{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-darrenhinde--openagentscontrol","slug":"darrenhinde--openagentscontrol","name":"OpenAgentsControl","type":"repo","url":"https://github.com/darrenhinde/OpenAgentsControl","page_url":"https://unfragile.ai/darrenhinde--openagentscontrol","categories":["ai-agents"],"tags":["ai-agents","ai-agents-framework","ai-t","automation","code-generation","code-validation","developer-tools","opencode","prompt-engineering"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-darrenhinde--openagentscontrol__cap_0","uri":"capability://planning.reasoning.registry.driven.agent.composition.with.hierarchical.delegation","name":"registry-driven agent composition with hierarchical delegation","description":"Defines a single-source-of-truth registry.json that declares all agents, subagents, contexts, and commands as composable components with metadata. The system uses a hierarchical agent architecture where primary orchestrators (OpenAgent, OpenCoder) delegate specialized tasks to subagents (TaskManager, CodeReviewer) through a registry lookup mechanism, enabling dynamic agent instantiation and capability routing without hardcoded dependencies.","intents":["I want to define a set of AI agents once and reuse them across multiple workflows without duplicating agent logic","I need to compose complex agent behaviors by combining specialized subagents that can be swapped or versioned independently","I want to maintain a canonical definition of all agent capabilities in my system that stays synchronized with actual implementations"],"best_for":["teams building multi-agent systems with shared component libraries","organizations needing version-controlled agent definitions across CI/CD pipelines","developers creating extensible agent frameworks where new agents can be added without modifying core orchestration logic"],"limitations":["Registry validation happens at install time, not runtime — changes to registry.json require reinstallation to take effect","No built-in conflict resolution for agents with overlapping capabilities — requires explicit priority ordering in registry","Circular dependencies between agents are not automatically detected; requires manual validation during component creation"],"requires":["registry.json file in repository root with valid JSON schema","Component metadata including name, type, version, and dependencies","Installation system (install.sh) to parse and deploy registry components"],"input_types":["JSON registry definition","component metadata objects","dependency declarations"],"output_types":["instantiated agent objects","delegated task routing","component dependency graph"],"categories":["planning-reasoning","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_1","uri":"capability://planning.reasoning.plan.first.execution.with.approval.gates.and.human.in.the.loop.validation","name":"plan-first execution with approval gates and human-in-the-loop validation","description":"Implements a workflow where agents first generate a detailed plan (broken down into discrete steps) before executing any code changes. The plan is presented to users for review and approval before execution proceeds, with built-in checkpoints that allow rejection, modification, or conditional execution of specific plan steps. This pattern is enforced through the command system and evaluation framework, which validates plan quality before allowing agent actions.","intents":["I want AI agents to show me their intended actions before they modify my codebase, so I can catch mistakes or unintended side effects","I need to enforce a review-before-execute workflow where complex operations require explicit human approval","I want to audit and log all agent decisions and the reasoning behind them before they take effect"],"best_for":["teams working on production codebases where unreviewed changes are risky","organizations with compliance requirements that mandate human oversight of automated changes","developers building safety-critical agent workflows where transparency is essential"],"limitations":["Plan generation adds latency before execution can begin — complex plans may take 10-30 seconds to generate","No automatic plan optimization; agents generate plans sequentially without considering resource constraints or execution order improvements","Approval gates require synchronous human interaction; asynchronous approval workflows are not natively supported"],"requires":["Agent configured with plan-generation capability","User interface or CLI that can display and collect approval decisions","Execution engine that respects approval gates and can halt execution on rejection"],"input_types":["user request or task description","codebase context","approval decision (approve/reject/modify)"],"output_types":["structured plan with discrete steps","execution results with audit trail","approval decision log"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_10","uri":"capability://tool.use.integration.openrepomanager.integration.with.abilities.system.for.repository.wide.operations","name":"openrepomanager integration with abilities system for repository-wide operations","description":"Integrates with OpenRepoManager to provide agents with repository-wide capabilities including file operations, code search, and dependency analysis. The abilities system exposes these capabilities as callable functions that agents can invoke to interact with the repository. Abilities are registered and discoverable, allowing agents to understand what operations are available without hardcoding them. The integration enables agents to perform complex repository operations like refactoring, dependency updates, and cross-file modifications.","intents":["I want agents to be able to understand and modify my entire codebase, not just individual files","I need agents to search for code patterns and dependencies across the repository","I want agents to perform repository-wide operations like refactoring or dependency updates"],"best_for":["teams working on large codebases where repository-wide context is important","organizations that want agents to perform complex refactoring operations","projects where agents need to understand dependencies and code relationships"],"limitations":["Repository-wide operations can be slow for large codebases; searching or analyzing large repositories may take significant time","Abilities are limited to what OpenRepoManager provides; custom repository operations require extending OpenRepoManager","Repository context can exceed token limits for very large codebases; requires selective context loading"],"requires":["OpenRepoManager installed and configured","Abilities system integrated into agent initialization","Repository indexed by OpenRepoManager","Agents configured to use abilities"],"input_types":["repository path","search queries","file paths","code patterns"],"output_types":["search results","file contents","dependency graphs","code modifications"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_11","uri":"capability://tool.use.integration.cross.ide.compatibility.layer.with.vs.code.and.opencode.support","name":"cross-ide compatibility layer with vs code and opencode support","description":"Provides a compatibility layer that allows agents to work with multiple IDEs including VS Code and OpenCode, abstracting away IDE-specific implementation details. The system detects the active IDE and loads appropriate IDE-specific plugins and configurations. Agents can invoke IDE operations (file operations, editor commands, terminal execution) through a unified interface that works across IDEs. IDE-specific context and capabilities are loaded dynamically based on the detected IDE.","intents":["I want agents to work seamlessly whether I'm using VS Code, OpenCode, or another IDE","I need agents to invoke IDE-specific operations without knowing which IDE is active","I want to extend agent capabilities with IDE-specific features without duplicating logic"],"best_for":["teams using multiple IDEs and wanting a unified agent experience","organizations building IDE-agnostic agent frameworks","developers who want to use agents across different development environments"],"limitations":["IDE support is limited to VS Code and OpenCode; other IDEs require custom plugin implementation","IDE-specific features may not be available across all IDEs; agents must gracefully degrade when features are unavailable","IDE detection is automatic but may fail in edge cases; requires manual configuration for non-standard setups"],"requires":["IDE detection logic","IDE-specific plugin implementations","Unified interface for IDE operations","IDE context loading system"],"input_types":["IDE identifier","IDE-specific configuration","operation requests"],"output_types":["IDE operation results","IDE-specific responses","unified operation results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_12","uri":"capability://automation.workflow.installation.system.with.configurable.component.profiles.and.selective.deployment","name":"installation system with configurable component profiles and selective deployment","description":"Provides an installation mechanism (install.sh) that allows users to select which components to install through configurable profiles (essential, standard, meta). The installer parses registry.json, resolves component dependencies, and deploys only the selected components. Different profiles can be used for different use cases (e.g., minimal installation for CI/CD, full installation for local development). Installation is idempotent and can be re-run to update components.","intents":["I want to install only the agents and components I need, not the entire framework","I need different component sets for different environments (CI/CD vs local development)","I want installation to automatically resolve and install dependencies"],"best_for":["teams with diverse use cases that need different component sets","organizations with resource constraints that want minimal installations","projects where different environments need different agent capabilities"],"limitations":["Installation profiles are predefined in the installer; custom profiles require modifying install.sh","Dependency resolution is static and happens at install time; runtime dependency changes are not supported","Installation requires shell access and may not work on all platforms (Windows requires WSL or Git Bash)"],"requires":["install.sh script in repository root","registry.json with component metadata","Shell environment (bash)","Git installed for cloning dependencies"],"input_types":["installation profile (essential/standard/meta)","component selection","installation path"],"output_types":["installed components","dependency resolution results","installation log"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_2","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.specific.validation.and.testing","name":"multi-language code generation with language-specific validation and testing","description":"Generates and validates code across TypeScript, Python, Go, and Rust through language-specific subagents that understand each language's syntax, idioms, and testing frameworks. Each language has dedicated validation logic that checks generated code for correctness before execution, with automatic test generation and execution through the evaluation framework. The system uses language-specific context files and prompt variants to guide code generation toward idiomatic patterns.","intents":["I want to generate code in multiple languages from a single high-level specification without manually translating between languages","I need generated code to follow language-specific best practices and idioms, not just be syntactically correct","I want automatic validation that generated code actually works before it's committed to my repository"],"best_for":["polyglot teams maintaining codebases in multiple languages","organizations building SDKs or libraries that need to support multiple language targets","developers who want language-specific code review and validation without manual inspection"],"limitations":["Language support is limited to TypeScript, Python, Go, and Rust — other languages require custom subagent implementation","Code generation quality varies by language; some languages (TypeScript) have more mature patterns in the prompt library than others","Testing assumes standard testing frameworks (Jest, pytest, Go testing, Rust cargo test) — custom test runners require additional configuration"],"requires":["Language-specific subagent implementation (e.g., TypeScriptCoder, PythonCoder)","Language runtime installed locally (Node.js for TypeScript, Python 3.9+, Go 1.18+, Rust toolchain)","Test framework configured for each language","Language-specific context files with idioms and patterns"],"input_types":["high-level code specification","target language identifier","existing codebase context","test requirements"],"output_types":["generated source code","generated test code","test execution results","validation report"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_3","uri":"capability://code.generation.editing.automated.code.review.with.specialized.reviewer.subagents","name":"automated code review with specialized reviewer subagents","description":"Deploys specialized CodeReviewer subagents that analyze generated code against configurable review criteria including style, performance, security, and architectural patterns. The review process is integrated into the evaluation framework and runs automatically after code generation, producing structured feedback that can block or request modifications to generated code. Review criteria are defined in context files and can be customized per project.","intents":["I want generated code to be automatically reviewed against my team's standards before it's committed","I need to enforce security and performance best practices in AI-generated code without manual review","I want consistent code review feedback that applies the same standards across all generated code"],"best_for":["teams with strict code quality standards who want to automate enforcement","security-conscious organizations that need to validate generated code for vulnerabilities","large teams where manual code review is a bottleneck"],"limitations":["Review quality depends on the quality of review criteria defined in context files — vague criteria produce vague feedback","Cannot detect all security vulnerabilities; specialized security scanning tools (SAST) should be used alongside this capability","Review feedback is generated by LLMs and may miss edge cases or context-specific issues that human reviewers would catch"],"requires":["CodeReviewer subagent configured and registered in registry.json","Review criteria defined in context files (style guide, security checklist, performance guidelines)","Integration with evaluation framework to run reviews automatically","Feedback mechanism to communicate review results back to code generation agent"],"input_types":["generated source code","review criteria","codebase context","language identifier"],"output_types":["structured review feedback","list of issues found","approval/rejection decision","suggested modifications"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_4","uri":"capability://memory.knowledge.context.aware.code.generation.with.dynamic.context.loading.and.mvi.pattern","name":"context-aware code generation with dynamic context loading and mvi pattern","description":"Loads and manages context files that contain codebase patterns, architectural standards, and domain-specific knowledge, then injects this context into agent prompts to guide code generation toward consistency with existing code. The system uses a Model-View-Intent (MVI) pattern for context organization where context is structured as reusable, composable modules that can be selectively loaded based on the task at hand. Context loading is dynamic and respects component dependencies defined in the registry.","intents":["I want generated code to follow the same patterns and conventions as my existing codebase without manually specifying them each time","I need to teach AI agents about my project's architecture and design decisions so they generate code that fits naturally","I want to version and evolve my coding standards alongside my codebase, with agents automatically using the latest standards"],"best_for":["teams with established architectural patterns who want to enforce consistency in AI-generated code","organizations with domain-specific coding conventions that need to be communicated to agents","projects where codebase context is large and complex, requiring selective context loading to stay within token limits"],"limitations":["Context files must be manually created and maintained — no automatic extraction of patterns from existing code","Large context files can exceed LLM token limits; requires careful curation of what context is loaded for each task","Context loading is static per task — agents cannot dynamically request additional context during generation if they discover they need it"],"requires":["Context files created and stored in the repository (typically in contexts/ directory)","Context metadata defined in registry.json with dependencies and categories","Context loading system integrated into agent initialization","Prompt templates that accept and incorporate context"],"input_types":["context file paths","task description","codebase patterns","architectural standards"],"output_types":["augmented prompts with injected context","context-aware code generation","consistency validation"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_5","uri":"capability://automation.workflow.evaluation.framework.with.golden.test.suite.and.real.execution.validation","name":"evaluation framework with golden test suite and real execution validation","description":"Provides an automated testing infrastructure that validates agent behavior through real code execution rather than static analysis. The framework includes a golden test suite (reference implementations of expected agent outputs), test runners that execute generated code in isolated environments, and evaluators that compare actual outputs against golden standards. Tests are executed using language-specific test frameworks and can validate both correctness and performance characteristics.","intents":["I want to validate that my agents generate correct, working code by actually running it, not just checking syntax","I need to establish baseline expectations for agent behavior (golden tests) and detect regressions when agent behavior changes","I want to measure agent performance and quality metrics across different configurations and prompt variants"],"best_for":["teams building production agent systems where correctness is critical","organizations that want to measure and track agent quality over time","developers creating new agent components who need to validate behavior before merging"],"limitations":["Golden test suite must be manually created and maintained — requires domain expertise to define expected outputs","Test execution is slow for large test suites; running all tests can take minutes to hours depending on complexity","Tests are limited to behaviors that can be validated through code execution; some agent behaviors (e.g., user experience) cannot be tested this way"],"requires":["Golden test suite defined with expected inputs and outputs","Test runner implementation for each supported language","Evaluators that can compare actual vs expected outputs","Isolated execution environment (containers or VMs) for safe test execution","Language runtimes installed (Node.js, Python, Go, Rust)"],"input_types":["test case definitions","agent configurations","code to be tested","expected outputs"],"output_types":["test execution results","pass/fail status","performance metrics","comparison against golden standards"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_6","uri":"capability://automation.workflow.version.controlled.agent.definitions.with.automated.version.bumping.and.changelog.generation","name":"version-controlled agent definitions with automated version bumping and changelog generation","description":"Manages agent versions through a VERSION file and CHANGELOG.md, with automated version bumping triggered by PR quality gates and component changes. The system detects which components have changed in a PR, determines the appropriate version bump (major/minor/patch), and automatically updates version metadata. Version history is maintained in CHANGELOG.md with structured entries describing what changed and why.","intents":["I want to track changes to agent definitions and know exactly what changed between versions","I need to automatically bump versions when components change, without manual version management","I want to maintain a clear changelog that documents agent evolution and breaking changes"],"best_for":["teams publishing agent frameworks or libraries that need semantic versioning","organizations with strict change management requirements","projects where agent versions need to be reproducible and auditable"],"limitations":["Automated version bumping relies on detecting component changes in diffs — requires consistent file structure and naming conventions","Changelog generation is automated but may require manual editing for clarity or additional context","Version bumping is tied to PR quality gates; if quality gates are not configured, version bumping may not trigger"],"requires":["VERSION file in repository root","CHANGELOG.md file with structured format","GitHub Actions workflow configured for version bumping","PR quality gates that trigger version bump detection","Component change detection logic"],"input_types":["PR diff","component metadata","quality gate results"],"output_types":["updated VERSION file","updated CHANGELOG.md","version bump commit"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_7","uri":"capability://automation.workflow.pr.quality.gates.with.registry.validation.and.component.standards.enforcement","name":"pr quality gates with registry validation and component standards enforcement","description":"Implements automated quality checks that run on every PR, validating that new or modified components meet standards defined in the component standards documentation. Checks include registry schema validation (ensuring registry.json is valid), component structure validation (verifying required files and metadata), and dependency validation (detecting circular dependencies or missing dependencies). Quality gates must pass before PRs can be merged, ensuring only compliant components are added to the system.","intents":["I want to ensure all new agents and components meet my team's standards before they're merged","I need to catch configuration errors and structural problems early, before they cause runtime failures","I want to enforce consistent component structure across the entire registry"],"best_for":["teams maintaining shared component registries with multiple contributors","organizations with strict architectural standards that need enforcement","projects where component quality directly impacts system reliability"],"limitations":["Quality gates can only validate structural and schema compliance; they cannot validate the quality of agent behavior itself","False positives are possible if standards are too strict or not well-documented","Quality gate failures block PRs, which can slow down development if gates are misconfigured"],"requires":["Quality gate workflow defined in GitHub Actions","Component standards documentation","Registry schema definition","Validation scripts that check components against standards"],"input_types":["PR diff","modified registry.json","new component files","component metadata"],"output_types":["validation results","pass/fail status","detailed error messages","remediation suggestions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_8","uri":"capability://tool.use.integration.command.system.with.version.control.integration.and.context.management","name":"command system with version control integration and context management","description":"Provides a command-based interface for agent operations including code generation, commits, context management, and code quality checks. Commands are registered in the registry and can be invoked through the CLI (oac) or programmatically. The commit command integrates with Git to stage, commit, and push changes with AI-generated commit messages. The context management command allows dynamic loading and unloading of context files. Commands are composable and can trigger other commands as part of their execution.","intents":["I want a unified CLI interface to invoke agent operations without writing custom scripts","I need agents to integrate with my Git workflow, automatically committing changes with meaningful messages","I want to dynamically manage which context files are loaded for different tasks"],"best_for":["developers who want to interact with agents through a CLI rather than a UI","teams integrating agents into existing Git-based workflows","organizations that want to automate commit messages and version control operations"],"limitations":["Command system is TypeScript-based; commands for other languages require custom implementation","Commit messages are AI-generated and may not always be accurate or follow team conventions","Context management commands are synchronous; loading large context files can block other operations"],"requires":["TypeScript CLI (oac) installed","Commands registered in registry.json","Git repository initialized","Node.js 18+ runtime"],"input_types":["command name","command arguments","codebase context","Git state"],"output_types":["command execution results","Git commits","context loading results","CLI output"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-darrenhinde--openagentscontrol__cap_9","uri":"capability://text.generation.language.prompt.library.with.language.specific.variants.and.dynamic.prompt.composition","name":"prompt library with language-specific variants and dynamic prompt composition","description":"Maintains a library of prompt templates that are specialized for different languages, agent types, and task contexts. Prompts are stored as variants that can be selected based on the target language or agent configuration. The system dynamically composes prompts by combining base templates with context files, task descriptions, and language-specific instructions. Prompt variants are versioned and can be updated independently, allowing prompt engineering improvements to be deployed without changing agent code.","intents":["I want to optimize prompts for different languages without maintaining separate agents for each language","I need to experiment with different prompt strategies and measure their impact on agent quality","I want to version and track prompt changes separately from agent code changes"],"best_for":["teams doing active prompt engineering and experimentation","organizations that want to measure the impact of prompt changes on agent quality","projects supporting multiple languages where language-specific prompting is important"],"limitations":["Prompt variants must be manually created and tested; no automatic prompt optimization","Prompt composition adds complexity to agent initialization; debugging prompt issues requires understanding the composition logic","Prompt quality is subjective and hard to measure; improvements require manual evaluation or A/B testing"],"requires":["Prompt templates stored in prompts/ directory","Prompt metadata defined in registry.json","Prompt composition logic in agent initialization","Language-specific prompt variants for each supported language"],"input_types":["base prompt template","language identifier","context files","task description"],"output_types":["composed prompt","language-specific prompt variant","prompt with injected context"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["registry.json file in repository root with valid JSON schema","Component metadata including name, type, version, and dependencies","Installation system (install.sh) to parse and deploy registry components","Agent configured with plan-generation capability","User interface or CLI that can display and collect approval decisions","Execution engine that respects approval gates and can halt execution on rejection","OpenRepoManager installed and configured","Abilities system integrated into agent initialization","Repository indexed by OpenRepoManager","Agents configured to use abilities"],"failure_modes":["Registry validation happens at install time, not runtime — changes to registry.json require reinstallation to take effect","No built-in conflict resolution for agents with overlapping capabilities — requires explicit priority ordering in registry","Circular dependencies between agents are not automatically detected; requires manual validation during component creation","Plan generation adds latency before execution can begin — complex plans may take 10-30 seconds to generate","No automatic plan optimization; agents generate plans sequentially without considering resource constraints or execution order improvements","Approval gates require synchronous human interaction; asynchronous approval workflows are not natively supported","Repository-wide operations can be slow for large codebases; searching or analyzing large repositories may take significant time","Abilities are limited to what OpenRepoManager provides; custom repository operations require extending OpenRepoManager","Repository context can exceed token limits for very large codebases; requires selective context loading","IDE support is limited to VS Code and OpenCode; other IDEs require custom plugin implementation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5551815532648663,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:37.060Z","last_commit":"2026-03-25T00:27:12Z"},"community":{"stars":3831,"forks":313,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=darrenhinde--openagentscontrol","compare_url":"https://unfragile.ai/compare?artifact=darrenhinde--openagentscontrol"}},"signature":"JTa0JpsX9fOLGqoacNsssbmq0XZMXNTNIeOHGzummmKcKawYO/I1YxCDG9TqF4GUe7qKclcRpxCYiZ0yzdSUAQ==","signedAt":"2026-06-19T18:10:35.808Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/darrenhinde--openagentscontrol","artifact":"https://unfragile.ai/darrenhinde--openagentscontrol","verify":"https://unfragile.ai/api/v1/verify?slug=darrenhinde--openagentscontrol","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"}}