AI app for it and development · no coding needed
Multi-agent coding workspace with persistent project memory
Keep agent work continuous and traceable in one owned workspace.
Made for: Software teams running several AI coding agents on shared projects
What it does for you
The problem
Agents lose project context between sessions and teams juggle separate tools for terminals, memory, review and traceability.
What it gives you
A searchable, source-linked workspace where agents resume with context
What you give it
Repository filesterminal sessionsagent conversationsproject decisions
Build your own version of Subspace, MeshPilot and more
One app with what these 10 AI tools do, yours to keep and change: Subspace, MeshPilot, hob, Phasr, PMB, Fabric CLI, ChetakAI, MemoryPlugin for OpenClaw, Context Overflow, Termexo.
Everything these tools do, in one app
- Multi-agent support Run multiple AI coding agents side by side in one workspace.Found in Subspace, MeshPilot, hob and 2 more
- Persistent memory Preserve project context across sessions so agents continue where they left off.Found in Subspace, MeshPilot, PMB and 3 more
- Unified workspace Combine terminals, files, docs, and other tools into a single application.Found in Subspace, MeshPilot, hob and 2 more
- Terminal sessions Run and manage terminal sessions for agents and manual work.Found in Subspace, MeshPilot, hob and 2 more
- Session recovery Restore conversation context, workspace layout, and model selection after a restart.Found in Termexo, Phasr, hob
- Searchable knowledge base Search past conversations, decisions, and solutions to reuse information.Found in Fabric CLI, MemoryPlugin for OpenClaw, Context Overflow and 1 more
- Workspace organization Group agents, terminals, docs, and files into per-project workspaces.Found in Subspace, MeshPilot, hob and 1 more
- Isolated worktrees Keep agent work separated per session to avoid collisions across projects.Found in hob, Phasr, Termexo
- Agent execution in terminals Let AI agents run commands directly in terminal sessions with visibility.Found in MeshPilot, hob, Termexo
- Keyboard-first interface Navigate and control agents quickly using keyboard shortcuts and a command palette.Found in Subspace
- Voice control Speak commands to control tools instead of clicking through menus.Found in MeshPilot
- Repo-aware assistant AI assistant reads project context from the repository instead of pasted snippets.Found in ChetakAI
- Commit and issue traceability Link commits, issues, and plans back to the conversation that produced them.Found in hob
- Built-in review surface Review code changes, images, and documents without leaving the app.Found in hob
- Notifications for agent status Get alerts when agents stall, error, or need input.Found in Phasr, Termexo
- Command pinning Pin important commands for quick access and keep long-running tasks alive.Found in Phasr
- Local-first storage Keep all project data and credentials on the local machine without cloud dependency.Found in PMB, Termexo
- Encrypted remote access Access workspace remotely with end-to-end encryption.Found in hob, MemoryPlugin for OpenClaw
How it works, step by step
- Run multiple AI coding agents side by side in one workspace
- Preserve project context across sessions so agents continue where they left off
- Combine terminals, files, docs and other tools into a single application
- Run and manage terminal sessions for agents and manual work
- Restore conversation context, workspace layout and model selection after a restart
- Search past conversations, decisions and solutions to reuse information
- Group agents, terminals, docs and files into per-project workspaces
- Keep agent work separated per session in isolated worktrees to avoid collisions
- Let agents run commands directly in terminal sessions with visibility
- Navigate and control agents with keyboard shortcuts and a command palette
- Accept spoken commands to control tools instead of clicking through menus
- Read project context from the repository instead of pasted snippets
- Link commits, issues and plans back to the conversation that produced them
- Review code changes, images and documents without leaving the app
- Alert when agents stall, error or need input
- Pin important commands for quick access and keep long-running tasks alive
- Keep project data and credentials on the local machine without cloud dependency
- Access the workspace remotely with end-to-end encryption
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned, source-linked workspace record with 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 coding workspace with persistent project memory 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 coding workspace with persistent project memory 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 data196 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
Keep agent work continuous and traceable in one owned workspace. For software teams running several AI coding agents on shared projects, convert repository files, terminal sessions, agent conversations and project decisions into a searchable, source-linked workspace where agents resume with context and humans review before merge. The benefit is a testable hypothesis, measured through context-recovery success rate and accepted agent-assisted changes per review hour; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository files, terminal sessions, agent conversations and project decisions, then follow this sequence: 1. Run multiple AI coding agents side by side in one workspace. 2. Preserve project context across sessions so agents continue where they left off. 3. Combine terminals, files, docs and other tools into a single application. Resolve uncertain cases with qualified reviewers, approve the searchable, source-linked workspace where agents resume with context, and measure context-recovery success rate and accepted agent-assisted changes per review hour 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. Local-first storage with encrypted remote access; final code review and merge 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 engineers approve substantive changes and merge scope. One repository layout and one agent runtime; final code review 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 layout and one agent runtime; final code review and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: run multiple AI coding agents side by side in one workspace; preserve project context across sessions. Support the third module with operator review: combine terminals, files, docs and other tools into a single application. 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
Team-owned repositories, authorized issue trackers and permitted documentation sources. Local file systems, version control, terminal environments and code hosting destinations. Start with file exchange and validate destination specifications before promising direct publishing. 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 agent board, Session and terminal view, Knowledge and traceability. Use a project switcher with per-project workspaces, a central pane for terminals and agent sessions, and a right-hand panel for context, memory and review. Let users compare agent runs side by side. Display running, stalled, needs-input and completed states. Provide a searchable knowledge view with links from commits and issues back to the conversation that produced them. Make the task-specific outcome a searchable, source-linked workspace where agents resume with context visible beside its evidence, review state and value baseline.
