An open-source SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skills and subagents, it handles different levels of tasks that could take minutes to hours.
Copy the command below to clone the repository to your machine.
English | 中文 | 日本語 | Français | Русский
On February 28th, 2026, DeerFlow claimed the 🏆 #1 spot on GitHub Trending following the launch of version 2. Thanks a million to our incredible community — you made this happen! 💪🔥
DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to do almost anything — powered by extensible skills.
https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18
[!NOTE]
DeerFlow 2.0 is a ground-up rewrite. It shares no code with v1. If you're looking for the original Deep Research framework, it's maintained on the1.xbranch — contributions there are still welcome. Active development has moved to 2.0.
Learn more and see real demos on our official website.
DeerFlow has newly integrated the intelligent search and crawling toolset independently developed by BytePlus--InfoQuest (supports free online experience)
If you use Claude Code, Codex, Cursor, Windsurf, or another coding agent, you can hand it the setup instructions in one sentence:
Help me clone DeerFlow if needed, then bootstrap it for local development by following https://raw.githubusercontent.com/bytedance/deer-flow/main/Install.md
That prompt is intended for coding agents. It tells the agent to clone the repo if needed, choose Docker when available, and stop with the exact next command plus any missing config the user still needs to provide.
Clone the DeerFlow repository
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow
Run the setup wizard
From the project root directory (deer-flow/), run:
make setup
This launches an interactive wizard that guides you through choosing an LLM provider, optional web search, and execution/safety preferences such as sandbox mode, bash access, and file-write tools. It generates a minimal config.yaml and writes your keys to .env. Takes about 2 minutes.
The wizard also lets you configure an optional web search provider, or skip it for now.
Run make doctor at any time to verify your setup and get actionable fix hints.
Manual model configuration examplesAdvanced / manual configuration: If you prefer to edit
config.yamldirectly, runmake configinstead to copy the full template. Seeconfig.example.yamlfor the complete reference including CLI-backed providers (Codex CLI, Claude Code OAuth), OpenRouter, Responses API, and more.
models:
- name: gpt-4o
display_name: GPT-4o
use: langchain_openai:ChatOpenAI
model: gpt-4o
api_key: $OPENAI_API_KEY
- name: openrouter-gemini-2.5-flash
display_name: Gemini 2.5 Flash (OpenRouter)
use: langchain_openai:ChatOpenAI
model: google/gemini-2.5-flash-preview
api_key: $OPENROUTER_API_KEY
base_url: https://openrouter.ai/api/v1
- name: gpt-5-responses
display_name: GPT-5 (Responses API)
use: langchain_openai:ChatOpenAI
model: gpt-5
api_key: $OPENAI_API_KEY
use_responses_api: true
output_version: responses/v1
- name: qwen3-32b-vllm
display_name: Qwen3 32B (vLLM)
use: deerflow.models.vllm_provider:VllmChatModel
model: Qwen/Qwen3-32B
api_key: $VLLM_API_KEY
base_url: http://localhost:8000/v1
supports_thinking: true
when_thinking_enabled:
extra_body:
chat_template_kwargs:
enable_thinking: true
OpenRouter and similar OpenAI-compatible gateways should be configured with langchain_openai:ChatOpenAI plus base_url. If you prefer a provider-specific environment variable name, point api_key at that variable explicitly (for example api_key: $OPENROUTER_API_KEY).
To route OpenAI models through /v1/responses, keep using langchain_openai:ChatOpenAI and set use_responses_api: true with output_version: responses/v1.
For vLLM 0.19.0, use deerflow.models.vllm_provider:VllmChatModel. For Qwen-style reasoning models, DeerFlow toggles reasoning with extra_body.chat_template_kwargs.enable_thinking and preserves vLLM's non-standard reasoning field across multi-turn tool-call conversations. Legacy thinking configs are normalized automatically for backward compatibility. Reasoning models may also require the server to be started with --reasoning-parser .... If your local vLLM deployment accepts any non-empty API key, you can still set VLLM_API_KEY to a placeholder value.
CLI-backed provider examples:
models:
- name: gpt-5.4
display_name: GPT-5.4 (Codex CLI)
use: deerflow.models.openai_codex_provider:CodexChatModel
model: gpt-5.4
supports_thinking: true
supports_reasoning_effort: true
- name: claude-sonnet-4.6
display_name: Claude Sonnet 4.6 (Claude Code OAuth)
use: deerflow.models.claude_provider:ClaudeChatModel
model: claude-sonnet-4-6
max_tokens: 4096
supports_thinking: true
~/.codex/auth.jsonCLAUDE_CODE_OAUTH_TOKEN, ANTHROPIC_AUTH_TOKEN, CLAUDE_CODE_CREDENTIALS_PATH, or ~/.claude/.credentials.jsonacp_agents.codex, point it at a Codex ACP adapter such as npx -y @zed-industries/codex-acpeval "$(python3 scripts/export_claude_code_oauth.py --print-export)"
API keys can also be set manually in .env (recommended) or exported in your shell:
OPENAI_API_KEY=your-openai-api-key
TAVILY_API_KEY=your-tavily-api-key
Use the table below as a practical starting point when choosing how to run DeerFlow:
| Deployment target | Starting point | Recommended | Notes |
|---|---|---|---|
Local evaluation / make dev |
4 vCPU, 8 GB RAM, 20 GB free SSD | 8 vCPU, 16 GB RAM | Good for one developer or one light session with hosted model APIs. 2 vCPU / 4 GB is usually not enough. |
Docker development / make docker-start |
4 vCPU, 8 GB RAM, 25 GB free SSD | 8 vCPU, 16 GB RAM | Image builds, bind mounts, and sandbox containers need more headroom than pure local dev. |
Long-running server / make up |
8 vCPU, 16 GB RAM, 40 GB free SSD | 16 vCPU, 32 GB RAM | Preferred for shared use, multi-agent runs, report generation, or heavier sandbox workloads. |
Development (hot-reload, source mounts):
make docker-init # Pull sandbox image (only once or when image updates)
make docker-start # Start services (auto-detects sandbox mode from config.yaml)
make docker-start starts provisioner only when config.yaml uses provisioner mode (sandbox.use: deerflow.community.aio_sandbox:AioSandboxProvider with provisioner_url).
Docker builds use the upstream uv registry by default. If you need faster mirrors in restricted networks, export UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple and NPM_REGISTRY=https://registry.npmmirror.com before running make docker-init or make docker-start.
Backend processes automatically pick up config.yaml changes on the next config access, so model metadata updates do not require a manual restart during development.
[!TIP]
On Linux, if Docker-based commands fail withpermission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock, add your user to thedockergroup and re-login before retrying. See CONTRIBUTING.md for the full fix.
Production (builds images locally, mounts runtime config and data):
make up # Build images and start all production services
make down # Stop and remove containers
[!NOTE]
The LangGraph agent server currently runs vialanggraph dev(the open-source CLI server).
Access: http://localhost:2026
See CONTRIBUTING.md for detailed Docker development guide.
If you prefer running services locally:
Prerequisite: complete the "Configuration" steps above first (make setup). make dev requires a valid config.yaml in the project root (can be overridden via DEER_FLOW_CONFIG_PATH). Run make doctor to verify your setup before starting.
On Windows, run the local development flow from Git Bash. Native cmd.exe and PowerShell shells are not supported for the bash-based service scripts, and WSL is not guaranteed because some scripts rely on Git for Windows utilities such as cygpath.
Check prerequisites:
make check # Verifies Node.js 22+, pnpm, uv, nginx
Install dependencies:
make install # Install backend + frontend dependencies
(Optional) Pre-pull sandbox image:
# Recommended if using Docker/Container-based sandbox
make setup-sandbox
(Optional) Load sample memory data for local review:
python scripts/load_memory_sample.py
This copies the sample fixture into the default local runtime memory file so reviewers can immediately test Settings > Memory.
See backend/docs/MEMORY_SETTINGS_REVIEW.md for the shortest review flow.
Start services:
make dev
Access: http://localhost:2026
DeerFlow supports multiple startup modes across two dimensions:
| Local Foreground | Local Daemon | Docker Dev | Docker Prod | |
|---|---|---|---|---|
| Dev | ./scripts/serve.sh --devmake dev |
./scripts/serve.sh --dev --daemonmake dev-daemon |
./scripts/docker.sh startmake docker-start |
— |
| Dev + Gateway | ./scripts/serve.sh --dev --gatewaymake dev-pro |
./scripts/serve.sh --dev --gateway --daemonmake dev-daemon-pro |
./scripts/docker.sh start --gatewaymake docker-start-pro |
— |
| Prod | ./scripts/serve.sh --prodmake start |
./scripts/serve.sh --prod --daemonmake start-daemon |
— | ./scripts/deploy.shmake up |
| Prod + Gateway | ./scripts/serve.sh --prod --gatewaymake start-pro |
./scripts/serve.sh --prod --gateway --daemonmake start-daemon-pro |
— | ./scripts/deploy.sh --gatewaymake up-pro |
| Action | Local | Docker Dev | Docker Prod |
|---|---|---|---|
| Stop | ./scripts/serve.sh --stopmake stop |
./scripts/docker.sh stopmake docker-stop |
./scripts/deploy.sh downmake down |
| Restart | ./scripts/serve.sh --restart [flags] |
./scripts/docker.sh restart |
— |
Gateway mode eliminates the LangGraph server process — the Gateway API handles agent execution directly via async tasks, managing its own concurrency.
In standard mode, DeerFlow runs a dedicated LangGraph Platform server alongside the Gateway API. This architecture works well but has trade-offs:
| Standard Mode | Gateway Mode | |
|---|---|---|
| Architecture | Gateway (REST API) + LangGraph (agent runtime) | Gateway embeds agent runtime |
| Concurrency | --n-jobs-per-worker per worker (requires license) |
--workers × async tasks (no per-worker cap) |
| Containers / Processes | 4 (frontend, gateway, langgraph, nginx) | 3 (frontend, gateway, nginx) |
| Resource usage | Higher (two Python runtimes) | Lower (single Python runtime) |
| LangGraph Platform license | Required for production images | Not required |
| Cold start | Slower (two services to initialize) | Faster |
Both modes are functionally equivalent — the same agents, tools, and skills work in either mode.
deploy.sh supports building and starting separately. Images are mode-agnostic — runtime mode is selected at start time:
# One-step (build + start)
deploy.sh # standard mode (default)
deploy.sh --gateway # gateway mode
# Two-step (build once, start with any mode)
deploy.sh build # build all images
deploy.sh start # start in standard mode
deploy.sh start --gateway # start in gateway mode
# Stop
deploy.sh down
DeerFlow supports multiple sandbox execution modes:
For Docker development, service startup follows config.yaml sandbox mode. In Local/Docker modes, provisioner is not started.
See the Sandbox Configuration Guide to configure your preferred mode.
DeerFlow supports configurable MCP servers and skills to extend its capabilities.
For HTTP/SSE MCP servers, OAuth token flows are supported (client_credentials, refresh_token).
See the MCP Server Guide for detailed instructions.
DeerFlow supports receiving tasks from messaging apps. Channels auto-start when configured — no public IP required for any of them.
| Channel | Transport | Difficulty |
|---|---|---|
| Telegram | Bot API (long-polling) | Easy |
| Slack | Socket Mode | Moderate |
| Feishu / Lark | WebSocket | Moderate |
| Tencent iLink (long-polling) | Moderate | |
| WeCom | WebSocket | Moderate |
Configuration in config.yaml:
channels:
# LangGraph Server URL (default: http://localhost:2024)
langgraph_url: http://localhost:2024
# Gateway API URL (default: http://localhost:8001)
gateway_url: http://localhost:8001
# Optional: global session defaults for all mobile channels
session:
assistant_id: lead_agent # or a custom agent name; custom agents are routed via lead_agent + agent_name
config:
recursion_limit: 100
context:
thinking_enabled: true
is_plan_mode: false
subagent_enabled: false
feishu:
enabled: true
app_id: $FEISHU_APP_ID
app_secret: $FEISHU_APP_SECRET
# domain: https://open.feishu.cn # China (default)
# domain: https://open.larksuite.com # International
wecom:
enabled: true
bot_id: $WECOM_BOT_ID
bot_secret: $WECOM_BOT_SECRET
slack:
enabled: true
bot_token: $SLACK_BOT_TOKEN # xoxb-...
app_token: $SLACK_APP_TOKEN # xapp-... (Socket Mode)
allowed_users: [] # empty = allow all
telegram:
enabled: true
bot_token: $TELEGRAM_BOT_TOKEN
allowed_users: [] # empty = allow all
wechat:
enabled: false
bot_token: $WECHAT_BOT_TOKEN
ilink_bot_id: $WECHAT_ILINK_BOT_ID
qrcode_login_enabled: true # optional: allow first-time QR bootstrap when bot_token is absent
allowed_users: [] # empty = allow all
polling_timeout: 35
state_dir: ./.deer-flow/wechat/state
max_inbound_image_bytes: 20971520
max_outbound_image_bytes: 20971520
max_inbound_file_bytes: 52428800
max_outbound_file_bytes: 52428800
# Optional: per-channel / per-user session settings
session:
assistant_id: mobile-agent # custom agent names are also supported here
context:
thinking_enabled: false
users:
"123456789":
assistant_id: vip-agent
config:
recursion_limit: 150
context:
thinking_enabled: true
subagent_enabled: true
Notes:
assistant_id: lead_agent calls the default LangGraph assistant directly.assistant_id is set to a custom agent name, DeerFlow still routes through lead_agent and injects that value as agent_name, so the custom agent's SOUL/config takes effect for IM channels.Set the corresponding API keys in your .env file:
# Telegram
TELEGRAM_BOT_TOKEN=123456789:ABCdefGHIjklMNOpqrSTUvwxYZ
# Slack
SLACK_BOT_TOKEN=xoxb-...
SLACK_APP_TOKEN=xapp-...
# Feishu / Lark
FEISHU_APP_ID=cli_xxxx
FEISHU_APP_SECRET=your_app_secret
# WeChat iLink
WECHAT_BOT_TOKEN=your_ilink_bot_token
WECHAT_ILINK_BOT_ID=your_ilink_bot_id
# WeCom
WECOM_BOT_ID=your_bot_id
WECOM_BOT_SECRET=your_bot_secret
Telegram Setup
/newbot, and copy the HTTP API token.TELEGRAM_BOT_TOKEN in .env and enable the channel in config.yaml.Slack Setup
app_mentions:read, chat:write, im:history, im:read, im:write, files:write.xapp-…) with connections:write scope.app_mention, message.im.SLACK_BOT_TOKEN and SLACK_APP_TOKEN in .env and enable the channel in config.yaml.Feishu / Lark Setup
im:message, im:message.p2p_msg:readonly, im:resource.im.message.receive_v1 and select Long Connection mode.FEISHU_APP_ID and FEISHU_APP_SECRET in .env and enable the channel in config.yaml.WeChat Setup
wechat channel in config.yaml.WECHAT_BOT_TOKEN in .env, or set qrcode_login_enabled: true for first-time QR bootstrap.bot_token is absent and QR bootstrap is enabled, watch backend logs for the QR content returned by iLink and complete the binding flow.state_dir for later restarts.state_dir on a persistent volume so the get_updates_buf cursor and saved auth state survive restarts.WeCom Setup
bot_id and bot_secret.channels.wecom in config.yaml and fill in bot_id / bot_secret.WECOM_BOT_ID and WECOM_BOT_SECRET in .env.wecom-aibot-python-sdk. The channel uses a WebSocket long connection and does not require a public callback URL.When DeerFlow runs in Docker Compose, IM channels execute inside the gateway container. In that case, do not point channels.langgraph_url or channels.gateway_url at localhost; use container service names such as http://langgraph:2024 and http://gateway:8001, or set DEER_FLOW_CHANNELS_LANGGRAPH_URL and DEER_FLOW_CHANNELS_GATEWAY_URL.
Commands
Once a channel is connected, you can interact with DeerFlow directly from the chat:
| Command | Description |
|---|---|
/new |
Start a new conversation |
/status |
Show current thread info |
/models |
List available models |
/memory |
View memory |
/help |
Show help |
Messages without a command prefix are treated as regular chat — DeerFlow creates a thread and responds conversationally.
DeerFlow has built-in LangSmith integration for observability. When enabled, all LLM calls, agent runs, and tool executions are traced and visible in the LangSmith dashboard.
Add the following to your .env file:
LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_API_KEY=lsv2_pt_xxxxxxxxxxxxxxxx
LANGSMITH_PROJECT=xxx
DeerFlow also supports Langfuse observability for LangChain-compatible runs.
Add the following to your .env file:
LANGFUSE_TRACING=true
LANGFUSE_PUBLIC_KEY=pk-lf-xxxxxxxxxxxxxxxx
LANGFUSE_SECRET_KEY=sk-lf-xxxxxxxxxxxxxxxx
LANGFUSE_BASE_URL=https://cloud.langfuse.com
If you are using a self-hosted Langfuse instance, set LANGFUSE_BASE_URL to your deployment URL.
If both LangSmith and Langfuse are enabled, DeerFlow attaches both tracing callbacks and reports the same model activity to both systems.
If a provider is explicitly enabled but missing required credentials, or if its callback fails to initialize, DeerFlow fails fast when tracing is initialized during model creation and the error message names the provider that caused the failure.
For Docker deployments, tracing is disabled by default. Set LANGSMITH_TRACING=true and LANGSMITH_API_KEY in your .env to enable it.
DeerFlow started as a Deep Research framework — and the community ran with it. Since launch, developers have pushed it far beyond research: building data pipelines, generating slide decks, spinning up dashboards, automating content workflows. Things we never anticipated.
That told us something important: DeerFlow wasn't just a research tool. It was a harness — a runtime that gives agents the infrastructure to actually get work done.
So we rebuilt it from scratch.
DeerFlow 2.0 is no longer a framework you wire together. It's a super agent harness — batteries included, fully extensible. Built on LangGraph and LangChain, it ships with everything an agent needs out of the box: a filesystem, memory, skills, sandbox-aware execution, and the ability to plan and spawn sub-agents for complex, multi-step tasks.
Use it as-is. Or tear it apart and make it yours.
Skills are what make DeerFlow do almost anything.
A standard Agent Skill is a structured capability module — a Markdown file that defines a workflow, best practices, and references to supporting resources. DeerFlow ships with built-in skills for research, report generation, slide creation, web pages, image and video generation, and more. But the real power is extensibility: add your own skills, replace the built-in ones, or combine them into compound workflows.
Skills are loaded progressively — only when the task needs them, not all at once. This keeps the context window lean and makes DeerFlow work well even with token-sensitive models.
When you install .skill archives through the Gateway, DeerFlow accepts standard optional frontmatter metadata such as version, author, and compatibility instead of rejecting otherwise valid external skills.
Tools follow the same philosophy. DeerFlow comes with a core toolset — web search, web fetch, file operations, bash execution — and supports custom tools via MCP servers and Python functions. Swap anything. Add anything.
Gateway-generated follow-up suggestions now normalize both plain-string model output and block/list-style rich content before parsing the JSON array response, so provider-specific content wrappers do not silently drop suggestions.
# Paths inside the sandbox container
/mnt/skills/public
├── research/SKILL.md
├── report-generation/SKILL.md
├── slide-creation/SKILL.md
├── web-page/SKILL.md
└── image-generation/SKILL.md
/mnt/skills/custom
└── your-custom-skill/SKILL.md ← yours
The claude-to-deerflow skill lets you interact with a running DeerFlow instance directly from Claude Code. Send research tasks, check status, manage threads — all without leaving the terminal.
Install the skill:
npx skills add https://github.com/bytedance/deer-flow --skill claude-to-deerflow
Then make sure DeerFlow is running (default at http://localhost:2026) and use the /claude-to-deerflow command in Claude Code.
What you can do:
Environment variables (optional, for custom endpoints):
DEERFLOW_URL=http://localhost:2026 # Unified proxy base URL
DEERFLOW_GATEWAY_URL=http://localhost:2026 # Gateway API
DEERFLOW_LANGGRAPH_URL=http://localhost:2026/api/langgraph # LangGraph API
See skills/public/claude-to-deerflow/SKILL.md for the full API reference.
Complex tasks rarely fit in a single pass. DeerFlow decomposes them.
The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output.
This is how DeerFlow handles tasks that take minutes to hours: a research task might fan out into a dozen sub-agents, each exploring a different angle, then converge into a single report — or a website — or a slide deck with generated visuals. One harness, many hands.
DeerFlow doesn't just talk about doing things. It has its own computer.
Each task gets its own execution environment with a full filesystem view — skills, workspace, uploads, outputs. The agent reads, writes, and edits files. It can view images and, when configured safely, execute shell commands.
With AioSandboxProvider, shell execution runs inside isolated containers. With LocalSandboxProvider, file tools still map to per-thread directories on the host, but host bash is disabled by default because it is not a secure isolation boundary. Re-enable host bash only for fully trusted local workflows.
This is the difference between a chatbot with tool access and an agent with an actual execution environment.
# Paths inside the sandbox container
/mnt/user-data/
├── uploads/ ← your files
├── workspace/ ← agents' working directory
└── outputs/ ← final deliverables
Isolated Sub-Agent Context: Each sub-agent runs in its own isolated context. This means that the sub-agent will not be able to see the context of the main agent or other sub-agents. This is important to ensure that the sub-agent is able to focus on the task at hand and not be distracted by the context of the main agent or other sub-agents.
Summarization: Within a session, DeerFlow manages context aggressively — summarizing completed sub-tasks, offloading intermediate results to the filesystem, compressing what's no longer immediately relevant. This lets it stay sharp across long, multi-step tasks without blowing the context window.
Strict Tool-Call Recovery: When a provider or middleware interrupts a tool-call loop, DeerFlow now strips provider-level raw tool-call metadata on forced-stop assistant messages and injects placeholder tool results for dangling calls before the next model invocation. This keeps OpenAI-compatible reasoning models that strictly validate tool_call_id sequences from failing with malformed history errors.
Most agents forget everything the moment a conversation ends. DeerFlow remembers.
Across sessions, DeerFlow builds a persistent memory of your profile, preferences, and accumulated knowledge. The more you use it, the better it knows you — your writing style, your technical stack, your recurring workflows. Memory is stored locally and stays under your control.
Memory updates now skip duplicate fact entries at apply time, so repeated preferences and context do not accumulate endlessly across sessions.
DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. That said, it performs best with models that support:
DeerFlow can be used as an embedded Python library without running the full HTTP services. The DeerFlowClient provides direct in-process access to all agent and Gateway capabilities, returning the same response schemas as the HTTP Gateway API. The HTTP Gateway also exposes DELETE /api/threads/{thread_id} to remove DeerFlow-managed local thread data after the LangGraph thread itself has been deleted:
from deerflow.client import DeerFlowClient
client = DeerFlowClient()
# Chat
response = client.chat("Analyze this paper for me", thread_id="my-thread")
# Streaming (LangGraph SSE protocol: values, messages-tuple, end)
for event in client.stream("hello"):
if event.type == "messages-tuple" and event.data.get("type") == "ai":
print(event.data["content"])
# Configuration & management — returns Gateway-aligned dicts
models = client.list_models() # {"models": [...]}
skills = client.list_skills() # {"skills": [...]}
client.update_skill("web-search", enabled=True)
client.upload_files("thread-1", ["./report.pdf"]) # {"success": True, "files": [...]}
All dict-returning methods are validated against Gateway Pydantic response models in CI (TestGatewayConformance), ensuring the embedded client stays in sync with the HTTP API schemas. See backend/packages/harness/deerflow/client.py for full API documentation.
DeerFlow has key high-privilege capabilities including system command execution, resource operations, and business logic invocation, and is designed by default to be deployed in a local trusted environment (accessible only via the 127.0.0.1 loopback interface). If you deploy the agent in untrusted environments — such as LAN networks, public cloud servers, or other multi-endpoint accessible environments — without strict security measures, it may introduce security risks, including:
Note: We strongly recommend deploying DeerFlow in a local trusted network environment. If you need cross-device or cross-network deployment, you must implement strict security measures, such as:
iptables, or deploy hardware firewalls / switches with Access Control Lists (ACL), to configure IP allowlist rules and deny access from all other IP addresses.We welcome contributions! Please see CONTRIBUTING.md for development setup, workflow, and guidelines.
Regression coverage includes Docker sandbox mode detection and provisioner kubeconfig-path handling tests in backend/tests/.
Gateway artifact serving now forces active web content types (text/html, application/xhtml+xml, image/svg+xml) to download as attachments instead of inline rendering, reducing XSS risk for generated artifacts.
This project is open source and available under the MIT License.
DeerFlow is built upon the incredible work of the open-source community. We are deeply grateful to all the projects and contributors whose efforts have made DeerFlow possible. Truly, we stand on the shoulders of giants.
We would like to extend our sincere appreciation to the following projects for their invaluable contributions:
These projects exemplify the transformative power of open-source collaboration, and we are proud to build upon their foundations.
A heartfelt thank you goes out to the core authors of DeerFlow, whose vision, passion, and dedication have brought this project to life:
Your unwavering commitment and expertise have been the driving force behind DeerFlow's success. We are honored to have you at the helm of this journey.