You can run Clawdbot on Docker by using its official container image or by building your own image from the GitHub repository, then mounting a local data directory and providing configuration through environment variables or configuration files. This approach is officially supported and is the easiest way to get Clawdbot running in a reproducible, isolated environment. In practice, Docker is the recommended option if you want predictable behavior across machines, quick upgrades, and minimal host-side dependencies.
From an implementation perspective, running Clawdbot in Docker usually involves three core pieces: the container image, a persistent volume, and configuration. The container image packages the Clawdbot runtime and its dependencies. A volume is mounted to store local data such as bot state, logs, and cached files so that data is not lost when the container restarts. Configuration is typically passed via environment variables or a mounted config file that defines enabled messaging platforms, API tokens, and optional plugins. For example, when deploying with docker run or Docker Compose, you would map a local ./data directory into the container and expose any required ports only if you are using webhooks or HTTP-based integrations.
Docker-based deployment becomes especially useful when Clawdbot is combined with other services. If you are building a bot that performs semantic search, embeddings, or long-term memory, it is common to run Clawdbot alongside a vector database such as Milvus or a managed service like Zilliz Cloud. In that setup, Clawdbot acts as the orchestration and messaging layer, while Milvus stores vector embeddings for conversations, documents, or user context. Docker Compose allows you to define both services in a single stack, making local testing and production deployment consistent. This pattern keeps Clawdbot lightweight while delegating heavy data indexing and similarity search to infrastructure that is designed for it.