skills.rest
FrekiManagarm
FrekiManagarmCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

ReasoningBank with AgentDB

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

Updated May 8, 2026
Search Tags:#vector database#reasoningbank#agentdb#memory distillation#experience replay#agent learning#trajectory tracking
One-click install
npx skills add https://github.com/FrekiManagarm/d-chambaud --skill reasoningbank-with-agentdb-frekimanagarm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/FrekiManagarm/d-chambaud/tree/main/.agents/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/FrekiManagarm/d-chambaud --skill reasoningbank-with-agentdb-frekimanagarm

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 provides a ReasoningBank implementation backed by AgentDB's vector database so agents can store experiences, judge outcomes, and retrieve relevant patterns to improve decision-making over time. ## Core Features & Use Cases - Trajectory Tracking & Verdict Judgment: Record agent execution paths with outcomes and judge new trajectories against similar past successes. - Memory Distillation & Pattern Recognition: Consolidate similar experiences into high-level 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 .swarm/memory.db databases. - 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 synthesized context and applies the proven approach. ## Quick Start Initialize an AgentDB ReasoningBank database and store a successful experience pattern so the agent can retrieve it for similar future tasks.

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 embedded experience patterns and retrieve them with retrieveWithReasoning. Enable learning and reasoning flags for the full plugin set.

How to migrate from legacy ReasoningBank memory.db to AgentDB?▼

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

Is AgentDB ReasoningBank compatible with the legacy ReasoningBank API?▼

Yes, legacy functions like retrieveMemories, judgeTrajectory, and distillMemories from agentic-flow/reasoningbank work unchanged and automatically use the AgentDB backend. No code changes are required for existing integrations.

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 with a higher k value to get richer, more diverse context for judgment.

How do I prevent AgentDB memory 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.

Related Skills

Explore More Software Engineering Skills→
sandmark78sandmark78

agent-optimizer

Record execution trajectories and gather rewards to optimize agent performance.

Community
Intermediate
deepnoodle-aideepnoodle-ai

planning-models

Analyze and synthesize human planning frameworks for strategic decision-making.

Official
Advanced
InnerScopeHearingInnerScopeHearing

embedding-drift-monitor

Compare Azure AI Search probe results against a baseline to detect embedding drift.

Official
Advanced
daemon-blockint-techdaemon-blockint-tech
7

ml-systems-engineer-rl-engineering

Orchestrate scalable RL training infrastructure with reproducible experiment pipelines.

Community
Advanced