coreCerebrum compared with Conductor, Vibe Kanban, AgentsRoom and Kanban AI.
Running agents in parallel is no longer the hard part. Several tools do it, some of them are free, and one of them is funded and good. Pretending otherwise would insult the person reading this. The difference is what survives when the session ends, and whether anyone can tell you what the work cost.
What is table stakes, and what is not.
What every tool in this category already does
Every serious tool here runs agent sessions in parallel, isolates each one in its own git worktree, gives you a board or a list to watch them from, and hands back a diff at the end. A tool that cannot do those four things in 2026 is not really competing.
What almost none of them do once the session ends
Almost none of them keep what an agent learned, or carry it across to your teammates. Almost none refuse an operation you can never undo. Almost none let you share and extend a common skill set, and none of them will tell you what the tokens actually cost.
Named, not anonymised.
| Capability | coreCerebrum | Conductor | Vibe Kanban | AgentsRoom | Kanban AI |
|---|---|---|---|---|---|
| parallel agent sessions | Yes | Yes | Yes | Yes | Assistant only |
| git worktree isolation | Yes | Yes | Yes | Yes | No |
| board as the interface | Yes | Workspace list | Yes | Workspace list | Yes |
| memory across sessions | Persistent | Not named | Not named | Not named | Not named |
| memory shared across the team | Synced | Not named | Not named | Not named | Not named |
| token and cost accounting | Per tool, per turn | Not named | Not named | Not named | Not named |
| destructive-operation guard | Hard deny, 10 hooks | Not named | Not named | Not named | Not named |
| shared skills and commands | 24 and 22 | Not named | Not named | Agent personas | Not named |
| dispatched review agents | 5, by diff | Not named | Visual review | Yes | Not named |
| docs sync | Included | Not named | Not named | Not named | Not named |
| work beyond the repository | Cowork surface | No | No | Non-code agents | Task planning |
| cloud execution | Local only | Yes | Local | Local | Hosted |
| mobile app | No | Yes | No | No | No |
| real-time multiplayer | No | Yes | No | No | No |
| SSO and SCIM | No | Enterprise | No | No | No |
| open source | No | No | Yes | No | No |
| price | $150 / user / mo | Free, $50, $60 / user | Free | Free tier | Tiered |
“Not named” means the capability does not appear on that product's public feature or pricing pages as reviewed in August 2026. It is not a claim that the product cannot do it, and any of these can change. If one of them ships these things, this table should change, and we will change it.
Where TOON fits
Token-Oriented Object Notation is a compact encoding of the JSON data model for use inside LLM prompts. It has no board, no agents, no worktrees and no sessions, so putting it in the table above would compare a file format to a workstation.
It is complementary. TOON reduces the tokens a prompt costs to send; our cost accounting measures what the tokens cost once sent. Same problem, opposite ends. It is answered here because “why are you better than TOON” is a real question that deserves a real answer.
Take the free tool you are weighing up and coreCerebrum, give both the same task on your own repository, and compare the two branches. We will help you set it up even though it might not go our way.
Check the table, then decide.
If cloud execution or a phone app is what you need most, buy the one that has it. If what you need is memory that survives and a number on the work, talk to us.
A real number in two business days, from an engineer. No sales deck, and no call just to book another call.