🦞 Lobster

An OpenClaw-native workflow shell: typed (JSON-first) pipelines, jobs, and approval gates.

Example of Lobster at work

OpenClaw (or any other AI agent) can use lobster as a workflow engine and avoid re-planning every step — saving tokens while improving determinism and resumability.

Watching a PR that hasn't had changes

node bin/lobster.js "workflows.run --name github.pr.monitor --args-json '{\"repo\":\"openclaw/openclaw\",\"pr\":1152}'"
[
  {
    "kind": "github.pr.monitor",
    "repo": "openclaw/openclaw",
    "prNumber": 1152,
    "key": "github.pr:openclaw/openclaw#1152",
    "changed": false,
    "summary": {
      "changedFields": [],
      "changes": {}
    },
    "prSnapshot": {
      "author": {
        "id": "MDQ6VXNlcjE0MzY4NTM=",
        "is_bot": false,
        "login": "vignesh07",
        "name": "Vignesh"
      },
      "baseRefName": "main",
      "headRefName": "feat/lobster-plugin",
      "isDraft": false,
      "mergeable": "MERGEABLE",
      "number": 1152,
      "reviewDecision": "",
      "state": "OPEN",
      "title": "feat: Add optional lobster plugin tool (typed workflows, approvals/resume)",
      "updatedAt": "2026-01-18T20:16:56Z",
      "url": "https://github.com/openclaw/openclaw/pull/1152"
    }
  }
]

And a PR that has a state change (in this case an approved PR)

 node bin/lobster.js "workflows.run --name github.pr.monitor --args-json '{\"repo\":\"openclaw/openclaw\",\"pr\":1200}'"
[
  {
    "kind": "github.pr.monitor",
    "repo": "openclaw/openclaw",
    "prNumber": 1200,
    "key": "github.pr:openclaw/openclaw#1200",
    "changed": true,
    "summary": {
      "changedFields": [
        "number",
        "title",
        "url",
        "state",
        "isDraft",
        "mergeable",
        "reviewDecision",
        "updatedAt",
        "baseRefName",
        "headRefName"
      ],
      "changes": {
        "number": {
          "from": null,
          "to": 1200
        },
        "title": {
          "from": null,
          "to": "feat(tui): add syntax highlighting for code blocks"
        },
        "url": {
          "from": null,
          "to": "https://github.com/openclaw/openclaw/pull/1200"
        },
        "state": {
          "from": null,
          "to": "MERGED"
        },
        "isDraft": {
          "from": null,
          "to": false
        },
        "mergeable": {
          "from": null,
          "to": "UNKNOWN"
        },
        "reviewDecision": {
          "from": null,
          "to": ""
        },
        "updatedAt": {
          "from": null,
          "to": "2026-01-19T05:06:09Z"
        },
        "baseRefName": {
          "from": null,
          "to": "main"
        },
        "headRefName": {
          "from": null,
          "to": "feat/tui-syntax-highlighting"
        }
      }
    },
    "prSnapshot": {
      "author": {
        "id": "MDQ6VXNlcjE0MzY4NTM=",
        "is_bot": false,
        "login": "vignesh07",
        "name": "Vignesh"
      },
      "baseRefName": "main",
      "headRefName": "feat/tui-syntax-highlighting",
      "isDraft": false,
      "mergeable": "UNKNOWN",
      "number": 1200,
      "reviewDecision": "",
      "state": "MERGED",
      "title": "feat(tui): add syntax highlighting for code blocks",
      "updatedAt": "2026-01-19T05:06:09Z",
      "url": "https://github.com/openclaw/openclaw/pull/1200"
    }
  }
]

Goals

• Typed pipelines (objects/arrays), not text pipes.
• Local-first execution.
• No new auth surface: Lobster must not own OAuth/tokens.
• Composable macros that OpenClaw (or any agent) can invoke in one step to save tokens.

Quick start

From this folder:

• pnpm install
• pnpm test
• pnpm lint
• node ./bin/lobster.js --help
• node ./bin/lobster.js doctor
• node ./bin/lobster.js "exec --json --shell 'echo [1,2,3]' | where '0>=0' | json"

Notes

• pnpm test runs tsc and then executes tests against dist/.
• bin/lobster.js prefers the compiled entrypoint in dist/ when present.

Commands

• exec: run OS commands
• exec --stdin raw|json|jsonl: feed pipeline input into subprocess stdin
• where, pick, head: data shaping
• json, table: renderers
• approve: approval gate (TTY prompt or --emit for OpenClaw integration)

Next steps

• OpenClaw integration: ship as an optional OpenClaw plugin tool.

Workflow files

Lobster workflow files are meant to read like small scripts:

• run: or command: for deterministic shell/CLI steps
• pipeline: for native Lobster stages like llm.invoke
• approval: for hard workflow gates between steps
• stdin: $step.stdout or stdin: $step.json to pass data forward

lobster run path/to/workflow.lobster
lobster run --file path/to/workflow.lobster --args-json '{"tag":"family"}'

Example file:

name: jacket-advice
args:
  location:
    default: Phoenix
steps:
  - id: fetch
    run: weather --json ${location}

  - id: confirm
    approval: Want jacket advice from the LLM?
    stdin: $fetch.json

  - id: advice
    pipeline: >
      llm.invoke --prompt "Given this weather data, should I wear a jacket?
      Be concise and return JSON."
    stdin: $fetch.json
    when: $confirm.approved

Notes:

• run: and command: are equivalent; run: is the preferred spelling for new files.
• pipeline: shares the same args/env/results model as shell steps, so later steps can still reference $step.stdout or $step.json.
• If you need a human checkpoint before an LLM call, use a dedicated approval: step in the workflow file rather than approve inside the nested pipeline.
• cwd, env, stdin, when, and condition work for both shell and pipeline steps.
• Use retry, timeout_ms, and on_error per step to control transient-failure behavior and recovery.
• Approval steps can optionally enforce identity constraints:
  • approval.required_approver (or requiredApprover) requires an exact approver id.
  • approval.require_different_approver (or requireDifferentApprover) requires approver id to differ from initiator.
  • approval.initiated_by (or initiatedBy) sets the initiator id for comparison.
  • LOBSTER_APPROVAL_INITIATED_BY can provide a default initiator id at run time.
  • LOBSTER_APPROVAL_APPROVED_BY is used at resume/approval time for identity checks.

Command-level input requests

Pipeline commands can call ctx.requestInput({ prompt, responseSchema, defaults, subject, suspendedState }) to pause in tool mode, workflows, or the SDK and resume the same command after a structured response. CLI/tool resume tokens store only a state key; the persisted state validates the suspended request metadata before returning the submitted response to the command. SDK same-command resumes store the command frame in the configured SDK state directory.

Commands are re-run on resume, so they must be idempotent until requestInput returns. Array-backed command input is snapshotted with bounds for replay; lazy stream input is not buffered and requires a compact JSON suspendedState supplied by the command. On resume, call ctx.requestInput.getSuspendedState() before reading lazy input to restore that command-owned continuation state.

Visualizing workflows

Use lobster graph to inspect workflow structure before execution.

lobster graph --file path/to/workflow.lobster
lobster graph --file path/to/workflow.lobster --format mermaid
lobster graph --file path/to/workflow.lobster --format dot
lobster graph --file path/to/workflow.lobster --format ascii
lobster graph --file path/to/workflow.lobster --args-json '{"location":"Seattle"}'

What gets visualized:

• each workflow step as a node (run, pipeline, approval, etc.)
• data-flow edges from stdin: $step.stdout / $step.json references
• conditional dependencies from when: / condition: expressions
• approval gates as diamond-shaped nodes in mermaid and dot output

Format notes:

• mermaid (default): emits flowchart TD text for GitHub/Markdown rendering
• dot: emits Graphviz DOT syntax
• ascii: emits a terminal-friendly node/edge list

Calling LLMs from workflows

Use llm.invoke from a native pipeline: step for model-backed work:

llm.invoke --prompt 'Summarize this diff'
llm.invoke --provider openclaw --prompt 'Summarize this diff'
llm.invoke --provider pi --prompt 'Summarize this diff'

Provider resolution order:

• --provider
• LOBSTER_LLM_PROVIDER
• auto-detect from environment

Built-in providers today:

• openclaw via OPENCLAW_URL / OPENCLAW_TOKEN
• pi via LOBSTER_PI_LLM_ADAPTER_URL (typically supplied by the Pi extension)
• http via LOBSTER_LLM_ADAPTER_URL

Workflow _meta.cost and cost_limit use a static pricing table plus optional overrides from LOBSTER_LLM_PRICING_JSON, for example {"my-model":{"input":1.0,"output":2.0}} in USD per million tokens. Unknown or missing model IDs still record token counts with zero estimated cost, but Lobster warns on stderr so stale or missing pricing does not fail silently.

A cached or replayed answer is not billed again: a model call is counted once, in the run that made it, however many later steps re-emit its answer. This holds while the answer stays inside Lobster: through pipelines, renderers, projections, run state, workflow: steps and a resume. It does not survive a stage that hands the items to an external process and reads them back — exec --stdin json --json ... — because what comes back is whatever that process printed, and Lobster cannot tell a faithful copy of a replay from a fresh claim to have made the call. Such a step is billed as a call, which is what earlier versions did everywhere. A workflow: step counts what its sub-workflow spent, and a replay the sub-workflow returned is not billed a second time by the run that composed it. Spend also survives a pause — a workflow that stops at an approval or input gate keeps what it has recorded, so _meta.cost covers the whole run after a resume and cost_limit applies to the whole run rather than to the steps after the last gate.

llm_task.invoke remains available as a backward-compatible alias for the OpenClaw provider.

Calling configured OpenClaw agents

Use openclaw.agent when a workflow needs a configured OpenClaw agent rather than a direct model call:

openclaw.agent --agent ops --prompt 'Summarize these logs'
openclaw.agent --agent ops --session-key incident-42 --model openai/gpt-5.4 --prompt 'Continue the investigation'

The command delegates agent identity, model defaults and overrides, sessions, authentication, and execution to the installed openclaw agent CLI. It accepts --agent, --session-key, --session-id, --model, --thinking, --timeout, and --local, and returns OpenClaw's structured --json response. Pipeline input is appended to the prompt as labeled JSONL.

pipeline: vs run: for LLM calls

• Use pipeline: for openclaw.agent, llm.invoke, and llm_task.invoke (they are Lobster pipeline stages, not shell executables).
• Use run: only for real binaries in your shell (for example openclaw.invoke).

Example (stdin from a prior step is passed to the LLM as artifacts):

steps:
  - id: make_words
    run: echo "One two three four five six"

  - id: count_words
    pipeline: llm_task.invoke --prompt "How many words have been pasted below?"
    stdin: $make_words.stdout

Calling OpenClaw tools from workflows

Shell run: steps execute in your system shell, so OpenClaw tool calls there must be real executables.

If you install Lobster via npm/pnpm, it installs a small shim executable named:

• openclaw.invoke (preferred)
• clawd.invoke (alias)

These shims forward to the Lobster pipeline command of the same name.

Example: invoke llm-task

Prereqs:

• OPENCLAW_URL points at a running OpenClaw gateway
• optionally OPENCLAW_TOKEN if auth is enabled

export OPENCLAW_URL=http://127.0.0.1:18789
# export OPENCLAW_TOKEN=...

In a workflow:

name: hello-world
steps:
  - id: greeting
    run: >
      openclaw.invoke --tool llm-task --action json --args-json '{"prompt":"Hello"}'

Passing data between steps (no temp files)

Use stdin: $stepId.stdout to pipe output from one step into the next.

Args and shell-safety

${arg} substitution is a raw string replace into the shell command text.

For anything that may contain quotes, $, backticks, or newlines, prefer env vars:

• every resolved workflow arg is exposed as LOBSTER_ARG_<NAME> (uppercased, non-alnum → _)
• the full args object is also available as LOBSTER_ARGS_JSON

Example:

args:
  text:
    default: ""
steps:
  - id: safe
    env:
      TEXT: "$LOBSTER_ARG_TEXT"
    command: |
      jq -n --arg text "$TEXT" '{"result": $text}'