{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_magic-ai","slug":"magic-ai","name":"Magic AI","type":"product","url":"https://magicai.ai","page_url":"https://unfragile.ai/magic-ai","categories":["chatbots-assistants","app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_magic-ai__cap_0","uri":"capability://automation.workflow.no.code.chatbot.builder.with.visual.workflow.composition","name":"no-code chatbot builder with visual workflow composition","description":"Enables non-technical users to construct conversational AI agents through drag-and-drop interface without writing code or prompts. The builder abstracts away prompt engineering by providing pre-configured conversation flows, intent routing, and response templates that map user inputs to predefined actions. Users connect knowledge sources, define conversation branches, and set response behaviors through visual node-based composition rather than manual prompt crafting.","intents":["I need to build a customer support chatbot without hiring a developer","I want to create an internal knowledge assistant without learning prompt engineering","I need to quickly prototype a chatbot for my team to test before investing in custom development"],"best_for":["non-technical business users and team leads","small to mid-size businesses without dedicated AI/ML teams","internal teams building knowledge assistants for employee support"],"limitations":["Visual builder abstracts away advanced customization — complex conditional logic or multi-turn reasoning patterns are difficult to implement","Limited ability to fine-tune model behavior or implement custom scoring/ranking logic","No direct access to underlying prompts or model parameters for optimization"],"requires":["Web browser with modern JavaScript support","Account creation (freemium tier available)","Knowledge source documents or data (optional but recommended)"],"input_types":["text (conversation input)","document files (for knowledge base)","structured data (optional for context)"],"output_types":["conversational text responses","structured conversation metadata","conversation logs"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_1","uri":"capability://memory.knowledge.knowledge.base.grounding.with.document.backed.response.generation","name":"knowledge base grounding with document-backed response generation","description":"Anchors chatbot responses to user-provided documents and data sources through retrieval-augmented generation (RAG) pattern, preventing hallucinations by forcing the model to cite and reference actual content from your knowledge base. The system ingests documents, creates searchable embeddings or indexes, and retrieves relevant passages during conversation to inject into the LLM context, ensuring responses are factually grounded in your actual data rather than model training data.","intents":["I want my chatbot to answer questions only based on our company documentation, not make things up","I need to ensure customer support responses cite actual product documentation or policies","I want to prevent the chatbot from confidently stating false information"],"best_for":["customer support teams requiring factual accuracy and compliance","organizations with sensitive or proprietary information requiring grounded responses","teams building internal knowledge assistants for employee onboarding"],"limitations":["Retrieval quality depends on document structure and indexing — poorly formatted or ambiguous documents reduce response accuracy","No built-in handling of document versioning or updates — requires manual re-indexing when knowledge base changes","Retrieval latency adds 200-500ms per query depending on knowledge base size","Limited control over retrieval ranking or relevance scoring algorithms"],"requires":["Document files in supported formats (PDF, DOCX, TXT, or similar)","Knowledge base ingestion API or UI upload mechanism","Sufficient storage quota on freemium or paid tier"],"input_types":["text documents","PDF files","structured data (optional)","conversation queries"],"output_types":["grounded text responses with citations","source document references","confidence scores (optional)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_2","uri":"capability://memory.knowledge.multi.source.knowledge.integration.and.data.consolidation","name":"multi-source knowledge integration and data consolidation","description":"Aggregates knowledge from multiple document sources, databases, or APIs into a unified knowledge base that the chatbot can query during conversations. The system provides connectors or import mechanisms for various data formats and sources, consolidating disparate information into a searchable index that serves as the single source of truth for chatbot responses. This enables teams to maintain one centralized knowledge repository rather than scattering information across multiple systems.","intents":["I need to consolidate knowledge from multiple documents, wikis, and databases into one searchable source","I want my chatbot to answer questions by pulling from both our help docs and product database","I need to keep the knowledge base synchronized as our documentation updates"],"best_for":["organizations with fragmented knowledge across multiple systems","teams managing large documentation libraries or product databases","enterprises requiring centralized knowledge governance"],"limitations":["Synchronization between sources is manual or requires custom integration — no built-in real-time sync with external systems","Conflicting information across sources is not automatically resolved — requires manual curation","Limited transformation or normalization of data from heterogeneous sources","No versioning or audit trail for knowledge base changes"],"requires":["Access to source documents or data systems","Supported file formats or API credentials for data sources","Sufficient knowledge base storage quota"],"input_types":["documents (PDF, DOCX, TXT)","structured data (CSV, JSON)","API endpoints (optional)","web content (optional)"],"output_types":["unified knowledge index","searchable knowledge base","consolidated metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_3","uri":"capability://planning.reasoning.conversational.intent.routing.and.multi.turn.dialogue.management","name":"conversational intent routing and multi-turn dialogue management","description":"Routes user inputs to appropriate responses or actions based on detected intent, maintaining conversation context across multiple turns to enable coherent multi-step dialogues. The system uses intent classification (rule-based or ML-based) to understand user goals, maintains conversation state to track context and previous exchanges, and orchestrates appropriate responses or actions based on the current dialogue state. This enables the chatbot to handle complex conversations that require understanding user intent and maintaining context rather than responding to isolated queries.","intents":["I want my chatbot to understand what the user is trying to do and route them to the right answer","I need the chatbot to remember context from earlier in the conversation and use it in later responses","I want to handle multi-step conversations where the chatbot asks clarifying questions"],"best_for":["customer support teams handling complex multi-step inquiries","internal assistants requiring context-aware responses","teams building conversational workflows with conditional branching"],"limitations":["Intent classification accuracy depends on training data and examples — ambiguous intents may be misrouted","Conversation state is typically session-scoped — no persistent memory across sessions without additional configuration","Complex conditional logic or multi-turn reasoning patterns are difficult to implement through visual builder","No built-in handling of intent conflicts or fallback strategies"],"requires":["Intent definitions or examples during chatbot configuration","Conversation flow design through visual builder","Session management infrastructure (typically provided by platform)"],"input_types":["text (user input)","conversation history","context metadata"],"output_types":["routed responses","action triggers","conversation state updates"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_4","uri":"capability://automation.workflow.pre.built.conversation.templates.and.response.customization","name":"pre-built conversation templates and response customization","description":"Provides ready-made conversation templates for common use cases (customer support, FAQ, onboarding) that users can customize without building from scratch. Templates include predefined intents, response patterns, and conversation flows that serve as starting points, reducing time to deployment. Users can modify templates through the visual builder, customize response text, adjust routing logic, and add domain-specific knowledge without rewriting entire conversation structures.","intents":["I want to quickly launch a chatbot using a template instead of building from scratch","I need to customize a template to match our brand voice and specific use cases","I want to reuse conversation patterns across multiple chatbots"],"best_for":["teams with limited time to design conversation flows","organizations launching their first chatbot","teams building multiple similar chatbots for different departments"],"limitations":["Templates may not fit niche or highly specialized use cases — customization may require significant effort","Limited ability to export or version control templates","Template updates from the platform may not automatically propagate to existing chatbots"],"requires":["Access to template library (typically included in freemium tier)","Basic understanding of your use case to select appropriate template","Ability to customize through visual builder"],"input_types":["template selection","customization parameters","knowledge base documents"],"output_types":["configured chatbot","conversation flows","response templates"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_5","uri":"capability://tool.use.integration.deployment.and.embedding.across.multiple.channels","name":"deployment and embedding across multiple channels","description":"Enables deployment of configured chatbots to multiple communication channels (web widget, Slack, Teams, email, etc.) from a single configuration without rebuilding for each platform. The system abstracts channel-specific protocols and formatting, allowing the same chatbot logic to operate across different interfaces. Users can enable/disable channels, customize channel-specific settings, and manage all deployments from a centralized dashboard.","intents":["I want to deploy my chatbot to our website, Slack, and Teams without rebuilding it three times","I need to embed a chatbot widget on our website with minimal code","I want to manage all chatbot deployments from one place"],"best_for":["teams deploying chatbots across multiple communication platforms","organizations with distributed teams using different communication tools","customer support teams needing omnichannel presence"],"limitations":["Channel-specific features or customizations may not be fully supported — some platforms may have limited capability parity","Embedding code requires basic HTML/JavaScript knowledge for web deployment","Channel authentication and permissions must be configured separately for each platform","No built-in analytics across channels — requires manual aggregation"],"requires":["Configured chatbot in Magic AI","Access credentials for target platforms (Slack workspace, Teams tenant, etc.)","Basic HTML/JavaScript knowledge for web widget embedding","API keys or OAuth tokens for channel integrations"],"input_types":["chatbot configuration","channel selection","deployment settings"],"output_types":["deployed chatbot instances","embedding code","channel-specific URLs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_6","uri":"capability://data.processing.analysis.conversation.analytics.and.performance.monitoring","name":"conversation analytics and performance monitoring","description":"Tracks chatbot interactions, user satisfaction, conversation outcomes, and performance metrics through built-in analytics dashboard. The system logs conversations, captures user feedback or ratings, measures response quality, identifies common failure patterns, and provides insights into chatbot effectiveness. Analytics help teams understand usage patterns, identify knowledge gaps, and optimize chatbot performance over time.","intents":["I want to see how many conversations my chatbot is handling and whether users are satisfied","I need to identify which questions the chatbot struggles with so I can improve the knowledge base","I want to measure the impact of my chatbot on support ticket volume"],"best_for":["teams optimizing chatbot performance iteratively","customer support leaders measuring chatbot ROI","organizations requiring conversation audit trails for compliance"],"limitations":["Analytics are limited to conversations within Magic AI — no integration with external analytics platforms","User satisfaction requires explicit feedback mechanism — implicit satisfaction signals are not captured","No predictive analytics or anomaly detection — requires manual analysis of metrics","Conversation logs may have retention limits depending on tier","Privacy considerations — conversation logging may require user consent in regulated industries"],"requires":["Active chatbot deployments with user interactions","Access to analytics dashboard (included in account)","Optional: user feedback mechanism configuration"],"input_types":["conversation logs","user feedback","interaction metadata"],"output_types":["analytics dashboards","performance metrics","conversation transcripts","usage reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_7","uri":"capability://safety.moderation.user.authentication.and.access.control.for.chatbot.management","name":"user authentication and access control for chatbot management","description":"Manages user roles, permissions, and access control for chatbot configuration and management within the platform. The system supports multiple user accounts per workspace, role-based access control (RBAC) to restrict who can edit chatbots or access analytics, and audit logging of administrative actions. This enables teams to collaborate on chatbot development while maintaining security and governance.","intents":["I need to give my team members access to edit chatbots without giving them access to billing or sensitive settings","I want to track who made changes to the chatbot configuration","I need to restrict access to certain chatbots or knowledge bases by team member"],"best_for":["teams with multiple members collaborating on chatbot development","organizations requiring access control and audit trails","enterprises with governance or compliance requirements"],"limitations":["Role granularity may be limited — fine-grained permissions per resource may not be available","No built-in SSO or SAML integration — may require manual user provisioning","Audit logs may have retention limits depending on tier","No field-level access control — users with edit access can modify all chatbot settings"],"requires":["Magic AI account with admin access","Team member email addresses for invitation","Optional: SSO/SAML provider for enterprise deployments"],"input_types":["user email addresses","role assignments","permission configurations"],"output_types":["user accounts","role assignments","audit logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_magic-ai__cap_8","uri":"capability://safety.moderation.response.quality.assurance.and.content.moderation","name":"response quality assurance and content moderation","description":"Implements safeguards to ensure chatbot responses meet quality standards and comply with content policies through built-in moderation and filtering. The system can flag potentially harmful responses, filter inappropriate content, enforce response guidelines, and provide human review workflows for sensitive conversations. This prevents the chatbot from generating harmful, biased, or off-brand responses.","intents":["I want to prevent my chatbot from generating inappropriate or harmful responses","I need to ensure all responses align with our brand voice and policies","I want to review sensitive conversations before they're sent to users"],"best_for":["customer-facing chatbots requiring brand consistency","organizations in regulated industries (healthcare, finance) requiring compliance","teams concerned about chatbot safety and alignment"],"limitations":["Content moderation rules may be limited to predefined categories — custom policies require manual configuration","No built-in bias detection or fairness auditing","Human review workflows add latency to response delivery","Moderation accuracy depends on rule configuration — false positives/negatives are possible"],"requires":["Content moderation rules or policies defined","Optional: human reviewers for sensitive conversations","Configuration of moderation thresholds and actions"],"input_types":["chatbot responses","moderation rules","user feedback"],"output_types":["flagged responses","moderation decisions","review queues"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Account creation (freemium tier available)","Knowledge source documents or data (optional but recommended)","Document files in supported formats (PDF, DOCX, TXT, or similar)","Knowledge base ingestion API or UI upload mechanism","Sufficient storage quota on freemium or paid tier","Access to source documents or data systems","Supported file formats or API credentials for data sources","Sufficient knowledge base storage quota","Intent definitions or examples during chatbot configuration"],"failure_modes":["Visual builder abstracts away advanced customization — complex conditional logic or multi-turn reasoning patterns are difficult to implement","Limited ability to fine-tune model behavior or implement custom scoring/ranking logic","No direct access to underlying prompts or model parameters for optimization","Retrieval quality depends on document structure and indexing — poorly formatted or ambiguous documents reduce response accuracy","No built-in handling of document versioning or updates — requires manual re-indexing when knowledge base changes","Retrieval latency adds 200-500ms per query depending on knowledge base size","Limited control over retrieval ranking or relevance scoring algorithms","Synchronization between sources is manual or requires custom integration — no built-in real-time sync with external systems","Conflicting information across sources is not automatically resolved — requires manual curation","Limited transformation or normalization of data from heterogeneous sources","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"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":"2026-05-24T12:16:31.857Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=magic-ai","compare_url":"https://unfragile.ai/compare?artifact=magic-ai"}},"signature":"IV69wLRyvssqb6GnTz1Vy6CkpT+b8Yt54WtcQKQjvS03EEVb4Z2VrBuNwq8U7UlW0K6zJoeaE9pVCZwiyVZ8DA==","signedAt":"2026-06-20T16:06:30.052Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/magic-ai","artifact":"https://unfragile.ai/magic-ai","verify":"https://unfragile.ai/api/v1/verify?slug=magic-ai","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"}}