DeepCode 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
Open-source AI coding agent that edits code, runs tools and tests, and resumes long tasks.
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 DeepCode if you need…
- Developers who want an open-source agent they can run on their own project
- People working on multi-step coding tasks that need verification with tests or builds
- Users who want to inspect diffs and task artifacts rather than accept suggestions blindly
- Researchers interested in agent harness and multi-agent orchestration ideas tied to the linked paper
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
| DeepCode | OpenHands | |
|---|---|---|
| Type | AI model / agent | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 16.8k | 92.5k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | See the README | Install with a package manager |
| Main language | Python | TypeScript |
| Last code update | 4 days ago | 4 days ago |
What each one is
DeepCode
DeepCode is an open-source coding agent meant for real software projects. You give it a small change or a larger goal, and it works through the project: reading code, editing files, running commands and tests, and checking results. The README says it can continue after an interruption, restart, or model switch. It offers terminal (TUI), desktop, and web clients that share one local service.
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
DeepCode
- Developers who want an open-source agent they can run on their own project
- People working on multi-step coding tasks that need verification with tests or builds
- Users who want to inspect diffs and task artifacts rather than accept suggestions blindly
- Researchers interested in agent harness and multi-agent orchestration ideas tied to the linked paper
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
DeepCode
- Covers terminal, desktop, and web clients over one shared local service
- README describes verification through tests, builds, diagnostics, and diffs
- MIT licensed and actively pushed to, with a linked research paper
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
DeepCode
- Non-programmers who want a no-code tool; the README assumes familiarity with software projects and terminals
- Anyone who cannot run Python 3.12 or newer, since the README lists that requirement
- Teams needing documented enterprise support or guarantees; the README does not describe any
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
DeepCode
- README is truncated and the install and usage details were not fully visible, so setup specifics are uncertain
- Describes many features (Skills, Goals, Automations, memory) whose depth is not explained in the visible text
- Agent behavior depends on models you configure; the README excerpt does not detail supported providers
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
DeepCode
Clone the repository
git clone https://github.com/HKUDS/DeepCodeOpenHands
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 DeepCode and OpenHands?
DeepCode: Open-source AI coding agent that edits code, runs tools and tests, and resumes long tasks. OpenHands: A self-hosted control panel that runs AI coding agents on your machine, in Docker, or in the cloud. DeepCode suits developers who want an open-source agent they can run on their own project, while OpenHands suits developers who want coding agents running on a server while their laptop is off.
Are DeepCode and OpenHands free?
DeepCode: Free and open source. OpenHands: Free and open source.
Is DeepCode or OpenHands more popular?
OpenHands has more GitHub stars (92.5k) than DeepCode (16.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DeepCode and OpenHands?
Other options people compare them with include GenericAgent, One API, LlamaGPT, codex-chatgpt-web, wechat-bot.
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.