Agentic AI 2026: When the Hackathon Fever Cools Down

Agentic AI 2026: When the Hackathon Fever Cools Down | INCLUSION AI

Skip to main content After the party cools down, we still want an inclusive AGI that more people can use.

Opening ​

Over the past year, we often described the LLM developer ecosystem as a “hackathon in the real world.” The phrase fits. It has energy, speed, luck, and flashes of talent. It also has noise, repeated work, short-lived projects, and repos that become famous overnight only to be covered by the next wave a few days later.

By mid-2026, the feeling is different. The change is no longer just “a few more Agent projects.” Something deeper is shifting: the way software is made is starting to move.

In the past, people used software. Software was designed around human hands, eyes, and attention: buttons, forms, editors, and chat boxes. Now agents are becoming a new kind of software user. They read files, call APIs, run commands, open PRs, write tests, review code, and wait for human approval before moving on. They do not always sit inside a chat box. They do not only answer questions. They are entering the inner workflow of software.

So the most useful question is not whether Agentic AI has a bubble. Of course it does, and it will have more. The better questions are: when the hackathon fever cools down, where will software go? What will developers become? What role is left for open source? And why do we need an inclusive AGI future?

Signals From Platforms ​

Models have not made software smaller. They have widened its boundary.

If we only watch product launches, it is easy to think that models are the whole story of AI. But when we look at GitHub and Hugging Face together, a different picture appears.

GitHub tells us what developers are building. Octoverse 2025 shows that GitHub has more than 180 million developers. In 2025, it added more than 36 million new developers, about one new developer every second. From January to April 2026, OpenDigger events recorded 13.016 million active developers and 27.107 million active repositories. Software production has not shrunk because models got stronger. It is still expanding.

The more interesting signal is automated accounts. In the first four months of 2017, only 112 bot or app actors were active in the GitHub event stream. In the same period in 2026, the number reached 17,285. That is 154 times larger across the ten-year window. The first four months of 2026 alone already doubled the same period in 2025. Today, open-source collaboration can no longer be imagined as “human developers working on GitHub, with a few CI bots doing chores on the side.” Automated accounts are entering the software production chain. They are becoming part of the collaboration network.

Hugging Face gives another signal. It shows how models are published, downloaded, changed, and reproduced. The number of public models has reached 2 million. It grew by more than 100% in the past year, and more than 540,000 models were added by May 2026 alone. A model platform is no longer just a display case for research models. It looks more like a busy factory. Some people publish foundation models. Some fine-tune them. Some quantize them. Some convert formats. Some upload adapters. Some move the same capability to different devices and runtime environments.

The GitHub collaboration list and star-growth list tell a similar story. AI projects have entered the core engineering world. Developer attention and curiosity are strongly focused on repos around new words like Agent, Claude Code, and Skills . But real collaboration still happens around the long-term foundations and complex systems of software. In other words, models have not eaten software. They are rewriting the division of labor inside software.

Models handle understanding, generation, reasoning, and tool use. Software puts models into reliable workflows. It manages data, permissions, state, cost, audit, and delivery. The closer models get to real work, the more software is needed to define boundaries, save process, connect systems, and handle failure. Models have not made software smaller. They have widened its boundary.

The Agentic AI Ecosystem Architecture ​

The ecosystem has moved from an LLM toolchain to a full execution stack for Agentic AI.

When we built the LLM developer landscape last year, the main question was still: which projects should be on the map? At that time, the ecosystem was slowly forming layers around LLM SDKs, RAG, agent frameworks, application platforms, and inference infrastructure. People cared about how to connect models, build RAG, write an agent, and run inference services.

By 2026, this question became harder. There are too many projects. They change too fast. Even many older projects are redefining themselves.

OpenClaw launched in November 2025 and passed 200,000 stars in February 2026. It took only 84 days. React, the software foundation that shaped modern frontend development, took almost ten years to reach the same number. This does not mean OpenClaw already has React’s long-term engineering impact. It means GitHub’s attention system, the speed of spread in the AI era, and developer expectations for agentic software have all changed.

Projects appear too fast, and attention moves too fast. A static map can easily capture only one moment. So this time we separated two jobs. The landscape map makes judgments and selects a set of representative projects worth watching. The dynamic leaderboard checks the temperature. It tracks Agentic AI projects that developers have actually worked on, and shows which projects suddenly became hot, which ones kept their heat, and which ones started to show real collaboration.

This dynamic leaderboard, built with OpenDigger, is now live on the inclusionAI website: https://www.inclusion-ai.org/insight

The latest monthly OpenRank Top 10 looks like a cross-section of the ecosystem. Claude Code, Codex, OpenCode, and Gemini CLI sit near the task-entry layer. vLLM, SGLang, TensorRT-LLM, and PyTorch sit near the infrastructure layer. Entry projects are closer to developers and users, so issues and PRs are busier. Infrastructure projects may have less scattered participation, but the collaboration density is high. What is heating up is not a single entry point. It is the whole execution system.

The structural change is clear. In 2025, the landscape was still sorting SDKs, RAG, ChatUI, and MLOps. In 2026, leading projects are being rearranged around the task execution...