What is AgentDB and how does it help AI agents?

AgentDB is a specialized persistent storage engine for AI agents that manages vector embeddings, session history, and learned patterns to provide long-term memory.

How does this skill improve Claude's performance?

It provides optimized retrieval patterns like HNSW indexing and caching, making memory lookups up to 12,500x faster than traditional search methods.

Can I integrate this with Claude Code via MCP?

Yes, this skill includes a built-in MCP server command ('npx agentdb@latest mcp') to connect AgentDB directly to your Claude Code environment.

Does it support different machine learning algorithms?

Yes, it includes templates for 9 different algorithms, including Decision Transformer, Q-Learning, SARSA, and Actor-Critic for advanced agent behavior.

Is AgentDB memory efficient for large datasets?

Yes, it supports binary and scalar quantization, which can reduce memory footprints by 4x to 32x while maintaining high search accuracy.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: Microck

byMicrock

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114

データサイエンスとML

Implements persistent, high-performance memory and learning patterns for AI agents using AgentDB vector storage.

概要

This skill provides advanced memory management patterns for AI agents, enabling them to maintain context, learn from interactions, and store long-term data using AgentDB's high-performance vector storage. It bridges the gap between stateless LLM interactions and stateful intelligent systems by providing structured patterns for session history, fact storage, and pattern-based learning. With built-in support for HNSW indexing and ReasoningBank integration, it allows developers to build agents that are significantly faster and more contextually aware than traditional solutions while maintaining full backward compatibility.

主な機能

Persistent session and long-term memory management for stateful agents
High-performance vector search with HNSW indexing and 100µs retrieval
Memory quantization (binary/scalar) for 4-32x reduction in storage size
Reinforcement learning algorithms including Decision Transformer and Actor-Critic
ReasoningBank integration for automated context synthesis and optimization
114 GitHub stars

ユースケース

Building intelligent assistants that remember user preferences and facts across sessions
Developing autonomous agents that optimize task execution through pattern learning
Implementing low-latency context retrieval for complex, multi-turn AI interactions

概要

主な機能

Persistent session and long-term memory management for stateful agents
High-performance vector search with HNSW indexing and 100µs retrieval
Memory quantization (binary/scalar) for 4-32x reduction in storage size
Reinforcement learning algorithms including Decision Transformer and Actor-Critic
ReasoningBank integration for automated context synthesis and optimization
114 GitHub stars

ユースケース

Building intelligent assistants that remember user preferences and facts across sessions
Developing autonomous agents that optimize task execution through pattern learning
Implementing low-latency context retrieval for complex, multi-turn AI interactions