for agents/docs/llms.txtv0.6.8 · 24 Sep 2026

Docs / Guides

Working with AI agents

sgit is designed to be driven by AI agents as well as humans. A vault is just a folder. The agent reads and writes files normally; sgit handles versioning, encryption, and sync. This page covers the agent-facing surface.

The session pattern

# start of session: get the workspace
$ sgit clone <vault-key> workspace   # or: sgit pull, if already cloned
# … agent works on files normally …
# end of session: persist the state
$ sgit commit -m "session: findings and next steps"
$ sgit push

The next session (hours or weeks later, on any machine) runs sgit pull and continues. State survives the context window, encrypted end to end.

sgit write, the surgical commit

When an agent needs to record one result, cloning a whole vault is waste. write commits a file directly to the vault HEAD in a single call: no working-directory scan, no full clone needed.

$ sgit write notes/finding.md --file result.md \
    --message "agent A: analysis" --push --json
{ "status": "pushed", "path": "notes/finding.md",
  "blob_id": "obj-cas-imm-9c2e41ab77d0" }

Machine-readable everything

NeedCommand
Read one file, structuredsgit cat <path> --json · sgit cat --id <blob-id> (zero network calls)
List files with fetch statesgit ls --json / --ids
History for pipelinessgit history log --json · history diff --json
Health checkssgit doctor --json

Fast cold starts

Agents run on time budgets. Three clone modes keep startup cheap:

Multi-agent collaboration

Give each agent a named branch; the two-branch model guarantees isolation of work-in-progress. Two commands make peer review safe:

# agent A  $ sgit branch new feature-analysis … commit … push
# agent B  $ sgit history show obj-cas-imm-c4e81a   # look, don't merge
# human    $ sgit pull → review in SG/Vault → merge
For Claude users: a packaged sgit Skill teaches a Claude session this entire workflow (install, clone, work, commit, push) so cross-session persistent state works out of the box.