01Agents: Kubernetes Alert Remediation System

A Kubernetes alert remediation system with a modern web interface and intelligent remediation agent. This platform analyzes monitoring alerts, fetches live cluster context, and generates executable remediation scripts through LLM‑powered workflows.

🏗️ Architecture Overview

The system follows a two‑tier hierarchical architecture:

Monitoring Alerts → L0 Client (UI) → L1 Remediation Agent → Remediation Scripts

L0 Client: React Router v7 Web Interface

Role: Modern web interface for alert management and visualization.
Port: Default 3000.

L1: Kubernetes Alert Remediation Agent

Role: LangGraph‑powered remediation specialist using specialized subagents.
Port: Default 10001.

🚀 Quick Start

Clone the Repository

git clone git@github.com:BerryBytes/01agent.git
cd 01agent

Choose Your Deployment Approach

Approach 1: Using Pre-built Images (Recommended for most users)
Approach 2: Building Custom Images (For developers modifying source code)

Approach 1: Using Pre-built Images

Deploy using ready-to-use images from the 01community registry. Perfect for quick setup and production use.

Prerequisites

Kubernetes cluster (Kind, Minikube, or Cloud)
Helm 3+
LLM API Key (OpenRouter, DeepSeek, Google, or Anthropic)

Step 1: Install MCP Server for Kubernetes

The MCP (Model Context Protocol) Server provides Kubernetes tools for the agents.

Clone the MCP Server repository

git clone https://github.com/Flux159/mcp-server-kubernetes.git

Update the schema (Required)

python3 -c "
import json
with open('mcp-server-kubernetes/helm-chart/values.schema.json') as f:
    schema = json.load(f)

schema['properties']['observability'] = {
    'type': 'object',
    'additionalProperties': True
}

with open('mcp-server-kubernetes/helm-chart/values.schema.json', 'w') as f:
    json.dump(schema, f, indent=2)
print('Schema updated successfully!')
"

Install the MCP Server

helm install mcp-server ./mcp-server-kubernetes/helm-chart \
  --set kubeconfig.provider=serviceaccount \
  --set transport.mode=http \
  --set transport.service.type=ClusterIP \
  --set security.allowOnlyNonDestructive=false \
  --create-namespace \
  --namespace mcp-system

Step 2: Install PostgreSQL Operator

The agents use PostgreSQL for long-term memory. We use the CrunchyData Operator to manage it.

Install OLM (Operator Lifecycle Manager)

curl -sL https://github.com/operator-framework/operator-lifecycle-manager/releases/download/v0.40.0/install.sh | bash -s v0.40.0

Important

OLM installation can take a few minutes. Please ensure all OLM pods in the olm namespace are in the Running state before proceeding.

Install the PostgreSQL Operator

kubectl create -f https://operatorhub.io/install/postgresql.yaml

Step 3: Configure Agent Settings

Edit helm-chart/values.yaml to configure your LLM provider and API key. You must set the MODEL_PROVIDER, MODEL_NAME and provide the corresponding API key in the secret section.

agents:
  - name: l0
    enabled: true
    image: 01community/agent-l0:v1

  - name: l1
    enabled: true
    image: 01community/agent-l1:v1
    env:
      MODEL_PROVIDER: deepseek # options: gemini, openai, openrouter, anthropic, deepseek
      MODEL_NAME: deepseek-chat # examples: gemini-2.0-flash, gpt-4o, claude-3-5-sonnet
      MCP_SERVER_URL: http://mcp-server-mcp-server-kubernetes.mcp-system.svc.cluster.local:3001/mcp
      ENABLE_K8S_TOOLS: "true"
      STM_ENABLE_POSTGRES: "true"
    usePostgresql: true
    secret:
      DEEPSEEK_API_KEY: "your-api-key-here"
      # GOOGLE_API_KEY: "your-api-key"
      # OPENAI_API_KEY: "your-api-key"
      # OPENROUTER_API_KEY: "your-api-key"
      # ANTHROPIC_API_KEY: "your-api-key"

Step 4: Deploy 01Agents

helm upgrade --install 01agent ./helm-chart -n 01cloud --create-namespace

Important

Ensure all the pods in the namespace have initialized successfully and running

Step 5: Access the Application

Port-forward the UI service:

kubectl port-forward svc/agent-l0 -n 01cloud 3000:3000

Open your browser and navigate to http://localhost:3000

Step 6: Monitoring & Observability (Optional)

For advanced monitoring with Grafana, Loki, Tempo, and OpenTelemetry, see OTEL-setup.md. These features are disabled by default and should only be enabled if you have the observability stack configured.

Approach 2: Building Custom Images

Build and deploy your own modified images from source code. Ideal for developers customizing the system.

Prerequisites

Kubernetes cluster (Kind, Minikube, or Cloud)
Helm 3+
Docker (for building images)
Node.js 25+ & npm (for L0 Client modifications)
Python 3.11+ (for L1 Agent modifications)
LLM API Key (OpenRouter, DeepSeek, Google, or Anthropic)

Step 1: Install MCP Server for Kubernetes

The MCP (Model Context Protocol) Server provides Kubernetes tools for the agents.

Clone the MCP Server repository

git clone https://github.com/Flux159/mcp-server-kubernetes.git

Update the schema (Required)

python3 -c "
import json
with open('mcp-server-kubernetes/helm-chart/values.schema.json') as f:
    schema = json.load(f)

schema['properties']['observability'] = {
    'type': 'object',
    'additionalProperties': True
}

with open('mcp-server-kubernetes/helm-chart/values.schema.json', 'w') as f:
    json.dump(schema, f, indent=2)
print('Schema updated successfully!')
"

Install the MCP Server

helm install mcp-server ./mcp-server-kubernetes/helm-chart \
  --set kubeconfig.provider=serviceaccount \
  --set transport.mode=http \
  --set transport.service.type=ClusterIP \
  --set security.allowOnlyNonDestructive=false \
  --create-namespace \
  --namespace mcp-system

Step 2: Install PostgreSQL Operator

The agents use PostgreSQL for long-term memory. We use the CrunchyData Operator to manage it.

Install OLM (Operator Lifecycle Manager)

curl -sL https://github.com/operator-framework/operator-lifecycle-manager/releases/download/v0.40.0/install.sh | bash -s v0.40.0

Important

OLM installation can take a few minutes. Please ensure all OLM pods in the olm namespace are in the Running state before proceeding.

Install the PostgreSQL Operator

kubectl create -f https://operatorhub.io/install/postgresql.yaml

Step 3: Build Custom Images

Navigate to the repository root and build your images:

Build L0 Frontend

docker build -t your-registry/agent-l0:latest ./k8s-agent/level-0-agent

Build L1 Backend Agent

docker build -t your-registry/agent-l1:latest ./k8s-agent/level-1-agent

Step 4: Push or Load Images

For remote clusters - Push to your registry:

docker push your-registry/agent-l0:latest
docker push your-registry/agent-l1:latest

For local Kind clusters - Load images directly:

kind load docker-image your-registry/agent-l0:latest --name 01cloud-cluster
kind load docker-image your-registry/agent-l1:latest --name 01cloud-cluster

Step 5: Configure Agent Settings

Edit helm-chart/values.yaml to use your custom images and configure your LLM provider. You must set the MODEL_PROVIDER, MODEL_NAME, image and provide the corresponding API key in the secret section.

agents:
  - name: l0
    enabled: true
    image: your-registry/agent-l0:latest  # Your custom image

  - name: l1
    enabled: true
    image: your-registry/agent-l1:latest  # Your custom image
    env:
      MODEL_PROVIDER: deepseek # options: gemini, openai, openrouter, anthropic, deepseek
      MODEL_NAME: deepseek-chat # examples: gemini-2.0-flash, gpt-4o, claude-3-5-sonnet
      MCP_SERVER_URL: http://mcp-server-mcp-server-kubernetes.mcp-system.svc.cluster.local:3001/mcp
      ENABLE_K8S_TOOLS: "true"
      STM_ENABLE_POSTGRES: "true"
    usePostgresql: true
    secret:
      DEEPSEEK_API_KEY: "your-api-key-here"
      # GOOGLE_API_KEY: "your-api-key"
      # OPENAI_API_KEY: "your-api-key"
      # OPENROUTER_API_KEY: "your-api-key"
      # ANTHROPIC_API_KEY: "your-api-key"

Step 6: Deploy 01Agents

helm upgrade --install 01agent ./helm-chart -n 01cloud --create-namespace

Important

Ensure all the pods in the namespace have initialized successfully and running

Step 7: Access the Application

Port-forward the UI service:

kubectl port-forward svc/agent-l0 -n 01cloud 3000:3000

Open your browser and navigate to http://localhost:3000

Step 8: Monitoring & Observability (Optional)

For advanced monitoring with Grafana, Loki, Tempo, and OpenTelemetry, see OTEL-setup.md.

🔌 Local Development Tools

Access these services via port-forwarding for development and debugging:

# PostgreSQL Database
kubectl port-forward svc/agents-primary -n 01cloud 5432:5432

# MCP Server
kubectl port-forward svc/mcp-server-mcp-server-kubernetes -n mcp-system 3001:3001

📁 Repository Structure

helm-chart/: Core Helm chart for deployment
k8s-agent/: Source code for L0 and L1 agents
OTEL-setup.md: Optional guide for monitoring (Prometheus, Grafana, Loki)

🤝 Contributing

We welcome contributions from the community! Whether you are reporting a bug, suggesting a feature, or submitting a pull request, your help is appreciated.

Found a bug? Open an issue
Have a feature idea? Suggest it here
Want to contribute code? Check out our Contributing Guidelines

This project is maintained by Bishal Singh (@bsalsingh) and the 01Cloud community.

📜 Code of Conduct

To ensure a welcoming and inclusive community, please review and follow our Code of Conduct.

⚖️ License

This project is licensed under the MIT License.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
.github		.github
docs		docs
helm-chart		helm-chart
k8s-agent		k8s-agent
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.prettierrc		.prettierrc
.stylelintrc.json		.stylelintrc.json
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE.md		LICENSE.md
OTEL-setup.md		OTEL-setup.md
README.md		README.md
commitlint.config.cjs		commitlint.config.cjs
package-lock.json		package-lock.json
package.json		package.json

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01Agents: Kubernetes Alert Remediation System

🏗️ Architecture Overview

L0 Client: React Router v7 Web Interface

L1: Kubernetes Alert Remediation Agent

🚀 Quick Start

Clone the Repository

Choose Your Deployment Approach

Approach 1: Using Pre-built Images

Prerequisites

Step 1: Install MCP Server for Kubernetes

Clone the MCP Server repository

Update the schema (Required)

Install the MCP Server

Step 2: Install PostgreSQL Operator

Install OLM (Operator Lifecycle Manager)

Install the PostgreSQL Operator

Step 3: Configure Agent Settings

Step 4: Deploy 01Agents

Step 5: Access the Application

Step 6: Monitoring & Observability (Optional)

Approach 2: Building Custom Images

Prerequisites

Step 1: Install MCP Server for Kubernetes

Clone the MCP Server repository

Update the schema (Required)

Install the MCP Server

Step 2: Install PostgreSQL Operator

Install OLM (Operator Lifecycle Manager)

Install the PostgreSQL Operator

Step 3: Build Custom Images

Build L0 Frontend

Build L1 Backend Agent

Step 4: Push or Load Images

Step 5: Configure Agent Settings

Step 6: Deploy 01Agents

Step 7: Access the Application

Step 8: Monitoring & Observability (Optional)

🔌 Local Development Tools

📁 Repository Structure

🤝 Contributing

📜 Code of Conduct

⚖️ License

📚 Component Documentation

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