{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_imbue","slug":"imbue","name":"Imbue","type":"agent","url":"https://imbue.com","page_url":"https://unfragile.ai/imbue","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_imbue__cap_0","uri":"capability://automation.workflow.autonomous.web.browsing.and.navigation","name":"autonomous web browsing and navigation","description":"Imbue agents can autonomously navigate web browsers, interpret visual page layouts, locate and click interactive elements, and extract information from websites without human intervention. The system likely uses computer vision to understand page structure combined with DOM interaction APIs or browser automation frameworks (Selenium/Playwright-style) to execute navigation commands. Agents maintain session state across multiple page loads and can handle dynamic content loading.","intents":["I need an AI agent to research competitor pricing across 20 websites and compile results into a spreadsheet","Automate data collection from multiple web sources that don't have public APIs","Have an agent monitor specific web pages for changes and alert me when updates occur","Execute multi-step workflows that require navigating through web forms and portals"],"best_for":["researchers automating information gathering from web sources","business analysts needing competitive intelligence across multiple sites","early-adopter professionals willing to tolerate occasional navigation failures"],"limitations":["Reliability degrades significantly with JavaScript-heavy SPAs or sites with aggressive anti-bot detection","Cannot reliably handle CAPTCHA challenges or multi-factor authentication flows","Session state management is inconsistent across complex multi-step workflows requiring 5+ page transitions","No built-in retry logic for transient network failures or page load timeouts"],"requires":["Active internet connection","Target websites must be publicly accessible (no authentication required or agent must handle login)","Modern web browser installed on agent runtime environment"],"input_types":["natural language task description","URL or website domain","structured data (e.g., list of URLs to visit)"],"output_types":["extracted text data","structured data (tables, JSON)","screenshots of pages visited","execution logs with success/failure status"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_1","uri":"capability://automation.workflow.cross.application.workflow.automation","name":"cross-application workflow automation","description":"Imbue agents can interact with desktop and web applications beyond browsers—opening files, manipulating application UIs, copying data between tools, and executing application-specific commands. This likely leverages accessibility APIs (Windows UI Automation, macOS Accessibility Framework) or application-level automation protocols combined with visual understanding to identify UI elements. Agents maintain context about which applications are open and can switch between them intelligently.","intents":["Automate data entry workflows that require copying information from one application to another","Execute multi-application processes like exporting data from a CRM, processing it, and importing into accounting software","Automate repetitive tasks across desktop applications without building custom integrations","Create workflows that combine web-based and desktop tools in a single automated sequence"],"best_for":["operations teams automating cross-tool data workflows","small businesses without engineering resources to build custom integrations","professionals automating personal productivity workflows across multiple SaaS tools"],"limitations":["Reliability is highly dependent on application UI stability—updates to application layouts frequently break workflows","No support for applications requiring specialized hardware (e.g., CAD software with 3D viewport interaction)","Latency between application interactions is high (500ms-2s per action) making real-time workflows impractical","Limited ability to handle modal dialogs, permission prompts, or unexpected UI state changes mid-workflow","No persistent workflow templates—each task execution requires re-specification of steps"],"requires":["Target applications must be installed and accessible on the agent runtime environment","Applications must have accessible UI elements (not heavily obfuscated or custom-rendered)","Sufficient system resources to run multiple applications simultaneously"],"input_types":["natural language workflow description","application names and target actions","data to be transferred between applications"],"output_types":["completed tasks with confirmation status","extracted data from applications","execution logs showing which applications were accessed"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_2","uri":"capability://planning.reasoning.multi.step.task.decomposition.and.execution","name":"multi-step task decomposition and execution","description":"Imbue agents can break down complex, multi-step user requests into intermediate subtasks, execute them sequentially or in parallel, and adapt execution based on intermediate results. The system likely uses chain-of-thought reasoning or task planning patterns to decompose goals, maintains execution state across steps, and includes decision logic to handle conditional branching based on task outcomes. Agents can recover from partial failures by retrying steps or adjusting subsequent tasks.","intents":["I need an agent to research a topic, compile findings, create a presentation, and email it to stakeholders—all from a single request","Automate a complex business process with multiple decision points and conditional branches","Execute a workflow where later steps depend on data extracted from earlier steps","Have an agent handle a task that requires trying multiple approaches if the first one fails"],"best_for":["business process automation requiring conditional logic and decision-making","research and analysis workflows with multiple sequential information-gathering steps","teams automating complex operational processes with variable outcomes"],"limitations":["Task decomposition is opaque—users cannot see or edit the intermediate steps the agent creates","Agents frequently fail at complex decompositions requiring 5+ steps, with success rates dropping significantly","No explicit error recovery strategy—agents may abandon workflows rather than attempting alternative approaches","Context window limitations prevent agents from maintaining state across very long workflows (10+ steps)","Conditional branching logic is brittle and fails when intermediate results don't match expected formats"],"requires":["Clear, well-specified user intent (vague requests lead to poor decomposition)","Tasks must be decomposable into discrete subtasks that can be executed independently","Sufficient API quota or rate limits for services accessed during multi-step execution"],"input_types":["natural language task description","structured task specification with dependencies","reference data or context for task execution"],"output_types":["final task result","intermediate results from each step","execution trace showing which steps succeeded/failed","structured data combining results from multiple steps"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_3","uri":"capability://text.generation.language.natural.language.task.specification.with.adaptive.execution","name":"natural language task specification with adaptive execution","description":"Users can describe tasks in natural language and Imbue agents interpret intent, determine required capabilities, and execute without explicit step-by-step instructions. The system uses LLM-based instruction interpretation combined with capability routing logic to map natural language requests to available agent actions (browsing, application interaction, data processing). Agents can ask clarifying questions if task specification is ambiguous and adapt execution strategy based on user feedback.","intents":["Describe what I want done in plain English without learning a workflow syntax or API","Have an agent ask me clarifying questions when my request is ambiguous rather than failing silently","Modify a task mid-execution by giving the agent new instructions without restarting","Use the same natural language interface for completely different types of tasks (research, automation, analysis)"],"best_for":["non-technical users automating tasks without programming knowledge","rapid prototyping of automation workflows without upfront specification","exploratory use cases where exact requirements aren't known in advance"],"limitations":["Interpretation accuracy degrades with complex, multi-faceted requests—agents frequently misunderstand intent","No way to specify exact execution constraints (timeout limits, cost budgets, approval gates)","Agents cannot reliably handle domain-specific terminology or industry jargon","Ambiguous requests often result in agents making incorrect assumptions rather than asking clarifying questions","No audit trail of how natural language was interpreted into executable steps"],"requires":["Clear, reasonably specific task description (extremely vague requests lead to poor execution)","Tasks must be within scope of agent capabilities (cannot request capabilities agent doesn't have)"],"input_types":["natural language task description","conversational clarifications and feedback"],"output_types":["task execution result","clarifying questions asked by agent","execution summary in natural language"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_4","uri":"capability://image.visual.visual.page.understanding.and.element.identification","name":"visual page understanding and element identification","description":"Imbue agents can analyze visual renderings of web pages and application UIs to identify interactive elements (buttons, forms, links), understand page structure and content hierarchy, and locate specific information without relying on HTML parsing or DOM inspection. This likely uses computer vision models trained on UI screenshots combined with OCR for text recognition. Agents can identify elements even when HTML structure is obfuscated or when pages use custom rendering frameworks.","intents":["Have an agent navigate websites with complex JavaScript-heavy layouts that traditional scraping tools can't parse","Automate interaction with web applications that use canvas-based or custom-rendered UIs","Extract information from pages where content is rendered as images or in non-standard formats","Identify and click on UI elements without needing to inspect HTML or CSS selectors"],"best_for":["automation of modern web applications with complex rendering","workflows targeting websites with anti-scraping measures","scenarios where HTML structure is unreliable or frequently changes"],"limitations":["Computer vision-based element identification has lower accuracy than DOM-based approaches (85-95% vs 99%+)","Struggles with small UI elements, overlapping elements, or elements with poor contrast","Cannot reliably identify elements in pages with heavy visual clutter or non-standard layouts","Latency is higher than DOM-based approaches due to image processing overhead (500ms-2s per page analysis)","Fails on pages with dynamic content that changes rapidly or requires scroll-based lazy loading"],"requires":["Page must be visually renderable (not text-only or heavily obfuscated)","Sufficient computational resources for image processing and vision model inference"],"input_types":["page screenshots or rendered HTML","natural language descriptions of elements to find","visual coordinates or element descriptions"],"output_types":["identified UI element locations and types","extracted text from pages","structured data about page layout and content"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_5","uri":"capability://memory.knowledge.session.state.management.across.multi.step.workflows","name":"session state management across multi-step workflows","description":"Imbue agents maintain execution context and state across multiple sequential actions—remembering login credentials, maintaining browser sessions, preserving extracted data, and tracking workflow progress. The system likely uses in-memory state stores or session management APIs to persist context between agent actions. Agents can reference previously extracted data in later steps and maintain authentication state across multiple page navigations.","intents":["Execute a workflow that requires logging into a website once and then performing multiple authenticated actions","Have an agent extract data from step 1, use that data to inform decisions in step 3, and reference it again in step 5","Maintain a workflow session across multiple user interactions without losing progress","Preserve agent context when switching between different applications or websites"],"best_for":["multi-step workflows requiring authentication or session persistence","complex automation scenarios with data dependencies between steps","long-running workflows that need to maintain state across hours or days"],"limitations":["Session state is not persisted across agent restarts—workflows must complete in a single session","Memory usage grows with workflow length, causing performance degradation for workflows with 10+ steps","No explicit state versioning or rollback capability—cannot revert to earlier workflow states","State management is opaque to users—no visibility into what data is being maintained or how","Session timeout handling is inconsistent—agents may lose state if workflows take too long"],"requires":["Continuous agent runtime (state is lost if agent process terminates)","Sufficient memory for storing intermediate results and context"],"input_types":["initial task specification","user feedback and corrections during execution"],"output_types":["final workflow result","intermediate state snapshots","execution logs showing state transitions"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_6","uri":"capability://planning.reasoning.agent.feedback.integration.and.mid.workflow.correction","name":"agent feedback integration and mid-workflow correction","description":"Users can observe agent execution in real-time, provide feedback or corrections, and agents adapt subsequent steps based on user input without restarting the workflow. The system likely implements a feedback loop where agents pause at decision points or after failures, present options to users, and incorporate user guidance into execution strategy. Agents can learn from corrections within a single workflow session.","intents":["Watch an agent execute a task and correct it mid-workflow if it's going in the wrong direction","Have an agent ask me which option to choose when it encounters ambiguity rather than making a wrong guess","Provide the agent with additional information mid-workflow to help it complete a task","Approve or reject agent actions before they're executed (e.g., before sending emails or making purchases)"],"best_for":["high-stakes workflows where human oversight is required (financial transactions, sensitive data)","exploratory automation where exact requirements aren't known in advance","scenarios where agent reliability is low and human correction is expected"],"limitations":["Feedback loop adds significant latency—workflows with frequent user interaction become slow","Agents don't reliably learn from corrections—the same mistakes may recur in later steps","No structured feedback mechanism—users must provide corrections in natural language which agents may misinterpret","Feedback integration is inconsistent—some types of corrections are ignored or cause workflow failures","No approval workflow or permission system—any user can approve any agent action"],"requires":["User availability to monitor and provide feedback during workflow execution","Real-time communication channel between agent and user"],"input_types":["user feedback in natural language","approval/rejection decisions","corrected data or instructions"],"output_types":["updated workflow execution based on feedback","clarifying questions from agent","execution logs showing where feedback was incorporated"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_7","uri":"capability://automation.workflow.free.tier.experimentation.without.financial.commitment","name":"free-tier experimentation without financial commitment","description":"Imbue offers a free tier that allows users to experiment with agent capabilities, test automation workflows, and evaluate the platform without requiring payment or credit card. The free tier likely includes limited monthly action quotas or rate limits but provides sufficient capacity for prototyping and small-scale automation. This removes friction for initial adoption and allows users to assess whether the platform meets their needs before committing financially.","intents":["Try out Imbue's agent capabilities without financial risk to evaluate if it solves my problem","Prototype an automation workflow before committing to a paid plan","Test whether specific tasks can be automated before building out full workflows","Experiment with agent capabilities for research or learning purposes"],"best_for":["early-adopter professionals and researchers evaluating AI agents","small teams and solo developers prototyping automation ideas","non-technical users wanting to experiment with automation without upfront cost"],"limitations":["Free tier quotas are restrictive—typically 50-500 agent actions per month, insufficient for production use","No SLA or uptime guarantees on free tier—reliability may be lower than paid plans","Free tier features may be limited compared to paid plans (e.g., no advanced reasoning, limited integrations)","Free tier accounts may have lower priority for support or bug fixes","Quota resets may not align with calendar months, creating unpredictable availability"],"requires":["Email address or account creation","No payment method required"],"input_types":["task specifications","workflow definitions"],"output_types":["task execution results","usage statistics showing remaining quota"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_imbue__cap_8","uri":"capability://safety.moderation.agent.capability.transparency.and.limitation.documentation","name":"agent capability transparency and limitation documentation","description":"Imbue provides documentation and UI indicators showing what tasks agents can reliably accomplish, what limitations exist, and what types of workflows are likely to fail. This likely includes capability matrices, example workflows, and explicit statements about reliability rates for different task categories. Users can understand upfront whether their specific use case is supported rather than discovering limitations through failed executions.","intents":["Understand which types of tasks Imbue agents can reliably handle before investing time in automation","Know what limitations and failure modes to expect for my specific use case","Assess whether Imbue is appropriate for my workflow or if I should use alternative tools","Get clear guidance on what tasks are experimental vs production-ready"],"best_for":["teams evaluating Imbue for specific use cases and needing realistic capability assessment","professionals making build-vs-buy decisions for automation","organizations with compliance requirements needing to understand tool limitations"],"limitations":["Documentation is vague and incomplete—specific capability limitations are not clearly stated","No published reliability metrics or success rates for different task categories","Capability boundaries are not clearly defined—unclear which tasks are supported vs experimental","Documentation does not include failure modes or common error patterns","No public roadmap showing which limitations will be addressed in future versions"],"requires":["Access to Imbue documentation and website"],"input_types":["task description for capability assessment"],"output_types":["capability assessment","limitation documentation","reliability expectations"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Active internet connection","Target websites must be publicly accessible (no authentication required or agent must handle login)","Modern web browser installed on agent runtime environment","Target applications must be installed and accessible on the agent runtime environment","Applications must have accessible UI elements (not heavily obfuscated or custom-rendered)","Sufficient system resources to run multiple applications simultaneously","Clear, well-specified user intent (vague requests lead to poor decomposition)","Tasks must be decomposable into discrete subtasks that can be executed independently","Sufficient API quota or rate limits for services accessed during multi-step execution","Clear, reasonably specific task description (extremely vague requests lead to poor execution)"],"failure_modes":["Reliability degrades significantly with JavaScript-heavy SPAs or sites with aggressive anti-bot detection","Cannot reliably handle CAPTCHA challenges or multi-factor authentication flows","Session state management is inconsistent across complex multi-step workflows requiring 5+ page transitions","No built-in retry logic for transient network failures or page load timeouts","Reliability is highly dependent on application UI stability—updates to application layouts frequently break workflows","No support for applications requiring specialized hardware (e.g., CAD software with 3D viewport interaction)","Latency between application interactions is high (500ms-2s per action) making real-time workflows impractical","Limited ability to handle modal dialogs, permission prompts, or unexpected UI state changes mid-workflow","No persistent workflow templates—each task execution requires re-specification of steps","Task decomposition is opaque—users cannot see or edit the intermediate steps the agent creates","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":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.445Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=imbue","compare_url":"https://unfragile.ai/compare?artifact=imbue"}},"signature":"TnI5jQwG6UcZsEyGhXx8/8cTCFyI6cwTfQAOWMK2CczV6LVMiWZ82O996sIlDS8IyBkygcwLsLtBv4Bt7/uVAg==","signedAt":"2026-06-23T01:08:02.109Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/imbue","artifact":"https://unfragile.ai/imbue","verify":"https://unfragile.ai/api/v1/verify?slug=imbue","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"}}