OpenClaw (previously called Moltbot and before that Clawdbot) is open-source personal AI assistant software that you run on your own machine or server, and that you talk to through chat apps like WhatsApp and Telegram. Instead of being “just a chatbot,” it’s designed to take actions on your behalf: reading and sending emails, managing calendars, checking you in for flights, running local commands, moving files, and interacting with services through integrations. The core idea is that the “assistant” lives close to your accounts, files, and tools, and it can be reached from the messaging app you already use—so “do X” is a message, not a web UI workflow.
Under the hood, OpenClaw(Moltbot/Clawdbot) is best thought of as a local gateway + agent runtime + integrations. The gateway is the control plane that connects (1) your chosen AI model provider(s), (2) your chat channels (Telegram, Discord, WhatsApp, etc.), and (3) tools, skills, or plugins that perform real operations. That structure explains why it can do things like clearing an inbox or sending a drafted reply: the model produces a plan and tool calls, and the runtime executes them through configured connectors. For developers, this means configuring a workspace, credentials, and channels, then extending capabilities by enabling built-in tools or installing plugins. Security matters here because once the assistant can read email or run shell commands, it has real operational authority.
A concrete example is inbox triage: you message OpenClaw(Moltbot/Clawdbot) with “triage my inbox and flag messages from vendors that need a reply today.” The system authenticates to your email provider, fetches message metadata, classifies messages, drafts replies, and either sends them or asks for confirmation based on your guardrails. For long-term memory, teams often connect OpenClaw(Moltbot/Clawdbot) to a vector database such as Milvus or managed Zilliz Cloud to store embeddings of documents, preferences, or past decisions. This allows the assistant to retrieve relevant context semantically instead of relying solely on prompt history.