DeerFlow vs LangGraph
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 Python framework for building long-running agents that keep state and can resume after failures.
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 LangGraph if you need…
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
Side-by-side
| DeerFlow | LangGraph | |
|---|---|---|
| Type | AI model / agent | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 84.9k | 44.3k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Run with Docker | Install with a package manager |
| Main language | Python | Python |
| 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.
LangGraph
LangGraph is a low-level framework for building workflows and AI agents that keep track of their state over time. It is designed for tasks that run for a long stretch, can pause for human review, and need to pick up where they left off after an interruption. It is produced by LangChain Inc, but the README says it can be used without LangChain.
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
LangGraph
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
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
LangGraph
- Built-in durable execution that resumes workflows from where they stopped, per the README
- Supports human-in-the-loop checkpoints and both short-term and long-term memory
- Open source under the MIT license with extensive documentation and a free course
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
LangGraph
- Beginners who just want a ready-made agent: the README points to Deep Agents, a higher-level package built on LangGraph, for quicker starts
- Non-programmers, since it is a Python library that requires writing code
- JavaScript/TypeScript-only teams, who are pointed to the separate LangGraph.js project
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
LangGraph
- It is a low-level framework, so it has a steeper learning curve than higher-level tools
- Several of the most useful features (debugging, deployment) point to LangSmith, a separate product
- The README is truncated and does not show full API details, so the depth of specific features is hard to verify from it alone
Getting started
DeerFlow
Clone the repository
git clone https://github.com/bytedance/deer-flow.gitFrequently asked questions
What is the difference between DeerFlow and LangGraph?
DeerFlow: An open-source agent framework that coordinates sub-agents, memory, and sandboxes for long, multi-step tasks. LangGraph: A Python framework for building long-running agents that keep state and can resume after failures. DeerFlow suits developers who want to experiment with multi-agent workflows on their own infrastructure, while LangGraph suits python developers building multi-step ai agents that must survive failures or long waits.
Are DeerFlow and LangGraph free?
DeerFlow: Free and open source. LangGraph: Free and open source.
Which is easier to set up, DeerFlow or LangGraph?
LangGraph is easier to get started with (rated intermediate); DeerFlow is rated advanced and usually needs more setup.
Is DeerFlow or LangGraph more popular?
DeerFlow has more GitHub stars (84.9k) than LangGraph (44.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DeerFlow and LangGraph?
Other options people compare them with include CrewAI, OpenHands, 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.