openfang README

The project's README from github.com/RightNow-AI/openfang. As one markdown file: README.md.

OpenFang Logo

OpenFang

The Agent Operating System

Open-source Agent OS built in Rust. 137K LOC. 14 crates. 1,767+ tests. Zero clippy warnings.
One binary. Battle-tested. Agents that actually work for you.

Documentation • Quick Start • Twitter / X

Rust MIT v0.6.9 Tests Clippy Buy Me A Coffee


v0.5.10 (April 2026)

OpenFang is feature complete but still pre-1.0. Expect rough edges and breaking changes between minor versions. We ship fast and fix fast. Pin to a specific commit for production use until v1.0. Report issues here.


What is OpenFang?

OpenFang is an open-source Agent Operating System. Not a chatbot framework. Not a Python wrapper around an LLM. Not a "multi-agent orchestrator." A full operating system for autonomous agents, built from scratch in Rust.

Traditional agent frameworks wait for you to type something. OpenFang runs autonomous agents that work for you: on schedules, 24/7, building knowledge graphs, monitoring targets, generating leads, managing your social media, and reporting results to your dashboard.

The entire system compiles to a single ~32MB binary. One install, one command, your agents are live.

curl -fsSL https://openfang.sh/install | sh
openfang init
openfang start
# Dashboard live at http://localhost:4200
Windows
irm https://openfang.sh/install.ps1 | iex
openfang init
openfang start

Hands: Agents That Actually Do Things

"Traditional agents wait for you to type. Hands work for you."

Hands are OpenFang's core innovation. Pre-built autonomous capability packages that run independently, on schedules, without you having to prompt them. This is not a chatbot. This is an agent that wakes up at 6 AM, researches your competitors, builds a knowledge graph, scores the findings, and delivers a report to your Telegram before you've had coffee.

Each Hand bundles:

All compiled into the binary. No downloading, no pip install, no Docker pull.

The 7 Bundled Hands

Hand What It Actually Does
Clip Takes a YouTube URL, downloads it, identifies the best moments, cuts them into vertical shorts with captions and thumbnails, optionally adds AI voice-over, and publishes to Telegram and WhatsApp. 8-phase pipeline. FFmpeg + yt-dlp + 5 STT backends.
Lead Runs daily. Discovers prospects matching your ICP, enriches them with web research, scores 0-100, deduplicates against your existing database, and delivers qualified leads in CSV/JSON/Markdown. Builds ICP profiles over time.
Collector OSINT grade intelligence. You give it a target (company, person, topic). It monitors continuously: change detection, sentiment tracking, knowledge graph construction, and critical alerts when something important shifts.
Predictor Superforecasting engine. Collects signals from multiple sources, builds calibrated reasoning chains, makes predictions with confidence intervals, and tracks its own accuracy using Brier scores. Has a contrarian mode that deliberately argues against consensus.
Researcher Deep autonomous researcher. Cross-references multiple sources, evaluates credibility using CRAAP criteria (Currency, Relevance, Authority, Accuracy, Purpose), generates cited reports with APA formatting, supports multiple languages.
Twitter Autonomous Twitter/X account manager. Creates content in 7 rotating formats, schedules posts for optimal engagement, responds to mentions, tracks performance metrics. Has an approval queue, so nothing posts without your OK.
Browser Web automation agent. Navigates sites, fills forms, clicks buttons, handles multi-step workflows. Uses Playwright bridge with session persistence. Mandatory purchase approval gate: it will never spend your money without explicit confirmation.
# Activate the Researcher Hand. It starts working immediately.
openfang hand activate researcher

# Check its progress anytime
openfang hand status researcher

# Activate lead generation on a daily schedule
openfang hand activate lead

# Pause without losing state
openfang hand pause lead

# See all available Hands
openfang hand list

Build your own. Define a HAND.toml with tools, settings, and a system prompt. Publish to FangHub.


OpenFang vs The Landscape

OpenFang vs OpenClaw vs ZeroClaw

Benchmarks: Measured, Not Marketed

All data from official documentation and public repositories, February 2026.

Cold Start Time (lower is better)

ZeroClaw   ██░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   10 ms
OpenFang   ██████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  180 ms    ★
LangGraph  █████████████████░░░░░░░░░░░░░░░░░░░░░░░░░  2.5 sec
CrewAI     ████████████████████░░░░░░░░░░░░░░░░░░░░░░  3.0 sec
AutoGen    ██████████████████████████░░░░░░░░░░░░░░░░░  4.0 sec
OpenClaw   █████████████████████████████████████████░░  5.98 sec

Idle Memory Usage (lower is better)

ZeroClaw   █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    5 MB
OpenFang   ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   40 MB    ★
LangGraph  ██████████████████░░░░░░░░░░░░░░░░░░░░░░░░░  180 MB
CrewAI     ████████████████████░░░░░░░░░░░░░░░░░░░░░░░  200 MB
AutoGen    █████████████████████████░░░░░░░░░░░░░░░░░░  250 MB
OpenClaw   ████████████████████████████████████████░░░░  394 MB

Install Size (lower is better)

ZeroClaw   █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  8.8 MB
OpenFang   ███░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   32 MB    ★
CrewAI     ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  100 MB
LangGraph  ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  150 MB
AutoGen    ████████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░  200 MB
OpenClaw   ████████████████████████████████████████░░░░  500 MB

Security Systems (higher is better)

OpenFang   ████████████████████████████████████████████   16      ★
ZeroClaw   ███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░    6
OpenClaw   ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    3
AutoGen    █████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    2
LangGraph  █████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    2
CrewAI     ███░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    1

Channel Adapters (higher is better)

OpenFang   ████████████████████████████████████████████   40      ★
ZeroClaw   ███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░   15
OpenClaw   █████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   13
CrewAI     ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    0
AutoGen    ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    0
LangGraph  ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    0

LLM Providers (higher is better)

ZeroClaw   ████████████████████████████████████████████   28
OpenFang   ██████████████████████████████████████████░░   27      ★
LangGraph  ██████████████████████░░░░░░░░░░░░░░░░░░░░░   15
CrewAI     ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   10
OpenClaw   ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   10
AutoGen    ███████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    8

Feature-by-Feature Comparison

Feature OpenFang OpenClaw ZeroClaw CrewAI AutoGen LangGraph
Language Rust TypeScript Rust Python Python Python
Autonomous Hands 7 built-in None None None None None
Security Layers 16 discrete 3 basic 6 layers 1 basic Docker AES enc.
Agent Sandbox WASM dual-metered None Allowlists None Docker None
Channel Adapters 40 13 15 0 0 0
Built-in Tools 53 + MCP + A2A 50+ 12 Plugins MCP LC tools
Memory SQLite + vector File-based SQLite FTS5 4-layer External Checkpoints
Desktop App Tauri 2.0 None None None Studio None
Audit Trail Merkle hash-chain Logs Logs Tracing Logs Checkpoints
Cold Start <200ms ~6s ~10ms ~3s ~4s ~2.5s
Install Size ~32 MB ~500 MB ~8.8 MB ~100 MB ~200 MB ~150 MB
License MIT MIT MIT MIT Apache 2.0 MIT

16 Security Systems: Defense in Depth

OpenFang doesn't bolt security on after the fact. Every layer is independently testable and operates without a single point of failure.

# System What It Does
1 WASM Dual-Metered Sandbox Tool code runs in WebAssembly with fuel metering + epoch interruption. A watchdog thread kills runaway code.
2 Merkle Hash-Chain Audit Trail Every action is cryptographically linked to the previous one. Tamper with one entry and the entire chain breaks.
3 Information Flow Taint Tracking Labels propagate through execution. Secrets are tracked from source to sink.
4 Ed25519 Signed Agent Manifests Every agent identity and capability set is cryptographically signed.
5 SSRF Protection Blocks private IPs, cloud metadata endpoints, and DNS rebinding attacks.
6 Secret Zeroization Zeroizing<String> auto-wipes API keys from memory the instant they're no longer needed.
7 OFP Mutual Authentication HMAC-SHA256 nonce-based, constant-time verification for P2P networking.
8 Capability Gates Role based access control. Agents declare required tools, the kernel enforces it.
9 Security Headers CSP, X-Frame-Options, HSTS, X-Content-Type-Options on every response.
10 Health Endpoint Redaction Public health check returns minimal info. Full diagnostics require authentication.
11 Subprocess Sandbox env_clear() + selective variable passthrough. Process tree isolation with cross-platform kill.
12 Prompt Injection Scanner Detects override attempts, data exfiltration patterns, and shell reference injection in skills.
13 Loop Guard SHA256-based tool call loop detection with circuit breaker. Handles ping-pong patterns.
14 Session Repair 7-phase message history validation and automatic recovery from corruption.
15 Path Traversal Prevention Canonicalization with symlink escape prevention. ../ doesn't work here.
16 GCRA Rate Limiter Cost-aware token bucket rate limiting with per-IP tracking and stale cleanup.

Architecture

14 Rust crates. 137,728 lines of code. Modular kernel design.

openfang-kernel      Orchestration, workflows, metering, RBAC, scheduler, budget tracking
openfang-runtime     Agent loop, 3 LLM drivers, 53 tools, WASM sandbox, MCP, A2A
openfang-api         140+ REST/WS/SSE endpoints, OpenAI-compatible API, dashboard
openfang-channels    40 messaging adapters with rate limiting, DM/group policies
openfang-memory      SQLite persistence, vector embeddings, canonical sessions, compaction
openfang-types       Core types, taint tracking, Ed25519 manifest signing, model catalog
openfang-skills      60 bundled skills, SKILL.md parser, FangHub marketplace
openfang-hands       7 autonomous Hands, HAND.toml parser, lifecycle management
openfang-extensions  25 MCP templates, AES-256-GCM credential vault, OAuth2 PKCE
openfang-wire        OFP P2P protocol with HMAC-SHA256 mutual authentication
openfang-cli         CLI with daemon management, TUI dashboard, MCP server mode
openfang-desktop     Tauri 2.0 native app (system tray, notifications, global shortcuts)
openfang-migrate     OpenClaw, LangChain, AutoGPT migration engine
xtask                Build automation

40 Channel Adapters

Connect your agents to every platform your users are on.

Core: Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email (IMAP/SMTP) Enterprise: Microsoft Teams, Mattermost, Google Chat, Webex, Feishu/Lark, Zulip Social: LINE, Viber, Facebook Messenger, Mastodon, Bluesky, Reddit, LinkedIn, Twitch Community: IRC, XMPP, Guilded, Revolt, Keybase, Discourse, Gitter Privacy: Threema, Nostr, Mumble, Nextcloud Talk, Rocket.Chat, Ntfy, Gotify Workplace: Pumble, Flock, Twist, DingTalk, Zalo, Webhooks

Each adapter supports per-channel model overrides, DM/group policies, rate limiting, and output formatting.


WhatsApp Web Gateway (QR Code)

Connect your personal WhatsApp account to OpenFang via QR code, just like WhatsApp Web. No Meta Business account required.

Prerequisites

Setup

1. Install the gateway dependencies:

cd packages/whatsapp-gateway
npm install

2. Configure config.toml:

[channels.whatsapp]
mode = "web"
default_agent = "assistant"

3. Set the gateway URL (choose one):

Add to your shell profile for persistence:

# macOS / Linux
echo 'export WHATSAPP_WEB_GATEWAY_URL="http://127.0.0.1:3009"' >> ~/.zshrc
source ~/.zshrc

Or set it inline when starting the gateway:

export WHATSAPP_WEB_GATEWAY_URL="http://127.0.0.1:3009"

4. Start the gateway:

node packages/whatsapp-gateway/index.js

The gateway listens on port 3009 by default. Override with WHATSAPP_GATEWAY_PORT.

5. Start OpenFang:

openfang start
# Dashboard at http://localhost:4200

6. Scan the QR code:

Open the dashboard → Channels → WhatsApp. A QR code will appear. Scan it with your phone:

WhatsApp → Settings → Linked Devices → Link a Device

Once scanned, the status changes to connected and incoming messages are routed to your configured agent.

Gateway Environment Variables

Variable Description Default
WHATSAPP_WEB_GATEWAY_URL Gateway URL for OpenFang to connect to (empty = disabled)
WHATSAPP_GATEWAY_PORT Port the gateway listens on 3009
OPENFANG_URL OpenFang API URL the gateway reports to http://127.0.0.1:4200
OPENFANG_DEFAULT_AGENT Agent that handles incoming messages assistant

Gateway API Endpoints

Method Route Description
POST /login/start Generate QR code (returns base64 PNG)
GET /login/status Connection status (disconnected, qr_ready, connected)
POST /message/send Send a message ({ "to": "5511999999999", "text": "Hello" })
GET /health Health check

Alternative: WhatsApp Cloud API

For production workloads, use the WhatsApp Cloud API with a Meta Business account. See the Cloud API configuration docs.


27 LLM Providers, 123+ Models

3 native drivers (Anthropic, Gemini, OpenAI-compatible) route to 27 providers:

Anthropic, Gemini, OpenAI, Groq, DeepSeek, OpenRouter, Together, Mistral, Fireworks, Cohere, Perplexity, xAI, AI21, Cerebras, SambaNova, HuggingFace, Replicate, Ollama, vLLM, LM Studio, Qwen, MiniMax, Zhipu, Moonshot, Qianfan, Bedrock, and more.

Intelligent routing with task complexity scoring, automatic fallback, cost tracking, and per-model pricing.


Migrate from OpenClaw

Already running OpenClaw? One

openfang (RightNow-AI/openfang)

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18.2K ·GitHub
这是什么?OpenFang 是一个用 Rust 编写的开源 Agent 操作系统。它不是一个等着你输入提示词的聊天机器人,而是让自主智能体 7×24 小时不间断运行——按计划执行任务、监控目标、构建知识图谱,并把结果汇报到仪表盘上。
那它是需要我写代码对接的框架,还是拿来就能用的成品?它是成品,不是起步模板:所有东西都编译进一个约 32MB 的二进制文件,智能体、工具和技能都已内置——不用 pip install,也不用 Docker pull。你通过 TOML 文件来配置,而不需要写代码。
我怎么让第一个智能体跑起来?为你的平台安装 OpenFang 二进制文件,然后运行一条命令——README 里有 Windows 和其他平台的安装章节。配置好 LLM 服务商之后,你的智能体就上线了。
在它能干活之前,我需要准备什么——API 密钥、Docker 还是 Node?只需要 27 家受支持的 LLM 服务商中任意一家的 API 密钥:Anthropic、Gemini 和任何 OpenAI 兼容的接口是三种原生驱动。Node.js >= 18 只在需要可选的 WhatsApp Web 网关时才用得到。
它能用在生产环境里吗?还不行:v0.5.10 功能已经齐全,但还不到 1.0——次版本之间可能有小毛病和破坏性变更,在 v1.0 发布之前,生产环境请固定使用某个具体的 commit。
Clip Hand 能把一个 YouTube 链接变成什么?直接可发布的竖屏短视频:它会下载视频、挑出精彩片段、配上字幕和封面图进行剪辑,可选添加 AI 配音,然后发布到 Telegram 和 WhatsApp——这是一条基于 FFmpeg、yt-dlp 和 5 种语音识别后端的 8 阶段流水线。
Predictor Hand 对预测的准确性有多较真?它会构建经过校准的推理链,给出带置信区间的预测,并用 Brier 分数长期追踪自己的准确率。它甚至有一个唱反调模式,会故意和主流共识唱反调。
它为什么自称是操作系统?因为它的结构就像一个操作系统:由 14 个 crate、13.7 万行代码组成的模块化 Rust 内核,负责调度智能体、执行能力门控、对进程做沙箱隔离,并保留防篡改的审计记录——智能体就是它的进程。
我怎么绑定我的个人 WhatsApp 账号?运行 WhatsApp Web 网关(需要 Node.js >= 18,默认监听 3009 端口),然后用手机扫描仪表盘的 Channels 页面显示的二维码——不需要 Meta Business 账号。生产环境建议改用 Cloud API。
What are similar projects?brigade, Raven, nullclaw, turnstone, hive, ruflo, starnet, opensquilla
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