DeerFlow
What is DeerFlow?
DeerFlow is an open-source harness for building and operating long-horizon AI agent systems. It combines agent orchestration with memory, tools, skills, subagents, context management, and sandboxed execution. Developers and technical teams can use the Harness to create custom agent products, while the DeerFlow App provides a deployable web workspace and API gateway. Its main differentiator is the runtime environment for multi-step work rather than a simple chat interface, supporting research, coding, data analysis, report generation, and internal automation. DeerFlow is released under the MIT License and is designed for self-hosted deployment, although external model or infrastructure costs may still apply.
How to use DeerFlow?
1. Clone the DeerFlow repository and install the required Docker, Docker Compose, server, and model-provider prerequisites. 2. Configure the application with your preferred language-model credentials, runtime settings, tools, skills, and optional integrations. 3. Start the services, open the web workspace, and submit a task to generate research, code, reports, files, or other agent-created outputs.
DeerFlow's Core Features
Long-Horizon Agent Runtime: Coordinate multi-step tasks that may run from minutes to hours without reducing the workflow to a single chat turn.
Subagent Orchestration: Break complex assignments into parallel or delegated subtasks to support broader agent workflows.
Sandboxed Execution: Let agents work with files and run code or commands inside configurable execution environments.
Persistent Memory: Retain selected context and recurring information across sessions to support more consistent agent behavior.
Extensible Skills: Load task-specific capabilities and workflows without rebuilding the complete agent runtime.
Tool and MCP Support: Connect agents to external tools and MCP servers through configurable extensions.
Multi-Model Configuration: Configure supported language-model providers to match project requirements and deployment preferences.
Web Conversation Workspace: Manage threads, file uploads, custom agents, skill selection, streaming responses, and generated artifacts in a browser interface.
Artifact Viewer: Preview and download files and outputs created during agent execution from the application workspace.
Gateway API: Integrate the application into other systems through its FastAPI-based REST gateway and LangGraph-compatible runtime.
DeerFlow's Use Cases
- #1
Research teams conducting multi-source investigations that require planning, web search, source synthesis, and structured reports
- #2
Developers building custom AI agent products with reusable tools, skills, memory, subagents, and sandboxed execution
- #3
Data analysts using an AI agent to run code, inspect files, process information, and produce analysis artifacts
- #4
Operations teams automating internal workflows that involve multiple steps, documents, tools, and persistent context
- #5
Product teams deploying a self-hosted conversational workspace with custom agents, file uploads, threads, and streaming responses
- #6
Engineering teams creating coding agents that can work with files, execute commands, and return generated artifacts
- #7
Organizations that need greater control over agent infrastructure, configuration, model providers, and stored conversation data
Frequently Asked Questions
Analytics of DeerFlow
Monthly Visits Trend: Jun 2025 - Aug 2026
Traffic Sources
Top Regions
| Region | Traffic Share |
|---|---|
| China | 36.16% |
| United States | 13.52% |
| Hong Kong | 7.14% |
| France | 6.84% |
| Singapore | 5.42% |
Top Keywords
| Keyword | Traffic | CPC |
|---|---|---|
| deerflow | 15.0K | $4.18 |
| deerflow 2.0 | 2.4K | $1.32 |
| deer flow | 680 | $4.51 |
| deer-flow | 310 | $3.83 |
| deep agent | 2.2K | $0.80 |
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