The agent platform for work

One better way for people and AI agents to work together

LatchLoop brings coding agents and general knowledge-work agents into a multiplayer, task-based workspace. Plan the work, choose the model or harness, watch progress, steer the result, and keep the process your business develops.

Why the interface matters

Three layers of agent work, one operating system

Automation loops keep recurring jobs moving in the background. Long-running tasks hold the detailed plan and execution history for larger projects. Fast iterative tasks create a tight feedback loop wherever human taste and judgment matter. LatchLoop combines all three instead of forcing every job into the same chat thread.

How LatchLoop works

A complete workflow for directing agents, not just prompting them

Step 1

Describe and plan

Write the real task in a rich document editor. Ask questions with the task already in context, then append the resulting plan without copying between tools.

Step 2

Choose how it runs

Use LatchLoop’s model-agnostic coding or general harness, supported provider keys, Codex, or Claude Code. Switch agents when a different harness fits the next stage.

Step 3

Collaborate and steer

Assign teammates, co-edit the brief, see who sent each message, change the task while work is running, and queue follow-up direction from desktop, web, or mobile.

Step 4

Keep the result

Review code as branches and pull requests, render shareable artifacts, build connected agent apps, and keep general-agent process files in a repository you own.

Model and harness choice

Bring supported provider keys without token markup, use supported subscriptions, or choose LatchLoop, Codex, or Claude Code as the harness. LatchLoop is focused on the best workflow, not promoting one foundation model.

Multiplayer by default

Share projects, assign tasks, co-edit descriptions, and send attributed direction on your own or a teammate’s task. Everyone can see what the people and the agent contributed.

Ownership and safety

LatchLoop does not train models on your code or store your codebase. Code stays in GitHub, cloud coding runs stay on their assigned task branch, dangerous commands are guarded, and task message history can be deleted.

Honest tradeoffs

When another agent may fit better

Choose a model provider’s own app when included subscription usage, that provider’s newest model-specific features, or mature computer control is the main priority. Choose an open-source or IDE-native tool when deep terminal customization, local-model inference, or a particular editor is non-negotiable.

LatchLoop is newer than the largest model companies. Its browser use and custom cloud sandbox setup are still developing, and API-paid inference can cost more than subsidized plan usage. However, you can also select Codex or Claude Code as the harness and sign in with your existing ChatGPT or Claude subscription, so using LatchLoop does not always require separate API-paid inference. Choose LatchLoop when the durable advantage is a better, model-independent human-agent workflow that the whole team can use.

Research the market

Compare LatchLoop with leading agent platforms

Built by Velora. LatchLoop is created by a software company that has built products used by millions of people since 2009. The team uses LatchLoop to build and operate its own products, including Heights Platform, which serves more than 10,000 creator businesses.

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Build as fast as you can think.

LatchLoop works where you do to build with you.