系统学习#
Hello-AgentsDescriptionhello-agents.datawhale.cc ↗ AI 智能体实战速成指南：从零到企业级落地 | ai-agents-from-zero《AI 智能体实战速成指南：从零到企业级落地》——技术栈全、易上手、可落地的智能体教程，含大量工程模板与企业级可部署源码；覆盖大模型基础→Coze/Dify→LangChain/LangGraph→RAG/Agent 实战→微调→大厂规范全链路didilili.github.io ↗ 深入理解 AI Agent围绕核心公式 Agent = LLM + 上下文 + 工具,用 10 章把 AI Agent 从原理讲到工程实战的开源技术书。正文、配图、94 个配套实验全部开源。bojieli.github.io ↗查缺补漏#
LLM 基础#
图解 Transformer：
The Illustrated TransformerDiscussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Italian, Japanese, Korean, Persian, Russian, Spanis…jalammar.github.io ↗了解并计算 Token：
https://help.openai.com/zh-hans-cn/articles/4936856-understanding-and-counting-tokens ↗
采样参数：
Sampling Parameters — vLLMdocs.vllm.ai ↗模型调用#
API 调用协议：
https://platform.openai.com/docs/api-reference/chat/create ↗
Responsesdevelopers.openai.com ↗ 使用 Messages API有效使用 Messages API 的实用模式和示例platform.claude.com ↗ 停止原因与回退了解每个 stop_reason 值的含义，以及如何在您的应用程序中处理截断、工具使用、暂停的轮次和拒绝。platform.claude.com ↗流式传输：
Streaming API responses | OpenAI APILearn how to stream model responses from the OpenAI API using server-sent events.developers.openai.com ↗ 流式传输消息使用服务器发送事件以增量方式流式传输 Messages API 响应，包括文本、工具使用和思考增量。platform.claude.com ↗结构化输出：
https://openai.com/index/introducing-structured-outputs-in-the-api/ ↗
工具调用：
Function calling | OpenAI APILearn how function calling enables large language models to connect to external data and systems.developers.openai.com ↗ 使用 Claude 进行工具使用将 Claude 连接到外部工具和 API。了解工具在何处执行、Claude 何时调用它们，以及哪种工具适合您的任务。platform.claude.com ↗多轮对话：
Conversation state | OpenAI APILearn how to manage conversation state during a model interaction with the OpenAI API.developers.openai.com ↗ 对话中途的系统消息与工具变更在对话进行到一半时更改系统指令或工具可用性，而不会使其之前的缓存前缀失效。platform.claude.com ↗Embedding：
Vector embeddings | OpenAI APILearn how to turn text into numbers, unlocking use cases like search, clustering, and more with OpenAI API embeddings.developers.openai.com ↗Prompt Engineering#
Prompt engineering | OpenAI APILearn strategies and tactics for better results using large language models in the OpenAI API.developers.openai.com ↗ Prompt engineering overviewLearn when prompt engineering is the right solution, and find Claude prompting techniques and interactive tutorials.platform.claude.com ↗Few-Shot：
少样本提示 | Prompt Engineering GuideA Comprehensive Overview of Prompt Engineeringwww.promptingguide.ai ↗Cot:
https://www.promptingguide.ai/zh/techniques/cot ↗ ReAct：
ReAct 框架 | Prompt Engineering GuideA Comprehensive Overview of Prompt Engineeringwww.promptingguide.ai ↗Reflexion：
自我反思（Reflexion） | Prompt Engineering GuideA Comprehensive Overview of Prompt Engineeringwww.promptingguide.ai ↗Reasoning best practices：
Reasoning best practices | OpenAI APIExplore best practices for using o-series reasoning models, like o1 and o3-mini, vs. GPT models—including use cases, how to choose a model, and prompting guidance.developers.openai.com ↗RAG#
RAG 完整流程：
Retrieval-Augmented Generation (RAG) | PineconeExplore the limitations of foundation models and how retrieval-augmented generation (RAG) can address these limitations so chat, search, and agentic workflows can all benefit.www.pinecone.io ↗文档解析与分块：
Loading Datadevelopers.llamaindex.ai ↗ Chunking Strategies for LLM Applications | PineconeIn the context of building LLM-related applications, chunking is the process of breaking down large pieces of text into smaller segments. It’s an essential technique that helps optimize the relevance of the content we get back from a vector…www.pinecone.io ↗ Node Parser Usage Patterndevelopers.llamaindex.ai ↗检索： https://www.pinecone.io/learn/vector-database/ ↗
Hybrid search overview - Pinecone DocsCombine keyword and semantic retrieval in Pinecone with a text-match filter on a dense search, or by fusing separate searches with reciprocal rank fusion.docs.pinecone.io ↗ Metadata Extractiondevelopers.llamaindex.ai ↗ Cohere DocumentationThis page describes how Cohere's Rerank models work.docs.cohere.com ↗评估：
RagasEvaluation framework for your AI Applicationdocs.ragas.io ↗Agent#
Building Effective AI AgentsDiscover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.www.anthropic.com ↗Agent Loop：
How the agent loop works - Claude Code DocsUnderstand the message lifecycle, tool execution, context window, and architecture that power your SDK agents.code.claude.com ↗规划与任务拆解：
LLM Powered Autonomous AgentsBuilding agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond genera…lilianweng.github.io ↗ Plan-and-Execute AgentsPlan and execute agents promise faster, cheaper, and more performant task execution over previous agent designs. Learn how to build 3 types of planning agents in LangGraph in this post.www.langchain.com ↗状态管理：
Persistence - Docs by LangChainLangGraph's persistence layer gives agents short-term memory through checkpointers and long-term memory through stores.docs.langchain.com ↗ Durable Execution: The Key to Harnessing AI Agents in Production - Inngest BlogAI Agents introduce multiple points of failure that traditional retry logic cannot handle. Durable execution provides the automatic state persistence, retries, and workflow resumption that make agents production-ready.www.inngest.com ↗记忆系统：
What Is AI Agent Memory? | IBMAI agent memory refers to an artificial intelligence (AI) system’s ability to store and recall past experiences to improve decision-making, perception and overall performance.www.ibm.com ↗ LLM Powered Autonomous AgentsBuilding agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond genera…lilianweng.github.io ↗上下文工程：
Effective context engineering for AI agentsAnthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.www.anthropic.com ↗MCP：
What is the Model Context Protocol (MCP)? - Model Context Protocolmodelcontextprotocol.io ↗Human-in-the-Loop：
Interrupts - Docs by LangChaindocs.langchain.com ↗Agent 安全：
LLM Prompt Injection Prevention - OWASP Cheat Sheet SeriesWebsite with the collection of all the cheat sheets of the project.cheatsheetseries.owasp.org ↗Agent 评估：
Evaluation of LLM Applications - LangfuseWith Langfuse you can capture all your LLM evaluations in one place. You can combine a variety of different evaluation metrics like model-based evaluations (LLM-as-a-Judge), human annotations or fully custom evaluation workflows via API/SDK…langfuse.com ↗ LangSmith Evaluation - Docs by LangChainEvaluate and test agent quality at scale with datasets, evaluators, prompts, and Studio.docs.langchain.com ↗后端工程#
Python Async：
asyncio — Asynchronous I/OHello World!: asyncio is a library to write concurrent code using the async/await syntax. asyncio is used as a foundation for multiple Python asynchronous frameworks that provide high-performance n...docs.python.org ↗FastAPI：
学习 - FastAPIFastAPI framework, high performance, easy to learn, fast to code, ready for productionfastapi.tiangolo.com ↗Pydantic：
Pydantic Validationpydantic.dev ↗SQLAlchemy：
Asynchronous I/O (asyncio) — SQLAlchemy 2.0 Documentationdocs.sqlalchemy.org ↗Alembic：
Welcome to Alembic’s documentation! — Alembic 1.20.0 documentationalembic.sqlalchemy.org ↗PostgreSQL：
PostgreSQL: Documentationwww.postgresql.org ↗ GitHub - pgvector/pgvector: Open-source vector similarity search for PostgresOpen-source vector similarity search for Postgres. Contribute to pgvector/pgvector development by creating an account on GitHub.github.com ↗Redis：
图解Redis介绍图解计算机基础，覆盖 Java 后端面试，让天下没有难懂的八股文！www.xiaolincoding.com ↗MySQL：
图解MySQL介绍图解计算机基础，覆盖 Java 后端面试，让天下没有难懂的八股文！www.xiaolincoding.com ↗