lfx 0.1.6

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

lfx - Langflow Executor

lfx is a command-line tool for running Langflow workflows. It provides two main commands: serve and run.

Installation

From PyPI (recommended)

# Install globally
uv pip install lfx

# Or run without installing using uvx
uvx lfx serve my_flow.json
uvx lfx run my_flow.json "input"

From source (development)

# Clone and run in workspace
git clone https://github.com/langflow-ai/langflow
cd langflow/src/lfx
uv run lfx serve my_flow.json

Commands

lfx serve - Run flows as an API

Serve a Langflow workflow as a REST API.

Important: You must set the LANGFLOW_API_KEY environment variable before running the serve command.

export LANGFLOW_API_KEY=your-secret-key
uv run lfx serve my_flow.json --port 8000

This creates a FastAPI server with your flow available at /flows/{flow_id}/run. The actual flow ID will be displayed when the server starts.

Options:

• --host, -h: Host to bind server (default: 127.0.0.1)
• --port, -p: Port to bind server (default: 8000)
• --verbose, -v: Show diagnostic output
• --env-file: Path to .env file
• --log-level: Set logging level (debug, info, warning, error, critical)
• --check-variables/--no-check-variables: Check global variables for environment compatibility (default: check)

Example:

# Set API key (required)
export LANGFLOW_API_KEY=your-secret-key

# Start server
uv run lfx serve simple_chat.json --host 0.0.0.0 --port 8000

# The server will display the flow ID, e.g.:
# Flow ID: af9edd65-6393-58e2-9ae5-d5f012e714f4

# Call API using the displayed flow ID
curl -X POST http://localhost:8000/flows/af9edd65-6393-58e2-9ae5-d5f012e714f4/run \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-secret-key" \
  -d '{"input_value": "Hello, world!"}'

lfx run - Run flows directly

Execute a Langflow workflow and get results immediately.

uv run lfx run my_flow.json "What is AI?"

Options:

• --format, -f: Output format (json, text, message, result) (default: json)
• --verbose: Show diagnostic output
• --input-value: Input value to pass to the graph (alternative to positional argument)
• --flow-json: Inline JSON flow content as a string
• --stdin: Read JSON flow from stdin
• --check-variables/--no-check-variables: Check global variables for environment compatibility (default: check)

Examples:

# Basic execution
uv run lfx run simple_chat.json "Tell me a joke"

# JSON output (default)
uv run lfx run simple_chat.json "input text" --format json

# Text output only
uv run lfx run simple_chat.json "Hello" --format text

# Using --input-value flag
uv run lfx run simple_chat.json --input-value "Hello world"

# From stdin (requires --input-value for input)
echo '{"data": {"nodes": [...], "edges": [...]}}' | uv run lfx run --stdin --input-value "Your message"

# Inline JSON
uv run lfx run --flow-json '{"data": {"nodes": [...], "edges": [...]}}' --input-value "Test"

Complete Agent Example

Here's a step-by-step example of creating and running an agent workflow with dependencies:

Step 1: Create the agent script

Create a file called simple_agent.py:

"""A simple agent flow example for Langflow.

This script demonstrates how to set up a conversational agent using Langflow's
Agent component with web search capabilities.

Features:
- Configures logging to 'langflow.log' at INFO level
- Creates an agent with OpenAI GPT model
- Provides web search tools via URLComponent
- Connects ChatInput → Agent → ChatOutput

Usage:
    uv run lfx run simple_agent.py "How are you?"
"""

import os
from pathlib import Path

from lfx.components.agents.agent import AgentComponent
from lfx.components.data.url import URLComponent
from lfx.components.input_output import ChatInput, ChatOutput
from lfx.graph import Graph
from lfx.lfx_logging.logger import LogConfig

log_config = LogConfig(
    log_level="INFO",
    log_file=Path("langflow.log"),
)
chat_input = ChatInput()
agent = AgentComponent()
url_component = URLComponent()
tools = url_component.to_toolkit()
agent.set(
    model_name="gpt-4.1-mini",
    agent_llm="OpenAI",
    api_key=os.getenv("OPENAI_API_KEY"),
    input_value=chat_input.message_response,
    tools=tools,
)
chat_output = ChatOutput().set(input_value=agent.message_response)

graph = Graph(chat_input, chat_output, log_config=log_config)

Step 2: Install dependencies

# Install lfx (if not already installed)
uv pip install lfx

# Install additional dependencies required for the agent
uv pip install langchain-community langchain beautifulsoup4 lxml langchain-openai

Step 3: Set up environment

# Set your OpenAI API key
export OPENAI_API_KEY=your-openai-api-key-here

Step 4: Run the agent

# Run with verbose output to see detailed execution
uv run lfx run simple_agent.py "How are you?" --verbose

# Run with different questions
uv run lfx run simple_agent.py "What's the weather like today?"
uv run lfx run simple_agent.py "Search for the latest news about AI"

This creates an intelligent agent that can:

• Answer questions using the GPT model
• Search the web for current information
• Process and respond to natural language queries

The --verbose flag shows detailed execution information including timing and component details.

Input Sources

Both commands support multiple input sources:

• File path: uv run lfx serve my_flow.json
• Inline JSON: uv run lfx serve --flow-json '{"data": {"nodes": [...], "edges": [...]}}'
• Stdin: uv run lfx serve --stdin

Development

# Install development dependencies
make dev

# Run tests
make test

# Format code
make format

License

MIT License. See LICENSE for details.