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.
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。
Given a target memory (an episode, a failed test, an outgoing change), traverse the causal graph backwards through edges to surface the chain of preceding events that explain it.
agentdb_causal_explain(
targetMemoryId: <id>
maxDepth?: 3
minConfidence?: 0.5
edgeWeights?: 'uplift' | 'confidence' | 'product' // ranking strategy
)
Returns a path or DAG of (node, edge, node) tuples ranked by combined confidence × |uplift|. Each step carries the relation (caused, supersedes, depends-on, etc.) so the explanation reads naturally.
Why did "deploy-2026-05-04 failed migration" happen?
┌─ skill[migrate-add-not-null-column] confidence 0.92
│ ─[supersedes]→ skill[v1: migrate-with-default]
│ ─[caused]→ episode[long-running migration on 50M rows]
│ ─[caused]→ episode[deploy-2026-05-04 failed migration]
│
└─ adr[ADR-046: zero-downtime migration policy] confidence 0.78
─[depends-on]→ skill[migrate-add-not-null-column]
For complex traces, dispatch the agentdb-investigator agent (this plugin) — it walks deeper, cross-references with hierarchical memory, and writes a postmortem-shaped report.
maxDepth > 5 casually — graph fan-out gets exponential and the bandit's confidence weights don't compensate.confidence column. A high-uplift edge with confidence 0.3 is gossip, not evidence.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。
Record a causal relationship between two memories in AgentDB — "X caused Y", "A supersedes B", "patch-foo depends-on patch-bar". Use when the user is documenting cause/effect, dependencies, supersessions, or after-action analysis.
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。