openfang

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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.

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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

irm https://openfang.sh/install.ps1 | iex
openfang init
openfang start

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Hands: Agents That Actually Do Things

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:

• HAND.toml: manifest declaring tools, settings, requirements, and dashboard metrics.
• System Prompt: multi-phase operational playbook. Not a one-liner. These are 500+ word expert procedures.
• SKILL.md: domain expertise reference injected into context at runtime.
• Guardrails: approval gates for sensitive actions (e.g. Browser Hand requires approval before any purchase).

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.

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OpenFang vs The Landscape

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

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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.

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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

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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.

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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

• Node.js >= 18 installed (download)
• OpenFang installed and initialized

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.

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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.

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Migrate from OpenClaw

Already running OpenClaw? One command:

# Migrate everything: agents, memory, skills, configs.
openfang migrate --from openclaw

# Migrate from a specific path
openfang migrate --from openclaw --path ~/.openclaw

# Dry run first to see what would change
openfang migrate --from openclaw --dry-run

The migration engine imports your agents, conversation history, skills, and configuration. OpenFang reads SKILL.md natively and is compatible with the ClawHub marketplace.

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OpenAI-Compatible API

Drop-in replacement. Point your existing tools at OpenFang:

curl -X POST localhost:4200/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "researcher",
    "messages": [{"role": "user", "content": "Analyze Q4 market trends"}],
    "stream": true
  }'

140+ REST/WS/SSE endpoints covering agents, memory, workflows, channels, models, skills, A2A, Hands, and more.

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Quick Start

# 1. Install (macOS/Linux)
curl -fsSL https://openfang.sh/install | sh

# 2. Initialize. Walks you through provider setup.
openfang init

# 3. Start the daemon
openfang start

# 4. Dashboard is live at http://localhost:4200

# 5. Activate a Hand. It starts working for you.
openfang hand activate researcher

# 6. Chat with an agent
openfang chat researcher
> "What are the emerging trends in AI agent frameworks?"

# 7. Spawn a pre-built agent
openfang agent spawn coder

irm https://openfang.sh/install.ps1 | iex
openfang init
openfang start

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Development

# Build the workspace
cargo build --workspace --lib

# Run all tests (1,767+)
cargo test --workspace

# Lint (must be 0 warnings)
cargo clippy --workspace --all-targets -- -D warnings

# Format
cargo fmt --all -- --check

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Stability Notice

OpenFang v0.5.10 is pre-1.0. The architecture is solid, the test suite is comprehensive, and the security model is deep. That said:

• Breaking changes may occur between minor versions until v1.0.
• Some Hands are more mature than others. Browser and Researcher are the most battle tested.
• Edge cases exist. If you find one, open an issue.
• Pin to a specific commit for production deployments until v1.0.

We ship fast and fix fast. The goal is a rock solid v1.0 by mid 2026.

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Security

To report a security vulnerability, email [email protected]. We take all reports seriously and will respond within 48 hours.

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License

MIT. Use it however you want.

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Links

• Website & Documentation
• Quick Start Guide
• GitHub
• Discord
• Twitter / X

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Built by RightNow

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