Command a team of AI agents from one chat — not a single chatbot.
Orkas is an open-source, local-first desktop app: a capable commander agent does the work itself and directs specialized sub-agents, all with your own LLM keys. Fully offline-capable. macOS · Windows · Linux.
One capable commander agent — with the strengths of a coding agent — does the work itself and directs a team of specialized sub-agents, all by conversation. No flowcharts, no orchestration code. Your conversations, files, and API keys never leave your machine.
- What it is — a desktop GUI app where you command a team of specialized AI agents through one chat. Not a single chatbot, not a code framework, not a hosted SaaS.
- A commander that does, not just delegates — the lead agent brings coding-agent strengths (precise file edits, careful tool use, engineering discipline, multi-step and long-horizon reasoning) and does the work itself; when a job needs a team, it assembles sub-agents to run in parallel or in series.
- Drives the open-source ecosystem — plug in external CLI coding agents (Claude Code, Codex, OpenCode, Cline) and onboard open-source projects like HyperFrames as local tools — so one commander can deliver code, research, data, video, and slides.
- Local-first — conversations, files, API keys, knowledge bases, and custom agents all stay on your disk. Model calls go straight from your machine to the provider — never through Orkas servers.
- Bring your own LLM keys — plug in Claude, OpenAI, Gemini, DeepSeek, Kimi, GLM, Qwen, MiniMax, or Doubao. Mix providers across agents. No vendor lock-in.
- Self-evolving — each agent has its own private skills and memory, and improves through reflection after each task.
⭐ If Orkas is useful to you, a star helps more people find the project.
- Automate recurring reports & market research — a sub-agent that gathers, summarizes, and ships a weekly report.
- Turn a product spec into dev tasks — the commander breaks a PRD into tasks and dispatches them across agents.
- Chat with your documents & run local data analysis — drop files in, keep the data on your machine.
- Go beyond code — video, slides, and more — the commander drives open-source tools like HyperFrames and hands off to CLI coding agents (Claude Code, Codex, OpenCode, Cline) and other local agents, so one chat produces code, research, video, and slide decks.
Explore use cases → research workflows · data analysis · chat with documents · for developers · automate your workspace
- Get the app → orkas.ai (macOS · Windows installers)
- Run from source → see Quick start below
| Tool | What it is | How Orkas differs |
|---|---|---|
| LangChain | A developer framework/library for building LLM apps and agents — code-first, embedded in your own Python/JS app. | Orkas is a no-code desktop GUI: you assemble and command a team of agents through chat, not by writing orchestration code. Data and keys stay local by default. |
| CrewAI | A Python framework for orchestrating role-playing autonomous agents — you define crews and agents in code. | Orkas gives you the same multi-agent orchestration without code, in a desktop app, with local-first storage and per-agent self-evolution built in. |
| Cloud agent platforms (SaaS orchestrators) | Server-hosted; conversations, files, and API keys live on the vendor's infrastructure. | Orkas is local-first: everything stays on your machine, and model API calls go straight to the provider — never archived by Orkas. |
| OpenClaw | A single always-on personal assistant reaching you across messaging channels. | Orkas builds a team of specialized agents directed from one desktop chat — and OpenClaw plugs in as an Orkas CLI backend. |
| Hermes-Agent | Nous Research's self-improving personal agent (TUI + multi-channel gateway). | Orkas is desktop-GUI and team-shaped, with per-agent private skills and meta-cognition — and Hermes-Agent plugs in as an Orkas CLI backend. |
Orkas is for you if you want a team of agents (not one assistant), a desktop GUI with file drop-in and visual agent management, and your data, keys, and agents on your own disk rather than a vendor cloud.
Not for you if you just want a single all-purpose chatbot, a fully hosted/cloud team where your data lives on a vendor's servers, or a pure code library to embed in your own app.
Full side-by-side comparisons → vs Claude Code · vs Cline · vs LangChain · vs ChatGPT · vs OpenClaw
What is Orkas? A local-first desktop app where you command a team of AI agents from one chat. A capable commander agent does the work itself and directs specialized sub-agents — not a single chatbot, not a code framework, not a hosted SaaS.
Is Orkas a local LLM? No. Orkas runs on your machine but calls the models you choose through your own API keys (or a local model endpoint). It orchestrates agents and tools — it is not itself a model.
Where are my API keys and data stored? On your disk. Conversations, files, knowledge bases, agents, and keys stay local; model calls go straight from your machine to the provider and are never proxied or archived by Orkas.
Does Orkas work offline? The app is fully offline-capable — only the model calls need network. Point agents at a local model endpoint and you can run without the cloud.
Can Orkas drive Claude Code and other CLI coding agents? Yes. Beyond its own commander and sub-agents, Orkas can drive external CLI coding agents — Claude Code, Codex, OpenCode, Cline — as local subprocesses, and onboard open-source projects like HyperFrames, all directed from the same chat.
How is Orkas different from Claude Desktop / CrewAI / LangChain? Claude Desktop is a single assistant; CrewAI and LangChain are code-first frameworks. Orkas is a no-code desktop app that commands a team of agents, keeps data and keys local, and gives each agent its own private skills and memory. See the full comparisons.
Is Orkas free and open source? Yes — MIT licensed. Bring your own model keys; you only ever pay your model providers.
Prefer the packaged app? Use the production installer links:
- macOS Apple Silicon -> Orkas-mac-arm64.dmg
- macOS Intel -> Orkas-mac-x64.dmg
- Windows x64 -> Orkas-Setup.exe
To run from source:
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 open Settings → AI Providers to add an API key or OAuth.
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 (
from==me ∨ to∋me ∨ mentions∋me). A worker never reads 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@, plan steps) funnels into the sameenqueueprimitive. No parallel routing paths. - Shared plan — when agents collaborate, the commander writes progress into one
plan.md, visible to every member.
- Structured dispatch — commander↔agent dispatches go through the
dispatch_to({to, message})tool call;@in prose is not treated as a dispatch signal (the user's@is still recognized — UX unchanged). - Deferred wake-up — a
dispatch_toonly stages; the recipient wakes only after the commander's turn finishes, preventing premature execution. - Turn-based safety stop — the runaway guard counts turns (
MAX_WORKER_TURNS=100), not wall-clock time, so a slow-but-progressing LLM isn't killed.
Each agent maintains, in its own directory:
meta/COMPETENCE.md— what it's good / not good at.meta/LEARNING_STRATEGIES.md— methods that have worked for it.
After each task the agent reflects and updates these; on the next task meta/ is fed back into the system prompt, so experience shapes the next run. Via the skill_manage tool an agent can also crystallize "how I solved X" into a private skill, reused directly next time.
Some core modules draw on these open-source projects — special thanks to: