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git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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OpenFang Project Research
Date: 2026-02-26 Scope: GitHub projects using the "OpenFang" name
Summary
There are three distinct projects on GitHub that share the "OpenFang" name:
| Project | Domain | Language | License | Stars | Status |
|---|---|---|---|---|---|
| RightNow-AI/openfang | Agent Operating System | Rust | MIT / Apache 2.0 | ~979 | Active (v0.1.0, Feb 2026) |
| anmaped/openfang | Camera firmware (Ingenic T20) | PHP/Shell | GPL-3.0 | ~188 | Dormant (last release 2018) |
| danshorstein/OpenFang | Python AI assistant | Python | Unknown | Low | Fork of OpenClaw |
1. RightNow-AI/openfang — Agent Operating System (Primary)
Website: openfang.sh Repo: github.com/RightNow-AI/openfang Built by: Jaber (RightNow AI)
What It Is
OpenFang is a production-grade Agent Operating System built from scratch in Rust. It is not a chatbot framework or a Python wrapper around an LLM — it is a full operating system for autonomous agents that run 24/7, building knowledge graphs, monitoring targets, generating leads, and managing social media.
The entire system compiles to a single ~32 MB binary.
Key Numbers
- 137,728 lines of Rust code across 14 crates
- 1,767+ passing tests, 0 clippy warnings
- 30 pre-built agents across 4 performance tiers
- 40 channel adapters (Telegram, Discord, Slack, WhatsApp, Signal, Matrix, etc.)
- 38 built-in tools + MCP integration
- 27 LLM providers supporting 123+ models
- 16 security systems
- v0.1.0 — first public release (February 2026)
Performance Benchmarks
| Metric | OpenFang | OpenClaw | CrewAI | AutoGen |
|---|---|---|---|---|
| Cold Start | 180ms | 5.98s | 3s | — |
| Idle Memory | 40MB | 394MB | 250MB | — |
| Install Size | 32MB | 500MB | — | 200MB |
The 7 "Hands" (Autonomous Agents)
- Clip — Video processing: downloads YouTube content, creates vertical shorts with captions
- Lead — Daily prospect discovery with ICP matching and qualification scoring
- Collector — OSINT intelligence with continuous monitoring and change detection
- Predictor — Superforecasting engine with calibrated reasoning and accuracy tracking
- Researcher — Cross-references sources using CRAAP criteria with APA citations
- Twitter — Account management across 7 content formats with approval gates
- Browser — Web automation with mandatory purchase approval safeguards
14 Core Rust Crates
| Crate | Purpose |
|---|---|
openfang-kernel |
Orchestration, workflows, RBAC |
openfang-runtime |
Agent loop, 53 tools, WASM sandbox |
openfang-api |
140+ REST/WS/SSE endpoints |
openfang-channels |
40 messaging adapters |
openfang-memory |
SQLite persistence, vector embeddings |
openfang-skills |
60 bundled skills |
openfang-hands |
Lifecycle management for autonomous agents |
openfang-extensions |
25 MCP templates |
openfang-wire |
P2P protocol |
openfang-cli |
Daemon management |
openfang-desktop |
Tauri 2.0 native app |
openfang-migrate |
OpenClaw/LangChain migration |
16 Security Systems
- WASM dual-metered sandbox (fuel + epoch interruption)
- Merkle hash-chain audit trails
- Information flow taint tracking
- Ed25519 signed agent manifests
- SSRF protection
- Secret zeroization
- OFP mutual authentication (HMAC-SHA256)
- Capability gates (role-based access)
- Security headers (CSP, HSTS, X-Frame-Options)
- Health endpoint redaction
- Subprocess sandbox with environment isolation
- Prompt injection scanner
- Loop guard with circuit breaker
- Session repair (7-phase validation)
- Path traversal prevention
- GCRA rate limiter
Protocol Support
- MCP (Model Context Protocol)
- A2A (Agent-to-Agent)
- OFP (OpenFang Protocol — proprietary P2P with HMAC-SHA256 mutual auth)
LLM Providers (27)
Anthropic, OpenAI, Google Gemini, Groq, DeepSeek, Mistral, xAI, Ollama, AWS Bedrock, and 18+ others — supporting 123+ models total.
Installation
# macOS/Linux
curl -fsSL https://openfang.sh/install | sh
openfang init
openfang start
# Dashboard: http://localhost:4200
# Windows
irm https://openfang.sh/install.ps1 | iex
Key Differentiators
- Single binary deployment — no Python, no Node, no Docker required
- OpenAI-compatible API — drop-in replacement capability
- Migration engine — imports from OpenClaw, LangChain, AutoGPT
- Dashboard-first — web UI at localhost:4200
- Desktop app — native Tauri 2.0 application with system tray
2. anmaped/openfang — Camera Firmware
Repo: github.com/anmaped/openfang
What It Is
An open-source bootloader, kernel, and toolchain for IP cameras using Ingenic T10 and T20 SoCs. This was one of the early community firmware projects for cheap Chinese IP cameras.
Supported Devices
| SoC | RAM | Cameras |
|---|---|---|
| Ingenic T20L | 64MB DDR | Xiaomi Mijia 2018, Xiaomi Xiaofang 1S |
| Ingenic T20N | 64MB DDR + SIMD128 | DIGOO DG W30 |
| Ingenic T20X | 128MB DDR | Wyze Cam V2, Xiaomi Dafang, Wyze Cam Pan |
Technical Details
- Kernel version 3.10.14
- U-Boot bootloader v2013.07
- Buildroot-based toolchain
- Docker support for compilation
- GPL-3.0 license
Status
- Last release: RC5 (November 2018) — dormant
- 188 stars, 43 forks, 10 contributors
- Largely superseded by OpenMiko, OpenIPC, and Thingino
3. danshorstein/OpenFang — Python AI Assistant
Repo: github.com/danshorstein/OpenFang
What It Is
An open-source fork of OpenClaw that rethinks personal AI agents. Built on the principle that "LLMs should write automations, not be automations."
Key Claims
- 90%+ reduction in token costs
- Faster execution and more reliable automations
- System gets cheaper over time as workflows graduate from LLM-orchestrated to Python-automated
Status
Low activity, small community. Positioned as a philosophical alternative to the mainstream agent frameworks.
Analysis & Relevance
Most Notable: RightNow-AI/openfang
The RightNow-AI variant is by far the most significant project:
- Active development with a February 2026 v0.1.0 release
- Rust-based architecture — high performance, single binary, low memory
- Comprehensive agent ecosystem — 30 agents, 40 channels, 38 tools
- Strong security posture — 16 dedicated security systems
- Production-oriented — not a research project or toy framework
Potential Relevance to RuVector
- The Rust architecture and WASM sandbox approach could inform solver optimization strategies
- The 14-crate modular design demonstrates a scalable Rust workspace pattern
- The security systems (especially taint tracking, prompt injection scanning) are relevant to any AI-adjacent system
- The performance benchmarks (180ms cold start, 40MB idle) set a useful reference point