Best of langgraph — 2025

  1. 1
    Article
    Avatar of freecodecampfreeCodeCamp·46w

    The Open Source LLM Agent Handbook: How to Automate Complex Tasks with LangGraph and CrewAI

    LLM agents are proactive AI systems that can break down complex tasks, make decisions, and use tools autonomously, unlike traditional reactive chatbots. The guide demonstrates building agents using open-source frameworks LangGraph and CrewAI to automate daily tasks like email summarization and schedule generation. LangGraph provides graph-based workflows for single agents, while CrewAI enables multi-agent collaboration with specialized roles. The tutorial includes practical code examples for creating an email processing agent that extracts meetings and deadlines, then formats them into organized daily schedules. Both frameworks integrate with OpenAI's models and offer structured approaches to agent development without requiring extensive custom code.

  2. 2
    Article
    Avatar of langchainLangChain·1y

    LangGraph 0.3 Release: Prebuilt Agents

    LangGraph has released version 0.3, introducing a new set of prebuilt agents in Python and JavaScript to make it easier for users to start with common agent patterns. The framework aims to remain low level but offers higher-level abstractions through its prebuilt libraries. Companies like Replit, Klarna, LinkedIn, and Uber are already leveraging LangGraph, and the team hopes to see a large collection of community-built agents in the future.

  3. 3
    Article
    Avatar of javarevisitedJavarevisited·33w

    LangGraph and n8n in 2025: The AI Stack You Can’t Ignore?

    LangGraph and n8n serve complementary roles in AI system architecture. n8n excels as a workflow automation tool for connecting APIs, databases, and services, while LangGraph specializes in building intelligent AI agents with multi-step reasoning, state management, and complex tool-calling capabilities. The key insight is using n8n for data movement and integrations, and LangGraph for AI reasoning and agent orchestration, rather than treating them as competing solutions.

  4. 4
    Video
    Avatar of TechWithTimTech With Tim·35w

    How to Build an Advanced AI Agent with Search (LangGraph, Python & More)

  5. 5
    Video
    Avatar of TechWithTimTech With Tim·45w

    Python Advanced AI Agent Tutorial - LangGraph, LangChain, Tools & More!

    A comprehensive tutorial on building advanced AI agents using LangGraph, LangChain, and Firecrawl. The guide demonstrates creating a coding research assistant that follows structured multi-step workflows to research developer tools and frameworks. It covers both simple agent creation using MCP servers and advanced implementations with custom workflows, structured outputs using Pydantic models, and controlled agent flow through graph-based state management.

  6. 6
    Article
    Avatar of auth0Auth0·38w

    How to build an AI Assistant with LangGraph and Next.js

    A comprehensive guide to building a production-ready AI assistant using LangGraph Server and Next.js. The tutorial covers migrating from Vercel AI SDK to LangGraph Server, implementing step-up authorization with Auth0 for secure API access, and integrating multiple tools including Google Calendar and custom APIs. Key features include handling authorization interrupts, streaming responses, and creating a scalable architecture for AI agents that can access external services securely.

  7. 7
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·25w

    Every LangGraph User We know is Making the Same Mistake!

    The supervisor pattern in LangGraph has a fundamental limitation: it routes queries to only one specialized agent at a time, failing when users ask multi-topic questions. An alternative approach using dynamic guideline matching (implemented in the open-source Parlant framework) loads multiple relevant guidelines simultaneously into context, enabling coherent responses across topics. While LangGraph excels at workflow automation, Parlant is designed for free-form conversations, and both can work together complementarily.

  8. 8
    Article
    Avatar of langchainLangChain·33w

    Building LangGraph: Designing an Agent Runtime from first principles

    LangGraph was designed as a low-level agent framework prioritizing production readiness over ease of getting started. Built to address LangChain's feedback about customization and scaling challenges, it focuses on six core features: parallelization, streaming, task queues, checkpointing, human-in-the-loop capabilities, and tracing. The framework uses a structured execution model based on the Pregel algorithm with channels and nodes, enabling deterministic concurrency and fault tolerance. Performance scales gracefully with agent complexity while maintaining low latency, making it suitable for production deployments at companies like LinkedIn, Uber, and Klarna.

  9. 9
    Article
    Avatar of tdsTowards Data Science·32w

    How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n

    A comprehensive guide to building an AI-powered budget planning system that automates CAPEX portfolio optimization. The solution combines LangGraph for agent orchestration, FastAPI for linear programming optimization, and n8n for workflow automation. Users can send budget requests via email and receive optimized project portfolios that maximize ROI while respecting constraints like sustainability requirements. The system includes email parsing, mathematical optimization using PuLP library, and automated report generation for executive decision-making.

  10. 10
    Article
    Avatar of nvidiadevNVIDIA Developer·34w

    How to Scale Your LangGraph Agents in Production From A Single User to 1,000 Coworkers

    NVIDIA shares their approach to scaling LangGraph AI agents from single-user prototypes to production systems supporting 1,000+ concurrent users. The process involves three key steps: profiling single-user performance to identify bottlenecks, conducting load tests to estimate hardware requirements, and implementing monitoring during phased rollouts. Using the NeMo Agent Toolkit, they deployed an internal AI-Q research agent, discovering critical issues like CPU misconfiguration and timeout handling that only emerged under load. The methodology includes evaluation tools, sizing calculators, and OpenTelemetry integration for comprehensive observability.

  11. 11
    Article
    Avatar of langchainLangChain·26w

    LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones

    LangChain and LangGraph have reached their 1.0 stable releases, marking a commitment to no breaking changes until 2.0. LangChain 1.0 introduces the create_agent abstraction with middleware support for customization, standardized content blocks across providers, and a streamlined package focused on core agent functionality. LangGraph 1.0 provides production-ready features including durable state, built-in persistence, and human-in-the-loop patterns for complex workflows. Both frameworks are backward compatible, with LangChain built on top of LangGraph's runtime, allowing developers to start with high-level abstractions and drop down to lower-level control when needed.

  12. 12
    Article
    Avatar of tdsTowards Data Science·36w

    LangGraph 101: Let’s Build A Deep Research Agent

    A comprehensive tutorial on building AI research agents using LangGraph, Google's open-source framework. Covers core concepts including graph-based workflow modeling with nodes and edges, state management for agent memory, structured outputs for reliable LLM responses, tool calling for web searches, conditional routing for decision-making, and parallel processing for concurrent operations. Uses Google's Deep Research Agent implementation as a practical example, demonstrating how to create agents that can autonomously search the web, evaluate results, and generate comprehensive reports with citations.

  13. 13
    Article
    Avatar of langchainLangChain·37w

    Introducing Open SWE: An Open-Source Asynchronous Coding Agent

    LangChain introduces Open SWE, an open-source asynchronous coding agent that operates in the cloud and integrates directly with GitHub repositories. The agent uses a multi-component architecture with Manager, Planner, and Programmer/Reviewer agents built on LangGraph. It can research codebases, create execution plans, write code, run tests, and open pull requests autonomously. Key features include human-in-the-loop control, isolated sandbox execution, and deep GitHub integration allowing task assignment through issue labels. The system is designed for complex, longer-running development tasks and runs asynchronously without consuming local resources.

  14. 14
    Article
    Avatar of langchainLangChain·45w

    Benchmarking Multi-Agent Architectures

    LangChain researchers benchmarked three multi-agent architectures (single agent, swarm, and supervisor) using a modified Tau-bench dataset with distractor domains. The swarm architecture performed best overall, while the single agent baseline degraded significantly with additional context. The supervisor architecture showed promise after optimizations like removing handoff messages and implementing message forwarding, achieving nearly 50% performance improvement. Multi-agent systems offer benefits in modularity, scalability, and handling multiple domains, though custom architectures typically outperform generic ones for specific applications.

  15. 15
    Article
    Avatar of langchainLangChain·40w

    Open Deep Research

    LangChain introduces an open-source deep research agent built on LangGraph that automates comprehensive research tasks. The system uses a three-phase approach: scoping (clarifying user requirements), research (using supervisor and sub-agents for parallel investigation), and writing (generating final reports). Key insights include using multi-agent architecture only for parallelizable tasks, isolating context across research topics to avoid token bloat, and implementing context engineering to manage computational costs. The agent flexibly adapts research strategies based on request complexity and is available through LangGraph Studio and Open Agent Platform.

  16. 16
    Article
    Avatar of tdsTowards Data Science·36w

    LangGraph + SciPy: Building an AI That Reads Documentation and Makes Decisions

    A comprehensive tutorial on building an AI agent that helps users choose appropriate statistical tests by combining LangGraph for multi-step decision making with RAG (Retrieval-Augmented Generation) using SciPy documentation. The agent classifies user questions, searches embedded documentation when needed, provides recommendations, and generates sample Python code. The implementation includes ChromaDB for vector storage, OpenAI GPT-4 for language processing, and a Streamlit frontend for user interaction.

  17. 17
    Article
    Avatar of javarevisitedJavarevisited·37w

    10 Best Udemy Courses to Learn Autonomous AI Agents and Auto-GPT in 2025

    A curated list of 10 Udemy courses for learning autonomous AI agents and Auto-GPT in 2025. The courses cover various frameworks including LangChain, LangGraph, CrewAI, and AutoGen, ranging from building agents from scratch with Python to creating multi-agent systems and RAG-integrated workflows. Each course focuses on hands-on projects and real-world applications, targeting developers who want to build production-ready AI agents for automation, business workflows, and agentic architectures.

  18. 18
    Article
    Avatar of tdsTowards Data Science·29w

    How to Build Effective Agentic Systems with LangGraph

    LangGraph is an agentic AI framework that simplifies building AI workflows by abstracting state management and tool usage. The framework uses graph-based architecture with nodes (LLM calls or tools) and edges (conditional logic) to create structured workflows. A practical example demonstrates building a document management system with intent classification routing to handle create, delete, and search operations. While LangGraph reduces boilerplate code and provides open-source flexibility, it still requires significant setup and may introduce framework-specific debugging challenges.

  19. 19
    Article
    Avatar of surmaSurma·44w

    LangGraph for complex workflows — surma.dev

    LangGraph enables building complex workflow architectures with cyclic patterns that LangChain's DAG-based approach cannot handle. The library supports local LLM orchestration, agent clusters, and multi-step automations through graph-based state management. Key features include structured responses, tool integration, conditional edges for loops, and parallel node execution with reducers for state reconciliation. The tutorial demonstrates building ReAct agents that can perform multi-step reasoning and tool invocation cycles.

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