Agiflow vs Autoplans
Agiflow and Autoplans arrive at the same idea from different ends: a project management tool with an MCP server, so the assistant you already use reads and changes the board rather than being told about it. Agiflow stays the board and leaves the work to whichever assistant you have connected. Autoplans also writes the backlog and ships the runtime that works it.
What Agiflow is
Agiflow is a hosted project board for people who already ask an AI assistant to organise their work; it describes itself as "a simple project board that works with the AI assistant you already use", and it is sold to solo builders and small teams — developers among them, though its guides also cover marketing publishing flows, sales pipelines and freelance client work. Work is arranged as projects, work units and tasks, where a work unit is the feature-level container that groups related tasks into one deliverable and holds the goal, the task order, the acceptance-criteria summary, decision notes and expected artifacts alongside them. Assistants reach it over MCP, and a connection is bound to a resource: its documentation states that organisation, project, work-unit and task endpoints expose different tool sets, so a narrower endpoint reduces what the assistant can read or update.
Stay with Agiflow if
- •Non-developers share the board. Agiflow’s guides cover marketing publishing flows, sales pipelines and freelance client work as well as engineering; Autoplans assumes a repository and a coding agent.
- •You want an assistant’s connection bound to one project, work unit or task rather than a key that reaches the whole account.
- •Work units are the level you think in. A feature-level container above the task is something Autoplans does not have — it goes project, task, subtask, and stops.
- •Several assistants write to the same board at once, and the workflow locks that keep them from colliding are worth having.
Consider Autoplans if
- •Writing the backlog is the part you want done for you, rather than typed onto a board by whichever assistant happens to be open.
- •You want the runtime as well as the board: a desktop app and a CLI that do the work, on the subscriptions already on your machine.
- •You want the plan committed with the code, so every app and every agent that opens the folder is pointed at the same backlog.
- •You want dependencies drawn rather than described — the task map renders the project as a graph, tasks as nodes and dependencies as edges.
Where they differ
| Autoplans | Agiflow | |
|---|---|---|
| Where the backlog comes from | A planning agent turns a description of the project into tasks with priorities, types, dependencies and done-criteria. You edit them. | Whatever your assistant writes through the MCP tools. Agiflow supplies the board, the tools and the skills; the planning is your assistant’s. |
| The object model | Project, task, subtask — one level deep — with dependencies as real edges and a task map that draws them. | Project, work unit, task. The work unit sits above the task as a feature-level container, and workflow locks coordinate work against a project, a work unit or a task. |
| How a connection is scoped | One account-wide endpoint, https://autoplans.dev/api/v1/mcp, with permission scopes on the key such as tasks:read, tasks:write and ai:execute. | Bound to a resource. Organisation, project, work-unit and task endpoints expose different tool sets, and the narrower one sees less. |
| Who runs the agent | Autoplans does, if you want it to: the desktop app and the CLI carry five agents and a runtime that uses the Claude, Copilot or Ollama access already on the machine. | Your assistant does. Agiflow states outright that it “does not run or host AI agents” and gives teams “a shared project board” that external assistants work from; it publishes an MIT-licensed plugin bundle on GitHub for the assistants people already run. |
| Who it is sold to | Developers. The model assumes a repository: .autoplans/project.json links a folder to a project, and pushed commits attach to tasks. | Solo builders and small teams, developers among them, alongside marketing, sales and freelance client work. |
Questions
Is Autoplans an Agiflow alternative?
They overlap more than any other pair on this page: both are a board an MCP client works from, both are aimed at small teams, and both leave the assistant you already use in charge of the conversation. Two questions decide it. Whether you want the breakdown generated rather than merely stored, which is what the planning agent is for; and whether you want the agent runtime supplied, which Agiflow deliberately does not do. If the board also carries work that is not engineering, that answers it the other way.
How should I choose an AI project management tool for developers?
Decide what you want it to do beyond storing tasks. If your assistant already writes and closes them and you only need somewhere durable to keep them, a board with an MCP server is enough, and there are now several. If you want the breakdown produced from a description, the dependencies recorded as data rather than prose, and the record kept current without being asked, that is the extra part. Either way the honest test is short: connect one, ask your agent what is left to do, and see whether the answer is useful.
Can an agent have both connected at once?
Yes. They are separate MCP servers with separate keys, and neither assumes it is the only one loaded. The practical limit is payload: the Autoplans server alone exposes around forty tool schemas, and attaching two full servers to every model call is a great deal to send — which is why the first-party desktop app and CLI load a bundled plugin of thirteen task-shaped tools instead of the whole server.
Our description of Agiflow was last reviewed 2026-08-28.