ReasoningBank with AgentDB

To enable adaptive reasoning for AI agents, you use ReasoningBank with AgentDB to perform trajectory tracking, verdict judgment, and memory distillation. It condenses past agent experiences into reusable high-level patterns for future decisions.

To track agent execution trajectories, you record sequences of actions and outcomes using ReasoningBank with AgentDB. It evaluates past trajectories to determine likely success and guide future decisions via verdict judgment.

Yes, you can use AgentDB with Claude Code MCP. Start the AgentDB MCP server and add it to Claude Code to enable fast vector storage, pattern retrieval, and context synthesis for your agents.

To initialize a ReasoningBank vector database, you need Node.js 18+ and the AgentDB package. Run the agentdb init command with a specified database path and a 1536 dimension to set up the vector backend.

To migrate existing memory databases to AgentDB, run the agentdb migrate command with the source database path. This transfers existing memory data into the ReasoningBank vector backend for adaptive reasoning.

You should use a vector backend like AgentDB for agent memory management when you need fast pattern retrieval and context synthesis across multiple domains, especially for self-learning agents requiring scalable memory distillation.