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.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Persist a memory into AgentDB so a future session can recall it. Three flavors — pick the right one for what's being remembered:
| User phrasing | Use |
|---|---|
| "remember that X works for Y" | Pattern — agentdb_pattern_store |
| "save what happened when I tried X" | Episode (Reflexion) — agentdb_reflexion_store |
| "this is a reusable approach for problem X" | Skill — agentdb_skill_create |
agentdb_pattern_store(
content: "JWT refresh token rotation pattern: ...",
metadata: { topic, project, success: true }
)
Use for facts, conventions, anti-patterns, or anything that should resurface as a hint when a similar query comes up later.
agentdb_reflexion_store(
sessionId: <session>,
task: <what we were trying to do>,
input: <what we tried>,
output: <what happened>,
critique: <what we'd do differently>,
reward: 0..1,
success: true|false
)
Use right after a task completes — success OR failure. Failed episodes feed getCritiqueSummary; successful ones feed getSuccessStrategies.
agentdb_skill_create(
name: <short verb-noun>,
description: <what it does>,
precondition: <when to use>,
action: <how to do it>,
outcome: <what success looks like>
)
Use when a pattern has been validated 3+ times and you want it elevated to a first-class reusable skill (queryable by intent embedding).
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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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