Author
Ren builds agent infrastructure and writes copy-paste tutorials for engineers shipping LLM tool-use systems.
A runnable 2026 tutorial: build a write-capable MCP server in Python with FastMCP and the official MCP SDK. Add tools with @mcp.tool(), back them with SQLite, test in the Inspector, and connect it to Claude for Desktop.
A runnable 2026 tutorial: give an AI agent web search in Python with the Claude web_search server tool. Read citations, cap cost with max_uses, filter domains, and go multi turn.
A runnable 2026 tutorial: give a Claude agent long-term memory that survives across sessions using SQLite, embeddings, and cosine retrieval. No LangChain, FAISS, or vector database.
A hands-on LangGraph tutorial for 2026: build the same tool-using AI agent two ways, with the five-line create_agent helper and as an explicit StateGraph you can customize. Fully runnable Python.
The five parts every AI agent has, a runnable Python example in about 40 lines, and an honest guide to the guardrails and framework choices most tutorials skip.
A runnable 2026 guide to custom tools in the Claude Agent SDK: define functions with tool() / @tool, bundle them into an in-process MCP server, register with allowedTools, and gate risky calls per argument with canUseTool. TypeScript and Python.
A runnable 2026 tutorial: build short-term and long-term memory for a Claude agent in TypeScript with message buffers, rolling summaries, and a durable fact store. No vector database required.
A runnable comparison of the Claude Agent SDK and LangGraph in 2026, with the same agent built in both and an honest decision matrix for when each wins.
A runnable Python quickstart: install the Claude Agent SDK, stream a run with query(), then give the agent your own tools with the @tool decorator and ClaudeSDKClient. Builds a weather agent that chains two tools.
A runnable 2026 quickstart: install the Claude Agent SDK, wire a custom tool with tool() and createSdkMcpServer(), and let the agent loop call it for you in about 40 lines of TypeScript.
Should you use the Claude Agent SDK or hand-roll the agent loop with the Anthropic Client SDK? An honest 2026 comparison with runnable TypeScript for both and a decision table.
Speed up your Claude agent loop by running independent tool calls concurrently in TypeScript. Copy-paste code for parallel tool_use, is_error handling, and disable_parallel_tool_use.
