reasoningbank-with-agentdb

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

AI & Automation 57,130 stars 6508 forks Updated today MIT

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Skill Content

# ReasoningBank with AgentDB ## What This Skill Does Provides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility. **Performance**: 150x faster pattern retrieval, 500x faster batch operations, <1ms memory access. ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow) - Understanding of reinforcement learning concepts (optional) --- ## Quick Start with CLI ### Initialize ReasoningBank Database ```bash # Initialize AgentDB for ReasoningBank npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536 # Start MCP server for Claude Code integration npx agentdb@latest mcp claude mcp add agentdb npx agentdb@latest mcp ``` ### Migrate from Legacy ReasoningBank ```bash # Automatic migration with validation npx agentdb@latest migrate --source .swarm/memory.db # Verify migration npx agentdb@latest stats ./.agentdb/reasoningbank.db ``` --- ## Quick Start with API ```typescript import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank'; // Initialize ReasoningBank with AgentDB const rb = await createAgentDBAdapter({ dbPath: '.agentdb/reasoningbank.db', enableLearning: true, // Enable learning plugins enableReasoning: true, // Enable reasoning agents cacheSize: 1000, // 1000 pattern cache }); // Store...

Details

Author
ruvnet
Repository
ruvnet/ruflo
Created
12 months ago
Last Updated
today
Language
TypeScript
License
MIT

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