3 dépôts
Mechanisms for agents to refine reasoning patterns and strategies based on historical performance across multiple sessions.
Distinct from Session Tracking: Distinct from Session Tracking: focuses on the accumulation of learned strategies and performance optimization rather than just session correlation.
Explore 3 awesome GitHub repositories matching data & databases · Cross-Session Learning. Refine with filters or upvote what's useful.
SuperClaude Framework is an autonomous agent development platform designed for orchestrating complex software development lifecycles. It functions as a Python-based toolkit that enables the deployment of specialized, domain-specific agents capable of coordinating tasks, conducting multi-hop web research, and managing end-to-end project requirements through a unified command interface. The framework distinguishes itself through its iterative planning loops and persistent memory state, which allow agents to evaluate progress in real-time and refine their reasoning strategies across multiple ses
Tracks successful strategies across sessions to optimize future task execution and improve reasoning patterns.
PUA is an agentic workflow orchestrator and behavioral governance tool designed to enhance the reliability and autonomy of AI coding assistants. It functions as a prompting framework and extension that implements strict engineering standards and verification requirements to prevent hallucinations and premature task completion. The project distinguishes itself through high-agency enforcement mechanisms, including escalating prompt pressure and failure-driven recovery loops that automatically pivot problem-solving strategies after repeated errors. It utilizes a diagnosis-first workflow that man
Maintains a persistent long-term journal of lessons learned and failure patterns to prevent mistake repetition across sessions.
This project is an agentic development framework and autonomous software engineering system. It utilizes a coordinated network of specialized LLM agents to automate the full software development lifecycle, from codebase exploration and architectural planning to implementation and automated refactoring. The system is distinguished by an agentic memory system and a test-driven development orchestrator. It maintains project continuity across sessions by capturing architectural learnings and state in a persistent semantic database and enforces code quality through an automated cycle of generating
Stores and retrieves architectural learnings across sessions to refine agent reasoning and avoid repeated mistakes.