DeerFlow 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
An open-source agent framework that coordinates sub-agents, memory, and sandboxes for long, multi-step tasks.
Open source · MIT
A self-hosted control panel that runs AI coding agents on your machine, in Docker, or in the cloud.
Our verdict
DeerFlow is an open-source agent framework for developers and researchers who want to build or experiment with AI agents that split work across sub-agents, keep memory, and run code in sandboxes on long tasks. OpenHands is a self-hosted control panel for running AI coding agents on your own machine, in Docker, or in the cloud, and connecting them to tools like Slack and GitHub. They overlap in being free, MIT-licensed, and self-hosted, but DeerFlow is a harness to build on while OpenHands is an app to run coding agents day to day.
Pick DeerFlow if you are a developer or researcher who wants to experiment with multi-agent workflows and long-running memory-based investigations on your own infrastructure.
Pick OpenHands if you want a ready-to-run control panel to direct coding agents on a server or laptop and connect them to Slack, GitHub, or Linear.
| Key difference | DeerFlow | OpenHands |
|---|---|---|
| Type | Agent framework (harness) for multi-step AI tasks | Self-hosted app/control panel for coding agents |
| Setup difficulty | Advanced; Docker or local dev plus API keys | Intermediate; install via package manager, configure backends |
| Best at | Sub-agents, long-term memory, and sandboxed execution | Running and coordinating coding agents with integrations |
| Security posture | README warns improper deployment can introduce risks | Quickstart without a sandbox gives full filesystem access |
| Community size | About 84.7k GitHub stars | About 92.2k GitHub stars |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose DeerFlow if you need…
- Developers who want to experiment with multi-agent workflows on their own infrastructure
- Researchers running long, multi-step investigations that need memory and sub-agents
- Teams already using LangGraph or LangChain who want a reference agent harness
- Users who are comfortable configuring their own LLM API keys and running services locally or in Docker
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
| DeerFlow | OpenHands | |
|---|---|---|
| Type | AI model / agent | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 84.9k | 92.5k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Run with Docker | Install with a package manager |
| Main language | Python | TypeScript |
| Last code update | 4 days ago | 4 days ago |
What each one is
DeerFlow
DeerFlow is an open-source 'super agent harness' that runs an AI lead agent which can split work across sub-agents, keep long-term memory, and execute code or tasks inside sandboxes. It is built to handle tasks that may take minutes to hours, such as research, coding, and content creation. The README describes it as extensible through skills and tools.
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
DeerFlow
- Developers who want to experiment with multi-agent workflows on their own infrastructure
- Researchers running long, multi-step investigations that need memory and sub-agents
- Teams already using LangGraph or LangChain who want a reference agent harness
- Users who are comfortable configuring their own LLM API keys and running services locally or in Docker
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
DeerFlow
- Open-source under the MIT license, so the code can be inspected and modified
- Combines sub-agents, memory, sandboxes, and skills in one harness
- Documented deployment options including Docker and local development
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
DeerFlow
- Non-technical users looking for a ready-to-use consumer app, since setup involves configuration, Docker or local development, and API keys
- Anyone who needs a fully managed, hosted service with no operational work
- Security-sensitive deployments without reviewing the README's security notice, because the gateway admin role is equivalent to code execution
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
DeerFlow
- Setup requires multiple prerequisites and configuration, which the README describes as non-trivial
- The README includes a security notice warning that improper deployment can introduce risks
- The README is long and truncated here, so some details of setup and behavior could not be verified
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
DeerFlow
Clone the repository
git clone https://github.com/bytedance/deer-flow.gitOpenHands
Install globally with npm (no sandbox)
npm install -g @openhands/agent-canvasStart the full local stack
agent-canvasFrequently asked questions
Should I use DeerFlow or OpenHands?
DeerFlow is an open-source agent framework for developers and researchers who want to build or experiment with AI agents that split work across sub-agents, keep memory, and run code in sandboxes on long tasks. OpenHands is a self-hosted control panel for running AI coding agents on your own machine, in Docker, or in the cloud, and connecting them to tools like Slack and GitHub. They overlap in being free, MIT-licensed, and self-hosted, but DeerFlow is a harness to build on while OpenHands is an app to run coding agents day to day. Pick DeerFlow if you are a developer or researcher who wants to experiment with multi-agent workflows and long-running memory-based investigations on your own infrastructure. Pick OpenHands if you want a ready-to-run control panel to direct coding agents on a server or laptop and connect them to Slack, GitHub, or Linear.
What is the difference between DeerFlow and OpenHands?
DeerFlow: An open-source agent framework that coordinates sub-agents, memory, and sandboxes for long, multi-step tasks. OpenHands: A self-hosted control panel that runs AI coding agents on your machine, in Docker, or in the cloud. DeerFlow suits developers who want to experiment with multi-agent workflows on their own infrastructure, while OpenHands suits developers who want coding agents running on a server while their laptop is off.
Are DeerFlow and OpenHands free?
DeerFlow: Free and open source. OpenHands: Free and open source.
Which is easier to set up, DeerFlow or OpenHands?
OpenHands is easier to get started with (rated intermediate); DeerFlow is rated advanced and usually needs more setup.
Is DeerFlow or OpenHands more popular?
OpenHands has more GitHub stars (92.5k) than DeerFlow (84.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DeerFlow and OpenHands?
Other options people compare them with include LangGraph, CrewAI, Edict, SWE-agent, Open SWE.
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.