sandclaw-memory

What it does

A 3-layer temporal RAG memory library for Python. Zero external dependencies. just stdlib + sqlite3. 43KB installed. Core idea: a self-growing tag dictionary. Day 1, every tag extraction calls your AI. Day 90, ~90% resolve from a local keyword map instantly. Cost goes down over time. L1: 3-day Markdown logs. L2: 30-day AI summaries. L3: SQLite FTS5 permanent archive (~1ms search). Works with any AI, OpenAI, Claude, Gemini, or local models. pip install sandclaw-memory

• LA
  LocalGPT – A local-first AI assistant in Rust with persistent memoryFeb 2026 · github.com · ▲331

  I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system). It compiles to a single ~27MB binary — no Node.js, Docker, or Python required. Key features: - Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider:…

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  ByteRover Memory System for OpenClaw Mar 2026 · byterover.dev · ▲146

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• AM
  AI memory with biological decay (52% recall)Apr 2026 · github.com · ▲98

  Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…

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  memories.shFeb 2026 · ▲50

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• AL
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