Flows via API very slow

[eyalcats]

Hello LangFlow community,

I've noticed a significant disparity in response times between running chatbot flows in the LangFlow playground versus calling them via API (using Python or JavaScript). This performance difference persists whether I'm running LangFlow locally or hosting it on Render.

Specifically:
What are the typical causes of slower response times when calling flows via API compared to the playground?
Are there any recommended optimizations or best practices for improving API call performance in LangFlow?
Are there any configuration settings in LangFlow that can help reduce API response times?
Are there any debugging tools or metrics I can use to identify bottlenecks in my API calls?

I'd appreciate any insights, tweaks, or optimization strategies that could help bring the API performance closer to what I'm experiencing in the playground. Thank you for your help!"

[dosubot[bot]]

1. Typical Causes of Slower Response Times:

   • Network Latency: API calls involve network communication, which can introduce latency.
   • Overhead of HTTP Protocol: The HTTP protocol adds overhead compared to direct function calls in a playground.
   • Authentication and Authorization: API calls often include authentication and authorization checks, which can add to the response time.
   • Serialization/Deserialization: Data needs to be serialized to JSON for API calls and deserialized back, which can be time-consuming.
   • Caching: The playground might use in-memory caching more effectively than the API, which might rely on external caches.

2. Recommended Optimizations:

   • Use Caching: Ensure that caching is effectively used to store and retrieve frequently accessed data.
   • Batch Requests: If possible, batch multiple requests into a single API call to reduce the number of network round trips.
   • Optimize Serialization: Use efficient serialization libraries and minimize the amount of data being serialized.
   • Reduce Payload Size: Minimize the size of the data being sent over the network.
   • Asynchronous Processing: Use asynchronous processing to handle API requests more efficiently.

3. Configuration Settings in LangFlow:

   • Cache Settings: Ensure that caching is enabled and configured correctly to store intermediate results.
   • Concurrency Settings: Adjust the concurrency settings to allow more parallel processing of requests.
   • Timeout Settings: Configure appropriate timeout settings to avoid long-running requests blocking the system.

4. Debugging Tools and Metrics:

   • Logging: Use detailed logging to track the time taken for each step in the API call.
   • Profiling: Use profiling tools to identify bottlenecks in the code.
   • Monitoring: Implement monitoring to track the performance of the API, including response times and error rates.
   • Tracing: Use distributed tracing to follow the flow of requests through the system and identify slow components.

Here is an example of how you might implement caching and logging to improve and monitor performance:

from functools import lru_cache
from loguru import logger

@lru_cache(maxsize=128)
def get_cached_graph(flow_id, session, chat_service):
    return build_and_cache_graph(flow_id, session, chat_service)

@router.get("/build/{flow_id}/vertices", response_model=VerticesOrderResponse)
async def get_vertices(
    flow_id: str,
    stop_component_id: Optional[str] = None,
    start_component_id: Optional[str] = None,
    chat_service: "ChatService" = Depends(get_chat_service),
    session=Depends(get_session),
):
    start_time = time.perf_counter()
    try:
        graph = get_cached_graph(flow_id, session, chat_service)
        if stop_component_id or start_component_id:
            first_layer = graph.sort_vertices(stop_component_id, start_component_id)
        else:
            first_layer = graph.sort_vertices()
        
        for vertex_id in first_layer:
            graph.remove_from_predecessors(vertex_id)

        run_id = uuid.uuid4()
        graph.set_run_id(run_id)
        vertices_to_run = list(graph.vertices_to_run) + get_top_level_vertices(graph, graph.vertices_to_run)
        
        duration = time.perf_counter() - start_time
        logger.info(f"get_vertices completed in {duration:.4f} seconds")
        
        return VerticesOrderResponse(ids=first_layer, run_id=run_id, vertices_to_run=vertices_to_run)
    except Exception as exc:
        logger.error(f"Error checking build status: {exc}")
        raise HTTPException(status_code=500, detail=str(exc)) from exc

This example uses an LRU cache to store the graph and logs the time taken to process the request ^[1].

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[rudmac]

I've noticed a lot of delays when running via API.

[detodavide]

I've noticed the same problem. I'm running langflow-1.0.12 as a docker container, the playground seems much faster than using the webhook. I thought Langsmith was the cause of the problem but, although the playground without Langsmith is much faster, via API the problem still persist.

[Frenz86]

I have the same error, are there any fixes?

[conradchu]

+1 happens to us as well

[thebacons]

Yes, it happens to me also. Using Groq, it's incredibly fast when running the flow manually (a few seconds) but about 50 times slower or just times-out when using the playground. I also always get the "Server busy" pop-up message.

[devinbost]

It's not just "very slow", it's MANY TIMES slower, like 5x in some cases.
For example, a flow invoked in the UI is 10 seconds, but from the API, it's 20-50 seconds. Something is seriously different.

[github-wenli]

Check you browser to the latest version.

[thomasgzx]

I've been experiencing significant issues with the API too. It's considerably slower than the Playground, and the output quality is worse. While I consistently get good results in the Playground, using the API often gives me unexpected and poor-quality outputs.

[ilNikk]

I'm experiencing the same latency issues with API responses taking over 10 seconds, impossible to use in my case unfortnaly.
One likely bottleneck is the extremely verbose JSON response structure containing lots of non-essential information (the response have 6k character count just for say "hi" to LLM). This excessive payload size could be contributing significantly to the slow response times.

[tanpham380]

I have the same error, are there any fixes?

[shanghaiyangming]

me too

[yvann-ponce]

Same, this is a big issue when using Langflow in production

[abdo-alachari]

I have the same issue, sometimes a request that took me 1 sec using as simple open ai script takes more than 120 sec in langflow inside Docker

[frogFred]

I have the same issue

[Ege-BULUT]

I have the same issue, also @ilNikk is right, there is lots of unnecesarry things in the response json (such as artifacts etc) it returns same output 3 times, and when we return a big base64 value, it unnecesarrly returns 3 times in different places, even if I only want to return a simple "hi" it adds tons of unnecesarry details to response json, is there any way to overwrite langflow's default api response json format ?

[tatwong]

Have the same problem, it runs like 10 minutes and cannot finish to read a super small csv file, like 5kb.

Can’t seems to identify where the slow response coming from.