---
title: Event queuing/batching
description: Queuing/batching configuration for Langfuse Tracing.
---

# Event Queuing/Batching

Langfuse's client SDKs and integrations are all designed to queue and batch requests in the background to optimize API calls and network time. Batches are determined by a combination of time and size (number of events and size of batch).

### Configuration

All integrations have a sensible default configuration, but you can customize the batching behaviour to suit your needs.

| Option (Python) [SDK constructor, Environment]  | Option (JS)               | Description                                                 |
| ----------------------------------------------- | ------------------------- | ----------------------------------------------------------- |
| `flush_at`, `LANGFUSE_FLUSH_AT`                 | `flushAt`                 | The maximum number of events to batch up before sending.    |
| `flush_interval`, `LANGFUSE_FLUSH_INTERVAL` (s) | `flushInterval` (seconds) | The maximum time to wait before sending a batch in seconds. |

You can e.g. set `flushAt=1` to send every event immediately, or `flushInterval=1` to send every second.

### Manual flushing

  In short-lived environments like serverless functions (e.g., Vercel Functions, AWS Lambda), you should explicitly flush the traces before the process exits or the runtime environment is frozen. If you do not flush the client, you may lose events.

If you want to send a batch immediately, you can call the `flush` method on the client. In case of network issues, flush will log an error and retry the batch, it will never throw an exception.

<LangTabs items={["Python SDK","JS/TS SDK","OpenAI SDK (Python)","Langchain","Langchain (JS)"]}>

<Tab>

```python
from langfuse import get_client

# access the client directly

langfuse = get_client()

# Flush all pending observations

langfuse.flush()

```

If you exit the application, use `shutdown` method to make sure all requests are flushed and pending requests are awaited before the process exits. On success of this function, no more events will be sent to Langfuse API.

```python
from langfuse import get_client

langfuse = get_client()

langfuse.shutdown()
```

</Tab>
<Tab>

The `LangfuseSpanProcessor` buffers events and sends them in batches, so a final flush ensures no data is lost.

You can export the processor from your OTEL SDK setup file.

```ts filename="instrumentation.ts" /langfuseSpanProcessor/ /forceFlush/
import { NodeSDK } from "@opentelemetry/sdk-node";
import { LangfuseSpanProcessor } from "@langfuse/otel";

// Export the processor to be able to flush it
export const langfuseSpanProcessor = new LangfuseSpanProcessor();

const sdk = new NodeSDK({
  spanProcessors: [langfuseSpanProcessor],
});

sdk.start();
```

Then, in your serverless function handler, call `forceFlush()` before the function exits.

```ts filename="handler.ts"
import { langfuseSpanProcessor } from "./instrumentation";

export async function handler(event, context) {
  // ... your application logic ...

  // Flush before exiting
  await langfuseSpanProcessor.forceFlush();
}
```

</Tab>
<Tab>

```python
from langfuse import get_client

# access the client directly
langfuse = get_client()

# Flush all pending observations
langfuse.flush()
```

</Tab>
<Tab>

```python
from langfuse import get_client

langfuse = get_client()

langfuse.flush()

# access the client directly
langfuse_handler.client.flush()

```

</Tab>
<Tab>

```javascript
await langfuseHandler.flushAsync();
```

If you exit the application, use `shutdownAsync` method to make sure all requests are flushed and pending requests are awaited before the process exits.

```javascript
await langfuseHandler.shutdownAsync();
```

</Tab>
</LangTabs>

<!-- agent-instructions -->

---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/docs/observability/features/queuing-batching.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.