NanoClaw vs OpenHands
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A small, container-isolated AI assistant that connects to chat apps and runs on Anthropic's Agent SDK.
Open source · MIT
A self-hosted control panel that runs AI coding agents on your machine, in Docker, or in the cloud.
The short answer
Choose NanoClaw if you need…
- Developers who want a small codebase they can read and change
- People who want an assistant on chat apps with container-level isolation
- Users comfortable working with Claude Code to customize their fork
- Anyone who wants separate agents with separate memory per channel
Choose OpenHands if you need…
- Developers who want coding agents running on a server while their laptop is off
- Teams that want a shared agent server for tasks like code review or dependency updates
- People who want to connect agents to Slack, GitHub, or Linear through automations
- Users who want to choose their own LLM provider
Side-by-side
| NanoClaw | OpenHands | |
|---|---|---|
| Type | AI model / agent | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 31.1k | 92.5k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | TypeScript | TypeScript |
| Last code update | 4 days ago | 4 days ago |
What each one is
NanoClaw
NanoClaw is an open-source AI assistant you run on your own machine. It connects to messaging apps such as WhatsApp, Telegram, Slack, Discord and others, and each agent runs in its own Linux container so it can only see files you explicitly mount. The README describes it as a smaller alternative to OpenClaw, designed to be read and modified as code rather than configured through many settings.
OpenHands
Agent Canvas is a self-hosted control center for AI coding agents. You start conversations with agents and set up automations, such as generating reports that publish to Slack or breaking GitHub issues into tasks. It runs on your own machine by default and can also connect to agents running in Docker, on virtual machines, or on OpenHands Cloud or Enterprise infrastructure. It can run the open source OpenHands agent or third-party agents such as Claude Code, Codex, and Gemini CLI.
Best for
NanoClaw
- Developers who want a small codebase they can read and change
- People who want an assistant on chat apps with container-level isolation
- Users comfortable working with Claude Code to customize their fork
- Anyone who wants separate agents with separate memory per channel
OpenHands
- Developers who want coding agents running on a server while their laptop is off
- Teams that want a shared agent server for tasks like code review or dependency updates
- People who want to connect agents to Slack, GitHub, or Linear through automations
- Users who want to choose their own LLM provider
Strengths
NanoClaw
- Agents run in containers and only see explicitly mounted files, per the README
- The codebase is described as small, with one process and a handful of files
- Supports many channels and can use Claude, Codex, OpenCode, Cursor or local Ollama models
OpenHands
- Self-hosted, so you control where agents run and where your work is stored
- Supports several agents and backends from one frontend
- Released under the MIT license with a large public community (about 92,000 stars)
Not ideal for
NanoClaw
- Non-technical users who want a finished app with no setup, since install runs shell scripts and may require Docker and Node
- Users on native Windows, since the README lists macOS, Linux and WSL2 for containers
- People who expect a monitoring dashboard or debugging UI, which the README says is intentionally absent
OpenHands
- People who need strong isolation by default: the quickstart without a sandbox gives the agent full access to your filesystem
- Non-technical users who are not comfortable with Node.js, npm, Docker, or the command line
- Anyone who needs a fully hosted service with no setup, since the README centers on self-hosting
Trade-offs
NanoClaw
- Relies on Claude Code and Anthropic's Agent SDK for its default workflow, so it assumes that stack
- Setup and customization depend on Claude Code, and the README says there is no monitoring UI
- Some features, such as the credential gateway and prebuilt image, involve a third-party gateway or optional account
OpenHands
- Running without a sandbox gives the agent full access to your filesystem, so the Docker options need careful setup
- Multiple features depend on configuring backends, LLM keys, and integrations, which takes effort
- The README is truncated and does not fully document security hardening or every integration; see the linked docs
Getting started
NanoClaw
Clone the repository
git clone https://github.com/nanocoai/nanoclaw.git nanoclaw-v2Enter the directory
cd nanoclaw-v2OpenHands
Install globally with npm (no sandbox)
npm install -g @openhands/agent-canvasStart the full local stack
agent-canvasFrequently asked questions
What is the difference between NanoClaw and OpenHands?
NanoClaw: A small, container-isolated AI assistant that connects to chat apps and runs on Anthropic's Agent SDK. OpenHands: A self-hosted control panel that runs AI coding agents on your machine, in Docker, or in the cloud. NanoClaw suits developers who want a small codebase they can read and change, while OpenHands suits developers who want coding agents running on a server while their laptop is off.
Are NanoClaw and OpenHands free?
NanoClaw: Free and open source. OpenHands: Free and open source.
Is NanoClaw or OpenHands more popular?
OpenHands has more GitHub stars (92.5k) than NanoClaw (31.1k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to NanoClaw and OpenHands?
Other options people compare them with include OpenClaw, Edict, /brag, Hermes Agent, Ponytail.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.