How Do Skills, MCP, RAG, and Agents Relate in OpenClaw?

Preface

A newcomer asked what Skills, MCP, RAG, and Agent mean in OpenClaw. The author uses a story about an emperor and a court advisor to illustrate knowledge cutoff, RAG, action‑ability, Function Calling, MCP, and Skills.

01 A Story That Clarifies All Concepts

In the story, the emperor (user) asks the advisor (LLM) for frontier information. The advisor’s lack of real‑time knowledge illustrates the knowledge cutoff problem. Providing the advisor with maps and reports demonstrates RAG (Retrieval‑Augmented Generation) . The emperor’s request to command troops shows the action‑ability gap, solved by Function Calling . Defining a formal command format introduces MCP (Model Context Protocol) . Supplying a handbook of procedures creates Skills . Together these form a complete Agent that can perceive, decide, and act.

02 Concept Overview Diagram

An illustration (image omitted) shows the relationships among the layers; the article proceeds to unpack each layer.

03 Layer One: Core Brain – Agent

The Agent is an autonomous system that perceives the environment, makes decisions, and executes actions. OpenClaw implements the classic Observe‑Plan‑Act loop.

Observe : understand user intent and current state.

Plan : decompose the task and decide which tools to invoke.

Act : execute the chosen tool and obtain results.

Loop : repeat until the task is complete.

Each Agent has a workspace containing configuration files such as AGENTS.md, SOUL.md, TOOLS.md, IDENTITY.md, USER.md, and MEMORY.md, making the Agent transparent and auditable.

04 Layer Two: Memory System

Memory gives the AI persistent state. OpenClaw stores short‑term memory (raw recent dialogue) and long‑term memory (summarized entities) in SQLite. The core retrieval logic is shown in the following pseudo‑code:

// OpenClaw memory retrieval core logic (pseudo‑code)
async function searchMemory(queryVector, limit = 5) {
  try {
    // Fast path: native vector search via sqlite‑vec extension
    return await db.all(`
      SELECT c.text, vec_distance_cosine(v.embedding, ?) AS dist
      FROM chunks_vec v
      JOIN chunks c ON c.id = v.id
      ORDER BY dist ASC LIMIT ?
    `, [queryVector, limit]);
  } catch (err) {
    // Safe fallback: compute similarity in JavaScript
    const allChunks = await db.all("SELECT text, embedding FROM chunks");
    return allChunks
      .map(chunk => ({ ...chunk, dist: cosineSimilarity(queryVector, JSON.parse(chunk.embedding)) }))
      .sort((a, b) => a.dist - b.dist)
      .slice(0, limit);
  }
}

The design prefers the native extension for performance and gracefully degrades when it is unavailable.

05 Layer Three: Knowledge – RAG

RAG solves the model‑knowledge‑freeze problem by retrieving relevant documents before answering. OpenClaw vectorizes local Markdown files and stores them in SQLite, then follows a four‑step process: user query → knowledge base retrieval → combine results with the query → model generates answer.

06 Layer Four: Tool Layer – Function Call and MCP

Function Call

Function Call lets the model output a structured request to invoke a real function. Example:

// User asks: "How's the weather in Beijing?"
{
  "function": "get_weather",
  "parameters": { "city": "北京" }
}
// Developer calls the real API and returns the result to the model.

MCP

MCP is a standardised tool‑calling protocol proposed by Anthropic. OpenClaw deliberately does not support MCP for three reasons:

Security and privacy – multi‑model collaboration could leak data.

Technical flexibility – avoiding protocol lock‑in enables rapid iteration.

Resource optimisation – fewer dependencies lead to faster response times.

Instead, OpenClaw uses its own Skills mechanism.

07 Layer Five: Process – Skills

Skills encapsulate complete workflows, solving the problem of “when to use a tool, in what order, and how to combine them.” Examples of built‑in Skills include memory, web_search, browser, and file. Installation is simple:

clawhub install memory   # install memory skill
clawhub install browser  # install browser control skill

The ClawHub registry hosts thousands of skill plugins covering office automation, code management, data processing, and more.

08 OpenClaw: A Unified Agent Platform

OpenClaw (originally Clawdbot/Moltbot) was released in November 2025 by Austrian developer Peter Steinberger (founder of PSPDFKit) and renamed in early 2026. It is MIT‑licensed, has over 290 k GitHub stars, and more than 1 000 contributors.

Its four‑layer architecture:

Control Gateway Layer – manages inbound/outbound messages across 22+ platforms (Telegram, Feishu, DingTalk, etc.).

Reasoning Layer – integrates large models and runs the Observe‑Plan‑Act loop.

Memory & State Layer – persistent memory built on SQLite.

Skills & Execution Layer – invokes Skills to perform concrete actions.

Typical execution flow:

User sends a message (e.g., “organise my desktop files”).

Gateway forwards it to the Agent.

Agent analyses the task and calls the file skill.

Skill performs the file operation and returns the result.

Memory records the operation for future reference.

Agent replies with a confirmation.

09 Choosing OpenClaw vs. Dify

Comparison table (summarised):

Design philosophy : OpenClaw – autonomous Agent; Dify – controlled chatbot.

Process control : OpenClaw – dynamic planning; Dify – static workflow canvas.

Suitable scenarios : OpenClaw – high‑freedom, undefined processes; Dify – standardised, compliance‑heavy workflows.

Typical use cases : OpenClaw – system ops, file management, browser automation; Dify – customer service, approval pipelines.

In short, use OpenClaw when AI must figure out its own approach; use Dify when a fixed, auditable process is required. The two can be combined by wrapping Dify workflows as Skills for OpenClaw.

10 Security Reminder: Keep Your “Lobster” Under Control

OpenClaw can execute shell commands and manipulate the file system, giving it the same permissions as the user. Follow these safety rules:

Do not run it “bare metal” on your primary machine; use an old PC, VM, or a separate system account.

Never expose your API keys – avoid committing them to Git or sharing screenshots.

For long‑running deployments, use Docker or a VPS to isolate permissions.

Never treat OpenClaw as a simple file‑transfer tool; assume data may leak.

Conclusion

OpenClaw integrates Agent, RAG, Memory, MCP (optional), and Skills into a single open‑source platform that runs locally. Understanding each layer clarifies how the system works and helps decide when to adopt OpenClaw over alternatives like Dify.

Open Source Links

OpenClaw: https://github.com/openclaw