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.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Reports the live state of the AgentDB Cognitive Container backing this session.
agentdb_pattern_stats to get pattern count, hit rate, recent insert/search latencies.agentdb_reflexion_stats for episode count and per-task win rates.agentdb_bandit_stats (if agentdb-learning is installed) for arm reward summaries.patterns 12,847
episodes 384
skills 22
hit rate 94.2%
avg search 0.83 ms
learning gain +18% since init
storage 38 MB (.rvf)
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.