Agent skill · research science · proffesor-for-testing
ReasoningBank with AgentDB
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.
Why this skill is useful
Adds executable scripts for initializing and migrating ReasoningBank with AgentDB, enabling advanced adaptive learning capabilities.
What it needs
Requires node installed locally. About 5k tokens when loaded. Last updated 2026-08-06. 434 stars on the source repository.
What this skill does
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 Migrate from Legacy ReasoningBank --- Quick Start with API --- Core ReasoningBank Concepts 1. Trajectory Tracking Track agent execution paths and outcomes: 2. Verdict Judgment Judge whether a trajectory was successful: 3. Memory Distillation Consolidate similar experiences into patterns: --- Integration with Reasoning Agents AgentDB provides 4 reasoning modules that enhance ReasoningBank: 1. PatternMatcher Find similar successful patterns: 2. ContextSynthesizer Generate rich context from multiple memories: 3. MemoryOptimizer Automatically consolidate and prune: 4. ExperienceCurator Filter by quality and relevance: --- Legacy API Compatibility AgentDB maintains 100% backward compatibility with legacy ReasoningBank: --- Performance Characteristics Pattern Search: 150x faster (100µs vs 15ms) Memory Retrieval: <1ms (with cache) Batch Insert: 500x faster (2ms vs 1s for 100 patterns) Trajectory Judgment: <5ms (including retrieval + analysis) Memory Distillation: <50ms (consolidate 100 patterns) --- Advanced Patterns Hierarchical Memory Organize memories by abstraction level: Multi-Domain Learning Transfer learning across domains: --- CLI Operations Database Management Migration --- Troubleshooting Issue: Migration fails Issue: Low confidence scores Issue: Memory growing too large --- Learn More AgentDB Integration: nodemodules/agentic-flow/docs/AGENTDBINTEGRATION.md GitHub: https://github.com/ruvnet …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills proffesor-for-testing/reasoningbank-agentdb