Ask the AgentDB bandit which RL algorithm / skill / pattern fits the current task best. Use at task start when there are multiple plausible approaches and you want the data-driven pick.
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See how to installReview the instructions the agent will receive before you install.
Ask the Thompson Sampling bandit which approach to use for the current task.
agentdb_learning_route(
task: <description>
candidates?: [<skill_id> | <algo>, ...] // omit to consider everything
context?: { stack, project, ... }
)
Returns: { picked, expectedReward, confidence, alternatives: [...] }
Thompson Sampling: each candidate has a Beta(α, β) posterior over reward. The bandit samples once from each, picks the highest sample. Exploration emerges naturally — uncertain candidates get tried until their posterior tightens.
Four bandit decision points across AgentDB:
The router unifies them: it returns the picked candidate AND a decisionTrace showing which decision points fired.
const { picked } = await agentdb_learning_route(...)
const result = await runWith(picked)
agentdb_bandit_update(arm: picked, reward: result.reward)
The agentdb-feedback skill (this plugin) wraps the close-loop step.
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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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