What is the main benefit of using AgentDB with ReasoningBank?

AgentDB provides a high-performance vector backend that offers 150x faster pattern retrieval and 500x faster batch operations compared to legacy systems.

What is 'Memory Distillation' in this context?

It is the process of consolidating multiple similar experiences into high-level, reusable patterns to optimize storage and improve reasoning quality.

Do I need to rewrite my existing ReasoningBank code?

No, this skill maintains 100% backward compatibility with legacy APIs while automatically upgrading the backend to AgentDB performance.

What are reasoning modules like PatternMatcher?

These are specialized components that enhance memory retrieval by finding diverse, relevant, and high-success patterns for the agent to follow.

How do I integrate this with Claude Code?

You can add it via the MCP server by running the command: claude mcp add agentdb npx agentdb@latest mcp.

ReasoningBank with AgentDB

Name: ReasoningBank with AgentDB
Author: ricable

byricable

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数据科学与机器学习

Implements high-performance adaptive learning and experience replay for AI agents using AgentDB's ultra-fast vector storage.

关于

This skill integrates ReasoningBank's adaptive learning framework with AgentDB, providing a high-speed vector backend that is up to 150x faster than legacy solutions. It enables Claude to track agent execution trajectories, judge task outcomes, and distill memories into reusable patterns. By implementing advanced features like ContextSynthesizer and MemoryOptimizer, it allows developers to build self-improving agents that learn from every interaction and optimize decision-making through 100% backward-compatible APIs.

主要功能

Advanced trajectory tracking and outcome judgment
Automatic memory distillation and pattern consolidation
Seamless MCP integration with 100% backward compatibility
High-performance vector retrieval (150x-12,500x faster than legacy)
Rich context synthesis and memory pruning via reasoning modules
2 GitHub stars

使用场景

Migrating legacy agent memory systems to high-performance vector backends
Building self-learning agents that improve accuracy through experience replay
Optimizing complex decision-making processes in multi-agent workflows

关于

主要功能

Advanced trajectory tracking and outcome judgment
Automatic memory distillation and pattern consolidation
Seamless MCP integration with 100% backward compatibility
High-performance vector retrieval (150x-12,500x faster than legacy)
Rich context synthesis and memory pruning via reasoning modules
2 GitHub stars

使用场景

Migrating legacy agent memory systems to high-performance vector backends
Building self-learning agents that improve accuracy through experience replay
Optimizing complex decision-making processes in multi-agent workflows