{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ruvnet--ruflo","slug":"ruvnet--ruflo","name":"ruflo","type":"agent","url":"https://Cognitum.One","page_url":"https://unfragile.ai/ruvnet--ruflo","categories":["ai-agents","rag-knowledge","deployment-infra"],"tags":["agentic-ai","agentic-engineering","agentic-framework","agentic-rag","agentic-workflow","agents","ai-assistant","ai-tools","anthropic-claude","autonomous-agents","claude-code","claude-code-skills","codex","huggingface","mcp-server","model-context-protocol","multi-agent","multi-agent-systems","swarm","swarm-intelligence"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ruvnet--ruflo__cap_0","uri":"capability://planning.reasoning.multi.agent.swarm.orchestration.with.dual.mode.collaboration","name":"multi-agent swarm orchestration with dual-mode collaboration","description":"Coordinates specialized AI agents (architect, coder, reviewer, tester, security-architect) working in parallel or sequential patterns through a centralized orchestration layer. Uses YAML-based agent configuration with role-specific prompts, hook-based routing logic, and a Hive Mind coordination system that manages task distribution, dependency resolution, and inter-agent communication. Agents can operate in autonomous mode (self-directed execution) or collaborative mode (Claude Code integration for human-in-the-loop oversight).","intents":["Deploy multiple specialized agents that each handle different aspects of software engineering (architecture, coding, testing, security review)","Coordinate agents to work on complex multi-stage tasks without manual intervention between stages","Switch between fully autonomous agent execution and human-supervised collaborative workflows","Route tasks to the most appropriate agent based on task type and current system state"],"best_for":["Teams building complex software systems requiring specialized expertise across architecture, development, testing, and security","Organizations migrating from single-agent to multi-agent AI systems","Developers building agentic workflows that need human oversight and control"],"limitations":["Swarm coordination adds latency proportional to agent count and task complexity — no built-in optimization for real-time constraints","Hook-based routing requires manual configuration of agent selection logic; no automatic agent capability discovery","Dual-mode collaboration requires Claude Code integration; standalone autonomous mode has limited observability","Agent state synchronization relies on AgentDB v3 — distributed deployments require external state persistence"],"requires":["Node.js 18+","TypeScript 4.9+","Claude API key (Anthropic)","AgentDB v3 backend for state management","YAML configuration files for agent definitions"],"input_types":["task descriptions (text)","code repositories (file paths)","configuration files (YAML)","user feedback (text)"],"output_types":["code artifacts","architectural decisions","test suites","security reports","structured task results"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_1","uri":"capability://tool.use.integration.mcp.server.with.schema.based.tool.exposure.and.multi.provider.function.calling","name":"mcp server with schema-based tool exposure and multi-provider function calling","description":"Exposes Ruflo's agent orchestration, memory, and task execution capabilities as Model Context Protocol (MCP) tools that Claude and other MCP-compatible clients can invoke. Implements a schema-based function registry (agent-tools, memory-tools, task-tools, hooks-tools, neural-tools, performance-tools, system-tools, terminal-tools, daa-tools, hive-mind-tools) with native bindings for OpenAI and Anthropic function-calling APIs. The MCP server runs as a persistent daemon and handles tool invocation, parameter validation, and result serialization.","intents":["Expose Ruflo agent orchestration capabilities to Claude so it can programmatically spawn, monitor, and coordinate agents","Allow Claude to query and manipulate agent memory, context, and learned patterns through standardized MCP tool calls","Enable Claude to trigger hooks, manage task routing, and inspect system performance metrics","Integrate Ruflo into broader MCP ecosystems where multiple tools/servers coordinate via Claude as the orchestrator"],"best_for":["Teams using Claude as the primary AI interface and wanting to extend it with Ruflo's multi-agent capabilities","Developers building MCP-compatible AI applications that need agent orchestration","Organizations standardizing on MCP for tool integration across multiple AI services"],"limitations":["MCP tool invocation is synchronous — long-running agent tasks block the tool call until completion","Schema validation adds ~50-100ms overhead per tool invocation for parameter checking","Tool result serialization to JSON may truncate large outputs (code artifacts, logs) — requires pagination for large results","No built-in rate limiting or quota management per MCP client — relies on upstream Claude API rate limits"],"requires":["Node.js 18+","MCP client implementation (Claude, or custom MCP-compatible client)","Ruflo daemon running and accessible (local socket or network endpoint)","API credentials for Anthropic or OpenAI (for function-calling schema support)"],"input_types":["tool invocation requests (JSON-RPC)","function parameters (typed, schema-validated)","context from Claude conversation"],"output_types":["tool results (JSON)","agent execution logs","memory query results","performance metrics","structured task outputs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_10","uri":"capability://safety.moderation.guidance.control.plane.with.alignment.and.governance","name":"guidance control plane with alignment and governance","description":"Provides a control plane for managing agent behavior alignment and governance policies. Allows operators to define constraints on agent actions (e.g., 'agents cannot delete production databases', 'code changes require review'), which are enforced at runtime. The guidance system uses a declarative policy language to specify allowed/disallowed actions. Policies can be scoped to specific agents, tasks, or users. Violations are logged and can trigger alerts or block execution. The control plane integrates with the hook system to enforce policies at decision points.","intents":["Define and enforce governance policies on agent behavior","Prevent agents from taking dangerous actions (deleting data, accessing secrets)","Ensure code changes meet organizational standards (require review, follow patterns)","Maintain audit trails of policy violations for compliance"],"best_for":["Organizations with strict governance requirements","Teams wanting to constrain agent autonomy for safety","Developers building compliant AI systems"],"limitations":["Policy language is custom and requires learning — no standard policy format","Policy enforcement is hook-based — policies only apply at defined decision points","No dynamic policy updates — policy changes require daemon restart","Policy conflicts are not automatically resolved — overlapping policies require manual prioritization"],"requires":["Node.js 18+","Policy definitions (custom language)","Hook configuration for policy enforcement points"],"input_types":["policy definitions (text/structured)","agent actions (structured)"],"output_types":["policy enforcement decisions (allow/deny)","audit logs","violation alerts"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_11","uri":"capability://memory.knowledge.infinite.context.management.with.adr.051.architecture","name":"infinite context management with adr-051 architecture","description":"Implements infinite context support through ADR-051 (Architecture Decision Record 051) which uses a hierarchical context compression strategy. Long conversations are automatically summarized and compressed into context summaries that preserve key decisions and information. Summaries are stored in memory and retrieved when relevant, allowing agents to maintain context across arbitrarily long conversations. The system uses semantic similarity to determine which summaries to retrieve, avoiding context window overflow. Compression is configurable and can be tuned for different use cases.","intents":["Enable agents to maintain context across very long conversations without hitting token limits","Preserve important decisions and information through automatic summarization","Retrieve relevant historical context without loading entire conversation history","Support long-running projects that span multiple sessions"],"best_for":["Long-running agent projects spanning weeks or months","Teams with large codebases requiring extensive context","Organizations wanting agents to maintain institutional knowledge"],"limitations":["Context compression is lossy — some information is lost during summarization","Compression adds latency proportional to conversation length — very long conversations may have noticeable delays","Retrieval of compressed context is probabilistic — relevant information may not be retrieved if semantic similarity is low","Compression strategy is fixed — no adaptive compression based on task type"],"requires":["Node.js 18+","Memory backend for storing context summaries","Embeddings service for semantic similarity","Compression configuration"],"input_types":["conversation messages (text)","context summaries (text)"],"output_types":["compressed context (text)","retrieved summaries (text)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_12","uri":"capability://automation.workflow.rvfa.appliance.deployment.with.containerized.environment","name":"rvfa appliance deployment with containerized environment","description":"Provides a containerized deployment appliance (RVFA) that packages Ruflo with all dependencies (Node.js, databases, embeddings service) into a single deployable unit. The appliance includes pre-configured settings, security hardening, and monitoring. Supports deployment to cloud platforms (AWS, GCP, Azure) and on-premises infrastructure. Includes automated scaling based on agent load and health monitoring with automatic recovery.","intents":["Deploy Ruflo to production without manual configuration","Run Ruflo in containerized environments (Docker, Kubernetes)","Scale agent deployments based on workload","Ensure security and monitoring in production"],"best_for":["Organizations deploying Ruflo to production","Teams using containerized infrastructure (Docker, Kubernetes)","Developers wanting turnkey deployment without configuration"],"limitations":["RVFA appliance is opinionated — customization requires rebuilding container","Scaling is horizontal (more instances) — vertical scaling requires manual configuration","Monitoring is basic — integration with external monitoring tools requires additional setup","Database persistence requires external storage — no built-in data durability"],"requires":["Docker or Kubernetes runtime","Cloud account or on-premises infrastructure","External database for persistence (PostgreSQL, etc.)","Network access to Anthropic API"],"input_types":["deployment configuration (YAML)","environment variables"],"output_types":["running Ruflo instance","health status","logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_13","uri":"capability://text.generation.language.ruvocal.chat.ui.with.conversational.agent.interaction","name":"ruvocal chat ui with conversational agent interaction","description":"Provides a web-based chat interface (RuVocal) for interacting with Ruflo agents through natural language. Users can chat with individual agents or the swarm, and the UI displays agent reasoning, decisions, and execution progress. The interface supports file uploads for code/documentation context, displays generated artifacts (code, reports), and provides controls for agent behavior (pause, resume, adjust parameters). Real-time updates show agent activity and task progress.","intents":["Interact with Ruflo agents through a user-friendly chat interface","Monitor agent execution and see reasoning in real-time","Upload context (code, documentation) for agents to work with","Control agent behavior and adjust parameters during execution"],"best_for":["Non-technical users wanting to interact with agents","Teams wanting visibility into agent execution","Developers debugging agent behavior through UI"],"limitations":["Chat UI is web-based and requires browser access — not suitable for headless deployments","Real-time updates rely on WebSocket connections — network latency affects responsiveness","File uploads are limited by browser constraints — large codebases may require chunking","UI is read-only for some operations — complex agent control requires CLI"],"requires":["Web browser (modern, with WebSocket support)","Ruflo instance running with RuVocal service","Network access to Ruflo instance"],"input_types":["chat messages (text)","file uploads (code, documentation)","control commands (pause, resume)"],"output_types":["agent responses (text)","generated artifacts (code, reports)","execution progress (real-time updates)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_2","uri":"capability://memory.knowledge.persistent.distributed.memory.system.with.agentdb.v3.and.context.persistence","name":"persistent distributed memory system with agentdb v3 and context persistence","description":"Maintains agent state, conversation history, learned patterns, and task context across sessions using AgentDB v3 controllers with pluggable backends (SQLite, PostgreSQL, Redis, custom). Implements context persistence through a memory bridge that automatically serializes/deserializes agent state, embeddings, and decision history. RuVector integration enables semantic memory queries (find similar past decisions, retrieve relevant context). SONA pattern learning system identifies recurring decision patterns and optimizes future agent behavior based on historical outcomes.","intents":["Preserve agent state and conversation history across restarts so agents can resume work without losing context","Enable agents to learn from past decisions and outcomes through pattern analysis","Query semantic memory to find similar past tasks or decisions relevant to current work","Support multi-session workflows where agents build on previous work incrementally"],"best_for":["Long-running agent systems that need to maintain state across deployments","Teams building learning agents that improve over time through pattern recognition","Organizations requiring audit trails and decision history for compliance","Distributed agent deployments where state must be shared across multiple instances"],"limitations":["Memory query latency depends on backend choice — SQLite suitable for <100k records, PostgreSQL/Redis for larger datasets","SONA pattern learning requires sufficient historical data (minimum 50-100 similar decisions) to identify meaningful patterns","Embeddings service adds ~200-500ms per memory write for semantic indexing — can be disabled for performance-critical paths","Context persistence serialization overhead increases with agent state size — large codebases may require chunking strategies"],"requires":["Node.js 18+","AgentDB v3 backend (SQLite, PostgreSQL, Redis, or custom controller)","RuVector embeddings service (local or remote)","Sufficient disk/database storage for agent state and history"],"input_types":["agent state objects (JSON)","conversation messages (text)","decision records (structured)","task outcomes (text/structured)"],"output_types":["retrieved context (text/JSON)","learned patterns (structured)","semantic search results","agent state snapshots"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_3","uri":"capability://planning.reasoning.hook.based.intelligent.routing.and.task.distribution","name":"hook-based intelligent routing and task distribution","description":"Routes tasks to appropriate agents using a declarative hook system that evaluates task characteristics against agent capabilities. Hooks are lifecycle events (pre-task, post-task, on-error, on-completion) with conditional logic that determines which agent should handle a task. The routing engine uses task metadata (type, complexity, domain), current agent load, and learned performance history to make routing decisions. Hooks can be chained to create complex workflows (e.g., architect → coder → reviewer → tester).","intents":["Automatically route tasks to the most appropriate agent based on task type and complexity","Create multi-stage workflows where output from one agent feeds into the next","Implement conditional logic that adapts routing based on task outcomes and system state","Optimize agent utilization by distributing load based on agent availability and specialization"],"best_for":["Teams with complex multi-stage workflows that need intelligent task routing","Organizations wanting to optimize agent utilization without manual task assignment","Developers building adaptive workflows that adjust routing based on outcomes"],"limitations":["Hook routing logic is declarative and requires manual configuration — no automatic capability discovery from agent prompts","Routing decisions are synchronous and must complete within hook execution window — complex routing logic may add latency","No built-in conflict resolution when multiple hooks match the same task — requires explicit priority ordering","Hook state is not persisted across daemon restarts — complex routing workflows may lose intermediate state"],"requires":["Node.js 18+","Hook configuration in settings.json or CLAUDE.md","Agent definitions with clear capability descriptions","Task metadata schema that hooks can evaluate"],"input_types":["task descriptions (text)","task metadata (structured)","agent availability status","previous task outcomes"],"output_types":["routing decisions (agent ID)","task assignments (structured)","workflow execution logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_4","uri":"capability://code.generation.editing.claude.code.integration.with.skill.library.and.code.execution","name":"claude code integration with skill library and code execution","description":"Integrates Ruflo agents with Claude Code (Anthropic's code execution environment) to enable agents to write, test, and execute code directly. Provides a skill library of pre-built code patterns and utilities that agents can reference. Agents can use Claude Code to prototype solutions, run tests, and validate architectural decisions before committing to production. The integration includes bidirectional communication: Claude Code can invoke Ruflo agents for specialized tasks, and Ruflo agents can trigger Claude Code execution for code generation/testing.","intents":["Enable agents to write and execute code directly without human intervention","Provide agents with a library of proven code patterns and utilities","Allow agents to validate architectural decisions through code prototyping","Create feedback loops where code execution results inform agent decision-making"],"best_for":["Teams using Claude Code as their primary development environment","Organizations wanting agents to autonomously write and test code","Developers building code-generation workflows with validation"],"limitations":["Claude Code execution is sandboxed and has resource limits — long-running tests or large compilations may timeout","Skill library requires manual curation and updates — no automatic discovery of new patterns","Code execution results must be parsed and interpreted by agents — complex output may require additional processing","Bidirectional communication adds latency — agent → Claude Code → agent roundtrips can exceed 5-10 seconds"],"requires":["Claude Code environment (Anthropic)","API credentials for Claude Code execution","Skill library definitions (code snippets, patterns)","Sandbox configuration for code execution limits"],"input_types":["code generation prompts (text)","test specifications (text/code)","architectural requirements (text)"],"output_types":["generated code (source files)","test results (structured)","execution logs","validation reports"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_5","uri":"capability://memory.knowledge.rag.enabled.context.augmentation.with.semantic.search.and.embeddings","name":"rag-enabled context augmentation with semantic search and embeddings","description":"Augments agent context with relevant information from knowledge bases using semantic search powered by RuVector embeddings. When agents encounter tasks, the RAG system automatically retrieves similar past decisions, relevant documentation, and related code patterns. Embeddings are computed for all stored context (agent memories, code snippets, decision records) and indexed for fast semantic retrieval. The system supports multiple embedding backends and can be configured for different retrieval strategies (top-k, similarity threshold, diversity sampling).","intents":["Automatically provide agents with relevant context from past work without explicit retrieval queries","Enable agents to discover similar past decisions and reuse successful patterns","Augment agent prompts with relevant documentation and code examples","Improve agent decision quality by grounding decisions in historical context"],"best_for":["Organizations with large knowledge bases or extensive project history","Teams wanting agents to learn from past work without explicit training","Developers building context-aware agents that improve with more historical data"],"limitations":["Embedding computation adds ~200-500ms per context write — high-frequency updates may impact performance","Semantic search quality depends on embedding model and data quality — poor embeddings lead to irrelevant retrievals","RAG retrieval is non-deterministic — same query may return different results depending on embedding model updates","Large knowledge bases (>1M embeddings) require external vector database — in-memory storage becomes impractical"],"requires":["RuVector embeddings service (local or remote)","Knowledge base with indexed content","Vector storage backend (in-memory, PostgreSQL pgvector, external vector DB)","Embedding model configuration"],"input_types":["task descriptions (text)","code snippets (text)","decision records (structured)","documentation (text)"],"output_types":["retrieved context (text/code)","similarity scores","ranked results"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_6","uri":"capability://automation.workflow.performance.monitoring.and.metrics.collection.with.statusline.and.background.workers","name":"performance monitoring and metrics collection with statusline and background workers","description":"Continuously monitors agent performance, system health, and task execution metrics through background worker processes. Collects metrics including agent response times, task success rates, memory usage, embedding latency, and hook execution times. Exposes metrics through a statusline interface (shell-based dashboard) and programmatic APIs. Background workers run as daemon processes and aggregate metrics at configurable intervals. Performance data is stored in AgentDB and can be queried for trend analysis and optimization.","intents":["Monitor agent performance and identify bottlenecks in multi-agent workflows","Track task success rates and agent reliability over time","Identify which agents are most effective for different task types","Optimize system configuration based on performance metrics"],"best_for":["Teams running production multi-agent systems that need observability","Organizations wanting to optimize agent performance based on metrics","Developers debugging agent behavior and workflow bottlenecks"],"limitations":["Metrics collection adds ~5-10% overhead to agent execution time","Statusline dashboard is shell-based and requires terminal access — not suitable for remote monitoring","Metrics aggregation is in-process — distributed deployments require external metrics collection","Historical metrics retention depends on storage backend — large-scale deployments may require data pruning"],"requires":["Node.js 18+","Background worker daemon running","AgentDB backend for metrics storage","Terminal access for statusline dashboard (optional)"],"input_types":["agent execution events","task completion records","system resource usage"],"output_types":["performance metrics (JSON)","statusline dashboard (text)","trend analysis (structured)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_7","uri":"capability://automation.workflow.daemon.management.and.background.service.orchestration","name":"daemon management and background service orchestration","description":"Manages long-running background services (worker daemons, MCP server, embeddings service, memory backend) as coordinated processes. Provides lifecycle management (start, stop, restart, status) for all background services through CLI commands and programmatic APIs. Daemon processes are monitored for health and automatically restarted on failure. The system maintains a single source of truth for daemon state and coordinates startup/shutdown sequences to ensure dependencies are satisfied.","intents":["Run Ruflo as a persistent service that agents can connect to","Manage multiple background services (MCP server, embeddings, memory backend) as a coordinated system","Monitor daemon health and automatically recover from failures","Provide CLI interface for operators to manage daemon lifecycle"],"best_for":["Production deployments where Ruflo runs as a persistent service","Teams wanting automated daemon management without manual process supervision","Organizations requiring high availability and automatic failure recovery"],"limitations":["Daemon management is process-based — no container orchestration integration (Kubernetes, Docker Compose)","Health monitoring is basic (process alive check) — no deep health checks for service functionality","Startup/shutdown sequences are hardcoded — no dynamic dependency resolution","Daemon logs are written to files — no centralized logging integration"],"requires":["Node.js 18+","Unix-like OS (Linux, macOS) for process management","File system access for daemon state and logs","Sufficient system resources for background processes"],"input_types":["daemon lifecycle commands (start, stop, restart)","configuration files"],"output_types":["daemon status (running/stopped)","health checks (pass/fail)","logs (text)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_8","uri":"capability://tool.use.integration.plugin.ecosystem.with.extensible.agent.capabilities","name":"plugin ecosystem with extensible agent capabilities","description":"Provides a plugin system that allows developers to extend agent capabilities with custom tools, skills, and integrations. Plugins are TypeScript modules that register new tools with the MCP server, add skills to the skill library, or extend agent behavior through hooks. The plugin system includes a plugin registry, lifecycle hooks (on-load, on-unload), and dependency management. Official plugins include Claude Code integration, HuggingFace model access, and specialized domain tools.","intents":["Extend agent capabilities with custom tools and integrations","Add domain-specific skills to agents without modifying core code","Integrate third-party services (HuggingFace, external APIs) with agents","Share agent extensions across teams through a plugin registry"],"best_for":["Teams wanting to customize agent capabilities for domain-specific tasks","Organizations building proprietary agent extensions","Developers contributing to the Ruflo ecosystem"],"limitations":["Plugin system requires TypeScript knowledge — no low-code plugin authoring","Plugins run in the same process as agents — buggy plugins can crash the entire system","Plugin dependency management is manual — no automatic version resolution","Plugin registry is centralized — no support for private/internal plugin repositories"],"requires":["Node.js 18+","TypeScript 4.9+","Plugin development kit (types, utilities)","Understanding of MCP tool schema"],"input_types":["plugin source code (TypeScript)","plugin configuration (JSON)"],"output_types":["registered tools (MCP schema)","extended agent capabilities"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ruvnet--ruflo__cap_9","uri":"capability://safety.moderation.security.scanning.and.input.validation.with.continuegate","name":"security scanning and input validation with continuegate","description":"Implements security scanning for agent-generated code and user inputs through a configurable validation pipeline. ContinueGate is a safety gate that inspects code before execution, checking for common vulnerabilities (SQL injection, command injection, unsafe file operations). Input validation applies schema-based validation to all MCP tool invocations and agent parameters. Security scanning includes dependency vulnerability checks and code pattern analysis. Results are logged for audit trails and can trigger alerts or block execution based on severity.","intents":["Prevent agents from executing malicious or vulnerable code","Validate user inputs and agent parameters against security schemas","Scan dependencies for known vulnerabilities","Maintain audit trails of security decisions for compliance"],"best_for":["Organizations running agents in production with security requirements","Teams handling sensitive data or code","Developers wanting to prevent common code vulnerabilities"],"limitations":["Security scanning is pattern-based — cannot detect sophisticated attacks or zero-days","ContinueGate adds ~100-200ms latency per code execution — may impact performance-critical workflows","Vulnerability database requires regular updates — outdated patterns miss new vulnerabilities","False positives are common — legitimate code patterns may be flagged as unsafe"],"requires":["Node.js 18+","Security scanning configuration","Vulnerability database (local or remote)","Code pattern definitions"],"input_types":["agent-generated code (source)","user inputs (text/structured)","dependencies (package lists)"],"output_types":["security scan results (structured)","vulnerability reports","audit logs"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","TypeScript 4.9+","Claude API key (Anthropic)","AgentDB v3 backend for state management","YAML configuration files for agent definitions","MCP client implementation (Claude, or custom MCP-compatible client)","Ruflo daemon running and accessible (local socket or network endpoint)","API credentials for Anthropic or OpenAI (for function-calling schema support)","Policy definitions (custom language)","Hook configuration for policy enforcement points"],"failure_modes":["Swarm coordination adds latency proportional to agent count and task complexity — no built-in optimization for real-time constraints","Hook-based routing requires manual configuration of agent selection logic; no automatic agent capability discovery","Dual-mode collaboration requires Claude Code integration; standalone autonomous mode has limited observability","Agent state synchronization relies on AgentDB v3 — distributed deployments require external state persistence","MCP tool invocation is synchronous — long-running agent tasks block the tool call until completion","Schema validation adds ~50-100ms overhead per tool invocation for parameter checking","Tool result serialization to JSON may truncate large outputs (code artifacts, logs) — requires pagination for large results","No built-in rate limiting or quota management per MCP client — relies on upstream Claude API rate limits","Policy language is custom and requires learning — no standard policy format","Policy enforcement is hook-based — policies only apply at defined decision points","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8024379022905771,"quality":0.5,"ecosystem":0.8,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-03T02:29:31Z"},"community":{"stars":37809,"forks":4312,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ruvnet--ruflo","compare_url":"https://unfragile.ai/compare?artifact=ruvnet--ruflo"}},"signature":"5VRzV2JRWQvPsiuIxr1HOH07VrUQdbD60KwjqfdWP5sRBBnDMGooNh9MNmVTU71idmlidDJLT813j7+wJVrKBA==","signedAt":"2026-06-21T02:50:47.426Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ruvnet--ruflo","artifact":"https://unfragile.ai/ruvnet--ruflo","verify":"https://unfragile.ai/api/v1/verify?slug=ruvnet--ruflo","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"}}