One Open Source Project a Day (61): Hello-Agents — A Practical Guide to Building AI Native Agents from Scratch

Source: Dev.to

What is Hello-Agents?

Hello-Agents is an open‑source educational project led by the Datawhale community. Its core objective is to break open the “black box” of Agent development, allowing developers to master the core architecture of agents starting from foundational logic.

Unlike platforms that focus on workflow orchestration (like Dify or Coze), Hello‑Agents emphasizes autonomous reasoning, dynamic planning, and self‑evolution. It not only covers basic theory but also provides a lightweight experimental framework, HelloAgents, for learners to validate their ideas through code.

Key Highlights

• Systematic Learning Path – From foundational theory to engineering implementation and advanced extensions (e.g., RLHF, MCP), the project offers a complete progression curve.
• Theory Meets Practice – Every chapter includes detailed core code implementation, teaching you how to hand‑write a ReAct loop or a memory system rather than just clicking buttons.
• Embracing Modern Standards – One of the first comprehensive tutorials in China to deeply integrate and explain the Model Context Protocol (MCP), keeping pace with international technical frontiers.
• Deep Dive into Agentic RL – Covers the evolution from SFT to GRPO, exploring how to empower agents with stronger reasoning capabilities through reinforcement learning.

Technical Breakdown: How to Build an Agent?

Hello‑Agents decomposes agent construction into several core elements:

1. Reasoning and Planning (Thinking Loop)
   In‑depth explanations of mainstream paradigms such as ReAct (Reason + Act), Plan‑and‑Solve, and Reflection, showing how to move beyond LLMs merely outputting text to having them “observe” the environment and “think” about the next step.

2. Tool Calling and MCP
   Goes beyond simple function calling. Highlights the Model Context Protocol (MCP) proposed by Anthropic—a standardized open protocol that lets agents connect to various data sources and local tools in a unified way.

3. Memory System
   Explores building sensory‑capable memory systems, including long‑term memory enhanced by RAG and session management based on state persistence.

4. RL and Self‑Evolution
   Discusses principles of reasoning models represented by OpenAI o1 and how Agentic RL technology enables agents to self‑correct and optimize through repeated practice.

Who is Hello-Agents for?

• Developers – Engineers familiar with Python and LLM basics who want to master agent system architecture and low‑level implementations.
• Students / Researchers – Learners seeking a systematic study of the latest research findings and engineering techniques in the agent field.
• Product Managers – Professionals who need a deep understanding of AI‑Native product logic to design more competitive agent applications.

Conclusion

In 2025, agents have evolved from “software engineering logic” to “AI‑Native logic.” Hello‑Agents is the perfect ticket to this new world. It doesn’t just teach you how to write code; it teaches a new way of thinking—how to build digital lives that can reason, learn, and act autonomously.

GitHub:

Online Tutorial: