Demystifying OpenClaw: How Agents, RAG, Memory, and Skills Power AI Automation

Introduction

Yesterday a newcomer asked about OpenClaw’s many terms—Skills, MCP, RAG, Agent—so I compare OpenClaw to a lobster: the lobster is the core, the claws, brain and recipe are its components.

OpenClaw has become a star‑project on GitHub with nearly 300 k stars in early 2026, but many users are confused by the new concepts. This article explains them with a story and a diagram.

Concept diagram

Story that explains the concepts

Imagine an emperor (the user) and a minister (the AI model) who lives in the cloud and cannot see the frontier. The emperor wants real‑time information, which illustrates the knowledge cutoff problem.

Providing the minister with maps and reports solves it—this is RAG (Retrieval‑Augmented Generation) .

If the emperor wants the minister to command troops directly, the minister lacks action capability .

Giving the minister a formal order format (e.g., “dispatch troops: general=Zhang San, count=1000”) and a messenger implements Function Calling .

When many different resources (troops, supplies, labor) must be coordinated, a messenger protocol called MCP (Model Context Protocol) defines the format and timing.

Finally, a handbook that tells the minister when and how to use each resource represents Skills .

OpenClaw packages all these abilities—Agent, Memory, RAG, Function Calling, MCP, Skills—into a single runtime that can be invoked from a chat app.

Four‑layer architecture

01 Agent (core brain)

An Agent perceives the environment, plans a task, and acts. OpenClaw follows an Observe‑Plan‑Act loop. When a user sends a message, the Agent:

Observe : understand intent and current state.

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

Act : execute the chosen tool and collect the result.

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. This makes the Agent transparent and auditable.

02 Memory system

Memory gives the model persistent state. Short‑term memory stores recent dialogue verbatim; long‑term memory compresses history into summaries and stores them in a SQLite database. Retrieval uses a hybrid “vector + keyword” approach, preferring the native sqlite‑vec extension and falling back to JavaScript similarity calculations when the extension is unavailable.

03 Knowledge layer – RAG

RAG solves the “knowledge freeze” problem by retrieving up‑to‑date documents before generating an answer. OpenClaw vectorizes local Markdown files and stores them in SQLite; at query time it fetches relevant chunks and feeds them to the LLM.

04 Tool layer – Function Call and MCP

Function Call allows the model to output a JSON‑like request such as {"function":"get_weather","parameters":{"city":"Beijing"}}. The developer then invokes the real API and returns the result to the model.

MCP is deliberately omitted in OpenClaw to avoid privacy risks, maintain flexibility, and reduce system complexity. Instead, OpenClaw uses a lightweight Skills mechanism.

05 Skills – workflow abstraction

Skills are packaged procedures that combine multiple tools (memory, web_search, browser, file, etc.) into a single callable unit. Installation is as simple as:

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

The public ClawHub registry offers thousands of skill plugins for office automation, code management, data processing, and more.

Putting it all together

OpenClaw’s four layers work as follows: the gateway receives a message, forwards it to the Agent, the Agent plans and calls a skill (e.g., file), the skill performs the operation, Memory records the action, and the Agent replies to the user.

OpenClaw vs. Dify/Workflow

OpenClaw excels in scenarios requiring high freedom and dynamic decision‑making, while Dify/Workflow is better for fixed, auditable processes. The two can be combined by wrapping a Dify workflow as a Skill.

Security considerations

Because OpenClaw can execute shell commands and manipulate the file system, it has the same privileges as the user. Recommended safeguards include running on a dedicated machine or VM, never exposing API keys, using Docker/VPS for long‑running services, and treating the system as a potential data‑leak vector.

Conclusion

Agent is the overarching concept; RAG provides external knowledge; Memory gives persistence; MCP is a standard tool‑calling protocol that OpenClaw opts out of; Skills are OpenClaw’s own workflow modules; and OpenClaw integrates all of them into an open‑source, locally runnable AI agent platform.

Resources

GitHub repository: https://github.com/openclaw

One‑click deployment guide (Alibaba Cloud): https://www.aliyun.com/activity/ecs/clawdbot