菜单
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.
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基于 SOC 职业分类
| name | agentdb-hierarchical-store |
| description | 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. |
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.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.
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.
Initialize an AgentDB Cognitive Container (.rvf file) in the current project. Sets up storage, embedder config, and the agentdb MCP server. Use when the user is starting a new project that needs vector memory, or asks to "set up agentdb" / "init agentdb".
Show AgentDB health — pattern count, embedder status, cache hit rate, learning gain since init. Use when the user asks "is agentdb working?", "how many memories?", "show agentdb stats", or after long-running sessions to confirm state.
Execute Cypher queries against AgentDB's graph backend. Use when the user wants to write a custom traversal that the standard tools don't cover, or when explaining graph state.
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.