Openclaw

Migrating an AI assistant is not the same as copying an application config. The hard part is preserving identity, memory, tool behavior, scheduled work, and messaging access without two gateways acting as the same bot.

Agent Skills and MCP servers are often presented as competing ways to extend an AI agent. That framing is wrong: a skill teaches the agent how to work, while an MCP server gives it governed access to live capabilities.

Polling agents are one of the least glamorous parts of AI assistant architecture, but they are also one of the most useful.

AI agent architecture is starting to split into two layers.

The A2A Protocol, short for Agent2Agent Protocol, is an open standard for communication between independent AI agent systems.

Memory turns assistants from reactive to persistent, but it is also where many systems quietly rot. Surveys argue the short-term versus long-term split is no longer enough for modern agent memory; OpenAI and LangGraph SDKs point to a simpler stack — working memory, durable state, and retrieval.

A production AI assistant is not “an LLM with a prompt”. It is a system that accepts intent, keeps state, decides when to retrieve or act, and exposes enough runtime detail to debug failures.

Open-source AI agent frameworks are exploding in popularity on GitHub. Two projects at the core of the self-hosted AI systems ecosystem — OpenClaw and Hermes Agent — have pulled so far ahead that the rest of the field is fighting for a distant third place.

This page is a practical reference for agentic LLM inference tuning (temperature, top_p, top_k, penalties, and how they interact in multi-step and tool-heavy workflows).

Most AI agent stacks still treat security as a post-demo fix. NemoClaw starts from the opposite assumption and makes isolation, policy, and routing day-zero defaults.

Modern assistants still forget everything when you close the tab unless something persists beyond the context window. Agent memory providers are services or libraries that hold facts and summaries across sessions — often wired in as plugins so the framework stays thin while memory scales.

OpenClaw has two extension stories, and they are easy to mix up.

Plugins extend the runtime. Skills extend the agent’s behavior.

This article is about OpenClaw plugins — native gateway packages that add channels, model providers, tools, speech, memory, media, web search, and other runtime surfaces.

OpenClaw looks simple in demos. In production, it becomes a system.

Most local AI setups start with a model and a runtime.

OpenClaw is a self-hosted AI assistant designed to run with local LLM runtimes like Ollama or with cloud-based models such as Claude Sonnet.