MIT-licensed TypeScript CLI for turning repository goals into gated pull requests, with TUI, detached headless runs, multiple backend options, and explicit local-only, resume, and re-plan controls.
DSH integration
Ecosystem-related
Safety audit
Unaudited
Last verified
2026-09-13
License
MIT
01What can it help you accomplish?
Turn a repository goal into a machine-checkable plan and parallel implementation run
A pull request that has passed the declared gates
DeepSeek Harness users evaluating an autonomous coding CLI
Run coding work in the background and inspect progress remotely
A detached run with an ID, milestone watching, raw logs, run listing, and stop control
Teams connecting DeepSeek Harness workflows to CI or automation
Choose an available model backend for each run
A baro run using Claude, Codex, OpenAI-compatible, or another documented backend option
Developers who want to use DeepSeek Harness alongside other coding-agent backends
02How to install into DeepSeek Harness
Prerequisites
- Node.js 20 or newer
- git
- At least one backend: the `claude` CLI, `codex`, or an OpenAI-compatible endpoint
Installation steps
- 01
Install the CLI globally with `npm install -g baro-ai`.
$ npm install -g baro-ai
- 02
Change into a repository and run `baro "<goal>"`.
Verify the integration
- Run `baro --doctor`; the README says it checks your setup.
03DSH integration and capability boundaries
Contract-first planning
A repository goal→Machine-checkable invariants and obligations before code is written
Parallel story execution
A planned goal→Stories built in parallel across isolated git worktrees
Runs can create multiple isolated git worktreesFail-closed merge gates
A proposed change and its captured command output→Merge blocking based on declared tests, build, evidence, and write-surface checks
A failed gate blocks the mergeRun lifecycle controls
A run ID or an interrupted repository run→Watch, logs, runs, stop, resume, and continue commands
`--continue` always re-plans, while `--resume` never re-plans
04Who is it for? When not to use it?
Good for
- DeepSeek Harness users evaluating an autonomous coding CLI
- Teams connecting DeepSeek Harness workflows to CI or automation
- Developers who want to use DeepSeek Harness alongside other coding-agent backends
Not for
- The README requires Node 20+, git, and at least one supported model backend.
- The documented `--local-only` mode disables pushes and pull requests.
05Compatibility, maintenance and safety notes
- The README requires Node 20+, git, and at least one supported model backend.
- The documented `--local-only` mode disables pushes and pull requests.
- Resume and continue have different planning behavior: resume does not re-plan, while continue does.
The README describes baro as an autonomous software factory; the repository metadata identifies the project as MIT-licensed.
06Frequently asked questions
Does baro have a native DeepSeek Harness integration?
The README does not document a native DeepSeek Harness runtime, MCP server, or DSH-specific setup command. This record therefore classifies baro as ecosystem-related rather than native or compatible.
What do I need before running baro from a DSH workflow?
Install Node 20 or newer, git, and at least one supported backend. Install `baro-ai` globally, then run `baro --doctor` to check the setup.
Can baro run unattended for automation?
Yes. The README documents `baro --headless --detach --goal-file goal.txt`, which prints a run ID and returns immediately. Use `baro watch`, `baro logs`, `baro runs`, and `baro stop` to operate the run.
What is the difference between `--resume` and `--continue`?
`--resume` resumes an interrupted run from `prd.json` and never re-plans. `--continue` follows up on the current branch and always re-plans.
How can I prevent pushes and pull requests?
Use `--local-only`. The README describes that mode as having no pushes and no pull requests with hard isolation.
07Related DSH workflows
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NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of production-proven infrastructure and a WYSIWYG no-code interface, so you get both speed and reliability.
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Harness engineering beginner tutorial, from 0 to 1
08Data and sources
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-09-13. Found an error? Submit a correction.
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