Clawdbot is a self-hosted, personal AI assistant platform that you run on your own devices, allowing you to interact with an AI through familiar messaging channels such as WhatsApp, Telegram, Slack, Discord, Signal, and iMessage. It consists of a core Gateway process that acts as the always-on control plane and one or more agents that handle user conversations, state, and execution of actions. The design emphasizes local execution, persistent memory, and the ability to integrate with real tools and systems on your machine, making it more of a utility-oriented assistant than a simple chatbot. The assistant listens on the channels you configure and routes messages through the Gateway to the appropriate agent, which then produces responses and executes any configured actions. The system is open source, runs on Node-based tooling, and is intended for developers and power users comfortable managing services on their own infrastructure.
Architecturally, Clawdbot works by establishing a Gateway daemon that remains running on your host, usually binding to localhost or another specified interface. This Gateway connects to messaging surfaces via APIs or web protocols, for example using the WhatsApp Web protocol, Telegram Bot API, or Discord Bot API, and listens for inbound messages. When a message arrives, the Gateway routes it to an AI agent process associated with that messaging session. The agent uses configured LLM backends to generate replies and can also use structured “skills” or scripts to perform tasks on your machine or external systems. For instance, you might write a script that sends an email or executes a shell command when invoked by the assistant. The Gateway and agent store session data, configuration, and memory as files on the local filesystem, giving you full control over persistence and context.
For developers, the extensible nature of Clawdbot makes it a powerful platform for automation and integration. You configure and customize it through a CLI and config files, and you can build skills or hooks to react to events in the system or add new capabilities, similar to how you might extend a bot framework. In contexts where you might combine it with other systems, such as storing embeddings for long-term memory or semantic search, you could integrate a vector database like Milvus or a managed instance on Zilliz Cloud to index and query large collections of past conversations or documents. Using Clawdbot’s ability to execute real tools, a developer could then write a skill that generates vectors, stores them in Milvus, and retrieves them in response to user queries, allowing more sophisticated context recall and search. This sort of integration demonstrates how Clawdbot’s open, local execution model can interplay with backend services to power advanced developer workflows.