TLDR;
OpenClaw is a local Node.js service that stays running on your machine. Messages from Telegram or Slack go through a channel plugin into the Gateway, which routes them to an AI agent. The agent can call tools like GitHub, files, and shell commands, and the Gateway can pause risky actions for your approval in the Control UI. Conversations are saved on disk and compacted when they get long, while older context is recalled using a local memory index with semantic search. That is why it can both automate tasks and continue work from days ago without you repeating everything.
If you have used AI tools that feel helpful in the moment but forget everything the next day, OpenClaw feels like a different category. It runs locally as a server, stays on, connects to your chat apps, and can resume work even after a gap of a few days. Once you know what parts are doing what, it becomes much easier to decide what to enable and what permissions to allow.
What OpenClaw ?
OpenClaw is a Node.js service that keeps running on your machine. When you start the gateway command, you are launching an HTTP and WebSocket server that listens on port 18789 by default. This gateway is basically the hub. Messages come in from Telegram or Slack, the agent processes them with tool access, and the gateway sends replies back out. The main difference from using an AI in a browser is that this thing does not disappear when you close a tab. It can keep conversations, run scheduled tasks, and even notify you when something needs attention.
Put diagram here Request flow from chat app to gateway to agent to tools and back
How it knows to check GitHub
It is not guessing. GitHub access is provided through a plugin and a skill. When you ask to check pull requests, the agent sees that GitHub is an available tool, calls it, and then turns the results into a readable summary. The reason it feels fast is because the gateway is already running and the integrations are already connected, so it is not starting from zero each time.
A simple way to think about the flow is this
A message arrives from Telegram or Slack
The channel plugin translates it into OpenClaw’s internal format
The gateway routes it to the agent
The agent calls tools if needed such as GitHub or shell
The gateway sends the final response back to the channel
The Memory Part.
When people hear persistent conversations, they assume the entire chat lives inside the model forever. That is not what is happening.
OpenClaw stores conversations on disk as JSONL files under a transcripts folder. For each new reply, it loads only the recent relevant part of the thread into the model context. If the thread becomes too large, it compacts it by summarizing older parts so the model does not have to carry a massive history every time.
Long term recall works differently. Old transcripts get indexed into a local database that supports vector search. Before answering, the agent can search that index and pull back the most relevant bits from previous sessions. That is how it can resume a bug investigation from five days ago without you re explaining the whole story.
Put diagram here Short term context versus long term recall with search
What starts up when you run the gateway
When the gateway boots, it reads your config file, loads plugins, and starts listening for connections. It also brings up the control UI so you can watch what the agent is doing and approve sensitive actions. In the background, it can start schedulers for timed automations, heartbeat style check ins, and a memory indexer that keeps new information searchable.
The Architecture.
You can understand OpenClaw as five layers working together
Channel layer connects Telegram Slack WhatsApp and friends
Gateway layer routes messages and controls approvals
Plugin layer adds integrations tools and background services
Agent layer calls the model and decides when to use tools
Memory layer stores conversations and retrieves older context when needed
Why this matters for real use
Once you see that OpenClaw is basically a local automation server with an AI agent in the middle, the security questions become clearer. You can choose what plugins to enable, what tools should require approval, and what information should be stored long term. That is the difference between a toy assistant and something you can actually trust on your own machine.
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