Tutorial

GitHub Copilot CLI went generally available today with full agent mode — and it’s the most significant upgrade to terminal-native AI development since GitHub CLI launched. This guide gets you set up and running productive agent workflows from the shell without touching a browser or IDE. Prerequisites GitHub account with an active Copilot Individual, Team, or Enterprise subscription Node.js 18+ (for the npm package) GitHub CLI (gh) version 2.40 or later A Unix-like terminal (macOS Terminal, iTerm2, Windows Terminal with WSL, or any Linux terminal) Step 1: Install the Copilot CLI Extension Copilot CLI installs as a gh extension. If you have gh installed and authenticated, run: ...

If OpenClaw is throwing 403 permission_error when it tries to call Claude, your OAuth session has been revoked by Anthropic. This is not a bug you can wait out — it’s a deliberate policy change. Here’s exactly what to do. Time estimate: 10–20 minutes Difficulty: Easy Who this affects: OpenClaw users who signed in with Claude Pro or Max subscription credentials (OAuth flow) rather than a direct API key First: Confirm You’re Affected Check your OpenClaw logs. If you see something like: ...

Alibaba’s CoPaw just went open-source and it’s one of the cleanest personal agent setups I’ve seen for developers who want full control over their stack. This guide walks you through a working deployment in under 30 minutes — locally on a Mac, or on a cheap Linux VPS. Prerequisites: Python 3.11+ or Docker A machine with at least 4GB RAM (8GB+ for local models) Optional: Anthropic/OpenAI API key, or a local model via llama.cpp or Ollama Step 1: Clone the Repository git clone https://github.com/agentscope-ai/CoPaw.git cd CoPaw The repo includes a docker-compose.yml for containerized deployment and a standard Python requirements.txt for bare-metal installs. ...

Most multi-agent tutorials stop at “here’s how to wire two agents together.” Production systems need more: structured message passing, durable state across restarts, and an audit trail you can debug when something goes wrong at 2am. This guide builds a Planner/Executor/Validator architecture with LangGraph that’s actually ready for production. Architecture Overview The system uses three specialized agents: Planner — Receives a task, decomposes it into steps, publishes to the message bus Executor — Consumes steps from the bus, executes them, publishes results Validator — Checks Executor outputs against criteria, flags failures, loops back to Planner if needed These agents communicate via a structured ACP-style message bus (Pydantic schemas), checkpoint state to SQLite via langgraph-checkpoint-sqlite, and log every message to JSONL for auditability. ...

Claude Code v2.1.63 ships a significant change to how you hook into the agent lifecycle: shell-based hooks are out, HTTP webhooks are in. If you’ve been using hooks in your Claude Code pipeline, this guide walks through the migration and shows you why the new pattern is worth the effort. Why HTTP Hooks? The old shell hook model had a fundamental problem: crossing the shell boundary destroyed structure. You serialized data to a string, passed it through a shell command, and parsed it back out on the other side. Every step introduced escaping issues, subprocess overhead, and opportunities for silent failure. ...