OpenAgentsControl
AgentFreeAI agent framework for plan-first development workflows with approval-based execution. Multi-language support (TypeScript, Python, Go, Rust) with automatic testing, code review, and validation built for OpenCode
Capabilities13 decomposed
registry-driven agent composition with hierarchical delegation
Medium confidenceDefines 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.
Uses a declarative registry.json as the single source of truth for agent definitions, enabling agents to be discovered and composed dynamically at runtime rather than through hardcoded imports. The hierarchical delegation pattern (primary agents → subagents) is explicitly modeled in the registry with typed component categories (Agents, Subagents, Contexts, Commands), allowing the framework to enforce composition rules and validate agent relationships during installation.
More maintainable than agent frameworks that require code changes to add new agents, and more flexible than monolithic agent designs because agents can be versioned, swapped, and composed independently through registry metadata rather than tight coupling.
plan-first execution with approval gates and human-in-the-loop validation
Medium confidenceImplements 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.
Enforces a mandatory planning phase before execution through the command system architecture, where agents must decompose tasks into discrete, reviewable steps before any code modifications occur. The approval gate is not a post-hoc safety layer but a first-class architectural pattern integrated into the agent execution flow, with explicit support for plan modification and conditional step execution.
Provides stronger safety guarantees than agents that execute immediately with only post-execution rollback, because the plan is visible and modifiable before any changes take effect. More practical than purely autonomous agents because it acknowledges that human judgment is needed for complex decisions while still automating the planning and execution of approved actions.
openrepomanager integration with abilities system for repository-wide operations
Medium confidenceIntegrates 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.
Exposes repository operations as discoverable, callable abilities that agents can invoke dynamically, rather than hardcoding repository access patterns in agent code. The abilities system allows agents to understand what operations are available and invoke them with appropriate parameters, enabling complex repository-wide operations.
More flexible than agents that can only modify individual files because it enables repository-wide operations and cross-file modifications. More discoverable than hardcoded repository operations because abilities are registered and agents can query what's available.
cross-ide compatibility layer with vs code and opencode support
Medium confidenceProvides 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.
Implements a compatibility layer that abstracts IDE-specific details behind a unified interface, allowing agents to invoke IDE operations without knowing which IDE is active. IDE-specific plugins are loaded dynamically based on the detected IDE, enabling IDE-specific features without duplicating agent logic.
More portable than IDE-specific agents because the same agent code works across multiple IDEs. More maintainable than duplicating agent logic for each IDE because the compatibility layer centralizes IDE-specific handling.
installation system with configurable component profiles and selective deployment
Medium confidenceProvides 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.
Uses configurable profiles to allow selective installation of components based on use case, rather than requiring all-or-nothing installation. Profiles are defined in the installer and can be combined with manual component selection, providing flexibility for different deployment scenarios.
More flexible than monolithic installation because users can choose which components to install. More maintainable than manual component installation because dependencies are resolved automatically.
multi-language code generation with language-specific validation and testing
Medium confidenceGenerates 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.
Uses language-specific subagents paired with language-specific prompt variants and context files to generate idiomatic code rather than generic code that happens to be syntactically valid. The evaluation framework automatically generates and executes tests for each language using native testing frameworks, providing real validation that generated code works rather than relying on static analysis.
More sophisticated than generic code generators that produce syntactically correct but non-idiomatic code, because it explicitly models language-specific patterns and validates through actual test execution. Supports multiple languages in a single framework without requiring separate tools for each language.
automated code review with specialized reviewer subagents
Medium confidenceDeploys 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.
Implements code review as a first-class subagent in the agent hierarchy rather than as a post-processing step, allowing review feedback to directly influence code generation through iterative refinement. Review criteria are declaratively defined in context files and can be versioned alongside code, ensuring review standards evolve with the codebase.
More integrated than external code review tools because it's part of the agent workflow and can trigger code regeneration, whereas external tools typically only report issues. More flexible than hardcoded linting rules because review criteria can be customized and updated without code changes.
context-aware code generation with dynamic context loading and mvi pattern
Medium confidenceLoads 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.
Uses the MVI (Model-View-Intent) pattern to structure context as composable, reusable modules that can be selectively loaded based on task requirements, rather than loading all context for every task. Context is declared in the registry with explicit dependencies, allowing the system to automatically resolve which context files are needed for a given task and load them in the correct order.
More maintainable than embedding patterns in prompts because context is versioned separately and can be updated without changing agent code. More efficient than loading all available context because selective loading respects token limits and reduces noise in agent prompts.
evaluation framework with golden test suite and real execution validation
Medium confidenceProvides 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.
Validates agent behavior through actual code execution in isolated environments rather than static analysis or LLM-based evaluation, providing ground truth about whether generated code actually works. The golden test suite pattern establishes reference implementations that serve as the source of truth for expected agent behavior, enabling regression detection and quality tracking over time.
More rigorous than LLM-based evaluation because it uses real execution to validate correctness, catching runtime errors and logic bugs that static analysis would miss. More maintainable than manual testing because tests are automated and can be run continuously in CI/CD pipelines.
version-controlled agent definitions with automated version bumping and changelog generation
Medium confidenceManages 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.
Integrates version management directly into the CI/CD pipeline through GitHub Actions, automatically detecting component changes and bumping versions without manual intervention. Version bumping is tied to component changes and quality gate results, ensuring versions accurately reflect what changed and whether changes meet quality standards.
More reliable than manual version management because it's automated and enforced by CI/CD, reducing human error. More informative than simple version numbers because it maintains a detailed changelog that documents what changed and why.
pr quality gates with registry validation and component standards enforcement
Medium confidenceImplements 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.
Embeds component standards validation directly into the PR workflow through GitHub Actions, making standards enforcement automatic and preventing non-compliant components from being merged. Standards are defined declaratively in component standards documentation and validated programmatically, making them enforceable without manual review.
More effective than manual code review for catching structural problems because it's automated and consistent. More scalable than requiring expert review of every component because standards are enforced automatically.
command system with version control integration and context management
Medium confidenceProvides 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.
Implements commands as first-class registry components that can be discovered, versioned, and composed, rather than hardcoding commands in the CLI. Commands integrate directly with Git operations and context management, allowing agents to perform end-to-end workflows from code generation through commit and context updates.
More flexible than hardcoded CLI commands because new commands can be added through the registry without modifying the CLI code. More integrated than separate tools because commands can compose and trigger other commands as part of their execution.
prompt library with language-specific variants and dynamic prompt composition
Medium confidenceMaintains 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.
Treats prompts as versioned, composable artifacts that are declared in the registry and can be selected and combined dynamically, rather than hardcoding prompts in agent code. Language-specific prompt variants allow the same agent to be optimized for different languages without code duplication.
More maintainable than hardcoded prompts because prompt changes don't require code changes. More flexible than static prompts because variants can be selected and composed dynamically based on task context.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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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
- ✓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
- ✓teams working on large codebases where repository-wide context is important
- ✓organizations that want agents to perform complex refactoring operations
Known 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
- ⚠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
Requirements
Input / Output
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Repository Details
Last commit: Mar 25, 2026
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AI agent framework for plan-first development workflows with approval-based execution. Multi-language support (TypeScript, Python, Go, Rust) with automatic testing, code review, and validation built for OpenCode
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