OpenClaw CLI with Python and AI Integration

What if your code could think? That's LangChain. LangChain is a framework that lets you build apps powered by LLMs (like GPT or Claude) - with memory, tools, and logic. Here's how simple it is to build a chatbot with memory in Python: from langchain_openai import ChatOpenAI from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationChain llm = ChatOpenAI(model="gpt-4") memory = ConversationBufferMemory() chain = ConversationChain(llm=llm, memory=memory) chain.predict(input="My name is Virat") chain.predict(input="What's my name?") # → "Your name is Virat." Without memory → every message is a fresh conversation. With memory → the model remembers context across turns. LangChain also lets you: 🔹 Connect LLMs to your own documents (RAG) 🔹 Give the model tools — search, calculator, APIs 🔹 Build multi-step AI agents that reason and act 🔹 Chain prompts together for complex workflows #LangChain #Python #LLM #MachineLearning #BackendDevelopment #LearningInPublic #Java #SpringBoot #AI

🚀 New open-source project: playwright-ai-pilot I'm building an AI-powered test automation framework using Playwright, Python, and Claude. Three AI pillars so far: 🔧 Self-healing locators — when a selector breaks, Claude finds an alternative automatically 🤖 AI test generation — point it at a URL and get a full pytest test file back 📋 AI test planning — give it a user story and get a structured test plan with risks and automation notes Work in progress — Windows/NTLM and MFA authentication mocking coming next. GitHub: https://lnkd.in/g7hiN6Fq Would love feedback from other automation engineers. What would you add? #TestAutomation #Playwright #Python #AI #SDET #OpenSource

How to Build a Secure Local-First Agent Runtime with OpenClaw Gateway, Skills, and Controlled Tool Execution In this tutorial, we build and operate a fully local, schema-valid OpenClaw runtime. We configure the OpenClaw gateway with strict loopback binding, set up authenticated model access through environment variables, and define a secure execution environment using the built-in exec tool. We then create a structured custom skill that the OpenClaw agent can discover and invoke deterministically. Instead of manually running Python scripts, we allow OpenClaw to orchestrate model reasoning, skill selection, and controlled tool execution through its agent runtime....

With low-quality tests, you're paying tokens for fixing them with little to no benefit that tests should provide Let's be real. Most of the Python tests out there are a waste of time. They are there to make the manager happy, to pass the compliance review, or to exercise dominance. Talking about tests that: - break due to unrelated changes, - make you restart the CI/CD pipeline and hope that they pass on the next run, - take forever to run, - pass, but the production is broken. Back in the day, one complained about having to work with such tests. Nowadays, we're paying LLM tokens while Claude Code is fixing them over and over. Pure waste of time and money. In my latest article, I'm describing 7 qualities of highly valuable tests that every developer should know. Qualities of tests that help you ship faster with AI without losing confidence or turning your status page into a traffic light🚦 Don't forget to subscribe to not miss the next tip 🔔

🎯 I built a Number Guessing Game in Python — and it taught me more than I expected! As a developer expanding into AI automation, I've committed to strengthening my Python fundamentals through hands-on projects. This classic beginner project turned into a real debugging session — and that's where the actual learning happened. 💡 🔍 What the game does: • Accepts a custom Min & Max range from the user • Generates a secret random number within that range • Tracks every valid guess attempt • Validates input and handles errors gracefully • Gives real-time feedback — too high, too low, or correct! 🛠️ Concepts I applied: → random module & randint() → Type hints for cleaner, readable code → Input validation with try/except ValueError → while loops, break & continue → Conditional logic & f-string formatting 🐛 Bugs I caught & fixed in my own code: → Secret number was hardcoded to 1–10, ignoring user's range → Wrong comparison operator (Min <= guess >= Max) → Invalid guesses were being counted as attempts → Bare except: replaced with except ValueError: Debugging your own code is one of the best teachers. Every bug = a lesson you won't forget! 🚀 📂 GitHub: https://lnkd.in/dfSTE3J4 I'm currently building Python skills alongside AI automation using n8n — more projects dropping soon. Follow along if you're on a similar journey! 🙌 #Python #PythonProgramming #100DaysOfCode #LearningInPublic #Debugging #BeginnerDeveloper #CodingJourney #SoftwareDevelopment #AIAutomation #PakistanTech

I thought error handling was just “avoiding crashes”… I was wrong. Today I practiced handling multiple exceptions in Python — and it completely changed how I look at writing reliable code. What I worked on: Taking user input safely using int(input()) Handling invalid inputs with ValueError Preventing runtime crashes like division by zero (ZeroDivisionError) Structuring multiple except blocks for different failure cases What’s actually happening behind the scenes: Python executes the try block normally If an error occurs → it immediately jumps to the matching except block Each except targets a specific failure scenario The program doesn’t crash — it responds gracefully Why this matters (real understanding): Before this, I wrote code assuming users would behave “correctly.” Now I design code assuming they won’t. That shift changes everything. Real-world relevance: Every backend system, API, or production app deals with unpredictable inputs. Error handling isn’t optional — it’s what makes software robust. What changed for me: I’ve stopped writing “happy path only” code. Now I think in terms of: → What can go wrong? → How should my program respond? Small shift. Big impact. Consistency over intensity. Building step by step. How do you usually approach error handling — after writing logic or while designing it? #Python #ErrorHandling #BackendDevelopment #APIs #Programming #SoftwareEngineering #LearnInPublic #DeveloperJourney #CodingPractice #GenAI

You can vibe code almost everything in 2026 🚀 But when it comes to client data, sensitive logic, and production-ready code, would you really trust AI to do everything on its own? 🤔 That is exactly where blind vibe coding starts to fall short. I always say this: ask AI for the logic, verify it properly, understand what it is doing, and then integrate it into your main codebase with confidence. That is why knowing a programming language is still not optional — it is essential. And in this AI era, one skill has become even more valuable and almost non-negotiable: Python. 🐍 The reason is pretty clear by now — Python is simple, powerful, versatile, AI friendly and one of the best languages to actually build with. So yes, in contrast to the first paragraph 😂 I vibe coded this entire repo in just 24 hours and made it public for anyone who wants to get better hands-on practice. Introducing PythonVx — a Python coding platform with continuous animation of code flow, designed to help beginners understand Python in a more visual and interactive way. If you are someone who is just starting out and wants to feel more familiar with how Python actually works, this might be useful for you. Check it out here: https://lnkd.in/gCZ26mep Always welcome for contributions 🙌 Leave a star ⭐️ It is much appreciated. ❤️ Btw, have you ever solved this question in this way? 😅 #Python #Coding #Programming #SoftwareDevelopment #Developer #CodeNewbie #LearnToCode #CodingLife #ArtificialIntelligence #AI #MachineLearning #GenerativeAI #TechLearning #Upskill #CareerGrowth #OpenSource #GitHub #BuildInPublic #DevCommunity #TechContent #Innovation #TechTrends #100DaysOfCode #PythonProjects #LearnPython #PythonBeginner #InteractiveLearning

🐍 Python Term of the Day: Lovable (AI Coding Tools) An AI-powered full-stack platform that generates and deploys web applications from natural language descriptions. https://lnkd.in/gW--_n-T

Built something exciting: CodeMailer AI – turning emails into AI-powered code reviews! Ever wished your inbox could review code for you? I built CodeMailer AI, an end-to-end Python automation tool that transforms Gmail into a smart code reviewer. 💡 What it does: Detects unread emails with Python attachments Analyzes code using advanced AI models (via OpenRouter) Generates structured insights—bugs, logic, execution flow & more Creates a clean, syntax-highlighted HTML report Automatically replies back to the sender with the review ✨ Key highlights: Seamless Gmail OAuth integration Deep AI-powered code analysis with inline comments Beautiful dark-mode HTML reports with Pygments styling Fully automated pipeline (read → analyze → generate → reply) Smart fallback to local files when inbox is empty ⚙️ Built using: Python, Gmail API, OpenRouter (Gemini 2.0 Flash), Jinja2, Pygments This project was a great hands-on experience in combining automation + AI + real-world workflows to build something practical and scalable. Would love your feedback! 🙌 #AI #Python #Automation #MachineLearning #Developers #Projects #OpenSource #BuildInPublic #Hack2Skill GitHub Project Link: https://lnkd.in/gYsGvBJw

PYTHON NO LONGER ENDS WITH CODE. It begins where the architecture of intelligence begins. For years, Python was seen as a programming language. A practical tool. A clean syntax. A fast way to build software. But that description is no longer enough. TODAY, PYTHON IS BECOMING SOMETHING FAR GREATER. It is turning into a language of orchestration: of models, of tools, of agents, of reasoning chains, of decision layers, of context, and of action. Not long ago, a developer wrote functions. NOW, MORE AND MORE OFTEN, A DEVELOPER DESIGNS BEHAVIOR. That is a profound shift. Because the real question is no longer: Can you write code? The real question is: CAN YOU BUILD A SYSTEM IN WHICH CODE, MODEL, DATA, MEMORY, AND CONTEXT BEGIN TO WORK AS ONE? This is exactly why Python is not disappearing in the age of AI. Quite the opposite. ITS STRATEGIC ROLE IS GROWING. Because very few languages combine so much at once: simplicity, abstraction, integration, automation, experimentation, and the ability to move from idea to working system with extraordinary speed. And that is why the future will not belong to those who merely write code. IT WILL BELONG TO THOSE WHO CAN DESIGN THE ARCHITECTURE OF DECISION. The engineer of the coming years will not be judged only by syntax. Not only by frameworks. Not only by whether a script runs. They will be judged by whether they can create structures in which intelligence becomes usable, directed, and real. PYTHON IS NO LONGER JUST A LANGUAGE OF SOFTWARE. IT IS BECOMING A LANGUAGE OF AGENCY. A language for building systems that do not merely execute instructions, but coordinate meaning, logic, memory, and response. So the real question is no longer: Should people still learn Python? The real question is: CAN YOU USE IT TO BUILD SYSTEMS THAT THINK WITH YOU, ACT WITH YOU, AND EXTEND HUMAN CAPABILITY? That is where the game is now. And many still do not see it. #Python #AI #LLM #MachineLearning #SoftwareArchitecture #Agents #Automation #FutureOfWork