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ReasoningBank with AgentDB

Implements adaptive learning memory for agents using AgentDB vector storage and reasoning modules.

1|Updated Jun 3, 2026
Search Tags:#vector database#adaptive learning#reasoningbank#agentdb#memory distillation#agent memory#trajectory tracking
One-click install
npx skills add https://github.com/KentwareDemo/RuView --skill reasoningbank-with-agentdb-kentwaredemo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/KentwareDemo/RuView/tree/main/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/KentwareDemo/RuView --skill reasoningbank-with-agentdb-kentwaredemo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Agents that cannot retain and learn from past experiences repeat mistakes and fail to improve over time. This Skill provides a ReasoningBank implementation backed by AgentDB's vector database so agents can store trajectories, judge outcomes, distill memories, and retrieve relevant patterns for better decision-making. ## Core Features & Use Cases - Trajectory Tracking & Verdict Judgment: Record agent execution paths with outcomes and judge whether new trajectories are likely to succeed based on similar past experiences. - Memory Distillation & Pattern Recognition: Consolidate similar experiences into high-level patterns using four reasoning modules (PatternMatcher, ContextSynthesizer, MemoryOptimizer, ExperienceCurator). - Legacy Migration: Migrate existing ReasoningBank data from .swarm/memory.db with 100% backward-compatible legacy APIs. - Use Case: A coding agent stores each successful debugging session as an embedded pattern; when a similar bug appears, it retrieves the top-k matching experiences with MMR diversity and applies the proven fix approach. ## Quick Start Initialize an AgentDB database with npx agentdb@latest init, then use the createAgentDBAdapter API to store and retrieve experience patterns for my agent.

Frequently Asked Questions about ReasoningBank with AgentDB

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement ReasoningBank adaptive learning with AgentDB?▼

Initialize a database with npx agentdb@latest init, then use createAgentDBAdapter from agentic-flow/reasoningbank to insert experience patterns with embeddings and retrieve them via retrieveWithReasoning. Enable learning and reasoning flags for the full plugin set.

How to migrate from legacy ReasoningBank to AgentDB?▼

Run npx agentdb@latest migrate --source .swarm/memory.db to transfer existing data automatically. Verify the result with npx agentdb@latest stats on the new database file.

Is AgentDB compatible with the legacy ReasoningBank API?▼

Yes, AgentDB maintains 100% backward compatibility. Legacy functions like retrieveMemories, judgeTrajectory, and distillMemories from agentic-flow/reasoningbank work unchanged and use the AgentDB backend automatically.

What are the performance characteristics of AgentDB pattern retrieval?▼

Pattern search runs about 150x faster than the legacy backend (100µs vs 15ms), memory retrieval is under 1ms with caching, and batch inserts of 100 patterns complete in about 2ms.

Why are my ReasoningBank confidence scores low?▼

Low confidence usually comes from sparse or low-quality retrieved memories. Enable synthesizeContext and useMMR with a higher k value in retrieveWithReasoning to get richer, more diverse context for judgment.

How do I prevent the ReasoningBank memory store from growing too large?▼

Pass optimizeMemory: true in retrieval options to trigger automatic consolidation of similar patterns and pruning of low-quality entries. You can also call rb.optimize() manually for on-demand cleanup.

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