ai-agents-from-zero
ai-agents-from-zero is a Chinese-language open-source tutorial and learning path for building AI agents and large language model applications. It covers frameworks like LangChain and LangGraph, low-code platforms like Dify and Coze, and includes executable projects, interview prep, and enterprise deployment guidance.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | didilili/ai-agents-from-zero |
| Owner | didilili |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 2.6k |
| Forks | 353 |
| Open issues | 12 |
| Latest release | Unknown |
| Last updated | 2026-06-23 |
| Source | https://github.com/didilili/ai-agents-from-zero |
What ai-agents-from-zero is
A Python-focused educational repository combining LLM fundamentals, prompt engineering, Tool Calling, RAG (vector + sparse + reranking), LangGraph workflows, MCP protocol integration, and multi-agent patterns. Includes two completed end-to-end projects: NL2SQL e-commerce Q&A (LangGraph + MySQL) and multi-agent deep research (DeepAgents framework).
Get the ai-agents-from-zero source
Clone the repository and explore it locally.
git clone https://github.com/didilili/ai-agents-from-zero.gitcd ai-agents-from-zero# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- All projects use external LLM APIs (OpenAI, cloud-hosted models) or local deployments (Ollama, Xinference); plan for cost and latency accordingly.
- Tutorials reference Coze (Tencent platform), Dify, and other low-code tools—adoption depends on infrastructure availability and internal tooling strategy.
- Multi-step RAG pipelines (vector + sparse + reranking) add operational complexity; evaluate RAGAS evaluation metrics and monitoring requirements upfront.
- Projects use PostgreSQL, Qdrant, Elasticsearch, Neo4j for different retrieval patterns—data pipeline setup is non-trivial.
- LangGraph and MCP are production-grade but younger than LangChain; community support and breaking changes should be monitored.
When to avoid it — and what to weigh
- Non-Python tech stacks (Java, C#, Go) — Content is almost entirely Python-centric (LangChain, LangGraph). Java equivalents (langchain4j) are explicitly not covered.
- Non-English speakers requiring English-only resources — Repository and tutorial are primarily in Chinese. Not suitable for English-only teams without translation capacity.
- Proprietary/closed-source enterprise deployment requirements — While MIT-licensed, content focuses on open-source and some cloud-platform (Coze, Dify) deployment. May not align with strict on-premise or closed-model mandates.
- Beginners with zero coding experience — Assumes Python familiarity and software engineering fundamentals. Target is engineers transitioning to AI development, not absolute beginners.
License & commercial use
Licensed under MIT License. Permissive open-source license allows commercial use, modification, and distribution with minimal restrictions. Requires only attribution and preservation of license notice.
MIT License explicitly permits commercial use of the educational content and code examples. However, projects in the tutorial may depend on external APIs (OpenAI, Coze, Dify) or third-party libraries with their own license restrictions. Always review transitive dependencies and API terms of service before deploying to production. No warranty or liability guarantees implied.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit or vulnerability disclosure process documented. Standard considerations apply: secure storage of API keys (use environment variables, secrets management), input validation for user prompts (injection risk), rate limiting on external API calls, and PII handling in chat logs. MCP server implementations should enforce authentication and authorization. No statement on data retention or compliance (GDPR, etc.).
Alternatives to consider
LangChain official documentation + LangGraph tutorials
Official source, actively maintained by LangChain team; narrower scope (no full project arc or interview prep) but authoritative for framework details.
Dify + Coze native documentation + tutorials
Vendor-native resources for low-code agent building; less emphasis on custom Python development or multi-framework comparison.
DeepLearning.AI short courses (LangChain, RAG, Agents)
English-language, shorter-form structured courses; less comprehensive enterprise project coverage but higher production value and live instructor support.
Build on ai-agents-from-zero with DEV.co software developers
Explore the tutorial repository, run the executable projects, and prepare for AI engineer interviews—all in one integrated curriculum.
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ai-agents-from-zero FAQ
Is this suitable for production use, or just learning?
Do I need to know LangChain before starting?
Are the projects runnable out of the box?
Is this only for Chinese speakers?
Software developers & web developers for hire
Adopting ai-agents-from-zero is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate rag frameworks software in production.
Ready to build AI agents? Start your learning path.
Explore the tutorial repository, run the executable projects, and prepare for AI engineer interviews—all in one integrated curriculum.