Chinese AI GitHub Stars in 2026: Agent Project Snapshot

If you searched for “Chinese AI GitHub stars,” start with the snapshot date.

On August 14, 2026, OpenClaw had about 386,000 GitHub stars, Dify about 152,000, and both Agent-Reach and MetaGPT were close to 70,000. The figures below came from the GitHub Repository API on that date.

These numbers measure public attention. They do not prove active users, deployments, revenue, code quality, or the nationality of every contributor. This article therefore uses the broader scope “Chinese-rooted or deeply connected to the Chinese-language ecosystem,” then keeps the repository, public positioning, and snapshot date together.

A nine-project GitHub snapshot

Project	Stars	Public repository positioning	Ecosystem role
OpenClaw	386,184	Personal AI assistant across operating systems and platforms	Personal agent product
Dify	152,351	Agentic workflows, RAG, and AI application platform	Application platform
Agent-Reach	71,403	Multi-site reading and search for agents	Content access tool
MetaGPT	69,805	Multi-agent framework for software collaboration	Multi-agent framework
Cherry Studio	50,419	Desktop workspace for models, assistants, and agents	Agent client
nanobot	46,942	Lightweight, self-hosted personal agent framework	Personal agent framework
AstrBot	39,108	Agent assistant and framework for multiple messaging platforms	Messaging interface
Langchain-Chatchat	38,544	RAG and agent applications for local language models	Knowledge and RAG
FastGPT	29,352	Knowledge, RAG, and visual AI workflow platform	Application platform

Stars are the values observed on August 14, 2026, and will continue to change. This is neither an exhaustive directory nor a ranking of “Chinese projects.” It gives search-result numbers a verifiable and interpretable boundary.

Stars measure attention, not technical fit

Stars are useful for developer tools. They show whether a repository has reached public attention and can help you discover issues, plugins, tutorials, and contributors.

They become misleading when unlike products are placed on one leaderboard.

A desktop agent client and a multi-agent framework solve different problems. Dify helps teams build, publish, and monitor AI applications. MetaGPT provides a model for multi-agent software collaboration. Agent-Reach connects agents to content across multiple sites. Sorting all three by stars produces a popularity table, not an architecture decision.

A better first question is which layer the project owns. You can then ask whether its workflow, deployment model, and governance boundary fit that layer.

Four layers now appear in the Chinese-language agent ecosystem

User-facing agent products

OpenClaw, Cherry Studio, AstrBot, and nanobot give users a relatively direct path to an agent that can chat, call tools, or work through messaging platforms. Their engineering questions are concrete. Which models can connect, where does state live, how are tool permissions controlled, and will configuration survive an upgrade?

These projects often create value through accessible interfaces first. A large star count does not mean that the product is the right runtime to embed in your backend.

Application and workflow platforms

Dify and FastGPT are closer to complete platforms. Dify's official documentation places orchestration, publishing, monitoring, knowledge, and integrations in one product, with both cloud and self-hosted options. FastGPT combines knowledge bases, RAG, and visual workflow orchestration.

A platform can save time when product, operations, and engineering need to maintain an application together. It can also be too heavy when a team already owns its runtime, state system, and deployment pipeline.

Multi-agent and runtime frameworks

MetaGPT brings software-team roles into multi-agent collaboration. Nanobot emphasizes a lightweight, self-hosted personal-agent runtime. Both can be called frameworks, but their abstraction levels are far apart.

Selection at this layer should examine termination, state recovery, information passed between agents, and who verifies that a reported completion is real. Adding more role names does not automatically produce reliable collaboration.

Knowledge and access to the outside world

Langchain-Chatchat focuses on local knowledge and RAG. Agent-Reach handles reading and search across internet platforms. The quality of an agent's external evidence is often decided at this layer.

This layer is easy to hide inside a framework feature list. “Supports tools” does not tell you how search sources, failures, evidence formats, or budgets are handled.

What gives this ecosystem a recognizable shape

Three engineering needs recur in the repositories' public positioning.

First, agents need many interfaces. Web applications, desktop clients, enterprise messaging, QQ, Telegram, and Chinese content platforms can all become working surfaces. AstrBot and Agent-Reach grew directly from these interface differences.

Second, self-hosting matters. Model choice, data boundaries, network conditions, and cost push teams to preserve local or private deployment paths. Dify, nanobot, Langchain-Chatchat, and FastGPT all expose that option in their public product shape.

Third, knowledge and search rarely stay minor add-ons. Chinese-language material is spread across code repositories, communities, video sites, and content platforms. An agent has to handle access constraints, source differences, and result structure, so connectivity becomes a project layer of its own.

These are observations from the repositories' current public positioning. They should not be generalized into one development style shared by every Chinese developer.

How to narrow the choice

Start with the job.

For building and publishing an AI application quickly, inspect the platform boundaries of Dify or FastGPT.
For multi-agent software collaboration, start with MetaGPT's role and process model.
For a personal agent or messaging interface, compare the runtime models of OpenClaw, nanobot, Cherry Studio, and AstrBot.
For local knowledge or web evidence, evaluate RAG, search sources, failure semantics, and provenance as a separate layer.

The 2026 AI Agent Ecosystem Landscape maps these layers into the broader framework, platform, and MCP market. If your question has narrowed to a traceable web-search path for an agent, Agent Search MCP is one multi-source implementation with visible failure states. Its current behavior and limitations are inspectable in lennney/agent-search-mcp.

Method and limitations

On August 14, 2026, this article read public star counts and descriptions through the GitHub Repository API, then grouped projects by their stated functions. The list comes from a previous survey of high-attention projects. It does not represent market share and does not verify deployments, active users, or commercial revenue.

Stars and project positioning both change. Re-read the API before quoting these figures in a new report instead of copying this table indefinitely.