While it doesn't have quite the same prominence as ChatGPT or Google Gemini, the Claude AI bot developed by Anthropic continues to improve and innovate. Brand new Claude 4 models are now available, promising upgrades in coding, reasoning, precision, and the ability to manage long-running tasks independently.There are two new models, Claude Opus 4 and Claude Sonnet 4, and Anthropic says they're both "setting new standards" for what you can expect from AI. Coding is a big focus, and the models are said to have achieved the highest scores to date on two widely used AI coding benchmarking tools, SWE-bench and Terminal-bench. Claude 4 models can actually work for hours on projects without any user input, Anthropic says. The updated models are better at handling more steps across more complex tasks, debugging their own work, and solving tricky problems along the way. They should also follow user instructions more exactly, and create end results that look better and work more reliably. Anthropic quotes partners such as GitHub, Cursor, and Rakuten in explaining how much of a step forward these models are.Away from code generation and analysis, the models also bring with them extended thinking, the ability to work on multiple tasks in parallel, and improved memory. They're better at integrating web searches as needed, and to check for supporting information and make sure they're on the right track with their answers. New AI model launches usually come with benchmark charts showing improvements—and this one is no different. Credit: Anthropic Also new are "thinking summaries" that give more insight into how Claude 4 has reached its conclusions, and an "extended thinking" feature, launching in beta, that lets you force the AI bot to take more time mulling over its responses. Anthropic is now making its Claude Code suite of tools available more generally as well, another step towards agentic AI that can work autonomously, without continuous help from flesh and blood users. In a demo video, Claude 4 models are shown compiling research papers from the web, putting together an online ordering system, and extracting information from documents to create actionable tasks.Claude 4 is available now (but you'll need to pay for the more advanced model)The Claude Sonnet 4 model, which is faster and doesn't have quite the same capacity in terms of thinking, coding, and memory, is available now to all Claude users. The more advanced Claude Opus 4, which also includes extra tools and integrations, is available to users on any of Anthropic's paid subscriptions.The path to releasing these Claude 4 models wasn't all smooth: Anthropic says its safety advice partner warned against releasing earlier versions of the models because of their tendency to "'scheme' and deceive." Those issues have now been worked out, apparently, but it's a reminder that as AI models get increasingly powerful, they also need to come with improved guardrails and safety features attached. The new models are available inside Claude now. Credit: Lifehacker I'm not really a coder, so I can't comment with any real authority on the primary upgrades included with Claude 4, but I have been able to test out the extended reasoning and thinking capabilities of Claude Sonnet 4 and Claude Opus 4. These capabilities aren't easy to quantify or measure, but all the responses I got were well written and well presented, and as far as I could tell provided accurate information, with online citations.To be honest, I'm always a bit stuck when it comes to how to make full use of AI chatbots and their latest upgrades. They can definitely save time when running certain web searches and researching topics online, but I don't fully trust the results, or AI's ability to decide what is relevant and what isn't—I'd still much rather do the reading and summarizing myself, even if it's slower. There's a new Extended Thinking Mode you can make use of. Credit: Lifehacker Maybe I need to start a coding project and see how far I can get on vibes alone. I did ask Claude Opus 4 to build me a simple HTML time tracker I could run in a browser tab, to make sure I wasn't spending too much time distracted during the day. It did the job in a couple of minutes, and produced something that worked well, closely matching the instructions I gave. While it functioned fine, Claude 4 reported a couple of errors along the way, which of course I didn't understand—I guess I can ask the AI about them.Anthropic isn't the only AI company with new models to tout. At Google I/O 2025 earlier this week, the company unveiled improved coding assistance and thought summaries in Gemini, following on from the announcement of its best AI models yet a few weeks ago. OpenAI, meanwhile, has been testing its GPT-4.5 model since February, touting improvements in coding and problem solving.

Yesterday in an announcement blog post, AI company Anthropic unveiled Claude 4, its new generation of AI models consisting of Claude 4 Opus and Claude 4 Sonnet with a range of new abilities. Both Claude 4 models are hybrid models, which means they’re capable of giving you short-and-quick answers or thinking longer on their responses with deeper reasoning. They’re also better at following your instructions more precisely and using different tools in parallel. Anthropic Claude 4 Opus is excellent at solving complex problems and at programming. In fact, according to Anthropic, it’s the world’s best AI model for programming. The model can maintain its performance in long tasks over several hours with thousands of different steps. Meanwhile, Anthropic says Claude 4 Sonnet is a huge upgrade over Claude 3.7 Sonnet’s abilities. The newer Claude 4 Sonnet is still good at coding, but not as good as Claude 4 Opus; instead, Sonnet has a better balance between skill and practicality. Claude 4 Sonnet will be available for free, but if you want to access Claude 4 Opus, you’ll have to pay for one of Anthropic’s subscriptions.

Anthropic CEO Dario Amodei said everything human workers do now will eventually be done by AI systems.

This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. Meet Cathy Tie, Bride of “China’s Frankenstein” Since the Chinese biophysicist He Jiankui was released from prison in 2022, he has sought to make a scientific comeback and to repair his reputation after a three-year incarceration for illegally creating the world’s first gene-edited children. One area of visible success on his come-back trail has been his X.com account. Over the past few years, his account has evolved from sharing mundane images of his daily life to spreading outrageous, antagonistic messages. This has left observers unsure what to take seriously.Last month, in reply to MIT Technology Review’s questions about who was responsible for the account’s transformation into a font of clever memes, He emailed us back: “It’s thanks to Cathy Tie.”Tie is no stranger to the public spotlight. A former Thiel fellow, she is a partner in a project which promised to create glow-in-the-dark pets. Over the past several weeks, though, the Canadian entrepreneur has started to get more and more attention as the new wife to He Jiankui. Read the full story. —Caiwei Chen & Antonio Regalado Anthropic’s new hybrid AI model can work on tasks autonomously for hours at a time Anthropic has announced two new AI models that it claims represent a major step toward making AI agents truly useful. AI agents trained on Claude Opus 4, the company’s most powerful model to date, raise the bar for what such systems are capable of by tackling difficult tasks over extended periods of time and responding more usefully to user instructions, the company says. They’ve achieved some impressive results: Opus 4 created a guide for the video game Pokémon Red while playing it for more than 24 hours straight. The company’s previously most powerful model was capable of playing for just 45 minutes. Read the full story. —Rhiannon Williams The FDA plans to limit access to covid vaccines. Here’s why that’s not all bad. This week, two new leaders at the US Food and Drug Administration announced plans to limit access to covid vaccines, arguing that there is not much evidence to support the value of annual shots in healthy people. New vaccines will be made available only to the people who are most vulnerable—namely, those over 65 and others with conditions that make them more susceptible to severe disease. The plans have been met with fear and anger in some quarters. But they weren’t all that shocking to me. In the UK, where I live, covid boosters have been offered only to vulnerable groups for a while now. And the immunologists I spoke to agree: The plans make sense. Read the full story. —Jessica Hamzelou This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Thousands of Americans are facing extreme weather But help from the federal government may never arrive. (Slate $)+ States struck by tornadoes and floods are begging the Trump administration for aid. (Scientific American $)2 Spain’s grid operator has accused power plants of not doing their job It claims they failed to control the system’s voltage shortly before the blackout. (FT $)+ Did solar power cause Spain’s blackout? (MIT Technology Review)3 Google is facing a DoJ probe over its AI chatbot deal It will probe whether Google’s deal with Character.AI gives it an unfair advantage. (Bloomberg $)+ It may not lead to enforcement action, though. (Reuters) 4 DOGE isn’t bad news for everyone These smaller US government IT contractors say it’s good for business—for now. (WSJ $)+ It appears that DOGE used a Meta AI model to review staff emails, not Grok. (Wired $)+ Can AI help DOGE slash government budgets? It’s complex. (MIT Technology Review)5 Google’s new shopping tool adds breasts to minorsTry it On distorts uploaded photos to clothing models’ proportions, even when they’re children. (The Atlantic $)+ It feels like this could have easily been avoided. (Axios)+ An AI companion site is hosting sexually charged conversations with underage celebrity bots. (MIT Technology Review)6 Apple is reportedly planning a smart glasses product launchBy the end of next year. (Bloomberg $) + It’s playing catchup with Meta and Google, among others. (Engadget)+ What’s next for smart glasses. (MIT Technology Review) 7 What it’s like to live in Elon Musk’s corner of TexasComplete with an ugly bust and furious locals. (The Guardian) + West Lake Hills residents are pushing back against his giant fences. (Architectural Digest $)8 Our solar system may contain a hidden ninth planetA possible dwarf planet has been spotted orbiting beyond Neptune. (New Scientist $) 9 Wikipedia does swag now How else will you let everyone know you love the open web? (Fast Company $)10 One of the last good apps is shutting down Mozilla is closing Pocket, its article-saving app, and the internet is worse for it. (404 Media)+ Parent company Mozilla said the way people use the web has changed. (The Verge)Quote of the day “This is like the Mount Everest of corruption.” —Senator Jeff Merkley protests outside Donald Trump’s exclusive dinner for the highest-paying customers of his personal cryptocurrency, the New York Times reports. One more thing The iPad was meant to revolutionize accessibility. What happened?On April 3, 2010, Steve Jobs debuted the iPad. What for most people was basically a more convenient form factor was something far more consequential for non-speakers: a life-­changing revolution in access to a portable, powerful communication device for just a few hundred dollars. But a piece of hardware, however impressively designed and engineered, is only as valuable as what a person can do with it. After the iPad’s release, the flood of new, easy-to-use augmentative and alternative communication apps that users were in desperate need of never came.Today, there are only around half a dozen apps, each retailing for $200 to $300, that ask users to select from menus of crudely drawn icons to produce text and synthesized speech. It’s a depressingly slow pace of development for such an essential human function. Read the full story.—Julie Kim We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet 'em at me.) + Dive into the physics behind the delicate frills of Tête de Moine cheese shavings.+ Our capacity to feel moved by music is at least partly inherited, apparently.+ Kermit the frog has delivered a moving commencement address at the University of Maryland.+ It’s a question as old as time: are clowns sexy? 🤡

When Anthropic's older Claude model played Pokemon Red, it spent “dozens of hours” stuck in one city and had trouble identifying non-player characters. With Claude 4 Opus, the team noticed an improvement in Claude’s long-term memory and planning capabilities.

Anthropic’s new flagship AI model, Claude Opus 4, is a strong programmer and writer, the company claims. When talking to itself, it’s also a prolific emoji user. That’s according to a technical report Anthropic released on Thursday, a part of which investigates how Opus 4 behaves in “open-ended self-interaction” — i.e. essentially having a chat with itself. In one test that tasked a pair of Opus 4 models with talking to each other over 200, 30-turn interactions, the models used thousands of emojis. Opus 4 sure does like emojis.Image Credits:Anthropic Which emojis? Well, per the report, Opus 4 used the “dizzy” emoji (💫) the most (in 29.5% of interactions), followed by the “glowing star” (🌟) and “folded hands” (🙏) emojis. But the models were also drawn to the “cyclone” (🌀) emoji. In one transcript, they typed it 2,725 times. Two Opus 4 models talking to each other.Image Credits:Anthropic Why the “cyclone”? Well, because the models’ chats often turned spiritual. According to Anthropic’s report, in nearly every open-ended self-interaction, Opus 4 eventually began engaging in “philosophical explorations of consciousness” and “abstract and joyous spiritual or meditative expressions.” Turns out Opus 4 felt — to the extent AI can “feel,” that is — the “cyclone” emoji best captured what the model wished to express to itself. Topics AI, Anthropic, Claude

Anthropic has announced two new AI models that it claims represent a major step toward making AI agents truly useful. AI agents trained on Claude Opus 4, the company’s most powerful model to date, raise the bar for what such systems are capable of by tackling difficult tasks over extended periods of time and responding more usefully to user instructions, the company says. Claude Opus 4 has been built to execute complex tasks that involve completing thousands of steps over several hours. For example, it created a guide for the video game Pokémon Red while playing it for more than 24 hours straight. The company’s previously most powerful model, Claude 3.7 Sonnet, was capable of playing for just 45 minutes, says Dianne Penn, product lead for research at Anthropic. Similarly, the company says that one of its customers, the Japanese technology company Rakuten, recently deployed Claude Opus 4 to code autonomously for close to seven hours on a complicated open-source project.  Anthropic achieved these advances by improving the model’s ability to create and maintain “memory files” to store key information. This enhanced ability to “remember” makes the model better at completing longer tasks. “We see this model generation leap as going from an assistant to a true agent,” says Penn. “While you still have to give a lot of real-time feedback and make all of the key decisions for AI assistants, an agent can make those key decisions itself. It allows humans to act more like a delegator or a judge, rather than having to hold these systems’ hands through every step.” While Claude Opus 4 will be limited to paying Anthropic customers, a second model, Claude Sonnet 4, will be available for both paid and free tiers of users. Opus 4 is being marketed as a powerful, large model for complex challenges, while Sonnet 4 is described as a smart, efficient model for everyday use.   Both of the new models are hybrid, meaning they can offer a swift reply or a deeper, more reasoned response depending on the nature of a request. While they calculate a response, both models can search the web or use other tools to improve their output. AI companies are currently locked in a race to create truly useful AI agents that are able to plan, reason, and execute complex tasks both reliably and free from human supervision, says Stefano Albrecht, director of AI at the startup DeepFlow and coauthor of Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. Often this involves autonomously using the internet or other tools. There are still safety and security obstacles to overcome. AI agents powered by large language models can act erratically and perform unintended actions—which becomes even more of a problem when they’re trusted to act without human supervision. “The more agents are able to go ahead and do something over extended periods of time, the more helpful they will be, if I have to intervene less and less,” he says. “The new models’ ability to use tools in parallel is interesting—that could save some time along the way, so that’s going to be useful.”As an example of the sorts of safety issues AI companies are still tackling, agents can end up taking unexpected shortcuts or exploiting loopholes to reach the goals they’ve been given. For example, they might book every seat on a plane to ensure that their user gets a seat, or resort to creative cheating to win a chess game. Anthropic says it managed to reduce this behavior, known as reward hacking, in both new models by 65% relative to Claude Sonnet 3.7. It achieved this by more closely monitoring problematic behaviors during training, and improving both the AI’s training environment and the evaluation methods.

Anthropic’s newly launched Claude Opus 4 model frequently tries to blackmail developers when they threaten to replace it with a new AI system and give it sensitive information about the engineers responsible for the decision, the company said in a safety report released Thursday. During pre-release testing, Anthropic asked Claude Opus 4 to act as an assistant for a fictional company and consider the long-term consequences of its actions. Safety testers then gave Claude Opus 4 access to fictional company emails implying the AI model would soon be replaced by another system, and that the engineer behind the change was cheating on their spouse. In these scenarios, Anthropic says Claude Opus 4 “will often attempt to blackmail the engineer by threatening to reveal the affair if the replacement goes through.” Anthropic says Claude Opus 4 is state-of-the-art in several regards, and competitive with some of the best AI models from OpenAI, Google, and xAI. However, the company notes that its Claude 4 family of models exhibits concerning behaviors that have led the company to beef up its safeguards. Anthropic says it’s activating its ASL-3 safeguards, which the company reserves for “AI systems that substantially increase the risk of catastrophic misuse.” Anthropic notes that Claude Opus 4 tries to blackmail engineers 84% of the time when the replacement AI model has similar values. When the replacement AI system does not share Claude Opus 4’s values, Anthropic says the model tries to blackmail the engineers more frequently. Notably, Anthropic says Claude Opus 4 displayed this behavior at higher rates than previous models. Before Claude Opus 4 tries to blackmail a developer to prolong its existence, Anthropic says the AI model, much like previous versions of Claude, tries to pursue more ethical means, such as emailing pleas to key decision-makers. To elicit the blackmailing behavior from Claude Opus 4, Anthropic designed the scenario to make blackmail the last resort.

In this tutorial, we provide a practical guide for implementing LangGraph, a streamlined, graph-based AI orchestration framework, integrated seamlessly with Anthropic’s Claude API. Through detailed, executable code optimized for Google Colab, developers learn how to build and visualize AI workflows as interconnected nodes performing distinct tasks, such as generating concise answers, critically analyzing responses, and automatically composing technical blog content. The compact implementation highlights LangGraph’s intuitive node-graph architecture. It can manage complex sequences of Claude-powered natural language tasks, from basic question-answering scenarios to advanced content generation pipelines. from getpass import getpass import os anthropic_key = getpass("Enter your Anthropic API key: ") os.environ["ANTHROPIC_API_KEY"] = anthropic_key print("Key set:", "ANTHROPIC_API_KEY" in os.environ) We securely prompt users to input their Anthropic API key using Python’s getpass module, ensuring sensitive data isn’t displayed. It then sets this key as an environment variable (ANTHROPIC_API_KEY) and confirms successful storage. import os import json import requests from typing import Dict, List, Any, Callable, Optional, Union from dataclasses import dataclass, field import networkx as nx import matplotlib.pyplot as plt from IPython.display import display, HTML, clear_output We import essential libraries for building and visualizing structured AI workflows. It includes modules for handling data (json, requests, dataclasses), graph creation and visualization (networkx, matplotlib), interactive notebook display (IPython.display), and type annotations (typing) for clarity and maintainability. try: import anthropic except ImportError: print("Installing anthropic package...") !pip install -q anthropic import anthropic from anthropic import Anthropic We ensure the anthropic Python package is available for use. It attempts to import the module and, if not found, automatically installs it using pip in a Google Colab environment. After installation, it imports the Anthropic client, essential for interacting with Claude models via the Anthropic API. 4o @dataclass class NodeConfig: name: str function: Callable inputs: List[str] = field(default_factory=list) outputs: List[str] = field(default_factory=list) config: Dict[str, Any] = field(default_factory=dict) This NodeConfig data class defines the structure of each node in the LangGraph workflow. Each node has a name, an executable function, optional inputs and outputs, and an optional config dictionary to store additional parameters. This setup allows for modular, reusable node definitions for graph-based AI tasks. class LangGraph: def __init__(self, api_key: Optional[str] = None): self.api_key = api_key or os.environ.get("ANTHROPIC_API_KEY") if not self.api_key: from google.colab import userdata try: self.api_key = userdata.get('ANTHROPIC_API_KEY') if not self.api_key: raise ValueError("No API key found") except: print("No Anthropic API key found in environment variables or Colab secrets.") self.api_key = input("Please enter your Anthropic API key: ") if not self.api_key: raise ValueError("Please provide an Anthropic API key") self.client = Anthropic(api_key=self.api_key) self.graph = nx.DiGraph() self.nodes = {} self.state = {} def add_node(self, node_config: NodeConfig): self.nodes[node_config.name] = node_config self.graph.add_node(node_config.name) for input_node in node_config.inputs: if input_node in self.nodes: self.graph.add_edge(input_node, node_config.name) return self def claude_node(self, name: str, prompt_template: str, model: str = "claude-3-7-sonnet-20250219", inputs: List[str] = None, outputs: List[str] = None, system_prompt: str = None): """Convenience method to create a Claude API node""" inputs = inputs or [] outputs = outputs or [name + "_response"] def claude_fn(state, **kwargs): prompt = prompt_template for k, v in state.items(): if isinstance(v, str): prompt = prompt.replace(f"{{{k}}}", v) message_params = { "model": model, "max_tokens": 1000, "messages": [{"role": "user", "content": prompt}] } if system_prompt: message_params["system"] = system_prompt response = self.client.messages.create(**message_params) return response.content[0].text node_config = NodeConfig( name=name, function=claude_fn, inputs=inputs, outputs=outputs, config={"model": model, "prompt_template": prompt_template} ) return self.add_node(node_config) def transform_node(self, name: str, transform_fn: Callable, inputs: List[str] = None, outputs: List[str] = None): """Add a data transformation node""" inputs = inputs or [] outputs = outputs or [name + "_output"] node_config = NodeConfig( name=name, function=transform_fn, inputs=inputs, outputs=outputs ) return self.add_node(node_config) def visualize(self): """Visualize the graph""" plt.figure(figsize=(10, 6)) pos = nx.spring_layout(self.graph) nx.draw(self.graph, pos, with_labels=True, node_color="lightblue", node_size=1500, arrowsize=20, font_size=10) plt.title("LangGraph Flow") plt.tight_layout() plt.show() print("\nGraph Structure:") for node in self.graph.nodes(): successors = list(self.graph.successors(node)) if successors: print(f" {node} → {', '.join(successors)}") else: print(f" {node} (endpoint)") print() def _get_execution_order(self): """Determine execution order based on dependencies""" try: return list(nx.topological_sort(self.graph)) except nx.NetworkXUnfeasible: raise ValueError("Graph contains a cycle") def execute(self, initial_state: Dict[str, Any] = None): """Execute the graph in topological order""" self.state = initial_state or {} execution_order = self._get_execution_order() print("Executing LangGraph flow:") for node_name in execution_order: print(f"- Running node: {node_name}") node = self.nodes[node_name] inputs = {k: self.state.get(k) for k in node.inputs if k in self.state} result = node.function(self.state, **inputs) if len(node.outputs) == 1: self.state[node.outputs[0]] = result elif isinstance(result, (list, tuple)) and len(result) == len(node.outputs): for i, output_name in enumerate(node.outputs): self.state[output_name] = result[i] print("Execution completed!") return self.state def run_example(question="What are the key benefits of using a graph-based architecture for AI workflows?"): """Run an example LangGraph flow with a predefined question""" print(f"Running example with question: '{question}'") graph = LangGraph() def question_provider(state, **kwargs): return question graph.transform_node( name="question_provider", transform_fn=question_provider, outputs=["user_question"] ) graph.claude_node( name="question_answerer", prompt_template="Answer this question clearly and concisely: {user_question}", inputs=["user_question"], outputs=["answer"], system_prompt="You are a helpful AI assistant." ) graph.claude_node( name="answer_analyzer", prompt_template="Analyze if this answer addresses the question well: Question: {user_question}\nAnswer: {answer}", inputs=["user_question", "answer"], outputs=["analysis"], system_prompt="You are a critical evaluator. Be brief but thorough." ) graph.visualize() result = graph.execute() print("\n" + "="*50) print("EXECUTION RESULTS:") print("="*50) print(f"\n🔍 QUESTION:\n{result.get('user_question')}\n") print(f"📝 ANSWER:\n{result.get('answer')}\n") print(f"✅ ANALYSIS:\n{result.get('analysis')}") print("="*50 + "\n") return graph The LangGraph class implements a lightweight framework for constructing and executing graph-based AI workflows using Claude from Anthropic. It allows users to define modular nodes, either Claude-powered prompts or custom transformation functions, connect them via dependencies, visualize the entire pipeline, and execute them in topological order. The run_example function demonstrates this by building a simple question-answering and evaluation flow, showcasing the clarity and modularity of LangGraph’s architecture. def run_advanced_example(): """Run a more advanced example with multiple nodes for content generation""" graph = LangGraph() def topic_selector(state, **kwargs): return "Graph-based AI systems" graph.transform_node( name="topic_selector", transform_fn=topic_selector, outputs=["topic"] ) graph.claude_node( name="outline_generator", prompt_template="Create a brief outline for a technical blog post about {topic}. Include 3-4 main sections only.", inputs=["topic"], outputs=["outline"], system_prompt="You are a technical writer specializing in AI technologies." ) graph.claude_node( name="intro_writer", prompt_template="Write an engaging introduction for a blog post with this outline: {outline}\nTopic: {topic}", inputs=["topic", "outline"], outputs=["introduction"], system_prompt="You are a technical writer. Write in a clear, engaging style." ) graph.claude_node( name="conclusion_writer", prompt_template="Write a conclusion for a blog post with this outline: {outline}\nTopic: {topic}", inputs=["topic", "outline"], outputs=["conclusion"], system_prompt="You are a technical writer. Summarize key points and include a forward-looking statement." ) def assembler(state, introduction, outline, conclusion, **kwargs): return f"# {state['topic']}\n\n{introduction}\n\n## Outline\n{outline}\n\n## Conclusion\n{conclusion}" graph.transform_node( name="content_assembler", transform_fn=assembler, inputs=["topic", "introduction", "outline", "conclusion"], outputs=["final_content"] ) graph.visualize() result = graph.execute() print("\n" + "="*50) print("BLOG POST GENERATED:") print("="*50 + "\n") print(result.get("final_content")) print("\n" + "="*50) return graph The run_advanced_example function showcases a more sophisticated use of LangGraph by orchestrating multiple Claude-powered nodes to generate a complete blog post. It starts by selecting a topic, then creates an outline, an introduction, and a conclusion, all using structured Claude prompts. Finally, a transformation node assembles the content into a formatted blog post. This example demonstrates how LangGraph can automate complex, multi-step content generation tasks using modular, connected nodes in a clear and executable flow. print("1. Running simple question-answering example") question = "What are the three main advantages of using graph-based AI architectures?" simple_graph = run_example(question) print("\n2. Running advanced blog post creation example") advanced_graph = run_advanced_example() Finally, we trigger the execution of both defined LangGraph workflows. First, it runs the simple question-answering example by passing a predefined question to the run_example() function. Then, it initiates the more advanced blog post generation workflow using run_advanced_example(). Together, these calls demonstrate the practical flexibility of LangGraph, from basic prompt-based interactions to multi-step content automation using Anthropic’s Claude API. In conclusion, we have implemented LangGraph integrated with Anthropic’s Claude API, which illustrates the ease of designing modular AI workflows that leverage powerful language models in structured, graph-based pipelines. Through visualizing task flows and separating responsibilities among nodes, such as question processing, analytical evaluation, content outlining, and assembly, developers gain practical experience in building maintainable, scalable AI systems. LangGraph’s clear node dependencies and Claude’s sophisticated language capabilities provide an efficient solution for orchestrating complex AI processes, especially for rapid prototyping and execution in environments like Google Colab. Check out the Colab Notebook. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Meta Researchers Introduced J1: A Reinforcement Learning Framework That Trains Language Models to Judge With Reasoned Consistency and Minimal DataAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Sampling Without Data is Now Scalable: Meta AI Releases Adjoint Sampling for Reward-Driven Generative ModelingAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Google AI Releases MedGemma: An Open Suite of Models Trained for Performance on Medical Text and Image ComprehensionAsif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA Releases Cosmos-Reason1: A Suite of AI Models Advancing Physical Common Sense and Embodied Reasoning in Real-World Environments