Taking the Pulse of Agentic AI from the Developer Community at the End of Q1 2026

Taking the Pulse of Agentic AI from the Developer Community at the End of Q1 2026 | INCLUSION AI

Skip to main content Today, I want to share some observations on the Agentic AI ecosystem from the vantage point of 2026's first quarter—technical trends read from popular projects, portraits of AI developers, and the subtle relationship between developers and AI tools. This is not meant to be comprehensive; we welcome the community to share more observations and reflections.

Agentic Ecosystem in 2026 ​

This year, everyone seems to be in a state where FOMO and excitement intertwine. There's a sense that AI application deployment has reached an unprecedented acceleration point—perhaps even a tipping point. But is this tipping point real or emotionally amplified? Let's calibrate our intuition with two metrics.

This chart shows the top 20 projects by OpenRank last month and the top 20 by Star growth this year—the most active and most-watched projects. I've highlighted LLM-related projects, and unsurprisingly, OpenClaw occupies the #1 and #2 spots on both lists.

Developer attention has completely flowed toward the Agent ecosystem , although the Star count list includes many awesome-collection type projects (which naturally attract more attention). Just looking at the project names, you can feel they're permutations of a few words: OpenClaw, Skills, Claude, Claude Skills, OpenClaw Skills. The actual developer effort reflected in activity metrics is somewhat more honest, but even so, LLM-related projects account for about 40%.

Expanding the scope to the top 1000 most-watched repositories, after rough labeling, we can see 81% are Agent-related. The most frequently tagged keywords in project Topics are: Agent, Claude, LLM, Code, Skill .

Looking back over the past few years, you can feel the rotation of technological ecosystem dominance from the naming of popular projects emerging at different stages. Popular projects created around 2023-2024 were mostly related to GPT and Llama , such as AutoGPT, MetaGPT, Ollama, llama.cpp. As time turns, there are always technologies that serve as unavoidable coordinates. In 2025, that coordinate was called Claude Code , and thus projects like Clawdbot (later OpenClaw) and Claude-Mem emerged.

Based on the currently most popular and active projects, we've compiled the latest map of the Agentic AI ecosystem, covering about 50+ projects. Many should look familiar, while some are new faces. Let's follow a few specific projects to examine current technical trends.

Technical Trends from Popular Projects ​

From Context Management to Complexity Harness ​

The optimizations we made under the capability constraints of the foundation models were essentially about managing information in the model's attention window: feeding more effective prompts to the model, invoking tools like browsers, connecting external background knowledge the model needs (RAG), and maintaining memory across multi-turn conversations. This path accumulated into a practice called " Context Engineering. "

Claude-Mem and Context7 are two open-source tools created around mid-last-year, each now with tens of thousands of Stars. They each found interesting entry points, but essentially solve the same thing: telling the model more effective background knowledge—and making sure it doesn't forget.

Claude-Mem is a Claude Code plugin that compresses all conversation outputs during Claude Code's task execution using a model, providing them as context for future conversations to ensure the Coding Agent has longer conversation memory.

Context7 provides both MCP service and Skill loading modes. Every time a task is executed, it fetches the latest documentation of involved dependency libraries to ensure the Coding Agent doesn't execute outdated code.

But "Context Engineering" as a term is starting to feel insufficient this year, because the problem is no longer just "is there enough information," but "will the Agent lose control?" Developers have likely experienced this: during autonomous task execution, the Agent either crashes the entire system or stops halfway without saying anything.

Oh-My-OpenAgent (formerly oh-my-opencode, a plugin for OpenCode) calls itself the "strongest Agent Harness" in its project description. It built a continuous execution Enforcer called "Sisyphus": as long as TODO tasks aren't complete, it forces the Agent to keep restarting or finding new paths until 100% achievement—like Sisyphus endlessly pushing the stone up the mountain.

So I understand Harness as providing background knowledge while further constraining the Agent's behavioral boundaries—not just letting the Agent know "what is," but making clear "what it can touch" and "what it can't," and knowing what to do when stuck. Context Engineering manages input quality; Harness Engineering manages execution discipline .

Software Development Shifts from Human-Centric to Agent-Centric ​

This trend can already be felt from the projects above: these newly emerging tools are designed not to serve developers, but with the Agent as the execution subject. Interestingly, what humans have accumulated in software development practices is now migrating to Agents. Developers need to consult the latest documentation—so do Agents; developers need to collaborate in teams—Agents are starting to need that too.

Vibe-Kanban brings traditional task boards to the Agent team collaboration scenario, turning it into the Agent's command center. Each task creates an entry with clear acceptance criteria (AC) on the board. Agents execute against AC, while human engineers do task preview and Diff Review through an integrated UI. This is essentially a Harness too—just constraining not individual Agent execution behavior, but the entire development process.

A fitting analogy: model-driven code generation is a powerful but directionless horse; Harness is the equipment composed of constraints, guardrails, and feedback mechanisms; humans are riders, responsible for giving direction, not running themselves.

The Agent "Evolution" Proposition—Lobsters, Cats, and Bees ​

Agents are clearly no longer satisfied with fixed process orchestration—self-evolution is the new proposition. OpenClaw started the "raising lobsters" trend first, and soon a new batch of cats and lobsters appeared. These projects, inspired by OpenClaw, each made tradeoffs in different dimensions.

nanoclaw…