AI app for it and development · no coding needed
Multi-agent software delivery workspace
Reduce coordination overhead while keeping changes reviewable and mergeable.
Made for: Engineering leads and product teams coordinating several AI coding agents on one codebase
What it does for you
The problem
Multiple AI coding agents work in isolation, so plans, code, tests and reviews are scattered and hard to verify before merge.
What it gives you
Human-approved, tested changes linked to a merge decision
What you give it
Shared repositorytask backlogrole definitionsreview rules
Build your own version of Tonkotsu, Crew44 and more
One app with what these 5 AI tools do, yours to keep and change: Tonkotsu, Crew44, Gas City 1.0, ChatDev, SmolAgents.
Everything these tools do, in one app
- Multi-agent orchestration Coordinates multiple AI agents with different roles to work together on software tasks.Found in Tonkotsu, Crew44, Gas City 1.0 and 1 more
- Role-based agent assignment Lets you assign specific roles or responsibilities to different agents or models.Found in Tonkotsu, Crew44, ChatDev
- Shared project context Keeps project state and context accessible to all agents and humans involved.Found in Tonkotsu, Crew44
- Structured handoffs Passes work between agents in an organized way so they can build on each other's output.Found in Tonkotsu, Crew44
- Built-in verification Provides test plans and diff-based reviews to check changes before they are merged.Found in Tonkotsu
- Human review interaction Allows human reviewers to give feedback and suggestions to the agents.Found in Tonkotsu, ChatDev
- Local-first storage Stores session state and data on your own machine by default.Found in Crew44
- Open source Source code is available for inspection, modification, and community contributions.Found in Crew44, Gas City 1.0, ChatDev and 1 more
- No account required Can be used without creating an account or subscription.Found in Crew44
- Cross-project memory Optionally carries useful context from one project to another.Found in Crew44
- Workspace and packaging Organizes projects into workspaces and reusable configuration packages.Found in Gas City 1.0
- Lifecycle support Covers building, deploying, operating, and maintaining software produced by agent workflows.Found in Gas City 1.0
- Task scheduling and mixing Schedules tasks and mixes outputs from multiple models with configurable priorities.Found in Gas City 1.0
- Git integration Connects to Git for version control and tracking changes.Found in ChatDev
- Art generation Generates software-related images through a dedicated art designer agent.Found in ChatDev
- Minimal codebase Keeps the core implementation small to reduce overhead and simplify understanding.Found in SmolAgents
- Code-first configuration Uses code rather than complex JSON files to define agent behavior.Found in SmolAgents
- Sandboxed execution Runs agent-generated code in a secure sandbox to prevent unsafe operations.Found in SmolAgents
- Hugging Face Hub integration Provides easy access to models and tools from the Hugging Face Hub.Found in SmolAgents
- Multiple LLM providers Supports using different large language model providers for flexibility.Found in SmolAgents
How it works, step by step
- Coordinate multiple AI agents with different roles on one software task
- Assign specific roles or models to different agents
- Keep shared project state and context accessible to agents and humans
- Pass work between agents through structured handoffs
- Generate test plans and diff-based reviews before merge
- Let human reviewers give feedback and suggestions to agents
- Store session state and data locally by default
- Keep source code open for inspection and modification
- Allow use without an account or subscription
- Carry useful context from one project to another when enabled
- Organize projects into workspaces and reusable configuration packages
- Cover build, deploy, operate and maintain stages
- Schedule tasks and mix outputs from multiple models with configurable priorities
- Connect to Git for version control and change tracking
- Generate software-related images through a dedicated art designer agent
- Keep the core implementation small to reduce overhead
- Define agent behavior in code rather than complex JSON
- Run agent-generated code in a sandbox
- Access models and tools from the Hugging Face Hub
- Support multiple large language model providers
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned human-approved, tested changes linked to a merge decision with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Multi-agent software delivery workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Multi-agent software delivery workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data200 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce coordination overhead while keeping changes reviewable and mergeable. For engineering leads and product teams coordinating several AI coding agents on one codebase, convert a shared repository, task backlog, role definitions and review rules into human-approved, tested changes linked to a merge decision. The benefit is a testable hypothesis, measured through accepted changes per engineering hour and defects found after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect a shared repository, task backlog, role definitions and review rules, then follow this sequence: 1. Coordinate multiple AI agents with different roles on one software task. 2. Assign specific roles or models to different agents. 3. Keep shared project state and context accessible to agents and humans. 4. Pass work between agents through structured handoffs. 5. Generate test plans and diff-based reviews before merge. Resolve uncertain cases with qualified reviewers, approve human-approved, tested changes linked to a merge decision, and measure accepted changes per engineering hour and defects found after merge against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository and one supported language stack; final architecture, security and merge decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license compliance and usage permissions. Engineering leads approve substantive changes and merge scope. One repository and one supported language stack; final architecture, security and merge decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One repository and one supported language stack; final architecture, security and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: coordinate multiple AI agents with different roles on one software task; assign specific roles or models to different agents. Support the third module with operator review: keep shared project state and context accessible to agents and humans. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned repositories, issue trackers and CI pipelines. Cloud code storage, Git hosting and deployment destinations. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Workspace and task board, Agent run and diff review, Merge and release record. Use a project list for workspaces, a central run view with per-agent steps, and a right-hand panel for context, roles and review comments. Let users compare agent outputs side by side. Display planned, running, changes requested and approved states. Provide a reviewer view with comments anchored to the relevant diff line. Make the task-specific outcome human-approved, tested changes linked to a merge decision visible beside its evidence, review state and value baseline.
