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ReasoningBank with AgentDB

Implement adaptive agent learning with trajectory tracking and vector-based memory retrieval.

Updated Feb 17, 2026
Search Tags:#vector database#adaptive learning#reasoningbank#agentdb#memory distillation#agentic-flow#trajectory tracking
One-click install
npx skills add https://github.com/derbalimajd04-dot/al-wasat --skill reasoningbank-with-agentdb-derbalimajd04-dot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/derbalimajd04-dot/al-wasat/tree/main/.agents/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/derbalimajd04-dot/al-wasat --skill reasoningbank-with-agentdb-derbalimajd04-dot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Agents that cannot learn from past executions repeat mistakes and rediscover solutions. This Skill implements ReasoningBank adaptive learning on AgentDB's vector database so agents can store experiences, judge outcomes, distill patterns, and retrieve relevant memories for better decisions. ## Core Features & Use Cases - Trajectory Tracking & Verdict Judgment: Record agent execution paths with outcomes and judge success by comparing against similar past trajectories. - Memory Distillation & Pattern Recognition: Consolidate similar experiences into high-level patterns using reasoning modules like PatternMatcher, ContextSynthesizer, MemoryOptimizer, and ExperienceCurator. - Use Case: A coding agent stores each successful API optimization as an embedded pattern; when facing a new endpoint, it retrieves similar past optimizations with MMR-based diversity and applies the distilled approach, cutting resolution time. ## 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 for an AI agent?▼

Initialize an AgentDB database with npx agentdb@latest init, then create an adapter via createAgentDBAdapter from agentic-flow/reasoningbank. Store experiences with insertPattern and retrieve similar ones with retrieveWithReasoning using embedding-based search.

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, and legacy API calls continue working unchanged on the AgentDB backend.

Does AgentDB work with existing ReasoningBank code?▼

Yes, AgentDB maintains 100% backward compatibility with the legacy ReasoningBank API. Functions like retrieveMemories, judgeTrajectory, and distillMemories work unchanged while using the faster AgentDB backend automatically.

Why are my memory retrieval confidence scores low?▼

Low confidence usually results from sparse or low-quality stored patterns. Enable context synthesis and MMR diversity in retrieveWithReasoning with synthesizeContext and useMMR options, and increase k to retrieve more candidate memories.

How do I prevent the agent memory database from growing too large?▼

Enable automatic memory optimization by passing optimizeMemory: true during retrieval, which consolidates similar patterns and prunes low-quality entries. You can also call rb.optimize() manually to run consolidation on demand.

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