Run flows with LFX
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.
Prerequisites
-
A flow JSON file. Download the Simple Agent starter 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" -
The flow's required API keys. The Simple Agent flow uses OpenAI, which requires
OPENAI_API_KEY.
Install flow dependencies
uv pip install lfx and uvx lfx install the LFX engine only. Before running a flow, install the extensions that provide the flow's components.
If a component is missing, LFX reports component-not-found with a hint naming the bundle to install, for example:
component-not-found-with-hint: DuckDuckGoSearchComponent not found.
Hint: try ext:duckduckgo:DuckDuckGoSearchComponent@official
Use the bundle name from the hint to find the pip package in the Bundle list.
For more information, see Install LFX with bundle components.
Generate requirements.txt
Extension packages cover component code. Some flows still need additional Python packages at runtime (third-party SDKs imported by those components).
To list them from a flow JSON file, use lfx requirements:
lfx requirements simple-agent-flow.json
Install the reported packages in the same environment before running the flow.
Run a flow
To run a flow from a JSON file, run:
uv run lfx run simple-agent-flow.json "Hello world"
Alternatively, use the --input-value flag:
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:
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:
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:
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:
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:
"""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:
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. |
Control which components load
LFX loads all component categories by default. Use environment variables to restrict which categories are available when loading flows.
| Variable | Description |
|---|---|
LANGFLOW_COMPONENT_CATEGORY_ALLOWLIST | Comma-separated list of category names to include. If empty, all categories are included. |
LANGFLOW_COMPONENT_CATEGORY_BLOCKLIST | Comma-separated list of category names to exclude. Applied after the allowlist. |
Category names are case-insensitive. The virtual keyword core in the allowlist or blocklist expands to all core component categories.
Component category names don't always match the labels displayed in the Langflow visual editor. To view the component categories, see Bundle list.
To restrict components to specific categories:
export LANGFLOW_COMPONENT_CATEGORY_ALLOWLIST="openai,anthropic,processing,input_output"
uv run lfx serve my_flow.json
To exclude specific categories:
export LANGFLOW_COMPONENT_CATEGORY_BLOCKLIST="prototypes,langchain_utilities"
uv run lfx run my_flow.json "Hello"
To allow only core categories:
export LANGFLOW_COMPONENT_CATEGORY_ALLOWLIST="core"
uv run lfx serve my_flow.json
See also
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