learning-engine

To make AI agents learn from past failures, you use a continuous self-learning loop that observes task outcomes, reflects on errors, and proposes actionable improvements. This engine automates that cycle using the SICA approach to boost performance.

Memory consolidation for AI workflows is the process of storing episodic task traces and converting them into reusable semantic memory. This engine implements a Mem0-style approach to retain knowledge across agent tasks.

To generate new skills from repeated patterns, you can use dynamic skill generation mechanisms. This engine implements a ReCreate-style approach that observes recurring task patterns and automatically creates new capabilities for agents.

Yes, you can automate error recovery to reduce manual interventions by using structured recovery strategies. This engine provides VIGIL-style error recovery to help agents self-heal and minimize downtime during workflow execution.

No external dependencies are required to run this self-learning engine. It operates independently using local CLI scripts and YAML metadata configurations to define and control the learning behaviors for your agent workflows.

The best way to configure an agent's learning loop behavior is through YAML metadata. You can define parameters for the Observe-Reflect-Propose-Test-Integrate cycle and manage execution via the engine's local CLI scripts.