skills.rest
dmuhoro
dmuhoroCOMMUNITY
Explore all skills by this author →
GitHub

ReasoningBank with AgentDB

Implements adaptive agent learning with trajectory tracking, verdict judgment, and memory distillation on AgentDB.

Updated Mar 25, 2026
Search Tags:#embeddings#vector database#reasoningbank#agentdb#memory distillation#experience replay#agent learning
One-click install
npx skills add https://github.com/dmuhoro/EasyTutor --skill reasoningbank-with-agentdb-dmuhoro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/dmuhoro/EasyTutor/tree/main/archive/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/dmuhoro/EasyTutor --skill reasoningbank-with-agentdb-dmuhoro

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 re-derive solutions. This Skill provides ReasoningBank patterns backed by AgentDB's vector database so agents can store experiences, judge outcomes, distill memories, and improve decision-making over time. ## 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 reusable patterns using reasoning modules like PatternMatcher, ContextSynthesizer, MemoryOptimizer, and ExperienceCurator. - Legacy Compatibility & Migration: Existing ReasoningBank APIs work unchanged on the AgentDB backend, with CLI migration from legacy memory databases. - Use Case: A coding agent stores each successful debugging session as an embedded pattern; on the next similar bug, it retrieves the top-k matching experiences via vector search and injects them as context, cutting resolution time. ## Quick Start Initialize an AgentDB database with npx agentdb init, then use the createAgentDBAdapter API to store and retrieve experience patterns for your 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 experience replay for AI agents?▼

Store each agent experience as an embedded pattern with insertPattern, then retrieve similar past experiences with retrieveWithReasoning using vector search. Successful trajectories inform future decisions through context synthesis.

How to migrate from legacy ReasoningBank to AgentDB?▼

Run npx agentdb migrate with the source pointing to your legacy memory.db file. The CLI validates the migration, and legacy APIs like retrieveMemories and judgeTrajectory continue working unchanged on the new backend.

Does AgentDB work with Claude Code via MCP?▼

Yes, start the MCP server with npx agentdb mcp and register it using claude mcp add. This exposes the ReasoningBank database to Claude Code for pattern storage and retrieval.

Why are my retrieved memories low confidence?▼

Low confidence usually results from sparse or noisy stored patterns. Enable synthesizeContext and useMMR in retrieval options, increase k, and set minConfidence to filter out low-quality experiences.

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

Enable optimizeMemory during retrieval to automatically consolidate similar patterns and prune low-quality entries. You can also call the optimize method manually to compact the database.

Related Skills

Explore More Software Engineering Skills→
pjherronpjherron

ReasoningBank with AgentDB

Implement adaptive agent learning with trajectory tracking and memory distillation using AgentDB.

Community
Intermediate
javierhbrjavierhbr

deepagents-architecture

Guide architectural decisions for Deep Agents applications, including backend selection and subagent design.

Community
Intermediate
sergeyitalysergeyitaly
1

skill-usage-insights

Analyze AI skill usage logs to classify skills and recommend additions or removals.

Community
Intermediate
svssdevasvssdeva
1

vector-index-tuning

Optimizes HNSW parameters and quantization for target latency, recall, and memory.

Community
Intermediate