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End-to-end Langfuse workshop using a TypeScript Agent to teach the AI engineering loop: tracing, prompt management, monitoring, datasets, experiments, and evaluation.
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This is a step-by-step Langfuse workshop built on a small TypeScript sample application: the Dad IT Support Agent. The workshop covers the full AI engineering loop with Langfuse: tracing, prompt management, monitoring, datasets, experiments, and evaluation.
You can complete the workshop on your own from the learner lessons, or use the instructor notes to teach the same material to a group.
Start with the learner lessons in docs/learner/. Each lesson tells you which checkpoint to check out, what to change in code or configure in Langfuse, and how to verify the result.
Use the learner path if you want to:
No instructor is required. The learner lessons are complete enough to run the workshop in self-guided mode.
The instructor guide is for people who want to teach Langfuse to others. Use the notes in docs/instructor/ alongside the learner lessons when you are facilitating a live workshop, recording a walkthrough, or adapting the material for a team.
For preparation we included a guide and slides on how to prepare for the workshop docs/preparation_and_materials/.
The workshop does not depend on an instructor. The instructor notes add teaching points, demo rhythm, setup reminders, and common pitfalls; they are not required for someone completing the workshop alone.
The sample app is a small web chat where Dad opens the chat to get iPhone help. The agent is named Specs and answers with step-by-step instructions. Under the hood, it is a normal OpenAI tool-calling loop with two local tools.
main contains the complete reference app and current workshop docs. Use it to inspect the finished implementation or compare your work against the end state. The exercises themselves start from checkpoint tags.
| Step |
|---|
| Learner lesson |
|---|
| Instructor notes |
|---|
| Checkpoint |
|---|
| What you will learn |
|---|
| 00 | Setup | Instructor notes | checkpoint/00-setup | Keys, install, run the app. |
| 01 | Base App | Instructor notes | checkpoint/01-base-app | Tour the running app. Nothing to build. |
| 02 | Tracing | Instructor notes | checkpoint/02-tracing | Log every agent step: generations, agent root, tool spans. |
| 03 | Prompt Management | Instructor notes | checkpoint/03-prompt-management | Move the system prompt into Langfuse. |
| 04 | Monitoring | Instructor notes | checkpoint/04-monitoring | Catch out-of-scope requests and user disagreement. |
| 05 | Dataset | Instructor notes | checkpoint/05-dataset | Turn product scope into reusable examples. |
| 06 | Experiments | Instructor notes | checkpoint/06-experiments | Run the agent against the dataset and score every item. |
| 07 | Evaluation | Instructor notes | checkpoint/07-evaluation | Change one thing, rerun the dataset, compare runs. |
| 08 | Wrap-up | Instructor notes | checkpoint/08-wrap-up | Apply the loop to your own app. |
main is the complete reference implementation. For hands-on work, check out the checkpoint named in the lesson you are starting, make the missing changes, and verify the result before moving on.
checkpoint/00-setup and checkpoint/01-base-app intentionally contain the same untraced base app. Setup uses that state to validate keys, dependencies, and ports; Base App uses it for orientation. Tracing starts in 02-tracing.
The workshop server and npm helper scripts treat the repository .env as the Node-side source of truth. Local shell exports from other projects do not override .env, so change workshop keys, Langfuse host, prompt labels, and dataset names in that file.
The workshop is small enough to finish in a sitting, and every module can also be used independently if you only care about one part of the loop.
docs/learner/ contains the self-guided workshop lessons.docs/instructor/ contains facilitator notes for teaching the same lessons.docs/images/ contains screenshots and diagrams, grouped by lesson.src/ contains the sample app.scripts/ contains dataset and prompt helper scripts used in later lessons.docs/learner/00-setup.md./langfuse) to apply the patterns from this workshop to your own codebase.