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noravth merged 1 commit into from
Oct 26, 2026
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improved flow #24273

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80 changes: 45 additions & 35 deletions tutorials/ai-core-prior-labs-langgraph/ai-core-prior-labs-langgraph.md
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---

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content/body-no-steps 

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parser: v2
auto_validation: true
time: 60
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retrieve such a service key, and store the credentials in your .env file. Instructions will follow below.

The keys are available in a GitHub repo, and are rotated (old ones expired, new
ones minted) on a regular basis. They can be found in the
ones minted) on a regular basis. Always chose the latest one and ask a Developer Advocate for the password. They can be found in the
[keys/](https://github.com/SAP-samples/teched2026-sk-gen/tree/main/keys)
directory of the `SAP-samples/teched2026-sk-gen` repo on GitHub, stored in
password-protected ZIP files, which are suffixed with a timestamp
Expand Down Expand Up @@ -68,6 +68,8 @@

Before writing any code you need a running deployment of TabPFN-3.5 Plus in your SAP AI Core instance.

> 🚨 As part of the Developer Garage at TechEd 2026 use this deployment ID: `d845a8b8465ba1e6` and move on to the next step.

Use the following values when creating the deployment:

| Field | Value |
Expand All @@ -82,9 +84,17 @@

---

### Create your project folder

Create a new empty folder on your machine for this project — for example `prior-labs-agent`. Open it in VS Code using **File > Open Folder**.

All files you create in this tutorial (`.env`, `payload_data.py`, and `agent-prior-labs.py`) go in this folder.

---

### Configure environment variables

Create a file named `.env` in your project folder and add the following variables. Use the [.env.example](https://github.com/sap-tutorials/Tutorials/blob/master/tutorials/ai-core-prior-labs-langgraph/.env.example) Never commit this file! Add `.env` to your `.gitignore`. When deploying to Cloud Foundry you can simply bind those variables using [environmental variables](https://docs.cloudfoundry.org/devguide/deploy-apps/environment-variable.html).
Create a file named `.env` in your project folder and add the following variables. Use the [.env.example](https://github.com/sap-tutorials/Tutorials/blob/master/tutorials/ai-core-prior-labs-langgraph/.env.example) as a reference. Never commit this file! Add `.env` to your `.gitignore`. When deploying to Cloud Foundry you can simply bind those variables using [environmental variables](https://docs.cloudfoundry.org/devguide/deploy-apps/environment-variable.html).

```
AICORE_AUTH_URL=https://<your-subdomain>.authentication.<region>.hana.ondemand.com/oauth/token
Expand All @@ -100,15 +110,13 @@

> Note: `LLM_MODEL` is optional. This agent defaults to `sap/amazon--nova-lite` if the variable is not set.

> 🚨 You can find the deployment ID for the TabPFN model deployed as part of the Developer Garage in the [.env.example](https://github.com/sap-tutorials/Tutorials/blob/master/tutorials/ai-core-prior-labs-langgraph/.env.example) file.

---

### Install Python dependencies

In your terminal/IDE of choice: Create and activate a virtual environment, then install the required packages:

> 🚨 For the Developer Garage at TechEd 2026 in Berlin open VS Code (Visual Studio Code) on your machine and open the integrated terminal (**Terminal > New Terminal**). Create and activate a virtual environment using the commands for your OS, then run the install command.
> 🚨 For the Developer Garage at TechEd 2026 in Berlin, open the integrated terminal in VS Code (**Terminal > New Terminal**). Create and activate a virtual environment using the commands for your OS, then run the install command.
>
> **macOS/Linux:**
> ```
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)
```

**Main**

Invoke the agent with a user message and print the results:

```python
def main():
result = forecast_agent.invoke(
{"messages": [{"role": "user", "content": "Run the TabPFN predictions and write a concise logistics operations summary."}]}
)

print("\n" + "=" * 50)
print("E-commerce Logistics Report:")
print("=" * 50)
print(result["messages"][-1].content)

for msg in result["messages"]:
if hasattr(msg, "content") and isinstance(msg.content, str):
try:
data = json.loads(msg.content)
if "predictions" in data:
evaluate_predictions(msg.content)
break
except (json.JSONDecodeError, KeyError):
pass

if __name__ == "__main__":
main()
```

---

### Add the evaluation function
Expand Down Expand Up @@ -476,6 +455,37 @@

---

### Add the main function

Finally, add `main()` to invoke the agent and print the results, followed by the entry-point guard:

```python
def main():
result = forecast_agent.invoke(
{"messages": [{"role": "user", "content": "Run the TabPFN predictions and write a concise logistics operations summary."}]}
)

print("\n" + "=" * 50)
print("E-commerce Logistics Report:")
print("=" * 50)
print(result["messages"][-1].content)

for msg in result["messages"]:
if hasattr(msg, "content") and isinstance(msg.content, str):
try:
data = json.loads(msg.content)
if "predictions" in data:
evaluate_predictions(msg.content)
break
except (json.JSONDecodeError, KeyError):
pass

if __name__ == "__main__":
main()
```

---

### Run the agent

Run the script from your project folder:
Expand Down Expand Up @@ -517,4 +527,4 @@

The complete agent script is available at [agent-prior-labs.py](https://github.com/sap-tutorials/Tutorials/blob/master/tutorials/ai-core-prior-labs-langgraph/agent-prior-labs.py).

A template for all required environment variables is provided in [env.example](https://github.com/sap-tutorials/Tutorials/blob/master/tutorials/ai-core-prior-labs-langgraph/env.example). Copy it to `.env` and fill in your values before running the agent.
A template for all required environment variables is provided in [.env.example](https://github.com/sap-tutorials/Tutorials/blob/master/tutorials/ai-core-prior-labs-langgraph/.env.example). Copy it to `.env` and fill in your values before running the agent.
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