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.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Close the loop on a memory or routing decision so AgentDB's bandit learns.
agentdb_record_feedback(
id: <pattern/skill/episode/decision id>
reward: -1..1
context?: { task, outcome, latency, ... }
)
agentdb_bandit_update(
arm: <bandit arm name>
reward: -1..1
)
| Outcome | Reward |
|---|---|
| Used the suggestion, task succeeded | +1.0 |
| Used the suggestion, task partial success | +0.5 |
| Used the suggestion, didn't help | 0.0 |
| Used the suggestion, made things worse | -0.5 |
| Ignored the suggestion (other reason) | -0.1 (mild downweight) |
| Rejected as wrong / harmful | -1.0 |
const hits = await agentdb_pattern_search(query)
for (const h of useful(hits)) {
use(h)
await agentdb_record_feedback(h.id, +1)
}
for (const h of skipped(hits)) {
await agentdb_record_feedback(h.id, -0.1)
}
Without negative feedback, the bandit only sees "winners" and exploration starves.
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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.
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.
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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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