How does this improve agent performance?

By utilizing HNSW indexing and intelligent caching, it achieves search times under 100µs and batch insertion speeds 500x faster than traditional solutions.

Is it compatible with existing ReasoningBank implementations?

Yes, it provides 100% backward compatibility with the ReasoningBank API and includes a dedicated migration tool for legacy databases.

What is AgentDB Memory Patterns?

It is a specialized capability for Claude Code that enables the implementation of persistent memory, session management, and learning behaviors in AI agents using the AgentDB framework.

Which learning algorithms are supported?

The skill includes 9 algorithms, including Decision Transformer, Q-Learning, SARSA, Actor-Critic, and Curiosity-Driven learning.

How do I connect this to Claude Code?

You can use the built-in MCP server by running 'npx agentdb@latest mcp' and adding it to your Claude configuration.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: while-basic

bywhile-basic

데이터 과학 및 ML

Empowers AI agents with persistent, high-performance memory and learning patterns using AgentDB and ReasoningBank.

소개

AgentDB Memory Patterns provides a sophisticated framework for managing AI agent state, enabling persistent long-term memory, session context, and advanced pattern learning. Built on top of AgentDB with HNSW indexing, this skill allows Claude to implement memory systems that are up to 12,500x faster than traditional solutions. It includes pre-built modules for reinforcement learning—such as Decision Transformers and Q-Learning—memory consolidation, and hierarchical storage, making it an essential tool for developers building complex, stateful agents or intelligent assistants that need to evolve and learn from past interactions.

주요 기능

Automatic context synthesis and memory consolidation
Persistent session and long-term memory storage
High-speed pattern matching with HNSW vector indexing
Efficient quantization options for up to 32x memory reduction
0 GitHub stars
Integrated reinforcement learning algorithms including Decision Transformer and Q-Learning

사용 사례

Building stateful AI assistants that remember user preferences and context across sessions
Implementing self-learning agents that optimize task strategies based on historical outcomes
Developing high-scale chat systems requiring sub-millisecond context retrieval and reasoning

소개

주요 기능

Automatic context synthesis and memory consolidation
Persistent session and long-term memory storage
High-speed pattern matching with HNSW vector indexing
Efficient quantization options for up to 32x memory reduction
0 GitHub stars
Integrated reinforcement learning algorithms including Decision Transformer and Q-Learning

사용 사례

Building stateful AI assistants that remember user preferences and context across sessions
Implementing self-learning agents that optimize task strategies based on historical outcomes
Developing high-scale chat systems requiring sub-millisecond context retrieval and reasoning