Run flows with LFX

import LfxPrereqs from '@site/docs/_partial-lfx-run-serve-prereqs.mdx'; import LfxComponentCategories from '@site/docs/_partial-lfx-component-categories.mdx';

lfx run executes a flow once and streams the results to stdout. No API key is required.

To expose flows as HTTP endpoints instead, see Serve flows with LFX.

Both lfx run and lfx serve accept a .json flow file or a .py Python script.

Run a flow {#run-a-flow}

To run a flow from a JSON file, run:

bash

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

Alternatively, use the --input-value flag:

bash

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

Other ways to provide a flow

To run a flow JSON from stdin, run:

bash

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

To fetch and run a flow from a remote API, run:

bash

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

To modify a flow before running it, for example, to change the model to gpt-4o, run:

bash

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'

To pass a flow JSON directly as a string argument, run:

bash

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

Run a flow from a Python script

In addition to JSON files, lfx run accepts .py Python scripts that define flows programmatically.

Create a file called simple_agent.py:

python

"""A simple agent flow example.

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:
    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)

Run the flow:

bash

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

For more examples, see the Complete Agent Example on PyPI.

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: `json`, `text`, `message`, or `result`. Default: `json`.
`--input-value`	Input value to pass to the graph. Required with `--stdin` and `--flow-json`.
`--session-id`	Session ID for conversation tracking. Auto-generated if not set.
`--stdin`	Read JSON flow content from `stdin`.
`--timing`	Include detailed timing information in output.
`--upgrade-flow`	Compatibility mode: `check` reports issues and fails; `safe` applies safe upgrades in memory before running.
`--verbose`, `-v`	Show basic progress and diagnostic output.
`-vv`	Show detailed progress and debug information.
`-vvv`	Show full debugging output including component logs.

Run a flow {#run-a-flow}

Other ways to provide a flow

Run a flow from a Python script

lfx run options

See also