{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_open","slug":"open","name":"Open","type":"product","url":"https://opens.so","page_url":"https://unfragile.ai/open","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_open__cap_0","uri":"capability://tool.use.integration.multichannel.message.aggregation.and.unified.inbox","name":"multichannel message aggregation and unified inbox","description":"Consolidates inbound messages from email, chat, social media, and other channels into a single inbox interface, using a normalized message schema that abstracts channel-specific protocols (SMTP, WebSocket, REST APIs) into a unified conversation thread model. Messages are deduplicated by sender identity and conversation context rather than raw channel data, enabling agents to view complete customer interaction history across all touchpoints without context switching.","intents":["I need to respond to customer inquiries across email, chat, and social without logging into five different platforms","Show me all messages from a single customer regardless of which channel they contacted us on","Reduce the time agents spend switching between support tools to find relevant customer context"],"best_for":["small e-commerce teams managing customer inquiries across multiple channels","early-stage SaaS companies without dedicated support infrastructure","bootstrapped startups needing operational efficiency without Zendesk/Intercom costs"],"limitations":["Channel integration breadth unknown — documentation does not specify which platforms are supported beyond 'email, chat, social'","No visibility into message deduplication logic or conflict resolution when same customer contacts via multiple channels simultaneously","Unclear whether channel-specific metadata (e.g., social media engagement metrics, email headers) is preserved or normalized away"],"requires":["Active accounts on supported communication channels (email provider, chat platform, social media)","API credentials or OAuth tokens for each channel to be integrated","Internet connectivity for real-time message polling/webhooks"],"input_types":["email (SMTP/IMAP)","chat messages (WebSocket or REST)","social media messages (platform-specific APIs)"],"output_types":["unified conversation thread (JSON/structured format)","agent-facing UI with chronological message history"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_1","uri":"capability://text.generation.language.ai.powered.response.suggestion.and.auto.reply.generation","name":"ai-powered response suggestion and auto-reply generation","description":"Analyzes incoming customer messages using a language model to generate contextually appropriate response suggestions or fully automated replies based on message intent classification and historical response patterns. The system likely uses prompt engineering or fine-tuning to map customer inquiries to response templates, with a confidence threshold determining whether to auto-reply or surface suggestions to agents for review. Responses are generated in real-time with latency optimizations (caching, batch inference) to meet support SLA expectations.","intents":["Automatically respond to common customer questions (order status, refund policy, password reset) without agent intervention","Get AI-suggested responses that I can edit and send in seconds instead of typing from scratch","Reduce response time for high-volume support queues by having AI handle repetitive inquiries"],"best_for":["e-commerce businesses with high-volume, repetitive support inquiries","SaaS companies handling onboarding and FAQ-style questions at scale","teams with limited support staff seeking to increase throughput without hiring"],"limitations":["No public documentation on model selection, fine-tuning approach, or which LLM provider powers responses (OpenAI, Anthropic, proprietary model unknown)","Confidence threshold for auto-reply triggering is not disclosed — risk of inappropriate automated responses damaging customer relationships","No visibility into response quality metrics, hallucination rates, or how the system handles edge cases outside training distribution","Unclear whether responses are personalized per customer or generic template-based"],"requires":["Sufficient historical support conversation data to train intent classifiers and response patterns","API access to LLM provider (if using third-party model) or computational resources for inference","Configuration of response templates or examples for the system to learn from"],"input_types":["customer message (text)","conversation history (structured)","customer metadata (account status, order history, etc.)"],"output_types":["suggested response text","confidence score for auto-reply","intent classification label"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_10","uri":"capability://text.generation.language.multi.language.support.and.translation","name":"multi-language support and translation","description":"Supports customer inquiries and agent responses in multiple languages, using automatic translation to enable agents to respond to customers in their preferred language without requiring multilingual staff. The system likely uses a translation API (Google Translate, DeepL, or similar) to translate incoming messages to the agent's language and outgoing responses back to the customer's language. Language detection is automatic based on incoming message content.","intents":["Support customers who write to us in Spanish, French, or other languages without hiring multilingual agents","Automatically translate customer messages so my English-speaking agents can understand them","Send responses in the customer's language even though my team only speaks English"],"best_for":["global e-commerce businesses serving customers in multiple countries","SaaS companies with international user bases but English-only support teams","bootstrapped startups needing to support multiple languages without hiring costs"],"limitations":["Supported languages are not disclosed — unclear which language pairs are supported","Translation quality and accuracy are not documented — risk of mistranslations damaging customer relationships","No visibility into whether translation is bidirectional (customer language ↔ agent language) or one-way","Unclear whether cultural context or domain-specific terminology is handled (e.g., product names, technical terms)"],"requires":["Translation API access (Google Translate, DeepL, or similar)","Language detection model or library","Integration with message processing pipeline"],"input_types":["customer message (text in any supported language)","agent response (text in agent's language)"],"output_types":["translated message (in target language)","detected language label"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_11","uri":"capability://tool.use.integration.webhook.based.event.streaming.and.external.system.integration","name":"webhook-based event streaming and external system integration","description":"Exposes webhook endpoints that fire events for key support actions (message received, ticket created, ticket resolved, customer feedback submitted) enabling external systems to react to support events in real-time. This allows integration with CRM systems, analytics platforms, or custom workflows without requiring Open to natively support every integration. Webhooks include full conversation context and metadata, enabling downstream systems to make informed decisions.","intents":["When a customer opens a support ticket, automatically create a record in our CRM so sales knows they're having issues","Send support events to our analytics platform so we can correlate support interactions with customer churn","Trigger a Slack notification when a high-priority ticket is created so our team responds immediately"],"best_for":["teams with existing CRM, analytics, or workflow automation platforms","businesses needing to integrate support data with other business systems","developers building custom support workflows on top of Open"],"limitations":["Webhook event types and payload schema are not documented — unclear which events are available or what data is included","No documentation on webhook delivery guarantees (at-least-once, exactly-once) or retry logic","Unclear whether webhooks support filtering or if all events are sent to all endpoints","No visibility into webhook signing/verification mechanism for security"],"requires":["Publicly accessible webhook endpoint (HTTPS)","Webhook configuration in Open dashboard (URL, event types, optional filtering)","Ability to parse JSON payloads"],"input_types":["support events (message received, ticket created, resolved, feedback submitted)","full conversation context and metadata"],"output_types":["HTTP POST request to webhook endpoint","JSON payload with event data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_2","uri":"capability://memory.knowledge.conversation.context.and.customer.history.retrieval","name":"conversation context and customer history retrieval","description":"Maintains a queryable store of customer conversation history, account metadata, and interaction patterns that agents can access to understand customer context before responding. The system likely indexes conversations by customer identity, timestamp, and intent to enable fast retrieval of relevant prior interactions. This context is surfaced to agents in the UI and may be automatically injected into AI response generation prompts to improve relevance and personalization.","intents":["When a customer contacts us, show me their previous support tickets and conversation history so I understand their issue context","Search for all conversations with a specific customer to identify patterns or recurring issues","Use customer history to personalize AI-generated responses with account-specific details"],"best_for":["support teams handling repeat customers with complex account histories","businesses needing to identify high-value customers or repeat issue patterns","teams seeking to improve response quality through contextual personalization"],"limitations":["No documentation on context window size — unclear how much historical conversation is retrieved or whether there are cutoffs for very old interactions","Retention policy for customer data is not disclosed — unclear how long conversation history is stored or whether GDPR/CCPA deletion requests are supported","No visibility into indexing strategy or retrieval latency — potential for slow context lookups during high-volume support periods","Unclear whether context retrieval is automatic or requires agent manual search"],"requires":["Customer identity resolution mechanism (email, account ID, phone number) to link conversations","Persistent storage backend with indexing capability (database, vector store, or search engine)","Integration with customer account system to retrieve metadata"],"input_types":["customer identifier (email, account ID)","search query (natural language or structured)","timestamp range (optional)"],"output_types":["conversation history (chronological list)","customer metadata (account status, lifetime value, etc.)","relevant prior interactions (ranked by relevance)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_3","uri":"capability://planning.reasoning.ai.powered.intent.classification.and.ticket.routing","name":"ai-powered intent classification and ticket routing","description":"Classifies incoming customer messages into predefined intent categories (e.g., 'refund request', 'technical issue', 'billing question') using a text classification model, then automatically routes tickets to appropriate support teams, queues, or specialized agents based on intent and priority signals. The system likely uses supervised learning on historical support data or prompt-based classification with an LLM, with fallback to manual routing for low-confidence predictions. Routing rules can be configured to assign tickets based on intent, customer segment, or SLA requirements.","intents":["Automatically route customer inquiries to the right support team (billing, technical, sales) based on what they're asking about","Prioritize urgent issues (account compromised, payment failed) so they reach senior agents first","Reduce time spent on ticket triage by having AI classify and assign tickets automatically"],"best_for":["support teams with multiple specialized queues or departments","businesses with high-volume support where manual triage is a bottleneck","companies needing to enforce SLA-based routing (urgent issues to senior agents)"],"limitations":["Intent taxonomy is not disclosed — unclear which categories are supported or whether custom intents can be defined","No documentation on classification accuracy, false positive rates, or how misclassified tickets are handled","Routing rule configuration is not described — unclear whether rules are UI-based, code-based, or require engineering support","No visibility into how the system handles ambiguous or multi-intent messages"],"requires":["Historical support ticket data with intent labels to train or fine-tune classifier","Defined routing rules or team assignments for each intent category","Integration with support team structure or queue system"],"input_types":["customer message (text)","customer metadata (segment, account status)","conversation history (optional)"],"output_types":["intent classification label","confidence score","assigned team or queue","priority level"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_4","uri":"capability://automation.workflow.real.time.agent.collaboration.and.presence.awareness","name":"real-time agent collaboration and presence awareness","description":"Provides real-time visibility into agent availability, active conversations, and workload distribution, enabling agents to collaborate on complex tickets or hand off conversations without losing context. The system likely uses WebSocket-based presence updates and conversation locking mechanisms to prevent duplicate responses. Agents can see which colleagues are online, how many active conversations each agent has, and can transfer tickets with full conversation history preserved.","intents":["See which support agents are online and available before assigning a ticket to them","Hand off a complex customer conversation to a senior agent without the customer having to re-explain their issue","Prevent two agents from responding to the same ticket simultaneously"],"best_for":["distributed support teams needing real-time coordination","businesses with escalation workflows where tickets move between agents","teams seeking to reduce customer frustration from repeated explanations"],"limitations":["No documentation on presence update latency or how stale presence data is handled during network outages","Conversation locking mechanism is not described — unclear whether locks are time-based, manual, or automatic","No visibility into how the system handles agent disconnections or whether presence is automatically cleared","Unclear whether collaboration features (e.g., internal notes, agent-to-agent messaging) are supported"],"requires":["WebSocket or similar real-time communication protocol support","Agent authentication and session management","Conversation state tracking to prevent concurrent edits"],"input_types":["agent login/logout events","conversation assignment events","ticket transfer requests"],"output_types":["agent presence status (online, away, offline)","active conversation count per agent","conversation lock status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_5","uri":"capability://memory.knowledge.knowledge.base.integration.and.faq.auto.linking","name":"knowledge base integration and faq auto-linking","description":"Integrates with or embeds a knowledge base of FAQs, documentation, and support articles, automatically linking relevant articles to incoming customer inquiries based on semantic similarity or keyword matching. When an agent is composing a response, the system suggests relevant knowledge base articles that can be included in the response or sent directly to the customer. This reduces response time for common questions and ensures consistent information delivery.","intents":["When a customer asks about our refund policy, automatically show them the relevant FAQ article instead of typing a response from scratch","Suggest knowledge base articles to include in my response so customers get comprehensive, consistent information","Reduce support volume by making it easy for customers to find answers in our knowledge base"],"best_for":["businesses with extensive documentation or FAQ content","support teams seeking to reduce response time for common questions","companies wanting to improve answer consistency across agents"],"limitations":["Knowledge base integration method is not disclosed — unclear whether Open hosts the KB or integrates with external systems (Notion, Zendesk, custom)","Article relevance matching algorithm is not described — unclear whether it uses semantic search, keyword matching, or LLM-based similarity","No documentation on how stale or outdated articles are handled or whether there's a review workflow for KB content","Unclear whether customers can access the knowledge base directly or only through agent-provided links"],"requires":["Existing knowledge base or FAQ content (hosted internally or in external system)","Integration credentials if using external KB system","Semantic search capability (embeddings, vector store, or keyword index)"],"input_types":["customer message (text)","knowledge base articles (text, markdown, HTML)"],"output_types":["ranked list of relevant articles","article preview or summary","shareable article link"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_6","uri":"capability://planning.reasoning.sentiment.analysis.and.escalation.triggering","name":"sentiment analysis and escalation triggering","description":"Analyzes customer message sentiment (positive, neutral, negative, angry) using a text classification model to identify frustrated or at-risk customers, automatically escalating high-priority emotional states to senior agents or flagging conversations for immediate attention. The system may use lexical analysis, supervised learning, or LLM-based sentiment scoring, with configurable thresholds for escalation. Sentiment trends over a conversation can be tracked to identify deteriorating customer satisfaction.","intents":["Automatically escalate angry customer messages to senior agents so they get priority attention","Identify customers who are becoming frustrated across multiple messages so we can intervene before they churn","Track sentiment trends in conversations to measure support quality and identify training needs"],"best_for":["support teams managing high-volume inquiries where escalation decisions are critical","businesses with churn risk where early intervention on frustrated customers is valuable","companies seeking to measure support quality through sentiment metrics"],"limitations":["Sentiment model accuracy is not disclosed — unclear whether it handles sarcasm, context-dependent emotion, or cultural language variations","Escalation threshold configuration is not described — unclear whether thresholds are configurable or fixed","No documentation on false positive rates (e.g., detecting frustration in technical jargon) or how misclassifications impact agent workload","Unclear whether sentiment analysis works across all languages or only English"],"requires":["Text classification model (trained on sentiment-labeled support data or using pre-trained model)","Escalation rule configuration (thresholds, target teams)","Integration with ticket routing system"],"input_types":["customer message (text)","conversation history (for trend analysis)"],"output_types":["sentiment label (positive, neutral, negative, angry)","confidence score","escalation flag","sentiment trend over conversation"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_7","uri":"capability://data.processing.analysis.performance.analytics.and.agent.quality.metrics","name":"performance analytics and agent quality metrics","description":"Tracks and reports on support team performance metrics including response time, resolution time, customer satisfaction (CSAT), sentiment trends, and individual agent productivity. The system aggregates data from conversations, agent actions, and customer feedback to generate dashboards and reports. Metrics can be filtered by time period, team, agent, or customer segment to identify trends and training opportunities. This data may also feed into AI model improvements (e.g., retraining response suggestion models on high-quality agent responses).","intents":["See how fast my support team is responding to customers and identify bottlenecks","Compare agent performance to identify top performers and training needs","Track customer satisfaction trends to measure support quality improvements"],"best_for":["support managers seeking visibility into team performance and SLA compliance","businesses using support metrics to drive hiring and training decisions","teams wanting to measure ROI of AI support features"],"limitations":["Metric definitions and calculation methods are not disclosed — unclear whether response time includes AI-suggested responses or only agent-sent messages","No documentation on data retention or historical trend analysis capabilities","Unclear whether CSAT is collected via surveys, implicit signals, or not at all","No visibility into whether metrics are real-time or batch-calculated, impacting decision latency"],"requires":["Conversation event logging (message sent, response time, resolution)","Agent action tracking (login, logout, ticket assignment)","Optional customer feedback mechanism (CSAT survey, rating)"],"input_types":["conversation events (timestamps, agent ID, message count)","agent actions (login/logout, ticket assignment)","customer feedback (optional)"],"output_types":["dashboard with KPI cards","time-series charts (response time, resolution time trends)","agent leaderboards","exportable reports (CSV, PDF)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_8","uri":"capability://automation.workflow.customizable.response.templates.and.macros","name":"customizable response templates and macros","description":"Allows support teams to create and manage reusable response templates and macros that agents can insert into messages with a single click or keyboard shortcut. Templates can include placeholders for customer name, account details, or other dynamic variables that are automatically filled in based on conversation context. This reduces typing time for common responses and ensures consistency in messaging. Templates may be organized by category, searchable, and versioned for updates.","intents":["Create a template for our standard refund response so agents don't have to type it every time","Use placeholders in templates so customer names and order numbers are automatically filled in","Organize templates by category so agents can quickly find the right response"],"best_for":["support teams with repetitive, standardized responses","businesses seeking to reduce agent typing time and response latency","teams wanting to enforce consistent messaging across all agents"],"limitations":["Template management UI/UX is not described — unclear whether templates are created in-app or require engineering support","No documentation on placeholder syntax or supported variable types (customer name, order ID, account status, etc.)","Unclear whether templates support conditional logic (e.g., different text for VIP customers) or are simple text substitution","No visibility into version control or approval workflows for template changes"],"requires":["Template storage backend (database)","Variable resolution mechanism to fill placeholders with conversation context","UI for template creation and management"],"input_types":["template text with placeholders","variable definitions (customer name, order ID, etc.)","conversation context (for variable resolution)"],"output_types":["rendered template with variables filled in","template list for agent selection"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_open__cap_9","uri":"capability://data.processing.analysis.customer.feedback.collection.and.satisfaction.tracking","name":"customer feedback collection and satisfaction tracking","description":"Collects customer satisfaction feedback after support interactions through surveys, ratings, or implicit signals (e.g., whether customer reopens a ticket). Feedback is aggregated to calculate CSAT or NPS metrics and linked to individual agents or conversation topics to identify quality issues. This data may be used to retrain AI models, identify training needs for agents, or trigger follow-up actions (e.g., escalation for low-satisfaction interactions).","intents":["After we resolve a customer issue, ask them if they're satisfied with the support they received","Identify which agents or topics have the lowest satisfaction scores so we can improve","Use customer feedback to measure the impact of our AI support features"],"best_for":["support teams seeking to measure customer satisfaction and identify quality issues","businesses using feedback to drive agent training and performance management","teams wanting to validate AI response quality through customer perception"],"limitations":["Feedback collection method is not disclosed — unclear whether surveys are sent via email, in-app, or SMS","Survey design and question types are not described — unclear whether CSAT, NPS, or custom questions are supported","No documentation on response rates or how non-responses are handled","Unclear whether feedback is collected for all interactions or sampled"],"requires":["Customer contact information (email, phone) for survey delivery","Survey platform or integration (internal or third-party)","Feedback storage and aggregation backend"],"input_types":["customer rating (1-5 scale, NPS 0-10, etc.)","optional feedback text","conversation metadata (agent, topic, resolution time)"],"output_types":["CSAT/NPS score","feedback text","agent satisfaction ranking","topic-based satisfaction trends"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active accounts on supported communication channels (email provider, chat platform, social media)","API credentials or OAuth tokens for each channel to be integrated","Internet connectivity for real-time message polling/webhooks","Sufficient historical support conversation data to train intent classifiers and response patterns","API access to LLM provider (if using third-party model) or computational resources for inference","Configuration of response templates or examples for the system to learn from","Translation API access (Google Translate, DeepL, or similar)","Language detection model or library","Integration with message processing pipeline","Publicly accessible webhook endpoint (HTTPS)"],"failure_modes":["Channel integration breadth unknown — documentation does not specify which platforms are supported beyond 'email, chat, social'","No visibility into message deduplication logic or conflict resolution when same customer contacts via multiple channels simultaneously","Unclear whether channel-specific metadata (e.g., social media engagement metrics, email headers) is preserved or normalized away","No public documentation on model selection, fine-tuning approach, or which LLM provider powers responses (OpenAI, Anthropic, proprietary model unknown)","Confidence threshold for auto-reply triggering is not disclosed — risk of inappropriate automated responses damaging customer relationships","No visibility into response quality metrics, hallucination rates, or how the system handles edge cases outside training distribution","Unclear whether responses are personalized per customer or generic template-based","Supported languages are not disclosed — unclear which language pairs are supported","Translation quality and accuracy are not documented — risk of mistranslations damaging customer relationships","No visibility into whether translation is bidirectional (customer language ↔ agent language) or one-way","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3333333333333333,"quality":0.74,"ecosystem":0.2,"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":"2026-05-24T12:16:31.859Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=open","compare_url":"https://unfragile.ai/compare?artifact=open"}},"signature":"zOcXoWUPWyxlkwlAa4/56N8eZUC+fq0M/iF0WHMD2DxhujnGnC92kKiJAzyvZ1Vw+5D5Lw3ICSpTO3ajVFY/Ag==","signedAt":"2026-06-22T00:30:44.805Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/open","artifact":"https://unfragile.ai/open","verify":"https://unfragile.ai/api/v1/verify?slug=open","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"}}