K-hop traversal from a starting node in AgentDB's graph. Use to explore neighborhoods, find reachable nodes, or visualize a memory's "context".
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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。
Walk outward from a node up to K hops, returning the visited subgraph.
agentdb_kHopNeighbors(
startId: <node id>
k: 1..5 // hops
edgeFilter?: [<relation>, ...] // limit to specific relations
direction?: 'in' | 'out' | 'both'
maxNodes?: 100 // safety cap
)
Returns: { nodes, edges, stats: { reachable, capped } }
| Goal | Pattern |
|---|---|
| Forward influence | direction: 'out', k: 3 |
| Backward dependencies | direction: 'in', k: 3 |
| Local neighborhood | direction: 'both', k: 1 |
| Causal chain only | edgeFilter: ['caused', 'supersedes'] |
| Skill composition graph | edgeFilter: ['composes', 'requires'], start: skill id |
K-hop fans out exponentially. With average degree d, k hops returns up to d^k nodes. AgentDB defaults maxNodes: 100 — when you hit the cap, stats.capped: true and you should rerun with smaller k or tighter edgeFilter.
k > 4 without edgeFilter — explosive fan-out destroys the answer's signal.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.
中文简介正在准备中。当前显示原文说明。
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中文简介正在准备中。当前显示原文说明。