider list with status","knowledge base inventory","configuration validation results","audit logs of configuration changes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_7","uri":"capability://image.visual.video.annotation.and.review.workflow.with.asset.management","name":"video annotation and review workflow with asset management","description":"Implements a specialized workflow for video annotation, review, and asset management (VideoBackend.js, video annotation system in web/src/App.js). Supports uploading videos, frame extraction, annotation markup, and collaborative review. Integrated with the asset scanning system for security validation and metadata extraction. Videos are stored via the provider storage abstraction and indexed for search.","intents":["I want to annotate video frames and collaborate with team members on reviews","I need to extract metadata from videos and make them searchable","I want to scan uploaded videos for security/compliance issues"],"best_for":["teams in media, security, or medical domains requiring video annotation","enterprises with video content management needs","organizations requiring collaborative review workflows"],"limitations":["Video processing is provider-dependent; no built-in transcoding or format conversion","Frame extraction is basic; no advanced computer vision features (object detection, etc.)","Annotation storage is flat; no hierarchical or temporal annotation support","Collaborative editing is not real-time; annotations are eventually consistent"],"requires":["Video storage backend configured (S3, local filesystem, etc.)","Video processing libraries (ffmpeg or equivalent) for frame extraction","Sufficient storage for video files and extracted frames","Browser support for HTML5 video playback"],"input_types":["video files (MP4, WebM, etc.)","annotation markup (text, coordinates)","metadata (title, description)"],"output_types":["extracted frames (images)","annotation data (JSON)","video metadata (duration, resolution, etc.)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_8","uri":"capability://safety.moderation.asset.security.scanning.and.compliance.validation","name":"asset security scanning and compliance validation","description":"Implements automated security scanning of uploaded assets (documents, videos, images) using configurable scanning providers. The system validates file types, scans for malware/sensitive content, and enforces compliance policies before indexing. Scanning is integrated into the ingestion pipeline (asset scanning system in web/src/App.js) and can block uploads that fail validation. Results are logged for audit purposes.","intents":["I want to prevent malicious files from being uploaded to the knowledge base","I need to enforce compliance policies (no PII, no sensitive data) on uploaded documents","I want to audit all file uploads for security purposes"],"best_for":["enterprises with strict security/compliance requirements","organizations handling sensitive data (healthcare, finance)","teams requiring audit trails for file uploads"],"limitations":["Scanning is synchronous; large files may cause upload delays","Scanning providers are external; no built-in malware detection","False positives may block legitimate files; no manual override mechanism","Scanning results are not cached; repeated uploads trigger re-scanning"],"requires":["Security scanning provider configured (antivirus, DLP, etc.)","Network connectivity to scanning provider","Audit logging enabled","File size limits configured"],"input_types":["uploaded files (any type)","scanning policies (rules, thresholds)","compliance requirements (PII patterns, etc.)"],"output_types":["scan results (pass/fail)","threat/compliance violation details","audit log entries"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-casibase--casibase__cap_9","uri":"capability://tool.use.integration.remote.access.tunneling.for.secure.agent.communication","name":"remote access tunneling for secure agent communication","description":"Implements remote access tunneling (tunnel.go) enabling secure, encrypted communication between Casibase instances and external agents/services without exposing ports. Uses a tunnel protocol to establish bidirectional communication channels, allowing agents to access Casibase APIs and knowledge bases securely. Tunneling is managed via the provider system and supports authentication/authorization for tunnel endpoints.","intents":["I want external agents to access my Casibase instance securely without opening firewall ports","I need to establish secure communication between Casibase and remote AI agents","I want to enable agent-to-agent communication across network boundaries"],"best_for":["teams deploying Casibase in restricted network environments","enterprises requiring secure inter-agent communication","organizations with strict firewall/network policies"],"limitations":["Tunneling adds latency (~100-200ms per request); not suitable for real-time applications","Tunnel protocol is custom; no standard tunnel protocol support (SSH, WireGuard)","Tunnel authentication is basic; no mutual TLS or certificate pinning","No built-in tunnel monitoring or performance metrics"],"requires":["Tunnel endpoint configured on both sides","Network connectivity between tunnel endpoints","Authentication credentials for tunnel access","TLS certificates for encrypted communication"],"input_types":["tunnel configuration (endpoints, credentials)","API requests from remote agents","authentication tokens"],"output_types":["tunneled API responses","tunnel status/health metrics","error messages if tunnel fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["API keys for desired providers (OpenAI, Anthropic, etc.)","Go 1.16+","Configured provider entries in Casibase database","Embedding provider configured (OpenAI, HuggingFace, etc.)","Knowledge base (Store) with documents uploaded","Vector storage backend (configured via provider system)","Minimum 512MB RAM for in-memory vector caching","Email provider configured (SMTP, SendGrid, etc.)","User email addresses populated in database","Background task system running (for async delivery)"],"failure_modes":["Provider-specific features (vision, function calling) require custom adapter code per provider","Response latency varies by provider; no built-in load balancing across providers","Token counting and cost estimation must be implemented per-provider","Embedding quality depends on provider choice; no built-in re-ranking of retrieved documents","Vector storage is provider-dependent (no native vector DB abstraction); requires external vector store configuration","Context window limits may truncate retrieved documents; no automatic chunking strategy optimization","Semantic search may retrieve irrelevant documents if embedding model is misaligned with domain","Email delivery is asynchronous; no guarantee of delivery or read receipts","Email templates are basic; no rich HTML formatting or dynamic content","Notification rules are coarse-grained; no fine-grained filtering","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5836513069813298,"quality":0.6,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-04-22T08:01:48.366Z","last_commit":"2026-04-21T16:22:59Z"},"community":{"stars":4500,"forks":529,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=casibase--casibase","compare_url":"https://unfragile.ai/compare?artifact=casibase--casibase"}},"signature":"AIsmkGdGaF4RMRJ2YNJJX+gXqHvCQIncPNusqWwsiNOQqvaCr1HdUydDto3hBMBjgssWeJjUhYJhX+pWxfvMAg==","signedAt":"2026-06-20T10:52:10.055Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/casibase--casibase","artifact":"https://unfragile.ai/casibase--casibase","verify":"https://unfragile.ai/api/v1/verify?slug=casibase--casibase","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"}}