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ReasoningBank with AgentDB

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

Updated Aug 18, 2026
Search Tags:#vector database#adaptive learning#reasoningbank#agentdb#memory distillation#agentic-flow#trajectory tracking
One-click install
npx skills add https://github.com/wandreandrade2018-maker/claude-config --skill reasoningbank-with-agentdb-wandreandrade2018-maker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/wandreandrade2018-maker/claude-config/tree/main/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/wandreandrade2018-maker/claude-config --skill reasoningbank-with-agentdb-wandreandrade2018-maker

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 to 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 high-level patterns using reasoning modules (PatternMatcher, ContextSynthesizer, MemoryOptimizer, ExperienceCurator). - Legacy Migration & Compatibility: Migrate existing ReasoningBank databases 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. ## Quick Start Ask the agent to initialize an AgentDB ReasoningBank database and store a successful task experience so it can retrieve similar patterns on future runs.

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 patterns and retrieve memories with retrieveWithReasoning. Enable learning and reasoning flags for the full plugin set.

How to migrate a legacy ReasoningBank database to AgentDB?▼

Run npx agentdb@latest migrate --source .swarm/memory.db to move existing data into the new AgentDB backend. Validate the result with npx agentdb@latest stats on the target database.

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, memory retrieval is under 1ms with caching, and batch inserts of 100 patterns complete in roughly 2ms. Trajectory judgment including retrieval and analysis takes under 5ms.

Why is my ReasoningBank memory growing too large?▼

Accumulated experiences without consolidation cause unbounded growth. Enable optimizeMemory in retrieveWithReasoning to automatically consolidate similar patterns and prune low-quality entries, or call rb.optimize() manually.

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