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?"
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Pull relevant past memories into the current context.
| Need | Tool | Returns |
|---|---|---|
| Similar past tasks | agentdb_reflexion_recall | Episodes, top-k by similarity |
| Lessons from past failures | agentdb_critique_summary | Combined critique text |
| What worked last time | agentdb_success_strategies | Approach summaries from high-reward episodes |
| Generic patterns / facts | agentdb_pattern_search | Patterns ranked by similarity |
| Reusable skills by intent | agentdb_skill_search | Skills ranked by precondition match |
agentdb_reflexion_recall with k=5, minReward=0.5.agentdb_pattern_search with the same query, k=5.agentdb_record_feedback (or recordFeedback via the library) so the bandit learns.minReward — drop low-quality matches (default 0.3).onlyFailures — explicitly query the postmortem set when debugging.onlySuccesses — only winning approaches when copying a strategy.timeWindowDays — recent context only when the codebase has shifted.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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