LangGraph has become the go-to framework for orchestrating complex AI agents, delivering a runtime that simplifies orchestration and debugging across production workloads. The recent 0.3 release introduces prebuilt agent templates, cutting setup time from hours to minutes.
Key highlights: - Stateful graph execution that preserves context across nodes. - Memory management with long‑term and short‑term options. - Tight integration with LangSmith for real‑time monitoring. - Adaptive deliberation loops that let agents refine decisions on the fly.
The community is rapidly adopting LangGraph for everything from conversational bots to data‑driven research agents. With upcoming releases promising even richer tooling and tighter Snowflake integration, the framework is poised to power the next wave of AI‑driven automation.
CrewAI is an open‑source framework that turns a collection of autonomous agents into a cohesive crew, enabling complex task orchestration with minimal friction.
Adopting CrewAI accelerates product development, reduces integration overhead, and provides a clear path from prototype to production.
AutoGen is Microsoft’s open‑source framework that turns large language models into collaborative agents. By defining conversational patterns, developers can orchestrate multiple LLMs to solve tasks that single agents struggle with.
Key capabilities include: - GroupChat orchestration for parallel agent dialogue - Auto‑scaling of resources based on workload - Low‑code AutoGen Studio for rapid workflow prototyping - Seamless migration path to the newer Agent Framework
Whether you’re building a chatbot, automating data pipelines, or experimenting with AI‑powered research, AutoGen provides the tooling and community support to accelerate your projects. Check out the official docs, migration guide, and video tutorials to get started today.
LangGraph sets the foundation for how we can build and scale AI workloads — from conversational agents, complex task automation, to custom LLM-backed experiences that 'just work'. The next chapter in building complex production-ready features with LLMs is agentic, and with LangGraph and LangSmith, LangChain delivers an out-of-the-box solution to iterate quickly, debug immediately, and scale effortlessly.”
Read about the latest product updates, events, and content from the LangChain team · By Chester Curme and Mason Daugherty As the addressable task length of AI agents continues to grow, effective context management becomes critical to prevent context
Qodo Command isn’t just a new product. It’s a new chapter in how we think about development automation. <strong>By choosing LangGraph again, we doubled down on flexibility, speed, and cross-surface intelligence</strong> — and we’re already seeing the payoff.
Today, we are <strong>splitting that out of langgraph as part of a 0.3 release, and moving it into langgraph-prebuilt</strong>.
The three reasons I can think of: · 1. <strong>by predeclaring the structure, you can show debugging UI of the full graph, even if you've only executed part of it</strong> 2. Pregel can make it easier to reason about global state transitions (you can accomplish this in other systems too, if you set it up right) ...
As I refine and expand GPT-Newspaper, I’m enthusiastic about LangGraph’s potential. Its capacity to handle cyclic operations and progressively improve outputs could revolutionize other applications, extending the limits of what language models can accomplish.
LangSmith: LangSmith is a specialized API for building and managing LLMs within LangGraph. It provides tools for initializing LLMs, adding conditional edges and optimizing performance. By combining these components in innovative ways, users can build more sophisticated AI workflows that use the strengths of individual components. ... Get curated insights on the most important—and intriguing—AI news.
Today marks a new chapter, merging LangChain’s past and future: the team has completely rewritten langchain in its 1.0 release to be opinionated, focused and powered by LangGraph’s runtime.
Together, this state enables the agent, tools, and checkpointing system to maintain a coherent conversation, allowing each node in the LangGraph workflow to access the full context and contribute incrementally.
How to Design an Agentic AI Architecture with LangGraph and OpenAI Using Adaptive Deliberation, Memory Graphs, and Reflexion Loops
... All the power of AMP Cloud, deployed securely on your own infrastructure — on-prem or private VPCs in AWS, Azure, or GCP ... <strong>An open-source orchestration framework with high-level abstractions and low-level APIs for building complex, agent-driven workflows</strong>.
<strong>Framework for orchestrating role-playing, autonomous AI agents</strong>. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. - crewAIInc/crewAI
Code - https://github.com/campusx-official/memory-in-llms
In this tutorial, we build a real AI research agent from scratch — not a chatbot, not a demo. Using LangGraph, Tavily, and OpenAI, we design a goal-driven ag...
LangGraph is the most popular framework for building stateful AI agents which perform complex tasks. In this tutorial you'll learn the basics of LangGraph, b...
In this video, we continue the Agentic AI using LangGraph series by learning how to implement short-term memory in LLM-based systems. The video explains why ...
Download PyCharm and use it for free forever with one month of Pro included: https://www.jetbrains.com/pycharm/In this video, you're going to learn how to bu...
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👋 Hi everyone! In this tutorial, I'm excited to walk you through building a Multi-Agent AI System from Scratch – no frameworks needed! 🤖 This Python-based ...
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On this video we are going to deep dive into Microsoft Agent Framework for AutoGen users. Eric will show a practical migration path. It starts by covering wh...
AutoGen is an open-source framework from Microsoft that enables developers to create large language model (LLM) applications using multiple agents that can c...