Orkas
A local-first desktop workforce where a Commander coordinates specialist agents for complex work.
What does this agent do, and when should you use it?
Orkas is an MIT-licensed, local-first desktop application for people who need coordinated agent work on complex tasks. In one chat, its Commander interprets a goal, breaks it down, and dispatches specialist agents when needed, while also handling general analysis, writing, research, file work, and automation itself. Agents can work in parallel or in sequence with focused skills, memory, and task context, while collaboration progress is shared through plan.md. The application runs on macOS, Windows, and Linux; conversations, files, knowledge bases, agent configurations, and model keys remain on the local machine, while model calls go directly to the chosen provider. It supports providers including Claude, OpenAI, Gemini, DeepSeek, Kimi, GLM, Qwen, MiniMax, and Doubao, and can drive local CLI coding agents such as Claude Code, Codex, OpenCode, and Cline.
A user submits a goal in the main chat, and the Commander uses the relevant context before staging work for specialists with dispatch_to({to, message}); recipients wake after the Commander’s current turn finishes. Every dispatch is routed through enqueue, and the Commander records collaborative progress in the shared plan.md. The main conversation is stored as jsonl, while workers see only messages matching from==me, a recipient list containing them, or mentions containing them. Each agent keeps meta/COMPETENCE.md and meta/LEARNING_STRATEGIES.md in its own directory and can use skill_manage to crystallize a solution into a private skill. Orkas can also run Claude Code, Codex, OpenCode, and Cline as local subprocesses.
- A product manager wants to turn a PRD into actionable development tasks and distribute the work among specialists.
- A researcher needs to gather, summarize, and deliver recurring market research while keeping working files on the local machine.
- A team handling sensitive local material wants to drop files into a desktop app for document chat or data analysis.
- A developer wants to coordinate Codex, Claude Code, OpenCode, or Cline from the same conversational workflow.
- A small team needs one coordinated workflow that can produce code, research, video, and slide decks using local tools.
What are this agent's strengths and limitations?
- Local-first storage keeps conversations, files, knowledge bases, agent configuration, and keys on the machine; model calls are not proxied or archived by Orkas servers.
- It provides Commander-led multi-agent coordination through a desktop chat instead of requiring orchestration code.
- It supports multiple model providers and can mix providers across agents.
- It can incorporate Claude Code, Codex, OpenCode, and Cline as local subprocesses from the same chat workflow.
- Per-agent meta/ memory and private skills support reflection and reuse after tasks.
- Model calls still need network access unless a local model endpoint is configured; offline-capable does not make cloud models available offline.
- Users must supply their own provider API keys or OAuth and pay their model providers.
- Linux has no packaged installer and currently requires running from source.
- Source startup requires Node 20+ and Python 3, and first launch downloads an approximately 95 MB embedding model.
- The supplied documentation does not establish support for enterprise identity, centralized key management, or cloud-based team sharing.
How do you install or deploy this agent?
Packaged installers are available from orkas.ai for macOS and Windows; Linux currently requires running from source. Source execution requires Node 20+, Python 3, and macOS, Windows 10+, or recent Linux:
git clone https://github.com/Orkas-AI/Orkas.git
cd Orkas
./run.sh
On Windows, use run.cmd. The scripts install dependencies and download an approximately 95 MB embedding model. After first launch, add a model-provider API key or OAuth in Settings → AI Providers.
How do you use this agent?
After first launch, configure an API key or OAuth in Settings → AI Providers, then describe the goal in the main chat. The Commander either completes general work directly or dispatches specialist agents as needed, with shared progress recorded in plan.md. Claude Code, Codex, OpenCode, and Cline can be used as CLI coding agents in the same workflow.
How does this agent compare with similar options?
Compared with code-first frameworks such as LangChain and CrewAI, Orkas offers a local desktop chat interface for coordinating work. Compared with cloud agent orchestrators, it keeps data and keys on the local machine. It is distinct from a single always-on assistant such as OpenClaw or a single-assistant experience such as Claude Desktop: Orkas coordinates multiple specialists through its Commander, while OpenClaw and Hermes-Agent can serve as CLI backends.