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ReasoningBank with AgentDB

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

11|Updated Jan 20, 2026
Search Tags:#vector database#reasoningbank#agentdb#memory distillation#experience replay#agent learning#trajectory tracking
One-click install
npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill reasoningbank-with-agentdb-ishandutta2007
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/ishandutta2007/claude-agent-orchestration/tree/main/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill reasoningbank-with-agentdb-ishandutta2007

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 via vector similarity. - Memory Distillation & Pattern Recognition: Consolidate similar experiences into high-level patterns using reasoning modules like PatternMatcher, ContextSynthesizer, MemoryOptimizer, and ExperienceCurator. - Legacy Migration & Compatibility: Migrate existing ReasoningBank databases with the agentdb CLI while keeping the legacy API (retrieveMemories, judgeTrajectory, distillMemories) fully functional. - Use Case: A coding agent stores each successful API optimization as an embedded pattern; when facing a new N+1 query problem, it retrieves similar past fixes with synthesized context and applies the proven approach. ## Quick Start Initialize an AgentDB ReasoningBank database with the agentdb CLI, then store and retrieve experience patterns using the createAgentDBAdapter API from agentic-flow.

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 with AgentDB?▼

Use createAgentDBAdapter from agentic-flow/reasoningbank to initialize a database, then store experiences via insertPattern with embeddings from computeEmbedding. Retrieve similar experiences with retrieveWithReasoning, which supports MMR diversity and context synthesis.

How to migrate from legacy ReasoningBank to AgentDB?▼

Run npx agentdb@latest migrate --source .swarm/memory.db to transfer data automatically, then verify with npx agentdb@latest stats. The legacy API functions retrieveMemories, judgeTrajectory, and distillMemories continue working unchanged on the AgentDB backend.

Does AgentDB ReasoningBank work with Claude Code via MCP?▼

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

Why are my retrieved memories returning low confidence scores?▼

Low confidence usually results from sparse or low-quality stored patterns. Enable synthesizeContext and useMMR in retrieveWithReasoning, increase k to gather more candidates, and filter with minConfidence to keep only high-quality experiences.

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

Enable optimizeMemory in retrieval options to automatically consolidate similar patterns and prune low-quality entries, or call rb.optimize() manually. The MemoryOptimizer module reports consolidated and pruned counts after each run.

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