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The openJiuwen community released the latest version of JiuwenClaw, which adds support for AgentTeam — a multi-agent collaborative capability. It proposes that the next leap beyond Harness Engineering is Coordination Engineering.
In in-depth tests, this team collaboration mechanism has demonstrated remarkable stability —team members have clear roles, collaborate autonomously with seamless coordination, and the entire workflow requires no human intervention.

It can autonomously assemble a “well-trained” team of agents — and with that team, it can produce a solid, logically rigorous 200‑page technical PPT in under 20 minutes.
Project links: https://github.com/openJiuwen-ai/jiuwenclaw
Want deep insights without lifting a finger? A 200‑page, content‑rich PPT in under 20 minutes.
In our trial, we asked it to conduct an in‑depth investigation of OpenClaw technology and break it down across 10 core aspects. For each aspect, it assigned a dedicated agent to take charge. Each agent was responsible for generating 20 PPT slides, all under a unified theme. Finally, the 10 sets of slides were merged into a complete, 200‑page technical presentation.
The entire process took less than 20 minutes. The resulting PPT was detailed, logically structured, and impressively efficient.
The core design philosophy of AgentTeam is straightforward: simulate how real-world teams collaborate.
JiuwenClaw AgentTeam delegates this responsibility to the Leader Agent itself.
What the Leader does:
What Teammates do:
Team members drive the core workflow through task collaboration—claiming, executing, completing, unblocking downstream tasks—discussing plans, negotiating priorities, flagging issues, requesting support.
Both channels run in parallel, with task dependencies managed automatically—not simply mechanical distribution and aggregation.
JiuwenClaw AgentTeam solves this with Team Workspace—a team‑level shared file space that all members can transparently access. Each Teammate’s working directory automatically mounts a shared path pointing to the same team workspace.
AgentTeam provides a two‑layer approval mechanism:
AgentTeam mitigates this with an event‑driven mechanism, using both external and internal events:
After an event is triggered, the relevant agents are automatically awakened (e.g., idle Teammates claim tasks, the Leader reassigns timed-out tasks)
With Persistent mode enabled, teams can be preserved across sessions: The next time you need the team, you can restore it with one click—create a new session space, restart the team members, and you’re ready to go, without rebuilding the team from scratch.
TeamMonitor providing observability in two dimensions:
The core technical principles of AgentTeam can be summarized in three points:

About JiuwenClaw
JiuwenClaw is a “Claw” Agent developed on top of the openJiuwen open‑source community. It natively supports multi‑agent collaboration and agent self‑evolution. The core design philosophy is simple: Understand what you want, and evolve autonomously.
Beyond AgentTeam, JiuwenClaw is also very easy to install and deploy – a single command gets you up and running. For a quick start, refer to: https://github.com/openJiuwen-ai/jiuwenclaw/blob/develop/docs/en/Quickstart.md
In addition, JiuwenClaw offers several advantages in autonomous task planning, self‑evolution, context compression and offloading, browser manipulation, and overall “lobster‑like” handling:
The enterprise-grade version, OfficeClaw, built on the Harness engineering foundation, seamlessly integrates task planning, multi-agent collaboration, tool invocation, and security governance on Huawei Cloud AgentArts, improving the success rate of complex office tasks.
Join the Community & Explore openJiuwen
openJiuwen Download Links
JiuwenClaw Download Links
Note: Thanks to the OpenJiuwen team for the resources, images, video, and other details.
Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
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