---

name: executor-cli description: "Use and maintain lfx, lfx run, lfx serve, lfx-mcp, Flow DevOps commands, and stateless executor semantics." disable-model-invocation: true metadata: disco-role: operating license: MIT

Executor CLI

Use this sub-skill when the task involves the lightweight Langflow Executor (lfx), Flow DevOps CLI workflows, local flow execution, serving flow JSON as a small API, lfx-mcp, or debugging stateless executor behavior.

Read First

• CLI reference: command map, lfx run, lfx serve, Flow DevOps commands, extension commands, lfx-mcp, and safe command templates.
• Stateless runtime: what persists, what does not, credential/request-variable behavior, NoopSession, --flow-dir, and memory/session caveats.
• Troubleshooting: install/import, optional dependency, flow schema, variable, API-key, network, and runtime failure signals.
• Flow validation helper: offline JSON parse/shape check that prints safe lfx validate, lfx run, and lfx serve command templates without executing the flow.

Use This For

• Running a local flow JSON or Python graph with lfx run, including path, --stdin, and --flow-json modes.
• Serving one or more flow JSON files with lfx serve, including API-key setup, .env files, --flow-dir, multi-worker behavior, uploads, streaming, and --no-env-fallback.
• Flow DevOps tasks with lfx init, create, validate, requirements, upgrade, status, push, pull, and export.
• MCP server startup and remote Langflow flow-building control through lfx-mcp.
• Explaining why LFX does not provide full Langflow database/server persistence.

Route Elsewhere

• Full Langflow server, database, auth, migrations, and service internals belong in ../backend-runtime/SKILL.md.
• SDK client code, REST client implementation, and Python API integrations belong in ../sdk-and-api-clients/SKILL.md.
• Component class authoring, bundles, and component-index maintenance belong in ../component-development/SKILL.md.
• Production container/deployment operations beyond lfx serve belong in ../deployment-and-operations/SKILL.md.

Fast Workflow

1. Identify whether the user needs one-shot execution (lfx run), an HTTP endpoint (lfx serve), Flow DevOps sync (status/push/pull/export), extension inspection (lfx extension ...), or MCP (lfx-mcp).
2. Validate the flow file offline before suggesting execution:

   python scripts/validate_lfx_flow.py path/to/flow.json
   

3. If the flow contains provider components, confirm required component packages and credentials are installed/configured before running or serving.
4. For lfx serve, require a local LANGFLOW_API_KEY or LFX_API_KEY token and decide whether process-env fallback is acceptable; use --no-env-fallback for request-scoped credentials.
5. For multi-worker serving, use --flow-dir with JSON flows only; do not use .py script flows with --workers > 1 --flow-dir.

Safety Notes

• Do not execute untrusted flow JSON or Python graph scripts just to inspect them; use the bundled helper and lfx validate first.
• Do not paste real API keys into examples, flow JSON, or committed .lfx/environments.yaml; store key values in environment variables.
• Treat lfx run and lfx serve as stateless executor paths, not replacements for the full Langflow app database.