MCP Standards

# Reasoning Agents for Claude-Flow ## Overview This directory contains reasoning and goal-planning agents that leverage ReasoningBank's closed-loop learning to provide intelligent, adaptive task execution with continuous improvement. ## Available Agents ### 🎯 goal-planner **Goal-Oriented Action Planning (GOAP) specialist** Uses gaming AI techniques to dynamically create intelligent plans to achieve complex objectives. Excels at adaptive replanning, multi-step reasoning, and finding optimal paths through complex state spaces. **Key Features:** - Dynamic Planning: A* search algorithms for optimal paths - Precondition Analysis: Evaluate action requirements - Effect Prediction: Model state changes - Adaptive Replanning: Adjust based on execution results - Goal Decomposition: Break complex objectives into sub-goals **Best for:** - Complex multi-step deployments - Tasks with many dependencies - High-level goals needing decomposition - Adaptive planning scenarios **Usage:** ```bash claude-flow init --agent reasoning # Or directly with agentic-flow: npx agentic-flow --agent goal-planner --task "Deploy application with prerequisites" ``` ### 🎯 sublinear-goal-planner **Sub-linear complexity goal planning** Specialized version optimized for large-scale state spaces with sub-linear time complexity. **Best for:** - Large-scale systems - Performance-critical planning - Massive state spaces ## Integration with ReasoningBank All reasoning agents integrate with ReasoningBank for: - **RETRIEVE**: Pull relevant memories from past executions - **JUDGE**: Evaluate success/failure of trajectories - **DISTILL**: Extract learnable patterns - **CONSOLIDATE**: Merge and optimize memory ## Performance Benefits Based on ReasoningBank benchmarks: - **+26% success rate** (70% → 88%) - **-25% token usage** (cost savings) - **3.2x learning velocity** (faster improvement) - **0% → 95% success** over 5 iterations ## Quick Start ### 1. Initialize with Reasoning Agents ```bash claude-flow init --agent reasoning ``` This will: - Set up ReasoningBank memory system - Configure reasoning agents - Initialize learning capabilities ### 2. Use Reasoning Agents ```bash # Via claude-flow (when integrated) claude-flow agent run goal-planner "Complex deployment task" # Via agentic-flow directly npx agentic-flow --agent goal-planner --task "Multi-step task" ``` ### 3. Enable Learning Mode ```bash export REASONINGBANK_ENABLED=true export AGENTIC_FLOW_TRAINING=true ``` ## Architecture ``` User Task ↓ [goal-planner analyzes] ↓ ReasoningBank.retrieve() → Get relevant memories ↓ Plan generation (A* search) ↓ Execute with monitoring (OODA loop) ↓ ReasoningBank.judge() → Evaluate success ↓ ReasoningBank.distill() → Extract learnings ↓ Store for future use ``` ## Configuration ### Memory Database Default location: `.swarm/memory.db` Configure via: ```bash export REASONINGBANK_DB_PATH="/custom/path/memory.db" ``` ### Retrieval Settings ```bash export REASONINGBANK_K=3 # Top-k memories to retrieve export REASONINGBANK_MIN_CONFIDENCE=0.5 # Minimum confidence threshold ``` ## Advanced Usage ### 1. Multi-Step Planning ```bash npx agentic-flow --agent goal-planner \ --task "Deploy application" \ --enable-memory \ --memory-domain "deployment" ``` ### 2. Learning from Failures The system automatically learns from both successes and failures: - Failed attempts store "what went wrong" - Successful attempts store "what worked" - Future tasks benefit from both ### 3. Cross-Domain Transfer Patterns learned in one domain can transfer to similar tasks: - Authentication patterns → Authorization patterns - Deployment patterns → Migration patterns - Testing strategies → Debugging strategies ## Documentation - **REASONING-AGENTS.md**: Detailed technical documentation - **REASONINGBANK-DEMO.md**: Live demo comparison - **REASONINGBANK-CLI-INTEGRATION.md**: CLI integration guide - **REASONINGBANK-BENCHMARK.md**: Performance benchmarks ## Future Agents (Coming Soon) The following reasoning agents are planned for future releases: - **adaptive-learner**: Learn from experience and improve over time - **pattern-matcher**: Recognize patterns and transfer proven solutions - **memory-optimizer**: Maintain memory system health and performance - **context-synthesizer**: Build rich situational awareness from multiple sources - **experience-curator**: Ensure high-quality learnings through rigorous curation - **reasoning-optimized**: Meta-reasoning orchestrator coordinating all reasoning agents ## Support For issues or questions: - GitHub: https://github.com/ruvnet/claude-flow/issues - Documentation: https://github.com/ruvnet/claude-flow --- **Remember: Reasoning agents learn from experience and get better over time!** 🧠✨