DeerFlow
FreeOpen-source SuperAgent harness for research, coding, and creation.
By Tanmay Verma, Founder · Last verified 06 Jul 2026
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Added 7d ago
69/100MonitorIn short
DeerFlow — Open-source SuperAgent harness for research, coding, and creation. Best for Developers building custom AI agents with full control, Power users automating complex multi-step research and coding tasks, Open-source enthusiasts wanting self-hosted agent orchestration. Free to use.
Is DeerFlow actually worth it?
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Editorial Verdict
Best for
Developers building custom AI agents with full controlPower users automating complex multi-step research and coding tasksOpen-source enthusiasts wanting self-hosted agent orchestrationAI researchers experimenting with multi-agent workflowsUsers needing a sandboxed environment for safe code execution
Not ideal for
Non-technical users seeking a no-code solutionTeams needing managed cloud hosting and SLAsUsers who require out-of-the-box integrations with paid services (e.g., Slack, Zapier)Those wanting a polished UI with minimal setup effort
An open-source choice for developers who need full control over multi-agent workflows with sandboxed execution. Its 2.0 RC adds memory and planning, elevating it beyond simple research agents. Best for technical users willing to self-host; if you need a managed cloud experience, consider alternatives like AutoGPT or CrewAI cloud.
Skip DeerFlow if Skip DeerFlow if you need a managed AI agent platform with a polished UI, cloud hosting, and official integrations with SaaS tools.
Compare with: DeerFlow vs Marvin, DeerFlow vs Zhipu GLM, DeerFlow vs Undermind
Last verified: July 2026
What's new in DeerFlow
Checked 4 days agoAcross the latest 3 updates: 1 launch and 2 changelog entries.
LaunchBlog·Apr 8Newest
DeerFlow 2.0 RC
DeerFlow 2.0 release candidate announced — open source MIT licensed, evolving from deep research to full-stack SuperAgent with memory, planning, and persistent sandbox.
ChangelogBlog·Apr 6
周报 - 2026-04-06
Chinese weekly update for DeerFlow project, covering recent developments and community contributions.
ChangelogBlog·Apr 6
Weekly - 2026-04-06
English weekly update for DeerFlow project, detailing progress on 2.0 features and community activities.
Viability Score
69/100
MonitorHow likely is DeerFlow to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
40
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Multi-agent orchestration for deep research
- Long-running task execution with planning and sub-tasking
- Long-term and short-term memory for context
- Docker-based sandbox with Browser, Shell, File, MCP, VSCode Server
- Progressive loading of Agent Skills
- Custom skill files via SKILL.md
- Multi-model support: Doubao, DeepSeek, OpenAI, Gemini
- File persistence and management within sandbox
- Code generation and execution
- Video generation from text/scenes
- Image generation from text/scenes
- Web scraping and data collection
- Summarization and report generation
- Subagents for complex tasks
- Self-hosted with full data control
About DeerFlow
FreeAdvancedNo APICLI
DeerFlow is an open-source, full-stack SuperAgent harness that researches, codes, and creates. It leverages sandboxes, memories, tools, skills, and subagents to handle tasks ranging from quick research to hours-long complex workflows. Designed for developers and power users, it provides a persistent Docker-based sandbox environment with a built-in filesystem, allowing agents to run long-running tasks, execute commands, and manage files securely. The platform supports context engineering with long- and short-term memory, enabling the agent to understand user preferences over time. A library of Agent Skills loads progressively, and users can extend capabilities with custom skill files. DeerFlow 2.0 RC, released in April 2026, marks a significant evolution from a deep research agent into a full-stack SuperAgent. New features include long/short-term memory, long task running with planning and sub-tasking, extensible skills and tools, a persistent sandbox filesystem, and flexible multi-model support (Doubao, DeepSeek, OpenAI, Gemini, etc.). The agent can generate videos and images from text, write and execute code, and perform deep research across multiple sources. DeerFlow is free and open-source under the MIT License, emphasizing self-hosting and full control. It distinguishes itself from other agent frameworks by offering a self-hosted, sandboxed environment with progressive skill loading. Unlike many research-only tools, DeerFlow handles diverse tasks from coding to video generation, and its modular architecture makes it highly extensible. The active community and frequent updates (including a Chinese-language weekly) suggest a vibrant development pace.
Behind the Verdict
DeerFlow's strength is its modular, self-hosted architecture. The long/short-term memory and persistent sandbox allow agents to learn from past interactions and run complex tasks without timeouts. The progressive skill loading is efficient—only the skills needed are loaded, keeping the agent responsive. Multi-model support (Doubao, DeepSeek, OpenAI, Gemini) gives flexibility in choosing the best model for each task. However, as a release candidate, stability may vary. It requires Docker and self-hosting, which is a barrier for non-technical users. The lack of a cloud version and official integrations with SaaS tools means you must build your own connections. For developers and AI researchers, it's a powerful sandbox for experimentation. For teams that need a plug-and-play solution, look at managed alternatives like Relevance AI or Browserbase.
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Real-world workflow fit
Concrete scenarios for the personas DeerFlow actually fits — and what changes day-one when you adopt it.
Solo developer building a research agent
You want an agent that can scrape web content, summarize, and generate a report with code and images, all in one sandbox.
Outcome: Deploy DeerFlow via Docker, write a SKILL.md for your custom research pipeline, and run a task that scrapes articles, generates charts, and compiles a PDF report with AI-generated images.
AI researcher testing multi-agent coordination
You need to test how multiple specialized agents (e.g., a researcher, a coder, a writer) collaborate on a complex project.
Outcome: Use DeerFlow's subagent orchestration to have a research agent find data, a coder agent write analysis scripts, and a writer agent produce a final summary — all within the persistent sandbox.
Use Cases
- Conduct deep research on any topic, summarizing findings into comprehensive reports.
- Generate a video based on a specific scene from a novel.
- Create comic strips explaining complex AI architectures (e.g., MOE) to teenagers.
- Perform exploratory data analysis on datasets and identify key factors.
- Scrape and summarize podcast appearances of a public figure.
- Deploy a frontend design agent that can write and execute code.
Models Under the Hood
DoubaoDeepSeekOpenAI (GPT-4, etc.)Gemini
as of 2026-07-06
Limitations
- DeerFlow is currently in Release Candidate (2.0 RC), so stability may vary.
- It is self-hosted, requiring Docker setup and technical maintenance.
- The platform does not provide a cloud-hosted version, so users must manage their own infrastructure.
as of 2026-07-06
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published DeerFlow tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (MIT)
$0/mo
Ideal for
Developers and researchers who want full control, self-hosting, and ability to customize the agent for any use case.
What this tier adds
Starting tier — free, MIT licensed, all features included; no premium tiers exist.
Where the pricing makes sense
The company stage and team size where DeerFlow's pricing actually pencils out — and where peers do it cheaper.
DeerFlow is free (MIT) — you pay only for infrastructure and API usage. For developers who already have compute resources, it's cheaper than managed platforms like Relevance AI ($20+/mo). Non-technical users who would need to hire a DevOps person may find managed alternatives more cost-effective.
Setup time & first value
How long it actually takes to get something useful out of DeerFlow — broken out by persona, not the marketing-page minute.
For developers familiar with Docker, setup takes about 30 minutes to deploy the sandbox and start using the agent. Custom skills or integrations may add a few hours depending on complexity.
Switching to or from DeerFlow
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From AutoGPT: Move your custom tools and skill definitions to DeerFlow's SKILL.md format; the multi-model support makes it easy to reuse prompts.
- →From CrewAI: Replicate your agent roles and tasks using DeerFlow's subagent configuration in JSON or SKILL.md files.
Migrating out
- ↗To AutoGPT: Export your skill files and adapt them to AutoGPT's plugin structure; DeerFlow's open nature makes transition straightforward.
- ↗To CrewAI: Translate DeerFlow's subagent logic into CrewAI's agent definitions; the conceptual model is similar.
Resources & Guides
Frequently Asked Questions
Tools that pair well with DeerFlow
Common stack mates teams adopt alongside DeerFlow, with the specific reason each pairing earns its keep.
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