AI app for it and development · no coding needed
Multi-agent canvas delivery workspace
Reduce coordination overhead while keeping agent work inspectable and under named human approval.
Made for: Engineering teams and technical leads coordinating several AI agents on one delivery
What it does for you
The problem
Agent work is scattered across separate tools, so context, decisions and review state are lost between runs.
What it gives you
Reviewed multi-agent canvas run linked to delivery evidence
What you give it
Agent definitionstool configsmodel keysproject context
Build your own version of Canvas by MindPal, Catenary and more
One app with what these 5 AI tools do, yours to keep and change: Canvas by MindPal, Catenary, PraisonAI, Cursor Glass, Doop.
Everything these tools do, in one app
- Multi-agent management Allows users to create, run, and coordinate multiple AI agents simultaneously.Found in Canvas by MindPal, PraisonAI, Cursor Glass and 1 more
- Infinite canvas workspace Provides an unlimited 2D space to arrange and organize AI interactions or tools.Found in Canvas by MindPal, Catenary, Doop
- Non-linear conversation branching Enables branching conversations in any direction without losing context.Found in Canvas by MindPal
- Workflow chaining Connects outputs from one AI workflow or agent to another for sequential processing.Found in Canvas by MindPal, Catenary
- Local-first processing Runs all processing on the user's device, keeping data and keys local.Found in Catenary
- No telemetry Sends zero usage data or analytics to external servers.Found in Catenary
- Cross-platform support Works on multiple operating systems like macOS, Windows, and Linux.Found in Catenary
- Integrated code editor Includes a built-in code editor for writing and editing code within the workspace.Found in Catenary
- Terminal integration Provides native terminal emulation for command-line interactions.Found in Catenary
- Browser previews Displays local webviews for previewing web content alongside other tools.Found in Catenary
- Low-code framework Simplifies building complex AI systems with minimal coding.Found in PraisonAI
- Custom tool integration Allows adding custom tools and configuring them via files like YAML.Found in PraisonAI
- Support for many LLMs Works with a wide range of large language models (e.g., over 100).Found in PraisonAI
- Cloud handoff Switches tasks between local and cloud environments mid-execution.Found in Cursor Glass
- Parallel agent orchestration Coordinates multiple agents running concurrently to reduce context-switching.Found in Cursor Glass
- Visibility and monitoring Provides tools to monitor and track agent activities and performance.Found in Cursor Glass
- Shared memory system Stores design rules, decisions, and context in an editable file and knowledge graph.Found in Doop
- Bring-your-own-AI model Connects existing AI subscriptions (e.g., Claude, ChatGPT) without platform markup.Found in Doop
- Agent-to-agent review Enables multiple AI agents to critique each other's work on the same canvas.Found in Doop
- Open source Provides source code that can be inspected, modified, or self-hosted.Found in Doop
How it works, step by step
- Create, run and coordinate multiple AI agents at once
- Arrange agents and tools on an unlimited 2D canvas
- Branch conversations in any direction without losing context
- Chain outputs from one agent or workflow into the next
- Run processing locally on the user's device with local keys
- Send zero telemetry or usage analytics to external servers
- Support macOS, Windows and Linux
- Provide an integrated code editor in the workspace
- Provide native terminal emulation beside the canvas
- Show local webview previews next to other tools
- Build agent systems with a low-code framework
- Add custom tools configured through YAML files
- Connect a wide range of large language models
- Hand off tasks between local and cloud mid-execution
- Orchestrate concurrent agents in parallel
- Monitor agent activity and performance
- Store design rules, decisions and context in an editable file and knowledge graph
- Connect existing AI subscriptions without platform markup
- Let agents critique each other's work on the same canvas
- Inspect, modify or self-host the source code
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed multi-agent canvas run linked to delivery evidence 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 canvas 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 canvas 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 Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data201 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 agent work inspectable and under named human approval. For engineering teams and technical leads coordinating several AI agents on one delivery, convert agent definitions, tool configs, model keys and project context into a reviewed multi-agent canvas run linked to delivery evidence. The benefit is a testable hypothesis, measured through accepted agent tasks per delivery hour and rework after review; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent definitions, tool configs, model keys and project context, then follow this sequence: 1. Create, run and coordinate multiple AI agents at once. 2. Arrange agents and tools on an unlimited 2D canvas. 3. Branch conversations in any direction without losing context. 4. Chain outputs from one agent or workflow into the next. Resolve uncertain cases with qualified reviewers, approve reviewed multi-agent canvas run linked to delivery evidence, and measure accepted agent tasks per delivery hour and rework after review against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the 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 fixed agent topology and approved model list; final code, security and delivery decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, code accuracy and usage permissions. Named owners approve substantive changes and deployment scope. One fixed agent topology and approved model list; final code, security and delivery 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 fixed agent topology and approved model list; final code, security and delivery decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create, run and coordinate multiple AI agents at once; arrange agents and tools on an unlimited 2D canvas. Support the third module with operator review: branch conversations in any direction without losing context. 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, agent definitions, tool configs and permitted model providers. Cloud compute, code hosting, terminal and preview 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: Agent and tool setup, Editable canvas run, Review and delivery. Use a thumbnail gallery for projects, a large central canvas for arranging agents and branches, and a right-hand panel for context, memory and comments. Let users compare run versions side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant agent step. Make the task-specific outcome reviewed multi-agent canvas run linked to delivery evidence visible beside its evidence, review state and value baseline.
