Memory Framework

Problem Addressed

In long-term interactions of AI assistants and agent systems, context information is easily lost, leading to models failing to learn from historical conversations, repeating errors, or making inconsistent decisions. The Memory Framework addresses this by providing a structured memory management solution, ensuring data persistence, efficient retrieval, and continuous optimization.

Core Capabilities and Working Principle

Based on OpenClaw improvements, the framework's core features include:
- Three-Tier Memory Hierarchy: L1, L2, L3 manage memory at different granularities, such as SESSION-STATE.md for short-term session state, brain/plan.md for long-term goals, and brain/me/identity.md for identity storage.
- WAL Protocol: Write-Ahead Logging ensures decisions are recorded before responses, guaranteeing data consistency and traceability.
- Working Buffer: A context buffering mechanism that automatically adjusts when context usage reaches 60%, referencing working-buffer.md to prevent overflow.
- Learnings System: Errors are logged via learnings/errors.json, supporting review and learning to reduce repeated mistakes.
- Daily Review: Conducted in the brain/tasks/daily/ directory, enabling daily summaries for continuous optimization of memory and decision processes.

Quick start involves editing configuration files: defining identity in brain/me/identity.md, setting goals in brain/plan.md, and managing current tasks in brain/tasks/active.md.

Applicable Scenarios and Cautions

• Applicable Scenarios: Personal AI assistants, team collaboration knowledge management systems; suitable for environments requiring long-term memory, error learning, and task tracking.
• Cautions: The framework relies on manual editing of configuration files, not suitable for fully automated systems; memory hierarchy needs proper setup to avoid redundancy; regular reviews are crucial, otherwise learning effects are limited; not applicable to scenarios requiring real-time high-concurrency memory.