lfx-nightly 0.4.0.dev34

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

LFX - Langflow Executor

The Langflow Executor (LFX) is a command-line tool that serves and runs flows statelessly from flow JSON files with minimal dependencies.

Running a flow with LFX is similar to running flows with the --backend-only environment variable enabled, but even more lightweight because the Langflow package and all of its dependencies don't need to be installed.

LFX uses a no-op database interface called NoopSession for all operations that require persistent state. There is no langflow.db database file when using LFX. You can run flows with the API, but any stateful operations that depend on the Langflow database, like saving flows, storing messages, or managing users will not persist data. Operations that depend on langflow.db will not work as they do in the full Langflow application.

LFX includes two commands for executing flows:

• lfx serve: Starts a FastAPI server hosting a Langflow API endpoint with your flow available at /flows/{flow_id}/run. The flow graph is stored in memory at all times, so there is less overhead for loading the graph from a database.
• lfx run: Executes a flow locally and returns the results to stdout.

Prerequisites

• Install Python.

• Install uv.

• Create or download a flow JSON file. For example, download the Simple Agent flow from the repository:

  curl -o simple-agent-flow.json "https://raw.githubusercontent.com/langflow-ai/langflow/main/src/backend/base/langflow/initial_setup/starter_projects/Simple%20Agent.json"
  

• Create an OpenAI API key.

• Create a Langflow API key. For LFX, you can generate a secure token locally (see Serve the simple agent starter flow with lfx serve), or create one through the Langflow server UI or CLI.

Install LFX

LFX can be installed in multiple ways. If you have installed Langflow OSS version >=1.6, lfx is already included.

Clone repository

1. Clone the Langflow repository:

   git clone https://github.com/langflow-ai/langflow
   

2. Change directory to langflow/src/lfx:

   cd langflow/src/lfx
   

   From this directory, you can run lfx commands using uv run lfx as shown in lfx serve or lfx run.

Install from PyPI

1. Create and activate a virtual environment:

   uv venv lfx-venv
   source lfx-venv/bin/activate
   

2. Install the LFX package from PyPI:

   uv pip install lfx
   

   To install the latest nightly version of LFX:

   uv pip install lfx-nightly
   

   To run lfx commands, continue to lfx serve or lfx run.

Run without installing

Run LFX without installing it locally using uvx.

1. Create a Langflow API key (see Serve), and set LANGFLOW_API_KEY in the same terminal session as lfx:

   export LANGFLOW_API_KEY="sk..."
   

2. Run lfx serve using uvx:

   uvx lfx serve simple-agent-flow.json
   

   This command downloads and runs LFX in a temporary environment without permanent installation. From the same environment, you can also run flows directly with lfx run.

Serve the simple agent starter flow with lfx serve

To serve a flow as a REST API endpoint, set a LANGFLOW_API_KEY and run the flow JSON.

The API key is required for security because lfx serve can create a publicly accessible FastAPI server.

This example uses the Agent component's built-in OpenAI model, which requires an OpenAI API key. If you want to use a different provider, edit the model provider, model name, and credentials accordingly.

1. Generate a Langflow API key.

   For LFX, you can generate a secure token locally to use as your LANGFLOW_API_KEY:

   uv run python -c "import secrets; print(secrets.token_urlsafe(32))"
   

   This is different from creating a Langflow API key through the Langflow server UI or CLI, which stores the key in the Langflow database. For LFX, you only need a secure token string to authenticate requests to your LFX server.

2. Set up your environment variables using one of the following options.

   Option: .env file

   Create a .env file and populate it with your flow's variables. The LANGFLOW_API_KEY is required. This example assumes the flow requires an OpenAI API key.

   LANGFLOW_API_KEY="sk..."
   OPENAI_API_KEY="sk-..."
   

   Option: Export variables

   Export your variables in the same terminal session where you'll start the server. You must declare your variables before the server starts for the server to pick them up.

   export LANGFLOW_API_KEY="sk..."
   export OPENAI_API_KEY="sk-..."
   

3. Install dependencies.

   If you already have Langflow installed, or if you're running from source at src/lfx, LFX is included with Langflow and all dependencies are already available. You don't need to install additional dependencies.

   If you install the standalone lfx package from PyPI or run LFX with uvx, you need to manually install the dependencies required by the components in your flow.

   To find which dependencies your flow requires:

   1. Run your flow with lfx run:

      uv run lfx run simple-agent-flow.json "test input"
      

      LFX reports any missing dependencies in the subsequent error message.

   2. Install the missing dependencies that LFX reports.

   For example, to run the simple agent template flow, install these dependencies in your environment before running the simple agent flow:

   uv pip install "langchain~=0.3.23" "langchain-core<1.0.0" "langchain-community" "langchain-openai" "langchain-text-splitters" beautifulsoup4 lxml requests
   

4. Start the server with your variable values using one of the following options.

   Option: .env file

   This example assumes your flow file and .env file are in the current directory:

   uv run lfx serve simple-agent-flow.json --env-file .env
   

   If your .env file is in a different location, provide the full or relative path:

   uv run lfx serve simple-agent-flow.json --env-file /path/to/.env
   

   Option: Export variables

   If you exported your variables, the command to start the server automatically picks up the values when it starts:

   uv run lfx serve simple-agent-flow.json
   

   To export new values, stop the server, export the variables, and then start the server again.

5. The startup process displays a flow_id value in the output. Copy the flow_id to use in the test API call in the next step. In this example, the flow_id is c1dab29d-3364-58ef-8fef-99311d32ee42:

    ╭───────────────────────────── LFX Server ─────────────────────────────╮
    │ 🎯 Single Flow Served Successfully!                                  │
    │                                                                      │
    │ Source: /Users/mendonkissling/Downloads/simple-agent-flow.json       │
    │ Server: http://127.0.0.1:8000                                        │
    │ API Key: sk-...                                                      │
    │                                                                      │
    │ Send POST requests to:                                               │
    │ http://127.0.0.1:8000/flows/c1dab29d-3364-58ef-8fef-99311d32ee42/run │
    │                                                                      │
    │ With headers:                                                        │
    │ x-api-key: sk-...                                                    │
    │                                                                      │
    │ Or query parameter:                                                  │
    │ ?x-api-key=sk-...                                                    │
    │                                                                      │
    │ Request body:                                                        │
    │ {'input_value': 'Your input message'}                                │
    ╰──────────────────────────────────────────────────────────────────────╯
   

6. To send a test request to the server, open a new terminal and export your flow_id and Langflow API key values as variables:

   export LANGFLOW_API_KEY="sk..."
   export FLOW_ID="c1dab29d-3364-58ef-8fef-99311d32ee42"
   

7. Test the server with an API call to the /flows/flow_id/run endpoint:

   curl -X POST http://localhost:8000/flows/$FLOW_ID/run \
     -H "Content-Type: application/json" \
     -H "x-api-key: $LANGFLOW_API_KEY" \
     -d '{"input_value": "Hello, world!"}'
   

   Successful response example:

   {
     "result": "Hello world! 👋\n\nHow can I help you today? If you have any questions or need assistance, just let me know!",
     "success": true,
     "logs": "\n\n\u001b[1m> Entering new None chain...\u001b[0m\n\u001b[32;1m\u001b[1;3mHello world! 👋\n\nHow can I help you today?...\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n",
     "type": "message",
     "component": "Chat Output"
   }
   

Your flow is now running as a lightweight API endpoint, with only the flow's required dependencies and no visual builder installed. Users who call your endpoint don't need to install Langflow or configure their own LLM provider keys.

To make your server publicly accessible, use a tunneling service like ngrok or deploy to a public cloud provider.

LFX response schema

The LFX server's response schema is different from the Langflow API /run endpoint's schema. Requests to the LFX server's /flows/{flow_id}/run endpoint return the following fields:

{
  "result": "string",      // Output result from the flow execution
  "success": true,         // Whether execution was successful
  "logs": "string",        // Captured logs from execution
  "type": "message",       // Type of result
  "component": "string"    // The component that generated the result (e.g. "Chat Output")
}

To view the LFX server's API docs and schema, see the /docs endpoint at http://localhost:8000/docs.

LFX serve options

Option	Description
--check-variables / --no-check-variables	Check global variables for environment compatibility. Default: --check-variables.
--env-file	Path to the .env file containing environment variables.
--host, -h	Host to bind the server to. Default: 127.0.0.1.
--log-level	Logging level. One of: debug, info, warning, error, critical. Default: warning.
--port, -p	Port to bind the server to. Default: 8000.
--verbose, -v	Show diagnostic output and execution details.
--flow-json	Read inline flow JSON content as a string. Example: uv run lfx serve --flow-json '{...}'.
--stdin	Read JSON flow content from stdin. Example: `cat flow.json

Run the simple agent flow with lfx run

The lfx run command runs a flow from a JSON file without serving it, and the output is sent to stdout. Input to lfx run can be a path to the JSON file, inline JSON passed with --input-value, or read from stdin. No Langflow API key is required.

This example uses the Agent component's built-in OpenAI model, which requires an OpenAI API key. If you want to use a different provider, edit the model provider, model name, and credentials accordingly.

1. Export your variables in the same terminal session where you'll run the flow:

   export OPENAI_API_KEY="sk-..."
   

2. Install dependencies.

   If you already have Langflow installed, or if you're running from source at src/lfx, LFX is included with Langflow and all dependencies are already available. You don't need to install additional dependencies.

   If you install the standalone lfx package from PyPI or run LFX with uvx, you need to manually install the dependencies required by the components in your flow.

   To find which dependencies your flow requires:

   1. Run your flow with lfx run:

      uv run lfx run simple-agent-flow.json "test input"
      

      LFX reports any missing dependencies in the subsequent error message.

   2. Install the missing dependencies that LFX reports.

   For example, to run the simple agent template flow, install these dependencies in your environment before running the simple agent flow:

   uv pip install "langchain~=0.3.23" "langchain-core<1.0.0" "langchain-community" "langchain-openai" "langchain-text-splitters" beautifulsoup4 lxml requests
   

3. Run the flow from a flow JSON file:

   uv run lfx run simple-agent-flow.json "Hello world"
   

   This flow expects a Message input, which is a simple text string.

   You can also use the --input-value flag instead of a positional argument:

   uv run lfx run simple-agent-flow.json --input-value "Hello world"
   

   The --input-value flag is required when using --stdin or --flow-json options, since those options use the positional argument for the flow definition instead of the input value.

In addition to running flows from JSON files, lfx run supports other input methods, described below.

Run flows from stdin

The --stdin option lets you run flows from dynamic sources (APIs, databases) or after modifying a flow before execution. The command reads the flow's JSON definition from stdin, validates the JSON structure, and runs the flow. The --input-value flag is required when using --stdin.

Read a flow JSON from stdin:

cat simple-agent-flow.json | uv run lfx run --stdin \
  --input-value "Hello world" \
  --format json | jq '.result'

Fetch a flow JSON from a remote API and run it:

curl https://api.example.com/flows/my-agent-flow | uv run lfx run --stdin \
  --input-value "Hello world"

Modify a flow created in the visual builder before execution (e.g. change the OpenAI model to gpt-4o):

cat simple-agent-flow.json | jq '(.data.nodes[] | select(.data.node.template.model_name.value) | .data.node.template.model_name.value) = "gpt-4o"' | \
  uv run lfx run --stdin \
  --input-value "Hello world" \
  --format json | jq '.result'

Run flows with inline JSON

Instead of piping from stdin or reading from a JSON file, you can pass the flow JSON directly as a string argument. The --input-value flag is required when using --flow-json.

uv run lfx run --flow-json '{"data": {"nodes": [...], "edges": [...]}}' \
  --input-value "Hello world"

LFX run options

Option	Description
--check-variables / --no-check-variables	Validate the flow's global variables. Default: check.
--flow-json	Load inline JSON flow content as a string.
--format, -f	Output format. One of: json, text, message, result. Default: json.
--input-value	Input value to pass to the graph.
--stdin	Read JSON flow content from stdin.
--timing	Include detailed timing information in output.
--verbose, -v	Show basic progress and diagnostic output.
-vv	Show detailed progress and debug information.
-vvv	Show full debugging output including component logs.

In addition to running flows from JSON files, you can use lfx run with Python scripts that define flows programmatically. This approach allows you to create flows directly in Python code without the visual builder.

For a complete example of creating an agent flow programmatically using LFX components, see the Complete Agent Example on PyPI or the Complete Agent Example below.

Complete agent example

Create a file called simple_agent.py:

"""A simple agent flow example for Langflow.

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

import os
from pathlib import Path

from lfx import components as cp
from lfx.graph import Graph
from lfx.log.logger import LogConfig


async def get_graph() -> Graph:
    """Create and return the graph with async component initialization."""
    log_config = LogConfig(
        log_level="INFO",
        log_file=Path("langflow.log"),
    )

    chat_input = cp.ChatInput()
    agent = cp.AgentComponent()
    url_component = cp.URLComponent()
    tools = await 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 = cp.ChatOutput().set(input_value=agent.message_response)

    return Graph(chat_input, chat_output, log_config=log_config)

Install dependencies and set your OpenAI API key, then run:

uv run lfx run simple_agent.py "How are you?" --verbose

Development

# Install development dependencies
make dev

# Run tests
make test

# Format code
make format

Pluggable services

LFX supports a pluggable service architecture that lets you customize and extend its behavior. You can replace built-in services (storage, telemetry, tracing, etc.) with your own implementations or use Langflow's full-featured services.

For more information, see PLUGGABLE_SERVICES.md.

Flattened component access

LFX supports simplified component imports for a better developer experience when building flows in Python. You get simpler imports, easier discovery via cp.ComponentName, and full backward compatibility with the traditional import method.

Before (old import style):

from lfx.components.agents.agent import AgentComponent
from lfx.components.data.url import URLComponent
from lfx.components.input_output import ChatInput, ChatOutput

Now (flattened style):

from lfx import components as cp

chat_input = cp.ChatInput()
agent = cp.AgentComponent()
url_component = cp.URLComponent()
chat_output = cp.ChatOutput()

Component category allowlist and blocklist

You can restrict which component categories are available when loading flows by using an allowlist or a blocklist.

Environment variables

Both settings are optional. When unset or empty, all categories from the component index are loaded.

Variable	Description
LANGFLOW_COMPONENT_CATEGORY_ALLOWLIST	Comma-separated list of component category names to include. If empty (default), all categories are included. If set, only the listed categories are available.
LANGFLOW_COMPONENT_CATEGORY_BLOCKLIST	Comma-separated list of component category names to exclude. If empty (default), no categories are excluded. Applied after the allowlist.

Category names are case-insensitive.

Component categories

Category names in the allowlist and blocklist match the component index (e.g. top-level folders under lfx.components). The virtual keyword core in the allowlist or blocklist expands to the following core categories (aligned with the frontend sidebar):

• input_output, data_source, models_and_agents, llm_operations, files_and_knowledge, processing, flow_controls, utilities, prototypes, tools, agents, data, logic, helpers, models, vectorstores, inputs, outputs, prompts, chains, documentloaders, link_extractors, output_parsers, retrievers, textsplitters, toolkits

Provider-specific and other categories (e.g. openai, anthropic, google, langchain_utilities) are also valid; the full set depends on your LFX version and index.

How to use in LFX

1. Set one or both environment variables before running lfx serve or lfx run. The filter is applied when the component index is loaded.

Allowlist only — restrict to specific categories:

export LANGFLOW_COMPONENT_CATEGORY_ALLOWLIST="openai,anthropic,google,processing,input_output"
uv run lfx serve my_flow.json

Blocklist only — load all categories except the ones you exclude:

export LANGFLOW_COMPONENT_CATEGORY_BLOCKLIST="prototypes,langchain_utilities"
uv run lfx run my_flow.json "Hello"

Virtual core keyword — use core in the allowlist or blocklist to refer to all core categories at once (e.g. allow only core categories, or exclude all core from a broader set):

export LANGFLOW_COMPONENT_CATEGORY_ALLOWLIST="core"
uv run lfx serve my_flow.json

License

MIT License. See LICENSE for details.