memory-checkpoint

Solid

Defines how to save and load agent state as checkpoint files.

AI & Automation 3 stars 0 forks Updated yesterday MIT

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Skill Content

# Memory Checkpoint Defines how to save a checkpoint and how to load one into SI. When to checkpoint is determined by the calling context — the project, the war room, or the agent's own judgment. --- ## How to Save Create a timestamped folder under `memory/checkpoints/`. Inside it, write one file per agent indexed by agent number. ``` memory/checkpoints/YYYYMMDD_HHMMSS/ ├── agent-0.md ├── agent-1.md └── ... ``` Each `agent-N.md` captures that agent's state. No length constraints — summarise what is needed to resume. A good checkpoint captures what would be hard to reconstruct, not a verbatim log. ```markdown # Agent <N> — <timestamp> **Role**: <role> **Trigger**: <what caused this checkpoint> **Completed**: <what was just finished> **Next**: <exact next action> ## State <key facts, file paths, env state, decisions made, blockers — whatever is needed to resume> ``` After writing all agent files, update `memory/SUMMARY.md` — rewrite it to point to the latest checkpoint. ```markdown # Summary - **Last checkpoint**: `memory/checkpoints/<timestamp>/` - **Status**: <not_started | in_progress | blocked | complete> ## State <key facts — file paths, important values, decisions that affect what comes next> ## Resume From <one clear instruction: what to do next, any preconditions> ``` --- ## How to Load On resume, inject into SI in this order: 1. **Read `memory/SUMMARY.md`** — always first. Get current status and the latest checkpoint path. 2. **Read the relevant `agen...

Details

Author
Daisybastioned440
Repository
Daisybastioned440/lite-research-agents
Created
3 months ago
Last Updated
yesterday
Language
Python
License
MIT

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