{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_stackbear","slug":"stackbear","name":"Stackbear","type":"product","url":"https://stackbear.com","page_url":"https://unfragile.ai/stackbear","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_stackbear__cap_0","uri":"capability://automation.workflow.no.code.chatbot.builder.with.visual.conversation.flow.designer","name":"no-code chatbot builder with visual conversation flow designer","description":"Provides a drag-and-drop interface for constructing multi-turn conversation flows without coding, likely using a state-machine or directed-graph architecture where nodes represent conversation states and edges represent user intents or message triggers. The builder abstracts away prompt engineering and API orchestration, allowing non-technical users to define branching logic, conditional responses, and fallback handlers through visual composition rather than writing LLM prompts directly.","intents":["I need to build a customer support chatbot without hiring a developer","I want to create multiple conversation paths based on user input without writing code","I need to quickly prototype a chatbot workflow and test it with real users"],"best_for":["non-technical founders and SMB operators validating chatbot ROI","customer support teams building first-line triage bots","sales teams automating lead qualification workflows"],"limitations":["Visual builder likely has ceiling on complexity — deeply nested conditional logic becomes unwieldy in UI","No visibility into whether builder supports advanced patterns like dynamic context injection or multi-turn memory management","Abstraction over LLM calls may limit fine-grained control over temperature, token limits, or model selection per node"],"requires":["Web browser with modern JavaScript support","Account creation on Stackbear platform","No coding knowledge required"],"input_types":["text descriptions of conversation flows","user intent labels","response templates"],"output_types":["executable chatbot conversation state machine","embeddable chatbot widget code","conversation logs and analytics"],"categories":["automation-workflow","no-code-platform"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stackbear__cap_1","uri":"capability://memory.knowledge.personalized.ai.model.fine.tuning.on.custom.business.data","name":"personalized ai model fine-tuning on custom business data","description":"Enables users to upload or connect business documents, FAQs, product catalogs, or knowledge bases to customize the underlying LLM's responses beyond generic outputs. The system likely uses retrieval-augmented generation (RAG) or lightweight fine-tuning to inject domain-specific context into the model's response generation, allowing the chatbot to answer questions about specific products, policies, or procedures rather than relying solely on the base model's training data.","intents":["I want my chatbot to answer questions about my specific products and services, not generic LLM knowledge","I need the bot to reflect my brand voice and company policies in every response","I want to upload my FAQ and have the chatbot automatically answer customer questions from it"],"best_for":["e-commerce businesses with product-specific customer questions","SaaS companies needing support bots trained on their documentation","enterprises with proprietary knowledge bases requiring brand-consistent responses"],"limitations":["No detail on whether fine-tuning is full model retraining (expensive, slow) or RAG-based context injection (faster, cheaper but context-window limited)","Unclear if system supports incremental updates to training data or requires full retraining cycles","No visibility into hallucination mitigation — whether system includes confidence scoring or fallback-to-human routing for out-of-domain queries"],"requires":["Stackbear account with chatbot created","Business documents in supported formats (likely PDF, TXT, CSV, or web URLs)","Minimum document size/quality unknown"],"input_types":["PDF documents","text files","CSV data","web URLs","FAQ lists"],"output_types":["fine-tuned model weights or embeddings","context-augmented LLM responses","confidence scores or relevance rankings"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stackbear__cap_2","uri":"capability://text.generation.language.multilingual.conversation.support.with.automatic.language.detection.and.response.translation","name":"multilingual conversation support with automatic language detection and response translation","description":"Detects the language of incoming user messages and routes them to language-specific response generation or translation pipelines, enabling a single chatbot to serve customers in multiple languages without separate bot instances. The system likely uses language detection models (e.g., fastText or transformer-based classifiers) on input, then either generates responses in the detected language or translates base responses using neural machine translation (NMT), maintaining conversation context across language switches.","intents":["I need one chatbot to serve customers in 5+ languages without building separate bots","I want customer support conversations to happen in the customer's native language automatically","I need to expand to new markets without rebuilding my chatbot infrastructure"],"best_for":["global e-commerce platforms with multilingual customer bases","SaaS companies serving international markets","support teams managing customers across multiple regions and languages"],"limitations":["Unclear which languages are supported — likely covers major languages (EN, ES, FR, DE, ZH, JA) but may not include low-resource languages","Translation quality depends on underlying NMT model — no visibility into whether system uses proprietary or open-source translation (e.g., Google Translate vs. local models)","Context loss risk in translation — multi-turn conversations may degrade in quality as context accumulates across language switches","No detail on whether system preserves conversation tone and brand voice across translations"],"requires":["Stackbear chatbot instance","No explicit language configuration — automatic detection","Supported language list (unknown scope)"],"input_types":["text in any supported language"],"output_types":["responses in detected language","translated responses","language metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stackbear__cap_3","uri":"capability://tool.use.integration.cost.optimized.message.routing.and.llm.provider.abstraction","name":"cost-optimized message routing and llm provider abstraction","description":"Abstracts underlying LLM provider selection (likely OpenAI, Anthropic, or local models) and routes messages to the most cost-effective option based on query complexity, conversation history, or configured policies. The system may use a provider abstraction layer that normalizes API calls across different LLM backends, allowing users to switch providers or use fallback models without rebuilding chatbot logic, and may implement cost-aware routing that uses cheaper models for simple queries and reserves expensive models for complex reasoning.","intents":["I want to reduce my chatbot's LLM API costs without sacrificing quality","I need to switch LLM providers (e.g., from OpenAI to Anthropic) without rebuilding my bot","I want to use multiple LLM providers as fallbacks if one goes down"],"best_for":["cost-conscious startups running high-volume chatbots","teams wanting to avoid vendor lock-in with a single LLM provider","businesses needing redundancy across multiple LLM APIs"],"limitations":["No visibility into routing logic — unclear if system uses latency-aware routing, cost-per-token optimization, or simple round-robin","Abstraction layer may add latency overhead (100-300ms per request) compared to direct API calls","No detail on whether system supports custom routing policies or is limited to preset strategies","Unclear if cost optimization is transparent to users or requires manual configuration"],"requires":["Stackbear account","API keys for at least one supported LLM provider","Understanding of cost trade-offs between providers"],"input_types":["user messages","conversation context","routing policy configuration"],"output_types":["routed API request to selected provider","cost metadata and provider selection logs","unified response format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stackbear__cap_4","uri":"capability://data.processing.analysis.conversation.analytics.and.performance.monitoring.dashboard","name":"conversation analytics and performance monitoring dashboard","description":"Aggregates conversation logs, user interactions, and chatbot performance metrics into a dashboard showing conversation volume, user satisfaction, common intents, fallback rates, and response quality indicators. The system likely uses event streaming or log aggregation to collect conversation data, then applies analytics queries to surface trends, bottlenecks, and opportunities for improvement, potentially including sentiment analysis or intent classification on historical conversations.","intents":["I need to understand which customer questions my chatbot is failing to answer","I want to track chatbot performance over time and identify improvement areas","I need to see which conversation topics are most common to prioritize training data"],"best_for":["customer support teams optimizing chatbot effectiveness","product managers measuring chatbot ROI and impact","businesses iterating on chatbot training data based on real usage patterns"],"limitations":["No detail on analytics granularity — unclear if system provides per-conversation, per-user, or per-intent breakdowns","Retention policy unknown — unclear how long conversation history is stored or if there are export limitations","No visibility into whether analytics include human-in-the-loop feedback or are purely automated metrics","Unclear if dashboard supports custom metrics or is limited to preset KPIs"],"requires":["Active Stackbear chatbot with conversation traffic","Web browser to access dashboard","Minimum conversation volume (unknown threshold)"],"input_types":["conversation logs","user interactions","chatbot responses"],"output_types":["dashboard visualizations","performance metrics and KPIs","trend analysis and recommendations","exportable reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stackbear__cap_5","uri":"capability://tool.use.integration.embeddable.chatbot.widget.with.customizable.ui.and.deployment.options","name":"embeddable chatbot widget with customizable ui and deployment options","description":"Generates embeddable JavaScript code that deploys the chatbot as a widget on websites, mobile apps, or messaging platforms (e.g., WhatsApp, Facebook Messenger). The system likely provides a widget SDK that handles message rendering, user input capture, and API communication, with configuration options for colors, positioning, and behavior (e.g., auto-open, greeting messages, typing indicators). Deployment may support multiple channels through a unified backend, allowing conversations to flow across web, mobile, and messaging platforms.","intents":["I want to add a chatbot to my website without building custom UI","I need my chatbot to work on WhatsApp and Facebook Messenger, not just my website","I want to customize the chatbot's appearance to match my brand"],"best_for":["e-commerce sites adding customer support without custom development","businesses wanting omnichannel customer engagement (web + messaging apps)","teams with limited frontend engineering resources"],"limitations":["Widget customization likely limited to preset themes and color schemes — no custom CSS or component replacement","Unclear which messaging platforms are supported (WhatsApp, Facebook Messenger, Telegram, etc.)","No detail on widget performance impact — embedding may add 100-500ms to page load time","Unclear if widget supports offline mode or requires persistent internet connection"],"requires":["Stackbear chatbot instance","Website or app to embed widget into","Ability to add JavaScript snippet to website (or API access for mobile apps)","Optional: messaging platform API credentials for omnichannel deployment"],"input_types":["widget configuration (colors, position, greeting text)","messaging platform credentials"],"output_types":["embeddable JavaScript code","mobile SDK or API endpoints","conversation data in unified format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stackbear__cap_6","uri":"capability://memory.knowledge.conversation.context.management.and.multi.turn.memory","name":"conversation context management and multi-turn memory","description":"Maintains conversation state across multiple user turns, preserving user intent, previous responses, and relevant context to enable coherent multi-turn dialogues. The system likely uses a conversation store (e.g., in-memory cache, database, or vector store) to track conversation history, and implements context windowing or summarization to manage token limits when conversations grow long. The architecture may support context injection into LLM prompts, allowing the model to reference previous turns without explicitly including full conversation history.","intents":["I want my chatbot to remember what the user asked earlier in the conversation","I need the bot to handle follow-up questions that reference previous context","I want to avoid the bot repeating information it already provided"],"best_for":["customer support bots handling multi-step troubleshooting","sales bots qualifying leads over multiple conversation turns","conversational AI requiring coherent, context-aware responses"],"limitations":["No detail on context window size — unclear if system supports 5-turn, 50-turn, or unlimited conversation history","Unclear if system uses context summarization (lossy) or full history retention (expensive)","No visibility into whether context is preserved across sessions or reset on new conversations","Unclear if system supports user identification and cross-session context (e.g., remembering user preferences across days)"],"requires":["Stackbear chatbot instance","Conversation storage backend (likely managed by platform)","User session management (likely automatic)"],"input_types":["current user message","conversation history"],"output_types":["context-augmented LLM prompt","response with awareness of previous turns","conversation state"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stackbear__cap_7","uri":"capability://planning.reasoning.intent.classification.and.conversation.routing.to.specialized.handlers","name":"intent classification and conversation routing to specialized handlers","description":"Automatically classifies incoming user messages into predefined intents (e.g., 'billing question', 'product inquiry', 'complaint') and routes conversations to specialized handlers, fallback queues, or human agents based on intent confidence and routing rules. The system likely uses text classification models (e.g., transformers or intent classifiers) trained on conversation examples, and implements a routing engine that applies rules (e.g., 'if intent=complaint AND confidence<0.7, escalate to human'). This enables the chatbot to handle different conversation types with appropriate logic and gracefully hand off to humans when needed.","intents":["I want my chatbot to recognize different types of customer questions and handle each appropriately","I need to automatically escalate complaints or high-priority issues to human agents","I want to route billing questions to a different handler than product questions"],"best_for":["customer support teams needing intelligent triage and escalation","businesses with diverse customer needs requiring specialized handling","teams wanting to reduce human agent workload by automating routine inquiries"],"limitations":["Intent classification accuracy depends on training data — no detail on how many examples are needed or what accuracy is achievable","Unclear if system supports custom intent definitions or is limited to preset intents","No visibility into how system handles ambiguous messages with low confidence scores","Unclear if routing rules are configurable or hardcoded"],"requires":["Stackbear chatbot instance","Intent definitions and examples (likely provided by user or platform)","Routing rules configuration"],"input_types":["user message text","conversation context"],"output_types":["intent classification with confidence score","routing decision (handle locally, escalate, queue)","routed conversation to appropriate handler"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Account creation on Stackbear platform","No coding knowledge required","Stackbear account with chatbot created","Business documents in supported formats (likely PDF, TXT, CSV, or web URLs)","Minimum document size/quality unknown","Stackbear chatbot instance","No explicit language configuration — automatic detection","Supported language list (unknown scope)","Stackbear account"],"failure_modes":["Visual builder likely has ceiling on complexity — deeply nested conditional logic becomes unwieldy in UI","No visibility into whether builder supports advanced patterns like dynamic context injection or multi-turn memory management","Abstraction over LLM calls may limit fine-grained control over temperature, token limits, or model selection per node","No detail on whether fine-tuning is full model retraining (expensive, slow) or RAG-based context injection (faster, cheaper but context-window limited)","Unclear if system supports incremental updates to training data or requires full retraining cycles","No visibility into hallucination mitigation — whether system includes confidence scoring or fallback-to-human routing for out-of-domain queries","Unclear which languages are supported — likely covers major languages (EN, ES, FR, DE, ZH, JA) but may not include low-resource languages","Translation quality depends on underlying NMT model — no visibility into whether system uses proprietary or open-source translation (e.g., Google Translate vs. local models)","Context loss risk in translation — multi-turn conversations may degrade in quality as context accumulates across language switches","No detail on whether system preserves conversation tone and brand voice across translations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"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":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stackbear","compare_url":"https://unfragile.ai/compare?artifact=stackbear"}},"signature":"S1IOmEDKDDAs3IVHCQynd2+OL6AnuH34vAkDuiKxBS8hBa4gw8dS5oy6pZ6MYrdt2PTyHGb8UM87KFKEnLKsCQ==","signedAt":"2026-06-20T01:01:33.466Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stackbear","artifact":"https://unfragile.ai/stackbear","verify":"https://unfragile.ai/api/v1/verify?slug=stackbear","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"}}