Orkas

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Open-source, local-first multi-agent AI desktop client — command your AI agent team with chat.

★ 1.9k Stars Category · Other Very popular

51FMRS · D

Orkas is an open-source, local-first multi-agent desktop app: a Commander plans and dispatches work, nine specialist agents ship ready to use, and you bring your own model keys or local endpoint while data and keys stay on your machine. It also positions itself around MCP client capabilities, coordinating external CLI coding agents and open-source tools. It is a good fit for people who want a local agent team, and a poor fit for those who want a single chatbot, a pure code framework, or a fully hosted cloud team. The main risks are locally stored keys, the broad permission scope of running external subprocesses, and the Linux run-from-source constraint.

Reliability

8/20

Security and permissions

10/20

Maintenance

11/20

Documentation

12/20

Setup experience

10/20

Read the FMRS scoring method →

Orkas is an open-source, local-first multi-agent AI desktop client. You describe a goal; its Commander plans the work, handles the general parts itself, and coordinates specialist agents in parallel or in sequence. Nine specialist agents ship with the app (out of 30 in the marketplace), ready the moment you launch it. Bring your own model keys (Claude, OpenAI, Gemini, DeepSeek, Kimi, GLM, Qwen, MiniMax, Doubao) or point it at an OpenAI-compatible local endpoint. Conversations, files, knowledge bases, API keys, and custom agents all stay on your disk; model calls go straight from your machine to the provider and are never proxied or archived by Orkas. Each agent has its own private skills (SKILL.md) and memory and self-evolves through reflection and skill crystallization. macOS, Windows 10+, and glibc 2.34+ Linux x64/arm64 (Linux currently runs from source). MIT licensed.

Tools

hand_off_to

Commander hands the task to an agent and ends its turn; that agent's reply stands as the final answer the user sees.

dispatch_to

Commander dispatches a task to an agent; the agent's reply is visible to the user and the result returns to the Commander, which continues with a named next step.

run_worker

Commander dispatches an ephemeral sub-task with private input not shown to the user; the result returns to the Commander, which continues. Emitting several calls in one response runs them concurrently.

memory_search

Search the local knowledge base index over stored documents and past sessions (FTS5 full text merged with vector similarity by reciprocal rank fusion).

memory_read

Read document content from the local knowledge base index.

skill_manage

Crystallize "how I solved X" into a private SKILL.md that the agent can use from the next turn on.

Setup

For packaged installers: download and install the macOS (Apple Silicon or Intel) or Windows x64 build from the official download page. 2. To run from source (currently the way to run Orkas on Linux): ensure Node 20+, then run git clone https://github.com/Orkas-AI/Orkas.git, cd Orkas, and execute ./run.sh (macOS/Linux) or run.cmd (Windows). 3. First launch installs the locked npm dependency tree and prepares the pinned Python, uv, Node, embedding model (~95 MB), OfficeCLI, FFmpeg, whisper.cpp, and multilingual speech model resources; Linux startup then verifies platform-native modules and the Whisper runtime under Electron's ABI before opening the app. 4. First launch creates a workspace under ~/.orkas/ (macOS/Linux) or <smallest non-system drive>:\.orkas\ (Windows). 5. Open Settings → AI Providers to add an API key or OAuth; for local models choose Custom (OpenAI-compatible) and point the base URL at your endpoint. Note: the source build has no account layer and boots straight into the app, while the packaged app asks you to sign in on first launch.

Fit and risk

Best for

People who want a team of specialist agents coordinated by a Commander rather than a single chatbot
Users who insist their data, keys, and agents stay on their own disk instead of a vendor cloud
Users who want to mix multiple model providers or local model endpoints
Users who want agents with private skills and memory that improve through reflection

Not for

People who just want a single all-purpose chatbot
Users who want a fully hosted/cloud multi-agent team and don't mind their data living on a vendor's servers
Developers who want a pure code library to embed in their own app (e.g. LangChain or CrewAI-style frameworks)
Users who cannot run Node 20+ or are on musl-based distributions such as Alpine

Required permissions

Read and write a local workspace directory (~/.orkas/ on macOS/Linux, <smallest non-system drive>:\.orkas\ on Windows)
Store conversations, files, knowledge bases, custom agents, and API keys on your local disk
Outbound network access to the model provider endpoints you configure (and to cloud embedding providers if you choose them)
Run external CLI coding agents (Claude Code, Codex, OpenCode, Cline) as local subprocesses
Download and verify local resources on first launch (embedding model ~95 MB, whisper.cpp runtime and multilingual speech model, FFmpeg, OfficeCLI, and others)

Risks and side effects

API keys are stored on your machine's disk and can be read by any process or person with access to that account or device, so OS account security and disk encryption matter
The Commander can drive external CLI coding agents and run them as local subprocesses, so a model may take actions with side effects; scope the working directory and permissions yourself
Building from source installs a large dependency tree and prepares native runtimes, making first launch long and network-dependent
On Linux, only glibc 2.34+ x64/arm64 is supported; Alpine and other musl-based distributions are not
The packaged desktop app asks you to sign in on first launch; avoiding any account layer means using the source build in this repository
The optional built-in Orkas model is billed by Orkas in credits, so paths other than your own keys incur additional cost

Troubleshooting

If the app won't start, verify Node 20+ (source build) and that the platform is macOS / Windows 10+ / glibc 2.34+ Linux x64 or arm64
On Linux, native-module or Whisper runtime errors usually mean the glibc requirement is unmet; keep Python 3 and a C/C++ build toolchain available to rebuild native npm packages locally
If model calls fail, check that your key or OAuth under Settings → AI Providers is valid; for local models confirm the Custom (OpenAI-compatible) base URL points to the right local endpoint
Local speech transcription downloads and verifies the whisper.cpp runtime and multilingual model on first use, so a network-restricted environment disables that feature
If knowledge base retrieval returns nothing, confirm the documents were chunked and indexed and that the embedding model (~95 MB) downloaded successfully
OCR and skill-specific Python packages are installed into isolated local environments when those features are first used; if installation fails, the corresponding feature is unavailable

Use cases

Automate recurring reports and market research, with a specialist agent gathering, summarizing, and shipping a weekly report

Turn a product requirement document (PRD) into dev tasks and dispatch them across agents

Chat with your documents and run local data analysis while keeping the data on your machine

Produce code, research, video, and slide decks from one chat, with the Commander driving open-source tools like HyperFrames and handing off to CLI coding agents (Claude Code, Codex, OpenCode, Cline)

Mix providers across agents — one agent on Claude, another on DeepSeek or a local endpoint

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