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.
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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.还没有评价。欢迎分享你的使用感受。
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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.
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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.
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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.
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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.
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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.
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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.
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