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Tsinghua University releases PilotDeck 2.0 open-source agent OS

By PulseAugur Editorial · [1 sources] ·

Tsinghua University, in collaboration with ModelBest and OpenBMB, has launched PilotDeck 2.0, an open-source operating system for AI agents. This new version features isolated WorkSpaces for individual projects and makes agent memory visible and editable. A key innovation is its smart routing capability, which directs simpler subtasks to less resource-intensive models, resulting in an estimated 83% cost reduction for long tasks during team testing. AI

IMPACT This open-source agent operating system could streamline AI agent development and reduce operational costs through efficient task routing.

RANK_REASON This is a release of an open-source operating system for AI agents, which falls under tooling.

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Tsinghua University releases PilotDeck 2.0 open-source agent OS

How we ranked this

Signal score
8 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
This is a release of an open-source operating system for AI agents, which falls under tooling.
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

  1. Pandaily TIER_1 English(EN) · [email protected] (Pandaily) ·

    Tsinghua, ModelBest and OpenBMB Release PilotDeck 2.0, an Open-Source Agent Operating System

    PilotDeck 2.0 isolates each project in its own WorkSpace, makes agent memory visible and editable, and routes routine subtasks to lighter models, cutting long-task cost by about 83% in team tests.

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