CLAI

Control teams of AI agents. Local-first, open source, sandboxed.

One team per workspace — every agent with its own provider, files, tools, and permissions, working in parallel, on your schedule, and reporting to you from one Fleet cockpit.

▶ Watch the full demo

What it is

CLAI organizes work into workspaces. A workspace is an ongoing conversation with a main agent — a configurable LLM that owns the workspace's provider, tools, skills, and permissions. The main agent can:

• Chat with you in a surface that stays front and center
• Use tools through attached MCP servers and a local shell sandbox
• Delegate to helper agents in the same workspace, each with their own skills, tools, and execution policy
• Persist context — memories and artifacts it writes are inspectable from the workspace header
• Run on a schedule — periodic workspaces fire on their interval

The Fleet view supervises everything: scheduled workspaces float to the top, anything needing attention (failed or blocked tasks, input prompts) is flagged, and selecting a card slides in a live chat preview.

Features

• Workspace-local agent teams — Add helpers (e.g. a Code Reviewer or SoW Tracker) with their own prompts, skills, MCP servers, providers, and execution policy. The main agent calls them as tools, and you can read their full transcripts.
• Multiple providers — API connections (OpenAI-compatible or Anthropic-compatible) and local CLI agents such as Claude Code, OpenAI Codex, and OpenCode. Each agent picks its own.
• MCP-native tools — Configure MCP servers once in Settings, then attach them per workspace or per agent. HTTP and stdio transports.
• Local execution sandbox — Per-agent filesystem grants and three shell modes: Off (no generic local file access), Restricted (only allowed command prefixes — kubectl get permits kubectl get pods but not kubectl delete), and Full.
• A shared agent library — Teammates are defined once in Settings → Agents and added to the workspaces that need them. Editing one reaches every workspace using it; each workspace still owns its own main agent, history and schedule.
• Default skills — CLAI registers the read-only clairun/clai-skills repository by default.
• Inspectable tasks — Delegated work streams a live transcript: the helper agent's full conversation, tool calls, and verdict.
• Memory & artifacts — Agents persist findings to the workspace directory; the drawer surfaces both, with read-only previews (rendered markdown, pretty JSON, multi-file HTML).
• Run notices — A run that hits a policy denial finishes in an amber "warnings" state and surfaces in the Fleet, instead of failing silently.

Local execution sandboxing is platform-specific. On Linux, shell commands run through bubblewrap. On macOS, they run through Seatbelt via sandbox-exec. On Windows there is no sandbox backend: shell execution is labeled as a host shell, and while CLAI still validates the command's working directory against the filesystem grants, nothing stops the command itself from reaching outside them. Windows also ships no POSIX shell, so bash_exec looks for Git Bash, then MSYS2, then bash on PATH, and fails with an "install Git for Windows" notice when it finds none. The allow/block lists are enforced by CLAI before a command spawns, on every platform.

Install

Download the latest build for your platform from the Releases page:

Platform	Download
Windows	.msi / .exe
macOS	.dmg
Linux	.deb, .rpm, or .flatpak
Arch Linux	AUR: clai-desktop-bin

On Debian/Ubuntu you can install from the CLAI apt repository instead — updates then arrive through apt upgrade like any other package:

sudo curl -fsSLo /usr/share/keyrings/clai-archive-keyring.gpg \
  https://download.clai.run/apt/clai-archive-keyring.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/clai-archive-keyring.gpg] https://download.clai.run/apt stable main" | \
  sudo tee /etc/apt/sources.list.d/clai.list
sudo apt update && sudo apt install clai

On Fedora/RHEL, the CLAI rpm repository does the same for dnf upgrade:

sudo curl -fsSLo /etc/yum.repos.d/clai.repo https://download.clai.run/rpm/clai.repo
sudo dnf install clai

openSUSE: sudo zypper ar -f https://download.clai.run/rpm clai && sudo zypper install clai

(Installing the .deb/.rpm from the Releases page enrolls the same repository automatically; see packaging/linux-repo/README.md, including the opt-outs.)

Getting started

1. Add a provider — In Settings, connect an API provider or point CLAI at a local CLI agent. Supported CLI agents are auto-detected and pre-wired into new workspaces.
2. Add MCP servers (optional) — Register local or remote MCP servers for external tools.
3. Create a workspace — From the Fleet view. Open its settings (gear icon in the header) to attach a provider, MCP servers, and skills.
4. Chat with the main agent.
5. Add helper agents (optional) — Define them once in Settings → Agents, then add them to a workspace's team from its settings; the main agent can then delegate to them.
6. Make it periodic (optional) — Toggle Schedule to run the main agent on an interval.

Providers

• API connections — Add an OpenAI-compatible or Anthropic-compatible provider with an API key and optional custom base URL. Works with OpenAI, Anthropic, together.ai, Groq, and local endpoints (vLLM, llama.cpp, Ollama).
• CLI agents — Drive a locally installed coding CLI directly, such as Claude Code, OpenAI Codex, or OpenCode. CLAI exposes its tools to them over MCP and streams their output like any other run.

Each agent chooses its own provider, so you can mix — for example a Claude Code main agent with an OpenAI-compatible reviewer.

Development

git clone https://github.com/clairun/clai.git
cd clai
npm install
make dev            # run the desktop app in development

Before pushing:

npm run lint && npm run format:check && npm run build
cargo fmt --manifest-path src-tauri/Cargo.toml --check
cargo clippy --manifest-path src-tauri/Cargo.toml --all-targets -- -D warnings
cargo test --manifest-path src-tauri/Cargo.toml

Architecture

• Frontend — React + Tauri; a chat-first workspace with a drawer for agents / tasks / memories / artifacts and slide-out transcript and file panels.
• Runtime — Each workspace owns its main agent, its assignments of shared agents, and a persistent session. Built-in tools (shell execution, inter-agent calls, task management) plus MCP tools, gated by each agent's policy. An agent is resolved fresh at the start of every turn, so a shared edit lands on the next turn and never mid-tool-sequence.
• Scheduler — Periodic workspaces run from the agent runner, emitting the same streaming events as interactive chat.
• Skills — Discovered from read-only local or git sources. The app-managed default source is clairun/clai-skills.

License

MIT