DeerFlow vs SWE-agent
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
An AI agent that tries to fix GitHub issues automatically, using the language model you choose.
The short answer
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 SWE-agent if you need…
- Researchers studying autonomous coding agents or running SWE-bench experiments
- Developers who want to experiment with an LLM attempting issue fixes in their own repositories
- Students learning how agent-computer interfaces work, since the README describes it as simple and hackable
- People who want a configurable agent controlled by a single YAML file
Side-by-side
| DeerFlow | SWE-agent | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 84.9k | 20.7k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Run with Docker | Clone and build from source |
| Main language | Python | Python |
| Last code update | 4 days ago | 10 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.
SWE-agent
SWE-agent takes a GitHub issue and lets a language model you pick (the README mentions GPT-4o and Claude Sonnet 4 as examples) use tools to try to fix it in a real repository. It is also described as usable for offensive cybersecurity (capture-the-flag) challenges and custom coding tasks. It is a research project from Princeton and Stanford, and its README says its main development has moved to the simpler mini-SWE-agent.
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
SWE-agent
- Researchers studying autonomous coding agents or running SWE-bench experiments
- Developers who want to experiment with an LLM attempting issue fixes in their own repositories
- Students learning how agent-computer interfaces work, since the README describes it as simple and hackable
- People who want a configurable agent controlled by a single YAML file
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
SWE-agent
- The README reports state-of-the-art results on SWE-bench among open-source projects
- Configuration is handled by a single YAML file and the README says it is fully documented
- MIT licensed with a public docs site, Slack, and a Codespaces option to try it in a browser
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
SWE-agent
- Teams wanting the project's actively recommended tool: the README says most development has moved to mini-SWE-agent, which it recommends instead
- Anyone needing a polished, finished product: the project is framed as research code
- People who cannot pay for or access a language model API, since the README assumes you bring your own model
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
SWE-agent
- The README says mini-SWE-agent has superseded it and is recommended instead going forward
- The EnIGMA cybersecurity mode currently points users to older version 0.7 rather than 1.0
- Results depend on the language model and API you supply; the README does not state costs or hardware needs
Getting started
DeerFlow
Clone the repository
git clone https://github.com/bytedance/deer-flow.gitSWE-agent
Clone the repository
git clone https://github.com/SWE-agent/SWE-agent.gitFrequently asked questions
What is the difference between DeerFlow and SWE-agent?
DeerFlow: An open-source agent framework that coordinates sub-agents, memory, and sandboxes for long, multi-step tasks. SWE-agent: An AI agent that tries to fix GitHub issues automatically, using the language model you choose. DeerFlow suits developers who want to experiment with multi-agent workflows on their own infrastructure, while SWE-agent suits researchers studying autonomous coding agents or running swe-bench experiments.
Are DeerFlow and SWE-agent free?
DeerFlow: Free and open source. SWE-agent: Free and open source.
Which is easier to set up, DeerFlow or SWE-agent?
SWE-agent is easier to get started with (rated intermediate); DeerFlow is rated advanced and usually needs more setup.
Is DeerFlow or SWE-agent more popular?
DeerFlow has more GitHub stars (84.9k) than SWE-agent (20.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DeerFlow and SWE-agent?
Other options people compare them with include LangGraph, CrewAI, OpenHands, Edict, 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.