ByteDance open-sourced DeerFlow 2.0 on February 27, 2026 — a full SuperAgent harness rebuilt on LangGraph 1.0 that shipped with persistent memory, sandboxed execution, file system access, skills, and sub-agent support baked in. It hit GitHub Trending #1 within 24 hours and crossed 25,000+ stars in days.

If you want to try a production-grade agent framework without building the plumbing yourself, DeerFlow 2.0 is one of the most complete starting points available right now. Here’s how to get it running locally.

What You’ll Need

  • Python 3.11 or 3.12 (3.10 works but 3.11+ recommended)
  • Git
  • Docker (required for sandbox execution)
  • An API key for at least one LLM provider (OpenAI, Anthropic, or local via Ollama)

Step 1 — Clone the Repository

git clone https://github.com/bytedance/deer-flow
cd deer-flow

Step 2 — Set Up Your Python Environment

DeerFlow uses Poetry for dependency management. If you don’t have Poetry installed:

pip install poetry

Then install dependencies:

poetry install

Or if you prefer a plain virtualenv:

python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Step 3 — Configure Your LLM Provider

Copy the example environment file:

cp .env.example .env

Then edit .env and add your API credentials. For OpenAI:

OPENAI_API_KEY=your-key-here
OPENAI_MODEL=gpt-4o

For Anthropic Claude:

ANTHROPIC_API_KEY=your-key-here
ANTHROPIC_MODEL=claude-sonnet-4-6

DeerFlow supports local models via Ollama too — set OLLAMA_BASE_URL and OLLAMA_MODEL if you want to run fully offline.

Step 4 — Start the Docker Sandbox

DeerFlow’s sandboxed code execution requires Docker. Make sure Docker Desktop (or Docker Engine on Linux) is running, then:

docker compose up -d sandbox

This spins up the isolated execution environment where your agents will run code. It’s separate from your main process — agents generate and execute code inside the sandbox without touching your host filesystem directly.

Step 5 — Run Your First Agent

Start DeerFlow’s development server:

poetry run python -m deerflow.server

Or with plain Python after activating your venv:

python -m deerflow.server

The server starts on http://localhost:8000. Open the web UI in your browser — DeerFlow ships with a built-in chat interface for testing agents interactively.

What DeerFlow Gives You Out of the Box

Once running, you’ll have access to DeerFlow’s core capabilities:

Persistent Memory. Agents remember context across sessions. You don’t need to re-explain your setup every time — DeerFlow stores structured memories in a local SQLite database by default.

Sub-agents. You can define a multi-agent workflow where a coordinator agent delegates to specialists. DeerFlow’s LangGraph backbone makes the coordination graph explicit and debuggable.

Skills. Pre-built capability modules (web search, code execution, file operations) that agents can invoke. You can also write custom skills and register them with a few lines of code.

Sandboxed Execution. When an agent generates code to run, it executes in the Docker sandbox — not on your host. This is a significant safety improvement over frameworks that run agent-generated code directly.

Defining a Custom Agent

DeerFlow uses YAML for agent configuration. Here’s a minimal example:

# agents/my_researcher.yaml
name: researcher
description: Researches topics and summarizes findings
model: gpt-4o
memory: true
skills:
  - web_search
  - file_write
instructions: |
  You are a focused research agent. When given a topic, search for
  recent information, synthesize key findings, and write a structured
  summary to a file. Be concise and cite your sources.

Load it in your Python code:

from deerflow import AgentRunner

runner = AgentRunner.from_yaml("agents/my_researcher.yaml")
result = runner.run("Research the latest developments in agentic AI frameworks")
print(result.output)

Troubleshooting Common Issues

Docker sandbox won’t start: Make sure Docker is running (docker ps should work) and that port 8001 isn’t already in use by another process.

API key errors: Double-check your .env file is in the project root and that you’ve activated the correct virtualenv if not using Poetry.

Memory not persisting: DeerFlow’s default SQLite memory store is in .deerflow/memory.db. Make sure you’re running from the same project directory across sessions.

LangGraph version conflicts: DeerFlow 2.0 requires LangGraph 1.0+. If you’re upgrading from a project that used LangGraph 0.x, pin your version in requirements and test carefully.

Sources

  1. DeerFlow 2.0 Official GitHub Repository
  2. VentureBeat — ByteDance DeerFlow 2.0 Coverage

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260324-0800

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