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Niuniu

Set the goal. An agent team finishes it.

Niuniu is a local-first AI work platform: write the work as issues, and many agents plan, execute, and verify in parallel on Claude Code / Codex / any model — from writing code to building spreadsheets, querying data, and drafting docs. It only comes back when truly blocked.

Screenshot of the Niuniu workstation: parallel task list, chat panel, commit feed, and terminal

Live demo

Many agents, running in parallel

Turn single-session Claude Code's “one chat you drive” into many workspaces driving many agents at once — each picking its own flow from your kanban stages, looping autonomously.

Niuniu demo animation: 3 workspaces each running an agent, each picking its own kanban stages, looping autonomously in parallel
3 workspaces, each driving one issue; agents advance in parallel, fully isolated, finishing on their own. Illustrative animation (not a screen capture of the desktop app), shown to convey the parallel scheduling model.

Core capability

Goal in. Done out.

You define the goal; the agent owns plan → execute → verify on its own, and only comes back to you when it actually needs you.

  1. 1

    Write the goal

    File the work as an issue — one sentence is enough; no need to spell out the code.

  2. 2

    Agent plans

    Inside the workspace, the agent reads context, breaks the work down, and proposes a plan.

  3. 3

    Parallel execution

    Many workspaces pursue their own goals in parallel; each agent works directly in its own git worktree.

  4. 4

    Self-verification

    After changes, the agent runs tests, reads the diff, self-reviews, and iterates on failures.

  5. 5

    Only when blocked

    You only get pinged for real blockers — missing credentials, decisions, risky actions. Otherwise it ships quietly.

Pick your deployment

Two flavors — from solo desktop to enterprise self-host.

🖥

Personal

Local-first · 3 platforms · Zero deps

  • macOS / Windows / Linux
  • git worktree isolation
  • SQLite backend
🐳

Self-Hosted Team

Docker · On-prem · Unlimited

  • docker compose deploy
  • Private data
  • Custom audit

One platform across software · office · data · content

🎯

Goal-driven, autohosted loop

Write the goal; the agent plans, executes, verifies, and wraps up on its own — an unattended autohost watchdog keeps it going and only pings you when blocked.

🗂

Parallel workspaces, worktree-isolated

Each workspace gets its own git worktree + isolated agent session, running in parallel across projects and repos without collisions.

🔀

Any agent, any model — no lock-in

Drive Claude Code / Codex / Qwen Code, and point them at GLM / DeepSeek / MiniMax / Kimi or any compatible endpoint via scene env presets.

📋

Kanban + Harness engineering gates

Track issues / checklists / comments / timeline; Harness wires lint / tests / AI review into a pre-commit gate.

🎬

Scenes: one-click work modes

18 built-in scenes spanning software / office / data / content, declaratively projecting MCP + plugins + quick actions — auto-recommended, layerable, forkable.

📊

Data intelligence: query + live dashboards

Connect SQL / Redis / Mongo / ES / HTTP, run governed read queries, and pin inline charts as continuously refreshing dashboards.

🧠

Knowledge base + project memory

Ingest local directories into a searchable knowledge base; a white-box project memory captures patterns, decisions, and gotchas — searchable, mergeable, restorable.

💬

IM bots: Lark / DingTalk / Telegram

Drive work conversationally from group chat — inbound text & images, markdown out, one bot routed across multiple projects.

Unattended runs & schedule management

Give a workspace a cron schedule to auto-trigger the agent; the autohost watchdog keeps it moving unattended, with run history you can review.

Why Niuniu

Niuniu vs Claude Desktop vs OpenAI Codex

All three run agents in parallel and support MCP. The difference: Niuniu isn't locked to one vendor, spans software through office / data / content, and can run entirely on your own machine or network.

🐮 Niuniu Desktop Claude Desktop OpenAI Codex Desktop
Model / engine Drives Claude Code / Codex / Qwen + any compatible endpoint (GLM / DeepSeek / Kimi), switchable per workspace — no vendor lock-in Claude models only OpenAI models only
Work types Software + office + data + content, 18 built-in scenes one click away General assistant + coding (Chat / Cowork / Code) Focused on agentic coding
How work is organized Kanban, issue-driven: columns / checklists / comments / timeline / execution plans Chats / projects Tasks / sessions
Parallel execution Many workspaces, each its own worktree, scheduled across projects from one board Cowork can run parallel subagents (within a local folder) Built-in worktrees, multiple agents in parallel / cloud
Workflow & gates Harness engineering gates (pre-commit / AI-review) + kanban stages + unattended autohost watchdog Cowork multi-step runs Automations
Data intelligence Connect SQL / Redis / Mongo / ES / HTTP, governed queries + pinned live dashboards Bring your own via MCP No built-in data platform
IM collaboration Lark / DingTalk / Telegram two-way conversational, routed across projects None None
Knowledge & memory Knowledge-base ingestion + white-box searchable project memory Memory / projects Limited
Deployment & data sovereignty Local-first (SQLite); self-hosted team edition on docker + PG, multi-tenant / audit, data can stay on-prem Cloud account (Cowork accesses a local folder) Cloud account + local
Platforms macOS / Windows / Linux macOS / Windows macOS / Windows
MCP support Yes — and exposes its own context to agents as tools Yes — most mature ecosystem Yes (plugins)

Compiled from publicly available 2026 product information; competitor capabilities evolve — for reference only.

Niuniu dashboard screenshot

One workstation. Many parallel AI Agent sessions.

Community & traction

Developers are shipping in parallel with Niuniu

Free to use, local-first, actively iterating — follow releases and feedback on GitHub.

Free
Desktop edition, free to use
3
Platforms · macOS / Windows / Linux
100%
Local-first · data stays on your machine by default
Unlimited workspaces
Single-session Claude Code only let me watch one task at a time. With three workspaces running at once I ship roughly twice as much before end of day.
Indie developer Full-stack · SaaS side project
One git worktree per workspace means agents never step on each other — parallel refactors no longer risk breaking someone else's branch.
Backend engineer Mid-size dev team
The self-hosted team edition runs on our own machines, so code never leaves the network. Our compliance team finally signed off.
Engineering lead Fintech startup

Ready to get started?

Download the desktop edition, or explore Self-Hosted Team.