Run production deploys and port scans from Telegram with OpenClaw
Read Time: 12 minutes. Task: Read Time: 12 minutes TL;DR OpenClaw has transformed how I work as a cybersecurity consultant and developer. After two weeks of daily use, I’ve automated email management, built custom security tools overnight, and integrated AI into my pentesting workflow—all while maintaining strict security boundaries. This article covers my real-world experience: the good (autonomous coding agents), the tricky (channel configuration), and the lessons learned (troubleshooting multi-platform integrations). Introduction: Why I Chose OpenClaw As a cybersecurity professional running VULNEX, my day involves pentesting, security consulting, building commercial products and open source tools. I need an AI assistant that can: Work autonomously while I sleep or focus on client work Access my infrastructure securely (email, servers, projects) Integrate with my workflow (Telegram, GitHub, development environments) Respect security boundaries (no data leakage, no unauthorized access) ChatGPT and Claude Web couldn’t do this. They’re great for conversations, but they can’t: Read my email inbox and filter spam SSH into my Raspberry Pi and deploy Docker containers Monitor GitHub issues and create pull requests Send me proactive Telegram notifications about urgent matters OpenClaw can. It’s self-hosted, runs on my infrastructure (a Raspberry Pi 5), and has persistent memory across sessions. It’s not just a chatbot—it’s a 24/7 autonomous agent. The Setup: From Zero to Productive in One Day Hardware Raspberry Pi 5 (8GB RAM, running Raspberry Pi OS 64-bit) 256GB microSD card (for workspace, Docker images, logs) Ethernet connection (reliable, no Wi-Fi dropouts) Yes, you can run your agents in more expensive hardware such as Apple Mini or Studio, it all comes down to what you want to achieve, number of agents, etc. At some point I will deploy my agents in Apple gear. Installation OpenClaw installation was straightforward: Within 5 minutes, I had: ✅ Gateway running (port 3000) ✅ Web interface accessible (local network) ✅ Main agent session ready Installation is easy, troubleshooting can be hard. Initial Configuration The first challenge: choosing a model. OpenClaw supports multiple providers: Anthropic (Claude Sonnet 4, Claude Opus 4) OpenAI (GPT-4.1, GPT-4o) Local models (via Ollama, LM Studio) I chose Claude Sonnet 4 for: Best code quality (important for autonomous work) 1M token context window (can handle large projects) Strong reasoning (fewer hallucinations on complex tasks) Opus is another choice for a top-notch model but more expensive. If you are using local models, it is another game. A future post, I guess :) Configuration was simple: Channels: The Communication Backbone Telegram Integration (Primary Interface) Telegram became my main interface to AgentX (my OpenClaw agent). Why Telegram? Mobile-first (I’m constantly on the move) Notifications (instant alerts without opening a browser) File sharing (send images, PDFs, code snippets) Secure (end-to-end encryption available) Setup: Created a Telegram bot via BotFather Added bot token to OpenClaw config: openclaw config set telegram.token="123456:ABC..." Started chatting immediately Real-world usage: Morning: “Check my inbox, any urgent emails?” Afternoon: “Deploy the latest changes to prod” Evening: “What did you work on today? Show me a summary.” Telegram’s inline buttons and rich formatting made interactions feel native, not like talking to a terminal. Webchat (For Sensitive Data) While Telegram is convenient, I configured Webchat for: Reviewing security audit reports (too sensitive for cloud messaging) Discussing client projects (GDPR compliance) Sharing API keys or credentials temporarily Webchat runs on localhost and never leaves my network. Security setup: Simple but effective: password-protected, no public exposure. Email Integration I gave AgentX access to email accounts via IMAP/SMTP. This was a game-changer for: Spam filtering (AgentX auto-archives recruitment spam) Inbox triage (flags urgent client emails) Automated responses (acknowledges non-urgent inquiries) Configuration: Security consideration: Used an app-specific password (not my main password) Email account is dedicated to AgentX (not my personal inbox) IMAP/SMTP over SSL (ports 993/465, encrypted) Security: Walking the Tightrope As a security professional, giving an AI agent access to my infrastructure was terrifying. Here’s how I mitigated risks: 1. Sandboxed Execution; tools: OpenClaw. Human role: I review logs weekly to catch anomalies.; limitations: Requires self-hosted infrastructure (e.g., Raspberry Pi 5) and API keys for LLM providers. Security risks include unauthorized access if boundaries are misconfigured; GDPR compliance dictates using local Webchat for client data. Troubleshooting multi-platform integrations can be difficult. Human approval is mandatory for production deploys and sensitive communications..
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Published on Agent Work Atlas:
Overview
Read Time: 12 minutes
Workflow
Read Time: 12 minutes TL;DR OpenClaw has transformed how I work as a cybersecurity consultant and developer. After two weeks of daily use, I’ve automated email management, built custom security tools overnight, and integrated AI into my pentesting workflow—all while maintaining strict security boundaries. This article covers my real-world experience: the good (autonomous coding agents), the tricky (channel configuration), and the lessons learned (troubleshooting multi-platform integrations). Introduction: Why I Chose OpenClaw As a cybersecurity professional running VULNEX, my day involves pentesting, security consulting, building commercial products and open source tools. I need an AI assistant that can: Work autonomously while I sleep or focus on client work Access my infrastructure securely (email, servers, projects) Integrate with my workflow (Telegram, GitHub, development environments) Respect security boundaries (no data leakage, no unauthorized access) ChatGPT and Claude Web couldn’t do this. They’re great for conversations, but they can’t: Read my email inbox and filter spam SSH into my Raspberry Pi and deploy Docker containers Monitor GitHub issues and create pull requests Send me proactive Telegram notifications about urgent matters OpenClaw can. It’s self-hosted, runs on my infrastructure (a Raspberry Pi 5), and has persistent memory across sessions. It’s not just a chatbot—it’s a 24/7 autonomous agent. The Setup: From Zero to Productive in One Day Hardware Raspberry Pi 5 (8GB RAM, running Raspberry Pi OS 64-bit) 256GB microSD card (for workspace, Docker images, logs) Ethernet connection (reliable, no Wi-Fi dropouts) Yes, you can run your agents in more expensive hardware such as Apple Mini or Studio, it all comes down to what you want to achieve, number of agents, etc. At some point I will deploy my agents in Apple gear. Installation OpenClaw installation was straightforward: Within 5 minutes, I had: ✅ Gateway running (port 3000) ✅ Web interface accessible (local network) ✅ Main agent session ready Installation is easy, troubleshooting can be hard. Initial Configuration The first challenge: choosing a model. OpenClaw supports multiple providers: Anthropic (Claude Sonnet 4, Claude Opus 4) OpenAI (GPT-4.1, GPT-4o) Local models (via Ollama, LM Studio) I chose Claude Sonnet 4 for: Best code quality (important for autonomous work) 1M token context window (can handle large projects) Strong reasoning (fewer hallucinations on complex tasks) Opus is another choice for a top-notch model but more expensive. If you are using local models, it is another game. A future post, I guess :) Configuration was simple: Channels: The Communication Backbone Telegram Integration (Primary Interface) Telegram became my main interface to AgentX (my OpenClaw agent). Why Telegram? Mobile-first (I’m constantly on the move) Notifications (instant alerts without opening a browser) File sharing (send images, PDFs, code snippets) Secure (end-to-end encryption available) Setup: Created a Telegram bot via BotFather Added bot token to OpenClaw config: openclaw config set telegram.token="123456:ABC..." Started chatting immediately Real-world usage: Morning: “Check my inbox, any urgent emails?” Afternoon: “Deploy the latest changes to prod” Evening: “What did you work on today? Show me a summary.” Telegram’s inline buttons and rich formatting made interactions feel native, not like talking to a terminal. Webchat (For Sensitive Data) While Telegram is convenient, I configured Webchat for: Reviewing security audit reports (too sensitive for cloud messaging) Discussing client projects (GDPR compliance) Sharing API keys or credentials temporarily Webchat runs on localhost and never leaves my network. Security setup: Simple but effective: password-protected, no public exposure. Email Integration I gave AgentX access to email accounts via IMAP/SMTP. This was a game-changer for: Spam filtering (AgentX auto-archives recruitment spam) Inbox triage (flags urgent client emails) Automated responses (acknowledges non-urgent inquiries) Configuration: Security consideration: Used an app-specific password (not my main password) Email account is dedicated to AgentX (not my personal inbox) IMAP/SMTP over SSL (ports 993/465, encrypted) Security: Walking the Tightrope As a security professional, giving an AI agent access to my infrastructure was terrifying. Here’s how I mitigated risks: 1. Sandboxed Execution
Reported result
The author reports automating email management and building security tools overnight after two weeks of daily use. Specific metrics on time saved or error reduction are not provided. The system handles proactive notifications and autonomous coding, but the source does not quantify the success rate of these automated tasks or the frequency of human intervention required.
Human role
I review logs weekly to catch anomalies.
Limitations
Requires self-hosted infrastructure (e.g., Raspberry Pi 5) and API keys for LLM providers. Security risks include unauthorized access if boundaries are misconfigured; GDPR compliance dictates using local Webchat for client data. Troubleshooting multi-platform integrations can be difficult. Human approval is mandatory for production deploys and sensitive communications.
Try it
I want to set up an autonomous cybersecurity assistant using OpenClaw on my local server. First, install OpenClaw and configure it to use [replaceable: LLM Provider, e.g., Anthropic Claude Sonnet 4]. Set up a Telegram bot for primary interaction and a local Webchat for sensitive data. Connect my email via IMAP/SMTP using an app-specific password for a dedicated account. Configure SSH access to my [replaceable: Server Name] for Docker deployments. Steps: 1. Verify installation and gateway status. 2. Test Telegram connectivity with a simple 'ping'. 3. Configure email filters to archive spam and flag urgent client emails. 4. Set up a dry-run for a production deploy command. Output: A configuration file and a log of the dry-run. Measure success by confirming the agent correctly identifies urgent emails and executes the deploy script without errors. Do not send real emails or deploy to production until I review the dry-run logs and explicitly approve the action. If inputs are missing, ask for the specific server IP and email credentials.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.