awesome-agentic-ai-zh
A trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。
Runs with mockssource: GitHubhomepagePythonMITcommit dce3048e1f9f
Python, MIT licensed. The project labels itself: agentic ai, agentic workflows, ai agent, ai agents, awesome list, chinese llm, claude code and claude skills.
awesome-agentic-ai-zh runs, with stand-ins for the services it depends on. An Argusic agent installed it in 14 minutes and hit 8 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
The mkdocs site builds to _build/site/, the mdBook builds to book/dist/, and all 25 example test suites (stages 1-7) pass with mock/offline mocks, no real API keys required.
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
Subject data from GitHub, linked at the top of this page, refreshed . 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.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- missing langchain-core, langchain-openai, crewai, smolagents, pydantic-ai for stage-4 tests3 minutes
- missing numpy, chromadb for stage-6 tests2 minutes
- python3 pip not installed initially1 minute
- pytest not installed (6 test scripts failing)1 minute
- missing openai, anthropic packages for stage-1/2/3 tests1 minute
- missing fastapi, uvicorn, httpx for stage-7/05-deploy test1 minute
- Install time
- 14 minutes
- Cold machine to finish
- 21 minutes
- Errors hit and fixed
- 8 hit, 8 fixed with no human help
- Model tokens used
- 188,159
- Exact commit tested
- dce3048e1f9f
- Container image
- sha256:4c3d41857be3
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.
- How the result was proved
- runs before 2026-09-07 did not record the command used to check the result
- 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.
The warnings are all pre-existing content linking patterns in the docs — these are known existing i… Let me verify the final state: exec /bin/sh -lc 'cd /work/repo && echo "=== Site files ===" && ls -la _build/site/index.html 2>/dev/nul… succeeded in 35497ms: 25/25 example test suites passed All 25 example test suites pass, both builds (mkdocs and mdbook) succeed, and all content verificat… Here's the report: tokens used 188,159 All 25 example test suites pass, both builds (mkdocs and mdbook) succeed, and all content verificat… Here's the report:
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 8 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 14 minutes to install, slower than the median of the 7 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 8 agentic-ai projects Argusic has installed and timed, awesome-agentic-ai-zh was the 5th fastest to reach a running state, and 5 of 8 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 | 0.09 |
Topics (from GitHub)
agentic-aiagentic-workflowsai-agentai-agentsawesome-listchinese-llmclaude-codeclaude-skillsclilearning-roadmapllmllm-agentsmcpmodel-context-protocolmulti-agent-systemsprompt-engineeringragtrilingualtutorial
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/awesome-agentic-ai-zh)Questions
- Does awesome-agentic-ai-zh run?
- awesome-agentic-ai-zh runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 14 minutes, hitting 8 errors on the way, and recorded the session.
- How did Argusic test awesome-agentic-ai-zh?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit dce3048e1f9f. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test awesome-agentic-ai-zh?
- The run that produced this verdict cost $0.09: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does awesome-agentic-ai-zh take to install?
- 14 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 awesome-agentic-ai-zh need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing awesome-agentic-ai-zh?
- 8 things broke in the recorded run, and 8 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does awesome-agentic-ai-zh compare with the alternatives?
- Of the 8 agentic-ai projects Argusic has installed and timed, awesome-agentic-ai-zh was the 5th fastest to reach a running state, and 5 of 8 reached one at all.
- Where is the evidence for awesome-agentic-ai-zh?
- 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.