agent-factory: generate agent auto-retrieve-files-from-a-reposito

.image-version

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+1.0.0

Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    curl \
+    ca-certificates \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app/ ./app/
+
+RUN useradd -m -u 1001 appuser && \
+    chown -R appuser:appuser /app
+
+USER appuser
+
+EXPOSE 8080
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=15s --retries=3 \
+    CMD curl -f http://localhost:8080/health || exit 1
+
+CMD ["uvicorn", "app.agent:app", "--host", "0.0.0.0", "--port", "8080"]

app/agent.py

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+"""
+AutoRetrieveFilesFromAReposito Agent — auto-generated by Agent Factory.
+"""
+import asyncio
+import logging
+import os
+
+import click
+import httpx
+import uvicorn
+from contextlib import asynccontextmanager
+from dotenv import load_dotenv
+from langchain_core.rate_limiters import InMemoryRateLimiter
+from langchain_openai import AzureChatOpenAI
+
+from starlette.applications import Starlette
+from starlette.requests import Request
+from starlette.responses import JSONResponse
+from starlette.routing import Route
+
+from app.config import (
+    AGENT_SELF_URL,
+    AZURE_OPENAI_API_KEY,
+    AZURE_OPENAI_API_VERSION,
+    AZURE_OPENAI_DEPLOYMENT,
+    AZURE_OPENAI_ENDPOINT,
+    LOG_LEVEL,
+    REGISTRY_URL,
+)
+from app.skills import AGENT_CONFIG, AUTO_RETRIEVE_FILES_FROM_A_REPOSITO_SKILLS
+from app.workflows.auto_retrieve_files_from_a_reposito_workflow import create_auto_retrieve_files_from_a_reposito_workflow
+
+load_dotenv()
+
+# ── Logging ──────────────────────────────────────────────────────────────
+logging.basicConfig(
+    level=getattr(logging, LOG_LEVEL.upper(), logging.INFO),
+    format="%(asctime)s %(name)s %(levelname)s %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+# ── LLM ──────────────────────────────────────────────────────────────────
+rate_limiter = InMemoryRateLimiter(
+    requests_per_second=10 / 60,
+    check_every_n_seconds=0.1,
+    max_bucket_size=10,
+)
+
+llm = AzureChatOpenAI(
+    temperature=0,
+    azure_deployment=AZURE_OPENAI_DEPLOYMENT,
+    api_version=AZURE_OPENAI_API_VERSION,
+    azure_endpoint=AZURE_OPENAI_ENDPOINT or "",
+    api_key=AZURE_OPENAI_API_KEY or "",
+    max_retries=5,
+    timeout=120,
+    rate_limiter=rate_limiter,
+)
+
+workflow = create_auto_retrieve_files_from_a_reposito_workflow(llm)
+
+
+# ── Endpoints ────────────────────────────────────────────────────────────
+
+async def health_check(request: Request) -> JSONResponse:
+    return JSONResponse({"status": "healthy", "agent": 'AutoRetrieveFilesFromAReposito'})
+
+
+async def agent_manifest(request: Request) -> JSONResponse:
+    """GET /.well-known/agent.json"""
+    return JSONResponse({
+        "name": AGENT_CONFIG["name"],
+        "version": AGENT_CONFIG["version"],
+        "description": AGENT_CONFIG["description"],
+        "url": "/",
+        "skills": [
+            {
+                "id": s.id,
+                "name": s.name,
+                "description": s.description,
+                "tags": s.tags,
+                "inputSchema": AGENT_CONFIG.get("input_schema"),
+                "outputSchema": AGENT_CONFIG.get("output_schema"),
+            } for s in AUTO_RETRIEVE_FILES_FROM_A_REPOSITO_SKILLS
+        ],
+        "capabilities": AGENT_CONFIG["capabilities"],
+    })
+
+
+async def process_endpoint(request: Request) -> JSONResponse:
+    """POST /process — run the agent workflow."""
+    try:
+        body = await request.json()
+        result = await workflow.ainvoke(body)
+        return JSONResponse(result)
+    except Exception as exc:
+        logger.error("Processing failed: %s", exc, exc_info=True)
+        return JSONResponse({"error": str(exc)}, status_code=500)
+
+
+# ── Self-registration ────────────────────────────────────────────────────
+
+async def _register_with_registry():
+    if not REGISTRY_URL:
+        logger.info("REGISTRY_URL not set — skipping self-registration")
+        return
+    await asyncio.sleep(2)
+    url = f"{REGISTRY_URL.rstrip('/')}/agents/register-url"
+    for attempt in range(3):
+        try:
+            async with httpx.AsyncClient(timeout=10.0) as client:
+                resp = await client.post(url, json={"endpoint": AGENT_SELF_URL})
+                if resp.status_code in (200, 201):
+                    logger.info("Self-registered with agent-registry at %s", REGISTRY_URL)
+                    return
+                logger.warning("Registration attempt %d: HTTP %d", attempt + 1, resp.status_code)
+        except Exception as exc:
+            logger.warning("Registration attempt %d failed: %s", attempt + 1, exc)
+        await asyncio.sleep(5)
+    logger.error("Failed to self-register after 3 attempts")
+
+
+@asynccontextmanager
+async def lifespan(app):
+    task = asyncio.create_task(_register_with_registry())
+    yield
+    task.cancel()
+
+
+app = Starlette(
+    routes=[
+        Route("/health", methods=["GET"], endpoint=health_check),
+        Route("/.well-known/agent.json", methods=["GET"], endpoint=agent_manifest),
+        Route("/process", methods=["POST"], endpoint=process_endpoint),
+    ],
+    lifespan=lifespan,
+)
+
+
+@click.command()
+@click.option("--host", default="0.0.0.0")
+@click.option("--port", default=8080, type=int)
+def main(host: str, port: int):
+    uvicorn.run(app, host=host, port=port, log_level=LOG_LEVEL.lower())
+
+
+if __name__ == "__main__":
+    main()

app/config.py

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+"""
+Configuration for the AutoRetrieveFilesFromAReposito agent.
+"""
+import os
+
+# ── Azure OpenAI ─────────────────────────────────────────────────────────
+AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
+AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
+AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview")
+AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT", "gpt-4o")
+
+# ── Agent Registry ───────────────────────────────────────────────────────
+REGISTRY_URL = os.getenv(
+    "REGISTRY_URL", "http://agent-gateway.agents.svc.cluster.local"
+)
+AGENT_SELF_URL = os.getenv(
+    "AGENT_SELF_URL", "http://auto-retrieve-files-from-a-reposito.agents.svc.cluster.local"
+)
+
+# ── Logging ──────────────────────────────────────────────────────────────
+LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")

app/nodes/core_node.py

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+async def process(state: dict) -> dict:
+    """
+    This node retrieves files from a repository based on the provided URL and update scope.
+    It uses an LLM to analyze and extract relevant workflow files from the repository.
+    """
+    from app.agent import llm
+    from langchain_core.messages import SystemMessage, HumanMessage
+
+    try:
+        repository_url = state.get("repository_url", "")
+        update_scope = state.get("update_scope", "")
+
+        if not repository_url or not update_scope:
+            raise ValueError("Both 'repository_url' and 'update_scope' must be provided.")
+
+        messages = [
+            SystemMessage(content="You are an assistant that retrieves workflow files from a repository."),
+            HumanMessage(content=f"Repository URL: {repository_url}\nUpdate Scope: {update_scope}")
+        ]
+
+        response = await llm.ainvoke(messages)
+        workflow_files = response.content.splitlines()  # Assuming the LLM returns file names as newline-separated strings.
+
+        return {"workflow_files": workflow_files, "phase": "complete"}
+    except Exception as exc:
+        return {"error": str(exc), "phase": "failed"}

app/skills.py

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+"""
+A2A skill declarations for AutoRetrieveFilesFromAReposito.
+"""
+from a2a.types import AgentSkill
+
+
+AUTO_RETRIEVE_FILES_FROM_A_REPOSITO_SKILLS = [
+    AgentSkill(
+        id="auto_retrieve_files_from_a_reposito_skill",
+        name="AutoRetrieveFilesFromAReposito",
+        description="Retrieve files from a repository",
+        tags=["auto-generated"],
+        examples=[],
+    ),
+]
+
+
+AGENT_CONFIG = {
+    "name": "AutoRetrieveFilesFromAReposito",
+    "description": "Fetch all Java workflow files from the Gitea repository",
+    "version": "1.0.0",
+    "framework": "LangGraph + Starlette",
+    "capabilities": {
+        "streaming": False,
+        "async": True,
+    },
+    "input_schema": {"repository_url": "string", "update_scope": "string"},
+    "output_schema": {"workflow_files": "string[]"},
+}

app/states/auto_retrieve_files_from_a_reposito_state.py

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+"""
+State definitions for AutoRetrieveFilesFromAReposito agent.
+"""
+from __future__ import annotations
+from typing import Any, Dict, List, Optional
+from langgraph.graph import MessagesState
+
+
+class AutoRetrieveFilesFromARepositoState(MessagesState):
+    """Workflow state for AutoRetrieveFilesFromAReposito."""
+    # ── Input fields ─────────────────────────────────────────────────────
+    pass
+    # ── Output fields ────────────────────────────────────────────────────
+    pass
+    # ── Internal ─────────────────────────────────────────────────────────
+    error: Optional[str]
+    phase: str

app/workflows/auto_retrieve_files_from_a_reposito_workflow.py

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+"""
+LangGraph workflow for AutoRetrieveFilesFromAReposito agent.
+"""
+from langgraph.graph import StateGraph, END
+
+from app.states.auto_retrieve_files_from_a_reposito_state import AutoRetrieveFilesFromARepositoState
+from app.nodes.core_node import process
+
+
+def create_auto_retrieve_files_from_a_reposito_workflow(llm):
+    """Build and compile the AutoRetrieveFilesFromAReposito workflow graph."""
+    graph = StateGraph(AutoRetrieveFilesFromARepositoState)
+
+    graph.add_node("process", process)
+
+    graph.set_entry_point("process")
+    graph.add_edge("process", END)
+
+    return graph.compile()

catalog-info.yaml

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+apiVersion: backstage.io/v1alpha1
+kind: Component
+metadata:
+  name: auto-retrieve-files-from-a-reposito
+  description: "Fetch all Java workflow files from the Gitea repository"
+  annotations:
+    backstage.io/kubernetes-label-selector: app=auto-retrieve-files-from-a-reposito
+    backstage.io/kubernetes-namespace: agents
+    backstage.io/techdocs-ref: dir:.
+    gitea.kyndemo.live/repo-slug: generated-agents/auto-retrieve-files-from-a-reposito
+    grafana/grafana-instance: default
+    grafana/alert-label-selector: app=auto-retrieve-files-from-a-reposito
+    grafana/dashboard-selector: uid == 'otel-app-observability-v2'
+    grafana.com/dashboard-url: https://grafana.kyndemo.live/d/otel-app-observability-v2/opentelemetry-application-observability?orgId=1&var-app=auto-retrieve-files-from-a-reposito
+  tags:
+    - agent
+    - a2a
+    - auto-generated
+  links:
+    - icon: github
+      title: Source Repository
+      url: https://gitea.kyndemo.live/generated-agents/auto-retrieve-files-from-a-reposito
+    - icon: code
+      title: CI/CD Pipelines
+      url: https://gitea.kyndemo.live/generated-agents/auto-retrieve-files-from-a-reposito/actions
+    - icon: dashboard
+      title: Grafana Dashboard
+      url: https://grafana.kyndemo.live/d/otel-app-observability-v2/opentelemetry-application-observability?orgId=1&var-app=auto-retrieve-files-from-a-reposito
+spec:
+  type: service
+  lifecycle: experimental
+  owner: group:default/agentic-agents
+  system: agentic-agents
+  dependsOn:
+    - resource:default/cjot-aks

k8s/configmap.yaml

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+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: auto-retrieve-files-from-a-reposito-config
+  namespace: agents
+data:
+  REGISTRY_URL: "http://agent-gateway.agents.svc.cluster.local"
+  AGENT_SELF_URL: "http://auto-retrieve-files-from-a-reposito.agents.svc.cluster.local"
+  AZURE_OPENAI_DEPLOYMENT: "gpt-4o"
+  AZURE_OPENAI_API_VERSION: "2024-08-01-preview"
+  LOG_LEVEL: "INFO"
+  OTEL_SERVICE_NAME: "auto-retrieve-files-from-a-reposito"
+  OTEL_EXPORTER_OTLP_ENDPOINT: "http://otel-collector.monitoring.svc.cluster.local:4318"

k8s/deployment.yaml

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+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: auto-retrieve-files-from-a-reposito
+  namespace: agents
+  labels:
+    app: auto-retrieve-files-from-a-reposito
+    component: agent
+    generated-by: agent-factory
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: auto-retrieve-files-from-a-reposito
+  template:
+    metadata:
+      labels:
+        app: auto-retrieve-files-from-a-reposito
+        azure.workload.identity/use: "true"
+      annotations:
+        instrumentation.opentelemetry.io/inject-python: "monitoring/otel-instrumentation"
+    spec:
+      serviceAccountName: agents-sa
+      containers:
+      - name: auto-retrieve-files-from-a-reposito
+        image: bstagecjotdevacr.azurecr.io/auto-retrieve-files-from-a-reposito:latest
+        imagePullPolicy: Always
+        ports:
+        - name: http
+          containerPort: 8080
+          protocol: TCP
+        envFrom:
+        - configMapRef:
+            name: auto-retrieve-files-from-a-reposito-config
+        env:
+        - name: AZURE_OPENAI_ENDPOINT
+          valueFrom:
+            secretKeyRef:
+              name: agents-kv-sync
+              key: azure-openai-endpoint
+        - name: AZURE_OPENAI_API_KEY
+          valueFrom:
+            secretKeyRef:
+              name: agents-kv-sync
+              key: azure-openai-api-key
+        - name: OTEL_SERVICE_NAME
+          value: "auto-retrieve-files-from-a-reposito"
+        - name: OTEL_RESOURCE_ATTRIBUTES
+          value: "service.namespace=agents,deployment.environment=production"
+        resources:
+          requests:
+            memory: "512Mi"
+            cpu: "250m"
+          limits:
+            memory: "1Gi"
+            cpu: "500m"
+        livenessProbe:
+          httpGet:
+            path: /health
+            port: http
+          initialDelaySeconds: 30
+          periodSeconds: 10
+          timeoutSeconds: 5
+          failureThreshold: 30
+        readinessProbe:
+          httpGet:
+            path: /health
+            port: http
+          initialDelaySeconds: 10
+          periodSeconds: 5
+        volumeMounts:
+        - name: secrets-store
+          mountPath: "/mnt/secrets-store"
+          readOnly: true
+      volumes:
+      - name: secrets-store
+        csi:
+          driver: secrets-store.csi.k8s.io
+          readOnly: true
+          volumeAttributes:
+            secretProviderClass: agents-kv-spc

k8s/service.yaml

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+apiVersion: v1
+kind: Service
+metadata:
+  name: auto-retrieve-files-from-a-reposito
+  namespace: agents
+  labels:
+    app: auto-retrieve-files-from-a-reposito
+spec:
+  type: ClusterIP
+  ports:
+  - port: 80
+    targetPort: http
+    protocol: TCP
+    name: http
+  selector:
+    app: auto-retrieve-files-from-a-reposito

requirements.txt

@@ -0,0 +1,18 @@
+# Auto-generated by Agent Factory
+uvicorn[standard]==0.32.1
+starlette>=0.28.0
+pydantic>=2.11.3
+python-dotenv==1.0.1
+httpx>=0.28.1
+click>=8.1.8
+
+# A2A Protocol
+a2a-sdk[http-server]>=0.3.0
+
+# LangChain & LangGraph
+langchain>=1.2.10
+langchain-openai>=0.2.12
+langchain-core>=1.2.11
+langgraph>=0.2.59
+langgraph-checkpoint>=2.0.6
+openai>=1.109.1

scripts/deploy.sh

@@ -0,0 +1,54 @@
+#!/bin/bash
+# deploy.sh — Build auto-retrieve-files-from-a-reposito image in ACR and deploy to AKS
+set -e
+
+SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+ACR_NAME="bstagecjotdevacr"
+APP_NAME="auto-retrieve-files-from-a-reposito"
+NAMESPACE="agents"
+VERSION_FILE="${SCRIPT_DIR}/../.image-version"
+TAG=""
+
+while [[ "$#" -gt 0 ]]; do
+    case $1 in
+        --tag) TAG="$2"; shift ;;
+    esac
+    shift
+done
+
+if [ -z "$TAG" ]; then
+    if [ -f "$VERSION_FILE" ]; then
+        CURRENT_VERSION=$(cat "$VERSION_FILE")
+    else
+        CURRENT_VERSION="1.0.0"
+    fi
+    IFS='.' read -r MAJOR MINOR PATCH <<< "$CURRENT_VERSION"
+    PATCH=$((PATCH + 1))
+    TAG="${MAJOR}.${MINOR}.${PATCH}"
+    echo "$TAG" > "$VERSION_FILE"
+fi
+
+IMAGE="${ACR_NAME}.azurecr.io/${APP_NAME}:${TAG}"
+
+echo "======================================================================"
+echo "  auto-retrieve-files-from-a-reposito — Build & Deploy"
+echo "  Image: ${IMAGE}"
+echo "======================================================================"
+
+az acr build --registry ${ACR_NAME} \
+    --image ${APP_NAME}:${TAG} \
+    --image ${APP_NAME}:latest \
+    "${SCRIPT_DIR}/.."
+
+kubectl apply -f "${SCRIPT_DIR}/../k8s/configmap.yaml" 2>/dev/null || true
+kubectl apply -f "${SCRIPT_DIR}/../k8s/deployment.yaml"
+kubectl apply -f "${SCRIPT_DIR}/../k8s/service.yaml" 2>/dev/null || true
+
+kubectl set image deployment/${APP_NAME} \
+    ${APP_NAME}=${IMAGE} \
+    -n ${NAMESPACE}
+
+kubectl rollout status deployment/${APP_NAME} -n ${NAMESPACE} --timeout=120s
+
+echo "✓ Done. Image: ${IMAGE}"
+echo "  Port forward:  kubectl port-forward -n ${NAMESPACE} deployment/${APP_NAME} 8080:8080"

scripts/full-deploy.sh

@@ -0,0 +1,66 @@
+#!/bin/bash
+# full-deploy.sh — Preflight + build + deploy auto-retrieve-files-from-a-reposito to AKS
+set -e
+
+GREEN='\033[0;32m'
+RED='\033[0;31m'
+NC='\033[0m'
+
+SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+ACR_NAME="bstagecjotdevacr"
+APP_NAME="auto-retrieve-files-from-a-reposito"
+NAMESPACE="agents"
+VERSION_FILE="${SCRIPT_DIR}/../.image-version"
+TAG=""
+
+while [[ "$#" -gt 0 ]]; do
+    case $1 in
+        --tag) TAG="$2"; shift ;;
+    esac
+    shift
+done
+
+print_success() { echo -e "${GREEN}✓ $1${NC}"; }
+print_error()   { echo -e "${RED}✗ $1${NC}"; exit 1; }
+
+command -v kubectl &>/dev/null || print_error "kubectl not found."
+command -v az &>/dev/null || print_error "Azure CLI not found."
+az account show &>/dev/null || print_error "Not logged in to Azure."
+kubectl cluster-info &>/dev/null || print_error "Cannot connect to cluster."
+CLUSTER=$(kubectl config current-context)
+print_success "Connected to cluster: ${CLUSTER}"
+
+if [ -z "$TAG" ]; then
+    if [ -f "$VERSION_FILE" ]; then
+        CURRENT_VERSION=$(cat "$VERSION_FILE")
+    else
+        CURRENT_VERSION="1.0.0"
+    fi
+    IFS='.' read -r MAJOR MINOR PATCH <<< "$CURRENT_VERSION"
+    PATCH=$((PATCH + 1))
+    TAG="${MAJOR}.${MINOR}.${PATCH}"
+    echo "$TAG" > "$VERSION_FILE"
+fi
+
+IMAGE="${ACR_NAME}.azurecr.io/${APP_NAME}:${TAG}"
+print_success "Image tag: ${TAG}"
+
+az acr build --registry ${ACR_NAME} \
+    --image ${APP_NAME}:${TAG} \
+    --image ${APP_NAME}:latest \
+    "${SCRIPT_DIR}/.." || print_error "ACR build failed."
+print_success "Image built: ${IMAGE}"
+
+kubectl apply -f "${SCRIPT_DIR}/../k8s/configmap.yaml" 2>/dev/null || true
+kubectl apply -f "${SCRIPT_DIR}/../k8s/deployment.yaml" || print_error "Failed to apply deployment."
+kubectl apply -f "${SCRIPT_DIR}/../k8s/service.yaml" || print_error "Failed to apply service."
+print_success "Manifests applied"
+
+kubectl set image deployment/${APP_NAME} ${APP_NAME}=${IMAGE} -n ${NAMESPACE}
+kubectl rollout status deployment/${APP_NAME} -n ${NAMESPACE} --timeout=180s || \
+    print_error "Rollout failed."
+print_success "Rollout complete"
+
+echo ""
+echo -e "${GREEN}  auto-retrieve-files-from-a-reposito deployed: ${IMAGE}${NC}"
+echo "  Port forward:  kubectl port-forward -n ${NAMESPACE} deployment/${APP_NAME} 8080:8080"