---
id: lfx
version: "1.11.3"
license: unclear
license_treatment: unclear
maintenance: active
---

# lfx — Langflow Executor - A lightweight CLI tool for executing and serving Langflow AI flows

License: unclear · Maintenance: active · Downloads: 113.4K/mo

## What it is and what it does

LFX is a standalone executor for Langflow flows that runs them without the full Langflow application or persistent database. It reads flow definitions from JSON files and either serves them as FastAPI endpoints or executes them directly from the command line, streaming results to stdout. The tool uses a no-op database interface for all stateful operations, meaning flows run purely in memory—any operations that would normally persist to langflow.db (saving flows, storing messages, managing users) will not retain data across invocations.

The package integrates LangChain, FastAPI, and document processing libraries to support a wide range of flow components. It's designed for lightweight deployment scenarios where you want to run pre-built flows without the overhead of the full Langflow server, but it trades persistence for that simplicity. If your flows depend on saved state, user management, or message history from the database, LFX is not a fit; if they're stateless or memory-only, it's a minimal alternative.

Use it for:

- Deploy a pre-built Langflow flow as a lightweight FastAPI microservice without running a full Langflow server.
- Execute a Langflow flow locally from the command line for testing or batch processing without database setup.
- Run flows in ephemeral environments (CI/CD, containers, serverless) where persistent state is not needed.
- Integrate a Langflow flow into an existing Python application as a library when the full Langflow package is too heavy.
- Serve multiple flows as stateless API endpoints for inference-only workloads.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

LFX is a lightweight command-line tool that executes Langflow AI flows from JSON files without requiring the full Langflow package, serving them as stateless FastAPI endpoints or running them locally.

Yes, if your flows are stateless or memory-only and you want to avoid the overhead of a full Langflow server. The low install friction and active maintenance make it a reasonable choice for lightweight deployment. However, verify the license terms first, and confirm that your flows do not depend on persistent state (saved flows, message history, user management) before committing to it.

## Install

pip install lfx
uv add lfx
poetry add lfx

## Installing lfx

Before you install: Low install friction with a pure Python wheel. Active maintenance with a release 3 days ago. The 49 runtime dependencies are substantial but typical for a framework that integrates LangChain, FastAPI, and document processing; most are already common in AI/LLM workflows.

License in practice: License treatment is unclear—no SPDX identifier or raw license text is available in the metadata. You should verify the actual license terms before using this in production or proprietary work.

Quickstart:

pip install lfx
lfx serve flow.json
# or
lfx run flow.json "input text"

Requires Python 3.10 or later (and <3.15). Flow components may require additional dependencies beyond LFX itself; the tool will report missing dependencies at runtime.

Verify before relying:

- Actual license terms (SPDX and raw license text are both absent from metadata)
- Whether stateless operation and no-op database fully satisfy your flow's persistence requirements
- Which component dependencies beyond the 49 listed runtime deps your specific flows will need

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 113.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

langflow flow executor, lightweight langflow runner, stateless flow execution, langflow cli tool, flow json runner, langflow api server, langflow without database, langflow-executor, stateless-flows, llm-deployment

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