This is unreleased documentation for Langflow Documentation 1.10.x (Next) version.
For up-to-date documentation, see the latest version (1.9.x).
Version: 1.10.x (Next)
OpenAI Responses API
Langflow includes an endpoint that is compatible with the OpenAI Responses API.
It is available at POST /api/v1/responses.
This endpoint allows you to use existing OpenAI client libraries with minimal code changes.
You only need to replace the model name, such as gpt-4, with your flow_id.
You can find Flow IDs in the code snippets on the API access pane or in a flow's URL.
To be compatible with Langflow's OpenAI Responses API endpoint, your flow and request must adhere to the following requirements:
Chat Input: Your flow must contain a Chat Input component.
Flows without this component return an error when passed to this endpoint.
The component types ChatInput and Chat Input are recognized as chat inputs.
Tools: The tools parameter isn't supported, and returns an error if provided.
Model Names: In your request, the model field must contain a valid flow ID or endpoint name.
Authentication: All requests require an API key passed in the x-api-key header.
For more information, see API keys and authentication.
Additional configuration for OpenAI client libraries
This endpoint is compatible with OpenAI's API, but requires special configuration when using OpenAI client libraries.
Langflow uses x-api-key headers for authentication, while OpenAI uses Authorization: Bearer headers.
When sending requests to Langflow with OpenAI client libraries, you must configure custom headers and include an api_key configuration.
The api_key parameter can have any value, such as "dummy-api-key" in the client examples, as the actual authentication is handled through the default_headers configuration.
In the following examples, replace the values for LANGFLOW_SERVER_URL, LANGFLOW_API_KEY, and FLOW_ID with values from your deployment.
OpenAI Python Client
OpenAI TypeScript Client
1import os
23from openai import OpenAI
45base = (os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")).rstrip("/")
6api_key = os.environ.get("LANGFLOW_API_KEY", "")
7flow_id = os.environ.get("FLOW_ID", "")
89client = OpenAI(
10 base_url=f"{base}/api/v1/",
11 default_headers={"x-api-key": api_key},
12 api_key="dummy-api-key", # Required by OpenAI SDK but not used by Langflow
13)
1415try:
16 response = client.responses.create(
17 model=flow_id,
18 input="There is an event that happens on the second wednesday of every month. What are the event dates in 2026?",
19 )
20except Exception as exc:
21 # Empty bootstrap flows return an error body; use a flow with ChatInput + ChatOutput in the UI.
22 print(exc)
23else:
24 try:
25 print(response.output_text)
26 except Exception:
27 print(response)
28
1import OpenAI from "openai";
23const client = new OpenAI({
4 baseURL: "LANGFLOW_SERVER_URL/api/v1/",
5 defaultHeaders: {
6 "x-api-key": "LANGFLOW_API_KEY"
7 },
8 apiKey: "dummy-api-key" // Required by OpenAI SDK but not used by Langflow
9});
1011const response = await client.responses.create({
12 model: "FLOW_ID",
13 input: "There is an event that happens on the second wednesday of every month. What are the event dates in 2026?"
14});
1516console.log(response.output_text);
17
Response
_14
Here are the event dates for the second Wednesday of each month in 2026:
_14
- January 14, 2026
_14
- February 11, 2026
_14
- March 11, 2026
_14
- April 8, 2026
_14
- May 13, 2026
_14
- June 10, 2026
_14
- July 8, 2026
_14
- August 12, 2026
_14
- September 9, 2026
_14
- October 14, 2026
_14
- November 11, 2026
_14
- December 9, 2026
_14
If you need these in a different format or want a downloadable calendar, let me know!
The response contains fields that Langflow sets dynamically and fields that use OpenAI-compatible defaults.
The OpenAI-compatible default values shown above are currently fixed and cannot be modified via the request.
They are included to maintain API compatibility and provide a consistent response format.
For your requests, you will only be setting the dynamic fields.
The default values are documented here for completeness and to show the full response structure.
Fields set dynamically by Langflow:
Field
Type
Description
id
string
Unique response identifier.
created_at
int
Unix timestamp of response creation.
model
string
The flow ID that was executed.
output
list[dict]
Array of output items (messages, tool calls, etc.).
previous_response_id
string
ID of previous response if continuing conversation.
usage
dict
Token usage statistics if the usage field is available. Contains prompt_tokens, completion_tokens, and total_tokens.
Fields with OpenAI-compatible default values
Field
Type
Default Value
Description
object
string
"response"
Always "response".
status
string
"completed"
Response status: "completed", "in_progress", or "failed".
error
dict
null
Error details (if any).
incomplete_details
dict
null
Incomplete response details (if any).
instructions
string
null
Response instructions (if any).
max_output_tokens
int
null
Maximum output tokens (if any).
parallel_tool_calls
boolean
true
Whether parallel tool calls are enabled.
reasoning
dict
{"effort": null, "summary": null}
Reasoning information with effort and summary.
store
boolean
true
Whether response is stored.
temperature
float
1.0
Temperature setting.
text
dict
{"format": {"type": "text"}}
Text format configuration.
tool_choice
string
"auto"
Tool choice setting.
tools
list[dict]
[]
Available tools.
top_p
float
1.0
Top-p setting.
truncation
string
"disabled"
Truncation setting.
usage
dict
null
Token usage statistics. Set dynamically when available from flow components, otherwise null. See Token usage tracking.
When you set "stream": true with your request, the API returns a stream where each chunk contains a small piece of the response as it's generated. This provides a real-time experience where users can see the AI's output appear word by word, similar to ChatGPT's typing effect.
Python
JavaScript
curl
1import os
23import requests
45url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/responses"
67headers = {
8 "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
9 "Content-Type": "application/json",
10}
1112payload = {"model": "$FLOW_ID", "input": "Tell me a story about a robot", "stream": True}
1314response = requests.request("POST", url, headers=headers, json=payload)
15response.raise_for_status()
1617print(response.text)
18
1const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
23const options = {
4 method: 'POST',
5 headers: {
6 "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
7 "Content-Type": `application/json`,
8 },
9 body: JSON.stringify({
10 "model": "$FLOW_ID",
11 "input": "Tell me a story about a robot",
12 "stream": true
13}),
14};
1516fetch(url, options)
17 .then(async (response) => {
18 if (!response.ok) {
19 throw new Error(`HTTP ${response.status}`);
20 }
21 const text = await response.text();
22 console.log(text);
23 })
24 .catch((error) => console.error(error));
25
1curl -X POST \
2 "$LANGFLOW_SERVER_URL/api/v1/responses" \
3 -H "x-api-key: $LANGFLOW_API_KEY" \
4 -H "Content-Type: application/json" \
5 -d '{
6 "model": "$FLOW_ID",
7 "input": "Tell me a story about a robot",
8 "stream": true
9 }'
10
Response status: "completed", "in_progress", or "failed" (optional).
The stream continues until a final chunk with "status": "completed" indicates the response is finished.
Final completion chunk
_10
{
_10
"id": "f7fcea36-f128-41c4-9ac1-e683137375d5",
_10
"object": "response.chunk",
_10
"created": 1756838094,
_10
"model": "ced2ec91-f325-4bf0-8754-f3198c2b1563",
_10
"delta": {},
_10
"status": "completed"
_10
}
Continue conversations with response and session IDs
Conversation continuity allows you to maintain context across multiple API calls, enabling multi-turn conversations with your flows. This is essential for building chat applications where users can have ongoing conversations.
When you make a request, the API returns a response with an id field. You can use this id as the previous_response_id in your next request to continue the conversation from where it left off.
1# Continuation requests use the same endpoint; add `previous_response_id` from a prior
2# response's `id` field. The bootstrap flow is empty; use a Playground flow with ChatInput +
3# ChatOutput for a full run. Same first message as continue-conversations-with-response-and-session-ids.py.
45import os
67import requests
89base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
10flow_id = os.environ.get("FLOW_ID", "")
11api_key = os.environ.get("LANGFLOW_API_KEY", "")
1213url = f"{base}/api/v1/responses"
1415headers = {
16 "x-api-key": api_key,
17 "Content-Type": "application/json",
18}
1920payload = {
21 "model": flow_id,
22 "input": "Hello, my name is Alice",
23 "stream": False,
24}
2526response = requests.post(url, headers=headers, json=payload, timeout=120)
27response.raise_for_status()
2829print(response.text)
30
When you send a request to the /api/v1/responses endpoint to run a flow that includes tools or function calls, you can retrieve the raw tool execution details by adding "include": ["tool_call.results"] to the request payload.
Without the include parameter, tool calls return basic function call information, but not the raw tool results.
For example:
_10
{
_10
"id": "fc_1",
_10
"type": "function_call",
_10
"status": "completed",
_10
"name": "evaluate_expression",
_10
"arguments": "{\"expression\": \"15*23\"}"
_10
},
To get the raw results of each tool execution, add include: ["tool_call.results"] to the request payload:
Variables passed with X-LANGFLOW-GLOBAL-VAR-{VARIABLE_NAME} are always available to your flow, regardless of whether they exist in the database.
If your flow components reference variables that aren't provided in headers or your Langflow database, the flow fails by default.
To avoid this, you can set the FALLBACK_TO_ENV_VARS environment variable is true, which allows the flow to use values from the .env file if they aren't otherwise specified.
In the above example, OPENAI_API_KEY will fall back to the database variable if not provided in the header.
USER_ID and ENVIRONMENT will fall back to environment variables if FALLBACK_TO_ENV_VARS is enabled.
Otherwise, the flow fails.
The OpenAI Responses API endpoint tracks token usage when your flow uses language model components that provide token usage information. The usage field in the response contains statistics about the number of tokens used for the request and response.
Token usage is automatically extracted from the flow execution results when the usage field is available.
The usage field follows OpenAI's format with prompt_tokens, completion_tokens, and total_tokens fields.
If token usage information is not available from the flow components, the usage field is null.
The usage field is always present in the response, either with token counts or as null. The conditional checks shown in the examples below are optional defensive programming to handle cases where usage might not be available.
