Arkavo Edge

Instant, secure orchestration for AI agents—launch, mesh, and monitor in real time.

Quick Start

Install on macOS

Download the installer from the releases page, open the .pkg file, and follow the installation wizard.

For advanced users: Install via Homebrew

brew tap arkavo-org/homebrew-arkavo
brew install arkavo

Install on Linux

brew tap arkavo-org/homebrew-arkavo
brew install arkavo

Raspberry Pi 5: Download ARM64 binary from releases. See deployment guide for setup. Recommended models: Ministral 3B (text+vision) or gemma-3 270M.

Install on Windows

Download the installer from the releases page and run the .exe file.

Launch

# Start an agent (zero config)
arkavo

# Or launch web UI
arkavo ui

That's it. No configuration files, no setup. Agents auto-discover via mDNS and form a mesh.

Coming from OpenClaw?

See the migration guide for a full comparison: what you gain (budget controls, TDF encryption, PII preflight, offline operation), what's different, and step-by-step setup.

Why Arkavo?

• Zero config: Just run arkavo. Auto-naming, auto-routing, auto-discovery.
• Fast: Low-latency agent-to-agent communication. Run cargo bench -p arkavo-protocol --bench a2a_latency to measure.
• Visual: See live agent communication flows in real-time.

Features

• Multi-provider routing (OpenAI, Anthropic, Gemini, Kimi, DeepSeek, local models)
• Ministral 3 support - Local edge models (3B/8B/14B) with vision via llama.cpp
• Cost-aware model selection
• GitHub issue orchestration
• iOS simulator automation (macOS only)
• Security scanning (Semgrep, OSV, SBOM)

Usage Examples

Chat

# Use any provider with API key
GEMINI_API_KEY=your-key arkavo chat --prompt "Hello"
DEEPSEEK_API_KEY=your-key arkavo chat --prompt "Explain Rust"

Context Control Demo

The Autonomous Refactor demo demonstrates Active Context Management. It simulates a large-scale "breaking change" refactor that generates extensive compiler output, showing how the Context Ledger maintains a small active window while preserving data access.

cd examples/autonomous_refactor
./run_demo.sh

Custom Agent Config (Optional)

arkavo agent init my-agent  # Creates AGENTS.md template
# Edit AGENTS.md to set model, capabilities, API keys
arkavo  # Runs with your config

Security (Optional)

OpenTDF Integration: Fine-grained access control for MCP tools via OpenTDF. Set OPENTDF_BASE_URL, OIDC_ISSUER, and AUD environment variables.

Coding Agent Toolset

Arkavo Edge includes a comprehensive suite of MCP tools for AI coding agents:

Code Search & Intelligence

• codegrep_search: Fast repository-wide code search with ripgrep
• struct_find_replace: Language-aware structural search and replace with Comby
• syntax_tree: AST parsing for syntax-aware code analysis with tree-sitter

Security & Quality

• sec_semgrep: SAST scanning with Semgrep
• deps_osv: Dependency vulnerability scanning with OSV-Scanner
• sbom_syft: SBOM generation with Syft

Test & Automation

• browser_cdp: Chrome DevTools Protocol automation via chromiumoxide
• test_run: Multi-language test runner (pytest, jest, go test, cargo test, xcodebuild)

GitHub Orchestration

# Auto-handle GitHub issues with AI agents
GITHUB_TOKEN=ghp_token arkavo orchestrator poll --repo owner/repo

Features: Issue classification, agent assignment, PR reviews, budget tracking. See orchestrator docs.

Ephemeral Workspaces

• workspace_container: Container-based isolated execution with resource quotas (Docker/Podman)

SWE-bench Evaluation

• swe_bench: Objective benchmarking harness with metrics tracking

See docs/coding-agent-toolset.md for complete tool documentation.

Platform Support

Platform	Architecture	Features
macOS	ARM64 (Apple Silicon)	Full support including iOS testing, local/remote LLM, mDNS
Linux	x86_64, ARM64	Full support with local/remote LLM, mDNS
Linux (musl)	x86_64	Static/slim binary with memory and mDNS support
Windows	x86_64	Memory, remote LLM, and mDNS support (no iOS testing)

mDNS discovery uses pure Rust implementation (mdns-sd crate) with no system dependencies

Note: iOS simulator automation and testing capabilities are only available on macOS.

Building from Source

Prerequisites

Install required build tools:

# macOS
brew install cmake ccache

# Linux (Debian/Ubuntu)
sudo apt install cmake ccache build-essential

# Linux (Fedora)
sudo dnf install cmake ccache gcc-c++

Setup llama.cpp

Clone the llama.cpp dependency (not tracked in git):

git clone https://github.com/ggerganov/llama.cpp vendor/llama.cpp
cd vendor/llama.cpp
git checkout d23355afc
cd ../..

Build

cargo build

The default build includes mDNS discovery using a pure Rust implementation (mdns-sd crate) that doesn't require system libraries like Avahi or Bonjour. This provides true portability across all platforms.