LangChain官方关于LLM测试的指南
4个成功的agent实践案例
Agentic AI Apps: Breakout Case Studies | LangChain
Dive into the stories of companies pushing the boundaries of AI agents. Learn "why" and "how" they made specific architecture, UX, prompt engineering, and evaluation choices for high-impact results.
www.langchain.com
AI编程工具中节省token的神器
GitHub - rtk-ai/rtk: CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies - rtk-ai/rtk
github.comM-FLOW · Documentation
docs.m-flow.aiGitHub - neo4j-labs/agent-memory: A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.
A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j. - neo4j-labs/ag...
github.com对记忆系统讲解得很深刻
首个 LLM 记忆智能体统一评估基准 https://github.com/HUST-AI-HYZ/MemoryAgentBench
</details>RAG
<details> <summary>基于图的rag</summary>GitHub - HKUDS/LightRAG: [EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation" - HKUDS/LightRAG
github.com