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.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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).
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。