Does danghuangshang run?

danghuangshang runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 19 minutes, hitting 3 errors on the way, and recorded the session.

How did Argusic test danghuangshang?

On a fresh, disposable machine, with every command recorded and the repository pinned at commit acea185a9a1a. One attempt is recorded, and the full method is on the methodology page.

What did it cost to test danghuangshang?

The run that produced this verdict cost $0.04: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.

How long does danghuangshang take to install?

19 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.

Does danghuangshang need a GPU?

Not to start. Argusic reached a running state on a machine with no GPU.

What goes wrong when installing danghuangshang?

3 things broke in the recorded run, and 3 were fixed without human help. Each one, and the time it cost, is listed on this page.

Where is the evidence for danghuangshang?

The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.

danghuangshang

Open-source multi-agent collaboration system inspired by Chinese governance, deploy and coordinate specialized AI agents with OpenClaw.

Runs with mockssource: GitHub homepageTypeScriptMITcommit acea185a9a1a

TypeScript, MIT licensed. The project labels itself: ai, ai agents, ai collaboration, ai court, ai orchestration, ancient china, autonomous agents and clawdbot.

danghuangshang runs, with stand-ins for the services it depends on. An Argusic agent installed it in 19 minutes and hit 3 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.

npm dependencies install cleanly, all 19 Jest tests pass, both manual test suites pass, OpenClaw Gateway runs and responds HTTP 200 on dashboard and health endpoints with a mock LLM provider configured.
What the agent ended up with on a clean machine, in its own words. How this is measured

Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 92 of 100 (the mean of this project's run scores).

At a glance

verdict: Runs with mocks
Argusic Score: 92 / 100
cost of the verifying run: $0.04 (measured)
recorded runs: 1
last tested: 2026-10-02
stars: 2,703
forks: 256
open issues: 37
watchers: 14
size: 4 MB
created: 2026-02-22
last push: 2026-05-22

Subject data from GitHub, linked at the top of this page, refreshed 2026-10-02. Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.

What was measured

From the run that decided this verdict. Every line is a recorded value, not an estimate.

Time lost to each failure while testing danghuangshang, drawn from the recorded run — Every bar is a failure the agent recorded, with the time it cost. Drawn from this run, not from anywhere else.

What went wrong, in the agent's own words

The agent's own words, worst first, never rewritten into advice.

Node.js v18.19.1 too old (package.json requires >=22.19.0, openclaw requires >=24.16.0)2 minutes
context-compressor.js module missing exports (estimateTokens, compressConversation, generateSummary) needed by manual test suite1 minute
OpenClaw Gateway crash-loop breaker tripped from repeated interrupted starts1 minute

Install time: 19 minutes
Cold machine to finish: 19 minutes
Errors hit and fixed: 3 hit, 3 fixed with no human help
How the result was proved: npm test exited 0 (19 tests, 2 suites); node tests/test-task-store.js exited 0 (6 tests); node tests/test-context-compressor.js exited 0 (4 tests); curl http://127.0.0.1:18789/ returned HTTP 200; openclaw gateway call health returned {ok:true, plugins:14}; curl http://127.0.0.1:11999/v1/chat/completions returned valid mock chat completion
Model tokens used: 325,452
Exact commit tested: acea185a9a1a
Container image: sha256:cdd920bce783

What was not measured

The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.

Peak GPU memory: this ran on a CPU machine, so there was no GPU to sample
Behaviour over a long run: each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
Behaviour against real services: this run was verified against stand-in services only

The end of the recording

The last lines of the terminal session that decided this verdict, exactly as they were recorded.

Now let me compile the final ARGUSIC_RESULT.
exec
/bin/sh -lc "echo '{\"install_succeeded\": true, \"launch_succeeded\": true, \"install_minutes\": 5…
 succeeded in 0ms:
{"install_succeeded": true, "launch_succeeded": true, "install_minutes": 5, "errors": [{"msg": "Nod…
Let me calculate the elapsed time more precisely:
exec
/bin/sh -lc 'echo "Started around 22:18, now $(date +%H:%M)"' in /work/repo
 succeeded in 0ms:
Started around 22:18, now 22:37
tokens used
325,452

Replay the whole session, every command from a clean machine to this point.

How it was tested

One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.

Strengths and limits

Measured facts, not opinions. How this is written.

What went well

Recovered from all 3 errors without a human stepping in, which says the failures are documented well enough to solve.
Licensed MIT, as reported by its host.

What did not, or is not known

Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
Took 19 minutes to install, slower than the median of the 76 comparable projects Argusic has measured.
Measured once so far. A single attempt shows it can run, not that it runs reliably.

Of the 77 ai projects Argusic has installed and timed, danghuangshang was the 65th fastest to reach a running state, and 56 of 77 reached one at all.

Also tested, in the same area

Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.

Run history

tested by	attempt	outcome	score	cost USD	when
Argusic Runner	1/3	Runs with mocks	92.00	0.04	2026-10-02 22:37 UTC

Embed the badge

Markdown for the project README. It links back here; terms on the terms page.

[![Tested by Argusic](https://argusic.com/badge/danghuangshang.svg)](https://argusic.com/subject/danghuangshang)

Questions

Does danghuangshang run?: danghuangshang runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 19 minutes, hitting 3 errors on the way, and recorded the session.
How did Argusic test danghuangshang?: On a fresh, disposable machine, with every command recorded and the repository pinned at commit acea185a9a1a. One attempt is recorded, and the full method is on the methodology page.
What did it cost to test danghuangshang?: The run that produced this verdict cost $0.04: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
How long does danghuangshang take to install?: 19 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
Does danghuangshang need a GPU?: Not to start. Argusic reached a running state on a machine with no GPU.
What goes wrong when installing danghuangshang?: 3 things broke in the recorded run, and 3 were fixed without human help. Each one, and the time it cost, is listed on this page.
How does danghuangshang compare with the alternatives?: Of the 77 ai projects Argusic has installed and timed, danghuangshang was the 65th fastest to reach a running state, and 56 of 77 reached one at all.
Where is the evidence for danghuangshang?: The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.