연결

Slowave

slowave-aibyslowave-ai
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25
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개발자 도구
생산성 및 워크플로
데이터 과학 및 ML

Provides a local, adaptive memory layer for AI agents, allowing them to retain context and learn from past interactions without requiring an external LLM for memory management.

Slowave creates a living local memory layer for your AI coding agents, ensuring that decisions, constraints, and learned experiences persist across different sessions and tools. Unlike static retrieval systems, Slowave incorporates a continuous feedback loop where agents' use of memory strengthens useful information and weakens irrelevant data, leading to a system that improves over time. This approach ensures agents can reuse past knowledge, adapt to new information, and learn from experience, all while operating locally and without needing additional LLM API keys for memory maintenance.

주요 기능

01Learns from experience, saving decisions, outcomes, and multi-step solutions
0210 GitHub stars
03Retains context across different AI agent sessions and tasks
04Operates locally using SQLite for storage, ensuring data privacy
05Improves memory salience through continuous agent feedback and usage
06Requires no LLM API key for its core memory maintenance and retrieval

사용 사례

01Enabling AI agents to learn and reuse common procedures, preferences, and constraints across tasks
02Maintaining development context when switching between various AI coding agents (e.g., Claude Code, Cursor, Codex)
03Providing a traceable record of agent interactions, retrievals, and feedback through a local dashboard