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LangChain Community Spotlight featuring Poland Ambassador Simon Budziak's post on five controls for running LangGraph agents in production: explicit state machines, per-turn tracing, evaluation gates, idempotent side effects, and confidence-based human routing, drawn from two shipped case studies.
The post explains the difference between AI agent frameworks (libraries like ADK, LangGraph, CrewAI) and agent harnesses (pre-built agents like Claude Code, Codex, Deep Agents), and offers guidance on when to choose each as the boundaries between them blur.
LangChain is hosting an in-person SF meetup with TypeSafe AI featuring talks on building with decision models (Jev) in LangGraph, reframing them as System One Models, and using Jev as an LLM judge for agent evaluation compared against GPT-5.6 and Claude Sonnet 4.6.
SkyWorks 使用 LangGraph 和 LangSmith 部署智能体并统一运行评估，将根因分析时间从约20天缩短至10天。这是 LangChain 分享的客户案例。
A study of execution-time reliability in tool-using AI agents that isolates recovery decisions from language-model behavior, evaluating checkpoint state, dispatch authority, external-effect evidence, and reconciliation obligations across 336 histories on LangGraph and Temporal hosts, plus a component study of Arya.ai's InfinityOS.
A free online book, 'LangGraph in Small Pieces,' teaches building AI agents from fundamentals to production using real local-model runs, hands-on challenges, and a public GitHub repo. It covers state, nodes, tool calling, MCP, streaming, RAG, observability, and deploys, pinned to LangGraph 1.2.14.
作者开源了一个容器化的多智能体研究与报告生成管线，基于 LangGraph 四阶段状态机（Planner/Researcher/Critic/Writer）、MCP 工具服务器，以及 FastAPI + Celery + Redis + PostgreSQL 的分布式异步架构，并分享了生产化设计经验。
LangChain shares a case study on how Numan built Nu, an eval-first AI health coach on LangGraph and LangSmith, navigating medical device regulation by moving beyond static single-turn and scripted multi-turn evals to conversation-aware evaluation frameworks.
A detailed engineering walkthrough on making action-taking AI agents production-ready, covering failure modes like duplicate refunds, prompt injection, and runaway tool calls, with a full FastAPI + LangGraph architecture including idempotency, spend caps, human-in-the-loop interrupts, and a policy engine.
作者对比了手写 agent loop 和使用 LangGraph create_agent 构建购物助手，说明框架本质上是帮你运行同样的循环，并额外提供 checkpointer 记忆、流式输出、重试和追踪。建议先手写循环理解原理，再用框架落地。
A reflection on why AI agent frameworks tend to converge on workflow-engine-like requirements — durable state, retry policies, and resumable processes — and whether the agent framework or an external execution system should own that durable lifecycle.
FinSentry AI 是一个探索 AI 辅助支付调查的独立副项目演示，重点说明 AI 建议不等于批准，并展示了三项具体控制：证据缺失时弃权、请求者与审批者分离、以及审批必须针对精确操作而非模糊结果。
LangChain 分享 Snyk 如何将内部支持助手 Snyk Assist 打造为客户产品功能：基于 LangChain + LangGraph 构建，LangSmith 负责可观测性与评估，已处理 6 万次查询、85% 会话无需人工工单解决。
ZipHQ rebuilt their agent on LangGraph + LangSmith, cutting development time for new features and gaining automatic tracing for LLM calls, storage, and context without writing code.
LangChain's official "Agents From Scratch" guide offers a hands-on, four-step roadmap for building an email agent with long-term memory and human-machine collaboration, covering LangGraph workflows, LLM-as-Judge testing, human-in-the-loop confirmation, and LangGraph Store memory integration, with notebooks and source code included.
Sydney Runkle introduces a new LangGraph feature: interrupts with typed response schemas, allowing human responses to be validated before being passed back to the agent and enabling real response forms in UIs instead of raw JSON input.
LangChain announces a collaboration between Jev and LangGraph as indicated by their tweet.
An agentic CUDA kernel optimizer that automates GPU implementation generation through iterative code generation, correctness checks, benchmarking, and refinement, powered by LangGraph and OpenAI models.
A developer is seeking advice on choosing between Hermes/OpenClaw and LangGraph/Mastra for building a multi-tenant AI agent SaaS, focusing on production concerns like isolation and observability.
Included Health built Dot, an AI-powered federated multi-agent healthcare guide, using LangGraph and Deep Agents, resulting in a 75% increase in chat engagement and over 99% high-risk detection.