{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-pathonaiorg--litewebagent","slug":"pathonaiorg--litewebagent","name":"LiteWebAgent","type":"agent","url":"https://github.com/PathOnAIOrg/LiteWebAgent","page_url":"https://unfragile.ai/pathonaiorg--litewebagent","categories":["ai-agents"],"tags":["agent","agent-based-framework","agentic-agi","agentic-framework","agentic-workflow","ai-agent","ai-agents","autonomous-agent","autonomous-agents","fastapi","gpt","llm","llm-agent","llm-agents","llm-framework","web-agent","web-agents"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-pathonaiorg--litewebagent__cap_0","uri":"capability://image.visual.multi.modal.web.page.understanding.via.accessibility.trees.and.visual.analysis","name":"multi-modal web page understanding via accessibility trees and visual analysis","description":"Processes web pages by combining accessibility tree (axtree) extraction, DOM element parsing, and screenshot analysis to build a unified representation of page structure and content. The system extracts interactive elements, their positions, and semantic relationships, enabling VLMs to reason about page layout without raw HTML. This multi-modal approach allows agents to understand both the logical structure (via axtree) and visual presentation (via screenshots) simultaneously.","intents":["I need my agent to understand complex web page layouts with nested elements and dynamic content","I want to extract interactive elements and their relationships from a live webpage","I need to ground visual understanding with semantic accessibility information"],"best_for":["developers building VLM-based web automation agents","teams needing robust web page parsing that handles dynamic content","researchers evaluating web agent performance on complex UI layouts"],"limitations":["Accessibility tree extraction depends on page's ARIA implementation — poorly marked pages may have incomplete element trees","Screenshot-based analysis requires sufficient visual clarity and contrast for VLM interpretation","Real-time DOM changes may require re-extraction, adding latency per state change"],"requires":["Python 3.9+","Browser automation library (Playwright or Selenium)","Vision-Language Model with image input support (GPT-4V, Claude 3.5 Vision, etc.)"],"input_types":["live webpage URL","browser session state","DOM snapshot"],"output_types":["structured accessibility tree (JSON/dict)","interactive element list with coordinates","screenshot with element annotations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_1","uri":"capability://planning.reasoning.natural.language.to.action.sequence.planning.with.goal.decomposition","name":"natural language to action sequence planning with goal decomposition","description":"Converts high-level natural language instructions into executable multi-step action sequences using specialized planning agents (HighLevelPlanningAgent, ContextAwarePlanningAgent). The system decomposes complex goals into sub-tasks, reasons about dependencies, and generates structured action plans that can be executed by function-calling agents. Planning agents leverage VLM reasoning to understand task semantics and generate contextually appropriate action sequences.","intents":["I want to give my agent a high-level goal like 'book a flight' and have it break it into steps","I need my agent to understand task dependencies and plan actions in the right order","I want planning that adapts based on previous workflow history and context"],"best_for":["developers building multi-step web automation workflows","teams needing adaptive planning that learns from past executions","applications requiring explainable action sequences for user review"],"limitations":["Planning accuracy depends on VLM's understanding of domain-specific workflows — may fail on novel task types","No built-in constraint satisfaction — generated plans may be inefficient or violate implicit business rules","Context window limits may prevent planning for very long workflows (100+ steps)"],"requires":["Python 3.9+","Vision-Language Model API access (OpenAI, Anthropic, etc.)","Agent factory initialization with model configuration"],"input_types":["natural language goal/instruction (string)","current webpage state (screenshot + accessibility tree)","optional: previous workflow history (for context-aware planning)"],"output_types":["structured action plan (list of action objects)","action descriptions with parameters","reasoning/explanation for plan steps"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_10","uri":"capability://tool.use.integration.vision.language.model.integration.with.multi.provider.support","name":"vision-language model integration with multi-provider support","description":"Integrates multiple Vision-Language Model providers (OpenAI GPT-4V, Anthropic Claude, etc.) through a unified interface, handling model-specific API differences, function-calling schemas, and response formats. The system abstracts away provider-specific details, allowing agents to work with different VLMs without code changes. Configuration specifies the model provider and parameters, enabling easy model switching.","intents":["I want to use different VLM providers without rewriting agent code","I need to switch models based on cost, latency, or capability requirements","I want to compare agent performance across different VLMs"],"best_for":["developers building model-agnostic web agents","teams evaluating different VLM providers","applications requiring model flexibility for cost optimization"],"limitations":["Different VLMs have different capabilities — agents may behave differently across models","API rate limits and costs vary by provider — switching models affects operational costs","Function-calling schema differences may require model-specific prompt adjustments"],"requires":["Python 3.9+","API keys for desired VLM providers","Model-specific SDK or HTTP client"],"input_types":["model configuration (provider, model name, API key)","agent prompts","screenshots and context"],"output_types":["VLM responses (text, function calls)","structured action plans","reasoning traces"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_11","uri":"capability://automation.workflow.browser.automation.with.playwright.selenium.integration","name":"browser automation with playwright/selenium integration","description":"Provides browser automation capabilities through integration with Playwright and Selenium, handling browser lifecycle management, page navigation, element interaction, and screenshot capture. The system abstracts browser-specific details, providing a unified interface for common automation tasks (click, type, scroll, submit). Async support enables non-blocking browser operations for concurrent agent execution.","intents":["I want to automate browser interactions (click, type, navigate) from my agent","I need reliable element interaction with retry logic and error handling","I want to capture page state (screenshots, HTML) for agent analysis"],"best_for":["developers building web automation agents","teams needing reliable browser control with error recovery","applications requiring headless or headed browser execution"],"limitations":["Browser automation is slow — typical interaction latency is 500ms-2s per action","Some websites detect and block automation — may require anti-detection measures","Memory usage grows with number of concurrent browser sessions"],"requires":["Python 3.9+","Playwright or Selenium library","Browser binary (Chrome, Firefox, etc.)","Sufficient system memory for concurrent sessions"],"input_types":["browser configuration","page URL","element selectors or coordinates","interaction parameters (text to type, etc.)"],"output_types":["execution success/failure","page state (screenshot, HTML)","error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_12","uri":"capability://automation.workflow.workflow.execution.tracing.and.state.management","name":"workflow execution tracing and state management","description":"Tracks agent execution state throughout a workflow, capturing action sequences, page states, and outcomes at each step. The system maintains a complete execution trace that can be replayed, analyzed, or used for debugging. State management handles browser session state, agent memory state, and workflow progress, enabling recovery from failures and analysis of execution paths.","intents":["I want to see exactly what actions my agent took and why","I need to debug failed workflows by replaying execution traces","I want to analyze agent behavior patterns across multiple executions"],"best_for":["developers debugging agent failures","teams analyzing agent behavior for optimization","applications requiring execution auditability"],"limitations":["Execution traces consume significant storage — long workflows may require gigabytes of storage","Trace replay may not be deterministic if page state changed or external systems updated","No built-in trace analysis tools — requires custom analysis code"],"requires":["Python 3.9+","Storage for execution traces (file system or database)","Logging and tracing infrastructure"],"input_types":["agent execution events","page states (screenshots, HTML)","action parameters"],"output_types":["execution trace (structured log)","state snapshots","action sequence"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_2","uri":"capability://tool.use.integration.function.based.web.action.execution.with.structured.tool.registry","name":"function-based web action execution with structured tool registry","description":"Executes web interactions through a structured function-calling interface where web actions (click, type, scroll, submit) are registered as callable functions with defined schemas. The FunctionCallingAgent maps VLM-generated function calls to actual browser automation commands, handling parameter validation and execution. This approach decouples action planning from execution, enabling tool reuse across different agent types and VLM providers.","intents":["I want my agent to execute web actions through structured function calls rather than raw browser commands","I need to validate action parameters before execution to prevent invalid interactions","I want to support multiple VLM providers (OpenAI, Anthropic, etc.) without rewriting action logic"],"best_for":["developers integrating web agents with multiple VLM providers","teams needing auditable, structured action logs for compliance","applications requiring action validation and error recovery"],"limitations":["Tool registry must be pre-defined — agents cannot dynamically discover new actions at runtime","Function schema complexity may confuse VLMs, leading to malformed function calls","No built-in retry logic for failed actions — requires external error handling"],"requires":["Python 3.9+","Browser automation library (Playwright or Selenium)","VLM with function-calling support (OpenAI, Anthropic, etc.)"],"input_types":["VLM-generated function call (name + parameters)","current browser state","tool registry (function schemas)"],"output_types":["execution result (success/failure)","action outcome (new page state, error message)","structured execution log"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_3","uri":"capability://memory.knowledge.agent.workflow.memory.system.with.past.execution.integration","name":"agent workflow memory system with past execution integration","description":"Stores and retrieves past web automation workflows to inform future agent decisions through the Agent Workflow Memory (AWM) module. The system captures execution traces (states, actions, outcomes) and enables context-aware agents to retrieve relevant past workflows, learning from successes and failures. This memory integration allows agents to adapt behavior based on historical context without explicit fine-tuning.","intents":["I want my agent to learn from past successful workflows and reuse them","I need to track what actions worked in similar situations and apply that knowledge","I want to reduce redundant exploration by leveraging historical execution data"],"best_for":["teams running repeated web automation tasks with similar patterns","applications requiring continuous improvement through execution history","developers building adaptive agents that improve over time"],"limitations":["Memory retrieval relies on similarity matching — may fail to find relevant past workflows if current context differs significantly","No built-in persistence layer — requires external database or file storage for workflow history","Memory size grows unbounded — requires manual pruning or archival strategies for long-running systems"],"requires":["Python 3.9+","Workflow history storage (file system, database, or vector store)","ContextAwarePlanningAgent or equivalent memory-aware agent type"],"input_types":["current task/goal (string)","current webpage state","workflow history (list of past executions)"],"output_types":["retrieved relevant past workflows","similarity scores for ranking","adapted action plans based on history"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_4","uri":"capability://image.visual.set.of.mark.visual.element.interaction.with.prompt.based.control","name":"set-of-mark visual element interaction with prompt-based control","description":"Implements Set-of-Mark (SoM) technique where interactive elements on a webpage are visually marked with unique identifiers (numbers, labels) in a modified screenshot, and agents interact with elements by referencing these marks in natural language prompts. The PromptAgent uses this visual marking approach to ground agent instructions in specific UI elements without requiring precise coordinate calculations or DOM element selection.","intents":["I want my agent to interact with web elements using visual marks instead of coordinates","I need a more robust element targeting method that works even when page layout changes","I want agents to reason about UI elements in natural language rather than technical selectors"],"best_for":["developers building agents for highly dynamic or frequently-changing websites","teams needing more human-interpretable agent actions for debugging","applications where coordinate-based clicking is unreliable"],"limitations":["Visual marking adds computational overhead — requires screenshot modification and re-analysis per interaction","Mark density on complex pages may create visual clutter, confusing VLM interpretation","Requires VLM with strong visual grounding capabilities — may fail with weaker models"],"requires":["Python 3.9+","Vision-Language Model with image annotation understanding","Browser automation library for screenshot capture and modification"],"input_types":["webpage screenshot","interactive elements list","natural language instruction"],"output_types":["marked screenshot with element identifiers","natural language action referencing marks","execution result"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_5","uri":"capability://automation.workflow.multi.interface.agent.access.via.cli.web.ui.chrome.extension.and.python.api","name":"multi-interface agent access via cli, web ui, chrome extension, and python api","description":"Exposes agent capabilities through multiple user interfaces: command-line interface for scripting, web playground for interactive testing, Chrome extension for in-browser automation, and Python API for programmatic integration. Each interface connects to a shared FastAPI backend that manages agent lifecycle, state, and execution. This multi-interface design allows different user personas (developers, non-technical users, end-users) to interact with the same underlying agent system.","intents":["I want to run web agents from the command line for CI/CD integration","I need a web UI to test and debug agents interactively","I want to automate web tasks directly from my browser using a Chrome extension","I need to integrate web agents into my Python application"],"best_for":["development teams with diverse user personas (developers, QA, non-technical users)","organizations needing multiple deployment options for the same agent logic","applications requiring both interactive testing and programmatic automation"],"limitations":["State synchronization across interfaces may introduce race conditions if multiple interfaces access the same agent simultaneously","Chrome extension requires browser-specific permissions and may have limited access to certain web APIs","Web UI requires separate frontend deployment and maintenance"],"requires":["Python 3.9+","FastAPI server running","Node.js 18+ (for web UI development)","Chrome browser (for Chrome extension)"],"input_types":["CLI arguments","web form inputs","Python function calls","browser extension UI interactions"],"output_types":["CLI output (text, JSON)","web UI results (HTML, JSON)","Python API return values","browser extension notifications"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_6","uri":"capability://automation.workflow.fastapi.based.async.agent.backend.with.concurrent.execution","name":"fastapi-based async agent backend with concurrent execution","description":"Implements a FastAPI server that manages agent lifecycle, handles concurrent requests, and provides async execution of web automation tasks. The backend uses async/await patterns to enable non-blocking agent execution, allowing multiple agents to run concurrently without blocking the server. State management is handled through async API services that coordinate browser sessions, memory access, and result collection.","intents":["I want to run multiple web agents concurrently without blocking","I need a scalable backend that can handle multiple simultaneous automation requests","I want to integrate web agents into a larger async application"],"best_for":["teams building production web automation services","applications requiring high concurrency and low latency","developers integrating agents into async Python frameworks"],"limitations":["Concurrent browser sessions consume significant memory — practical limit is typically 5-20 concurrent agents per machine","Async execution adds complexity to error handling and state management","No built-in load balancing — requires external orchestration for multi-machine deployments"],"requires":["Python 3.9+","FastAPI 0.95+","Uvicorn or equivalent ASGI server","Browser automation library with async support (Playwright)"],"input_types":["HTTP requests (JSON)","agent configuration","task specifications"],"output_types":["HTTP responses (JSON)","execution results","status updates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_7","uri":"capability://tool.use.integration.agent.factory.pattern.with.pluggable.agent.type.selection","name":"agent factory pattern with pluggable agent type selection","description":"Implements a factory pattern (agent_factory.py) that centralizes agent instantiation and allows developers to select from multiple agent types (FunctionCallingAgent, PromptAgent, HighLevelPlanningAgent, ContextAwarePlanningAgent) through a unified interface. The factory handles model configuration, tool registry setup, and memory initialization, abstracting away the complexity of agent construction. This pattern enables easy switching between agent types without changing client code.","intents":["I want to easily switch between different agent types without rewriting code","I need a centralized place to configure all agent parameters","I want to experiment with different agent strategies without code duplication"],"best_for":["developers evaluating different agent architectures","teams needing flexible agent selection based on task type","researchers comparing agent performance across implementations"],"limitations":["Factory abstraction may hide important differences between agent types, leading to incorrect type selection","Adding new agent types requires modifying factory code — not fully extensible without inheritance","Configuration complexity grows with number of agent types and customization options"],"requires":["Python 3.9+","All agent type implementations imported","Model configuration (API keys, model names, etc.)"],"input_types":["agent type name (string)","configuration dictionary","model parameters"],"output_types":["instantiated agent object","configured tool registry","initialized memory system"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_8","uri":"capability://data.processing.analysis.evaluation.framework.with.webarena.and.x.webarena.benchmarking","name":"evaluation framework with webarena and x-webarena benchmarking","description":"Provides an evaluation suite that benchmarks agent performance against WebArena and X-WebArena datasets, which contain realistic web automation tasks with ground-truth solutions. The framework measures success rates, action efficiency, and other metrics to quantify agent performance. This enables systematic comparison of different agent types, models, and strategies on standardized benchmarks.","intents":["I want to measure my agent's performance on standard web automation benchmarks","I need to compare different agent types or models objectively","I want to track performance improvements over time"],"best_for":["researchers publishing web agent papers","teams evaluating agent quality before production deployment","developers optimizing agent performance"],"limitations":["Benchmark tasks may not represent real-world use cases — high benchmark scores don't guarantee production success","Evaluation requires access to WebArena/X-WebArena datasets and infrastructure","Metrics may not capture important aspects like user experience or cost efficiency"],"requires":["Python 3.9+","WebArena or X-WebArena dataset access","Evaluation infrastructure (test environment, metrics collection)"],"input_types":["agent instance","benchmark task specifications","ground-truth solutions"],"output_types":["success rate metrics","action efficiency scores","detailed execution logs","comparative performance reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathonaiorg--litewebagent__cap_9","uri":"capability://data.processing.analysis.interactive.element.extraction.and.coordinate.mapping","name":"interactive element extraction and coordinate mapping","description":"Extracts interactive elements (buttons, links, input fields, etc.) from web pages and maps them to precise coordinates and DOM selectors. The system identifies clickable regions, input targets, and form elements, providing agents with a structured list of available interactions. Coordinate mapping enables accurate element targeting for browser automation, while DOM selectors provide fallback targeting methods.","intents":["I need to identify all clickable elements on a page for my agent","I want to map visual elements to their DOM selectors for reliable targeting","I need to extract form fields and their input requirements"],"best_for":["developers building web agents that need precise element targeting","teams working with complex, dynamic web applications","applications requiring fallback targeting methods"],"limitations":["Element extraction may miss dynamically-created elements or shadow DOM content","Coordinate mapping breaks when page layout changes or elements move","Complex interactive elements (custom dropdowns, sliders) may not be correctly identified"],"requires":["Python 3.9+","Browser automation library (Playwright or Selenium)","Access to live webpage or DOM snapshot"],"input_types":["webpage URL or DOM snapshot","browser session"],"output_types":["structured element list (JSON)","element coordinates (x, y, width, height)","DOM selectors (CSS, XPath)","element types and attributes"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Browser automation library (Playwright or Selenium)","Vision-Language Model with image input support (GPT-4V, Claude 3.5 Vision, etc.)","Vision-Language Model API access (OpenAI, Anthropic, etc.)","Agent factory initialization with model configuration","API keys for desired VLM providers","Model-specific SDK or HTTP client","Playwright or Selenium library","Browser binary (Chrome, Firefox, etc.)","Sufficient system memory for concurrent sessions"],"failure_modes":["Accessibility tree extraction depends on page's ARIA implementation — poorly marked pages may have incomplete element trees","Screenshot-based analysis requires sufficient visual clarity and contrast for VLM interpretation","Real-time DOM changes may require re-extraction, adding latency per state change","Planning accuracy depends on VLM's understanding of domain-specific workflows — may fail on novel task types","No built-in constraint satisfaction — generated plans may be inefficient or violate implicit business rules","Context window limits may prevent planning for very long workflows (100+ steps)","Different VLMs have different capabilities — agents may behave differently across models","API rate limits and costs vary by provider — switching models affects operational costs","Function-calling schema differences may require model-specific prompt adjustments","Browser automation is slow — typical interaction latency is 500ms-2s per action","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.24586760698654891,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:09.058Z","last_commit":"2025-07-11T00:44:45Z"},"community":{"stars":149,"forks":27,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pathonaiorg--litewebagent","compare_url":"https://unfragile.ai/compare?artifact=pathonaiorg--litewebagent"}},"signature":"XMOy788tN1hwktUwn/dT59rrzHbLbJ4q9okZMfpGDi5zalCMfhoAwjbCj4/c2YRdysuZ+x94sFiVQY7Pdl1PDw==","signedAt":"2026-06-19T21:50:42.087Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pathonaiorg--litewebagent","artifact":"https://unfragile.ai/pathonaiorg--litewebagent","verify":"https://unfragile.ai/api/v1/verify?slug=pathonaiorg--litewebagent","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"}}