Hello-Agents: A Practical Open-Source Guide to Building AI-Native Agents from Scratch

Background and Context

The transition from 2024 to 2025 marks a definitive paradigm shift in the artificial intelligence landscape, moving from the era of foundational model scaling to the era of intelligent agents. While 2024 was characterized as the year of the "hundred-model war," 2025 has officially inaugurated the "Agent Year," where the industry focus has decisively shifted from training larger base models to constructing smarter, autonomous agent applications. This evolution represents a fundamental change in how AI is deployed, prioritizing application-level intelligence over raw model capacity. However, this rapid expansion has exposed a critical gap in the developer ecosystem: a severe shortage of systematic, practice-oriented educational resources. Most existing content either remains superficial, focusing only on low-code platform operations, or becomes overly theoretical, failing to provide actionable engineering guidance. This dichotomy has left many developers struggling to transition from simple model users to robust system architects.

In response to this market void, the Datawhale community launched Hello-Agents, an open-source project designed to bridge the divide between theoretical understanding and practical implementation. The project’s primary objective is to provide a comprehensive, from-scratch guide for building intelligent agent systems, emphasizing both foundational logic and hands-on engineering. Unlike many commercial tutorials that prioritize tool usage, Hello-Agents positions itself as a "bridge" in the industry ecosystem, aiming to cultivate developers who can deeply understand the underlying mechanics of agent architecture. By tracing the historical evolution from symbolic AI to Large Language Model (LLM)-driven agents, the project offers a macro-level technical perspective. This approach helps developers navigate the increasingly complex jungle of frameworks, enabling them to make informed architectural decisions rather than blindly adopting new tools. The initiative is particularly targeted at engineers and researchers who seek to evolve from being mere consumers of LLM APIs to becoming creators of native AI agent systems.

Deep Analysis

A core differentiator of the Hello-Agents project is its rigorous distinction between two distinct categories of agents: workflow-driven, low-code agents and true AI-native agents. The former, represented by platforms such as Dify, Coze, and n8n, relies on predefined software engineering workflows to orchestrate tasks. In contrast, AI-native agents are fundamentally driven by AI logic, requiring a deeper understanding of dynamic decision-making processes. Hello-Agents guides developers through this distinction, encouraging them to look beyond the surface-level abstractions of low-code tools to understand the core architectures of native agents. This analytical depth is crucial for developers who wish to build scalable, autonomous systems that can adapt to unstructured environments rather than merely executing static scripts. The project explicitly teaches that while low-code tools are efficient for simple automation, they often lack the flexibility and reasoning capabilities required for complex, multi-step autonomous tasks.

The curriculum provides a thorough exploration of classic agent paradigms, including ReAct, Plan-and-Solve, and Reflection, with a specific focus on code-level implementation. Rather than simply describing these concepts, the tutorial offers practical code examples that demonstrate how these patterns function in real-world scenarios. Beyond these foundational techniques, the project delves into advanced topics such as memory systems, Retrieval-Augmented Generation (RAG), and context engineering. A particularly unique aspect of Hello-Agents is its inclusion of a module dedicated to building an agent framework, named HelloAgents, from scratch using the OpenAI native API. This hands-on exercise allows developers to understand the底层 (underlying) logic of agent orchestration without relying on third-party libraries. Additionally, the project introduces Agentic Reinforcement Learning (RL), covering the full pipeline from Supervised Fine-Tuning (SFT) to Group Relative Policy Optimization (GRPO). This level of technical depth is rare in open-source tutorials and empowers developers to optimize agent behavior strategies, moving beyond simple prompt engineering to true model alignment and behavioral refinement.

Furthermore, the project addresses the critical infrastructure of multi-agent systems by covering communication protocols such as Model Context Protocol (MCP), Agent-to-Agent (A2A), and Agent Network Protocol (ANP). These protocols are essential for enabling interoperability and collaboration between different agents, a necessity for building complex, distributed AI systems. The tutorial also includes comprehensive sections on performance evaluation, providing developers with the metrics and methodologies needed to assess agent reliability and efficiency. By integrating these advanced topics, Hello-Agents ensures that learners are equipped with the full stack of skills required to design, build, and evaluate sophisticated agent ecosystems. The inclusion of practical case studies, such as developing an intelligent travel assistant or a cyber-town simulation, allows developers to apply their knowledge in a cohesive, end-to-end manner, reinforcing the theoretical concepts with tangible outcomes.

Industry Impact

The emergence of Hello-Agents has significant implications for the broader AI developer community and engineering teams. By providing a structured, high-quality learning path, the project lowers the barrier to entry for agent development while simultaneously raising the technical ceiling for practitioners. This dual effect is crucial for the maturation of the AI industry, as it helps shift the focus from experimental "toy" projects to production-grade applications. The project’s emphasis on understanding underlying principles rather than just mastering specific frameworks helps correct a prevalent trend in the community where developers often chase the latest tools without grasping the fundamental logic of agent design. This foundational approach fosters a more robust and sustainable engineering culture, where developers can adapt to new technologies and frameworks with greater ease and confidence. The open-source nature of the project also encourages community contribution and collaboration, leading to continuous improvement and refinement of the educational content.

The project’s structure is designed to accommodate developers at various skill levels, ensuring broad accessibility and impact. For beginners, the tutorial offers a gentle onboarding process, starting with the basics of agents and gradually progressing to low-code platform construction. For advanced developers and researchers, the content provides deep dives into framework self-development and reinforcement learning training. This tiered approach ensures that the resource is valuable to a wide audience, from students and academics to enterprise engineers. The high activity level and contribution enthusiasm within the Datawhale community further enhance the project’s value, ensuring that the documentation remains up-to-date and the code examples are well-tested. The availability of domestic acceleration for online reading also improves accessibility for developers in regions with limited internet connectivity, democratizing access to high-quality AI education.

Moreover, Hello-Agents serves as a catalyst for the standardization of agent development practices. By covering communication protocols and performance evaluation metrics, the project contributes to the establishment of best practices in the field. This standardization is essential for the interoperability and scalability of agent systems, enabling different agents to work together seamlessly in complex business environments. The project’s focus on multi-agent communication protocols, in particular, addresses a critical need in the industry for systems that can handle distributed tasks and collaborative problem-solving. By providing clear guidelines and practical examples, Hello-Agents helps developers avoid common pitfalls and design more efficient and reliable multi-agent architectures. This contribution to the industry’s technical infrastructure is a significant step forward in the evolution of AI agent development.

Outlook

Looking ahead, the Hello-Agents project faces the challenge of keeping pace with the rapid iteration of AI technologies. As new communication protocol standards emerge and more efficient reinforcement learning algorithms are developed, the tutorial must continuously update its content to remain relevant and comprehensive. This requirement for ongoing maintenance and expansion is a critical area for future development. The project team will need to allocate resources to monitor technological advancements and integrate new findings into the curriculum, ensuring that learners are exposed to the latest best practices and innovations. Additionally, the project may need to explore new topics such as the integration of multimodal models into agent systems, the development of more sophisticated reasoning capabilities, and the application of agents in specialized domains such as healthcare and finance. By staying at the forefront of technological change, Hello-Agents can continue to serve as a leading resource for AI agent education.

Another key area for exploration is the practical implementation of multi-agent collaboration in real-world business scenarios. While the tutorial provides a strong theoretical foundation and basic practical examples, there is a need for more case studies that address the complexities of deploying agent systems in production environments. Issues such as agent stability in long-duration tasks, error handling, and resource management are critical for enterprise adoption. By developing more advanced case studies and providing guidance on these practical challenges, Hello-Agents can help bridge the gap between academic research and industrial application. This focus on real-world applicability will enhance the project’s value to engineering teams and accelerate the adoption of AI agents in various industries.

Ultimately, Hello-Agents represents more than just a tutorial; it is an ecosystem that promotes the popularization and deepening of agent technology. Its open-source spirit and comprehensive content make it an indispensable learning resource for anyone interested in the frontiers of AI. As the industry continues to evolve, projects like Hello-Agents will play a vital role in shaping the next generation of AI developers and architects. By fostering a community of practice and encouraging continuous learning, the project contributes to the overall health and growth of the AI ecosystem. The success of Hello-Agents will depend on its ability to adapt to changing technological landscapes and meet the evolving needs of its community, ensuring that it remains a relevant and valuable resource for years to come.