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NanoClaw is an open-source personal AI assistant that connects messaging apps to AI agents running in Docker containers on a computer you manage. A Node.js host routes messages between the messaging channel and each agent session; Docker containers, explicit file mounts, and session-specific state are central parts of its documented design.
What is NanoClaw?
NanoClaw is a self-hosted, customizable assistant for people who want to message an AI agent through selected channels and manage the software themselves. Rather than shipping every possible integration in one large installation, the project is designed to be forked and extended with the channels, providers, and tools a user chooses. The official site describes it as a lightweight personal agent, and its repository emphasizes a small codebase and on-demand integrations.
The official website reported more than 30.9k GitHub stars when observed on October 7, 2026. That is a site-reported snapshot, not a stable measure of current popularity or an independent assessment of the software.
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A Node.js host receives and routes messages. It uses SQLite queues to pass inbound messages to an agent container and outbound replies back to the host for delivery through the relevant channel adapter. Each active session has its own container and message databases. Sessions within the same agent group share that group’s workspace and memory; separate groups maintain separate state.
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- A connected messaging adapter receives a message and passes it to the NanoClaw host.
- The host places the inbound message in a SQLite queue for the relevant session.
- The session’s Docker container runs the agent and its selected provider against the message.
- The agent places its response in an outbound SQLite queue, which the host reads and sends through the channel adapter.
The official site describes the container runtime as Bun plus the selected provider. The repository README identifies Claude as the default provider and describes alternatives that can be installed or selected for agent groups. Check the project’s current documentation for the available provider options and setup details.
Which messaging channels and AI providers can you use?
Project materials list potential messaging integrations including WhatsApp, Telegram, Slack, Discord, iMessage, Signal, Matrix, and Google Chat. Integrations are installed on demand, so a channel appearing in project documentation does not mean it is enabled in a fresh checkout. The supported list and installation instructions can change; consult the current repository before choosing a channel.
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Providers and tools are also modular. This lets a user keep a fork focused on the integrations they actually need, but it means setup may involve adding or configuring components rather than simply enabling every feature from a single prebuilt package.
What does NanoClaw require?
The current official site lists macOS and Linux, plus Windows through WSL2, with Docker as the supported container runtime. Its documented requirements include Node.js, pnpm, Docker, and credentials for an AI provider. The exact version requirements and installation steps are subject to change, so use the current project instructions rather than assuming an older command or dependency list still applies.
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The repository README gives a quick-start path: clone the official repository and run bash nanoclaw.sh. It says the installer can install missing dependencies, prepare the container, and pair an initial channel. This is a terminal-based setup, and users should expect to understand Git, manage Docker, and handle provider credentials. NanoClaw is therefore more suitable for technically comfortable users than for someone seeking a fully managed assistant with no local setup.
Is NanoClaw secure?
NanoClaw’s security documentation presents Docker as its primary isolation boundary. Agents run unprivileged, receive only explicitly mounted paths, and use session-specific state. The documentation also describes an allowlist and blocked-path checks for additional host directories. These are design claims, not proof that a particular deployment is secure: container isolation depends on configuration and implementation, and the cited project materials are not an independent security audit.
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The project treats incoming messages as potentially malicious or prompt-injected, regardless of who sent them. A message that appears to come from a trusted person should not automatically be treated as safe instructions for an agent.
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- Review every host directory you mount into an agent container, and give access only where it is needed.
- Keep host secrets and sensitive files out of folders agents can see.
- Control which senders can reach the assistant and consider what actions its tools can take.
- Read the current security documentation and verify the configuration you actually run before using NanoClaw with sensitive data.
How are provider credentials handled?
The current official site and repository security document describe OneCLI Agent Vault as the default route for outbound credentials, so raw provider credentials do not enter agent containers under that arrangement. The repository describes NanoClaw as defining an integration seam and installing a gateway through a separate skill; it does not describe NanoClaw itself as implementing the credential gateway. Check current installation and behavior details before relying on this setup.
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Is NanoClaw free?
The project’s official FAQ says NanoClaw is open source under the MIT license and that the software itself is free. Hosted AI providers may still charge for model usage, so the total cost depends in part on the provider and how much you use it. Review the provider’s current pricing and terms separately.
Who should choose NanoClaw?
NanoClaw is a reasonable fit for someone who wants a messaging-connected assistant they can run and customize, and who is prepared to work with a terminal, Git, Docker, and provider credentials. Its modular approach can suit users who prefer to add only the integrations they need.
It is a weaker fit for someone who wants a turnkey service, does not want to maintain software on a machine they control, or needs a security assurance that goes beyond a project’s documented design. When evaluating it against another agent system, compare the isolation boundary, channel and provider support, maintenance effort, customization expectations, credential handling, network access, and model costs. The available project materials do not establish a universal security or performance winner.
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