tailagent-local

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  step1: Connect agents (Auto)

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  Step2: Import your projects (Auto)

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  Tasks

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  Approvals

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  Workflow

Inspiration

I wanted an easy way to run and manage AI agents locally without sending project data anywhere else.

What it does

I use tailagent-local to run local agents, manage their progress on a Kanban board, handle approvals efficiently, and inspect activity through traces.

How we built it

I built tailagent-local as a lightweight local Node.js app with a simple browser-based interface. Everything runs on the user’s machine.

Challenges we ran into

I had to make agent execution, task states, approvals, and real-time updates work together without making the experience complicated.

Accomplishments that we're proud of

I’m proud that tailagent-local brings tasks, local agents, approvals, Git changes, and traces into one easy-to-use workspace.

What we learned

I learned that local-first agent tools need clear progress tracking, human control, and good visibility into what each agent is doing.

What's next for tailagent-local

I want to make local agent workflows even easier, improve automation and integrations, and provide better monitoring while keeping data private and local.

Built With

• codex
• typescript