ReasoningBank Intelligence

Use ReasoningBank's recordExperience method to log each task's approach, outcome metrics, and context. After accumulating enough experiences, call recommendStrategy to get the best-performing approach for new tasks of that type.

Use the learnPattern method to register patterns with triggers, actions, and confidence scores, then call matchPatterns against the current situation. ReasoningBank matches learned patterns to incoming contexts and returns relevant matches.

Yes, ReasoningBank integrates with AgentDB v3.0.0-alpha.10+ by setting the adapter to 'agentdb' during initialization. This enables durable storage, vector search for semantic pattern matching, and querying learned patterns by category and confidence.

Poor recommendations usually result from insufficient training data; aim for at least 100 recorded experiences per task type. Also ensure you record both successes and failures with rich context to improve pattern matching quality.

Transfer learning requires a similarity score between source and target domains, such as 0.8 for JavaScript to TypeScript code review. Low-similarity domain pairs will transfer unreliable knowledge, so validate transferred strategies with fresh experience recordings.