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Search with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.

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。

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.

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。

Store a memory in AgentDB — an episode (task + outcome + critique), a pattern, or a skill. Use when the user says "remember this", "save this for later", "add to memory", or when the agent has just succeeded/failed at a task and the lesson is worth keeping.

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。

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.

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。

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.

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。

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.

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。

K-hop traversal from a starting node in AgentDB's graph. Use to explore neighborhoods, find reachable nodes, or visualize a memory's "context".

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。

Close the learning loop — record reward signal for an action AgentDB suggested. Use after using anything from agentdb_pattern_search / reflexion_recall / skill_search / learning_route. The bandit needs the signal to improve.

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。

Promote a validated pattern into a reusable Skill in AgentDB's skill library. Use when the same approach has worked 3+ times across episodes, or when the user explicitly says "make this a skill" / "save this as reusable".

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。

Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

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。

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".

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。

Ask the AgentDB bandit which RL algorithm / skill / pattern fits the current task best. Use at task start when there are multiple plausible approaches and you want the data-driven pick.

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。

Train one of AgentDB's 9 RL algorithms on a stream of episodes. Use when the user has accumulated successful/failed episodes and wants to derive a policy, or when a task type is repeated enough to benefit from RL routing.

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。

Retrieve relevant memories for the current task from AgentDB. Use at the start of a task to load prior knowledge, when stuck to surface what worked before, or when the user asks "what do we know about X" / "have we done this before?"

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。

Maximal Marginal Relevance rerank — get diverse top-k instead of redundant top-k. Use when standard search returns 5 near-duplicates, or when you want broader coverage of a topic.

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。

Hybrid search — BM25 keyword + dense vector fused with Reciprocal Rank Fusion. Use when queries have specific identifiers, code symbols, or proper nouns that pure semantic search might miss.

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。

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。

Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges

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。

Turn a feature request into a minimal, file-level implementation plan before any code.

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。

Search and inspect the harness memory namespace (HNSW + emergent-time decay).

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。