AI Agent Tools: Sept 2026 GitHub Recheck, 3 Archived

Recheck date: 2026-09-17 (Taipei time). This time I rechecked the 26 GitHub repositories listed in the original article, reading each one's star count, archive status, license detection result, and the latest commit on its default branch. Dates in the tables below are all in UTC. This is a recheck of the same sample, not a full-market ranking, and not an installation test of the 26 tools.

When choosing an AI agent tool, it is easy to be carried along by star counts. Among the 26 projects in this recheck, the three original repositories Flowise, AgentGPT and Roo Code are still archived; and the default branches of the other four unarchived projects have had no new commit for more than four months as of the query date.

But "recently pushed" can also mislead. AutoGPT's repository shows a push on September 16, yet its default-branch latest commit stopped at September 4; SWE-agent's two dates are September 14 and July 16 respectively. Push date, default-branch commit, and substantive feature maintenance are three different things.

If you only want to try one tool first, I would start with a candidate that can complete your own task and still shows signs of recent maintenance, not with the framework that has the most stars. When you need to write it into your own product, then consider the framework, the license and the data flow.

Three original projects are still archived

GitHub archiving sets a repository to read-only; existing code can still be downloaded. This means the original repository will not accept new changes while it is archived, but it does not mean every fork has stopped patching, and you cannot claim that a security vulnerability exists based on the archive label alone. For the definition, see the GitHub archiving documentation.

You can also check the archive flag directly through the Flowise repository API by looking at archived. The table is a snapshot as of this date; if a repository is later unarchived, use its live status instead.

If you are already using these tools, you do not need to rebuild your whole system just because they are archived. First confirm the version you use, whether there is a still-maintained successor project or fork, and whether there are any unresolved dependency issues. For new purchases, ask first: when something breaks, who maintains it? Star counts cannot answer that question.

Four unarchived projects whose default branches have had no new commit for over four months

The "four months" here means calendar months: the query date for this check was September 17, and the latest commits in the table are all earlier than May 17. It does not treat every ~118 days as four months.

These dates do not prove that the authors have abandoned the projects: maintenance may have moved to another branch or another repository, or no changes may currently be needed. They are signals that warrant further checking. In particular, Langchain-Chatchat, which often appears in Chinese tutorials, should have its supported Python version, dependencies and model API versions verified before you follow an older tutorial to install it.

The original article placed Aider in the "more than four months" table; this time it has been removed. Its latest commit is May 22, and as of the recheck date it has not yet been four full calendar months. Slow updates are worth questioning, but the threshold cannot be changed just to fill out the list.

The other 19 samples: a commit does not mean it suits you

The following 14 unarchived repositories have their latest default-branch commit within about 48 hours before this query. This only measures commit activity; whether they fixed features or dependencies, or only changed documentation, still requires looking at the diffs, releases and issues.

Five more unarchived samples have older latest commits, but they do not reach the four-month threshold in the section above:

This also corrects an overly strong classification in the original article: browser-use is a browser automation package, and having a demo does not make it a finished product that requires no coding; mem0 is a memory layer, not a complete agent. Frameworks, functional packages, directly operable tools and visual platforms should be selected separately.

Custom model endpoints: three official sources found this time

The original article only searched READMEs and environment variables, which cannot answer which projects can switch endpoints. This time I checked the official integration documentation. What can be confirmed is the configuration capability listed below, not a compatibility guarantee from this site for every tool or every model feature.

Finding only OPENAI_BASE_URL proves only that something accepts the setting; it does not prove the whole agent works. Send a minimal text request first, then separately verify streaming, tool calling and the features you actually need. This site's LangChain setup tutorial can serve as a first small example; for editor scenarios, see Cursor fees and bringing your own key.

How to recheck: look at the repository and its default branch separately

This time I first obtained each default_branch from GET /repos/{owner}/{repo}, then used it to query GET /repos/{owner}/{repo}/commits?sha={default_branch}&per_page=1. Each sample used two GET requests, for 52 successful responses in total; I also checked the redirects for two old names. For fields and parameters, see the GitHub commits API.

Below is a query method you can rerun yourself, using LangChain as the example. First obtain the default branch name, then fill it into sha in the second step; do not assume every project is called main.

# repository level: stars, archived, default_branch, pushed_at, license
curl -fsSL https://api.github.com/repos/langchain-ai/langchain

# This check found default_branch=master; read the committer.date and html_url of the first commit
curl -fsSL 'https://api.github.com/repos/langchain-ai/langchain/commits?sha=master&per_page=1'

The unauthenticated GitHub API has request quotas, so check the response headers before running batch queries. license.spdx_id=NOASSERTION means the API did not recognize a standard SPDX identifier. It cannot be directly translated as "no license" or "not usable commercially"; a recognized result also does not mean that all subdirectories use the same license. For commercial deployment, open the repository's LICENSE and the related terms of service and read them.

This check also confirmed that geekan/MetaGPT redirects to FoundationAgents/MetaGPT, and All-Hands-AI/OpenHands redirects to OpenHands/OpenHands. Old URLs do not necessarily stop working, but documents should use the current names so that searches and issues line up.

When actually choosing a tool, validate it with a small task

For example: "read one public company document and answer three fixed questions with citations", or "fix a known failing test in a test repository". Write down the success criteria first, then choose the tool.

On the first trial, I record these items: whether it can be reproduced, whether it stops on failure, how many model requests it actually sends, how much it costs, and whether it needs to hand over browser login state or full project permissions. A fluent answer does not yet mean the workflow is complete.

Enterprise procurement should ask two more questions: where customer data will be sent, and whether the commercial license allows your intended use. GitHub stars, an MIT label and "can be self-hosted" cannot replace these two answers. If you need Taiwan company payment and expense-claim documents, see the AI API expense reimbursement guide separately; do not mix tool selection and invoice issues into one ranking.

TWD billing · Taiwan invoices · leading AI models · OpenAI-compatible API