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ReasoningBank with AgentDB

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

1|1|Updated Nov 28, 2025
Search Tags:#vector database#adaptive learning#reasoningbank#agentdb#memory distillation#self-learning agents#trajectory tracking
One-click install
npx skills add https://github.com/33may/robotics --skill reasoningbank-with-agentdb-33may
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/33may/robotics/tree/main/humanoid/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/33may/robotics --skill reasoningbank-with-agentdb-33may

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Agents that cannot learn from past experiences 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 improve decisions over time. ## Core Features & Use Cases - Trajectory Tracking & Verdict Judgment: Record agent execution paths and judge success by comparing against similar past trajectories. - Memory Distillation & Pattern Recognition: Consolidate similar experiences into reusable patterns using PatternMatcher, ContextSynthesizer, MemoryOptimizer, and ExperienceCurator modules. - Legacy Compatibility & Migration: Migrate existing ReasoningBank databases with the CLI while keeping the legacy API fully functional. - Use Case: A coding agent stores each successful optimization approach as an embedded pattern; when facing a new N+1 query problem, it retrieves similar past solutions with confidence scores and applies the highest-rated approach. ## Quick Start Ask the AI to initialize an AgentDB ReasoningBank database and store a successful task experience as a retrievable pattern.

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 init, then use createAgentDBAdapter from agentic-flow/reasoningbank to insert patterns and call retrieveWithReasoning for similarity-based memory retrieval with reasoning modules.

How to migrate a legacy ReasoningBank database to AgentDB?▼

Run npx agentdb migrate --source .swarm/memory.db to transfer existing data automatically. Validate the result with npx agentdb stats on the target database, and the legacy API continues working unchanged.

Does AgentDB ReasoningBank work with Claude Code via MCP?▼

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

Why are my ReasoningBank retrieval confidence scores low?▼

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

What are the limitations of vector-based memory distillation?▼

Distillation quality depends on embedding quality and sufficient sample sizes per domain. Small experience sets produce unreliable patterns, and cross-domain transfer requires manual adaptation of retrieved memories.

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