Add confidential AI examples

README.md

@@ -9,7 +9,7 @@
 
 **Example applications for [dstack](https://github.com/Dstack-TEE/dstack) - Deploy containerized apps to TEEs with end-to-end security in minutes**
 
-[Getting Started](#getting-started) • [Use Cases](#use-cases) • [Core Patterns](#core-patterns) • [Dev Tools](#dev-scaffolding) • [Starter Packs](#starter-packs) • [Other Use Cases](#other-use-cases)
+[Getting Started](#getting-started) • [Confidential AI](#confidential-ai) • [Tutorials](#tutorials) • [Use Cases](#use-cases) • [Core Patterns](#core-patterns) • [Dev Tools](#dev-scaffolding) • [Starter Packs](#starter-packs)
 
 </div>
 
@@ -44,7 +44,7 @@ phala simulator start
 ### Run an Example Locally
 
 ```bash
-cd tutorial/01-attestation-oracle
+cd tutorial/01-attestation
 docker compose run --rm \
   -v ~/.phala-cloud/simulator/0.5.3/dstack.sock:/var/run/dstack.sock \
   app
@@ -57,7 +57,23 @@ phala auth login
 phala deploy -n my-app -c docker-compose.yaml
 ```
 
-See [Phala Cloud](https://cloud.phala.network) for production TEE deployment.
+See [Phala Cloud](https://cloud.phala.com) for production TEE deployment.
+
+---
+
+## Confidential AI
+
+Run AI workloads where prompts, model weights, and inference stay encrypted in hardware.
+
+| Example | Description |
+|---------|-------------|
+| [confidential-ai/inference](./confidential-ai/inference) | Private LLM inference with vLLM on Confidential GPU |
+| [confidential-ai/training](./confidential-ai/training) | Confidential fine-tuning on sensitive data using Unsloth |
+| [confidential-ai/agents](./confidential-ai/agents) | Secure AI agent with TEE-derived wallet keys using LangChain and Confidential AI models |
+
+GPU deployments require: `--instance-type h200.small --region US-EAST-1 --image dstack-nvidia-dev-0.5.4.1`
+
+See [Confidential AI Guide](https://github.com/Dstack-TEE/dstack/blob/master/docs/confidential-ai.md) for concepts and security model.
 
 ---
 
@@ -67,10 +83,10 @@ Step-by-step guides covering core dstack concepts.
 
 | Tutorial | Description |
 |----------|-------------|
-| [01-attestation-oracle](./tutorial/01-attestation-oracle) | Use the guest SDK to work with attestations directly — build an oracle, bind data to TDX quotes via `report_data`, verify with local scripts |
-| [02-persistence-and-kms](./tutorial/02-persistence-and-kms) | Use `getKey()` for deterministic key derivation from a KMS — persistent wallets, same key across restarts |
-| [03-gateway-and-ingress](./tutorial/03-gateway-and-ingress) | Custom domains with automatic SSL, certificate evidence chain |
-| [04-upgrades](./tutorial/04-upgrades) | Extend `AppAuth.sol` with custom authorization logic — NFT-gated clusters, on-chain governance |
+| [01-attestation](./tutorial/01-attestation) | Build an oracle, bind data to TDX quotes via `report_data`, verify with local scripts |
+| [02-kms-and-signing](./tutorial/02-kms-and-signing) | Deterministic key derivation from KMS — persistent wallets, same key across restarts |
+| [03-gateway-and-tls](./tutorial/03-gateway-and-tls) | Custom domains with automatic SSL, certificate evidence chain |
+| [04-onchain-oracle](./tutorial/04-onchain-oracle) | AppAuth contracts, on-chain signature verification, multi-device deployment |
 
 ---
 
@@ -120,15 +136,6 @@ TLS termination, custom domains, external connectivity.
 | Example | Description |
 |---------|-------------|
 | [dstack-ingress](./custom-domain/dstack-ingress) | **Complete ingress solution** — auto SSL via Let's Encrypt, multi-domain, DNS validation, evidence generation with TDX quote chain |
-| [custom-domain](./custom-domain/custom-domain) | Simpler custom domain setup via zt-https |
-
-### Keys & Persistence
-
-Persistent keys across deployments via KMS.
-
-| Example | Description | Status |
-|---------|-------------|--------|
-| [get-key-basic](./get-key-basic) | `dstack.get_key()` — same key identity across machines | Coming Soon |
 
 ### On-Chain Interaction
 

confidential-ai/README.md

@@ -0,0 +1,23 @@
+# Confidential AI Examples
+
+Run AI workloads with hardware-enforced privacy. Your prompts, model weights, and computations stay encrypted in memory.
+
+| Example | Description | Status |
+|---------|-------------|--------|
+| [inference](./inference) | Private LLM with response signing | Ready to deploy |
+| [training](./training) | Fine-tuning on sensitive data | Requires local build |
+| [agents](./agents) | AI agent with TEE-derived keys | Requires local build |
+
+Start with inference—it deploys in one command and shows the full attestation flow.
+
+```bash
+cd inference
+phala auth login
+phala deploy -n my-llm -c docker-compose.yaml \
+  --instance-type h200.small \
+  -e TOKEN=your-secret-token
+```
+
+First deployment takes 10-15 minutes (large images + model loading). Check progress with `phala cvms serial-logs <app_id> --tail 100`.
+
+See the [Confidential AI Guide](https://github.com/Dstack-TEE/dstack/blob/master/docs/confidential-ai.md) for how the security model works.

confidential-ai/agents/Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY agent.py .
+
+EXPOSE 8080
+
+CMD ["python", "agent.py"]

confidential-ai/agents/README.md

@@ -0,0 +1,91 @@
+# Secure AI Agent
+
+Run AI agents with TEE-derived wallet keys. The agent calls a confidential LLM (redpill.ai), so prompts never leave encrypted memory.
+
+## Quick Start
+
+```bash
+phala auth login
+phala deploy -n my-agent -c docker-compose.yaml \
+  -e LLM_API_KEY=your-redpill-key
+```
+
+Your API key is encrypted client-side and only decrypted inside the TEE.
+
+Test it:
+
+```bash
+# Get agent info and wallet address
+curl https://<endpoint>/
+
+# Chat with the agent
+curl -X POST https://<endpoint>/chat \
+  -H "Content-Type: application/json" \
+  -d '{"message": "What is your wallet address?"}'
+
+# Sign a message
+curl -X POST https://<endpoint>/sign \
+  -H "Content-Type: application/json" \
+  -d '{"message": "Hello from TEE"}'
+```
+
+## How It Works
+
+```mermaid
+graph TB
+    User -->|TLS| Agent
+    subgraph TEE1[Agent CVM]
+        Agent[Agent Code]
+        Agent --> Wallet[TEE-derived wallet]
+    end
+    Agent -->|TLS| LLM
+    subgraph TEE2[LLM CVM]
+        LLM[redpill.ai]
+    end
+```
+
+The agent derives an Ethereum wallet from TEE keys:
+
+```python
+from dstack_sdk import DstackClient
+from dstack_sdk.ethereum import to_account
+
+client = DstackClient()
+eth_key = client.get_key("agent/wallet", "mainnet")
+account = to_account(eth_key)
+# Same path = same key, even across restarts
+```
+
+Both the agent and the LLM run in separate TEEs. User queries stay encrypted from browser to agent to LLM and back.
+
+## API
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/` | GET | Agent info, wallet address, TCB info |
+| `/attestation` | GET | TEE attestation quote |
+| `/chat` | POST | Chat with the agent |
+| `/sign` | POST | Sign a message with agent's wallet |
+
+## Using a Different LLM
+
+The agent uses redpill.ai by default for end-to-end confidentiality. To use a different OpenAI-compatible endpoint:
+
+```bash
+phala deploy -n my-agent -c docker-compose.yaml \
+  -e LLM_BASE_URL=https://api.openai.com/v1 \
+  -e LLM_API_KEY=sk-xxxxx
+```
+
+Note: Using a non-confidential LLM means prompts leave the encrypted environment.
+
+## Cleanup
+
+```bash
+phala cvms delete my-agent --force
+```
+
+## Further Reading
+
+- [Confidential AI Guide](https://github.com/Dstack-TEE/dstack/blob/master/docs/confidential-ai.md)
+- [dstack Python SDK](https://github.com/Dstack-TEE/dstack/tree/master/sdk/python)