ruvnet/ruflo

Features

• Autonomous Agent Orchestration - Functions as a platform for deploying modular agents with persistent memory to automate complex, multi-step workflows.
• Multi-Agent Orchestration Systems - Functions as a platform for coordinating multiple autonomous agents to execute complex, collaborative workflows.
• Long-term Memory Stores - Maintains long-term agent context and session history using persistent storage for retrieval across interactions.
• Agentic RAG Platforms - Integrates retrieval augmented generation and knowledge graphs for complex reasoning over linked data.
• Claude-Powered Orchestrators - Provides a framework for deploying and coordinating multi-agent swarms specifically powered by Claude.
• Autonomous Agent Loops - Provides the core logic for repeated planning, acting, and observing cycles to execute autonomous agent loops.
• Autonomous Workflow Automation - Implements tools for designing self-directed digital workers that execute multi-step autonomous processes.
• Multi-Agent Orchestrators - Provides a framework for coordinating teams of specialized AI agents to solve complex, multi-step tasks.
• Agent Swarms - Organizes autonomous agents into collaborative topologies to execute complex tasks through shared consensus.
• Hierarchical Coordination - Organizes autonomous agents into structured topologies to collaborate through shared consensus and goal decomposition.
• Topology Management - Organizes multiple agents into hierarchical or mesh topologies to collaborate through shared consensus.
• Agentic Goal Decomposition - Uses LLMs to recursively break high-level objectives into actionable sub-tasks that update as state changes.
• Agent Memory Management - Stores and retrieves long-term user memories and execution traces to maintain AI interaction continuity.
• Autonomous Planning Engines - Implements an autonomous planning engine that decomposes high-level goals into executable action sequences with adaptive pathfinding.
• Claude Integration Frameworks - Provides a framework for building agents leveraging Claude's tool calling and lifecycle capabilities.
• Model Request Routing - Distributes task payloads across multiple model providers using intelligent routing logic.
• Parallel Tool Execution - Enables the concurrent invocation of multiple functions or tool calls in a single model request to accelerate workflows.
• Provider Response Routing - Directs requests to specific AI service providers based on model capabilities to optimize for performance and cost.
• Trajectory-Based Agent Optimization - Refines agent behavior by capturing and scoring execution trajectories to improve cognitive reasoning patterns.
• Entity Relationships - Defines associations between database entities to enable complex reasoning over linked data sets.
• Knowledge Graphs - Utilizes knowledge graphs to map semantic relationships between entities for complex reasoning over linked data.
• AI Cost Monitoring - Provides utilities for tracking token usage and model efficiency to optimize operational expenses.
• AI Agent Behavior Monitors - Tracks agent interactions and performance via trust scoring and structured trails to ensure compliance.
• Goal Trajectory Visualizers - Ships a monitoring dashboard for visualizing goal-planning trajectories and auditing agent behavior.
• Web Chat Interfaces - Provides a web chat interface featuring parallel tool execution and visual goal-planning dashboards.
• AI Agents - Orchestration platform for Claude-based agents.

Star history