memory-checkpoint
SolidDefines how to save and load agent state as checkpoint files.
AI & Automation 3 stars
0 forks Updated yesterday MIT
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# 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
Integrates with
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