{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-simular-ai--agent-s","slug":"simular-ai--agent-s","name":"Agent-S","type":"agent","url":"https://www.simular.ai","page_url":"https://unfragile.ai/simular-ai--agent-s","categories":["ai-agents"],"tags":["agent-computer-interface","ai-agents","computer-automation","computer-use","computer-use-agent","cua","grounding","gui-agents","in-context-reinforcement-learning","memory","mllm","planning","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-simular-ai--agent-s__cap_0","uri":"capability://automation.workflow.multimodal.llm.based.gui.perception.and.action.planning","name":"multimodal llm-based gui perception and action planning","description":"Agent-S uses Large Multimodal Models (LMMs) to observe desktop screenshots, extract visual and textual elements through grounding mechanisms, and generate coordinate-based GUI actions. The system maintains a unified LMM provider abstraction layer supporting OpenAI, Anthropic, and other LMM backends, with message management that preserves visual context across multi-turn interactions. Actions are grounded to screen coordinates via PyAutoGUI execution primitives, enabling pixel-perfect GUI automation.","intents":["Build autonomous agents that can interact with any desktop application without API integration","Enable agents to understand and navigate complex GUIs by visual reasoning rather than DOM parsing","Create cross-platform automation that works on Linux, macOS, and Windows without platform-specific rewrites"],"best_for":["Teams building autonomous desktop automation agents","Researchers evaluating GUI-based task completion benchmarks","Developers needing cross-platform computer-use capabilities without application-specific APIs"],"limitations":["LMM inference latency (typically 2-5 seconds per action) limits real-time responsiveness for fast-paced interactions","Visual grounding accuracy depends on LMM's ability to localize UI elements; fails on novel or obfuscated interfaces","Coordinate-based actions cannot interact with elements outside the visible viewport without explicit scrolling","No native support for accessibility APIs; relies purely on visual perception"],"requires":["Python 3.9+","API credentials for LMM provider (OpenAI, Anthropic, or self-hosted Ollama)","X11/Wayland display server (Linux), Quartz (macOS), or Windows API access","Minimum 4GB RAM for screenshot processing and model inference"],"input_types":["Desktop screenshots (PNG/JPEG)","Natural language task descriptions","Structured action primitives (click, type, scroll)"],"output_types":["Coordinate-based GUI actions","Reasoning traces and planning steps","Execution logs with visual annotations"],"categories":["automation-workflow","image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_1","uri":"capability://planning.reasoning.hierarchical.task.decomposition.with.manager.worker.architecture","name":"hierarchical task decomposition with manager-worker architecture","description":"Agent-S2 implements a two-level planning hierarchy where a Manager agent decomposes high-level tasks into subtasks using DAG-based planning, and Worker agents execute individual subtasks with focused context. The Manager maintains task dependencies and execution order, while Workers operate with reduced context windows, improving efficiency and enabling parallel execution. This architecture is implemented via manager_step() and worker_step() methods with shared knowledge base integration for state synchronization.","intents":["Decompose complex multi-step tasks into manageable subtasks for more reliable execution","Reduce context window pressure by distributing task state across manager and worker agents","Enable parallel execution of independent subtasks to improve overall task completion speed"],"best_for":["Teams tackling complex, multi-stage automation workflows (e.g., data entry across multiple applications)","Scenarios with strict context window constraints requiring task decomposition","Benchmarks evaluating hierarchical planning capabilities"],"limitations":["Manager-worker synchronization adds 200-500ms overhead per task boundary","Incorrect task decomposition by Manager can cascade failures to all dependent Workers","Requires explicit task dependency specification; cannot automatically infer parallelizable subtasks","Knowledge base consistency issues if Workers modify shared state concurrently"],"requires":["Python 3.9+","LMM provider API credentials","External knowledge base (vector store or structured database) for state management","Task definition in structured format (JSON/YAML with dependency graph)"],"input_types":["High-level task descriptions","Task dependency graphs","Shared state/knowledge base"],"output_types":["Subtask execution logs","Dependency resolution order","Aggregated task completion status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_10","uri":"capability://code.generation.editing.local.coding.environment.with.sandboxed.python.execution","name":"local coding environment with sandboxed python execution","description":"Agent-S3 integrates a local coding environment where agents can generate and execute Python code directly for programmatic operations. The CodeAgent component generates Python scripts for tasks like file I/O, data processing, or API calls, executing them in a controlled environment. Execution results are captured and fed back to the agent for further planning. This capability enables agents to choose between GUI automation and direct code execution based on task requirements, improving efficiency for programmatic tasks.","intents":["Enable agents to execute programmatic operations without GUI interactions","Improve efficiency for data processing and file manipulation tasks","Allow agents to combine GUI automation with code execution for complex workflows"],"best_for":["Agents handling mixed automation scenarios (GUI + programmatic operations)","Tasks involving file I/O, data transformation, or API interactions","Systems where code execution is more efficient than GUI automation"],"limitations":["Code execution requires sandboxing for security; no built-in isolation mechanism","Agents may generate unsafe code (file deletion, network access); requires explicit safety constraints","Execution environment must have necessary libraries installed; dependency management is manual","Debugging generated code is difficult; agents may generate syntactically valid but logically incorrect code"],"requires":["Python 3.9+","Local Python environment with write access","Sandboxing mechanism (Docker, subprocess isolation, or similar)","Required Python libraries for agent tasks (requests, pandas, etc.)"],"input_types":["Task description","File system state","Data to process"],"output_types":["Generated Python code","Execution results","File system modifications"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_11","uri":"capability://automation.workflow.cross.platform.gui.automation.with.pyautogui.execution","name":"cross-platform gui automation with pyautogui execution","description":"Agent-S uses PyAutoGUI as the unified execution backend for GUI automation across Linux, macOS, and Windows. The system abstracts platform-specific differences through a coordinate-based action interface, translating high-level action descriptions (click, type, scroll) into PyAutoGUI commands. Platform-specific implementations handle display scaling, coordinate system differences, and OS-specific input methods. This approach enables agents to control any GUI application without platform-specific rewrites.","intents":["Build cross-platform automation agents that work on Linux, macOS, and Windows","Enable agents to interact with any GUI application without application-specific APIs","Provide unified action interface abstracting platform-specific implementation details"],"best_for":["Teams building cross-platform automation solutions","Scenarios requiring interaction with legacy applications or proprietary software","Systems where API-based integration is not available"],"limitations":["PyAutoGUI is slower than native APIs; typical action latency is 100-500ms","Coordinate-based actions are brittle; UI changes or different screen resolutions break automation","No access to application state beyond visual rendering; cannot interact with hidden elements","Platform-specific issues (permission errors, display server problems) require manual debugging"],"requires":["Python 3.9+","PyAutoGUI library","Display server access (X11/Wayland on Linux, Quartz on macOS, Windows API on Windows)","Administrator/root privileges for some operations (keyboard input, mouse control)"],"input_types":["Action primitives (click, type, scroll, drag)","Coordinates (x, y)","Text input"],"output_types":["GUI state changes","Screenshots after actions","Execution status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_12","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.embedding.based.knowledge.retrieval","name":"retrieval-augmented generation with embedding-based knowledge retrieval","description":"Agent-S integrates RAG capabilities through embedding engines that encode task descriptions, procedural memory, and historical execution traces into vector space. The system retrieves relevant examples and procedures based on semantic similarity to the current task, augmenting the agent's context with relevant knowledge. This approach combines procedural memory with dynamic retrieval, enabling agents to leverage task-specific knowledge without explicit prompt engineering.","intents":["Dynamically retrieve relevant procedural memory and examples based on task context","Reduce manual prompt engineering by automatically selecting relevant knowledge","Enable agents to leverage large knowledge bases without context window constraints"],"best_for":["Systems with large procedural memory or knowledge bases","Scenarios requiring dynamic knowledge selection based on task context","Teams building domain-specific agents with diverse task types"],"limitations":["Embedding quality depends on encoder model; poor embeddings lead to irrelevant retrievals","Retrieval latency adds overhead (typically 100-500ms per retrieval)","No mechanism to verify retrieved knowledge is correct or up-to-date","Scaling to very large knowledge bases requires efficient vector search infrastructure"],"requires":["Python 3.9+","Embedding model (sentence-transformers, OpenAI embeddings, etc.)","Vector database or search index (FAISS, Pinecone, Weaviate, etc.)","Knowledge base with encoded embeddings"],"input_types":["Task description","Query for knowledge retrieval","Knowledge base documents"],"output_types":["Retrieved relevant documents","Similarity scores","Augmented context for agent"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_13","uri":"capability://image.visual.ocr.based.ui.element.extraction.and.text.localization","name":"ocr-based ui element extraction and text localization","description":"Agent-S integrates OCR services (Tesseract, EasyOCR, or cloud-based) to extract text from screenshots and localize UI elements. The OCR pipeline identifies text regions, extracts content, and maps text to screen coordinates, enabling agents to ground natural language references to specific UI elements. This capability is essential for text-based grounding when visual features alone are insufficient. OCR results are cached and reused across multiple agent steps to reduce latency.","intents":["Extract text from screenshots for UI element identification and grounding","Localize UI text to enable agents to reference elements by content rather than coordinates","Support agents in understanding application state through text analysis"],"best_for":["Agents interacting with text-heavy applications (forms, documents, terminals)","Scenarios requiring text-based element grounding","Systems where visual features alone are insufficient for element identification"],"limitations":["OCR accuracy varies by font, size, and image quality; typical accuracy is 85-95%","Small text, rotated text, or low-contrast elements are often misrecognized","OCR latency is significant (500ms-2s per screenshot); caching is essential","Localization accuracy depends on OCR bounding box quality; may be off by several pixels"],"requires":["Python 3.9+","OCR engine (Tesseract, EasyOCR, or cloud API)","Screenshot input","Optional: OCR result caching mechanism"],"input_types":["Screenshots (PNG, JPEG)","OCR configuration (language, model)"],"output_types":["Extracted text","Text bounding boxes","Localized UI elements"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_14","uri":"capability://automation.workflow.signal.handling.and.graceful.shutdown.with.state.preservation","name":"signal handling and graceful shutdown with state preservation","description":"Agent-S implements signal handling for graceful shutdown, allowing agents to save execution state, close resources, and terminate cleanly on interrupt signals (SIGINT, SIGTERM). The system preserves execution traces, screenshots, and agent state to enable resumption or post-mortem analysis. This capability is essential for long-running agents where interruption is expected and state recovery is important.","intents":["Enable graceful shutdown of long-running agents with state preservation","Support resumption of interrupted tasks from saved state","Provide execution traces and debugging information for failed tasks"],"best_for":["Long-running automation tasks where interruption is expected","Systems requiring task resumption after failures or interruptions","Debugging scenarios where execution traces are essential"],"limitations":["State preservation adds overhead; large execution traces consume significant disk space","Resumption from saved state requires careful state synchronization; inconsistencies can cause failures","Signal handling complexity increases with multi-threaded or async agent implementations","No built-in mechanism to detect and recover from corrupted state files"],"requires":["Python 3.9+","Signal handling library (signal module)","State serialization mechanism (pickle, JSON, etc.)","Storage for execution traces and state files"],"input_types":["Signal events (SIGINT, SIGTERM)","Agent state"],"output_types":["Saved execution state","Execution traces","Shutdown status"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_2","uri":"capability://automation.workflow.flat.single.agent.architecture.with.integrated.code.execution","name":"flat single-agent architecture with integrated code execution","description":"Agent-S3 simplifies the architecture to a single Worker agent with integrated CodeAgent capability, eliminating manager overhead while maintaining task completion accuracy. The agent can generate and execute Python code directly in a local coding environment for programmatic operations, bypassing GUI interactions when more efficient. This flat design uses a single predict() method with reflection-based error recovery, reducing latency and complexity compared to hierarchical versions.","intents":["Build lightweight autonomous agents without hierarchical planning overhead","Enable agents to choose between GUI automation and direct code execution based on task requirements","Reduce latency for simple tasks by eliminating manager-worker synchronization"],"best_for":["Solo developers building single-purpose automation agents","Scenarios prioritizing latency over complex task decomposition","Tasks mixing GUI automation with programmatic operations (file I/O, data processing)"],"limitations":["Single context window limits ability to handle very complex multi-step workflows","Code execution in local environment requires sandboxing for security; no built-in isolation","Agent must learn to switch between GUI and code modalities; no explicit guidance mechanism","Reflection-based error recovery can enter infinite loops on persistent failures"],"requires":["Python 3.9+","LMM provider API credentials","Local Python environment with write access for code execution","PyAutoGUI for GUI automation"],"input_types":["Natural language task descriptions","Desktop screenshots","File system state"],"output_types":["GUI actions or executed Python code","Task completion status","Execution traces with reasoning"],"categories":["automation-workflow","code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_3","uri":"capability://planning.reasoning.behavior.best.of.n.bbon.sampling.with.rollout.based.refinement","name":"behavior best-of-n (bbon) sampling with rollout-based refinement","description":"Agent-S implements Behavior Best-of-N sampling where the agent generates multiple action trajectories (rollouts) in parallel, evaluates them using a scoring function, and selects the highest-scoring trajectory. This in-context reinforcement learning approach improves accuracy without retraining by leveraging the LMM's ability to reason about action quality. The system supports configurable rollout counts (typically 3-5) and can be combined with reflection mechanisms for iterative refinement.","intents":["Improve task completion accuracy by exploring multiple action sequences and selecting the best","Implement in-context learning without model fine-tuning or external RL training","Evaluate action quality through LMM reasoning rather than external reward models"],"best_for":["Scenarios where accuracy is critical and inference cost is acceptable","Benchmarks evaluating agent reasoning quality (OSWorld, WindowsAgentArena)","Teams without access to model fine-tuning or RL training infrastructure"],"limitations":["Rollout-based sampling increases inference cost linearly with rollout count (3x-5x more LMM calls)","Evaluation function quality directly impacts trajectory selection; poor scoring leads to suboptimal choices","Parallel rollout execution requires managing multiple agent states and screenshot contexts simultaneously","No guarantee that best-of-N selection finds globally optimal trajectory; limited to sampled space"],"requires":["Python 3.9+","LMM provider with sufficient rate limits for parallel inference","Evaluation function definition (scoring logic for trajectory comparison)","Sufficient compute for parallel rollout execution"],"input_types":["Task description","Initial screenshot state","Evaluation criteria"],"output_types":["Selected action trajectory","Rollout comparison scores","Execution trace with reasoning"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_4","uri":"capability://automation.workflow.agent.computer.interface.aci.with.visual.and.text.grounding","name":"agent-computer interface (aci) with visual and text grounding","description":"Agent-S defines a unified Agent-Computer Interface abstraction that standardizes how agents perceive and interact with computers. The ACI layer implements visual grounding (mapping LMM-generated descriptions to screen coordinates) and text grounding (extracting and localizing UI text elements). OSWorldACI is the primary implementation, using OCR services and coordinate systems to translate high-level action descriptions into pixel-precise PyAutoGUI commands. The system supports multiple coordinate systems and platform-specific implementations.","intents":["Standardize agent-computer interaction across different platforms and applications","Enable agents to ground natural language descriptions to precise screen coordinates","Provide abstraction layer for swapping different perception and execution backends"],"best_for":["Framework developers building agent systems requiring cross-platform compatibility","Teams evaluating different grounding strategies (OCR-based vs. DOM-based vs. accessibility APIs)","Researchers implementing platform-specific ACI variants"],"limitations":["OCR-based text grounding fails on small fonts, rotated text, or low-contrast UI elements","Coordinate system transformations add complexity when supporting multiple display scales and resolutions","Visual grounding accuracy depends on LMM's spatial reasoning; struggles with overlapping UI elements","Platform-specific implementations required for Windows, macOS, Linux; no unified API"],"requires":["Python 3.9+","OCR service (Tesseract, EasyOCR, or cloud-based)","PyAutoGUI for action execution","Platform-specific display APIs (X11, Quartz, Windows API)"],"input_types":["Screenshots with UI elements","Natural language action descriptions","Coordinate specifications"],"output_types":["Grounded coordinates for UI elements","Executed GUI actions (click, type, scroll)","Grounding confidence scores"],"categories":["automation-workflow","image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_5","uri":"capability://memory.knowledge.procedural.memory.and.prompt.management.system","name":"procedural memory and prompt management system","description":"Agent-S maintains procedural memory through structured prompt templates and in-context examples that guide agent behavior. The system stores successful action sequences, error recovery patterns, and task-specific procedures as reusable prompts. Memory is managed through a prompt registry that can be dynamically loaded based on task context, enabling agents to leverage past experiences without explicit fine-tuning. This approach combines static procedural knowledge with dynamic context selection.","intents":["Enable agents to learn from past successful interactions without model retraining","Reduce inference cost by providing relevant examples and procedures in-context","Build task-specific agent variants by composing different procedural memory modules"],"best_for":["Teams building domain-specific agents with recurring task patterns","Scenarios where procedural knowledge can be captured as prompt templates","Systems requiring rapid iteration on agent behavior without retraining"],"limitations":["Procedural memory quality depends on manual curation of examples and procedures","Context window constraints limit the amount of procedural memory that can be included per inference","No automatic mechanism to identify when procedural memory is outdated or incorrect","Scaling procedural memory across many tasks requires sophisticated prompt selection logic"],"requires":["Python 3.9+","Structured prompt template format (YAML/JSON)","Mechanism for prompt selection and composition","Storage for procedural memory (file system or database)"],"input_types":["Task descriptions","Historical execution traces","Prompt templates"],"output_types":["Selected procedural memory","Composed prompts with examples","Agent behavior guided by procedures"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_6","uri":"capability://planning.reasoning.graph.search.based.planning.with.hierarchical.exploration","name":"graph search-based planning with hierarchical exploration","description":"Agent-S1 implements GraphSearchAgent using graph-based planning where the agent explores a search tree of possible action sequences, maintaining state nodes and evaluating paths to the goal. The system uses hierarchical exploration with expand() and predict() methods to grow the search tree, pruning low-probability branches. This approach combines classical planning (graph search) with LMM-based heuristics for node evaluation, enabling systematic exploration of action spaces.","intents":["Systematically explore action sequences using graph search rather than greedy single-step planning","Implement backtracking and alternative path exploration when initial actions fail","Leverage LMM heuristics to guide search tree expansion toward promising branches"],"best_for":["Complex tasks requiring exploration of multiple action sequences","Scenarios where backtracking and alternative paths are necessary","Research on planning algorithms combining classical search with neural heuristics"],"limitations":["Graph search tree expansion grows exponentially with action space size; requires aggressive pruning","LMM-based heuristics may be inaccurate, leading to poor branch selection and wasted exploration","Maintaining and traversing search tree adds significant memory overhead compared to flat agents","Search depth is limited by context window and inference budget; cannot explore very deep trees"],"requires":["Python 3.9+","LMM provider API credentials","Graph data structure implementation (tree/DAG)","Heuristic evaluation function for node scoring"],"input_types":["Task description","Initial state (screenshot)","Goal specification"],"output_types":["Search tree with explored nodes","Selected action path to goal","Exploration statistics (nodes expanded, branches pruned)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_7","uri":"capability://tool.use.integration.multi.provider.lmm.abstraction.with.unified.message.management","name":"multi-provider lmm abstraction with unified message management","description":"Agent-S provides a unified LMM provider abstraction layer that normalizes interfaces across OpenAI, Anthropic, and other LMM backends. The system manages message history with vision context preservation, handling image encoding, token counting, and provider-specific API differences transparently. LMMAgent base class implements message management with support for multi-turn conversations, image attachment, and context window optimization. This abstraction enables swapping LMM providers without changing agent logic.","intents":["Build agents that work with multiple LMM providers without provider-specific code","Manage vision context across multi-turn conversations with automatic image encoding","Optimize context window usage through intelligent message truncation and token counting"],"best_for":["Teams evaluating multiple LMM providers for agent applications","Systems requiring provider flexibility for cost optimization or availability","Developers building LMM-agnostic agent frameworks"],"limitations":["Provider API differences (function calling, vision encoding, token limits) require abstraction complexity","Vision context preservation adds overhead; images must be re-encoded for each provider","Token counting is approximate; actual token usage may vary by provider","Some advanced provider features (streaming, tool use) may not be uniformly supported"],"requires":["Python 3.9+","API credentials for at least one LMM provider (OpenAI, Anthropic, etc.)","Provider-specific SDK or HTTP client","Image encoding libraries (PIL, base64)"],"input_types":["Text messages","Images (PNG, JPEG)","Structured function definitions"],"output_types":["Text responses","Function calls","Structured completions"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_8","uri":"capability://automation.workflow.osworld.and.windowsagentarena.benchmark.integration","name":"osworld and windowsagentarena benchmark integration","description":"Agent-S includes native integration with OSWorld and WindowsAgentArena evaluation frameworks, providing standardized task definitions, environment setup, and result evaluation. The system implements evaluation scripts that run agents against benchmark tasks, collect execution traces, and compute accuracy metrics. Integration includes parallel evaluation support for Azure deployment, enabling large-scale benchmark runs. Evaluation results are processed and compared against baseline performance.","intents":["Evaluate agent performance on standardized benchmarks (OSWorld, WindowsAgentArena, AndroidWorld)","Compare agent variants and architectural choices using consistent evaluation methodology","Run large-scale parallel evaluations for statistical significance testing"],"best_for":["Researchers publishing agent performance results on standard benchmarks","Teams comparing multiple agent architectures (S1, S2, S2.5, S3)","Systems requiring reproducible evaluation with standardized task definitions"],"limitations":["Benchmark tasks may not reflect real-world automation scenarios; agents may overfit to benchmark patterns","Parallel evaluation requires significant compute resources (Azure VMs or local cluster)","Evaluation latency is high (hours to days for full benchmark runs) due to LMM inference cost","Results are specific to benchmark environment; generalization to other applications is uncertain"],"requires":["Python 3.9+","OSWorld or WindowsAgentArena environment setup","LMM provider API credentials with sufficient quota","Azure subscription for parallel evaluation (optional but recommended)","Benchmark task definitions and ground truth labels"],"input_types":["Benchmark task specifications","Agent implementation","Environment configuration"],"output_types":["Task completion accuracy","Execution traces with screenshots","Performance metrics and statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-simular-ai--agent-s__cap_9","uri":"capability://planning.reasoning.reflection.based.error.recovery.and.trajectory.refinement","name":"reflection-based error recovery and trajectory refinement","description":"Agent-S implements reflection mechanisms where agents analyze failed actions, identify error causes, and generate corrective actions. The system uses LMM reasoning to understand why an action failed (e.g., 'button not found', 'incorrect input format') and generates alternative approaches. Reflection can be applied iteratively, building a history of failed attempts and lessons learned. This approach enables agents to recover from transient failures and adapt to unexpected UI changes.","intents":["Enable agents to recover from action failures without human intervention","Improve robustness by learning from failed attempts and generating alternatives","Handle unexpected UI states and application behavior through adaptive error recovery"],"best_for":["Long-running automation tasks where failures are expected and recovery is critical","Scenarios with unpredictable UI behavior or application state changes","Systems requiring high reliability without human oversight"],"limitations":["Reflection adds latency (additional LMM inference per failure); can increase task completion time significantly","Agents may enter infinite loops if reflection generates the same failing action repeatedly","Reflection quality depends on agent's ability to diagnose root causes; misdiagnosis leads to ineffective recovery","No built-in mechanism to distinguish transient failures (retry) from permanent failures (give up)"],"requires":["Python 3.9+","LMM provider API credentials","Error detection mechanism (screenshot comparison, action validation)","Failure history tracking"],"input_types":["Failed action description","Error screenshot","Previous attempt history"],"output_types":["Error diagnosis","Alternative action suggestions","Refined action trajectory"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API credentials for LMM provider (OpenAI, Anthropic, or self-hosted Ollama)","X11/Wayland display server (Linux), Quartz (macOS), or Windows API access","Minimum 4GB RAM for screenshot processing and model inference","LMM provider API credentials","External knowledge base (vector store or structured database) for state management","Task definition in structured format (JSON/YAML with dependency graph)","Local Python environment with write access","Sandboxing mechanism (Docker, subprocess isolation, or similar)","Required Python libraries for agent tasks (requests, pandas, etc.)"],"failure_modes":["LMM inference latency (typically 2-5 seconds per action) limits real-time responsiveness for fast-paced interactions","Visual grounding accuracy depends on LMM's ability to localize UI elements; fails on novel or obfuscated interfaces","Coordinate-based actions cannot interact with elements outside the visible viewport without explicit scrolling","No native support for accessibility APIs; relies purely on visual perception","Manager-worker synchronization adds 200-500ms overhead per task boundary","Incorrect task decomposition by Manager can cascade failures to all dependent Workers","Requires explicit task dependency specification; cannot automatically infer parallelizable subtasks","Knowledge base consistency issues if Workers modify shared state concurrently","Code execution requires sandboxing for security; no built-in isolation mechanism","Agents may generate unsafe code (file deletion, network access); requires explicit safety constraints","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.675980327336885,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2026-02-21T06:09:49Z"},"community":{"stars":11034,"forks":1286,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=simular-ai--agent-s","compare_url":"https://unfragile.ai/compare?artifact=simular-ai--agent-s"}},"signature":"abVjqaBJopQ1iU1LNhgU9cYJerr+Y7KRrlXqmjgMxHWpKDwWNf2Oe40Pmx4+QtEfUu2/f6Hxd/RD0ZHPJV9xCQ==","signedAt":"2026-06-22T18:47:01.138Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/simular-ai--agent-s","artifact":"https://unfragile.ai/simular-ai--agent-s","verify":"https://unfragile.ai/api/v1/verify?slug=simular-ai--agent-s","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"}}