---
title: I have setup Langfuse, but I do not see any traces in the dashboard. How to solve this?
seoTitle: "No Traces Showing in Langfuse? How to Fix It"
description: "Traces missing from your Langfuse dashboard? Tracing is asynchronous, so check flushing, credentials, host and region, and SDK version in that order."
tags: [evaluation, observability, observability-get-started]
---

# I do not see any traces in Langfuse ("Tracing Pending"). How to solve this?

Langfuse runs all tracing integrations asynchronously ([learn more](/docs/tracing#queuing-batching)). Here are a few steps to resolve this issue:

1. **Verify Integration**: Ensure that your application is correctly integrated with Langfuse. Follow the [quickstart guide](/docs/observability/get-started) to verify your setup.
2. **Check API Credentials**: Ensure that the API credentials used in your application match those configured in your Langfuse project settings. Set them before importing or instantiating the Langfuse SDKs. If you’re using Jupyter Notebook, try restarting the kernel or session.
3. **Inspect Tracing Configuration**: Ensure that your tracing configuration is correctly set up. For example, verify that the `LANGFUSE_BASE_URL` (Python & JS/TS) is set to the correct endpoint.
4. **Review Logs**: Check the logs of your application to see if there are any errors related to Langfuse. This can help identify issues with the integration or network connectivity. Optionally, you can enable [debug mode](/docs/observability/sdk/advanced-features#logging--debugging) to get more detailed information.
5. **Manual Flushing**: If you are using short-lived applications like serverless functions, local batch scripts or Jupyter Notebooks, ensure that you are manually flushing the events before the application exits. This is important to avoid losing events. Read more on this [here](/docs/observability/features/queuing-batching).
6. **Network Issues**: Check for any network issues that might be preventing your application from sending data to Langfuse. Ensure that your firewall or network settings allow outbound connections to Langfuse endpoints.
7. **Batching Configuration**: In high throughput applications, verify the batching configuration to ensure that events are being sent in a timely manner. You can adjust the `flushAt` and `flushInterval` settings to suit your needs.
8. **Conflicts with existing OpenTelemetry setup**: If you're using other observability tools that also use OpenTelemetry (such as Sentry, Datadog, Honeycomb, or Pydantic Logfire), they may conflict with Langfuse's tracing. See [Using Langfuse with an Existing OpenTelemetry Setup](/faq/all/existing-otel-setup) to resolve these conflicts.

By following these steps, you should be able to identify and resolve the issue preventing traces from appearing in the Langfuse dashboard. If you continue to have issues, please [reach out](/support), we are happy to help.

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<!-- 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/faq/all/missing-traces.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>.