ReasoningBank with AgentDB

Adaptive learning with ReasoningBank and AgentDB tracks agent execution trajectories, judges outcomes automatically, and distills experiences into reusable patterns. This enables agents to learn 150x faster and retrieve successful patterns in under 1ms, significantly reducing trial-and-error cycles in optimization tasks.

Yes. ReasoningBank integrates AgentDB's high-performance vector database to store embedding-based patterns from agent experiences. The system supports sub-millisecond memory access and backward compatibility with Node.js 18+, enabling efficient pattern storage and retrieval for self-learning agents.

Memory distillation consolidates similar trajectories into high-level patterns that capture decision-making insights. ReasoningBank automatically judges trajectory success and extracts patterns, allowing agents to apply proven approaches to new optimization problems without repeating failed attempts.

Initialize AgentDB with `npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536`, then start the MCP server with `npx agentdb@latest mcp`. ReasoningBank automatically tracks execution paths, judges outcomes, and enables agents to optimize queries and configurations through learned patterns.

ReasoningBank integrates via agentic-flow with AgentDB as dependencies and requires Node.js 18+. It maintains backward compatibility and works within existing agent architectures for trajectory tracking, verdict judgment, and memory distillation across code optimization, database tuning, and API optimization domains.

ReasoningBank delivers sub-millisecond memory access for pattern retrieval and supports high-performance vector search through AgentDB. For very large pattern repositories or concurrent multi-agent systems, performance depends on embedding dimension (default 1536) and database indexing; test with your specific trajectory volume.