Orkas — Open-Source Multi-Agent AI Desktop Client, Build and command your AI agent team through conversation
Open-source multi-agent AI desktop client for AI workflow orchestration. Build your AI team in one chat: a commander LLM assembles an agent team, dispatches sub-agents in parallel or in series, and lets agents self-evolve through reflection and skill crystallization. Local-first storage, BYO LLM API keys (Claude · OpenAI · Gemini · DeepSeek · Kimi · GLM · Qwen · MiniMax · Doubao), cross-platform on macOS, Windows, and Linux. A no-code, GUI-native team layer for local agents — OpenClaw, Hermes-Agent, Claude Code, Codex, and other local CLI agents all plug in seamlessly.
Your AI workforce · Open · Local · Yours forever
AI learns how you work · Stays private · Pays you back later
Multi-agent collaboration · Self-evolving agents · Local-first storage · Cross-platform desktop app
🌐 Want multi-device sync, remote control, team collaboration, and more? → Commercial edition
⭐ If Orkas helps you build better AI workflows, please consider giving it a star — it helps more people find the project.
Full design and hard constraints →
CLAUDE.md
In one chat there's a commander, N agents, and you — but each agent does not see the same conversation.
- Visibility slicing — the main conversation is one full jsonl; each agent only gets a slice in its own
visibility/<aid>.jsonl:from==me ∨ to∋me ∨ mentions∋me. The worker only reads its own slice and never the full main conversation — saves tokens and prevents private context from leaking across agents - One scheduling primitive — every dispatch (the commander's
dispatch_to, the user's@in text, steps split out from a plan) funnels into the sameenqueueprimitive. No parallel routing paths. Any new dispatch path must go through it, to avoid scattered "who-can-wake-whom" rules - Shared plan — when multiple agents collaborate, the commander writes the progress into one
plan.md, visible to every member
LLMs love using @ as a markdown decoration — recognizing @ in prose as a dispatch signal triggers false positives over and over. So:
- Structured dispatch — dispatches between commander and agents must go through the
dispatch_to({to, message})tool call (a structured channel);@in prose is not recognized as a dispatch signal (the user's@is still text-recognized — user UX unchanged) - Deferred wake-up — a
dispatch_tocall only stages; the recipient worker is woken up only after the commander's current turn finishes, preventing premature execution - Turn-based safety stop — the runaway-loop guard counts turns (
MAX_WORKER_TURNS=100), not wall-clock time. A slow LLM that's making progress isn't a runaway loop
Each agent maintains two kinds of self-knowledge in its own directory, written by the agent itself:
meta/COMPETENCE.md— what I'm good at / not good atmeta/LEARNING_STRATEGIES.md— methods that have worked for me
After each task, the agent reflects and updates these two files; on the next task, meta/ is fed in as part of the system prompt, so experience actually shapes the next run.
The other evolution path is the skill_manage tool: an agent can crystallize "this is how I solved X" into a skill that only belongs to itself (a private SkillStore, independent of the global skill library). The next similar task calls it directly — no need to re-derive it every time.
Orkas isn't a single personal AI assistant that follows you across messaging channels, and isn't a hosted SaaS — it's a desktop app where you assemble a team of specialized agents and command them through one chat.
| Tool | What it is | Where Orkas differs |
|---|---|---|
| OpenClaw | A personal AI assistant you run on your own devices, reaching you across the messaging channels you already use. Single-user, always-on, channel-native. | Orkas is a desktop multi-agent client: instead of one assistant on every channel, you build a team of specialized agents and direct them through a single desktop chat — visibility-sliced collaboration, a shared plan.md, and per-agent self-evolution. OpenClaw also plugs in as an Orkas CLI backend, so an Orkas agent can hand work off to your OpenClaw. |
| Hermes-Agent | Nous Research's self-improving personal AI agent — a TUI plus multi-channel gateway, with a built-in learning loop, scheduled automations, and the ability to run on a cheap VPS or serverless infra. | Orkas is desktop-GUI and team-shaped: a commander LLM dispatches a team of agents in parallel or in series through one chat; each agent has its own private skill library and meta-cognition, and the entire stack runs locally on your machine. Hermes-Agent is also pluggable as an Orkas CLI backend. |
| Cloud agent platforms (SaaS multi-agent orchestrators) | Server-hosted; conversations, files, and API keys live on the vendor's infrastructure. | Orkas is local-first: conversations, files, API keys, knowledge bases, custom agents / skills / memory all stay on your machine. Model API calls go straight from your machine to the provider — never through Orkas servers, and never archived. |
Orkas is for you if: you want a team of agents, not a single personal assistant; you want a desktop GUI with file drop-in and visual agent management; and you want your data, keys, and agents on your own disk rather than in a vendor cloud.
Requirements: Node 20+ · Python 3 · macOS / Windows 10+ / recent Linux
git clone https://github.com/Orkas-AI/Orkas.git
cd Orkas
./run.sh # macOS / Linux
run.cmd # Windowsrun.sh / run.cmd auto-installs dependencies and downloads the embedding model (~95 MB). First launch creates a workspace under ~/.orkas/ (macOS / Linux) or <smallest non-system drive>:\.orkas\ (Windows). Then go to Settings → AI Providers to configure an API key or OAuth.
⭐ Got Orkas running? A star on the repo goes a long way toward keeping the project moving.
Some core modules in this project draw on the following open-source projects — special thanks to: