Agent Operating System

The Agent
Operating System

Open-source Agent OS built in Rust. 7 autonomous Hands that run on schedules, build knowledge graphs, and report to your dashboard. 30 agents, 40 channels, 38 tools, 26 LLM providers. 16 security systems. One binary.

137K lines of Rust · 16.8k★ on GitHub

[ .SH ]
Install>curl -fsSL https://openfang.sh/install | sh
Update>curl -fsSL https://openfang.sh/update | sh
[ 01 / 07 ]Trusted By
01
0
Hands
02
0
Security Systems
03
0+
Channels
04
0
LLM Providers
[ 02 / 07 ]Main Features
Main Features

Everything in one binary

Nine primitives that ship compiled together — hands, agents, tools, security, memory, channels, protocols, and a native desktop app.

01[ HANDS ]

7 Autonomous Capability Packages

Pre-built agents that work FOR you. Clip turns video into shorts. Lead generates qualified leads. Collector monitors targets. Predictor forecasts with Brier scores. Researcher fact-checks with CRAAP. Twitter manages your X account. Browser automates the web. Activate, configure, check your dashboard.

02[ SECURITY ]

16 Security Systems

WASM dual-metered sandbox, Ed25519 manifest signing, Merkle audit trail, taint tracking, SSRF protection, secret zeroization, HMAC-SHA256 mutual auth, GCRA rate limiter, subprocess isolation, prompt injection scanner, path traversal prevention, and more.

03[ RUNTIME ]

Sandboxed Execution

Tool code runs inside WASM with dual metering (fuel + epoch interruption). File operations are workspace-confined. Subprocesses are env-cleared and timeout-enforced. 10-phase graceful shutdown.

04[ AGENTS ]

30 Pre-Built Agents

From orchestrators to code reviewers to customer support. Four performance tiers across Anthropic, Gemini, Groq, and DeepSeek. Spawn one in a single command.

05[ TOOLS ]

38 Built-In Tools + MCP

38 native tools plus Model Context Protocol client and server. Connect external MCP servers and expose OpenFang tools to other agents. Includes web search, browser automation, image generation, TTS, Docker, knowledge graphs.

06[ MEMORY ]

Persistent Memory

SQLite-backed storage with vector embeddings. Cross-channel canonical sessions, automatic LLM-based compaction, and JSONL session mirroring. Agents recall context across conversations and channels.

07[ CHANNELS ]

40 Channel Adapters

Telegram, Discord, Slack, WhatsApp, Teams, IRC, Matrix, and 33 more. Per-channel model overrides, DM/group policies, rate limiting, and output formatting. One agent, every platform.

08[ PROTOCOLS ]

MCP + A2A + OFP

Model Context Protocol (client + server), Google A2A agent-to-agent tasks, and OpenFang Protocol for P2P networking with HMAC-SHA256 mutual authentication. Your agents talk to the world.

09[ DESKTOP ]

Tauri 2.0 Native App

Native desktop application with system tray, notifications, single-instance enforcement, auto-start on login, and global shortcuts. Full dashboard in a native window.

[ 03 / 07 ]Comparison
Comparison

OpenFang versus the landscape

Ten dimensions, six frameworks. Measured from official docs and public repos.

[ FEATURE × FRAMEWORK ][ 10 × 6 ]
Feature
OpenFang
OpenClaw
ZeroClaw
CrewAI
AutoGen
LangGraph
01Language
Rust
TypeScript
Rust
Python
Python
Python
02Autonomous Hands
7 built-in
None
None
None
None
None
03Security Layers
16 discrete
3 basic
6 layers
1 basic
Docker
AES enc.
04Agent Sandbox
WASM dual
None
Allowlists
None
Docker
None
05Channel Adapters
40
13
15
0
0
0
06Built-in Tools
53 + MCP
50+
12
Plugins
MCP
LC tools
07Memory
SQLite + vec
File-based
SQLite FTS5
4-layer
External
Checkpoints
08Desktop App
Tauri 2.0
None
None
None
Studio
None
09Audit Trail
Merkle chain
Logs
Logs
Tracing
Logs
Checkpoints
10License
MIT
MIT
MIT
MIT
Apache 2.0
MIT
OpenFang 9
ZeroClaw 1
// winners highlighted per feature
[ 04 / 07 ]Benchmarks
Benchmarks

Measured, not marketed

Cold start, idle memory, install size, security depth, channel coverage, and provider support — across six leading agent frameworks.

OpenFang
Category winner
Others
Cold Start Time[ lower = better ]
ZeroClaw
10ms
OpenFang
180 ms
LangGraph
2500 ms
CrewAI
3000 ms
AutoGen
4000 ms
OpenClaw
5980 ms
Idle Memory Usage[ lower = better ]
ZeroClaw
5 MB
OpenFang
40 MB
LangGraph
180 MB
CrewAI
200 MB
AutoGen
250 MB
OpenClaw
394 MB
Install Size[ lower = better ]
ZeroClaw
8.8 MB
OpenFang
32 MB
CrewAI
100 MB
LangGraph
150 MB
AutoGen
200 MB
OpenClaw
500 MB
Security Systems[ higher = better ]
OpenFang
16 layers
ZeroClaw
6 layers
OpenClaw
3 layers
AutoGen
2 layers
LangGraph
2 layers
CrewAI
1 layers
Channel Adapters[ higher = better ]
OpenFang
40 built-in
ZeroClaw
15 built-in
OpenClaw
13 built-in
CrewAI
0
AutoGen
0
LangGraph
0
LLM Providers[ higher = better ]
ZeroClaw
28 native
OpenFang
27 native
LangGraph
15 native
CrewAI
10 native
OpenClaw
10 native
AutoGen
8 native
[ 05 / 07 ]The Seven Hands
The Seven Hands

Agents that work for you

Hands are pre-built autonomous capability packages. Unlike agents you chat with, Hands run on schedules, build knowledge graphs, and report to your dashboard.

>

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

Each Hand bundles a HAND.toml manifest, a multi-phase system prompt, SKILL.md expert knowledge, settings, and dashboard metrics — compiled into the binary at build time.

01 / 07[ Content ]

Clip.hand

Turns long-form video into viral short clips with captions, thumbnails, and optional AI voice-overs. Publishes to Telegram and WhatsApp.

8-phase pipelineFFmpeg + yt-dlp5 STT backendsAuto-publish
02 / 07[ Data ]

Lead.hand

Autonomous lead generation. Discovers, enriches, scores, and deduplicates qualified leads on a daily schedule. Builds ICP profiles and knowledge graphs.

ICP scoring 0-100Web research loopsCSV/JSON/Markdown exportScheduled delivery
03 / 07[ Intelligence ]

Collector.hand

OSINT-style intelligence collector. Monitors any target continuously with change detection, sentiment tracking, and knowledge graph construction.

Change detectionSentiment analysisKnowledge graphsCritical alerts
04 / 07[ Forecasting ]

Predictor.hand

Superforecasting engine. Collects signals, builds calibrated reasoning chains, makes predictions, and tracks accuracy with Brier scores.

Brier score calibrationContrarian modeEvidence chainsAccuracy tracking
05 / 07[ Productivity ]

Researcher.hand

Deep autonomous researcher. Cross-references sources, fact-checks claims with CRAAP evaluation, and generates cited reports in multiple languages.

CRAAP fact-checkingMulti-languageAPA citationsSource verification
06 / 07[ Communication ]

Twitter.hand

Autonomous Twitter/X manager. Creates content in 7 rotating formats, schedules posts, engages with mentions, and tracks performance metrics.

Approval queue7 content typesEngagement trackingBrand voice control
07 / 07[ Automation ]

Browser.hand

Web automation agent. Navigates sites, fills forms, clicks buttons, and completes multi-step workflows. Mandatory purchase approval gate.

Purchase gatePlaywright bridgeSession persistenceCAPTCHA detection
08 / 07[ Custom ]

Build your own.hand

Define a HAND.toml with tools, settings, requirements, and a system prompt. Publish it to FangHub.

Hand development guide
$openfang hand --help
[ .SH ]
activate<hand>Activate a Hand (spawns autonomous agent)
deactivate<hand>Stop a running Hand
status<hand>Check Hand metrics and status
listList all available Hands
pause<hand>Pause a running Hand
resume<hand>Resume a paused Hand
[ 06 / 07 ]Cookbook
Cookbook

Recipes for every use case

Guides, references, and examples to help you build with OpenFang.

$[ 12 ]
[ 07 / 07 ]Built By
[ Built & maintained by ]
RightNow AIRightNow Labs

OpenFang is built by the founders of RightNow Labs.

[ Sponsored by ]
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OpenFangopenfang

Open-source Agent OS built in Rust. 137K lines of Rust. 16.8k★ on GitHub. One binary.

© 2026 RightNow AI · All systems operational
v0.1.0 · main · signed