DeerFlow — a super agent harness for long research and build tasks
Overview
DeerFlow 2.0 (repository bytedance/deer-flow) is a ground-up rewrite. It shares no code with version 1, the original "Deep Research" framework, which now lives on the main-1.x branch. Do not mix instructions for the two.
The 2.0 harness has a lead agent that can delegate work to sub-agents through a task tool, keeps long-term memory, runs files and shell commands inside a sandbox, and loads capabilities from skills (the repository ships deep-research, data-analysis, consulting-analysis, ppt-generation, podcast-generation and others under skills/public/). It serves a web UI and a Gateway HTTP API on one port (2026 by default), and can also be embedded as a Python library.
An agent can realistically: clone and bootstrap it, write config.yaml, pick a sandbox, start and stop the services, run make doctor, and drive it through DeerFlowClient or the Gateway API. It cannot supply model API keys; the user creates those with their provider.
Instructions
Installation with Docker (recommended)
Needs Docker with Compose v2.24 or newer (docker compose version). The repository also ships Install.md, written for coding agents; it prefers Docker and stops with the next command rather than starting services.
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow
make setup # interactive wizard: provider, optional web search, sandbox and bash preferences
make docker-init # pulls the sandbox image (once)
make docker-start # development stack with hot reload
make docker-logs
The wizard writes a minimal config.yaml and puts keys in .env. If you only want the full template, run make config instead; it aborts when config.yaml already exists. Open http://localhost:2026 once the stack is up. The first visit goes through /setup to create the admin account; do this immediately on any host that is not loopback-only.
For a long-running server use the production stack:
make up # builds images, waits for the Gateway /health, then reports success
make down # stops and removes the containers
Installation without Docker
make check # verifies Node.js 22+, pnpm, uv and nginx
make install # backend and frontend dependencies
make dev # all services with hot reload, http://localhost:2026
On Windows run these from Git Bash. If a cold start is slow, raise the Gateway wait: DEER_FLOW_GATEWAY_STARTUP_TIMEOUT=240 make dev. make stop stops local services. Never run make clean unless the user wants local data gone: it deletes the database, users, threads, uploads, memory and secrets under backend/.deer-flow.
Configure a model
config.yaml in the project root has a models: list; the template config.example.yaml carries commented examples for several providers. A minimal OpenAI-compatible entry:
models:
- name: gpt-4
display_name: GPT-4
use: langchain_openai:ChatOpenAI
model: gpt-4
api_key: $OPENAI_API_KEY
request_timeout: 600.0
max_retries: 2
context_window: 128000
supports_vision: true
The $OPENAI_API_KEY form reads the variable from the environment or from .env; never paste a key into config.yaml. Local Ollama models need langchain_ollama:ChatOllama, not the OpenAI class. Backend processes pick up config.yaml changes on the next config access, so no restart is needed for model edits. After upgrading the repository, make config-upgrade merges new fields from the template into your file.
Move config and runtime state with DEER_FLOW_CONFIG_PATH, DEER_FLOW_PROJECT_ROOT, DEER_FLOW_HOME and DEER_FLOW_ENV_FILE.
Choose a sandbox
The sandbox: block selects where the agent runs commands:
sandbox:
use: deerflow.sandbox.local:LocalSandboxProvider
allow_host_bash: false
LocalSandboxProvider is the default and is not an isolation boundary; host bash stays disabled unless allow_host_bash: true, which is acceptable only for a single trusted user. For isolation switch to the container provider, use: deerflow.community.aio_sandbox:AioSandboxProvider, and pin an explicit image version (the template recommends all-in-one-sandbox:1.11.0 from the Volcengine registry because its :latest tag is frozen on an old build). Extra host folders are exposed with mounts entries containing host_path, container_path and read_only. Run make setup-sandbox to pre-pull the image.
Use it from Python
The embedded client runs the agent in-process with the same response shapes as the Gateway:
from deerflow.client import DeerFlowClient
client = DeerFlowClient() # or DeerFlowClient(config_path="config.yaml")
reply = client.chat("Compare managed Postgres offerings on price", thread_id="pg-comparison")
for event in client.stream("Continue with a recommendation", thread_id="pg-comparison"):
if event.type == "messages-tuple" and event.data.get("type") == "ai":
print(event.data["content"])
print(client.list_models())
print(client.list_skills())
Thread IDs must match ^[A-Za-z0-9_-]{1,64}$. Reuse the same thread_id to continue a conversation.
Drive it from Claude Code
The repository ships a claude-to-deerflow skill that sends tasks to a running instance. Install it with npx skills add https://github.com/bytedance/deer-flow --skill claude-to-deerflow. Point it elsewhere with DEERFLOW_URL (default http://localhost:2026).
Diagnose problems
make doctor # checks config and system requirements, prints fix hints
make support-bundle # redacted issue summary plus draft, under .deer-flow/support-bundles/
The bundle excludes .env, raw messages and user files, but read it before sharing.
Examples
Example 1: bring up DeerFlow on a laptop with Docker
Request: "Set up DeerFlow here so I can try its deep research mode."
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow
docker compose version # confirm v2.24 or newer
make setup # the user answers the provider and key prompts
make doctor
make docker-init
make docker-start
Result: make doctor reports a valid config.yaml and the user's key variable name; the stack answers on http://localhost:2026, where the user creates the admin account. Report the URL and any missing key; do not open .env.
Example 2: scripted research run on a pinned model
Request: "Write a script that asks DeerFlow for a vendor comparison and saves the answer."
from pathlib import Path
from deerflow.client import DeerFlowClient
client = DeerFlowClient(config_path="config.yaml")
answer = client.chat(
"Research Grafana Cloud, Datadog and Honeycomb pricing for 40 services and write a one-page comparison.",
thread_id="observability-pricing",
)
Path("observability-pricing.md").write_text(str(answer))
Result: the lead agent searches the web with the configured search tool, may delegate to sub-agents, and returns the text, which the script saves. Run it with the backend environment (uv run --project backend python script.py from the repository root).
Guidelines
- DeerFlow executes commands and writes files. Its authors design it for a trusted local environment on 127.0.0.1; the Docker stack publishes on loopback only. To reach it from another machine set
BIND_HOST in .env only after adding an IP allowlist and an authenticating reverse proxy.
- Gateway admin is equivalent to code execution on the host (admins can register stdio MCP servers). Grant it accordingly.
- Keep
allow_host_bash: false unless the machine is single-user and trusted; prefer the container sandbox for anything shared.
- The default web search tool is DuckDuckGo and the default fetch tool is Jina, both configured under
tools:; search quality and rate limits depend on them.
- Production defaults to one Gateway worker. Multiple workers need Postgres, a Redis stream bridge and database-backed run events.
- Plan for 4 vCPU and 8 GB RAM at minimum for local use, more for a shared server; a local LLM is sized separately.
- Do not use this skill for DeerFlow 1.x or for a hand-written LangGraph pipeline; those are different codebases.