Retrieve relevant memories for the current task from AgentDB. Use at the start of a task to load prior knowledge, when stuck to surface what worked before, or when the user asks "what do we know about X" / "have we done this before?"
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Pull relevant past memories into the current context.
| Need | Tool | Returns |
|---|---|---|
| Similar past tasks | agentdb_reflexion_recall | Episodes, top-k by similarity |
| Lessons from past failures | agentdb_critique_summary | Combined critique text |
| What worked last time | agentdb_success_strategies | Approach summaries from high-reward episodes |
| Generic patterns / facts | agentdb_pattern_search | Patterns ranked by similarity |
| Reusable skills by intent | agentdb_skill_search | Skills ranked by precondition match |
agentdb_reflexion_recall with k=5, minReward=0.5.agentdb_pattern_search with the same query, k=5.agentdb_record_feedback (or recordFeedback via the library) so the bandit learns.minReward — drop low-quality matches (default 0.3).onlyFailures — explicitly query the postmortem set when debugging.onlySuccesses — only winning approaches when copying a strategy.timeWindowDays — recent context only when the codebase has shifted.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.
日本語の概要は準備中です。原文の説明を表示しています。