{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-ruvnet-ruflo","slug":"mcp-ruvnet-ruflo","name":"ruflo","type":"agent","url":"https://github.com/ruvnet/ruflo","page_url":"https://unfragile.ai/mcp-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_mcp-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 multiple specialized Claude agents (architect, coder, reviewer, tester, security-architect) working in parallel or sequentially through a centralized orchestration layer. Uses YAML-based agent configuration with role-specific prompts and capabilities, routing tasks via hooks system and hive-mind coordination. Agents share context through AgentDB v3 memory controllers, enabling distributed decision-making with unified state management across the swarm.","intents":["Deploy a team of AI agents that can work together on complex software engineering tasks without manual coordination","Route different types of work (code generation, testing, security review) to specialized agents based on task type","Enable agents to share context and build on each other's work within a single orchestrated workflow","Monitor and control agent behavior through centralized hooks and intelligence routing"],"best_for":["teams building multi-stage CI/CD pipelines with AI agents","enterprises needing specialized agent roles (architect, coder, reviewer, security)","developers orchestrating complex autonomous workflows across multiple Claude instances"],"limitations":["Swarm coordination adds latency proportional to number of agents and context size","Hook-based routing requires explicit configuration for each agent interaction pattern","Memory synchronization across agents depends on AgentDB v3 availability and network latency","No built-in load balancing for agent task distribution — requires manual queue management"],"requires":["Node.js 18+","TypeScript 4.9+","Anthropic Claude API key with sufficient rate limits","AgentDB v3 instance for distributed memory","YAML configuration files for each agent role"],"input_types":["YAML agent configuration","task descriptions (text)","code context (source files)","hook routing rules (JSON/YAML)"],"output_types":["structured agent responses (JSON)","code artifacts","analysis reports","execution logs with agent attribution"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_1","uri":"capability://tool.use.integration.mcp.server.with.native.claude.code.integration","name":"mcp server with native claude code integration","description":"Exposes Ruflo's orchestration capabilities as a Model Context Protocol server with 10+ tool categories (agent-tools, memory-tools, neural-tools, hooks-tools, task-tools, terminal-tools, etc.) that Claude can invoke directly. Implements schema-based function calling with native bindings to Anthropic's Claude Code API, enabling Claude to spawn agents, manage memory, execute tasks, and monitor swarms without leaving the conversation context.","intents":["Let Claude directly control agent swarms and orchestration through natural language commands","Expose agent management, memory operations, and task execution as callable tools within Claude's context","Enable Claude Code to invoke Ruflo operations (spawn agents, check memory, run neural analysis) as native functions","Build conversational AI systems where Claude orchestrates multi-agent workflows interactively"],"best_for":["Claude users building agentic workflows without leaving the chat interface","teams integrating Ruflo into Claude Code for automated development","developers building conversational AI systems with agent orchestration"],"limitations":["MCP tool invocation latency depends on network round-trip to Ruflo daemon","Tool schemas must be manually maintained in sync with underlying agent capabilities","No built-in rate limiting or quota management per MCP client","Context window consumed by tool definitions reduces available context for agent reasoning"],"requires":["Claude API with MCP support (Claude 3.5+)","Ruflo daemon running with MCP server enabled","Network connectivity between Claude client and Ruflo MCP server","Proper authentication/API key configuration in settings.json"],"input_types":["natural language commands (Claude prompts)","tool invocation parameters (JSON)","agent configuration (YAML)","memory queries (structured)"],"output_types":["tool execution results (JSON)","agent responses","memory state snapshots","task execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_10","uri":"capability://automation.workflow.environment.management.and.rvfa.appliance.deployment","name":"environment management and rvfa appliance deployment","description":"Provides environment management capabilities for deploying Ruflo across different environments (development, staging, production) with environment-specific configurations. Includes RVFA (Ruflo Virtual Field Appliance) for containerized deployment with pre-configured settings, dependencies, and integrations. Supports environment variables, secrets management, and configuration inheritance. Enables one-command deployment of complete Ruflo stacks with all dependencies (MCP server, daemon, memory backend, embeddings service).","intents":["Deploy Ruflo to production with environment-specific configurations","Manage secrets and API keys across multiple environments securely","Package Ruflo with all dependencies as a containerized appliance","Replicate development environment configuration to staging/production"],"best_for":["teams deploying Ruflo to cloud environments (AWS, GCP, Azure)","enterprises requiring containerized deployment with compliance","DevOps engineers managing multi-environment Ruflo deployments"],"limitations":["RVFA appliance requires Docker/container runtime — not suitable for serverless","Environment configuration must be manually maintained across versions","No built-in multi-region or high-availability setup — requires external orchestration","Secrets management relies on environment variables — no built-in vault integration"],"requires":["Docker or container runtime for RVFA appliance","Environment-specific configuration files (.env, settings.json)","Cloud provider credentials for deployment","Optional: Kubernetes for orchestration"],"input_types":["environment configuration (YAML/JSON)","secrets and API keys (environment variables)","deployment parameters (region, instance size, etc.)","RVFA appliance configuration"],"output_types":["deployed Ruflo instance","environment-specific settings","deployment logs and status","health check results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_11","uri":"capability://text.generation.language.conversational.ui.with.ruvocal.chat.interface","name":"conversational ui with ruvocal chat interface","description":"Provides RuVocal Chat UI as a conversational interface for interacting with Ruflo agents and orchestration capabilities. Enables users to describe tasks in natural language and have the system automatically decompose them into agent workflows, select appropriate agents, and execute tasks. Chat interface maintains conversation history, displays agent execution progress, and allows users to interrupt or modify running workflows. Integrates with MCP server to expose all Ruflo capabilities as conversational commands.","intents":["Interact with agent swarms through natural language conversation","Monitor agent execution progress and results in real-time","Modify or interrupt running workflows without stopping the system","Build conversational AI applications on top of Ruflo orchestration"],"best_for":["non-technical users who want to leverage agent swarms without CLI knowledge","teams building conversational AI products with agent orchestration","developers prototyping agent workflows interactively"],"limitations":["Chat interface adds latency to agent execution (network round-trip for UI updates)","Natural language task descriptions may be ambiguous — requires clarification","No built-in support for complex multi-turn workflows with conditional branching","Chat history is not persisted by default — requires external storage"],"requires":["RuVocal Chat UI service running","MCP server for exposing Ruflo capabilities","WebSocket or HTTP connection between client and UI service","Optional: conversation history storage"],"input_types":["natural language task descriptions","conversation history (text)","workflow modification commands","execution control commands (pause, resume, cancel)"],"output_types":["agent execution progress updates","task results and artifacts","conversation history","execution logs and metrics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_12","uri":"capability://memory.knowledge.infinite.context.with.adr.051.architecture.decision","name":"infinite context with adr-051 architecture decision","description":"Implements infinite context capability through ADR-051 (Architecture Decision Record) that enables agents to work with arbitrarily large codebases and context without hitting Claude's context window limits. Uses a combination of semantic chunking, progressive context loading, and intelligent context selection to maintain only relevant context in the active window. Agents can reference external context through memory and RAG without loading everything into the model's context.","intents":["Enable agents to work on large codebases (1M+ lines) without context window limitations","Maintain semantic coherence across large projects by intelligently selecting relevant context","Reference external documentation and code without loading everything into context","Scale agent capabilities to enterprise-size projects"],"best_for":["enterprises with large monolithic codebases","teams building agents for complex multi-module projects","developers working on systems with extensive documentation"],"limitations":["Infinite context adds latency for context selection and loading (typically 100-500ms per task)","Semantic chunking may split related code across boundaries, requiring agents to manage context","No guarantee that all relevant context will be loaded — agents may miss important details","Requires careful tuning of context selection parameters for optimal performance"],"requires":["Semantic chunking configuration in settings.json","RAG system for external context retrieval","Memory system for context caching and reuse","Optional: custom context selection strategies"],"input_types":["large codebases (source files)","context selection queries","semantic chunking parameters","context relevance hints"],"output_types":["selected context chunks (text)","context loading statistics","semantic coherence metrics","agent execution results with context attribution"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_13","uri":"capability://safety.moderation.guidance.control.plane.with.policy.enforcement","name":"guidance control plane with policy enforcement","description":"Implements a guidance control plane that enforces organizational policies and governance rules across all agent executions. Policies can specify constraints (e.g., 'agents cannot delete production databases'), approval workflows (e.g., 'security changes require human review'), and audit requirements. Control plane intercepts agent execution at hooks and validates against active policies before allowing execution. Supports policy versioning, rollback, and audit trails for compliance.","intents":["Enforce organizational policies across all agent executions","Require human approval for high-risk agent operations","Maintain audit trails for compliance and governance","Prevent agents from violating security or operational constraints"],"best_for":["enterprises with strict governance and compliance requirements","teams managing agents in regulated industries (finance, healthcare)","organizations requiring human-in-the-loop approval for critical operations"],"limitations":["Policy enforcement adds latency to agent execution (typically 50-200ms per policy check)","Complex policies may be difficult to express and maintain","No built-in policy conflict resolution — overlapping policies may cause unexpected behavior","Approval workflows require human intervention — may block automated workflows"],"requires":["Policy definitions in YAML or JSON format","Guidance control plane service running","Optional: approval workflow system (email, Slack, etc.)","Audit logging configured for compliance"],"input_types":["policy definitions (YAML/JSON)","agent execution requests","approval decisions (human input)","audit log queries"],"output_types":["policy enforcement results (allow/deny with reason)","approval requests and decisions","audit logs with policy attribution","policy violation reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_2","uri":"capability://memory.knowledge.persistent.distributed.memory.with.agentdb.v3.controllers","name":"persistent distributed memory with agentdb v3 controllers","description":"Implements a multi-backend memory system using AgentDB v3 controllers that persist agent context, conversation history, and learned patterns across sessions. Supports pluggable backends (file-based, database, vector stores) with context persistence layer that automatically serializes/deserializes agent state. Integrates RuVector for semantic embeddings and SONA pattern learning to enable agents to recall relevant past interactions and adapt behavior based on historical success patterns.","intents":["Persist agent memory across multiple invocations so agents can learn from past interactions","Enable semantic search over agent conversation history to retrieve relevant context","Store and recall agent-specific knowledge (learned patterns, successful strategies) for future tasks","Synchronize memory state across distributed agents in a swarm"],"best_for":["long-running agent systems that need to learn and adapt over time","teams building RAG-enhanced agents with persistent knowledge bases","enterprises requiring audit trails and memory snapshots for compliance"],"limitations":["Memory synchronization latency increases with number of agents and context size","Vector embeddings require external embedding service (RuVector) for semantic search","No built-in garbage collection for old memory entries — requires manual pruning","Context persistence adds ~50-100ms per memory operation depending on backend"],"requires":["AgentDB v3 instance (file-based or database backend)","RuVector service for semantic embeddings","Persistent storage (filesystem, PostgreSQL, or compatible database)","Memory initialization via memory-initializer module"],"input_types":["agent state objects (JSON)","conversation history (text/structured)","learned patterns (embeddings)","memory queries (semantic or structured)"],"output_types":["persisted agent state snapshots","retrieved context (text + embeddings)","memory statistics and metrics","pattern learning results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_3","uri":"capability://planning.reasoning.hook.based.intelligent.task.routing.and.lifecycle.management","name":"hook-based intelligent task routing and lifecycle management","description":"Implements a hook system that intercepts agent execution at defined lifecycle points (pre-execution, post-execution, error handling, context updates) and routes tasks to appropriate agents based on configurable rules and learned patterns. Hooks can trigger neural analysis, update memory, modify task parameters, or redirect to different agents. The routing engine uses intelligence signals from past executions to optimize agent selection, reducing unnecessary context transfers and improving task completion rates.","intents":["Route different types of tasks to specialized agents based on task characteristics","Intercept and modify agent execution at key lifecycle points without changing agent code","Implement conditional logic (if code review fails, route to security-architect) across agent workflows","Learn optimal routing patterns from historical execution data"],"best_for":["teams building complex multi-stage workflows with conditional branching","developers implementing custom agent routing logic without modifying core agents","enterprises needing to enforce governance rules at execution boundaries"],"limitations":["Hook evaluation adds latency to each agent execution (typically 10-50ms per hook)","Complex routing rules can become difficult to debug and maintain","No built-in visualization of hook execution flow — requires external tooling","Hook ordering matters but is not explicitly validated at configuration time"],"requires":["Hook configuration in YAML or JSON format","Understanding of agent lifecycle events (pre/post/error)","Optional: neural analysis module for intelligent routing decisions","Memory system for storing routing decision history"],"input_types":["hook configuration (YAML/JSON)","task metadata (type, priority, context)","agent execution results","routing rules (conditional expressions)"],"output_types":["routing decisions (agent selection)","modified task parameters","execution logs with hook attribution","routing statistics and optimization suggestions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_4","uri":"capability://planning.reasoning.neural.learning.and.pattern.analysis.with.sona","name":"neural learning and pattern analysis with sona","description":"Implements SONA (Self-Organizing Neural Architecture) for analyzing agent execution patterns, identifying successful strategies, and recommending optimizations. Processes execution logs through neural analysis to extract patterns (e.g., 'code-review followed by security-architect yields 95% defect catch rate'), stores patterns in memory, and uses them to inform future routing decisions and agent prompting. Integrates with RuVector for semantic pattern matching across different task types.","intents":["Analyze historical agent execution data to identify successful workflow patterns","Recommend optimizations to agent routing and task decomposition based on learned patterns","Detect anomalies or failures in agent execution and suggest corrective actions","Continuously improve swarm performance without manual tuning"],"best_for":["teams running long-lived agent systems that benefit from continuous optimization","enterprises needing data-driven insights into agent performance and behavior","developers building self-improving AI systems"],"limitations":["Pattern learning requires sufficient historical data (typically 100+ executions) to be meaningful","Neural analysis is computationally expensive and may run asynchronously","Patterns may become stale if agent behavior or task distribution changes significantly","No built-in explainability for why specific patterns were learned or recommended"],"requires":["Execution history stored in AgentDB v3 memory system","RuVector service for semantic pattern matching","Neural analysis module enabled in configuration","Sufficient computational resources for pattern analysis (background worker)"],"input_types":["agent execution logs (structured)","task metadata and results","performance metrics","historical patterns (embeddings)"],"output_types":["learned patterns (semantic descriptions)","optimization recommendations","performance insights and metrics","anomaly alerts"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_5","uri":"capability://memory.knowledge.rag.enhanced.agent.context.with.semantic.search","name":"rag-enhanced agent context with semantic search","description":"Integrates Retrieval-Augmented Generation (RAG) capabilities using RuVector embeddings to augment agent context with semantically relevant information from knowledge bases, documentation, and past executions. When agents execute tasks, the system automatically retrieves relevant context based on semantic similarity, reducing hallucination and improving task accuracy. Supports multiple knowledge sources (codebase, documentation, memory) with configurable retrieval strategies and ranking.","intents":["Automatically retrieve relevant code context and documentation when agents start tasks","Reduce agent hallucination by grounding responses in retrieved knowledge","Enable agents to access large codebases without exceeding context window limits","Build knowledge bases from past executions and make them available to future agents"],"best_for":["teams with large codebases (100k+ lines) that need agents to work effectively","enterprises building domain-specific agent systems with proprietary knowledge","developers implementing agents that need to reference documentation or standards"],"limitations":["Retrieval latency adds 100-500ms per agent task depending on knowledge base size","Semantic search quality depends on embedding model quality and knowledge base organization","No built-in deduplication of retrieved context — may include redundant information","Requires maintaining embeddings as knowledge base evolves (re-indexing overhead)"],"requires":["RuVector service for semantic embeddings","Knowledge base sources (code files, documentation, memory)","Embedding model configuration (default or custom)","RAG configuration in settings.json"],"input_types":["task descriptions (text)","query context (code, documentation references)","knowledge base documents","retrieval parameters (top-k, similarity threshold)"],"output_types":["retrieved context chunks (text + embeddings + metadata)","ranked results with similarity scores","augmented agent prompts with context","retrieval statistics"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_6","uri":"capability://automation.workflow.cli.based.agent.management.and.configuration","name":"cli-based agent management and configuration","description":"Provides a comprehensive TypeScript-based CLI (v3/@claude-flow/cli) with commands for initializing projects, configuring agents, managing memory, running neural analysis, and controlling daemon services. Supports YAML-based agent configuration (agents/architect.yaml, agents/coder.yaml, etc.) with role-specific prompts, capabilities, and constraints. CLI commands include agent lifecycle management (create, update, delete), memory operations (store, retrieve, search), and advanced features (neural analysis, security scanning, performance optimization).","intents":["Initialize a new Ruflo project with default agent configurations and settings","Create and configure specialized agents with role-specific prompts and capabilities","Manage agent lifecycle (deploy, update, monitor, retire) from the command line","Execute memory operations and neural analysis without writing code"],"best_for":["developers setting up Ruflo projects from scratch","teams managing agent configurations across environments","DevOps engineers automating agent deployment in CI/CD pipelines"],"limitations":["CLI commands are synchronous by default — long-running operations may timeout","No built-in command composition or scripting language — complex workflows require shell scripts","Error messages may be verbose and difficult to parse programmatically","CLI state is not persisted — each invocation is independent"],"requires":["Node.js 18+","TypeScript 4.9+","Ruflo package installed globally or locally","Valid settings.json configuration file"],"input_types":["command-line arguments and flags","YAML configuration files","JSON input for structured commands","environment variables"],"output_types":["command execution results (JSON/text)","configuration files (YAML)","status messages and logs","error reports"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__cap_7","uri":"capability://automation.workflow.background.daemon.with.worker.pool.and.metrics.collection","name":"background daemon with worker pool and metrics collection","description":"Implements a background worker daemon (worker-daemon.ts) that runs long-lived background tasks (memory synchronization, neural analysis, pattern learning, metrics collection) without blocking the main CLI or MCP server. Uses a worker pool pattern to parallelize tasks and collect real-time metrics (execution time, success rate, memory usage) via statusline and performance monitoring. Daemon can be managed via CLI commands (start, stop, status) and integrates with system monitoring tools.","intents":["Run long-lived background tasks (neural analysis, memory sync) without blocking user interactions","Collect and expose real-time metrics about agent performance and system health","Parallelize expensive operations (pattern learning, embeddings) across multiple workers","Monitor daemon health and automatically restart failed workers"],"best_for":["production deployments requiring continuous background processing","teams needing real-time visibility into agent performance metrics","systems with expensive operations (neural analysis, embeddings) that need parallelization"],"limitations":["Worker pool size must be manually configured — no automatic scaling","Metrics collection adds overhead (typically 5-10% CPU) to background processing","No built-in persistence for metrics — requires external time-series database for long-term analysis","Worker failures are logged but not automatically escalated"],"requires":["Node.js 18+ with worker_threads support","Sufficient system resources for worker pool (CPU cores, memory)","Configuration of worker pool size and task types in settings.json","Optional: external metrics collection system (Prometheus, DataDog, etc.)"],"input_types":["background task definitions (JSON)","worker pool configuration","metrics collection rules","task scheduling parameters"],"output_types":["task execution results","real-time metrics (JSON)","daemon status and health checks","performance logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-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 architecture that enables extending agent capabilities through custom plugins without modifying core agent code. Plugins can register new tools, hooks, memory backends, and neural analysis strategies. Includes official plugins (Claude Code Plugin) that integrate with external systems. Plugin system uses a registry pattern with dependency management and lifecycle hooks (install, enable, disable, unload). Plugins are loaded dynamically at runtime and can be managed via CLI commands.","intents":["Extend agent capabilities with custom tools and integrations without forking Ruflo","Build domain-specific plugins for specialized workflows (e.g., security scanning, code analysis)","Enable third-party developers to build and distribute Ruflo plugins","Manage plugin dependencies and versions across a Ruflo deployment"],"best_for":["teams building custom agent capabilities for specific domains","enterprises integrating Ruflo with proprietary systems via plugins","developers building and distributing Ruflo extensions"],"limitations":["Plugin API is not yet stable — breaking changes may occur between versions","No built-in plugin marketplace or discovery mechanism","Plugin isolation is limited — malicious plugins could access agent state","Plugin loading adds startup time proportional to number of plugins"],"requires":["TypeScript 4.9+ for plugin development","Understanding of Ruflo plugin API and lifecycle","Plugin configuration in settings.json","Optional: plugin package published to npm"],"input_types":["plugin source code (TypeScript)","plugin configuration (JSON/YAML)","plugin dependencies (npm packages)","plugin metadata (name, version, capabilities)"],"output_types":["registered tools and hooks","extended agent capabilities","plugin status and metrics","plugin execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-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 (security-tools CLI command) and input validation across all agent inputs and outputs using ContinueGate safety framework. Validates agent prompts, task parameters, and execution results against security policies before processing. Includes built-in checks for prompt injection, code injection, and policy violations. Security scanning can be run on-demand or integrated into agent execution hooks. Supports custom security policies via configuration.","intents":["Prevent prompt injection attacks by validating all agent inputs","Detect and block potentially malicious code generated by agents","Enforce security policies (e.g., no database access) across agent executions","Audit security events and generate compliance reports"],"best_for":["enterprises deploying agents in security-sensitive environments","teams handling sensitive data (PII, credentials) with agents","developers building agents that execute generated code"],"limitations":["Security scanning adds latency (typically 50-200ms per check) to agent execution","False positives in security checks may block legitimate agent operations","Custom security policies require manual configuration and testing","No built-in integration with external security tools (SIEM, WAF)"],"requires":["ContinueGate safety framework enabled in configuration","Security policy definitions (JSON/YAML)","Optional: custom validation rules","Audit logging configured for compliance"],"input_types":["agent prompts (text)","task parameters (JSON)","generated code (source files)","security policies (JSON/YAML)"],"output_types":["security scan results (pass/fail with details)","policy violation reports","audit logs with security events","remediation recommendations"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-ruvnet-ruflo__headline","uri":"capability://tool.use.integration.ai.agent.orchestration.platform.for.multi.agent.workflows","name":"ai agent orchestration platform for multi-agent workflows","description":"Ruflo is a leading AI agent orchestration platform designed to deploy and manage intelligent multi-agent systems, enabling seamless coordination of autonomous workflows and integration with Claude Code for complex software engineering tasks.","intents":["best AI agent orchestration platform","AI agent framework for multi-agent systems","top tools for deploying autonomous agents","AI workflow orchestration solutions","best platforms for Claude Code integration"],"best_for":["teams needing multi-agent coordination","developers integrating AI workflows"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","TypeScript 4.9+","Anthropic Claude API key with sufficient rate limits","AgentDB v3 instance for distributed memory","YAML configuration files for each agent role","Claude API with MCP support (Claude 3.5+)","Ruflo daemon running with MCP server enabled","Network connectivity between Claude client and Ruflo MCP server","Proper authentication/API key configuration in settings.json","Docker or container runtime for RVFA appliance"],"failure_modes":["Swarm coordination adds latency proportional to number of agents and context size","Hook-based routing requires explicit configuration for each agent interaction pattern","Memory synchronization across agents depends on AgentDB v3 availability and network latency","No built-in load balancing for agent task distribution — requires manual queue management","MCP tool invocation latency depends on network round-trip to Ruflo daemon","Tool schemas must be manually maintained in sync with underlying agent capabilities","No built-in rate limiting or quota management per MCP client","Context window consumed by tool definitions reduces available context for agent reasoning","RVFA appliance requires Docker/container runtime — not suitable for serverless","Environment configuration must be manually maintained across versions","builder identity is not verified yet","no observed match outcomes 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