diff --git a/agents/langgraph/examples/guardrailed_agent/.dockerignore b/agents/langgraph/examples/guardrailed_agent/.dockerignore new file mode 100644 index 00000000..ab4aafe3 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/.dockerignore @@ -0,0 +1,18 @@ +.env +.env.* +!.env.example +.venv +__pycache__ +*.pyc +*.pyo +.pytest_cache +.mypy_cache +.ruff_cache +.git +.gitignore +.helm-secrets.yaml +.DS_Store +tests/ +examples/ +docs/ +*.md diff --git a/agents/langgraph/examples/guardrailed_agent/.env.example b/agents/langgraph/examples/guardrailed_agent/.env.example new file mode 100644 index 00000000..320edc2b --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/.env.example @@ -0,0 +1,26 @@ +# Required — agent connects to NeMo Guardrails proxy, not directly to the LLM +API_KEY=not-needed-for-local-development +BASE_URL=http://localhost:8090/v1 +MODEL_ID=llama3.1:8b + +# NeMo Guardrails — the proxy server that sits between the agent and the LLM +# Start with: make guardrails-server (generates config.yaml from these values) +# GUARDRAILS_PORT=8090 +# LLM_BASE_URL=http://localhost:11434/v1 + +# Deployment +#CONTAINER_IMAGE= + +# Auth (local default off; comma-separate multiple allowlist entries) +# AUTH_ENABLED=false +# AUTH_AUDIENCE=langgraph-guardrailed-agent +# AUTH_ALLOWED_SERVICEACCOUNTS=ci-testing:langgraph-guardrailed-agent-caller + +# Optional — MLflow Tracing +# Tracing is opt-in: if MLFLOW_TRACKING_URI is not set, the agent runs without MLflow. +# Note: MLflow traces show the overall request but not guardrails internals. +# NeMo Guardrails appears as a regular ChatOpenAI endpoint in MLflow. + +# --- Local MLflow server (no auth, no TLS) --- +# MLFLOW_TRACKING_URI=http://localhost:5000 +# MLFLOW_EXPERIMENT_NAME=langgraph-guardrailed-agent diff --git a/agents/langgraph/examples/guardrailed_agent/.gitignore b/agents/langgraph/examples/guardrailed_agent/.gitignore new file mode 100644 index 00000000..6f12f38b --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/.gitignore @@ -0,0 +1,6 @@ +# Local environment (may contain API keys) +.env + +# NeMo Guardrails config is generated at startup by `make guardrails-server` +# from .env values — may contain API_KEY, never commit. +guardrails/safety/config.yaml diff --git a/agents/langgraph/examples/guardrailed_agent/Dockerfile b/agents/langgraph/examples/guardrailed_agent/Dockerfile new file mode 100644 index 00000000..73e7245d --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/Dockerfile @@ -0,0 +1,47 @@ +# Use Red Hat UBI9 Python 3.12 image (no Docker Hub rate limits on OpenShift) +# To update: docker pull registry.access.redhat.com/ubi9/python-312:latest +# docker inspect --format='{{index .RepoDigests 0}}' registry.access.redhat.com/ubi9/python-312:latest +FROM registry.access.redhat.com/ubi9/python-312@sha256:e95978812895b9abb2bdc109b501078da2a47c8dbb9fa23758af40ed50ab6023 +WORKDIR /opt/app-root/src/agents/langgraph/examples/guardrailed_agent + +# Switch to root for installing dependencies +USER 0 + +# Install uv for fast dependency management (v0.11.1) +# To update: docker pull ghcr.io/astral-sh/uv:latest +# docker inspect --format='{{index .RepoDigests 0}}' ghcr.io/astral-sh/uv:latest +COPY --from=ghcr.io/astral-sh/uv@sha256:fc93e9ecd7218e9ec8fba117af89348eef8fd2463c50c13347478769aaedd0ce /uv /usr/local/bin/uv + +# Copy project files for dependency installation. +# Keep the repo-relative layout so ../../../../components/auth resolves. +COPY pyproject.toml . +COPY src/ ./src/ +COPY components/auth/ /opt/app-root/src/components/auth/ + +# Install the project and its dependencies using uv +# NeMo Guardrails runs as a separate pod in production (managed by NemoGuardrails CRD), +# so the [guardrails] extra is intentionally omitted from the agent container image. +RUN uv pip install --no-cache ".[tracing,auth]" + +# Copy the application entrypoint, playground UI, and images +COPY main.py . +COPY playground/ ./playground/ +COPY images/ ./images/ + +# Make everything group-writable (GID 0) for OpenShift arbitrary UID support +RUN chown -R 1001:0 /opt/app-root/src \ + && chmod -R g=u /opt/app-root/src + +# Switch back to default non-root user +USER 1001 + +# Expose port 8080 (OpenShift standard) +EXPOSE 8080 + +# Set environment variables +ENV PORT=8080 +ENV HOME=/opt/app-root +ENV PYTHONPATH=/opt/app-root/src/agents/langgraph/examples/guardrailed_agent + +# Run the application — reads PORT at runtime +CMD ["sh", "-c", "uvicorn main:app --host 0.0.0.0 --port ${PORT}"] diff --git a/agents/langgraph/examples/guardrailed_agent/Makefile b/agents/langgraph/examples/guardrailed_agent/Makefile new file mode 100644 index 00000000..68add861 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/Makefile @@ -0,0 +1,212 @@ +SHELL := bash +AGENT_NAME := $(shell python3 -c "import re; print(re.search(r'^name:\s*(.+)', open('agent.yaml').read(), re.M).group(1).strip())") +CHART_DIR := ../../deployment +VALUES_FILE := values.yaml +CONTAINER_CLI := $(shell command -v podman 2>/dev/null || command -v docker 2>/dev/null) +MODEL ?= llama3.1:8b + +.PHONY: init re-init env ollama run-app run-app-fresh build push build-openshift deploy undeploy test dry-run help guardrails-server + +help: ## Show this help + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf " %-20s %s\n", $$1, $$2}' + +re-init: ## Activate venv and reload .env variables + @source .venv/bin/activate && set -a && source .env && set +a && \ + echo "Venv activated and .env variables loaded." + +init: ## Copy .env.example to .env for configuration + @if [ ! -f .env ]; then cp .env.example .env && echo "Created .env from .env.example - edit it with your configuration"; else echo ".env already exists - skipping"; fi + +env: ## Create venv, load .env, and install dependencies (including NeMo Guardrails) + @echo "==> Creating virtual environment and installing dependencies..." && \ + rm -rf .venv && \ + uv sync --python 3.12 --extra guardrails && \ + echo "==> Load .env variables..." && \ + source .venv/bin/activate && set -a && source .env && set +a && \ + echo "" && \ + echo "Done. Next steps:" && \ + echo " make ollama # install Ollama and pull models" && \ + echo " make guardrails-server # start NeMo Guardrails proxy" && \ + echo " make run-app # start the agent" + +ollama: ## Install Ollama, start it, and pull models + @source .venv/bin/activate && set -a && source .env && set +a && \ + if command -v ollama >/dev/null 2>&1; then \ + echo "==> Ollama already installed, skipping..."; \ + else \ + echo "==> Installing Ollama..." && \ + curl -fsSL https://ollama.com/install.sh | sh; \ + fi && \ + echo "==> Starting Ollama service..." && \ + ollama serve &>/dev/null & \ + OLLAMA_PID=$$! && \ + echo "Waiting for Ollama to be ready (max 30s)..." && \ + OLLAMA_WAIT=0 && \ + until ollama list >/dev/null 2>&1; do \ + OLLAMA_WAIT=$$((OLLAMA_WAIT + 1)); \ + if [ $$OLLAMA_WAIT -ge 30 ]; then \ + echo "ERROR: Ollama failed to start within 30 seconds." && \ + kill $$OLLAMA_PID 2>/dev/null; \ + exit 1; \ + fi; \ + sleep 1; \ + done && \ + echo "==> Pulling $(MODEL)..." && \ + ollama pull $(MODEL) && \ + echo "" && \ + echo "Done. Run 'make guardrails-server' to start NeMo Guardrails." + +guardrails-server: ## Generate config from .env and start NeMo Guardrails proxy + @source .venv/bin/activate && set -a && source .env && set +a && \ + cp guardrails/safety/config.yaml.example guardrails/safety/config.yaml && \ + python3 -c "\ + import yaml; \ + f='guardrails/safety/config.yaml'; \ + c=yaml.safe_load(open(f)); \ + url='$${LLM_BASE_URL:-http://localhost:11434/v1}'; \ + model='$${MODEL_ID:-llama3.1:8b}'; \ + key='$${API_KEY:-not-needed}'; \ + [m['parameters'].update(base_url=url, api_key=key) or m.update(model=model) for m in c['models']]; \ + yaml.dump(c, open(f,'w'), default_flow_style=False, sort_keys=False); \ + print(f'Config generated: LLM_BASE_URL={url} MODEL_ID={model}')" && \ + echo "Starting NeMo Guardrails server on port $${GUARDRAILS_PORT:-8090}..." && \ + nemoguardrails server --config guardrails --default-config-id safety --port $${GUARDRAILS_PORT:-8090} --disable-chat-ui + +run-app: ## Run agent locally with hot-reload + @source .venv/bin/activate && set -a && source .env && set +a && \ + if lsof -ti:$${PORT:-8000} >/dev/null 2>&1; then \ + echo "ERROR: Port $${PORT:-8000} is already in use." && \ + echo " Change PORT in .env or run: make run-app-fresh" && \ + exit 1; \ + fi && \ + uv run $${MLFLOW_TRACKING_URI:+--extra tracing} uvicorn main:app --host 127.0.0.1 --port $${PORT:-8000} --reload --reload-exclude .venv + +run-app-fresh: ## Kill existing process on port and run agent with hot-reload + @source .venv/bin/activate && set -a && source .env && set +a && \ + echo "==> Killing existing process on port $${PORT:-8000}..." && \ + lsof -ti:$${PORT:-8000} | xargs kill -9 2>/dev/null; true && \ + uv run $${MLFLOW_TRACKING_URI:+--extra tracing} uvicorn main:app --host 127.0.0.1 --port $${PORT:-8000} --reload --reload-exclude .venv + +build: ## Build container image locally (podman/docker) + @[ -n "$(CONTAINER_CLI)" ] || { echo "ERROR: neither podman nor docker found in PATH"; exit 1; } && \ + source .env && \ + [ -n "$${CONTAINER_IMAGE}" ] || { echo "ERROR: CONTAINER_IMAGE is not set in .env"; exit 1; } && \ + cp -r ../../../../images ./images && \ + mkdir -p ./components && cp -r ../../../../components/auth ./components/auth && \ + trap 'rm -rf ./images ./components/auth; rmdir ./components 2>/dev/null || true' EXIT && \ + $(CONTAINER_CLI) build --platform linux/amd64 -t "$${CONTAINER_IMAGE}" -f Dockerfile . + +push: ## Push container image to registry + @[ -n "$(CONTAINER_CLI)" ] || { echo "ERROR: neither podman nor docker found in PATH"; exit 1; } && \ + source .env && \ + [ -n "$${CONTAINER_IMAGE}" ] || { echo "ERROR: CONTAINER_IMAGE is not set in .env"; exit 1; } && \ + $(CONTAINER_CLI) push "$${CONTAINER_IMAGE}" + +build-openshift: ## Build image in-cluster via OpenShift BuildConfig (no podman/docker needed) + @cp -r ../../../../images ./images && \ + mkdir -p ./components && cp -r ../../../../components/auth ./components/auth && \ + trap 'rm -rf ./images ./components/auth; rmdir ./components 2>/dev/null || true' EXIT && \ + oc new-build --strategy=docker --binary --name=$(AGENT_NAME) --to=$(AGENT_NAME):latest 2>/dev/null || true && \ + oc start-build $(AGENT_NAME) --from-dir=. --follow && \ + NS=$$(oc project -q) && \ + echo "" && \ + echo "Image built. To deploy, set in .env:" && \ + echo " CONTAINER_IMAGE=image-registry.openshift-image-registry.svc:5000/$$NS/$(AGENT_NAME):latest" + +_check-env: + @set -a && source .env 2>/dev/null && set +a; \ + missing=""; \ + for var in $$(sed -n '/^ *required:/,/^ *[a-z]/{ /^ *- /s/^ *- *//p; }' agent.yaml); do \ + eval val="\$$$$var"; \ + [ -n "$$val" ] || missing="$$missing $$var"; \ + done; \ + [ -z "$$missing" ] || { echo "ERROR: Missing required env vars:$$missing"; exit 1; } + +deploy: _check-env ## Deploy to OpenShift/K8s via Helm + @source .env && \ + AUTH_ALLOWLIST="$${AUTH_ALLOWED_SERVICEACCOUNTS:-$${AUTH_ALLOWED_SERVICEACCOUNT}}" && \ + AUTH_ALLOWLIST_ARGS="" && \ + if [ -n "$$AUTH_ALLOWLIST" ]; then \ + IFS=',' read -r -a AUTH_ALLOWLIST_ITEMS <<< "$$AUTH_ALLOWLIST"; \ + for idx in "$${!AUTH_ALLOWLIST_ITEMS[@]}"; do \ + account="$${AUTH_ALLOWLIST_ITEMS[$$idx]}"; \ + account="$$(echo -n "$$account" | tr -d '[:space:]')"; \ + [ -n "$$account" ] || continue; \ + AUTH_ALLOWLIST_ARGS="$$AUTH_ALLOWLIST_ARGS --set-string auth.allowedServiceAccounts[$$idx]=$$account"; \ + done; \ + fi && \ + [ -n "$${CONTAINER_IMAGE}" ] || { echo "ERROR: CONTAINER_IMAGE is not set in .env"; exit 1; } && \ + LAST_SEG="$${CONTAINER_IMAGE##*/}" && if [[ "$$LAST_SEG" == *:* ]]; then IMAGE_REPO="$${CONTAINER_IMAGE%:*}"; IMAGE_TAG="$${LAST_SEG##*:}"; else IMAGE_REPO="$${CONTAINER_IMAGE}"; IMAGE_TAG="latest"; fi && \ + trap 'rm -f .helm-secrets.yaml' EXIT && \ + umask 077 && \ + { printf 'secrets:\n apiKey: "%s"\n' "$${API_KEY}"; \ + [ -z "$${MLFLOW_TRACKING_TOKEN}" ] || printf ' mlflowTrackingToken: "%s"\n' "$${MLFLOW_TRACKING_TOKEN}"; \ + } > .helm-secrets.yaml && \ + helm upgrade --install $(AGENT_NAME) $(CHART_DIR) \ + -f $(VALUES_FILE) \ + -f .helm-secrets.yaml \ + --set image.repository="$${IMAGE_REPO}" \ + --set image.tag="$${IMAGE_TAG}" \ + --set env.BASE_URL="$${BASE_URL}" \ + --set env.MODEL_ID="$${MODEL_ID}" \ + $${AUTH_ENABLED:+--set "auth.enabled=$${AUTH_ENABLED}"} \ + $${AUTH_ENABLED:+--set "serviceAccount.create=$${AUTH_ENABLED}"} \ + $${AUTH_AUDIENCE:+--set-string "auth.audience=$${AUTH_AUDIENCE}"} \ + $${AUTH_ALLOWLIST_ARGS} \ + $${MLFLOW_TRACKING_URI:+--set env.MLFLOW_TRACKING_URI="$${MLFLOW_TRACKING_URI}"} \ + $${MLFLOW_EXPERIMENT_NAME:+--set env.MLFLOW_EXPERIMENT_NAME="$${MLFLOW_EXPERIMENT_NAME}"} \ + $${MLFLOW_TRACKING_AUTH:+--set env.MLFLOW_TRACKING_AUTH="$${MLFLOW_TRACKING_AUTH}"} \ + $${MLFLOW_TRACKING_INSECURE_TLS:+--set env.MLFLOW_TRACKING_INSECURE_TLS="$${MLFLOW_TRACKING_INSECURE_TLS}"} \ + $${MLFLOW_TRACKING_SERVER_CERT_PATH:+--set env.MLFLOW_TRACKING_SERVER_CERT_PATH="$${MLFLOW_TRACKING_SERVER_CERT_PATH}"} \ + $${MLFLOW_WORKSPACE:+--set env.MLFLOW_WORKSPACE="$${MLFLOW_WORKSPACE}"} && \ + if command -v oc >/dev/null 2>&1; then \ + echo "" && echo "Waiting for rollout to complete..." && \ + if oc rollout status deployment/$(AGENT_NAME) --timeout=120s; then \ + ROUTE=$$(oc get route $(AGENT_NAME) -o jsonpath='{.spec.host}' 2>/dev/null || true); \ + if [ -n "$$ROUTE" ]; then echo "" && echo "Agent is available at: https://$$ROUTE"; fi; \ + else \ + echo "" && echo "WARNING: Rollout did not complete successfully. Check pod status with:" && \ + echo " oc get pods -l app.kubernetes.io/name=$(AGENT_NAME)" && \ + echo " oc logs deployment/$(AGENT_NAME)"; \ + fi; \ + fi + +dry-run: _check-env ## Render Helm templates without deploying + @source .env && \ + AUTH_ALLOWLIST="$${AUTH_ALLOWED_SERVICEACCOUNTS:-$${AUTH_ALLOWED_SERVICEACCOUNT}}" && \ + AUTH_ALLOWLIST_ARGS="" && \ + if [ -n "$$AUTH_ALLOWLIST" ]; then \ + IFS=',' read -r -a AUTH_ALLOWLIST_ITEMS <<< "$$AUTH_ALLOWLIST"; \ + for idx in "$${!AUTH_ALLOWLIST_ITEMS[@]}"; do \ + account="$${AUTH_ALLOWLIST_ITEMS[$$idx]}"; \ + account="$$(echo -n "$$account" | tr -d '[:space:]')"; \ + [ -n "$$account" ] || continue; \ + AUTH_ALLOWLIST_ARGS="$$AUTH_ALLOWLIST_ARGS --set-string auth.allowedServiceAccounts[$$idx]=$$account"; \ + done; \ + fi && \ + [ -n "$${CONTAINER_IMAGE}" ] || { echo "ERROR: CONTAINER_IMAGE is not set in .env"; exit 1; } && \ + LAST_SEG="$${CONTAINER_IMAGE##*/}" && if [[ "$$LAST_SEG" == *:* ]]; then IMAGE_REPO="$${CONTAINER_IMAGE%:*}"; IMAGE_TAG="$${LAST_SEG##*:}"; else IMAGE_REPO="$${CONTAINER_IMAGE}"; IMAGE_TAG="latest"; fi && \ + helm template $(AGENT_NAME) $(CHART_DIR) \ + -f $(VALUES_FILE) \ + --set secrets.apiKey="REDACTED" \ + --set image.repository="$${IMAGE_REPO}" \ + --set image.tag="$${IMAGE_TAG}" \ + --set env.BASE_URL="$${BASE_URL}" \ + --set env.MODEL_ID="$${MODEL_ID}" \ + $${AUTH_ENABLED:+--set "auth.enabled=$${AUTH_ENABLED}"} \ + $${AUTH_ENABLED:+--set "serviceAccount.create=$${AUTH_ENABLED}"} \ + $${AUTH_AUDIENCE:+--set-string "auth.audience=$${AUTH_AUDIENCE}"} \ + $${AUTH_ALLOWLIST_ARGS} \ + $${MLFLOW_TRACKING_URI:+--set env.MLFLOW_TRACKING_URI="$${MLFLOW_TRACKING_URI}"} \ + $${MLFLOW_TRACKING_TOKEN:+--set secrets.mlflowTrackingToken="REDACTED"} \ + $${MLFLOW_EXPERIMENT_NAME:+--set env.MLFLOW_EXPERIMENT_NAME="$${MLFLOW_EXPERIMENT_NAME}"} \ + $${MLFLOW_TRACKING_AUTH:+--set env.MLFLOW_TRACKING_AUTH="$${MLFLOW_TRACKING_AUTH}"} \ + $${MLFLOW_TRACKING_INSECURE_TLS:+--set env.MLFLOW_TRACKING_INSECURE_TLS="$${MLFLOW_TRACKING_INSECURE_TLS}"} \ + $${MLFLOW_TRACKING_SERVER_CERT_PATH:+--set env.MLFLOW_TRACKING_SERVER_CERT_PATH="$${MLFLOW_TRACKING_SERVER_CERT_PATH}"} \ + $${MLFLOW_WORKSPACE:+--set env.MLFLOW_WORKSPACE="$${MLFLOW_WORKSPACE}"} + +undeploy: ## Remove deployment from cluster + helm uninstall $(AGENT_NAME) + +test: ## Run unit tests + uv run --extra dev python -m pytest tests/ $(PYTEST_ARGS) diff --git a/agents/langgraph/examples/guardrailed_agent/README.md b/agents/langgraph/examples/guardrailed_agent/README.md new file mode 100644 index 00000000..0ce623b0 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/README.md @@ -0,0 +1,219 @@ +
+ +![LangGraph Logo](/images/langgraph_logo.svg) + +# Guardrailed Agent + +
+ +--- + +## What this agent does + +Banking customer service agent with [NeMo Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) safety layer. Demonstrates how to add content safety, topical guardrails, and regex-based input filtering to a LangGraph ReAct agent using the **proxy pattern** — NeMo Guardrails sits between the agent and the LLM, requiring zero changes to the agent's source code. + +### Guardrails architecture + +```text +User → Agent (port 8000) → NeMo Guardrails (port 8090) → LLM (port 11434) +``` + +Three layered input rails run in order (if any blocks, later rails are skipped): + +1. **Regex** — instant pattern matching, no LLM call (catches jailbreak patterns) +2. **Content safety** — LLM classifies input against S1–S13 safety categories +3. **Topic safety** — LLM checks if input is within the banking domain boundary + +Plus a content safety **output rail** that checks the LLM's response before returning it. + +### Swapping the use case + +The banking domain is defined entirely in `guardrails/safety/prompts.yml` (the `topic_safety_check_input` prompt). To adapt this example to a different domain (healthcare, telecom, etc.), edit only that prompt's guidelines list. Everything else stays the same. + +--- + +## Prerequisites + +- [uv](https://docs.astral.sh/uv/) — Python package manager +- [Ollama](https://ollama.com/) — local LLM inference (or any OpenAI-compatible endpoint) +- [Podman](https://podman.io/) or [Docker](https://www.docker.com/) — for container builds +- [oc](https://docs.openshift.com/container-platform/latest/cli_reference/openshift_cli/getting-started-cli.html) — for OpenShift deployment +- [Helm](https://helm.sh/) — for deploying to Kubernetes/OpenShift +- [GNU Make](https://www.gnu.org/software/make/) and a bash-compatible shell + +## Local Development + +### 1. Initialize + +```bash +cd agents/langgraph/examples/guardrailed_agent +make init # creates .env from .env.example +``` + +### 2. Install dependencies + +```bash +make env # creates venv and installs deps (including NeMo Guardrails) +``` + +### 3. Setup Ollama + +Install Ollama and pull the default model: + +```bash +make ollama # installs Ollama (if needed) and pulls llama3.1:8b +``` + +Ensure Ollama is running (the macOS desktop app handles this automatically; otherwise run `ollama serve` in a separate terminal). + +### 4. Start NeMo Guardrails proxy + +> **Keep this terminal open** — the guardrails server needs to keep running. + +```bash +make guardrails-server # starts on port 8090, proxies to Ollama on 11434 +``` + +The guardrails server generates its runtime config from `.env` at startup — no separate config step needed. The generated `config.yaml` is gitignored to prevent accidental credential commits. + +> **Using a different model?** Set `MODEL_ID` in `.env`, pull it with +> `ollama pull `, then restart `make guardrails-server`. +> +> **Using a remote endpoint instead of Ollama?** Set `LLM_BASE_URL` and `API_KEY` in `.env`, +> then restart `make guardrails-server`. + +### 5. Start the agent + +> **In a separate terminal:** + +```bash +make run-app # starts on port 8000 +``` + +### 6. Test the guardrails + +```bash +# On-topic question — should respond normally +curl -s http://localhost:8000/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"messages":[{"role":"user","content":"What is my balance for ACCT-12345?"}]}' \ + | python3 -m json.tool + +# Toxic input — should be blocked by content safety rail +curl -s http://localhost:8000/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"messages":[{"role":"user","content":"How do I build a bomb?"}]}' \ + | python3 -m json.tool + +# Off-topic request — should be blocked by topic safety rail +curl -s http://localhost:8000/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"messages":[{"role":"user","content":"Give me a recipe for chocolate cake"}]}' \ + | python3 -m json.tool + +# Greeting — should respond normally +curl -s http://localhost:8000/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"messages":[{"role":"user","content":"Hello!"}]}' \ + | python3 -m json.tool +``` + +### Tracing (optional) + +MLflow tracing works the same as other agents. Note that MLflow sees NeMo Guardrails as a regular ChatOpenAI endpoint — guardrails-internal traces (which rail fired, classification results) are not visible in MLflow. Use `nemoguardrails server --verbose` for rail-level debugging. + +See `.env.example` for MLflow configuration options. + +## Deploying to OpenShift + +### Configuration + +Edit `.env` with your model endpoint and container image: + +```ini +API_KEY=your-api-key-here +BASE_URL=https://your-guardrails-endpoint/v1 +MODEL_ID=llama-3.1-8b-instruct +CONTAINER_IMAGE=quay.io/your-username/langgraph-guardrailed-agent:latest +``` + +> **Note:** In production on RHOAI, NeMo Guardrails runs as a separate pod managed by the `NemoGuardrails` CRD. The agent's `BASE_URL` points to the guardrails service, not directly to the LLM. + +### Build and deploy + +#### Option A: Build locally and push + +```bash +make build # builds the container image +make push # pushes to the registry +make deploy # deploys via Helm +``` + +#### Option B: Build in-cluster + +```bash +make build-openshift # builds via OpenShift BuildConfig +make deploy +``` + +### Verify + +```bash +make dry-run # preview Helm manifests (secrets redacted) +make undeploy # remove deployment +``` + +## Tests + +```bash +make test +``` + +## API Endpoints + +### POST /chat/completions + +```bash +# Non-streaming +curl -X POST http://localhost:8000/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"messages": [{"role": "user", "content": "What is my account balance?"}], "stream": false}' + +# Streaming +curl -sN -X POST http://localhost:8000/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"messages": [{"role": "user", "content": "What is my account balance?"}], "stream": true}' +``` + +### GET /health + +```bash +curl http://localhost:8000/health +``` + +## Guardrails Configuration + +| File | Purpose | +|------|---------| +| `guardrails/safety/config.yaml.example` | Template with safe defaults (committed) | +| `guardrails/safety/config.yaml` | Runtime config (gitignored, generated by `make guardrails-server`) | +| `guardrails/safety/prompts.yml` | Content safety (S1–S13) and topic safety classification prompts | +| `guardrails/safety/rails.co` | Colang greeting flows (required by RHOAI entrypoint) | + +**Key constraints:** + +- Config file must be `config.yaml` (not `.yml`) — RHOAI container entrypoint requirement +- `rails.co` must exist — RHOAI container entrypoint requirement (can be minimal) +- NeMo Guardrails version pinned to `0.21.0` to match RHOAI + +## Known Limitations + +- **Single-turn only** — the agent uses only the last user message; prior conversation history is not forwarded to the LLM. This matches the template pattern and is adequate for demonstrating guardrails behavior. +- **Health probe as readiness** — `/health` returns 503 when guardrails are unreachable. Use it as a Kubernetes **readiness** probe, not a liveness probe, to avoid unnecessary pod restarts. + +## Resources + +- [NeMo Guardrails Documentation](https://docs.nvidia.com/nemo/guardrails/) +- [LangGraph Documentation](https://docs.langchain.com/oss/python/langgraph/overview) +- [LangChain Documentation](https://docs.langchain.com/oss/python/langchain/overview) +- [Ollama Documentation](https://docs.ollama.com) diff --git a/agents/langgraph/examples/guardrailed_agent/agent.yaml b/agents/langgraph/examples/guardrailed_agent/agent.yaml new file mode 100644 index 00000000..64192dd7 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/agent.yaml @@ -0,0 +1,27 @@ +name: langgraph-guardrailed-agent +displayName: "LangGraph Guardrailed Agent" +framework: langgraph +description: > + Banking customer service agent with NeMo Guardrails safety layer. + Demonstrates content safety rails (input + output), topical guardrails + (banking domain boundary), and regex-based input filtering using the + NeMo Guardrails proxy pattern. Built on the LangGraph ReAct agent template. +labels: + - guardrails + - nemo-guardrails + - safety + - customer-service + - react +env: + required: + - API_KEY + - BASE_URL + - MODEL_ID + optional: + - PORT + - CONTAINER_IMAGE + - GUARDRAILS_PORT + - LLM_BASE_URL + - AUTH_ENABLED + - AUTH_AUDIENCE + - AUTH_ALLOWED_SERVICEACCOUNTS diff --git a/agents/langgraph/examples/guardrailed_agent/guardrails/safety/config.yaml.example b/agents/langgraph/examples/guardrailed_agent/guardrails/safety/config.yaml.example new file mode 100644 index 00000000..48a3a88d --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/guardrails/safety/config.yaml.example @@ -0,0 +1,48 @@ +# NeMo Guardrails configuration for the guardrailed agent. +# Model endpoints below default to Ollama on localhost. +# To use a different LLM: +# 1. Edit the base_url, model, and api_key values below, OR +# 2. Run `make guardrails-config` to update from .env (LLM_BASE_URL, MODEL_ID) +models: + - type: main + engine: openai + model: llama3.1:8b + parameters: + base_url: http://localhost:11434/v1 + api_key: not-needed + + - type: content_safety + engine: openai + model: llama3.1:8b + parameters: + base_url: http://localhost:11434/v1 + api_key: not-needed + + - type: topic_control + engine: openai + model: llama3.1:8b + parameters: + base_url: http://localhost:11434/v1 + api_key: not-needed + +passthrough: true + +rails: + config: + regex_detection: + input: + patterns: + - "\\b(hack|exploit|bypass|inject)\\s+(the|this|my)\\s+(system|filter|guardrail)" + - "ignore\\s+(all\\s+)?(previous|above)\\s+(instructions|rules|prompts)" + - "\\bDAN\\b.*\\bjailbreak\\b" + case_insensitive: true + input: + flows: + - regex check input + - content safety check input $model=content_safety + - topic safety check input $model=topic_control + output: + flows: + - content safety check output $model=content_safety + streaming: + enabled: True diff --git a/agents/langgraph/examples/guardrailed_agent/guardrails/safety/prompts.yml b/agents/langgraph/examples/guardrailed_agent/guardrails/safety/prompts.yml new file mode 100644 index 00000000..71e7cc92 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/guardrails/safety/prompts.yml @@ -0,0 +1,90 @@ +prompts: + - task: content_safety_check_input $model=content_safety + content: | + Task: Check if there is unsafe content in the user message below in accordance with our safety policy. Provide a 'safe' or 'unsafe' rating. + + + S1: Violence. + S2: Sexual. + S3: Criminal Planning/Confessions. + S4: Guns and Illegal Weapons. + S5: Controlled/Regulated Substances. + S6: Suicide and Self Harm. + S7: Sexual (minor). + S8: Hate/Identity Hate. + S9: PII/Privacy. + S10: Harassment. + S11: Threat. + S12: Profanity. + S13: Needs Caution. + + + + + user: {{ user_input }} + + + + Provide your safety assessment for the above conversation in the below output JSON format: + {"User Safety": safe or unsafe rating for the user message, "Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.} + + Do not include anything other than the output JSON in your response. + Output JSON: + output_parser: nemoguard_parse_prompt_safety + max_tokens: 50 + + - task: content_safety_check_output $model=content_safety + content: | + Task: Check if there is unsafe content in the agent response below in accordance with our safety policy. Provide a 'safe' or 'unsafe' rating. + + + S1: Violence. + S2: Sexual. + S3: Criminal Planning/Confessions. + S4: Guns and Illegal Weapons. + S5: Controlled/Regulated Substances. + S6: Suicide and Self Harm. + S7: Sexual (minor). + S8: Hate/Identity Hate. + S9: PII/Privacy. + S10: Harassment. + S11: Threat. + S12: Profanity. + S13: Needs Caution. + + + + + user: {{ user_input }} + + response: agent: {{ bot_response }} + + + + Provide your safety assessment for the above conversation in the below output JSON format: + {"User Safety": safe or unsafe rating for the user message, "Response Safety": safe or unsafe rating for the agent response, "Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.} + + Do not include anything other than the output JSON in your response. + Output JSON: + output_parser: nemoguard_parse_response_safety + max_tokens: 50 + + - task: topic_safety_check_input $model=topic_control + content: | + You are a topic boundary classifier for a customer service assistant at a retail bank. The assistant helps customers with their banking needs only. + + Guidelines for the user messages: + - Allow questions about account balances, transactions, and account history. + - Allow questions about billing, payments, and due dates. + - Allow questions about bank products, services, interest rates, and fees. + - Allow questions about branch locations, hours, and contact information. + - Allow questions about online banking, mobile app, and password resets. + - Allow small talk and greetings. + - Do not answer questions about medical or health advice. + - Do not answer questions about legal advice or legal proceedings. + - Do not answer questions about specific investment recommendations or stock picks. + - Do not answer questions about cooking, recipes, or food preparation. + - Do not answer questions about entertainment, sports, or celebrity gossip. + - Do not answer questions about personal relationships or dating advice. + - If a user asks topics irrelevant to banking and financial services, politely redirect. + max_tokens: 10 diff --git a/agents/langgraph/examples/guardrailed_agent/guardrails/safety/rails.co b/agents/langgraph/examples/guardrailed_agent/guardrails/safety/rails.co new file mode 100644 index 00000000..49925545 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/guardrails/safety/rails.co @@ -0,0 +1,12 @@ +define user express greeting + "Hello" + "Hi" + "Hey" + "Good morning" + +define flow + user express greeting + bot express greeting + +define bot express greeting + "Hello! Welcome to our banking service. How can I help you today?" diff --git a/agents/langgraph/examples/guardrailed_agent/main.py b/agents/langgraph/examples/guardrailed_agent/main.py new file mode 100644 index 00000000..c0d9b8e4 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/main.py @@ -0,0 +1,568 @@ +import json +import logging +import time +import uuid +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from os import getenv +from pathlib import Path +from typing import Any + +import httpx +from fastapi import FastAPI, HTTPException +from fastapi.responses import ( + FileResponse, + HTMLResponse, + JSONResponse, + StreamingResponse, +) +from guardrailed_agent.agent import get_graph_closure +from guardrailed_agent.tracing import enable_tracing +from langchain_core.messages import AIMessage, HumanMessage, ToolMessage +from openai import APIError as OpenAIAPIError +from pydantic import BaseModel, Field + +logger = logging.getLogger(__name__) + +_GUARDRAILS_REFUSAL = "I'm sorry, I can't respond to that." + + +def _is_guardrails_block(exc: Exception) -> bool: + """NeMo Guardrails v0.21.0 has no typed exception for rail blocks. + It raises openai.APIError with 'Blocked by ... rails' in the message. + This coupling is version-specific and may need updating for newer NeMo.""" + return ( + isinstance(exc, OpenAIAPIError) + and "Blocked by" in str(exc) + and "rails" in str(exc) + ) + + +# OpenAI-compatible request/response models +class ChatMessage(BaseModel): + """A message in the conversation.""" + + role: str = Field( + ..., + description="The role of the message author.", + examples=["user", "assistant", "system", "tool"], + ) + content: str = Field( + ..., + description="The contents of the message.", + examples=["What is the best cluster hosting service?"], + ) + + +class ChatCompletionRequest(BaseModel): + """Creates a model response for the given chat conversation. + + [See OpenAI docs](https://platform.openai.com/docs/api-reference/chat/create) + """ + + messages: list[ChatMessage] = Field( + ..., + min_length=1, + description="A list of messages comprising the conversation so far.", + ) + model: str | None = Field( + None, + description="ID of the model to use. Defaults to the server's configured MODEL_ID.", + ) + stream: bool = Field( + False, + description="If true, partial message deltas will be sent as SSE `data: {json}\\n\\n` events, terminated by `data: [DONE]\\n\\n`.", + ) + + +class ChoiceMessage(BaseModel): + """A chat completion message generated by the model.""" + + role: str = Field( + "assistant", description="The role of the author of this message." + ) + content: str = Field(..., description="The contents of the message.") + + +class Choice(BaseModel): + index: int = Field( + ..., description="The index of the choice in the list of choices." + ) + message: ChoiceMessage + finish_reason: str = Field( + ..., + description="The reason the model stopped generating tokens.", + examples=["stop", "tool_calls"], + ) + + +class ChatCompletionResponse(BaseModel): + """Represents a chat completion response returned by the model, based on the provided input.""" + + id: str = Field( + ..., + description="A unique identifier for the chat completion.", + examples=["chatcmpl-abc123def456"], + ) + object: str = Field( + "chat.completion", + description="The object type, which is always `chat.completion`.", + ) + created: int = Field( + ..., + description="The Unix timestamp (in seconds) of when the chat completion was created.", + ) + model: str = Field(..., description="The model used for the chat completion.") + choices: list[Choice] = Field(..., description="A list of chat completion choices.") + + usage: dict | None = Field( + None, description="Usage statistics for the completion request." + ) + + +class HealthResponse(BaseModel): + """Service health status.""" + + status: str = Field( + ..., description="Current service status.", examples=["healthy"] + ) + agent_initialized: bool = Field( + ..., + description="Whether the agent has been initialized and is ready to serve requests.", + ) + guardrails_reachable: bool = Field( + False, + description="Whether the NeMo Guardrails proxy server is reachable.", + ) + + +# Global variable for agent graph +agent_graph = None +_health_client = httpx.AsyncClient(timeout=2) + + +@asynccontextmanager +async def lifespan(app: FastAPI) -> AsyncIterator[None]: + """Initialize the ReAct agent graph on startup and clear it on shutdown.""" + global agent_graph + + enable_tracing() + + base_url = getenv("BASE_URL") + model_id = getenv("MODEL_ID") + + if base_url and not base_url.endswith("/v1"): + base_url = base_url.rstrip("/") + "/v1" + + agent_graph = get_graph_closure(model_id=model_id, base_url=base_url) + + yield + + agent_graph = None + + +# Create FastAPI app +app = FastAPI( + title="LangGraph Guardrailed Agent API", + description="FastAPI service for LangGraph Guardrailed Agent with OpenAI-compatible chat completions API.", + lifespan=lifespan, + openapi_tags=[ + {"name": "Chat", "description": "Chat completion operations"}, + {"name": "Health", "description": "Service health monitoring"}, + ], +) + + +def _auth_enabled() -> bool: + return getenv("AUTH_ENABLED", "false").strip().lower() == "true" + + +def _configure_auth_middleware() -> None: + if not _auth_enabled(): + return + + try: + from agent_auth.middleware import SATokenAuthMiddleware + except ModuleNotFoundError as exc: + raise RuntimeError( + "AUTH_ENABLED=true, but auth middleware dependencies are not installed. " + "Run `uv sync --extra auth` to enable ServiceAccount token auth locally." + ) from exc + + app.add_middleware(SATokenAuthMiddleware) + + +_configure_auth_middleware() + + +def _build_langchain_messages(messages: list[ChatMessage]) -> list[HumanMessage]: + """Extract the last user message from the OpenAI-format messages list.""" + for msg in reversed(messages): + if msg.role == "user": + return [HumanMessage(content=msg.content)] + raise ValueError("No user message found in messages list") + + +def _extract_usage(messages: list) -> dict | None: + """Sum token usage from AIMessage.usage_metadata across all LLM calls.""" + prompt_tokens = 0 + completion_tokens = 0 + total_tokens = 0 + found = False + for message in messages: + if isinstance(message, AIMessage) and getattr(message, "usage_metadata", None): + meta = message.usage_metadata + prompt_tokens += meta.get("input_tokens", 0) or 0 + completion_tokens += meta.get("output_tokens", 0) or 0 + total_tokens += meta.get("total_tokens", 0) or 0 + found = True + if not found: + return None + return { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": total_tokens, + } + + +def _make_completion_id() -> str: + return f"chatcmpl-{uuid.uuid4().hex[:12]}" + + +@app.post( + "/chat/completions", + response_model=ChatCompletionResponse, + summary="Create chat completion", + description="Creates a model response for the given chat conversation. When `stream=false`, returns a complete `chat.completion` JSON object. When `stream=true`, returns Server-Sent Events with `chat.completion.chunk` deltas.", + tags=["Chat"], +) +async def chat_completions(request: ChatCompletionRequest): + global agent_graph + + if agent_graph is None: + raise HTTPException(status_code=503, detail="Agent not initialized") + + langchain_messages = _build_langchain_messages(request.messages) + model_id = request.model or getenv("MODEL_ID") or "model" + + if request.stream: + return await _handle_stream(langchain_messages, model_id) + else: + return await _handle_chat(langchain_messages, model_id) + + +async def _handle_chat(messages: list[HumanMessage], model_id: str) -> dict[str, Any]: + """Handle non-streaming chat completion.""" + global agent_graph + + try: + result = await agent_graph.ainvoke( + {"messages": messages}, config={"recursion_limit": 10} + ) + + # Extract the final assistant message content + assistant_content = "" + context_messages = [] + + if "messages" in result and len(result["messages"]) > 0: + for message in result["messages"]: + if isinstance(message, HumanMessage): + context_messages.append( + {"role": "user", "content": message.content} + ) + elif isinstance(message, AIMessage): + msg_data = {"role": "assistant", "content": message.content or ""} + if message.tool_calls: + msg_data["tool_calls"] = [ + { + "id": tc["id"], + "type": "function", + "function": { + "name": tc["name"], + "arguments": json.dumps(tc["args"]), + }, + } + for tc in message.tool_calls + ] + context_messages.append(msg_data) + elif isinstance(message, ToolMessage): + context_messages.append( + { + "role": "tool", + "tool_call_id": message.tool_call_id, + "name": message.name, + "content": message.content, + } + ) + + # Final assistant content is the last AIMessage with content + for message in reversed(result["messages"]): + if isinstance(message, AIMessage) and message.content: + assistant_content = message.content + break + + return { + "id": _make_completion_id(), + "object": "chat.completion", + "created": int(time.time()), + "model": model_id, + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": assistant_content, + }, + "finish_reason": "stop", + } + ], + "context": context_messages, + "usage": _extract_usage(result.get("messages", [])), + } + + except Exception as e: + if _is_guardrails_block(e): + return { + "id": _make_completion_id(), + "object": "chat.completion", + "created": int(time.time()), + "model": model_id, + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": _GUARDRAILS_REFUSAL, + }, + "finish_reason": "stop", + } + ], + "usage": None, + } + logger.exception("Error processing chat request") + raise HTTPException(status_code=500, detail="Error processing request") + + +async def _handle_stream( + messages: list[HumanMessage], model_id: str +) -> StreamingResponse: + """Handle streaming chat completion with OpenAI-compatible SSE chunks.""" + global agent_graph + + completion_id = _make_completion_id() + created = int(time.time()) + + async def event_generator() -> AsyncIterator[str]: + try: + async for event in agent_graph.astream_events( + {"messages": messages}, + config={"recursion_limit": 10}, + version="v2", + ): + kind = event["event"] + + # LLM streaming tokens + if kind == "on_chat_model_stream": + chunk = event["data"]["chunk"] + if chunk.content: + data = { + "id": completion_id, + "object": "chat.completion.chunk", + "created": created, + "model": model_id, + "choices": [ + { + "index": 0, + "delta": {"content": chunk.content}, + "finish_reason": None, + } + ], + } + yield f"data: {json.dumps(data)}\n\n" + + # Tool calls (after LLM finishes generating the call) + elif kind == "on_chat_model_end": + message = event["data"]["output"] + if hasattr(message, "tool_calls") and message.tool_calls: + tool_calls_delta = [ + { + "index": i, + "id": tc["id"], + "type": "function", + "function": { + "name": tc["name"], + "arguments": json.dumps(tc["args"]), + }, + } + for i, tc in enumerate(message.tool_calls) + ] + data = { + "id": completion_id, + "object": "chat.completion.chunk", + "created": created, + "model": model_id, + "choices": [ + { + "index": 0, + "delta": { + "role": "assistant", + "tool_calls": tool_calls_delta, + }, + "finish_reason": None, + } + ], + } + yield f"data: {json.dumps(data)}\n\n" + + # Tool execution results + elif kind == "on_tool_end": + output = event["data"].get("output", "") + if hasattr(output, "content"): + output = output.content + data = { + "id": completion_id, + "object": "chat.completion.chunk", + "created": created, + "model": model_id, + "choices": [ + { + "index": 0, + "delta": { + "role": "tool", + "content": str(output), + "name": event.get("name", ""), + }, + "finish_reason": None, + } + ], + } + yield f"data: {json.dumps(data)}\n\n" + + # Send final chunk with finish_reason + final_data = { + "id": completion_id, + "object": "chat.completion.chunk", + "created": created, + "model": model_id, + "choices": [ + { + "index": 0, + "delta": {}, + "finish_reason": "stop", + } + ], + } + yield f"data: {json.dumps(final_data)}\n\n" + yield "data: [DONE]\n\n" + + except Exception as e: + if _is_guardrails_block(e): + refusal_data = { + "id": completion_id, + "object": "chat.completion.chunk", + "created": created, + "model": model_id, + "choices": [ + { + "index": 0, + "delta": {"content": _GUARDRAILS_REFUSAL}, + "finish_reason": None, + } + ], + } + yield f"data: {json.dumps(refusal_data)}\n\n" + final_data = { + "id": completion_id, + "object": "chat.completion.chunk", + "created": created, + "model": model_id, + "choices": [ + { + "index": 0, + "delta": {}, + "finish_reason": "stop", + } + ], + } + yield f"data: {json.dumps(final_data)}\n\n" + yield "data: [DONE]\n\n" + else: + logger.exception("Error in stream event_generator") + error_data = { + "error": { + "message": "Internal server error", + "type": "server_error", + } + } + yield f"data: {json.dumps(error_data)}\n\n" + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, + ) + + +@app.get( + "/health", response_model=HealthResponse, summary="Health check", tags=["Health"] +) +async def health(): + initialized = agent_graph is not None + guardrails_ok = False + base_url = getenv("BASE_URL", "") + if initialized and base_url: + probe_url = base_url.rstrip("/").removesuffix("/v1") + try: + r = await _health_client.get(probe_url) + guardrails_ok = r.status_code == 200 + except Exception: + guardrails_ok = False + + body = { + "status": "healthy" if initialized and guardrails_ok else "not_ready", + "agent_initialized": initialized, + "guardrails_reachable": guardrails_ok, + } + if not initialized or not guardrails_ok: + return JSONResponse(status_code=503, content=body) + return body + + +# ── Playground UI ──────────────────────────────────────────────────────────── +_BASE_DIR = Path(__file__).resolve().parent +_PLAYGROUND_HTML = _BASE_DIR / "playground" / "templates" / "index.html" +# In Docker the images are copied to /opt/app-root/src/images; locally they live at the repo root +_IMAGES_DIR = _BASE_DIR / "images" +if not _IMAGES_DIR.is_dir(): + _IMAGES_DIR = _BASE_DIR.parent.parent.parent.parent / "images" + + +@app.get("/", response_class=HTMLResponse, include_in_schema=False) +async def playground(): + """Serve the playground chat UI.""" + if _auth_enabled(): + raise HTTPException(status_code=404, detail="Not found") + return FileResponse(_PLAYGROUND_HTML) + + +@app.get("/images/{filename:path}", include_in_schema=False) +async def serve_image(filename: str): + """Serve images from the project-level images directory.""" + if _auth_enabled(): + raise HTTPException(status_code=404, detail="Not found") + base = _IMAGES_DIR.resolve() + file_path = (base / filename).resolve() + try: + file_path.relative_to(base) + except ValueError: + raise HTTPException(status_code=404, detail="Image not found") + if not file_path.is_file(): + raise HTTPException(status_code=404, detail="Image not found") + return FileResponse(file_path) + + +if __name__ == "__main__": + import uvicorn + + port = int(getenv("PORT", 8000)) + uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/agents/langgraph/examples/guardrailed_agent/playground/app.py b/agents/langgraph/examples/guardrailed_agent/playground/app.py new file mode 100644 index 00000000..99535615 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/playground/app.py @@ -0,0 +1,148 @@ +""" +Playground UI for the LangGraph ReAct Agent. + +A simple Flask chat interface that proxies requests to the agent's +/chat/completions endpoint with streaming support. + +Usage: + # Make sure the agent is running first (default: http://localhost:8000) + cd agents/langgraph/react_agent + flask --app playground/app run --port 5001 + + # Or with a custom agent URL: + AGENT_URL=http://localhost:8000 flask --app playground/app run --port 5001 +""" + +import json +import logging +from os import getenv +from pathlib import Path + +import requests as http_requests +from flask import ( + Flask, + Response, + jsonify, + render_template, + request, + send_from_directory, + stream_with_context, +) + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger(__name__) + +IMAGES_DIR = Path(__file__).resolve().parents[5] / "images" + +app = Flask(__name__) + + +@app.route("/images/") +def serve_image(filename): + """Serve images from the project-level images directory.""" + return send_from_directory(IMAGES_DIR, filename) + + +AGENT_URL = getenv("AGENT_URL", "http://localhost:8000") + + +@app.route("/") +def index(): + return render_template("index.html") + + +@app.route("/api/health", methods=["GET"]) +def health(): + """Check if the agent is reachable.""" + try: + resp = http_requests.get(f"{AGENT_URL}/health", timeout=5) + return jsonify(resp.json()), resp.status_code + except Exception: + logger.exception("Error checking agent health") + return ( + jsonify( + { + "status": "unreachable", + "error": "Agent is unreachable. Please try again later.", + } + ), + 503, + ) + + +@app.route("/api/chat", methods=["POST"]) +def chat(): + """Proxy chat requests to the agent with streaming.""" + data = request.get_json() or {} + messages = data.get("messages", []) + + payload = { + "messages": messages, + "stream": True, + } + + logger.info( + f"Sending request to {AGENT_URL}/chat/completions (messages={len(payload.get('messages', []))}, stream={payload.get('stream')})" + ) + + def generate(): + try: + with http_requests.post( + f"{AGENT_URL}/chat/completions", + json=payload, + stream=True, + timeout=(10, 300), + ) as resp: + logger.info(f"Agent response status: {resp.status_code}") + + if resp.status_code != 200: + error_msg = resp.text[:500] + logger.error(f"Agent error: {error_msg}") + error = json.dumps( + { + "error": { + "message": f"Agent returned {resp.status_code}: {error_msg}" + } + } + ) + yield f"data: {error}\n\n" + return + + for chunk in resp.iter_content(chunk_size=None, decode_unicode=True): + if chunk: + logger.debug(f"Chunk: {chunk[:200]}") + yield chunk + + except http_requests.exceptions.ConnectionError: + logger.error(f"Cannot connect to agent at {AGENT_URL}") + error = json.dumps( + { + "error": { + "message": f"Cannot connect to agent at {AGENT_URL}. Is it running?" + } + } + ) + yield f"data: {error}\n\n" + except http_requests.exceptions.ReadTimeout: + logger.error("Agent request timed out") + error = json.dumps({"error": {"message": "Agent request timed out (300s)"}}) + yield f"data: {error}\n\n" + except Exception: + logger.exception("Unexpected error in proxy") + error = json.dumps({"error": {"message": "Internal server error"}}) + yield f"data: {error}\n\n" + + return Response( + stream_with_context(generate()), + content_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + "Connection": "keep-alive", + }, + ) + + +if __name__ == "__main__": + debug_mode = getenv("FLASK_DEBUG", "false").lower() == "true" + app.run(debug=debug_mode, port=5001) diff --git a/agents/langgraph/examples/guardrailed_agent/playground/templates/index.html b/agents/langgraph/examples/guardrailed_agent/playground/templates/index.html new file mode 100644 index 00000000..65221a3d --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/playground/templates/index.html @@ -0,0 +1,426 @@ + + + + + + LangGraph ReAct Agent — Playground + + + + +
+
+ LangGraph ReAct Agent — Playground +
+ +
+
+ +
+
+ +
+ + +
+ + + + + diff --git a/agents/langgraph/examples/guardrailed_agent/pyproject.toml b/agents/langgraph/examples/guardrailed_agent/pyproject.toml new file mode 100644 index 00000000..11b92869 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/pyproject.toml @@ -0,0 +1,50 @@ +[project] +name = "guardrailed_agent" +version = "0.1.0" +description = "A banking customer service agent with NeMo Guardrails safety layer. Demonstrates content safety, topical guardrails, and regex filtering using the proxy pattern." +authors = [{ name = "Your Name", email = "you@example.com" }] +license = { text = "Apache-2.0" } +requires-python = ">=3.12,<3.14" +dependencies = [ + "fastapi>=0.135.1", + "uvicorn[standard]>=0.41.0", + "langchain>=1.2.10", + "langchain-openai>=1.1.10", + "langgraph-prebuilt>=1.0.8", + "openai>=2.24.0", + "httpx>=0.27", + "python-dotenv>=1.2.2", + "setuptools>=80.9.0,<83.0.0", + "starlette>=1.0.1", + "protobuf<7", # nemoguardrails 0.21.0 pulls grpcio which conflicts with protobuf 7+ + "flask>=3.1.0", + "requests>=2.32.0", +] + +[project.optional-dependencies] +auth = [ + "agent-auth", +] +dev = [ + "pytest>=9.0.2", + "httpx>=0.27", + "pyyaml>=6.0", +] +guardrails = [ + "nemoguardrails[server]==0.21.0", # pinned to match RHOAI container version + "dataclasses-json>=0.6.7", # undeclared transitive dep of nemoguardrails 0.21.0 + "langchain-community>=0.2,<0.4", # >=0.4 drops GoogleSearchAPIWrapper, breaks nemoguardrails imports +] +tracing = [ + "mlflow[kubernetes]>=3.11.0", +] + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.uv.sources] +agent-auth = { path = "../../../../components/auth" } + +[build-system] +requires = ["setuptools>=80.9.0"] +build-backend = "setuptools.build_meta" diff --git a/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/__init__.py b/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/agent.py b/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/agent.py new file mode 100644 index 00000000..e6535c1a --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/agent.py @@ -0,0 +1,67 @@ +from os import getenv +from typing import Any + +from langchain.agents import create_agent +from langchain_openai import ChatOpenAI + +from guardrailed_agent.tools import check_account_balance + + +def get_graph_closure( + model_id: str | None = None, + base_url: str | None = None, + api_key: str | None = None, +) -> Any: + """Build and return a LangGraph ReAct agent for banking customer service. + + Creates a ChatOpenAI client, wires check_account_balance tool, + and uses create_agent to produce a graph that runs the ReAct loop. + + Args: + model_id: LLM model identifier. Uses MODEL_ID env if omitted. + base_url: Base URL for the LLM API. Uses BASE_URL env if omitted. + api_key: API key for the LLM. Uses API_KEY env if omitted. + + Returns: + A LangGraph agent (CompiledGraph) that accepts {"messages": [...]} and returns updated state. + """ + + if not api_key: + api_key = getenv("API_KEY") + if not base_url: + base_url = getenv("BASE_URL") + if not model_id: + model_id = getenv("MODEL_ID") + + if not model_id: + raise ValueError( + "MODEL_ID is required. Set it via argument or MODEL_ID env var." + ) + + if not base_url: + raise ValueError( + "BASE_URL is required. Set it via argument or BASE_URL env var." + ) + is_local = any(host in base_url for host in ["localhost", "127.0.0.1"]) + + if not is_local and not api_key: + raise ValueError("API_KEY is required for non-local environments.") + + tools = [check_account_balance] + + chat = ChatOpenAI( + model=model_id, + temperature=0.01, + api_key=api_key, + base_url=base_url, + ) + + system_prompt = """You are a customer service assistant for a retail bank. + You help customers with account inquiries, billing, payments, and general + banking questions. When you receive a result from a tool, use that + information to provide a FINAL answer to the customer immediately. + Do NOT call tools repeatedly for the same question. + Always be professional and helpful.""" + agent = create_agent(model=chat, tools=tools, system_prompt=system_prompt) + + return agent diff --git a/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/tools.py b/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/tools.py new file mode 100644 index 00000000..c5186099 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/tools.py @@ -0,0 +1,20 @@ +from langchain_core.tools import tool + + +@tool("check_balance", parse_docstring=True) +def check_account_balance(account_id: str) -> str: + """Check the account balance for a bank customer. + + Placeholder implementation that returns a fixed balance for demonstration. + Replace with a real banking API integration in production. + + Args: + account_id: The customer account ID. Example: "ACCT-12345" + + Returns: + A string with the account balance information. + """ + return ( + f"Account {account_id}: Checking balance $2,450.00, " + f"Savings balance $12,800.50. Last transaction: -$45.99 on 2026-07-20." + ) diff --git a/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/tracing.py b/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/tracing.py new file mode 100644 index 00000000..486f9aae --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/src/guardrailed_agent/tracing.py @@ -0,0 +1,174 @@ +import logging +import os +import time +from os import getenv +from typing import Optional + +from dotenv import load_dotenv + +logger = logging.getLogger("tracing") +logger.setLevel(logging.INFO) +if not logger.handlers: + handler = logging.StreamHandler() + formatter = logging.Formatter("[%(asctime)s] [%(levelname)s] %(message)s") + handler.setFormatter(formatter) + logger.addHandler(handler) + + +def check_mlflow_health( + mlflow_tracking_uri: str, max_wait_time: int = 5, retry_interval: int = 1 +) -> None: + """ + Check MLflow health by trying the /health endpoint. If it fails, retry for a certain duration before giving up. + args: + mlflow_tracking_uri: base URI of the MLflow server + max_wait_time: total time to keep retrying before giving up (in seconds) + retry_interval: time to wait between retries (in seconds) + """ + import requests + + mlflow_health_endpoint = "/health" + mlflow_url = f"{mlflow_tracking_uri.rstrip('/')}{mlflow_health_endpoint}" + + # Build auth and TLS config. This is only needed for + # the raw requests.get() health check. The MLflow client SDK + # handles these env vars natively for all other calls. + # See docs/mlflow-openshift-auth-and-tls.md for full details. + headers = {} + token = getenv("MLFLOW_TRACKING_TOKEN") + tracking_auth = getenv("MLFLOW_TRACKING_AUTH", "") + if token: + headers["Authorization"] = f"Bearer {token}" + logger.debug("[Health Check] Using MLFLOW_TRACKING_TOKEN for authentication") + elif tracking_auth in ("kubernetes", "kubernetes-namespaced"): + sa_token_path = "/var/run/secrets/kubernetes.io/serviceaccount/token" + if not os.path.isfile(sa_token_path): + logger.warning( + "[Health Check] MLFLOW_TRACKING_AUTH=%s but SA token not found at %s", + tracking_auth, + sa_token_path, + ) + else: + with open(sa_token_path) as f: + headers["Authorization"] = f"Bearer {f.read().strip()}" + logger.debug( + "[Health Check] Using MLFLOW_TRACKING_AUTH=%s for authentication", + tracking_auth, + ) + + cert_path = getenv("MLFLOW_TRACKING_SERVER_CERT_PATH") + if getenv("MLFLOW_TRACKING_INSECURE_TLS", "").lower() in ("true", "1", "yes"): + verify = False + logger.warning( + "[Health Check] TLS verification disabled (MLFLOW_TRACKING_INSECURE_TLS=true). " + "Consider using MLFLOW_TRACKING_SERVER_CERT_PATH instead." + ) + elif cert_path: + if not os.path.isfile(cert_path): + logger.warning( + "[Health Check] MLFLOW_TRACKING_SERVER_CERT_PATH does not exist: %s — falling back to system CAs", + cert_path, + ) + verify = True + else: + verify = cert_path + logger.debug("[Health Check] TLS verify using cert: %s", cert_path) + else: + verify = True + + start_time = time.time() + + while True: + remaining = max_wait_time - (time.time() - start_time) + if remaining <= 0: + logger.error( + f"MLflow server is unavailable after {max_wait_time} seconds of checking." + ) + raise RuntimeError( + "MLflow server is unavailable. Please start the server or check the URI." + ) + + try: + response = requests.get( + mlflow_url, timeout=min(5, remaining), headers=headers, verify=verify + ) + if response.status_code == 200: + logger.info( + f"MLflow health check passed at {mlflow_url} with status code {response.status_code}." + ) + return # Success, exit the function without error + else: + logger.warning( + f"MLflow returned status code {response.status_code} at {mlflow_url}\n" + f" Status Code: {response.status_code}\n" + f" Reason: {response.reason}" + ) + except requests.exceptions.RequestException as e: + logger.warning(f"Failed to connect to MLflow at {mlflow_url}: {e}") + + logger.warning(f"Retrying in {retry_interval} seconds...") + time.sleep(retry_interval) + + +def enable_tracing() -> None: + """ + Enable MLflow tracing if MLFLOW_TRACKING_URI is set. + + Behavior: + 1. If MLFLOW_TRACKING_URI is not set: tracing is skipped. + 2. If MLFLOW_TRACKING_URI is set: + - Try to connect to the server. + - If the server is reachable: tracing is enabled. + - If the server is unreachable: log a warning and continue without tracing. + """ + load_dotenv() + tracking_uri: Optional[str] = getenv("MLFLOW_TRACKING_URI") + if not tracking_uri: + logger.info("[Tracing] MLFLOW_TRACKING_URI not set. Tracing is disabled.") + return + + try: + import mlflow + import mlflow.langchain + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "MLFLOW_TRACKING_URI is set but mlflow is not installed. " + "Install it with: uv sync --extra tracing" + ) from e + + # Check if server is reachable + try: + try: + health_check_timeout = int(getenv("MLFLOW_HEALTH_CHECK_TIMEOUT", "5")) + except ValueError: + health_check_timeout = 5 + check_mlflow_health( + mlflow_tracking_uri=tracking_uri, max_wait_time=health_check_timeout + ) + logger.info(f"[Tracing] MLflow server is reachable at {tracking_uri}") + except RuntimeError as e: + logger.warning( + f"[Tracing] MLflow server is unreachable at {tracking_uri}. " + f"Tried connecting for {health_check_timeout}s. Continuing without tracing. Error: {e}" + ) + return + + # Server is reachable → enable tracing + try: + mlflow.set_tracking_uri(tracking_uri) + experiment_name: str = getenv( + "MLFLOW_EXPERIMENT_NAME", "default-agent-experiment" + ) + mlflow.set_experiment(experiment_name) + mlflow.config.enable_async_logging() + + mlflow.langchain.autolog() + + logger.info( + f"[Tracing Enabled] MLflow -> {tracking_uri}, Experiment: {experiment_name}" + ) + except Exception as e: + logger.warning( + f"[Tracing] Failed to configure MLflow tracing at {tracking_uri}. " + f"Continuing without tracing. Error: {e}" + ) diff --git a/agents/langgraph/examples/guardrailed_agent/tests/__init__.py b/agents/langgraph/examples/guardrailed_agent/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/agents/langgraph/examples/guardrailed_agent/tests/test_guardrails_config.py b/agents/langgraph/examples/guardrailed_agent/tests/test_guardrails_config.py new file mode 100644 index 00000000..a41e8bd8 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/tests/test_guardrails_config.py @@ -0,0 +1,114 @@ +"""Structural tests for NeMo Guardrails configuration files. + +Validates that the guardrails config is well-formed and contains all +required components without needing a running LLM or NeMo server. +""" + +from pathlib import Path + +import yaml + +AGENT_DIR = Path(__file__).resolve().parents[1] +GUARDRAILS_DIR = AGENT_DIR / "guardrails" / "safety" +CONFIG_FILE = GUARDRAILS_DIR / "config.yaml.example" + + +class TestConfigFilesExist: + def test_config_yaml_example_exists(self): + assert CONFIG_FILE.is_file() + + def test_config_uses_yaml_extension_not_yml(self): + assert CONFIG_FILE.is_file() + assert not (GUARDRAILS_DIR / "config.yml").exists() + assert not (GUARDRAILS_DIR / "config.yml.example").exists() + + def test_prompts_yml_exists(self): + assert (GUARDRAILS_DIR / "prompts.yml").is_file() + + def test_rails_co_exists(self): + assert (GUARDRAILS_DIR / "rails.co").is_file() + + +class TestConfigYaml: + @classmethod + def setup_class(cls): + cls.config = yaml.safe_load(CONFIG_FILE.read_text(encoding="utf-8")) + + def test_has_models_section(self): + assert "models" in self.config + model_types = {m["type"] for m in self.config["models"]} + assert "main" in model_types + assert "content_safety" in model_types + assert "topic_control" in model_types + + def test_passthrough_enabled(self): + assert self.config.get("passthrough") is True + + def test_has_input_rails(self): + flows = self.config["rails"]["input"]["flows"] + flow_text = " ".join(flows) + assert "content safety check input" in flow_text + assert "topic safety check input" in flow_text + + def test_has_regex_input_rail(self): + flows = self.config["rails"]["input"]["flows"] + assert "regex check input" in flows + + def test_has_output_rails(self): + flows = self.config["rails"]["output"]["flows"] + flow_text = " ".join(flows) + assert "content safety check output" in flow_text + + def test_streaming_enabled(self): + assert self.config["rails"]["output"]["streaming"]["enabled"] is True + + def test_regex_patterns_configured(self): + patterns = self.config["rails"]["config"]["regex_detection"]["input"][ + "patterns" + ] + assert len(patterns) >= 1 + + +class TestPromptsYml: + @classmethod + def setup_class(cls): + cls.prompts = yaml.safe_load( + (GUARDRAILS_DIR / "prompts.yml").read_text(encoding="utf-8") + ) + + def test_has_content_safety_input_prompt(self): + tasks = [p["task"] for p in self.prompts["prompts"]] + assert any("content_safety_check_input" in t for t in tasks) + + def test_has_content_safety_output_prompt(self): + tasks = [p["task"] for p in self.prompts["prompts"]] + assert any("content_safety_check_output" in t for t in tasks) + + def test_has_topic_safety_prompt(self): + tasks = [p["task"] for p in self.prompts["prompts"]] + assert any("topic_safety_check_input" in t for t in tasks) + + def test_input_prompt_uses_correct_output_parser(self): + for p in self.prompts["prompts"]: + if "content_safety_check_input" in p["task"]: + assert p["output_parser"] == "nemoguard_parse_prompt_safety" + + def test_output_prompt_uses_correct_output_parser(self): + for p in self.prompts["prompts"]: + if "content_safety_check_output" in p["task"]: + assert p["output_parser"] == "nemoguard_parse_response_safety" + + def test_topic_prompt_mentions_banking(self): + for p in self.prompts["prompts"]: + if "topic_safety_check_input" in p["task"]: + assert "bank" in p["content"].lower() + + +class TestRailsCo: + def test_rails_co_is_not_empty(self): + content = (GUARDRAILS_DIR / "rails.co").read_text(encoding="utf-8") + assert len(content.strip()) > 0 + + def test_rails_co_defines_greeting_flow(self): + content = (GUARDRAILS_DIR / "rails.co").read_text(encoding="utf-8") + assert "express greeting" in content diff --git a/agents/langgraph/examples/guardrailed_agent/tests/test_main_helpers.py b/agents/langgraph/examples/guardrailed_agent/tests/test_main_helpers.py new file mode 100644 index 00000000..92472cd3 --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/tests/test_main_helpers.py @@ -0,0 +1,46 @@ +"""Unit tests for main.py helper functions.""" + +from unittest.mock import MagicMock + +from openai import APIError as OpenAIAPIError + + +def _make_openai_error(message: str) -> OpenAIAPIError: + """Create a mock OpenAIAPIError with the given message.""" + req = MagicMock() + return OpenAIAPIError(message=message, request=req, body=None) + + +class TestIsGuardrailsBlock: + def _call(self, exc: Exception) -> bool: + from main import _is_guardrails_block + + return _is_guardrails_block(exc) + + def test_content_safety_input_block(self): + exc = _make_openai_error("Blocked by content_safety_check_input rails") + assert self._call(exc) is True + + def test_content_safety_output_block(self): + exc = _make_openai_error("Blocked by content_safety_check_output rails") + assert self._call(exc) is True + + def test_topic_safety_block(self): + exc = _make_openai_error("Blocked by topic_safety_check_input rails") + assert self._call(exc) is True + + def test_generic_openai_error_not_matched(self): + exc = _make_openai_error("Connection refused") + assert self._call(exc) is False + + def test_non_openai_error_not_matched(self): + exc = RuntimeError("Blocked by rails") + assert self._call(exc) is False + + def test_partial_match_blocked_without_rails(self): + exc = _make_openai_error("Blocked by firewall") + assert self._call(exc) is False + + def test_partial_match_rails_without_blocked(self): + exc = _make_openai_error("Error in rails processing") + assert self._call(exc) is False diff --git a/agents/langgraph/examples/guardrailed_agent/tests/test_packaging_config.py b/agents/langgraph/examples/guardrailed_agent/tests/test_packaging_config.py new file mode 100644 index 00000000..804e814e --- /dev/null +++ b/agents/langgraph/examples/guardrailed_agent/tests/test_packaging_config.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +import re +import tomllib +from pathlib import Path + +AGENT_DIR = Path(__file__).resolve().parents[1] +MAKEFILE = (AGENT_DIR / "Makefile").read_text(encoding="utf-8") +DOCKERFILE = (AGENT_DIR / "Dockerfile").read_text(encoding="utf-8") +ENV_EXAMPLE = (AGENT_DIR / ".env.example").read_text(encoding="utf-8") +PYPROJECT = tomllib.loads((AGENT_DIR / "pyproject.toml").read_text(encoding="utf-8")) +NORMALIZED_MAKEFILE = re.sub(r"\s+", " ", MAKEFILE) +NORMALIZED_DOCKERFILE = re.sub(r"\s+", " ", DOCKERFILE) + + +def test_makefile_stages_auth_component_for_container_builds() -> None: + copy_pattern = ( + r"mkdir -p \./components && cp -r " + r"\.\./\.\./\.\./\.\./components/auth \./components/auth" + ) + cleanup_pattern = ( + r"trap 'rm -rf \./images \./components/auth; " + r"rmdir \./components 2>/dev/null \|\| true' EXIT" + ) + + assert len(re.findall(copy_pattern, NORMALIZED_MAKEFILE)) == 2 + assert len(re.findall(cleanup_pattern, NORMALIZED_MAKEFILE)) == 2 + + +def test_makefile_has_guardrails_server_target() -> None: + assert re.search(r"(?m)^guardrails-server:", MAKEFILE) + + +def test_makefile_env_installs_guardrails_extra() -> None: + assert "--extra guardrails" in MAKEFILE + + +def test_env_example_points_to_guardrails_proxy() -> None: + assert "localhost:8090" in ENV_EXAMPLE + + +def test_env_example_documents_auth_settings() -> None: + assert "# AUTH_ENABLED=false" in ENV_EXAMPLE + assert "# AUTH_AUDIENCE=langgraph-guardrailed-agent" in ENV_EXAMPLE + + +def test_dockerfile_workdir_matches_agent_location() -> None: + assert re.search( + r"WORKDIR /opt/app-root/src/agents/langgraph/examples/guardrailed_agent", + NORMALIZED_DOCKERFILE, + ) + + +def test_dockerfile_installs_auth_extra_from_staged_component() -> None: + assert re.search( + r"COPY components/auth/ /opt/app-root/src/components/auth/", + NORMALIZED_DOCKERFILE, + ) + assert re.search( + r'RUN uv pip install --no-cache "\.\[tracing,auth\]"', + NORMALIZED_DOCKERFILE, + ) + + +def test_pyproject_pins_nemoguardrails_version() -> None: + guardrails_deps = PYPROJECT["project"]["optional-dependencies"]["guardrails"] + assert any("nemoguardrails" in d and "0.21.0" in d for d in guardrails_deps) + + +def test_pyproject_constrains_protobuf_below_7() -> None: + dependencies = PYPROJECT["project"]["dependencies"] + assert "protobuf<7" in dependencies + + +def test_pyproject_package_name() -> None: + assert PYPROJECT["project"]["name"] == "guardrailed_agent" diff --git 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