{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-lsdefine--genericagent","slug":"lsdefine--genericagent","name":"GenericAgent","type":"agent","url":"https://github.com/lsdefine/GenericAgent","page_url":"https://unfragile.ai/lsdefine--genericagent","categories":["ai-agents"],"tags":["ai-agent","automation","autonomous-agent","browser-automation","claude","computer-control","desktop-automation","gemini","lightweight","llm-agent","memory-system","python","self-evolving","skill-tree","task-automation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-lsdefine--genericagent__cap_0","uri":"capability://planning.reasoning.sense.think.act.agent.loop.with.llm.agnostic.multi.backend.support","name":"sense-think-act agent loop with llm-agnostic multi-backend support","description":"Implements a core agent_runner_loop that orchestrates the sense-think-act cycle by accepting LLM responses, parsing tool calls from multiple backend protocols (OpenAI, Anthropic, Gemini), executing atomic tools, and feeding results back to the LLM in a closed feedback loop. The architecture abstracts backend differences through a unified LLM Communication Layer that normalizes function-calling schemas across providers, enabling seamless switching between Claude, GPT, and Gemini without code changes.","intents":["I want to build an autonomous agent that works with any LLM provider without rewriting tool integration logic","I need to switch between Claude and GPT mid-deployment without breaking my agent's tool-calling pipeline","I want to understand how my agent makes decisions and what tools it calls at each step"],"best_for":["developers building LLM agents who want provider flexibility","teams evaluating multiple LLM backends for cost/performance tradeoffs","researchers studying agent behavior across different model families"],"limitations":["Token overhead from protocol normalization adds ~50-100ms per loop iteration","Error handling and retry logic must be manually configured per provider (no automatic fallback between backends)","Sense-think-act cycle is synchronous — no built-in parallelization of tool execution across multiple branches"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, or Google Gemini)","agent_loop.py and agentmain.py from core codebase"],"input_types":["natural language task descriptions","structured tool call responses from LLM","tool execution results (stdout, file contents, HTML)"],"output_types":["parsed tool calls with arguments","execution results fed back to LLM context","agent decision logs and reasoning traces"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_1","uri":"capability://memory.knowledge.hierarchical.memory.system.with.axiom.based.governance.and.long.term.crystallization","name":"hierarchical memory system with axiom-based governance and long-term crystallization","description":"Implements a multi-layer memory architecture consisting of working memory (update_working_checkpoint), episodic memory (task execution logs), and long-term memory (crystallized procedures and learned SOPs). The system uses Core Axioms as governance rules that define how the agent thinks and operates, and triggers background memory refinement via start_long_term_update which distills repeated task patterns into reusable procedures. Memory operations are synchronized across layers to maintain consistency and prevent conflicting knowledge states.","intents":["I want my agent to learn and remember solutions to recurring problems across multiple task executions","I need the agent to maintain consistent reasoning principles (axioms) that guide all decisions","I want to inspect what the agent has learned and refine its knowledge base manually"],"best_for":["teams running agents on long-running tasks where learning compounds over time","developers who want interpretable agent reasoning grounded in explicit axioms","organizations needing audit trails of what the agent has learned and why"],"limitations":["Memory crystallization is asynchronous and non-deterministic — timing of long-term updates depends on task frequency and system load","No built-in conflict resolution when learned procedures contradict Core Axioms — requires manual intervention","Memory persistence requires external storage (file system or database) — no in-memory-only mode for stateless deployments","Axiom updates are not versioned — changing governance rules retroactively affects interpretation of old memories"],"requires":["Python 3.9+","Persistent file system or database for memory storage","Memory synchronization mechanism (file locks or distributed locks for multi-agent scenarios)"],"input_types":["task execution logs","tool call sequences","user feedback and corrections","axiom definitions (text-based rules)"],"output_types":["working memory checkpoints (JSON/structured format)","learned procedures (SOP documents)","memory crystallization reports","axiom compliance logs"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_10","uri":"capability://safety.moderation.human.in.the.loop.confirmation.with.ask.user.tool.and.interactive.decision.gates","name":"human-in-the-loop confirmation with ask_user tool and interactive decision gates","description":"The ask_user tool enables the agent to request human confirmation before executing irreversible or high-risk actions, implementing interactive decision gates in the agent's workflow. The tool blocks the agent loop until a human responds, allowing humans to inspect the agent's reasoning, provide corrections, or approve/reject proposed actions. This enables safe autonomous operation in domains where human oversight is required.","intents":["I want my agent to ask for approval before deleting files or making system changes","I need to inspect the agent's reasoning and provide corrections mid-task","I want to maintain human control over high-stakes decisions while allowing autonomous operation for routine tasks"],"best_for":["teams deploying agents in regulated industries (finance, healthcare, legal) requiring human oversight","developers building agents for destructive operations (file deletion, system configuration changes)","organizations wanting to gradually increase agent autonomy as trust builds"],"limitations":["ask_user blocks the agent loop — not suitable for high-frequency autonomous operation or time-sensitive tasks","No built-in timeout mechanism — agent may wait indefinitely if human doesn't respond","Human responses are unstructured text — agent must parse and interpret user intent, which may fail","No audit trail of human decisions — cannot easily track why humans approved or rejected specific actions","Requires active human monitoring — not suitable for 24/7 autonomous operation without on-call staff"],"requires":["Python 3.9+","User interaction mechanism (stdin, HTTP endpoint, chat platform, or UI)","Human availability to respond to prompts","Timeout and escalation policies for unresponded prompts"],"input_types":["question or prompt text","optional context (proposed action, reasoning, alternatives)","decision options (yes/no, multiple choice)"],"output_types":["human response (text or structured choice)","response timestamp","human identity (if tracked)","decision rationale (if provided)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_11","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.provider.specific.fallback.strategies","name":"error handling and retry logic with provider-specific fallback strategies","description":"Implements robust error handling and retry logic that gracefully handles LLM API failures, tool execution errors, and network timeouts. The system uses provider-specific fallback strategies (e.g., exponential backoff for rate limits, circuit breakers for service outages) and can automatically retry failed operations with modified parameters. Error states are logged and fed back into the memory system for learning.","intents":["I want my agent to gracefully handle LLM API failures and network timeouts","I need the agent to automatically retry failed operations with intelligent backoff strategies","I want to understand why the agent failed and what recovery strategies it attempted"],"best_for":["teams deploying agents in production environments with unreliable network or API availability","developers building agents that must handle transient failures gracefully","organizations needing detailed error logs and recovery telemetry"],"limitations":["Retry logic is provider-specific — different LLM providers have different rate-limit and error semantics","No built-in circuit breaker for cascading failures — agent may exhaust retries if underlying service is down","Retry parameters (max attempts, backoff strategy) must be manually configured per provider","Error recovery is reactive — agent cannot proactively detect and avoid failure conditions","Retry exhaustion may leave agent in inconsistent state — no automatic rollback or compensation"],"requires":["Python 3.9+","LLM API keys with rate-limit and error handling documentation","Logging infrastructure for error tracking","Monitoring and alerting for retry exhaustion"],"input_types":["LLM API requests","tool execution commands","error responses and exceptions"],"output_types":["retry decisions and backoff delays","error logs with context","recovery status and final outcomes","telemetry for monitoring"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_2","uri":"capability://tool.use.integration.atomic.tool.execution.with.code.runtime.manufacturing.and.os.level.control","name":"atomic tool execution with code runtime manufacturing and os-level control","description":"Provides 9 atomic tools (code_run, file_read, file_write, file_patch, web_scan, web_execute_js, ask_user, update_working_checkpoint, start_long_term_update) that give the LLM direct physical-level control over the computing system. The code_run tool executes Python or PowerShell code in isolated runtime contexts, enabling the agent to manufacture new tools dynamically, interact with the OS, and perform arbitrary computation. Each tool is designed as a minimal, composable primitive that can be combined to achieve complex tasks.","intents":["I want my agent to execute arbitrary code and see the results in real-time","I need the agent to read, write, and surgically patch files on disk","I want the agent to control a web browser and manipulate the DOM via JavaScript injection","I need the agent to ask for human confirmation before taking irreversible actions"],"best_for":["developers building agents for system automation and DevOps tasks","teams needing agents that can dynamically create new tools at runtime","researchers studying how LLMs interact with real computing environments"],"limitations":["code_run executes in the same Python process — no sandboxing or resource limits, poses security risk if agent is compromised","file_patch uses line-based diffing and may fail on complex multi-line edits or non-ASCII files","web_execute_js requires a live browser session (TMWebDriver) — cannot work offline or with static HTML","ask_user blocks the agent loop until human responds — not suitable for high-frequency autonomous operation","No built-in rate limiting or quota management — agent can exhaust system resources (disk, CPU, memory)"],"requires":["Python 3.9+","PowerShell 5.0+ (for Windows code_run)","Selenium WebDriver or equivalent (for web automation tools)","File system write permissions for file operations","User interaction mechanism (stdin, HTTP endpoint, or chat platform) for ask_user"],"input_types":["Python code strings","PowerShell command strings","file paths and content","HTML selectors and JavaScript code","user prompts"],"output_types":["stdout/stderr from code execution","file contents (with pagination support)","file write confirmations","DOM state after JS execution","user responses"],"categories":["tool-use-integration","code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_3","uri":"capability://data.processing.analysis.token.optimized.html.extraction.and.dom.perception.with.pagination","name":"token-optimized html extraction and dom perception with pagination","description":"The web_scan tool extracts and tokenizes HTML content from web pages using intelligent pagination and token budgeting to minimize context window consumption. The system analyzes page structure, identifies relevant content regions, and returns compressed HTML representations that preserve semantic meaning while reducing token count by orders of magnitude. This enables the agent to perceive large web pages without exhausting the LLM's context window.","intents":["I want my agent to browse web pages without consuming the entire context window on HTML markup","I need the agent to extract specific information from large, complex web pages efficiently","I want to understand what the agent actually sees when it looks at a web page"],"best_for":["developers building web automation agents with limited context budgets","teams running agents on long-running web scraping or monitoring tasks","researchers studying how LLMs perceive and reason about web content"],"limitations":["Token optimization is heuristic-based — may miss important content if page structure is non-standard or heavily JavaScript-rendered","Pagination requires multiple tool calls to retrieve full page content — adds latency for large pages","CSS styling and visual layout information is lost in HTML extraction — agent cannot reason about visual positioning or colors","Dynamic content loaded via JavaScript is not captured unless web_execute_js is called first"],"requires":["Python 3.9+","Selenium WebDriver or equivalent for page loading","HTML parser (BeautifulSoup or lxml)","Token counter compatible with target LLM (OpenAI, Anthropic, or Gemini)"],"input_types":["URLs or page handles from active browser session","optional CSS selectors or XPath for content targeting","token budget constraints"],"output_types":["compressed HTML representation","token count estimates","pagination metadata (page number, total pages)","extracted text content"],"categories":["data-processing-analysis","search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_4","uri":"capability://tool.use.integration.browser.dom.manipulation.via.javascript.injection.with.state.synchronization","name":"browser dom manipulation via javascript injection with state synchronization","description":"The web_execute_js tool injects and executes arbitrary JavaScript code in the browser's DOM context, enabling the agent to click elements, fill forms, scroll pages, and manipulate application state. The tool maintains synchronization between the agent's mental model of page state and the actual DOM state, returning execution results and updated page snapshots after each operation. This enables complex multi-step browser automation workflows.","intents":["I want my agent to interact with JavaScript-heavy web applications (SPAs, dashboards)","I need the agent to perform multi-step workflows like login, form filling, and data extraction","I want the agent to handle dynamic page updates and wait for asynchronous operations to complete"],"best_for":["developers automating complex web applications and SPA interactions","teams building agents for e-commerce, banking, or SaaS automation","researchers studying how LLMs reason about and manipulate interactive systems"],"limitations":["JavaScript execution is synchronous — no built-in support for waiting on async operations or network requests","State synchronization relies on manual snapshots — agent may have stale mental model if page updates occur between tool calls","Cross-origin restrictions prevent JavaScript injection on third-party iframes or popup windows","No built-in error recovery for JavaScript errors or DOM exceptions — agent must handle failures explicitly","Requires active browser session (TMWebDriver) — cannot work with headless or API-only services"],"requires":["Python 3.9+","Selenium WebDriver with JavaScript execution support","Active browser session with page loaded","Target page must allow JavaScript execution (no CSP restrictions)"],"input_types":["JavaScript code strings","CSS selectors or XPath for element targeting","optional wait conditions (timeout, element visibility)"],"output_types":["JavaScript execution results (return values)","updated DOM snapshots","execution errors or exceptions","page state after operation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_5","uri":"capability://code.generation.editing.surgical.file.patching.with.line.based.diffing.and.atomic.writes","name":"surgical file patching with line-based diffing and atomic writes","description":"The file_patch tool enables precise, surgical modifications to existing files using line-based diffing. Rather than rewriting entire files, it identifies the exact lines to modify, applies changes atomically, and validates the result. This approach minimizes token consumption (only changed lines are transmitted) and reduces the risk of corrupting files through accidental overwrites. The tool supports multi-line edits and preserves file formatting.","intents":["I want my agent to make precise edits to source code files without rewriting the entire file","I need the agent to modify configuration files while preserving comments and formatting","I want to minimize token consumption when the agent needs to edit large files"],"best_for":["developers building code-editing agents and AI-assisted refactoring tools","teams automating configuration management and infrastructure-as-code updates","researchers studying how LLMs perform surgical code modifications"],"limitations":["Line-based diffing fails on complex multi-line edits where context is ambiguous — may apply patches to wrong locations","No support for binary files or non-UTF8 encodings","Atomic writes assume POSIX-compliant file systems — behavior undefined on Windows with file locks or network shares","No built-in version control integration — agent cannot easily revert failed patches or maintain edit history","Patch validation is syntactic only — cannot detect logical errors or semantic issues introduced by edits"],"requires":["Python 3.9+","File system write permissions","UTF-8 encoded text files","Sufficient disk space for atomic write operations"],"input_types":["file paths","line numbers (start, end)","replacement text","optional context lines for disambiguation"],"output_types":["patch confirmation","updated file content (snippet around changes)","validation results","error messages if patch fails"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_6","uri":"capability://planning.reasoning.autonomous.task.planning.with.multi.mode.execution.task.map.plan.modes","name":"autonomous task planning with multi-mode execution (task, map, plan modes)","description":"Implements an Autonomous Operation Framework that decomposes complex user requests into executable tasks using three execution modes: Task Mode (single sequential task), Map Mode (parallel processing of independent subtasks), and Plan Mode (complex multi-step workflows with dependencies). The system uses a Task Planning System that analyzes user intent, generates task decompositions, and orchestrates execution through subagent instances. Reports and learning loops feed task outcomes back into the memory system for future optimization.","intents":["I want my agent to break down complex requests into manageable subtasks and execute them autonomously","I need the agent to parallelize independent work and coordinate dependent tasks","I want the agent to learn from task execution patterns and improve planning over time"],"best_for":["developers building autonomous workflow systems and task orchestration platforms","teams automating complex business processes with multiple dependent steps","organizations needing agents that can handle ambiguous, open-ended requests"],"limitations":["Task decomposition is LLM-driven and non-deterministic — same request may generate different plans on different runs","Map Mode parallelization assumes task independence — no built-in dependency resolution or task ordering","Plan Mode requires explicit dependency specification — agent cannot automatically infer task ordering from context","No built-in resource allocation or load balancing — parallel tasks may compete for system resources","Learning loop requires manual feedback — agent cannot autonomously detect and correct planning failures"],"requires":["Python 3.9+","LLM API access for task planning","Subagent instances (spawned dynamically or pre-allocated)","Task state persistence (file system or database)","Inter-process communication mechanism (file-based IPC or message queue)"],"input_types":["natural language task descriptions","optional task decomposition hints","execution mode specification (task/map/plan)","resource constraints and priorities"],"output_types":["task decomposition plan","execution status and progress","task results and aggregated outputs","learning reports and optimization suggestions"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_7","uri":"capability://tool.use.integration.multi.ui.integration.with.desktop.cli.chat.platform.and.file.based.modes","name":"multi-ui integration with desktop, cli, chat platform, and file-based modes","description":"Provides multiple user interface layers (Desktop UI via launch.pyw and Streamlit, CLI, Chat Platform Integrations, File-based Modes) that allow users to interact with the agent through their preferred channel. The system abstracts the underlying agent engine from UI concerns, enabling the same agent instance to be accessed via web browser, command line, Slack/WeChat, or file-based IPC. This enables flexible deployment across different organizational contexts.","intents":["I want to interact with my agent through a web browser without learning a new CLI","I need my agent to integrate with existing chat platforms (Slack, WeChat, Feishu)","I want to automate agent invocation through file-based task queues or CI/CD pipelines"],"best_for":["teams deploying agents across multiple user groups with different tool preferences","organizations with existing chat platform infrastructure (Slack, WeChat, Feishu)","developers building agent-as-a-service platforms with multiple access patterns"],"limitations":["UI abstraction adds latency — file-based IPC mode has higher round-trip times than direct API calls","Chat platform integrations require platform-specific authentication and rate-limit handling","Desktop UI (Streamlit) is single-user only — not suitable for multi-user concurrent access","File-based mode has no built-in locking — concurrent writes may corrupt task queues","UI state is not synchronized across channels — user may see inconsistent agent state if accessing from multiple UIs simultaneously"],"requires":["Python 3.9+","Streamlit (for Desktop UI)","Chat platform SDKs (Slack SDK, WeChat SDK, Feishu SDK) for platform integrations","File system access (for file-based mode)","HTTP server (for web UI)"],"input_types":["natural language commands (all UIs)","file paths and task definitions (file-based mode)","chat messages (chat platform mode)","web form submissions (desktop UI)"],"output_types":["agent responses (text, structured data)","execution logs and status updates","file-based task results","chat platform messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_8","uri":"capability://memory.knowledge.self.evolving.skill.tree.with.learned.procedure.crystallization.and.sop.generation","name":"self-evolving skill tree with learned procedure crystallization and sop generation","description":"Implements a self-evolution mechanism where the agent autonomously grows its capability set by recording learned procedures into the memory system as Standard Operating Procedures (SOPs). When the agent solves a novel task, it extracts the solution pattern, crystallizes it into a reusable procedure, and stores it in long-term memory. Future tasks can reference these learned SOPs, reducing token consumption and improving consistency. The skill tree grows organically from the initial 3K-line seed without manual intervention.","intents":["I want my agent to learn from solving tasks and apply those learnings to future similar tasks","I need the agent to reduce token consumption over time as it builds a library of learned procedures","I want to understand what skills the agent has learned and how it applies them"],"best_for":["teams running agents on long-running, repetitive task workloads where learning compounds","organizations wanting agents that improve over time without manual retraining","researchers studying emergent agent capabilities and skill acquisition"],"limitations":["Skill crystallization is heuristic-based — may generalize incorrectly and create buggy SOPs that propagate to future tasks","No built-in mechanism to detect and remove obsolete or contradictory procedures — skill tree can accumulate technical debt","Learning requires sufficient task repetition — rare or one-off tasks may not generate learnable patterns","SOP generation is LLM-driven and non-deterministic — same task may generate different procedures on different runs","No versioning or rollback mechanism — incorrect learned procedures cannot easily be reverted"],"requires":["Python 3.9+","Long-term memory persistence (file system or database)","Task execution logs with sufficient detail for pattern extraction","LLM API access for SOP generation and crystallization"],"input_types":["task execution logs","tool call sequences","task outcomes and success metrics","user feedback on learned procedures"],"output_types":["learned SOPs (text-based procedures)","skill tree snapshots","crystallization reports","procedure application logs"],"categories":["memory-knowledge","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lsdefine--genericagent__cap_9","uri":"capability://memory.knowledge.token.efficient.multi.turn.context.management.with.working.memory.checkpoints","name":"token-efficient multi-turn context management with working memory checkpoints","description":"Implements working memory checkpoints (update_working_checkpoint tool) that compress multi-turn conversation history into concise summaries, enabling the agent to maintain context across long task sequences without exhausting the LLM's context window. The system tracks which information is essential for future reasoning, prunes irrelevant details, and stores compressed state that can be restored in subsequent turns. This achieves the project's claimed 6x token reduction compared to naive context accumulation.","intents":["I want my agent to handle long-running tasks without hitting context window limits","I need the agent to maintain consistent reasoning across many tool calls and LLM turns","I want to understand what information the agent considers essential for future decisions"],"best_for":["developers building agents for long-running autonomous tasks (hours or days)","teams with strict token budgets or expensive LLM APIs","researchers studying how agents compress and manage context over time"],"limitations":["Checkpoint compression is lossy — important details may be discarded if summarization heuristics are too aggressive","Checkpoint restoration requires careful state synchronization — agent may have stale assumptions if checkpoint is outdated","No built-in mechanism to detect when checkpoints are needed — agent must explicitly call update_working_checkpoint","Checkpoint format is LLM-specific — checkpoints created for Claude may not work with GPT or Gemini","No versioning or branching — agent cannot explore multiple hypotheses and maintain separate checkpoints"],"requires":["Python 3.9+","Persistent storage for checkpoint data","Token counter compatible with target LLM","Summarization mechanism (LLM-based or heuristic)"],"input_types":["conversation history (tool calls, results, LLM responses)","optional compression hints (importance weights, retention policies)","checkpoint naming and metadata"],"output_types":["compressed checkpoint (text or structured format)","token count reduction metrics","checkpoint metadata (timestamp, task context)","restoration instructions"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, or Google Gemini)","agent_loop.py and agentmain.py from core codebase","Persistent file system or database for memory storage","Memory synchronization mechanism (file locks or distributed locks for multi-agent scenarios)","User interaction mechanism (stdin, HTTP endpoint, chat platform, or UI)","Human availability to respond to prompts","Timeout and escalation policies for unresponded prompts","LLM API keys with rate-limit and error handling documentation","Logging infrastructure for error tracking"],"failure_modes":["Token overhead from protocol normalization adds ~50-100ms per loop iteration","Error handling and retry logic must be manually configured per provider (no automatic fallback between backends)","Sense-think-act cycle is synchronous — no built-in parallelization of tool execution across multiple branches","Memory crystallization is asynchronous and non-deterministic — timing of long-term updates depends on task frequency and system load","No built-in conflict resolution when learned procedures contradict Core Axioms — requires manual intervention","Memory persistence requires external storage (file system or database) — no in-memory-only mode for stateless deployments","Axiom updates are not versioned — changing governance rules retroactively affects interpretation of old memories","ask_user blocks the agent loop — not suitable for high-frequency autonomous operation or time-sensitive tasks","No built-in timeout mechanism — agent may wait indefinitely if human doesn't respond","Human responses are unstructured text — agent must parse and interpret user intent, which may fail","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6531771596896325,"quality":0.49,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:22.061Z","last_scraped_at":"2026-05-03T13:57:04.027Z","last_commit":"2026-05-03T08:36:58Z"},"community":{"stars":8884,"forks":1022,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lsdefine--genericagent","compare_url":"https://unfragile.ai/compare?artifact=lsdefine--genericagent"}},"signature":"fheFBY7gcnWIXzbg7qWEoX09CYb0R/2zMfTH1QgRgKF5hCrnBIlyMWXowOqonUoGp5GKaGFJGNUrdYfszgUYDw==","signedAt":"2026-06-23T00:52:47.750Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lsdefine--genericagent","artifact":"https://unfragile.ai/lsdefine--genericagent","verify":"https://unfragile.ai/api/v1/verify?slug=lsdefine--genericagent","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"}}