ReasoningBank with AgentDB

Build self-learning agents by tracking action trajectories and judging outcomes to refine decisions. This adaptive learning approach uses memory distillation to consolidate past experiences into concise, actionable patterns for continuous improvement.

Accelerate vector database memory access using a high-performance backend that delivers sub-millisecond retrieval. This approach provides 150x faster memory operations, enabling autonomous agents to recognize patterns and optimize decisions efficiently.

Memory distillation in adaptive learning systems consolidates similar agent experiences into concise, actionable patterns. This process optimizes decision-making by recording trajectories and verdict judgments, then refining them into strategies for future tasks.

Yes, you can build a self-learning customer support agent using a high-performance vector database. It facilitates trajectory tracking and pattern recognition, enabling the agent to learn from past interactions and provide faster, more accurate responses.

Initialize a vector database for adaptive learning agents by running `npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536`. This command configures the high-performance memory store required for trajectory tracking and experience replay.