space_.
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Project context for teams and AI agents

The whole project,in one context.

Decisions, constraints, scope and specs live in one place instead of chat threads and people's heads. Your team and your AI agents work from the same context, and every screen answers before you finish typing.

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12 vs 53
MCP calls for 12 PM tasks, against Linear's server
0
network requests to open a new issue
1 s
to reload a workspace of 10,000 issues

Context leaks out of every project.

A decision on a call. A rule in an email. A spec in someone's drive. When a teammate leaves or an agent starts a task, it is gone. Space_. turns each one into a record with an owner, a source and a status.

Checkoutproject context
  • call notesClient: fine, no card data on our side
    DEC-2Card data never touches our serversdecision, client
  • emailOK, +3 days for Apple Pay. We'll pay.
    DEC-8+3 days, +$1,800, M2 due Wed 2 Decchange request
  • spreadsheetassume Stripe pays out in VND?
    ASM-4Stripe pays out in VND, owner Linhassumption
  • Slackheads up, Stripe review can take 2 weeks
    RSK-2Stripe account review slipsrisk
  • Linh's headwhy we skipped saved cards
    SPECcheckout-v2.md, linked to 6 issuesdocument

Built for agents. One call, the whole task.

get_context returns readiness, blockers, the rules that bind the task and its spec in one round trip. Fewer calls make agents faster, cheaper and harder to confuse.

12 vs 53
tool calls for 12 common PM tasks, Space_. against Linear's MCP
1 call
to create 30 issues from a spec, numbered in order, in 0.73 s
~10k tokens
to load every tool, about half of Linear's estimated 18–20k

Our benchmark on both official MCP servers, October 2026. Method and raw results on request.

Scope is context too.

  1. 1 of 4

    Freeze the scope.

    The client signs off twelve issues. That is the baseline.

  2. 2 of 4

    Catch the extra.

    Anything added later lands outside it, whoever adds it.

  3. 3 of 4

    Price it, or own it.

    A change request moves the date and the bill. Absorbed work stays on record.

  4. 4 of 4

    Agents play by it.

    AI agents hit the same door and read each decision as a rule.

Faster than you can type.

Screens read a local store in the browser and the server sends only what changed. Nothing you type waits on the network, for people or for agents.

0requests
to open a new issue. The composer is up in about 50 ms.
1.0s
to reload a workspace of 10,000 issues, pulling 24 KB. It was 1.8 s and 2.9 MB.
463B
on the wire to move 20 issues at once. It used to be 5.87 MB.

Measured with our Playwright perf suite on a production build, October 2026.

They track the work. We keep the context.

Space_. compared with Linear and Plane
Featurespace_.LinearPlane
the work
Issues, cycles, projects, timeline
Status from GitHub
MCP server for AI agents
Runs on your own server
the context
One MCP call returns a task's full context
Accepted decisions read by agents as hard rules
Decisions with who decided, days and cost
Risks, assumptions and dependencies register
Scope baseline with change request or absorb

Based on Linear's and Plane's public docs, October 2026. Check before you quote it. Product names belong to their owners.

Give your next project a memory.

Start with one client project. Your team and your agents share its context from day one.