{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-yagents","slug":"yagents","name":"yAgents","type":"agent","url":"https://github.com/yeagerai/yeagerai-agent","page_url":"https://unfragile.ai/yagents","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-yagents__cap_0","uri":"capability://code.generation.editing.agent.driven.code.generation.with.iterative.refinement","name":"agent-driven code generation with iterative refinement","description":"Generates code through an agentic loop that designs, implements, and validates solutions iteratively. The system decomposes user requirements into implementation steps, generates code artifacts, and uses feedback mechanisms to refine outputs across multiple iterations until functional requirements are met. This differs from single-pass code generation by maintaining context across refinement cycles and adapting based on validation results.","intents":["I need to generate a complete tool or service from a natural language specification","I want the system to automatically improve code quality through multiple design-test-refine cycles","I need code generation that understands context and can make architectural decisions autonomously"],"best_for":["teams building autonomous coding agents","developers prototyping tools without manual implementation","organizations automating code scaffolding and boilerplate generation"],"limitations":["Iterative refinement adds latency — multiple agent loops required for complex specifications","Quality depends on clarity of initial requirements — ambiguous specs may require many refinement cycles","No guaranteed convergence — some specifications may not reach functional completion within reasonable iteration budgets","Limited to code domains where validation can be automated (harder for UI/UX-heavy applications)"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible provider)","Agent orchestration framework (likely built-in to yAgents)","Code execution environment for validation"],"input_types":["natural language specifications","code requirements documents","structured task descriptions"],"output_types":["executable code (Python, JavaScript, etc.)","implementation artifacts","refinement logs and iteration history"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_1","uri":"capability://planning.reasoning.autonomous.tool.design.and.architecture.planning","name":"autonomous tool design and architecture planning","description":"Analyzes requirements and generates architectural designs for tools before implementation, decomposing complex specifications into modular components with defined interfaces. The agent reasons about design patterns, dependency structures, and scalability concerns, producing design documents and architecture diagrams that guide subsequent code generation. This planning phase enables better code generation by establishing clear contracts and component boundaries upfront.","intents":["I need the system to design the architecture of a tool before writing code","I want to understand the proposed component structure and interfaces before implementation begins","I need architectural decisions documented so I can review and modify the design"],"best_for":["architects and senior developers reviewing AI-generated designs","teams building complex, multi-component tools","organizations wanting design-first development workflows"],"limitations":["Design quality depends on LLM reasoning capability — may miss edge cases or non-obvious architectural concerns","No formal verification of design correctness — architectural decisions are heuristic-based","Limited to domains where standard design patterns apply","Design documents may not capture all implicit constraints or performance requirements"],"requires":["LLM with strong reasoning capabilities","Agent framework supporting multi-step planning","Access to design pattern knowledge base or training data"],"input_types":["natural language requirements","functional specifications","non-functional requirements (performance, scalability)"],"output_types":["architecture documents","component diagrams","interface specifications","design rationale"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_10","uri":"capability://code.generation.editing.tool.integration.and.api.binding.generation","name":"tool integration and api binding generation","description":"Generates integration code and API bindings to connect created tools with external systems, APIs, and frameworks. The system understands tool interfaces and generates appropriate adapters, middleware, and bindings for popular platforms and frameworks. This enables tools to be easily integrated into larger systems without manual integration work.","intents":["I need to integrate generated tools with external APIs and services","I want automatic generation of API bindings and adapters","I need tools that work seamlessly with my existing infrastructure"],"best_for":["teams building tools that need to integrate with external systems","organizations with complex infrastructure requiring multiple integrations","systems needing to generate integration code as part of tool creation"],"limitations":["Integration code quality depends on API documentation and specifications","May struggle with non-standard or poorly documented APIs","Generated bindings may require customization for specific use cases","Requires knowledge of target platforms and frameworks"],"requires":["API specifications for target systems","Integration templates for supported platforms","Knowledge of target framework conventions"],"input_types":["tool specifications","API documentation","integration requirements"],"output_types":["integration code","API bindings","adapter implementations","configuration files"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_2","uri":"capability://code.generation.editing.multi.turn.debugging.with.root.cause.analysis","name":"multi-turn debugging with root cause analysis","description":"Debugs code through iterative agent loops that identify failures, analyze root causes, and generate fixes. The system executes code, captures error traces and test failures, uses reasoning to determine underlying issues, and generates targeted fixes rather than random modifications. Maintains debugging context across multiple iterations, learning from previous failed attempts to avoid repeating mistakes.","intents":["I need the system to automatically identify and fix bugs in generated code","I want root cause analysis of failures, not just error messages","I need debugging that learns from previous attempts and avoids repeating failed fixes"],"best_for":["autonomous development workflows where human debugging is unavailable","teams validating AI-generated code quality","systems building self-healing code generation pipelines"],"limitations":["Debugging capability limited to errors that produce observable failures (harder for logic bugs without test cases)","May enter infinite loops if root cause is not discoverable through code inspection","Requires comprehensive test coverage to catch subtle bugs","Cannot debug issues requiring domain expertise or external system knowledge"],"requires":["Code execution environment with error capture","Test framework or validation harness","LLM with code reasoning capability","Agent loop with iteration budget"],"input_types":["executable code","error traces and stack traces","test failures","expected vs actual output"],"output_types":["fixed code","root cause analysis","debugging logs","fix rationale"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_3","uri":"capability://code.generation.editing.tool.validation.and.test.generation","name":"tool validation and test generation","description":"Automatically generates test cases and validation harnesses for tools, then executes them to verify correctness. The system reasons about edge cases, boundary conditions, and functional requirements to create comprehensive test suites. Validation results feed back into the code generation loop, enabling the agent to identify and fix failures before returning tools to users.","intents":["I need automated test generation for AI-generated code","I want the system to validate that generated tools meet requirements before delivery","I need comprehensive test coverage without manually writing test cases"],"best_for":["autonomous code generation pipelines requiring quality gates","teams building tools where manual testing is infeasible","systems needing automated validation of generated artifacts"],"limitations":["Test quality depends on LLM's ability to reason about edge cases — may miss subtle failure modes","Cannot generate tests for non-functional requirements (performance, security) without explicit specifications","Test generation may be incomplete for complex domains with implicit requirements","Validation is only as good as the test cases generated"],"requires":["Test framework (pytest, Jest, unittest, etc.)","Code execution environment","LLM with reasoning capability","Functional specification or requirements document"],"input_types":["code to validate","functional specifications","requirements documents"],"output_types":["test cases","validation reports","coverage metrics","failure analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_4","uri":"capability://tool.use.integration.agent.based.tool.composition.and.orchestration","name":"agent-based tool composition and orchestration","description":"Orchestrates multiple agents or tool instances to work together toward complex goals, managing communication, state passing, and coordination between components. The system decomposes complex tasks into sub-tasks assigned to specialized agents, coordinates their execution, and aggregates results. This enables building sophisticated multi-agent systems where individual agents handle specific domains or functions.","intents":["I need to build a multi-agent system where agents collaborate on complex tasks","I want agents to decompose work and delegate to specialized sub-agents","I need coordination and state management across multiple concurrent agents"],"best_for":["teams building complex autonomous systems with multiple specialized agents","organizations needing hierarchical task decomposition","systems requiring coordination between different tool types or domains"],"limitations":["Coordination overhead increases latency — each agent handoff adds round-trip time","State consistency across agents requires careful management — potential for race conditions or inconsistent state","Debugging multi-agent systems is complex — failures may be distributed across agents","Agent communication overhead scales with number of agents"],"requires":["Agent framework supporting multi-agent orchestration","Message passing or state management system","Coordination protocol (likely built into yAgents)","Monitoring and logging for multi-agent debugging"],"input_types":["complex task specifications","agent definitions","coordination rules"],"output_types":["aggregated results","execution logs","coordination traces"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_5","uri":"capability://code.generation.editing.natural.language.to.executable.tool.conversion","name":"natural language to executable tool conversion","description":"Converts natural language specifications directly into executable tools through end-to-end code generation, design, validation, and debugging. The system interprets user intent from natural language, generates appropriate code, validates functionality, and iteratively refines until the tool is production-ready. This is a meta-capability that orchestrates the design, generation, validation, and debugging capabilities into a cohesive workflow.","intents":["I want to describe a tool in natural language and get a working implementation","I need to rapidly prototype tools without writing code manually","I want the system to handle all aspects of tool creation from design to validation"],"best_for":["non-technical users building tools through natural language","rapid prototyping and MVP development","teams wanting to automate tool creation workflows"],"limitations":["Quality depends on clarity and completeness of natural language specification","Complex or ambiguous requirements may require multiple clarification rounds","Generated tools may not match all implicit user expectations","Requires iterative feedback for refinement"],"requires":["LLM with strong instruction-following capability","Complete agent framework (design, generation, validation, debugging)","Code execution environment","Feedback mechanism for iterative refinement"],"input_types":["natural language specifications","example usage patterns","requirements descriptions"],"output_types":["executable code","working tools","validation reports","implementation documentation"],"categories":["code-generation-editing","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_6","uri":"capability://code.generation.editing.context.aware.code.generation.with.codebase.understanding","name":"context-aware code generation with codebase understanding","description":"Generates code with awareness of existing codebase patterns, conventions, and architecture by analyzing the project structure and existing code. The system understands the codebase context, applies consistent patterns, and generates code that integrates seamlessly with existing implementations. This enables generating code that feels native to the project rather than generic or disconnected.","intents":["I need code generation that respects my project's existing patterns and conventions","I want generated code to integrate seamlessly with my codebase","I need the system to understand my project's architecture and generate code accordingly"],"best_for":["teams maintaining large codebases with strong conventions","projects with specific architectural patterns or frameworks","organizations wanting generated code to feel native to their codebase"],"limitations":["Requires access to codebase — may have privacy or security implications","Codebase analysis adds latency to code generation","May struggle with highly unconventional or novel architectural patterns","Context window limitations may prevent analyzing very large codebases"],"requires":["Access to project codebase","Code analysis capability (AST parsing, pattern detection)","LLM with sufficient context window","Codebase indexing or summarization"],"input_types":["code generation requests","codebase files","project structure"],"output_types":["context-aware code","pattern-consistent implementations"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_7","uri":"capability://planning.reasoning.agent.driven.requirement.clarification.and.refinement","name":"agent-driven requirement clarification and refinement","description":"Engages in multi-turn dialogue with users to clarify ambiguous requirements, identify missing specifications, and refine tool designs before implementation. The agent asks targeted questions about edge cases, performance requirements, and integration points, then uses clarified requirements to guide code generation. This reduces iteration cycles by ensuring requirements are complete before coding begins.","intents":["I have a vague idea for a tool and need help clarifying what it should do","I want the system to identify missing requirements before implementation","I need to refine my specification through interactive dialogue"],"best_for":["users with incomplete or ambiguous requirements","rapid prototyping where requirements evolve during development","teams wanting to reduce implementation rework through better upfront clarification"],"limitations":["Dialogue quality depends on LLM's ability to ask relevant questions","May not identify domain-specific requirements without expert knowledge","Clarification dialogue adds time before implementation begins","Users may not know answers to all clarification questions"],"requires":["LLM with strong dialogue and reasoning capability","Agent framework supporting multi-turn interaction","User feedback mechanism"],"input_types":["initial tool descriptions","user responses to clarification questions","example use cases"],"output_types":["clarified requirements","specification documents","edge case identification","implementation guidance"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_8","uri":"capability://code.generation.editing.tool.performance.optimization.and.refactoring","name":"tool performance optimization and refactoring","description":"Analyzes generated code for performance bottlenecks, algorithmic inefficiencies, and code quality issues, then generates optimized versions. The system profiles code execution, identifies slow operations, and refactors for better performance while maintaining correctness. This enables iterative improvement of generated tools beyond initial functionality.","intents":["I need the system to optimize generated code for performance","I want to identify and fix algorithmic inefficiencies automatically","I need refactoring that improves code quality without changing functionality"],"best_for":["performance-critical applications","teams wanting to improve generated code quality iteratively","systems requiring optimization as part of the generation pipeline"],"limitations":["Optimization requires profiling data — may not identify issues without execution","Some optimizations may require domain expertise or external knowledge","Refactoring may introduce subtle bugs if not validated thoroughly","Performance improvements may be marginal for already-efficient code"],"requires":["Code profiling capability","Performance metrics collection","Code analysis for identifying bottlenecks","Refactoring capability with validation"],"input_types":["executable code","performance requirements","profiling data"],"output_types":["optimized code","performance reports","refactoring rationale"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-yagents__cap_9","uri":"capability://text.generation.language.tool.documentation.and.specification.generation","name":"tool documentation and specification generation","description":"Automatically generates comprehensive documentation, API specifications, and usage examples for created tools. The system analyzes code structure and functionality to produce clear documentation, including function signatures, parameter descriptions, return types, and example usage patterns. Documentation is kept in sync with code through regeneration during refinement cycles.","intents":["I need documentation generated automatically for created tools","I want API specifications and usage examples without manual writing","I need documentation that stays synchronized with code changes"],"best_for":["teams building tools that require comprehensive documentation","organizations wanting to reduce documentation maintenance burden","systems needing to generate documentation as part of tool creation"],"limitations":["Documentation quality depends on code clarity and structure","May miss implicit requirements or non-obvious usage patterns","Generated examples may not cover all important use cases","Requires manual review and enhancement for production documentation"],"requires":["Code analysis capability","Documentation generation templates","Example generation capability"],"input_types":["source code","function signatures","type information"],"output_types":["API documentation","usage examples","parameter descriptions","specification documents"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible provider)","Agent orchestration framework (likely built-in to yAgents)","Code execution environment for validation","LLM with strong reasoning capabilities","Agent framework supporting multi-step planning","Access to design pattern knowledge base or training data","API specifications for target systems","Integration templates for supported platforms","Knowledge of target framework conventions"],"failure_modes":["Iterative refinement adds latency — multiple agent loops required for complex specifications","Quality depends on clarity of initial requirements — ambiguous specs may require many refinement cycles","No guaranteed convergence — some specifications may not reach functional completion within reasonable iteration budgets","Limited to code domains where validation can be automated (harder for UI/UX-heavy applications)","Design quality depends on LLM reasoning capability — may miss edge cases or non-obvious architectural concerns","No formal verification of design correctness — architectural decisions are heuristic-based","Limited to domains where standard design patterns apply","Design documents may not capture all implicit constraints or performance requirements","Integration code quality depends on API documentation and specifications","May struggle with non-standard or poorly documented APIs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.690Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=yagents","compare_url":"https://unfragile.ai/compare?artifact=yagents"}},"signature":"Oakxzp4+oOJAC5R0t5krd+ymIGGxvFJU+6WwA2wuzgRe7plRI+Jenw8c5/jBdeMJIiKmIvd2nq2Tap2Zb8apAg==","signedAt":"2026-06-21T14:47:29.081Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yagents","artifact":"https://unfragile.ai/yagents","verify":"https://unfragile.ai/api/v1/verify?slug=yagents","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"}}