Search and manage hyperedges — n-ary relationships between memories. Use for swarm membership, multi-cause incidents, or any "this involves all of (A, B, C, D)" relationship that doesn't fit a binary edge.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Hyperedges connect more than two nodes — useful when a single relationship genuinely involves many participants.
agentdb_hyperedge_create(
nodes: [<id>, <id>, ...]
description: <one-liner>
embedding?: Float32Array
confidence?: 0..1
metadata?: { ... }
)
agentdb_hyperedge_search(
query: <semantic | nodeId>
topK?: 10
)
agentdb_hyperedge_delete(
id: <hyperedge id>
)
The agentdb_hyperedge_delete tool was added in agentdb 3.0.0-alpha.13.
Hyperedges are searchable by their description embedding — useful for "find all multi-agent collaborations on auth-related work" without enumerating every binary edge.
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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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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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