Multi-agent coding workspace with persistent project memory

For

Software teams running several AI coding agents on shared projects

Solves

Agents lose project context between sessions and teams juggle separate tools for terminals, memory, review and traceability.

Delivers

A searchable, source-linked workspace where agents resume with context

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Keep agent work continuous and traceable in one owned workspace.

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.
4. Run and manage terminal sessions for agents and manual work.
5. Restore conversation context, workspace layout and model selection after a restart.
6. Search past conversations, decisions and solutions to reuse information.
7. Group agents, terminals, docs and files into per-project workspaces.
8. Keep agent work separated per session in isolated worktrees to avoid collisions.
9. Let agents run commands directly in terminal sessions with visibility.
10. Navigate and control agents with keyboard shortcuts and a command palette.
11. Accept spoken commands to control tools instead of clicking through menus.
12. Read project context from the repository instead of pasted snippets.
13. Link commits, issues and plans back to the conversation that produced them.
14. Review code changes, images and documents without leaving the app.
15. Alert when agents stall, error or need input.
16. Pin important commands for quick access and keep long-running tasks alive.
17. Keep project data and credentials on the local machine without cloud dependency.
18. Access the workspace remotely with end-to-end encryption.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned, source-linked workspace record with unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Repository files, terminal sessions, agent conversations and project decisions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository files

4. 3

   Terminal sessions

5. 4

   Agent conversations and project decisions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A searchable, source-linked workspace where agents resume with context

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, workspace versions, agent permissions, approval states, usage allowances, session limits, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: run multiple AI coding agents side by side in one workspace; preserve project context across sessions. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will software teams running several AI coding agents on shared projects use it to solve "agents lose project context between sessions and teams juggle separate tools for terminals, memory, review and traceability"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Context-recovery success rate and accepted agent-assisted changes per review hour.
4. Measure, then decide. Track context-recovery success rate and accepted agent-assisted changes per review hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the searchable, source-linked workspace where agents resume with context. Retain the explicit scope boundary: One repository layout and one agent runtime; final code review and merge decisions remain human.

What the build depends on. Repository upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository layout and one agent runtime; final code review and merge decisions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined workspace package. Offer a monthly production allowance after repeat demand. Quote complex multi-repo or enterprise integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, source-linked workspace where agents resume with context. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Keep agent work continuous and traceable in one owned workspace. Demonstrate a concrete searchable, source-linked workspace where agents resume with context using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams running several AI coding agents on shared projects professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable, source-linked workspace where agents resume with context from a small authorized input set, with a transparent calculation of context-recovery success rate and accepted agent-assisted changes per review hour and no promised savings.

The first 30 days

1. Week 1: interview five software teams running several AI coding agents on shared projects and inspect a recent example of agents losing project context between sessions and teams juggling separate tools.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure context-recovery success rate and accepted agent-assisted changes per review hour, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Context-recovery success rate and accepted agent-assisted changes per review hour. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Context-recovery success rate and accepted agent-assisted changes per review hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a searchable, source-linked workspace where agents resume with context. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved workspace configurations, agent handoff patterns and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software teams running several AI coding agents on shared projects. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Subspace, MeshPilot, hob, Phasr, PMB, Fabric CLI, ChetakAI, MemoryPlugin for OpenClaw, Context Overflow and Termexo. Compare this product with the buyer's present method on context-recovery success rate and accepted agent-assisted changes per review hour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, agent runtime hours, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the searchable, source-linked workspace where agents resume with context. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.