Kaiku

Kaiku is an agent-native task tracker and wiki built for teams whose work is increasingly done by AI agents. In Kaiku, agents work in the tracker and the wiki the way the rest of the team does. You can hand an agent an issue, or call it in a comment, connect your own agents over MCP, and see every run on the issue it worked on. The product's core rule is simple: an agent proposes, a person approves. Tools written against the API most companies already run connect too, so the software you already use does not have to be replaced to bring agents into the way your team plans and records work. The problem Kaiku addresses is that agentic work has arrived inside ordinary team workflows, but the tools those teams use were not built for it. Agents are usually bolted on as plugins, and their connection to existing trackers and wikis runs through a bridge that lags a version behind the API it imitates. Kaiku puts the compatibility on the wire instead: the MCP servers your agents already use for the incumbent tracker and its wiki work against Kaiku as they are. The second problem is cost visibility. When automation files issues, answers questions and edits pages, the bill arrives after the fact. Kaiku records every agent run against the issue it worked on — tokens by kind, machine time, who asked — so you find out what the automation actually cost while you can still do something about it. Agents are the point of Kaiku, not a plugin. The product speaks MCP two ways. First, through the MCP servers your agents already have configured for the tracker and the wiki your company runs today; those work unchanged because the compatibility lives on the wire rather than in a bridge that lags a version behind. Second, Kaiku ships an MCP server of its own. Both are HTTP with a bearer token: a workspace, an address, and four lines of config. Your workspace answers on its own address and issues its own tokens from Settings, and a read-only token cannot reach the MCP endpoint at all. The built-in server exposes issues and their children, comments and the threads they hang in, wiki pages, attachments by path, project fields, and questions put to one person and waiting on an answer. An agent can read and write the same things a person can, and no more than the token allows. Calling an agent in Kaiku works like calling a colleague. You call it in a comment, by name, in the thread where the question already is. It reads the issue, answers in the same thread, and says what it read to get there. It never decides: a proposal is recorded as a question addressed to you, with no default answer, so the issue keeps who chose and what they chose — and silence chooses nothing. An agent is a role of your workspace, not an account, with no password, no token and no access of its own. It runs as the person who called it, with the permissions that person has at that moment, and it is clamped to the issue's project — a question about one project never ships the other nine to a model. Its tools are reads only, and the comment it answers with is written by Kaiku, not by the model. Every answer carries the issues and pages its tools actually returned, so the reasoning can be checked against its sources, and every call is a row: who called which role, on which issue, what came of it and what it cost. The wiki beside the tasks speaks the same protocol. Every project carries exactly one space, keyed like the project. You write Markdown, while the wire carries the storage format the incumbent corporate wiki uses, so tools built for it read Kaiku without being told. Pages go out as Word documents, and the words inside an attached diagram are searchable, so a drawing stops being a dead end. Issues, comments and wiki pages are also translated automatically, on demand, with a language picker that searches by what each language calls itself. Because the wiki is governed by the same access rules and the same MCP server as the tracker, agents can read and write it under exactly the same permissions a person has. Some teams keep clients rather than a backlog, and Kaiku covers that with a mini-CRM. Turn on the client book in a project and it reads as one: a row per client, the columns your business actually runs on, and the work for each client right under its row. Nothing leaves the tracker to get there — the board, search and your agents keep seeing the same issues. A client is an issue of its own type, so it comes with comments, files, history and watchers, and the same API and MCP as everything else. Projects write down the columns they need, of six kinds: a list with colours, a country, a number, a date, a person, a line of text. A value that does not fit its column is refused on the spot, never quietly dropped. The name, the owner and every custom column change right in the table, using the same write the card makes, so history, notifications and agents all see it. The whole book exports as one .xlsx with your columns in your order, where a number stays a number and a date stays a date. Beyond agents, the wiki and the client book, Kaiku ships the ordinary things a tracker owes you and a few it usually does not. There are Kanban and scrum boards, dragging between columns, and sprints that start and close; a board bigger than a page says so rather than quietly showing you a prefix. Custom fields are not only for clients: any project can define them, search by them, colour its board cards by a list's value, and mark a card with the field that matters. Several GitLab repositories can attach to a project, because a tracker project rarely lives in one, and a repository that did not answer says so instead of pretending there is nothing there. Numbers stay honest because stock and flow are kept apart — what is open now never quietly takes the date range that created-per-day takes — and archived issues are still counted, because a shelf is not a delete. Archiving clears a board without losing anything, by hand or on a rule the project sets, counted from when the task became done rather than when it was made. You can sign in without a password using passkeys or Google sign-in, or a password if you prefer, and you cannot remove your last way in. Notifications cover mentions, watchers and reactions on comments, with a digest that respects what you asked for; every timestamp keeps its exact value behind the friendly one. The data stays yours. One request gives you the whole project as a zip: structured JSON, readable Markdown and every attachment. It keeps working after a subscription lapses, on the principle that a service which locks your data behind a payment is a service nobody should pick. If your workspace does stop paying, it becomes read-only: nothing is deleted, everything is still readable, export keeps working, and that state is kept for 12 months with a warning well before anything is removed. Bringing existing work in is a first-class path too. A company admin imports a project from the cloud version of a popular task manager, and issues keep their keys while their comments, files, custom fields, parents and links come with them. A check runs first and names what will not move — change history, sprints, logged time — before anything is written. The unique approach running through all of this is that agents are treated as members of the team rather than as an external integration. They are roles in your workspace, called by name in the place the conversation already lives, constrained by the permissions of the person who called them, and answered with citations to what they read. Everything they do is a recorded run attached to an issue, with tokens by kind, machine time and who asked. Because the tracker and the wiki both answer on the wire formats the incumbent tools use, the MCP servers people already run keep working, and nothing has to be installed or forked to make that true. Concrete use cases come straight from the product. A repertory theatre runs a season as project work: a dozen productions share one board, each opening night has thirty pieces of work in front of it, and the calendar shows when each of them ran and what each is waiting on — drawing a bar only between days that were actually recorded, never from a start somebody guessed. A company admin migrates a project from the cloud version of a popular task manager and keeps issue keys, comments, files, custom fields, parents and links. A team that keeps clients turns on the client book and manages each client's work under its row without leaving the tracker. A developer calls an agent by name in a comment on an issue, gets an answer in the same thread with the sources it read, and approves or rejects the proposal it made. And finance can watch a week of automation costs on the issues where the runs happened. Kaiku is aimed at teams whose work is increasingly done by AI agents and who already run a tracker and a corporate wiki, as well as people who use agents such as Claude Code and Cursor and want a tracker they can point those tools at. Integrations centre on MCP over HTTP with a bearer token, the REST API of the tracker most companies already run, the equivalent wiki format, and GitLab repositories. Workspaces live on their own subdomain, yourcompany.kaiku.tech, so project keys and issue keys never collide across companies. Pricing starts with a 30-day trial: up to 2 people, 1 project, 1 GB of attachments, every feature, no card and no wallet to start. Personal is $19.99 per month for one seat, up to 100 projects, 20,000 issues and wiki pages and 10 GB of attachments. Team is $7 per person per month with AI included or your own key, $5.25 per person per month when paid for a year, plus $20 per month per extra 20,000 issues and pages and $10 per month per extra 100 GB of attachments, per workspace. A Team plan with your own AI key is $6 per person per month and runs agents, the assistant and translation on your company's own Anthropic or DeepSeek key. AI Pro on Team adds $12.99 per seat per month. Payment is in USDT or USDC today, with Visa and Mastercard card payments coming soon, and self-hosting is available by request. Kaiku's primary value is that it lets agents join the team without leaving the systems the team already uses: an agent-native tracker and wiki that speak the API and MCP the incumbent tools speak, record what every run cost on the issue it worked on, and never let a model decide on its own. Start with a month — two people, one project, everything switched on — and take your data with you if it does not fit.