Your assistant can build the tools that manage itself. Ask for a workflow, and 30 minutes later you have a CLI plus pipelines that run as one call. Lobster is the missing piece: deterministic pipelines, explicit approvals, and resumable state.
Today, complex workflows require many back-and-forth tool calls. Each call costs tokens, and the LLM has to orchestrate every step. Lobster moves that orchestration into a typed runtime:
Lobster is intentionally small. The goal is not "a new language," it's a predictable, AI-friendly pipeline spec with first-class approvals and resume tokens.
- **Approve/resume is built in**: A normal program can prompt a human, but it can’t *pause and resume* with a durable token without you inventing that runtime yourself.
- **Determinism + auditability**: Pipelines are data, so they’re easy to log, diff, replay, and review.
- **Constrained surface for AI**: A tiny grammar + JSON piping reduces “creative” code paths and makes validation realistic.
- **Safety policy baked in**: Timeouts, output caps, sandbox checks, and allowlists are enforced by the runtime, not each script.
- **Still programmable**: Each step can call any CLI or script. If you want JS/TS, generate `.lobster` files from code.
Lobster can run YAML/JSON workflow files with `name`, `args`, `steps`, `env`, `condition`, and `approval` fields. In Clawdbot tool calls, set `pipeline` to the file path.
```yaml
name: inbox-triage
args:
tag:
default: "family"
steps:
- id: collect
command: inbox list --json
- id: categorize
command: inbox categorize --json
stdin: $collect.stdout
- id: approve
command: inbox apply --approve
stdin: $categorize.stdout
approval: required
- id: execute
command: inbox apply --execute
stdin: $categorize.stdout
condition: $approve.approved
```
Notes:
-`stdin: $step.stdout` and `stdin: $step.json` pass a prior step’s output.
-`condition` (or `when`) can gate steps on `$step.approved`.
Install the Lobster CLI on the **same host** that runs the Clawdbot Gateway (see the [Lobster repo](https://github.com/clawdbot/lobster)), and ensure `lobster` is on `PATH`.
Use `approve --preview-from-stdin --limit N` to attach a JSON preview to approval requests without custom jq/heredoc glue. Resume tokens are now compact: Lobster stores workflow resume state under its state dir and hands back a small token key.
OpenProse pairs well with Lobster: use `/prose` to orchestrate multi-agent prep, then run a Lobster pipeline for deterministic approvals. If a Prose program needs Lobster, allow the `lobster` tool for sub-agents via `tools.subagents.tools`. See [OpenProse](/prose).
One public example: a “second brain” CLI + Lobster pipelines that manage three Markdown vaults (personal, partner, shared). The CLI emits JSON for stats, inbox listings, and stale scans; Lobster chains those commands into workflows like `weekly-review`, `inbox-triage`, `memory-consolidation`, and `shared-task-sync`, each with approval gates. AI handles judgment (categorization) when available and falls back to deterministic rules when not.