Store memories in tier-aware hierarchical memory — working / short-term / long-term — and recall with tier filters. Use for working-set context that should fade, vs facts that should persist, vs patterns that should be searchable forever.
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。
Three-tier memory matching how humans actually use memory. Each tier has different retention, retrieval cost, and query semantics.
| Tier | Retention | Use for |
|---|---|---|
| working | minutes (auto-expires) | The current task's scratch — files open, hypotheses, in-flight decisions |
| short | hours-days | Recent session context — what was decided this morning |
| long | indefinite (until pruned) | Cross-session facts, patterns, lessons |
agentdb_hierarchical_store(
key: <namespace key> // e.g. 'task:auth-refactor:hypothesis'
tier: 'working' | 'short' | 'long'
value: <stringified content>
ttl?: <seconds> // overrides tier default
metadata?: { topic, project, ... }
)
agentdb_hierarchical_recall(
query: <semantic or exact key>
tier?: <filter> // omit to search all tiers
k?: 5
)
agentdb_hierarchical_delete(
key: <namespace key>
tier?: <filter>
)
The delete tool was added in agentdb 3.0.0-alpha.13. Before that, there was no first-class way to remove an entry — re-indexing workflows like ruflo's /adr-index couldn't purge stale ADRs. Now they can.
working first — it's smallest, freshest, highest signal for "what am I doing right now."short if working returns nothing.long last for established knowledge.tier=undefined and let the bandit weight by tier × similarity.working — they expire and you lose them.long — it pollutes searches forever.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。