TARS

TARS is a local-first AI project autopilot.
Unlike Claude Code, Aider, or Cursor, which mostly operate at the file and conversation level, TARS manages long-running projects autonomously: it plans phases with you, executes backlog work inside each phase, coordinates tools and worker agents, and only brings you back for approvals or real blockers. All in a single Go binary running on your machine.
Key Features
Project Autopilot
The killer feature. Describe what you want to build, and TARS handles the rest:
- Plan — Collects requirements through a short interview and turns them into a phase plan
- Phase Loop — Builds a backlog, selects the next task, executes it, evaluates the result, and replans when needed
- Capabilities — Combines built-in tools, skills, MCP servers, web research, and worker agents in one runtime
- Human-in-the-Loop — Escalates at phase approvals and real blockers instead of asking for every routine retry
- Dashboard — Live phase status, run status, pending decisions, blockers, and worker reports in a browser
tars init && tars serve
# In the TUI:
> todo 앱 만드는 프로젝트 시작해줘
Agent Runtime
- Terminal client with a Bubble Tea TUI + local HTTP API (
tars serve)
- Session lifecycle, transcript storage, and structured context compaction
- Agent loop with built-in file, process, scheduling, memory, and ops tools
- Built-in file tools with 2,000-line read pagination, continuation hints, and safe atomic writes
- Structured session compaction with identifier-preserving summaries, a safer recent-tail preserve policy, and manual
/compact [instructions]
- Parallel read-only chat subagents through the built-in
explorer gateway agent
- MCP transports for local stdio servers and remote HTTP/WebSocket endpoints, with bearer or OAuth auth for remote servers
- Semantic memory recall with Gemini embeddings (optional)
- Playwright-based browser automation
Extensibility
- Skill Hub —
tars skill search, tars plugin install, and tars mcp install from a vetted registry
- Plugins — Bundle skills and MCP servers with manifest metadata, runtime gating, and default project profiles
- Managed MCP Hub — Install checksum-verified MCP packages hosted in
tars-skills
- Skills — LLM instruction files (SKILL.md) with companion scripts and runtime gating by plugin, binary, env, and platform requirements
Install
Homebrew:
brew tap devlikebear/tap
brew install devlikebear/tap/tars
Curl:
curl -fsSL https://raw.githubusercontent.com/devlikebear/tars/main/install.sh | sh
Quick Start
# 1. Initialize workspace and config
tars init
# 2. Set your LLM provider
export OPENAI_API_KEY="your-api-key"
# Or use Claude Code CLI: set llm_provider: claude-code-cli in config
# 3. Validate setup
tars doctor --fix
# 4. Start the server
tars serve --config ./workspace/config/tars.config.yaml
# Or as a macOS background service:
tars service install && tars service start
# 5. Launch the TUI client
tars
Kick off a project from chat, or use the TUI commands directly:
/project board <project-id>
/project autopilot start <project-id>
/project autopilot advance <project-id>
The recommended path is planning first, then controlled phase advancement. advance runs one synchronous autopilot step so you can inspect approvals, blockers, and replans explicitly.
For read-heavy codebase research in chat, TARS can now fan out parallel explorer subagents and merge back compact summaries. The runtime defaults are:
gateway_subagents_max_threads: 4
gateway_subagents_max_depth: 1
Open the dashboard: http://127.0.0.1:43180/dashboards
Install trusted MCP packages from the hub:
tars mcp search
tars mcp install safe-time
Local stdio MCP servers still respect mcp_command_allowlist_json. For example, a Node-based MCP package requires a config allowlist such as:
mcp_command_allowlist_json: ["node"]
Requirements
- Go 1.25.6+ (for building from source)
- LLM provider credentials, or a local Claude Code CLI install
- Optional: Gemini API key for semantic memory embeddings
- Optional: Node.js for Playwright browser automation
Build
make build-bins
bin/tars version
Documentation
Status
Pre-1.0.0 — Module path: github.com/devlikebear/tars