Lobster is a workflow shell that lets OpenClaw run multi-step tool sequences as a single, deterministic operation with explicit approval checkpoints.

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:

• One call instead of many: OpenClaw runs one Lobster tool call and gets a structured result.
• Approvals built in: Side effects (send email, post comment) halt the workflow until explicitly approved.
• Resumable: Halted workflows return a token; approve and resume without re-running everything.

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.

OpenClaw launches the local lobster CLI in tool mode and parses a JSON envelope from stdout. If the pipeline pauses for approval, the tool returns a resumeToken so you can continue later.

Build tiny commands that speak JSON, then chain them into a single Lobster call. (Example command names below — swap in your own.)

inbox list --json
inbox categorize --json
inbox apply --json

{
  "action": "run",
  "pipeline": "exec --json --shell 'inbox list --json' | exec --stdin json --shell 'inbox categorize --json' | exec --stdin json --shell 'inbox apply --json' | approve --preview-from-stdin --limit 5 --prompt 'Apply changes?'",
  "timeoutMs": 30000
}

If the pipeline requests approval, resume with the token:

{
  "action": "resume",
  "token": "<resumeToken>",
  "approve": true
}

AI triggers the workflow; Lobster executes the steps. Approval gates keep side effects explicit and auditable.

Example: map input items into tool calls:

gog.gmail.search --query 'newer_than:1d' \
  | openclaw.invoke --tool message --action send --each --item-key message --args-json '{"provider":"telegram","to":"..."}'

For workflows that need a structured LLM step, enable the optional llm-task plugin tool and call it from Lobster. This keeps the workflow deterministic while still letting you classify/summarize/draft with a model.

Enable the tool:

{
  "plugins": {
    "entries": {
      "llm-task": { "enabled": true }
    }
  },
  "agents": {
    "list": [
      {
        "id": "main",
        "tools": { "allow": ["llm-task"] }
      }
    ]
  }
}

Use it in a pipeline:

openclaw.invoke --tool llm-task --action json --args-json '{
  "prompt": "Given the input email, return intent and draft.",
  "input": { "subject": "Hello", "body": "Can you help?" },
  "schema": {
    "type": "object",
    "properties": {
      "intent": { "type": "string" },
      "draft": { "type": "string" }
    },
    "required": ["intent", "draft"],
    "additionalProperties": false
  }
}'

See LLM Task for details and configuration options.

Lobster can run YAML/JSON workflow files with name, args, steps, env, condition, and approval fields. In OpenClaw tool calls, set pipeline to the file path.

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 OpenClaw Gateway (see the Lobster repo), and ensure lobster is on PATH. If you want to use a custom binary location, pass an absolute lobsterPath in the tool call.

Lobster is an optional plugin tool (not enabled by default).

Recommended (additive, safe):

{
  "tools": {
    "alsoAllow": ["lobster"]
  }
}

Or per-agent:

{
  "agents": {
    "list": [
      {
        "id": "main",
        "tools": {
          "alsoAllow": ["lobster"]
        }
      }
    ]
  }
}

Avoid using tools.allow: ["lobster"] unless you intend to run in restrictive allowlist mode.

Note: allowlists are opt-in for optional plugins. If your allowlist only names plugin tools (like lobster), OpenClaw keeps core tools enabled. To restrict core tools, include the core tools or groups you want in the allowlist too.

Without Lobster:

User: "Check my email and draft replies"
→ openclaw calls gmail.list
→ LLM summarizes
→ User: "draft replies to #2 and #5"
→ LLM drafts
→ User: "send #2"
→ openclaw calls gmail.send
(repeat daily, no memory of what was triaged)

With Lobster:

{
  "action": "run",
  "pipeline": "email.triage --limit 20",
  "timeoutMs": 30000
}

Returns a JSON envelope (truncated):

{
  "ok": true,
  "status": "needs_approval",
  "output": [{ "summary": "5 need replies, 2 need action" }],
  "requiresApproval": {
    "type": "approval_request",
    "prompt": "Send 2 draft replies?",
    "items": [],
    "resumeToken": "..."
  }
}

User approves → resume:

{
  "action": "resume",
  "token": "<resumeToken>",
  "approve": true
}

One workflow. Deterministic. Safe.

Run a pipeline in tool mode.

{
  "action": "run",
  "pipeline": "gog.gmail.search --query 'newer_than:1d' | email.triage",
  "cwd": "/path/to/workspace",
  "timeoutMs": 30000,
  "maxStdoutBytes": 512000
}

Run a workflow file with args:

{
  "action": "run",
  "pipeline": "/path/to/inbox-triage.lobster",
  "argsJson": "{\"tag\":\"family\"}"
}

Continue a halted workflow after approval.

{
  "action": "resume",
  "token": "<resumeToken>",
  "approve": true
}

• lobsterPath: Absolute path to the Lobster binary (omit to use PATH).
• cwd: Working directory for the pipeline (defaults to the current process working directory).
• timeoutMs: Kill the subprocess if it exceeds this duration (default: 20000).
• maxStdoutBytes: Kill the subprocess if stdout exceeds this size (default: 512000).
• argsJson: JSON string passed to lobster run --args-json (workflow files only).

Lobster returns a JSON envelope with one of three statuses:

• ok → finished successfully
• needs_approval → paused; requiresApproval.resumeToken is required to resume
• cancelled → explicitly denied or cancelled

The tool surfaces the envelope in both content (pretty JSON) and details (raw object).

If requiresApproval is present, inspect the prompt and decide:

• approve: true → resume and continue side effects
• approve: false → cancel and finalize the workflow

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.

• Local subprocess only — no network calls from the plugin itself.
• No secrets — Lobster doesn’t manage OAuth; it calls OpenClaw tools that do.
• Sandbox-aware — disabled when the tool context is sandboxed.
• Hardened — lobsterPath must be absolute if specified; timeouts and output caps enforced.

• lobster subprocess timed out → increase timeoutMs, or split a long pipeline.
• lobster output exceeded maxStdoutBytes → raise maxStdoutBytes or reduce output size.
• lobster returned invalid JSON → ensure the pipeline runs in tool mode and prints only JSON.
• lobster failed (code …) → run the same pipeline in a terminal to inspect stderr.

• Plugins
• Plugin tool authoring

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.

• Thread: https://x.com/plattenschieber/status/2014508656335770033
• Repo: https://github.com/bloomedai/brain-cli