d-o-hub Checkpoint & Handoff
When to Use
- Saving progress mid-episode for session recovery
- Creating handoff packs for agent-to-agent context transfer
- Resuming work from a previous agent's checkpoint
- Long-running tasks that may be interrupted
CLI Commands
| Command | Purpose |
|---|---|
do-memory-cli episode checkpoint <EPISODE_ID> |
Create checkpoint of current episode state |
do-memory-cli episode checkpoint <EPISODE_ID> --note "reason" |
Checkpoint with annotation |
MCP Tools
| Tool | Parameters | Purpose |
|---|---|---|
checkpoint_episode |
episode_id, note (optional) | Save episode state snapshot |
get_handoff_pack |
episode_id | Generate complete context pack for handoff |
resume_from_handoff |
handoff_pack | Resume episode from a handoff pack |
Workflow: Agent Handoff
Agent A: Working on episode...
1. checkpoint_episode(episode_id, note: "completed API design")
2. get_handoff_pack(episode_id)
→ Returns: { episode state, steps, patterns applied, context }
Agent B: Resuming work...
1. resume_from_handoff(handoff_pack)
→ Restores context, continues episode
Workflow: Session Recovery
1. Agent hits context limit or session ends
2. checkpoint_episode(episode_id, note: "context limit reached")
3. New session starts
4. get_handoff_pack(episode_id)
5. resume_from_handoff(pack)
6. Continue adding steps to the same episode
Abstention Checkpoints
The system automatically creates checkpoints when an agent abstains (decides not to act). These are queryable via list_abstention_checkpoints() for analyzing decision patterns.
Best Practices
- Checkpoint before expensive operations (in case of failure)
- Include meaningful notes describing what was accomplished
- Use handoff packs for cross-agent context, not just resumption
- Handoff packs contain patterns applied + episode steps — sufficient for cold-start