• How to Move from an Apartment to a House: A Step-by-Step Guide

    House in Palm Springs | © Sydney Turturro via Unsplash
    Moving from an apartment to a house is a major life milestone. It usually means more space, more responsibility, and more freedom. But unless you plan it right, it can also come with more stress. 
    Whether upgrading to accommodate a growing family or simply looking for more room to breathe, here are five essential steps to help you move from an apartment to a house confidently and easily.

    1. Start with a Clear Plan and Timeline
    One of the most common mistakes people make when moving from an apartment to a house is underestimating how long it takes. It’s not just about packing up your belongings; it’s about handling logistics, paperwork, and scheduling around daily life. 
    As soon as you know your moving date, create a moving checklist. 
    Outline what needs to be done weekly: notifying your landlord, scheduling the elevator if you’re in a high-rise, collecting packing materials, and booking your moving company.
    Planning ahead helps you avoid last-minute stress and unexpected costs. If possible, give yourself at least 6–8 weeks. That gives you enough time to declutter, coordinate with service providers, and ensure you’re fully prepared for move-in day. The earlier you start, the smoother everything flows.
    Hiring professional movers early in the process can also secure your preferred date and provide access to helpful services, such as packing, storage, or specialized transportation. With everything scheduled well in advance, you’ll have the peace of mind needed to focus on your new adventure.
    2. Downsize Before You Upsize
    It may sound counterintuitive, but moving to a bigger space doesn’t mean you should bring everything with you. 
    Over time, we accumulate furniture, clothes, gadgets, and kitchen tools that serve little purpose. And in a small apartment, many of these items might have been crammed into closets or storage bins and forgotten altogether. 
    Now’s the perfect time to declutter and lighten the load before the move.
    Start by walking through your apartment and taking stock of everything. Ask yourself what truly adds value to your life and what’s just taking up space. If you haven’t used something in over a year, it’s probably safe to let it go. You can donate, sell, or recycle items as needed. A good rule of thumb is to be ruthless. A cleaner move means a cleaner start in your new home.
    By purging the excess, you’ll reduce moving costs and arrive at your new house with a fresh mindset. There’s no sense in transporting items you no longer want or need. Instead, you’ll be able to unpack more quickly and enjoy your new space without clutter.
    3. Prepare for a Different Kind of Space
    Living in a house is very different from apartment life. The layout, square footage, and storage options all change, which means your furniture and lifestyle habits may also need to adapt. That cozy loveseat that fits perfectly in your apartment living room might look dwarfed in a larger house. 
    Before moving, visit the house if possible and take room measurements. 
    Consider creating a rough floor plan to determine where each piece of furniture will fit. This not only helps your local movers place items on move-in day, but it also ensures you don’t waste time and energy relocating things that won’t work in the new space. Apps and online tools can help you visualize your layout ahead of time.
    Beyond furniture, also think about what your new home may need. 

    Will you have a backyard that needs maintenance?
    A garage that requires storage shelving?
    An extra guest room or home office that needs furnishing? 

    While you don’t have to buy everything at once, it’s smart to budget for future purchases so you can comfortably and intentionally grow into your home.
    4. Handle Utilities, Address Changes, and Logistics

    © Peter Thomas via Unsplash

    © Mitch via Unsplash

    Unlike apartments, where utilities may be partially covered or managed by the landlord, houses typically require you to set up and manage everything yourself. 
    This includes electricity, water, gas, internet, trash collection, and sometimes even lawn care services. Don’t wait until the last minute; contact providers at least a week before your move to schedule installation or transfers.
    At the same time, update your address with your bank, subscription services, and relevant government agencies. This helps ensure you continue to receive important mail and prevents service disruptions. If you’re moving within the same city, this can be fairly straightforward. If you’re moving to a different region or province, however, make sure you check for local utility providers and regional services.
    Elevator bookings and parking permits are other key details to address, especially when moving out of an apartment in a busy area. 
    Confirm all building rules and moving hours in advance, and inform your movers so they can plan accordingly. Clear communication on these details makes the moving day smoother for everyone involved.
    5. Rely on a Trusted Moving Partner
    Perhaps the most important step in this process is choosing the right moving company. 
    Apartment-to-house moves require experience, careful coordination, and physical effort, especially when dealing with tight stairwells, narrow hallways, or long distances from the apartment to the truck. 
    A professional moving team can handle the logistics efficiently while protecting your belongings from damage.
    Instead of doing it all yourself, you can count on us to carry the load literally. We bring the right equipment, vehicles, and muscle, so you don’t have to worry about heavy lifting or unexpected delays. More importantly, we offer peace of mind during an exciting and overwhelming time.
    A good moving experience sets the tone for your new chapter. Let us help make that transition smooth, stress-free, and even enjoyable.
    Final Word
    Moving from an apartment to a house is more than just a physical shift; it’s a lifestyle change. With the right planning, thoughtful decisions, and support from experienced movers, you can make the process simple and even fun. Whether it’s your first home or your dream upgrade, we’re here to guide you every step of the way.
    Frequently Asked Questions 
    1. How far in advance should I book a moving company when moving from an apartment to a house?
    We recommend booking your moving company at least 4–6 weeks in advance, especially during peak moving seasons. This gives you the best chance to secure your preferred date and time while also allowing time for proper planning, packing, and coordination with building management if needed.
    2. What’s the best way to downsize before moving into a house?
    Even though you’re moving to a larger space, it’s wise to declutter before your move. Sort items by category, keep, donate, sell, or discard, and be honest about what you actually use. Unused furniture, duplicate kitchenware, and old clothes often don’t need to make the move. A lighter load means a faster, more affordable, and more organized transition.
    3. Will all my apartment furniture fit properly in a house? 
    Some furniture from a compact apartment may not always feel too small or awkward in a larger home. We recommend measuring key pieces and comparing them with the dimensions of the new space before moving. Our team can help you decide what’s worth moving and even assist with layout planning to ensure everything fits where it should.
    4. What should I do about utilities when moving into a house? 
    Unlike apartment living, homeowners are responsible for setting up all their utilities individually. Be sure to contact providers for electricity, water, internet, gas, and waste collection at least a week before your move. Scheduling ahead ensures your new home is move-in ready, and you won’t experience any service interruptions.
    5. Do I need professional movers for a short move from an apartment to a house?
    Even if you’re moving just a few blocks, a professional moving team makes the process faster, safer, and far less stressful. We handle the heavy lifting, stairs, tight corners, and transportation logistics, so you don’t have to. Our experience ensures that your belongings arrive safely, regardless of the distance.

    GuidesTips

    by ArchEyes Team
    Leave a comment
    #how #move #apartment #house #stepbystep
    How to Move from an Apartment to a House: A Step-by-Step Guide
    House in Palm Springs | © Sydney Turturro via Unsplash Moving from an apartment to a house is a major life milestone. It usually means more space, more responsibility, and more freedom. But unless you plan it right, it can also come with more stress.  Whether upgrading to accommodate a growing family or simply looking for more room to breathe, here are five essential steps to help you move from an apartment to a house confidently and easily. 1. Start with a Clear Plan and Timeline One of the most common mistakes people make when moving from an apartment to a house is underestimating how long it takes. It’s not just about packing up your belongings; it’s about handling logistics, paperwork, and scheduling around daily life.  As soon as you know your moving date, create a moving checklist.  Outline what needs to be done weekly: notifying your landlord, scheduling the elevator if you’re in a high-rise, collecting packing materials, and booking your moving company. Planning ahead helps you avoid last-minute stress and unexpected costs. If possible, give yourself at least 6–8 weeks. That gives you enough time to declutter, coordinate with service providers, and ensure you’re fully prepared for move-in day. The earlier you start, the smoother everything flows. Hiring professional movers early in the process can also secure your preferred date and provide access to helpful services, such as packing, storage, or specialized transportation. With everything scheduled well in advance, you’ll have the peace of mind needed to focus on your new adventure. 2. Downsize Before You Upsize It may sound counterintuitive, but moving to a bigger space doesn’t mean you should bring everything with you.  Over time, we accumulate furniture, clothes, gadgets, and kitchen tools that serve little purpose. And in a small apartment, many of these items might have been crammed into closets or storage bins and forgotten altogether.  Now’s the perfect time to declutter and lighten the load before the move. Start by walking through your apartment and taking stock of everything. Ask yourself what truly adds value to your life and what’s just taking up space. If you haven’t used something in over a year, it’s probably safe to let it go. You can donate, sell, or recycle items as needed. A good rule of thumb is to be ruthless. A cleaner move means a cleaner start in your new home. By purging the excess, you’ll reduce moving costs and arrive at your new house with a fresh mindset. There’s no sense in transporting items you no longer want or need. Instead, you’ll be able to unpack more quickly and enjoy your new space without clutter. 3. Prepare for a Different Kind of Space Living in a house is very different from apartment life. The layout, square footage, and storage options all change, which means your furniture and lifestyle habits may also need to adapt. That cozy loveseat that fits perfectly in your apartment living room might look dwarfed in a larger house.  Before moving, visit the house if possible and take room measurements.  Consider creating a rough floor plan to determine where each piece of furniture will fit. This not only helps your local movers place items on move-in day, but it also ensures you don’t waste time and energy relocating things that won’t work in the new space. Apps and online tools can help you visualize your layout ahead of time. Beyond furniture, also think about what your new home may need.  Will you have a backyard that needs maintenance? A garage that requires storage shelving? An extra guest room or home office that needs furnishing?  While you don’t have to buy everything at once, it’s smart to budget for future purchases so you can comfortably and intentionally grow into your home. 4. Handle Utilities, Address Changes, and Logistics © Peter Thomas via Unsplash © Mitch via Unsplash Unlike apartments, where utilities may be partially covered or managed by the landlord, houses typically require you to set up and manage everything yourself.  This includes electricity, water, gas, internet, trash collection, and sometimes even lawn care services. Don’t wait until the last minute; contact providers at least a week before your move to schedule installation or transfers. At the same time, update your address with your bank, subscription services, and relevant government agencies. This helps ensure you continue to receive important mail and prevents service disruptions. If you’re moving within the same city, this can be fairly straightforward. If you’re moving to a different region or province, however, make sure you check for local utility providers and regional services. Elevator bookings and parking permits are other key details to address, especially when moving out of an apartment in a busy area.  Confirm all building rules and moving hours in advance, and inform your movers so they can plan accordingly. Clear communication on these details makes the moving day smoother for everyone involved. 5. Rely on a Trusted Moving Partner Perhaps the most important step in this process is choosing the right moving company.  Apartment-to-house moves require experience, careful coordination, and physical effort, especially when dealing with tight stairwells, narrow hallways, or long distances from the apartment to the truck.  A professional moving team can handle the logistics efficiently while protecting your belongings from damage. Instead of doing it all yourself, you can count on us to carry the load literally. We bring the right equipment, vehicles, and muscle, so you don’t have to worry about heavy lifting or unexpected delays. More importantly, we offer peace of mind during an exciting and overwhelming time. A good moving experience sets the tone for your new chapter. Let us help make that transition smooth, stress-free, and even enjoyable. Final Word Moving from an apartment to a house is more than just a physical shift; it’s a lifestyle change. With the right planning, thoughtful decisions, and support from experienced movers, you can make the process simple and even fun. Whether it’s your first home or your dream upgrade, we’re here to guide you every step of the way. Frequently Asked Questions  1. How far in advance should I book a moving company when moving from an apartment to a house? We recommend booking your moving company at least 4–6 weeks in advance, especially during peak moving seasons. This gives you the best chance to secure your preferred date and time while also allowing time for proper planning, packing, and coordination with building management if needed. 2. What’s the best way to downsize before moving into a house? Even though you’re moving to a larger space, it’s wise to declutter before your move. Sort items by category, keep, donate, sell, or discard, and be honest about what you actually use. Unused furniture, duplicate kitchenware, and old clothes often don’t need to make the move. A lighter load means a faster, more affordable, and more organized transition. 3. Will all my apartment furniture fit properly in a house?  Some furniture from a compact apartment may not always feel too small or awkward in a larger home. We recommend measuring key pieces and comparing them with the dimensions of the new space before moving. Our team can help you decide what’s worth moving and even assist with layout planning to ensure everything fits where it should. 4. What should I do about utilities when moving into a house?  Unlike apartment living, homeowners are responsible for setting up all their utilities individually. Be sure to contact providers for electricity, water, internet, gas, and waste collection at least a week before your move. Scheduling ahead ensures your new home is move-in ready, and you won’t experience any service interruptions. 5. Do I need professional movers for a short move from an apartment to a house? Even if you’re moving just a few blocks, a professional moving team makes the process faster, safer, and far less stressful. We handle the heavy lifting, stairs, tight corners, and transportation logistics, so you don’t have to. Our experience ensures that your belongings arrive safely, regardless of the distance. GuidesTips by ArchEyes Team Leave a comment #how #move #apartment #house #stepbystep
    ARCHEYES.COM
    How to Move from an Apartment to a House: A Step-by-Step Guide
    House in Palm Springs | © Sydney Turturro via Unsplash Moving from an apartment to a house is a major life milestone. It usually means more space, more responsibility, and more freedom. But unless you plan it right, it can also come with more stress.  Whether upgrading to accommodate a growing family or simply looking for more room to breathe, here are five essential steps to help you move from an apartment to a house confidently and easily. 1. Start with a Clear Plan and Timeline One of the most common mistakes people make when moving from an apartment to a house is underestimating how long it takes. It’s not just about packing up your belongings; it’s about handling logistics, paperwork, and scheduling around daily life.  As soon as you know your moving date, create a moving checklist.  Outline what needs to be done weekly: notifying your landlord, scheduling the elevator if you’re in a high-rise, collecting packing materials, and booking your moving company. Planning ahead helps you avoid last-minute stress and unexpected costs. If possible, give yourself at least 6–8 weeks. That gives you enough time to declutter, coordinate with service providers, and ensure you’re fully prepared for move-in day. The earlier you start, the smoother everything flows. Hiring professional movers early in the process can also secure your preferred date and provide access to helpful services, such as packing, storage, or specialized transportation. With everything scheduled well in advance, you’ll have the peace of mind needed to focus on your new adventure. 2. Downsize Before You Upsize It may sound counterintuitive, but moving to a bigger space doesn’t mean you should bring everything with you.  Over time, we accumulate furniture, clothes, gadgets, and kitchen tools that serve little purpose. And in a small apartment, many of these items might have been crammed into closets or storage bins and forgotten altogether.  Now’s the perfect time to declutter and lighten the load before the move. Start by walking through your apartment and taking stock of everything. Ask yourself what truly adds value to your life and what’s just taking up space. If you haven’t used something in over a year, it’s probably safe to let it go. You can donate, sell, or recycle items as needed. A good rule of thumb is to be ruthless. A cleaner move means a cleaner start in your new home. By purging the excess, you’ll reduce moving costs and arrive at your new house with a fresh mindset. There’s no sense in transporting items you no longer want or need. Instead, you’ll be able to unpack more quickly and enjoy your new space without clutter. 3. Prepare for a Different Kind of Space Living in a house is very different from apartment life. The layout, square footage, and storage options all change, which means your furniture and lifestyle habits may also need to adapt. That cozy loveseat that fits perfectly in your apartment living room might look dwarfed in a larger house.  Before moving, visit the house if possible and take room measurements.  Consider creating a rough floor plan to determine where each piece of furniture will fit. This not only helps your local movers place items on move-in day, but it also ensures you don’t waste time and energy relocating things that won’t work in the new space. Apps and online tools can help you visualize your layout ahead of time. Beyond furniture, also think about what your new home may need.  Will you have a backyard that needs maintenance? A garage that requires storage shelving? An extra guest room or home office that needs furnishing?  While you don’t have to buy everything at once, it’s smart to budget for future purchases so you can comfortably and intentionally grow into your home. 4. Handle Utilities, Address Changes, and Logistics © Peter Thomas via Unsplash © Mitch via Unsplash Unlike apartments, where utilities may be partially covered or managed by the landlord, houses typically require you to set up and manage everything yourself.  This includes electricity, water, gas, internet, trash collection, and sometimes even lawn care services. Don’t wait until the last minute; contact providers at least a week before your move to schedule installation or transfers. At the same time, update your address with your bank, subscription services, and relevant government agencies. This helps ensure you continue to receive important mail and prevents service disruptions. If you’re moving within the same city, this can be fairly straightforward. If you’re moving to a different region or province, however, make sure you check for local utility providers and regional services. Elevator bookings and parking permits are other key details to address, especially when moving out of an apartment in a busy area.  Confirm all building rules and moving hours in advance, and inform your movers so they can plan accordingly. Clear communication on these details makes the moving day smoother for everyone involved. 5. Rely on a Trusted Moving Partner Perhaps the most important step in this process is choosing the right moving company.  Apartment-to-house moves require experience, careful coordination, and physical effort, especially when dealing with tight stairwells, narrow hallways, or long distances from the apartment to the truck.  A professional moving team can handle the logistics efficiently while protecting your belongings from damage. Instead of doing it all yourself, you can count on us to carry the load literally. We bring the right equipment, vehicles, and muscle, so you don’t have to worry about heavy lifting or unexpected delays. More importantly, we offer peace of mind during an exciting and overwhelming time. A good moving experience sets the tone for your new chapter. Let us help make that transition smooth, stress-free, and even enjoyable. Final Word Moving from an apartment to a house is more than just a physical shift; it’s a lifestyle change. With the right planning, thoughtful decisions, and support from experienced movers, you can make the process simple and even fun. Whether it’s your first home or your dream upgrade, we’re here to guide you every step of the way. Frequently Asked Questions  1. How far in advance should I book a moving company when moving from an apartment to a house? We recommend booking your moving company at least 4–6 weeks in advance, especially during peak moving seasons (spring and summer). This gives you the best chance to secure your preferred date and time while also allowing time for proper planning, packing, and coordination with building management if needed. 2. What’s the best way to downsize before moving into a house? Even though you’re moving to a larger space, it’s wise to declutter before your move. Sort items by category, keep, donate, sell, or discard, and be honest about what you actually use. Unused furniture, duplicate kitchenware, and old clothes often don’t need to make the move. A lighter load means a faster, more affordable, and more organized transition. 3. Will all my apartment furniture fit properly in a house?  Some furniture from a compact apartment may not always feel too small or awkward in a larger home. We recommend measuring key pieces and comparing them with the dimensions of the new space before moving. Our team can help you decide what’s worth moving and even assist with layout planning to ensure everything fits where it should. 4. What should I do about utilities when moving into a house?  Unlike apartment living, homeowners are responsible for setting up all their utilities individually. Be sure to contact providers for electricity, water, internet, gas, and waste collection at least a week before your move. Scheduling ahead ensures your new home is move-in ready, and you won’t experience any service interruptions. 5. Do I need professional movers for a short move from an apartment to a house? Even if you’re moving just a few blocks, a professional moving team makes the process faster, safer, and far less stressful. We handle the heavy lifting, stairs, tight corners, and transportation logistics, so you don’t have to. Our experience ensures that your belongings arrive safely, regardless of the distance. GuidesTips by ArchEyes Team Leave a comment
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  • Step-by-Step Guide to Getting Started on Blogsternation-com

    Posted on : May 30, 2025

    By

    Tech World Times

    SEO 

    Rate this post

    Starting a blog can feel confusing at first. Many platforms are available, but not all are straightforward. If you’re new to blogging, Blogsternation-com is a good place to start. This guide will help you begin your journey. Step by step, we’ll cover each part. You don’t need any tech skills to follow along.
    Step 1: Visit the Blogsternation-com Website
    Go to Blogsternation-com in your browser. Wait for the homepage to load. The design is clean and simple. You will see a clear “Sign Up” or “Join Now” button.
    Step 2: Create a New Account
    Click the “Sign Up” button. You will be asked for basic details. Enter your name and email address. Choose a strong password you can remember. Make sure your email is active and correct. You’ll need it for verification.
    Step 3: Verify Your Email Address
    After signing up, check your email inbox. Look for a message from Blogsternation-com. It will contain a link. Click on that link to verify your account. This step helps keep your account safe.
    Step 4: Log in to Your Account
    After verifying, go back to the website. Click “Login” at the top right. Enter your email and password. Click “Submit.” You are now inside your dashboard. This is your control panel. You will use this area to manage your blog.
    Step 5: Set Up Your Blogger Profile
    Click on your name or profile icon. Select “Edit Profile.” Add a profile photo. Write a short bio. Let people know who you are. This builds trust with your readers. Choose a username that fits your blog style.
    Step 6: Pick Your Blog Niche
    Before writing, decide your niche. A niche is your blog’s main topic. It could be travel, health, fashion, tech, or anything else. Stick to one area for now. This helps readers know what to expect. Pick a topic you love. That will keep you motivated.
    Step 7: Create Your First Blog
    Click “New Blog” or “Start Blogging.” A writing editor will open. Add a catchy title. Then start writing your content. Use short paragraphs and simple words. Make your blog easy to read. You don’t need to write long articles. Quality is more important than length.
    Step 8: Format Your Blog Post
    Use bold text for headings. Use bullet points or numbers for lists. Add images to make posts engaging. Blogsternation-com allows you to upload images directly. Use free stock photos if you don’t have your own. Always credit the source if needed.
    Step 9: Preview Before Publishing
    Once you finish writing, click “Preview.” This shows how your post will look. Check for grammar mistakes. Make sure links work. Edit anything that looks off. Take your time to make it right.
    Step 10: Publish Your Blog
    If everything looks good, hit “Publish.” Your blog is now live. Share it with friends and family. Use social media to get more readers. Keep sharing whenever you post something new.
    Step 11: Stay Consistent
    Try to post regularly. Once a week is a good start. Don’t disappear for months. Regular posts help build an audience. Over time, more people will visit your blog. Consistency also improves your writing skills.
    Step 12: Engage with Readers
    Reply to comments on your blog. Thank readers for their feedback. Ask them questions to start a conversation. This builds a community. Loyal readers are key to blog growth.
    Step 13: Learn from Other Bloggers
    Follow successful bloggers on Blogsternation-com. Read their posts. Notice their style and structure. See what works for them. Learning from others helps you grow faster.
    Step 14: Share Useful Content
    Your blog should help people. Give tips, guides, or real stories. Add value to your readers’ lives. Useful content gets shared more. That means more traffic and readers for you.
    Step 15: Use SEO Basics
    SEO stands for Search Engine Optimization. Use keywords people search for. Add them naturally in your post. Include keywords in your title and headings. Blogsternation-com has basic SEO tools you can use. These help your post show up on search engines.
    Step 16: Join Blogsternation-com Communities
    There are groups and forums on the site. Join communities related to your niche. Ask questions. Share your blogs. Support others. Networking helps you grow faster.
    Step 17: Check Your Blog Analytics
    Go to your dashboard. Click on “Analytics” or “Stats.” You’ll see how many people read your blog. You’ll also see which posts get the most views. Use this info to plan future posts.
    Step 18: Monetize Your BlogAfter you gain some traffic, think about monetizing. You can add ads or affiliate links. Some users also sell products or services. Don’t rush into it. Focus on building good content first. Monetization can come later.
    Step 19: Stay Updated
    Technology changes often. So does blogging. Blogsternation-com often shares updates and tips. Read their blog and help guides. These help you stay ahead.
    Step 20: Keep Improving
    Blogging is a journey. Don’t stop learning. Watch free YouTube videos on blogging. Take online courses if possible. Read other blogs for ideas. The more you learn, the better you blog.
    Bonus Tips for Success

    Always check grammar before posting
    Avoid copying content from others
    Write from your heart
    Be honest in your posts
    Keep your layout clean and easy to read
    Use headings to break long sections
    Back up your content regularly

    Final Thoughts
    Starting a blog can seem hard. But with the right steps, it gets easier. Blogsternation-com makes blogging simple. It’s beginner-friendly and full of helpful tools. Whether you’re sharing tips or stories, your voice matters. Start today and grow with time.
    Remember, everyone starts small. Your first post may not be perfect. That’s okay. The important thing is to keep going. The more you write, the better you become. Take that first step now.
    Good luck and happy blogging with Blogsternation-com!
    Tech World TimesTech World Times, a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com
    #stepbystep #guide #getting #started #blogsternationcom
    Step-by-Step Guide to Getting Started on Blogsternation-com
    Posted on : May 30, 2025 By Tech World Times SEO  Rate this post Starting a blog can feel confusing at first. Many platforms are available, but not all are straightforward. If you’re new to blogging, Blogsternation-com is a good place to start. This guide will help you begin your journey. Step by step, we’ll cover each part. You don’t need any tech skills to follow along. Step 1: Visit the Blogsternation-com Website Go to Blogsternation-com in your browser. Wait for the homepage to load. The design is clean and simple. You will see a clear “Sign Up” or “Join Now” button. Step 2: Create a New Account Click the “Sign Up” button. You will be asked for basic details. Enter your name and email address. Choose a strong password you can remember. Make sure your email is active and correct. You’ll need it for verification. Step 3: Verify Your Email Address After signing up, check your email inbox. Look for a message from Blogsternation-com. It will contain a link. Click on that link to verify your account. This step helps keep your account safe. Step 4: Log in to Your Account After verifying, go back to the website. Click “Login” at the top right. Enter your email and password. Click “Submit.” You are now inside your dashboard. This is your control panel. You will use this area to manage your blog. Step 5: Set Up Your Blogger Profile Click on your name or profile icon. Select “Edit Profile.” Add a profile photo. Write a short bio. Let people know who you are. This builds trust with your readers. Choose a username that fits your blog style. Step 6: Pick Your Blog Niche Before writing, decide your niche. A niche is your blog’s main topic. It could be travel, health, fashion, tech, or anything else. Stick to one area for now. This helps readers know what to expect. Pick a topic you love. That will keep you motivated. Step 7: Create Your First Blog Click “New Blog” or “Start Blogging.” A writing editor will open. Add a catchy title. Then start writing your content. Use short paragraphs and simple words. Make your blog easy to read. You don’t need to write long articles. Quality is more important than length. Step 8: Format Your Blog Post Use bold text for headings. Use bullet points or numbers for lists. Add images to make posts engaging. Blogsternation-com allows you to upload images directly. Use free stock photos if you don’t have your own. Always credit the source if needed. Step 9: Preview Before Publishing Once you finish writing, click “Preview.” This shows how your post will look. Check for grammar mistakes. Make sure links work. Edit anything that looks off. Take your time to make it right. Step 10: Publish Your Blog If everything looks good, hit “Publish.” Your blog is now live. Share it with friends and family. Use social media to get more readers. Keep sharing whenever you post something new. Step 11: Stay Consistent Try to post regularly. Once a week is a good start. Don’t disappear for months. Regular posts help build an audience. Over time, more people will visit your blog. Consistency also improves your writing skills. Step 12: Engage with Readers Reply to comments on your blog. Thank readers for their feedback. Ask them questions to start a conversation. This builds a community. Loyal readers are key to blog growth. Step 13: Learn from Other Bloggers Follow successful bloggers on Blogsternation-com. Read their posts. Notice their style and structure. See what works for them. Learning from others helps you grow faster. Step 14: Share Useful Content Your blog should help people. Give tips, guides, or real stories. Add value to your readers’ lives. Useful content gets shared more. That means more traffic and readers for you. Step 15: Use SEO Basics SEO stands for Search Engine Optimization. Use keywords people search for. Add them naturally in your post. Include keywords in your title and headings. Blogsternation-com has basic SEO tools you can use. These help your post show up on search engines. Step 16: Join Blogsternation-com Communities There are groups and forums on the site. Join communities related to your niche. Ask questions. Share your blogs. Support others. Networking helps you grow faster. Step 17: Check Your Blog Analytics Go to your dashboard. Click on “Analytics” or “Stats.” You’ll see how many people read your blog. You’ll also see which posts get the most views. Use this info to plan future posts. Step 18: Monetize Your BlogAfter you gain some traffic, think about monetizing. You can add ads or affiliate links. Some users also sell products or services. Don’t rush into it. Focus on building good content first. Monetization can come later. Step 19: Stay Updated Technology changes often. So does blogging. Blogsternation-com often shares updates and tips. Read their blog and help guides. These help you stay ahead. Step 20: Keep Improving Blogging is a journey. Don’t stop learning. Watch free YouTube videos on blogging. Take online courses if possible. Read other blogs for ideas. The more you learn, the better you blog. Bonus Tips for Success Always check grammar before posting Avoid copying content from others Write from your heart Be honest in your posts Keep your layout clean and easy to read Use headings to break long sections Back up your content regularly Final Thoughts Starting a blog can seem hard. But with the right steps, it gets easier. Blogsternation-com makes blogging simple. It’s beginner-friendly and full of helpful tools. Whether you’re sharing tips or stories, your voice matters. Start today and grow with time. Remember, everyone starts small. Your first post may not be perfect. That’s okay. The important thing is to keep going. The more you write, the better you become. Take that first step now. Good luck and happy blogging with Blogsternation-com! Tech World TimesTech World Times, a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com #stepbystep #guide #getting #started #blogsternationcom
    TECHWORLDTIMES.COM
    Step-by-Step Guide to Getting Started on Blogsternation-com
    Posted on : May 30, 2025 By Tech World Times SEO  Rate this post Starting a blog can feel confusing at first. Many platforms are available, but not all are straightforward. If you’re new to blogging, Blogsternation-com is a good place to start. This guide will help you begin your journey. Step by step, we’ll cover each part. You don’t need any tech skills to follow along. Step 1: Visit the Blogsternation-com Website Go to Blogsternation-com in your browser. Wait for the homepage to load. The design is clean and simple. You will see a clear “Sign Up” or “Join Now” button. Step 2: Create a New Account Click the “Sign Up” button. You will be asked for basic details. Enter your name and email address. Choose a strong password you can remember. Make sure your email is active and correct. You’ll need it for verification. Step 3: Verify Your Email Address After signing up, check your email inbox. Look for a message from Blogsternation-com. It will contain a link. Click on that link to verify your account. This step helps keep your account safe. Step 4: Log in to Your Account After verifying, go back to the website. Click “Login” at the top right. Enter your email and password. Click “Submit.” You are now inside your dashboard. This is your control panel. You will use this area to manage your blog. Step 5: Set Up Your Blogger Profile Click on your name or profile icon. Select “Edit Profile.” Add a profile photo. Write a short bio. Let people know who you are. This builds trust with your readers. Choose a username that fits your blog style. Step 6: Pick Your Blog Niche Before writing, decide your niche. A niche is your blog’s main topic. It could be travel, health, fashion, tech, or anything else. Stick to one area for now. This helps readers know what to expect. Pick a topic you love. That will keep you motivated. Step 7: Create Your First Blog Click “New Blog” or “Start Blogging.” A writing editor will open. Add a catchy title. Then start writing your content. Use short paragraphs and simple words. Make your blog easy to read. You don’t need to write long articles. Quality is more important than length. Step 8: Format Your Blog Post Use bold text for headings. Use bullet points or numbers for lists. Add images to make posts engaging. Blogsternation-com allows you to upload images directly. Use free stock photos if you don’t have your own. Always credit the source if needed. Step 9: Preview Before Publishing Once you finish writing, click “Preview.” This shows how your post will look. Check for grammar mistakes. Make sure links work. Edit anything that looks off. Take your time to make it right. Step 10: Publish Your Blog If everything looks good, hit “Publish.” Your blog is now live. Share it with friends and family. Use social media to get more readers. Keep sharing whenever you post something new. Step 11: Stay Consistent Try to post regularly. Once a week is a good start. Don’t disappear for months. Regular posts help build an audience. Over time, more people will visit your blog. Consistency also improves your writing skills. Step 12: Engage with Readers Reply to comments on your blog. Thank readers for their feedback. Ask them questions to start a conversation. This builds a community. Loyal readers are key to blog growth. Step 13: Learn from Other Bloggers Follow successful bloggers on Blogsternation-com. Read their posts. Notice their style and structure. See what works for them. Learning from others helps you grow faster. Step 14: Share Useful Content Your blog should help people. Give tips, guides, or real stories. Add value to your readers’ lives. Useful content gets shared more. That means more traffic and readers for you. Step 15: Use SEO Basics SEO stands for Search Engine Optimization. Use keywords people search for. Add them naturally in your post. Include keywords in your title and headings. Blogsternation-com has basic SEO tools you can use. These help your post show up on search engines. Step 16: Join Blogsternation-com Communities There are groups and forums on the site. Join communities related to your niche. Ask questions. Share your blogs. Support others. Networking helps you grow faster. Step 17: Check Your Blog Analytics Go to your dashboard. Click on “Analytics” or “Stats.” You’ll see how many people read your blog. You’ll also see which posts get the most views. Use this info to plan future posts. Step 18: Monetize Your Blog (Optional) After you gain some traffic, think about monetizing. You can add ads or affiliate links. Some users also sell products or services. Don’t rush into it. Focus on building good content first. Monetization can come later. Step 19: Stay Updated Technology changes often. So does blogging. Blogsternation-com often shares updates and tips. Read their blog and help guides. These help you stay ahead. Step 20: Keep Improving Blogging is a journey. Don’t stop learning. Watch free YouTube videos on blogging. Take online courses if possible. Read other blogs for ideas. The more you learn, the better you blog. Bonus Tips for Success Always check grammar before posting Avoid copying content from others Write from your heart Be honest in your posts Keep your layout clean and easy to read Use headings to break long sections Back up your content regularly Final Thoughts Starting a blog can seem hard. But with the right steps, it gets easier. Blogsternation-com makes blogging simple. It’s beginner-friendly and full of helpful tools. Whether you’re sharing tips or stories, your voice matters. Start today and grow with time. Remember, everyone starts small. Your first post may not be perfect. That’s okay. The important thing is to keep going. The more you write, the better you become. Take that first step now. Good luck and happy blogging with Blogsternation-com! Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com
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  • Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)

    Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used:

    LLMs train on AI-generated text

    Fraud systems simulate edge cases

    Vision models pretrain on fake images

    SDVis an open-source Python library that generates realistic tabular data using machine learning. It learns patterns from real data and creates high-quality synthetic data for safe sharing, testing, and model training.
    In this tutorial, we’ll use SDV to generate synthetic data step by step.
    pip install sdv
    We will first install the sdv library:
    from sdv.io.local import CSVHandler

    connector = CSVHandlerFOLDER_NAME = '.' # If the data is in the same directory

    data = connector.readsalesDf = dataNext, we import the necessary module and connect to our local folder containing the dataset files. This reads the CSV files from the specified folder and stores them as pandas DataFrames. In this case, we access the main dataset using data.
    from sdv.metadata import Metadata
    metadata = Metadata.load_from_jsonWe now import the metadata for our dataset. This metadata is stored in a JSON file and tells SDV how to interpret your data. It includes:

    The table name
    The primary key
    The data type of each columnOptional column formats like datetime patterns or ID patterns
    Table relationshipsHere is a sample metadata.json format:
    {
    "METADATA_SPEC_VERSION": "V1",
    "tables": {
    "your_table_name": {
    "primary_key": "your_primary_key_column",
    "columns": {
    "your_primary_key_column": { "sdtype": "id", "regex_format": "T{6}" },
    "date_column": { "sdtype": "datetime", "datetime_format": "%d-%m-%Y" },
    "category_column": { "sdtype": "categorical" },
    "numeric_column": { "sdtype": "numerical" }
    },
    "column_relationships":}
    }
    }
    from sdv.metadata import Metadata

    metadata = Metadata.detect_from_dataframesAlternatively, we can use the SDV library to automatically infer the metadata. However, the results may not always be accurate or complete, so you might need to review and update it if there are any discrepancies.
    from sdv.single_table import GaussianCopulaSynthesizer

    synthesizer = GaussianCopulaSynthesizersynthesizer.fitsynthetic_data = synthesizer.sampleWith the metadata and original dataset ready, we can now use SDV to train a model and generate synthetic data. The model learns the structure and patterns in your real dataset and uses that knowledge to create synthetic records.
    You can control how many rows to generate using the num_rows argument.
    from sdv.evaluation.single_table import evaluate_quality

    quality_report = evaluate_qualityThe SDV library also provides tools to evaluate the quality of your synthetic data by comparing it to the original dataset. A great place to start is by generating a quality report

    You can also visualize how the synthetic data compares to the real data using SDV’s built-in plotting tools. For example, import get_column_plot from sdv.evaluation.single_table to create comparison plots for specific columns:
    from sdv.evaluation.single_table import get_column_plot

    fig = get_column_plotfig.showWe can observe that the distribution of the ‘Sales’ column in the real and synthetic data is very similar. To explore further, we can use matplotlib to create more detailed comparisons—such as visualizing the average monthly sales trends across both datasets.
    import pandas as pd
    import matplotlib.pyplot as plt

    # Ensure 'Date' columns are datetime
    salesDf= pd.to_datetimesynthetic_data= pd.to_datetime# Extract 'Month' as year-month string
    salesDf= salesDf.dt.to_period.astypesynthetic_data= synthetic_data.dt.to_period.astype# Group by 'Month' and calculate average sales
    actual_avg_monthly = salesDf.groupby.mean.renamesynthetic_avg_monthly = synthetic_data.groupby.mean.rename# Merge the two series into a DataFrame
    avg_monthly_comparison = pd.concat.fillna# Plot
    plt.figure)
    plt.plotplt.plotplt.titleplt.xlabelplt.ylabelplt.xticksplt.gridplt.legendplt.ylim# y-axis starts at 0
    plt.tight_layoutplt.showThis chart also shows that the average monthly sales in both datasets are very similar, with only minimal differences.
    In this tutorial, we demonstrated how to prepare your data and metadata for synthetic data generation using the SDV library. By training a model on your original dataset, SDV can create high-quality synthetic data that closely mirrors the real data’s patterns and distributions. We also explored how to evaluate and visualize the synthetic data, confirming that key metrics like sales distributions and monthly trends remain consistent. Synthetic data offers a powerful way to overcome privacy and availability challenges while enabling robust data analysis and machine learning workflows.

    Check out the Notebook on GitHub. 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.
    Arham IslamI am a Civil Engineering Graduatefrom Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context ProtocolServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP Server
    #stepbystep #guide #creating #synthetic #data
    Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)
    Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used: LLMs train on AI-generated text Fraud systems simulate edge cases Vision models pretrain on fake images SDVis an open-source Python library that generates realistic tabular data using machine learning. It learns patterns from real data and creates high-quality synthetic data for safe sharing, testing, and model training. In this tutorial, we’ll use SDV to generate synthetic data step by step. pip install sdv We will first install the sdv library: from sdv.io.local import CSVHandler connector = CSVHandlerFOLDER_NAME = '.' # If the data is in the same directory data = connector.readsalesDf = dataNext, we import the necessary module and connect to our local folder containing the dataset files. This reads the CSV files from the specified folder and stores them as pandas DataFrames. In this case, we access the main dataset using data. from sdv.metadata import Metadata metadata = Metadata.load_from_jsonWe now import the metadata for our dataset. This metadata is stored in a JSON file and tells SDV how to interpret your data. It includes: The table name The primary key The data type of each columnOptional column formats like datetime patterns or ID patterns Table relationshipsHere is a sample metadata.json format: { "METADATA_SPEC_VERSION": "V1", "tables": { "your_table_name": { "primary_key": "your_primary_key_column", "columns": { "your_primary_key_column": { "sdtype": "id", "regex_format": "T{6}" }, "date_column": { "sdtype": "datetime", "datetime_format": "%d-%m-%Y" }, "category_column": { "sdtype": "categorical" }, "numeric_column": { "sdtype": "numerical" } }, "column_relationships":} } } from sdv.metadata import Metadata metadata = Metadata.detect_from_dataframesAlternatively, we can use the SDV library to automatically infer the metadata. However, the results may not always be accurate or complete, so you might need to review and update it if there are any discrepancies. from sdv.single_table import GaussianCopulaSynthesizer synthesizer = GaussianCopulaSynthesizersynthesizer.fitsynthetic_data = synthesizer.sampleWith the metadata and original dataset ready, we can now use SDV to train a model and generate synthetic data. The model learns the structure and patterns in your real dataset and uses that knowledge to create synthetic records. You can control how many rows to generate using the num_rows argument. from sdv.evaluation.single_table import evaluate_quality quality_report = evaluate_qualityThe SDV library also provides tools to evaluate the quality of your synthetic data by comparing it to the original dataset. A great place to start is by generating a quality report You can also visualize how the synthetic data compares to the real data using SDV’s built-in plotting tools. For example, import get_column_plot from sdv.evaluation.single_table to create comparison plots for specific columns: from sdv.evaluation.single_table import get_column_plot fig = get_column_plotfig.showWe can observe that the distribution of the ‘Sales’ column in the real and synthetic data is very similar. To explore further, we can use matplotlib to create more detailed comparisons—such as visualizing the average monthly sales trends across both datasets. import pandas as pd import matplotlib.pyplot as plt # Ensure 'Date' columns are datetime salesDf= pd.to_datetimesynthetic_data= pd.to_datetime# Extract 'Month' as year-month string salesDf= salesDf.dt.to_period.astypesynthetic_data= synthetic_data.dt.to_period.astype# Group by 'Month' and calculate average sales actual_avg_monthly = salesDf.groupby.mean.renamesynthetic_avg_monthly = synthetic_data.groupby.mean.rename# Merge the two series into a DataFrame avg_monthly_comparison = pd.concat.fillna# Plot plt.figure) plt.plotplt.plotplt.titleplt.xlabelplt.ylabelplt.xticksplt.gridplt.legendplt.ylim# y-axis starts at 0 plt.tight_layoutplt.showThis chart also shows that the average monthly sales in both datasets are very similar, with only minimal differences. In this tutorial, we demonstrated how to prepare your data and metadata for synthetic data generation using the SDV library. By training a model on your original dataset, SDV can create high-quality synthetic data that closely mirrors the real data’s patterns and distributions. We also explored how to evaluate and visualize the synthetic data, confirming that key metrics like sales distributions and monthly trends remain consistent. Synthetic data offers a powerful way to overcome privacy and availability challenges while enabling robust data analysis and machine learning workflows. Check out the Notebook on GitHub. 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. Arham IslamI am a Civil Engineering Graduatefrom Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context ProtocolServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP Server #stepbystep #guide #creating #synthetic #data
    WWW.MARKTECHPOST.COM
    Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)
    Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used: LLMs train on AI-generated text Fraud systems simulate edge cases Vision models pretrain on fake images SDV (Synthetic Data Vault) is an open-source Python library that generates realistic tabular data using machine learning. It learns patterns from real data and creates high-quality synthetic data for safe sharing, testing, and model training. In this tutorial, we’ll use SDV to generate synthetic data step by step. pip install sdv We will first install the sdv library: from sdv.io.local import CSVHandler connector = CSVHandler() FOLDER_NAME = '.' # If the data is in the same directory data = connector.read(folder_name=FOLDER_NAME) salesDf = data['data'] Next, we import the necessary module and connect to our local folder containing the dataset files. This reads the CSV files from the specified folder and stores them as pandas DataFrames. In this case, we access the main dataset using data[‘data’]. from sdv.metadata import Metadata metadata = Metadata.load_from_json('metadata.json') We now import the metadata for our dataset. This metadata is stored in a JSON file and tells SDV how to interpret your data. It includes: The table name The primary key The data type of each column (e.g., categorical, numerical, datetime, etc.) Optional column formats like datetime patterns or ID patterns Table relationships (for multi-table setups) Here is a sample metadata.json format: { "METADATA_SPEC_VERSION": "V1", "tables": { "your_table_name": { "primary_key": "your_primary_key_column", "columns": { "your_primary_key_column": { "sdtype": "id", "regex_format": "T[0-9]{6}" }, "date_column": { "sdtype": "datetime", "datetime_format": "%d-%m-%Y" }, "category_column": { "sdtype": "categorical" }, "numeric_column": { "sdtype": "numerical" } }, "column_relationships": [] } } } from sdv.metadata import Metadata metadata = Metadata.detect_from_dataframes(data) Alternatively, we can use the SDV library to automatically infer the metadata. However, the results may not always be accurate or complete, so you might need to review and update it if there are any discrepancies. from sdv.single_table import GaussianCopulaSynthesizer synthesizer = GaussianCopulaSynthesizer(metadata) synthesizer.fit(data=salesDf) synthetic_data = synthesizer.sample(num_rows=10000) With the metadata and original dataset ready, we can now use SDV to train a model and generate synthetic data. The model learns the structure and patterns in your real dataset and uses that knowledge to create synthetic records. You can control how many rows to generate using the num_rows argument. from sdv.evaluation.single_table import evaluate_quality quality_report = evaluate_quality( salesDf, synthetic_data, metadata) The SDV library also provides tools to evaluate the quality of your synthetic data by comparing it to the original dataset. A great place to start is by generating a quality report You can also visualize how the synthetic data compares to the real data using SDV’s built-in plotting tools. For example, import get_column_plot from sdv.evaluation.single_table to create comparison plots for specific columns: from sdv.evaluation.single_table import get_column_plot fig = get_column_plot( real_data=salesDf, synthetic_data=synthetic_data, column_name='Sales', metadata=metadata ) fig.show() We can observe that the distribution of the ‘Sales’ column in the real and synthetic data is very similar. To explore further, we can use matplotlib to create more detailed comparisons—such as visualizing the average monthly sales trends across both datasets. import pandas as pd import matplotlib.pyplot as plt # Ensure 'Date' columns are datetime salesDf['Date'] = pd.to_datetime(salesDf['Date'], format='%d-%m-%Y') synthetic_data['Date'] = pd.to_datetime(synthetic_data['Date'], format='%d-%m-%Y') # Extract 'Month' as year-month string salesDf['Month'] = salesDf['Date'].dt.to_period('M').astype(str) synthetic_data['Month'] = synthetic_data['Date'].dt.to_period('M').astype(str) # Group by 'Month' and calculate average sales actual_avg_monthly = salesDf.groupby('Month')['Sales'].mean().rename('Actual Average Sales') synthetic_avg_monthly = synthetic_data.groupby('Month')['Sales'].mean().rename('Synthetic Average Sales') # Merge the two series into a DataFrame avg_monthly_comparison = pd.concat([actual_avg_monthly, synthetic_avg_monthly], axis=1).fillna(0) # Plot plt.figure(figsize=(10, 6)) plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Actual Average Sales'], label='Actual Average Sales', marker='o') plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Synthetic Average Sales'], label='Synthetic Average Sales', marker='o') plt.title('Average Monthly Sales Comparison: Actual vs Synthetic') plt.xlabel('Month') plt.ylabel('Average Sales') plt.xticks(rotation=45) plt.grid(True) plt.legend() plt.ylim(bottom=0) # y-axis starts at 0 plt.tight_layout() plt.show() This chart also shows that the average monthly sales in both datasets are very similar, with only minimal differences. In this tutorial, we demonstrated how to prepare your data and metadata for synthetic data generation using the SDV library. By training a model on your original dataset, SDV can create high-quality synthetic data that closely mirrors the real data’s patterns and distributions. We also explored how to evaluate and visualize the synthetic data, confirming that key metrics like sales distributions and monthly trends remain consistent. Synthetic data offers a powerful way to overcome privacy and availability challenges while enabling robust data analysis and machine learning workflows. Check out the Notebook on GitHub. 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. Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP Server
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  • Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation

    In this comprehensive tutorial, we guide users through creating a powerful multi-tool AI agent using LangGraph and Claude, optimized for diverse tasks including mathematical computations, web searches, weather inquiries, text analysis, and real-time information retrieval. It begins by simplifying dependency installations to ensure effortless setup, even for beginners. Users are then introduced to structured implementations of specialized tools, such as a safe calculator, an efficient web-search utility leveraging DuckDuckGo, a mock weather information provider, a detailed text analyzer, and a time-fetching function. The tutorial also clearly delineates the integration of these tools within a sophisticated agent architecture built using LangGraph, illustrating practical usage through interactive examples and clear explanations, facilitating both beginners and advanced developers to deploy custom multi-functional AI agents rapidly.
    import subprocess
    import sys

    def install_packages:
    packages =for package in packages:
    try:
    subprocess.check_callprintexcept subprocess.CalledProcessError:
    printprintinstall_packagesprintWe automate the installation of essential Python packages required for building a LangGraph-based multi-tool AI agent. It leverages a subprocess to run pip commands silently and ensures each package, ranging from long-chain components to web search and environment handling tools, is installed successfully. This setup streamlines the environment preparation process, making the notebook portable and beginner-friendly.
    import os
    import json
    import math
    import requests
    from typing import Dict, List, Any, Annotated, TypedDict
    from datetime import datetime
    import operator

    from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage
    from langchain_core.tools import tool
    from langchain_anthropic import ChatAnthropic
    from langgraph.graph import StateGraph, START, END
    from langgraph.prebuilt import ToolNode
    from langgraph.checkpoint.memory import MemorySaver
    from duckduckgo_search import DDGS
    We import all the necessary libraries and modules for constructing the multi-tool AI agent. It includes Python standard libraries such as os, json, math, and datetime for general-purpose functionality and external libraries like requests for HTTP calls and duckduckgo_search for implementing web search. The LangChain and LangGraph ecosystems bring in message types, tool decorators, state graph components, and checkpointing utilities, while ChatAnthropic enables integration with the Claude model for conversational intelligence. These imports form the foundational building blocks for defining tools, agent workflows, and interactions.
    os.environ= "Use Your API Key Here"

    ANTHROPIC_API_KEY = os.getenvWe set and retrieve the Anthropic API key required to authenticate and interact with Claude models. The os.environ line assigns your API key, while os.getenv securely retrieves it for later use in model initialization. This approach ensures the key is accessible throughout the script without hardcoding it multiple times.
    from typing import TypedDict

    class AgentState:
    messages: Annotated, operator.add]

    @tool
    def calculator-> str:
    """
    Perform mathematical calculations. Supports basic arithmetic, trigonometry, and more.

    Args:
    expression: Mathematical expression as a string")

    Returns:
    Result of the calculation as a string
    """
    try:
    allowed_names = {
    'abs': abs, 'round': round, 'min': min, 'max': max,
    'sum': sum, 'pow': pow, 'sqrt': math.sqrt,
    'sin': math.sin, 'cos': math.cos, 'tan': math.tan,
    'log': math.log, 'log10': math.log10, 'exp': math.exp,
    'pi': math.pi, 'e': math.e
    }

    expression = expression.replaceresult = evalreturn f"Result: {result}"
    except Exception as e:
    return f"Error in calculation: {str}"
    We define the agent’s internal state and implement a robust calculator tool. The AgentState class uses TypedDict to structure agent memory, specifically tracking messages exchanged during the conversation. The calculator function, decorated with @tool to register it as an AI-usable utility, securely evaluates mathematical expressions. It allows for safe computation by limiting available functions to a predefined set from the math module and replacing common syntax like ^ with Python’s exponentiation operator. This ensures the tool can handle simple arithmetic and advanced functions like trigonometry or logarithms while preventing unsafe code execution.
    @tool
    def web_search-> str:
    """
    Search the web for information using DuckDuckGo.

    Args:
    query: Search query string
    num_results: Number of results to returnReturns:
    Search results as formatted string
    """
    try:
    num_results = min, 10)

    with DDGSas ddgs:
    results = list)

    if not results:
    return f"No search results found for: {query}"

    formatted_results = f"Search results for '{query}':\n\n"
    for i, result in enumerate:
    formatted_results += f"{i}. **{result}**\n"
    formatted_results += f" {result}\n"
    formatted_results += f" Source: {result}\n\n"

    return formatted_results
    except Exception as e:
    return f"Error performing web search: {str}"
    We define a web_search tool that enables the agent to fetch real-time information from the internet using the DuckDuckGo Search API via the duckduckgo_search Python package. The tool accepts a search query and an optional num_results parameter, ensuring that the number of results returned is between 1 and 10. It opens a DuckDuckGo search session, retrieves the results, and formats them neatly for user-friendly display. If no results are found or an error occurs, the function handles it gracefully by returning an informative message. This tool equips the agent with real-time search capabilities, enhancing responsiveness and utility.
    @tool
    def weather_info-> str:
    """
    Get current weather information for a city using OpenWeatherMap API.
    Note: This is a mock implementation for demo purposes.

    Args:
    city: Name of the city

    Returns:
    Weather information as a string
    """
    mock_weather = {
    "new york": {"temp": 22, "condition": "Partly Cloudy", "humidity": 65},
    "london": {"temp": 15, "condition": "Rainy", "humidity": 80},
    "tokyo": {"temp": 28, "condition": "Sunny", "humidity": 70},
    "paris": {"temp": 18, "condition": "Overcast", "humidity": 75}
    }

    city_lower = city.lowerif city_lower in mock_weather:
    weather = mock_weatherreturn f"Weather in {city}:\n" \
    f"Temperature: {weather}°C\n" \
    f"Condition: {weather}\n" \
    f"Humidity: {weather}%"
    else:
    return f"Weather data not available for {city}."
    We define a weather_info tool that simulates retrieving current weather data for a given city. While it does not connect to a live weather API, it uses a predefined dictionary of mock data for major cities like New York, London, Tokyo, and Paris. Upon receiving a city name, the function normalizes it to lowercase and checks for its presence in the mock dataset. It returns temperature, weather condition, and humidity in a readable format if found. Otherwise, it notifies the user that weather data is unavailable. This tool serves as a placeholder and can later be upgraded to fetch live data from an actual weather API.
    @tool
    def text_analyzer-> str:
    """
    Analyze text and provide statistics like word count, character count, etc.

    Args:
    text: Text to analyze

    Returns:
    Text analysis results
    """
    if not text.strip:
    return "Please provide text to analyze."

    words = text.splitsentences = text.split+ text.split+ text.splitsentences =analysis = f"Text Analysis Results:\n"
    analysis += f"• Characters: {len}\n"
    analysis += f"• Characters: {len)}\n"
    analysis += f"• Words: {len}\n"
    analysis += f"• Sentences: {len}\n"
    analysis += f"• Average words per sentence: {len/ max, 1):.1f}\n"
    analysis += f"• Most common word: {max, key=words.count) if words else 'N/A'}"

    return analysis
    The text_analyzer tool provides a detailed statistical analysis of a given text input. It calculates metrics such as character count, word count, sentence count, and average words per sentence, and it identifies the most frequently occurring word. The tool handles empty input gracefully by prompting the user to provide valid text. It uses simple string operations and Python’s set and max functions to extract meaningful insights. It is a valuable utility for language analysis or content quality checks in the AI agent’s toolkit.
    @tool
    def current_time-> str:
    """
    Get the current date and time.

    Returns:
    Current date and time as a formatted string
    """
    now = datetime.nowreturn f"Current date and time: {now.strftime}"
    The current_time tool provides a straightforward way to retrieve the current system date and time in a human-readable format. Using Python’s datetime module, it captures the present moment and formats it as YYYY-MM-DD HH:MM:SS. This utility is particularly useful for time-stamping responses or answering user queries about the current date and time within the AI agent’s interaction flow.
    tools =def create_llm:
    if ANTHROPIC_API_KEY:
    return ChatAnthropicelse:
    class MockLLM:
    def invoke:
    last_message = messages.content if messages else ""

    if anyfor word in):
    import re
    numbers = re.findall\s\w]+', last_message)
    expr = numbersif numbers else "2+2"
    return AIMessage}, "id": "calc1"}])
    elif anyfor word in):
    query = last_message.replace.replace.replace.stripif not query or len< 3:
    query = "python programming"
    return AIMessageelif anyfor word in):
    city = "New York"
    words = last_message.lower.splitfor i, word in enumerate:
    if word == 'in' and i + 1 < len:
    city = words.titlebreak
    return AIMessageelif anyfor word in):
    return AIMessageelif anyfor word in):
    text = last_message.replace.replace.stripif not text:
    text = "Sample text for analysis"
    return AIMessageelse:
    return AIMessagedef bind_tools:
    return self

    printreturn MockLLMllm = create_llmllm_with_tools = llm.bind_toolsWe initialize the language model that powers the AI agent. If a valid Anthropic API key is available, it uses the Claude 3 Haiku model for high-quality responses. Without an API key, a MockLLM is defined to simulate basic tool-routing behavior based on keyword matching, allowing the agent to function offline with limited capabilities. The bind_tools method links the defined tools to the model, enabling it to invoke them as needed.
    def agent_node-> Dict:
    """Main agent node that processes messages and decides on tool usage."""
    messages = stateresponse = llm_with_tools.invokereturn {"messages":}

    def should_continue-> str:
    """Determine whether to continue with tool calls or end."""
    last_message = stateif hasattrand last_message.tool_calls:
    return "tools"
    return END
    We define the agent’s core decision-making logic. The agent_node function handles incoming messages, invokes the language model, and returns the model’s response. The should_continue function then evaluates whether the model’s response includes tool calls. If so, it routes control to the tool execution node; otherwise, it directs the flow to end the interaction. These functions enable dynamic and conditional transitions within the agent’s workflow.
    def create_agent_graph:
    tool_node = ToolNodeworkflow = StateGraphworkflow.add_nodeworkflow.add_nodeworkflow.add_edgeworkflow.add_conditional_edgesworkflow.add_edgememory = MemorySaverapp = workflow.compilereturn app

    printagent = create_agent_graphprintWe construct the LangGraph-powered workflow that defines the AI agent’s operational structure. It initializes a ToolNode to handle tool executions and uses a StateGraph to organize the flow between agent decisions and tool usage. Nodes and edges are added to manage transitions: starting with the agent, conditionally routing to tools, and looping back as needed. A MemorySaver is integrated for persistent state tracking across turns. The graph is compiled into an executable application, enabling a structured, memory-aware multi-tool agent ready for deployment.
    def test_agent:
    """Test the agent with various queries."""
    config = {"configurable": {"thread_id": "test-thread"}}

    test_queries =printfor i, query in enumerate:
    printprinttry:
    response = agent.invoke]},
    config=config
    )

    last_message = responseprintexcept Exception as e:
    print}\n")
    The test_agent function is a validation utility that ensures that the LangGraph agent responds correctly across different use cases. It runs predefined queries, arithmetic, web search, weather, time, and text analysis, and prints the agent’s responses. Using a consistent thread_id for configuration, it invokes the agent with each query. It neatly displays the results, helping developers verify tool integration and conversational logic before moving to interactive or production use.
    def chat_with_agent:
    """Interactive chat function."""
    config = {"configurable": {"thread_id": "interactive-thread"}}

    printprintprintwhile True:
    try:
    user_input = input.stripif user_input.lowerin:
    printbreak
    elif user_input.lower== 'help':
    printprint?'")
    printprintprintprintprintcontinue
    elif not user_input:
    continue

    response = agent.invoke]},
    config=config
    )

    last_message = responseprintexcept KeyboardInterrupt:
    printbreak
    except Exception as e:
    print}\n")
    The chat_with_agent function provides an interactive command-line interface for real-time conversations with the LangGraph multi-tool agent. It supports natural language queries and recognizes commands like “help” for usage guidance and “quit” to exit. Each user input is processed through the agent, which dynamically selects and invokes appropriate response tools. The function enhances user engagement by simulating a conversational experience and showcasing the agent’s capabilities in handling various queries, from math and web search to weather, text analysis, and time retrieval.
    if __name__ == "__main__":
    test_agentprintprintprintchat_with_agentdef quick_demo:
    """Quick demonstration of agent capabilities."""
    config = {"configurable": {"thread_id": "demo"}}

    demos =printfor category, query in demos:
    printtry:
    response = agent.invoke]},
    config=config
    )
    printexcept Exception as e:
    print}\n")

    printprintprintprintprintfor a quick demonstration")
    printfor interactive chat")
    printprintprintFinally, we orchestrate the execution of the LangGraph multi-tool agent. If the script is run directly, it initiates test_agentto validate functionality with sample queries, followed by launching the interactive chat_with_agentmode for real-time interaction. The quick_demofunction also briefly showcases the agent’s capabilities in math, search, and time queries. Clear usage instructions are printed at the end, guiding users on configuring the API key, running demonstrations, and interacting with the agent. This provides a smooth onboarding experience for users to explore and extend the agent’s functionality.
    In conclusion, this step-by-step tutorial gives valuable insights into building an effective multi-tool AI agent leveraging LangGraph and Claude’s generative capabilities. With straightforward explanations and hands-on demonstrations, the guide empowers users to integrate diverse utilities into a cohesive and interactive system. The agent’s flexibility in performing tasks, from complex calculations to dynamic information retrieval, showcases the versatility of modern AI development frameworks. Also, the inclusion of user-friendly functions for both testing and interactive chat enhances practical understanding, enabling immediate application in various contexts. Developers can confidently extend and customize their AI agents with this foundational knowledge.

    Check out the Notebook on GitHub. 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/A Comprehensive Coding Guide to Crafting Advanced Round-Robin Multi-Agent Workflows with Microsoft AutoGenAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Microsoft AI Introduces Magentic-UI: An Open-Source Agent Prototype that Works with People to Complete Complex Tasks that Require Multi-Step Planning and Browser UseAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Anthropic Releases Claude Opus 4 and Claude Sonnet 4: A Technical Leap in Reasoning, Coding, and AI Agent DesignAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Technology Innovation Institute TII Releases Falcon-H1: Hybrid Transformer-SSM Language Models for Scalable, Multilingual, and Long-Context Understanding
    #stepbystep #guide #build #customizable #multitool
    Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation
    In this comprehensive tutorial, we guide users through creating a powerful multi-tool AI agent using LangGraph and Claude, optimized for diverse tasks including mathematical computations, web searches, weather inquiries, text analysis, and real-time information retrieval. It begins by simplifying dependency installations to ensure effortless setup, even for beginners. Users are then introduced to structured implementations of specialized tools, such as a safe calculator, an efficient web-search utility leveraging DuckDuckGo, a mock weather information provider, a detailed text analyzer, and a time-fetching function. The tutorial also clearly delineates the integration of these tools within a sophisticated agent architecture built using LangGraph, illustrating practical usage through interactive examples and clear explanations, facilitating both beginners and advanced developers to deploy custom multi-functional AI agents rapidly. import subprocess import sys def install_packages: packages =for package in packages: try: subprocess.check_callprintexcept subprocess.CalledProcessError: printprintinstall_packagesprintWe automate the installation of essential Python packages required for building a LangGraph-based multi-tool AI agent. It leverages a subprocess to run pip commands silently and ensures each package, ranging from long-chain components to web search and environment handling tools, is installed successfully. This setup streamlines the environment preparation process, making the notebook portable and beginner-friendly. import os import json import math import requests from typing import Dict, List, Any, Annotated, TypedDict from datetime import datetime import operator from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage from langchain_core.tools import tool from langchain_anthropic import ChatAnthropic from langgraph.graph import StateGraph, START, END from langgraph.prebuilt import ToolNode from langgraph.checkpoint.memory import MemorySaver from duckduckgo_search import DDGS We import all the necessary libraries and modules for constructing the multi-tool AI agent. It includes Python standard libraries such as os, json, math, and datetime for general-purpose functionality and external libraries like requests for HTTP calls and duckduckgo_search for implementing web search. The LangChain and LangGraph ecosystems bring in message types, tool decorators, state graph components, and checkpointing utilities, while ChatAnthropic enables integration with the Claude model for conversational intelligence. These imports form the foundational building blocks for defining tools, agent workflows, and interactions. os.environ= "Use Your API Key Here" ANTHROPIC_API_KEY = os.getenvWe set and retrieve the Anthropic API key required to authenticate and interact with Claude models. The os.environ line assigns your API key, while os.getenv securely retrieves it for later use in model initialization. This approach ensures the key is accessible throughout the script without hardcoding it multiple times. from typing import TypedDict class AgentState: messages: Annotated, operator.add] @tool def calculator-> str: """ Perform mathematical calculations. Supports basic arithmetic, trigonometry, and more. Args: expression: Mathematical expression as a string") Returns: Result of the calculation as a string """ try: allowed_names = { 'abs': abs, 'round': round, 'min': min, 'max': max, 'sum': sum, 'pow': pow, 'sqrt': math.sqrt, 'sin': math.sin, 'cos': math.cos, 'tan': math.tan, 'log': math.log, 'log10': math.log10, 'exp': math.exp, 'pi': math.pi, 'e': math.e } expression = expression.replaceresult = evalreturn f"Result: {result}" except Exception as e: return f"Error in calculation: {str}" We define the agent’s internal state and implement a robust calculator tool. The AgentState class uses TypedDict to structure agent memory, specifically tracking messages exchanged during the conversation. The calculator function, decorated with @tool to register it as an AI-usable utility, securely evaluates mathematical expressions. It allows for safe computation by limiting available functions to a predefined set from the math module and replacing common syntax like ^ with Python’s exponentiation operator. This ensures the tool can handle simple arithmetic and advanced functions like trigonometry or logarithms while preventing unsafe code execution. @tool def web_search-> str: """ Search the web for information using DuckDuckGo. Args: query: Search query string num_results: Number of results to returnReturns: Search results as formatted string """ try: num_results = min, 10) with DDGSas ddgs: results = list) if not results: return f"No search results found for: {query}" formatted_results = f"Search results for '{query}':\n\n" for i, result in enumerate: formatted_results += f"{i}. **{result}**\n" formatted_results += f" {result}\n" formatted_results += f" Source: {result}\n\n" return formatted_results except Exception as e: return f"Error performing web search: {str}" We define a web_search tool that enables the agent to fetch real-time information from the internet using the DuckDuckGo Search API via the duckduckgo_search Python package. The tool accepts a search query and an optional num_results parameter, ensuring that the number of results returned is between 1 and 10. It opens a DuckDuckGo search session, retrieves the results, and formats them neatly for user-friendly display. If no results are found or an error occurs, the function handles it gracefully by returning an informative message. This tool equips the agent with real-time search capabilities, enhancing responsiveness and utility. @tool def weather_info-> str: """ Get current weather information for a city using OpenWeatherMap API. Note: This is a mock implementation for demo purposes. Args: city: Name of the city Returns: Weather information as a string """ mock_weather = { "new york": {"temp": 22, "condition": "Partly Cloudy", "humidity": 65}, "london": {"temp": 15, "condition": "Rainy", "humidity": 80}, "tokyo": {"temp": 28, "condition": "Sunny", "humidity": 70}, "paris": {"temp": 18, "condition": "Overcast", "humidity": 75} } city_lower = city.lowerif city_lower in mock_weather: weather = mock_weatherreturn f"Weather in {city}:\n" \ f"Temperature: {weather}°C\n" \ f"Condition: {weather}\n" \ f"Humidity: {weather}%" else: return f"Weather data not available for {city}." We define a weather_info tool that simulates retrieving current weather data for a given city. While it does not connect to a live weather API, it uses a predefined dictionary of mock data for major cities like New York, London, Tokyo, and Paris. Upon receiving a city name, the function normalizes it to lowercase and checks for its presence in the mock dataset. It returns temperature, weather condition, and humidity in a readable format if found. Otherwise, it notifies the user that weather data is unavailable. This tool serves as a placeholder and can later be upgraded to fetch live data from an actual weather API. @tool def text_analyzer-> str: """ Analyze text and provide statistics like word count, character count, etc. Args: text: Text to analyze Returns: Text analysis results """ if not text.strip: return "Please provide text to analyze." words = text.splitsentences = text.split+ text.split+ text.splitsentences =analysis = f"Text Analysis Results:\n" analysis += f"• Characters: {len}\n" analysis += f"• Characters: {len)}\n" analysis += f"• Words: {len}\n" analysis += f"• Sentences: {len}\n" analysis += f"• Average words per sentence: {len/ max, 1):.1f}\n" analysis += f"• Most common word: {max, key=words.count) if words else 'N/A'}" return analysis The text_analyzer tool provides a detailed statistical analysis of a given text input. It calculates metrics such as character count, word count, sentence count, and average words per sentence, and it identifies the most frequently occurring word. The tool handles empty input gracefully by prompting the user to provide valid text. It uses simple string operations and Python’s set and max functions to extract meaningful insights. It is a valuable utility for language analysis or content quality checks in the AI agent’s toolkit. @tool def current_time-> str: """ Get the current date and time. Returns: Current date and time as a formatted string """ now = datetime.nowreturn f"Current date and time: {now.strftime}" The current_time tool provides a straightforward way to retrieve the current system date and time in a human-readable format. Using Python’s datetime module, it captures the present moment and formats it as YYYY-MM-DD HH:MM:SS. This utility is particularly useful for time-stamping responses or answering user queries about the current date and time within the AI agent’s interaction flow. tools =def create_llm: if ANTHROPIC_API_KEY: return ChatAnthropicelse: class MockLLM: def invoke: last_message = messages.content if messages else "" if anyfor word in): import re numbers = re.findall\s\w]+', last_message) expr = numbersif numbers else "2+2" return AIMessage}, "id": "calc1"}]) elif anyfor word in): query = last_message.replace.replace.replace.stripif not query or len< 3: query = "python programming" return AIMessageelif anyfor word in): city = "New York" words = last_message.lower.splitfor i, word in enumerate: if word == 'in' and i + 1 < len: city = words.titlebreak return AIMessageelif anyfor word in): return AIMessageelif anyfor word in): text = last_message.replace.replace.stripif not text: text = "Sample text for analysis" return AIMessageelse: return AIMessagedef bind_tools: return self printreturn MockLLMllm = create_llmllm_with_tools = llm.bind_toolsWe initialize the language model that powers the AI agent. If a valid Anthropic API key is available, it uses the Claude 3 Haiku model for high-quality responses. Without an API key, a MockLLM is defined to simulate basic tool-routing behavior based on keyword matching, allowing the agent to function offline with limited capabilities. The bind_tools method links the defined tools to the model, enabling it to invoke them as needed. def agent_node-> Dict: """Main agent node that processes messages and decides on tool usage.""" messages = stateresponse = llm_with_tools.invokereturn {"messages":} def should_continue-> str: """Determine whether to continue with tool calls or end.""" last_message = stateif hasattrand last_message.tool_calls: return "tools" return END We define the agent’s core decision-making logic. The agent_node function handles incoming messages, invokes the language model, and returns the model’s response. The should_continue function then evaluates whether the model’s response includes tool calls. If so, it routes control to the tool execution node; otherwise, it directs the flow to end the interaction. These functions enable dynamic and conditional transitions within the agent’s workflow. def create_agent_graph: tool_node = ToolNodeworkflow = StateGraphworkflow.add_nodeworkflow.add_nodeworkflow.add_edgeworkflow.add_conditional_edgesworkflow.add_edgememory = MemorySaverapp = workflow.compilereturn app printagent = create_agent_graphprintWe construct the LangGraph-powered workflow that defines the AI agent’s operational structure. It initializes a ToolNode to handle tool executions and uses a StateGraph to organize the flow between agent decisions and tool usage. Nodes and edges are added to manage transitions: starting with the agent, conditionally routing to tools, and looping back as needed. A MemorySaver is integrated for persistent state tracking across turns. The graph is compiled into an executable application, enabling a structured, memory-aware multi-tool agent ready for deployment. def test_agent: """Test the agent with various queries.""" config = {"configurable": {"thread_id": "test-thread"}} test_queries =printfor i, query in enumerate: printprinttry: response = agent.invoke]}, config=config ) last_message = responseprintexcept Exception as e: print}\n") The test_agent function is a validation utility that ensures that the LangGraph agent responds correctly across different use cases. It runs predefined queries, arithmetic, web search, weather, time, and text analysis, and prints the agent’s responses. Using a consistent thread_id for configuration, it invokes the agent with each query. It neatly displays the results, helping developers verify tool integration and conversational logic before moving to interactive or production use. def chat_with_agent: """Interactive chat function.""" config = {"configurable": {"thread_id": "interactive-thread"}} printprintprintwhile True: try: user_input = input.stripif user_input.lowerin: printbreak elif user_input.lower== 'help': printprint?'") printprintprintprintprintcontinue elif not user_input: continue response = agent.invoke]}, config=config ) last_message = responseprintexcept KeyboardInterrupt: printbreak except Exception as e: print}\n") The chat_with_agent function provides an interactive command-line interface for real-time conversations with the LangGraph multi-tool agent. It supports natural language queries and recognizes commands like “help” for usage guidance and “quit” to exit. Each user input is processed through the agent, which dynamically selects and invokes appropriate response tools. The function enhances user engagement by simulating a conversational experience and showcasing the agent’s capabilities in handling various queries, from math and web search to weather, text analysis, and time retrieval. if __name__ == "__main__": test_agentprintprintprintchat_with_agentdef quick_demo: """Quick demonstration of agent capabilities.""" config = {"configurable": {"thread_id": "demo"}} demos =printfor category, query in demos: printtry: response = agent.invoke]}, config=config ) printexcept Exception as e: print}\n") printprintprintprintprintfor a quick demonstration") printfor interactive chat") printprintprintFinally, we orchestrate the execution of the LangGraph multi-tool agent. If the script is run directly, it initiates test_agentto validate functionality with sample queries, followed by launching the interactive chat_with_agentmode for real-time interaction. The quick_demofunction also briefly showcases the agent’s capabilities in math, search, and time queries. Clear usage instructions are printed at the end, guiding users on configuring the API key, running demonstrations, and interacting with the agent. This provides a smooth onboarding experience for users to explore and extend the agent’s functionality. In conclusion, this step-by-step tutorial gives valuable insights into building an effective multi-tool AI agent leveraging LangGraph and Claude’s generative capabilities. With straightforward explanations and hands-on demonstrations, the guide empowers users to integrate diverse utilities into a cohesive and interactive system. The agent’s flexibility in performing tasks, from complex calculations to dynamic information retrieval, showcases the versatility of modern AI development frameworks. Also, the inclusion of user-friendly functions for both testing and interactive chat enhances practical understanding, enabling immediate application in various contexts. Developers can confidently extend and customize their AI agents with this foundational knowledge. Check out the Notebook on GitHub. 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/A Comprehensive Coding Guide to Crafting Advanced Round-Robin Multi-Agent Workflows with Microsoft AutoGenAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Microsoft AI Introduces Magentic-UI: An Open-Source Agent Prototype that Works with People to Complete Complex Tasks that Require Multi-Step Planning and Browser UseAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Anthropic Releases Claude Opus 4 and Claude Sonnet 4: A Technical Leap in Reasoning, Coding, and AI Agent DesignAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Technology Innovation Institute TII Releases Falcon-H1: Hybrid Transformer-SSM Language Models for Scalable, Multilingual, and Long-Context Understanding #stepbystep #guide #build #customizable #multitool
    WWW.MARKTECHPOST.COM
    Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation
    In this comprehensive tutorial, we guide users through creating a powerful multi-tool AI agent using LangGraph and Claude, optimized for diverse tasks including mathematical computations, web searches, weather inquiries, text analysis, and real-time information retrieval. It begins by simplifying dependency installations to ensure effortless setup, even for beginners. Users are then introduced to structured implementations of specialized tools, such as a safe calculator, an efficient web-search utility leveraging DuckDuckGo, a mock weather information provider, a detailed text analyzer, and a time-fetching function. The tutorial also clearly delineates the integration of these tools within a sophisticated agent architecture built using LangGraph, illustrating practical usage through interactive examples and clear explanations, facilitating both beginners and advanced developers to deploy custom multi-functional AI agents rapidly. import subprocess import sys def install_packages(): packages = [ "langgraph", "langchain", "langchain-anthropic", "langchain-community", "requests", "python-dotenv", "duckduckgo-search" ] for package in packages: try: subprocess.check_call([sys.executable, "-m", "pip", "install", package, "-q"]) print(f"✓ Installed {package}") except subprocess.CalledProcessError: print(f"✗ Failed to install {package}") print("Installing required packages...") install_packages() print("Installation complete!\n") We automate the installation of essential Python packages required for building a LangGraph-based multi-tool AI agent. It leverages a subprocess to run pip commands silently and ensures each package, ranging from long-chain components to web search and environment handling tools, is installed successfully. This setup streamlines the environment preparation process, making the notebook portable and beginner-friendly. import os import json import math import requests from typing import Dict, List, Any, Annotated, TypedDict from datetime import datetime import operator from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage from langchain_core.tools import tool from langchain_anthropic import ChatAnthropic from langgraph.graph import StateGraph, START, END from langgraph.prebuilt import ToolNode from langgraph.checkpoint.memory import MemorySaver from duckduckgo_search import DDGS We import all the necessary libraries and modules for constructing the multi-tool AI agent. It includes Python standard libraries such as os, json, math, and datetime for general-purpose functionality and external libraries like requests for HTTP calls and duckduckgo_search for implementing web search. The LangChain and LangGraph ecosystems bring in message types, tool decorators, state graph components, and checkpointing utilities, while ChatAnthropic enables integration with the Claude model for conversational intelligence. These imports form the foundational building blocks for defining tools, agent workflows, and interactions. os.environ["ANTHROPIC_API_KEY"] = "Use Your API Key Here" ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY") We set and retrieve the Anthropic API key required to authenticate and interact with Claude models. The os.environ line assigns your API key (which you should replace with a valid key), while os.getenv securely retrieves it for later use in model initialization. This approach ensures the key is accessible throughout the script without hardcoding it multiple times. from typing import TypedDict class AgentState(TypedDict): messages: Annotated[List[BaseMessage], operator.add] @tool def calculator(expression: str) -> str: """ Perform mathematical calculations. Supports basic arithmetic, trigonometry, and more. Args: expression: Mathematical expression as a string (e.g., "2 + 3 * 4", "sin(3.14159/2)") Returns: Result of the calculation as a string """ try: allowed_names = { 'abs': abs, 'round': round, 'min': min, 'max': max, 'sum': sum, 'pow': pow, 'sqrt': math.sqrt, 'sin': math.sin, 'cos': math.cos, 'tan': math.tan, 'log': math.log, 'log10': math.log10, 'exp': math.exp, 'pi': math.pi, 'e': math.e } expression = expression.replace('^', '**') result = eval(expression, {"__builtins__": {}}, allowed_names) return f"Result: {result}" except Exception as e: return f"Error in calculation: {str(e)}" We define the agent’s internal state and implement a robust calculator tool. The AgentState class uses TypedDict to structure agent memory, specifically tracking messages exchanged during the conversation. The calculator function, decorated with @tool to register it as an AI-usable utility, securely evaluates mathematical expressions. It allows for safe computation by limiting available functions to a predefined set from the math module and replacing common syntax like ^ with Python’s exponentiation operator. This ensures the tool can handle simple arithmetic and advanced functions like trigonometry or logarithms while preventing unsafe code execution. @tool def web_search(query: str, num_results: int = 3) -> str: """ Search the web for information using DuckDuckGo. Args: query: Search query string num_results: Number of results to return (default: 3, max: 10) Returns: Search results as formatted string """ try: num_results = min(max(num_results, 1), 10) with DDGS() as ddgs: results = list(ddgs.text(query, max_results=num_results)) if not results: return f"No search results found for: {query}" formatted_results = f"Search results for '{query}':\n\n" for i, result in enumerate(results, 1): formatted_results += f"{i}. **{result['title']}**\n" formatted_results += f" {result['body']}\n" formatted_results += f" Source: {result['href']}\n\n" return formatted_results except Exception as e: return f"Error performing web search: {str(e)}" We define a web_search tool that enables the agent to fetch real-time information from the internet using the DuckDuckGo Search API via the duckduckgo_search Python package. The tool accepts a search query and an optional num_results parameter, ensuring that the number of results returned is between 1 and 10. It opens a DuckDuckGo search session, retrieves the results, and formats them neatly for user-friendly display. If no results are found or an error occurs, the function handles it gracefully by returning an informative message. This tool equips the agent with real-time search capabilities, enhancing responsiveness and utility. @tool def weather_info(city: str) -> str: """ Get current weather information for a city using OpenWeatherMap API. Note: This is a mock implementation for demo purposes. Args: city: Name of the city Returns: Weather information as a string """ mock_weather = { "new york": {"temp": 22, "condition": "Partly Cloudy", "humidity": 65}, "london": {"temp": 15, "condition": "Rainy", "humidity": 80}, "tokyo": {"temp": 28, "condition": "Sunny", "humidity": 70}, "paris": {"temp": 18, "condition": "Overcast", "humidity": 75} } city_lower = city.lower() if city_lower in mock_weather: weather = mock_weather[city_lower] return f"Weather in {city}:\n" \ f"Temperature: {weather['temp']}°C\n" \ f"Condition: {weather['condition']}\n" \ f"Humidity: {weather['humidity']}%" else: return f"Weather data not available for {city}. (This is a demo with limited cities: New York, London, Tokyo, Paris)" We define a weather_info tool that simulates retrieving current weather data for a given city. While it does not connect to a live weather API, it uses a predefined dictionary of mock data for major cities like New York, London, Tokyo, and Paris. Upon receiving a city name, the function normalizes it to lowercase and checks for its presence in the mock dataset. It returns temperature, weather condition, and humidity in a readable format if found. Otherwise, it notifies the user that weather data is unavailable. This tool serves as a placeholder and can later be upgraded to fetch live data from an actual weather API. @tool def text_analyzer(text: str) -> str: """ Analyze text and provide statistics like word count, character count, etc. Args: text: Text to analyze Returns: Text analysis results """ if not text.strip(): return "Please provide text to analyze." words = text.split() sentences = text.split('.') + text.split('!') + text.split('?') sentences = [s.strip() for s in sentences if s.strip()] analysis = f"Text Analysis Results:\n" analysis += f"• Characters (with spaces): {len(text)}\n" analysis += f"• Characters (without spaces): {len(text.replace(' ', ''))}\n" analysis += f"• Words: {len(words)}\n" analysis += f"• Sentences: {len(sentences)}\n" analysis += f"• Average words per sentence: {len(words) / max(len(sentences), 1):.1f}\n" analysis += f"• Most common word: {max(set(words), key=words.count) if words else 'N/A'}" return analysis The text_analyzer tool provides a detailed statistical analysis of a given text input. It calculates metrics such as character count (with and without spaces), word count, sentence count, and average words per sentence, and it identifies the most frequently occurring word. The tool handles empty input gracefully by prompting the user to provide valid text. It uses simple string operations and Python’s set and max functions to extract meaningful insights. It is a valuable utility for language analysis or content quality checks in the AI agent’s toolkit. @tool def current_time() -> str: """ Get the current date and time. Returns: Current date and time as a formatted string """ now = datetime.now() return f"Current date and time: {now.strftime('%Y-%m-%d %H:%M:%S')}" The current_time tool provides a straightforward way to retrieve the current system date and time in a human-readable format. Using Python’s datetime module, it captures the present moment and formats it as YYYY-MM-DD HH:MM:SS. This utility is particularly useful for time-stamping responses or answering user queries about the current date and time within the AI agent’s interaction flow. tools = [calculator, web_search, weather_info, text_analyzer, current_time] def create_llm(): if ANTHROPIC_API_KEY: return ChatAnthropic( model="claude-3-haiku-20240307", temperature=0.1, max_tokens=1024 ) else: class MockLLM: def invoke(self, messages): last_message = messages[-1].content if messages else "" if any(word in last_message.lower() for word in ['calculate', 'math', '+', '-', '*', '/', 'sqrt', 'sin', 'cos']): import re numbers = re.findall(r'[\d\+\-\*/\.\(\)\s\w]+', last_message) expr = numbers[0] if numbers else "2+2" return AIMessage(content="I'll help you with that calculation.", tool_calls=[{"name": "calculator", "args": {"expression": expr.strip()}, "id": "calc1"}]) elif any(word in last_message.lower() for word in ['search', 'find', 'look up', 'information about']): query = last_message.replace('search for', '').replace('find', '').replace('look up', '').strip() if not query or len(query) < 3: query = "python programming" return AIMessage(content="I'll search for that information.", tool_calls=[{"name": "web_search", "args": {"query": query}, "id": "search1"}]) elif any(word in last_message.lower() for word in ['weather', 'temperature']): city = "New York" words = last_message.lower().split() for i, word in enumerate(words): if word == 'in' and i + 1 < len(words): city = words[i + 1].title() break return AIMessage(content="I'll get the weather information.", tool_calls=[{"name": "weather_info", "args": {"city": city}, "id": "weather1"}]) elif any(word in last_message.lower() for word in ['time', 'date']): return AIMessage(content="I'll get the current time.", tool_calls=[{"name": "current_time", "args": {}, "id": "time1"}]) elif any(word in last_message.lower() for word in ['analyze', 'analysis']): text = last_message.replace('analyze this text:', '').replace('analyze', '').strip() if not text: text = "Sample text for analysis" return AIMessage(content="I'll analyze that text for you.", tool_calls=[{"name": "text_analyzer", "args": {"text": text}, "id": "analyze1"}]) else: return AIMessage(content="Hello! I'm a multi-tool agent powered by Claude. I can help with:\n• Mathematical calculations\n• Web searches\n• Weather information\n• Text analysis\n• Current time/date\n\nWhat would you like me to help you with?") def bind_tools(self, tools): return self print("⚠️ Note: Using mock LLM for demo. Add your ANTHROPIC_API_KEY for full functionality.") return MockLLM() llm = create_llm() llm_with_tools = llm.bind_tools(tools) We initialize the language model that powers the AI agent. If a valid Anthropic API key is available, it uses the Claude 3 Haiku model for high-quality responses. Without an API key, a MockLLM is defined to simulate basic tool-routing behavior based on keyword matching, allowing the agent to function offline with limited capabilities. The bind_tools method links the defined tools to the model, enabling it to invoke them as needed. def agent_node(state: AgentState) -> Dict[str, Any]: """Main agent node that processes messages and decides on tool usage.""" messages = state["messages"] response = llm_with_tools.invoke(messages) return {"messages": [response]} def should_continue(state: AgentState) -> str: """Determine whether to continue with tool calls or end.""" last_message = state["messages"][-1] if hasattr(last_message, 'tool_calls') and last_message.tool_calls: return "tools" return END We define the agent’s core decision-making logic. The agent_node function handles incoming messages, invokes the language model (with tools), and returns the model’s response. The should_continue function then evaluates whether the model’s response includes tool calls. If so, it routes control to the tool execution node; otherwise, it directs the flow to end the interaction. These functions enable dynamic and conditional transitions within the agent’s workflow. def create_agent_graph(): tool_node = ToolNode(tools) workflow = StateGraph(AgentState) workflow.add_node("agent", agent_node) workflow.add_node("tools", tool_node) workflow.add_edge(START, "agent") workflow.add_conditional_edges("agent", should_continue, {"tools": "tools", END: END}) workflow.add_edge("tools", "agent") memory = MemorySaver() app = workflow.compile(checkpointer=memory) return app print("Creating LangGraph Multi-Tool Agent...") agent = create_agent_graph() print("✓ Agent created successfully!\n") We construct the LangGraph-powered workflow that defines the AI agent’s operational structure. It initializes a ToolNode to handle tool executions and uses a StateGraph to organize the flow between agent decisions and tool usage. Nodes and edges are added to manage transitions: starting with the agent, conditionally routing to tools, and looping back as needed. A MemorySaver is integrated for persistent state tracking across turns. The graph is compiled into an executable application (app), enabling a structured, memory-aware multi-tool agent ready for deployment. def test_agent(): """Test the agent with various queries.""" config = {"configurable": {"thread_id": "test-thread"}} test_queries = [ "What's 15 * 7 + 23?", "Search for information about Python programming", "What's the weather like in Tokyo?", "What time is it?", "Analyze this text: 'LangGraph is an amazing framework for building AI agents.'" ] print("🧪 Testing the agent with sample queries...\n") for i, query in enumerate(test_queries, 1): print(f"Query {i}: {query}") print("-" * 50) try: response = agent.invoke( {"messages": [HumanMessage(content=query)]}, config=config ) last_message = response["messages"][-1] print(f"Response: {last_message.content}\n") except Exception as e: print(f"Error: {str(e)}\n") The test_agent function is a validation utility that ensures that the LangGraph agent responds correctly across different use cases. It runs predefined queries, arithmetic, web search, weather, time, and text analysis, and prints the agent’s responses. Using a consistent thread_id for configuration, it invokes the agent with each query. It neatly displays the results, helping developers verify tool integration and conversational logic before moving to interactive or production use. def chat_with_agent(): """Interactive chat function.""" config = {"configurable": {"thread_id": "interactive-thread"}} print("🤖 Multi-Tool Agent Chat") print("Available tools: Calculator, Web Search, Weather Info, Text Analyzer, Current Time") print("Type 'quit' to exit, 'help' for available commands\n") while True: try: user_input = input("You: ").strip() if user_input.lower() in ['quit', 'exit', 'q']: print("Goodbye!") break elif user_input.lower() == 'help': print("\nAvailable commands:") print("• Calculator: 'Calculate 15 * 7 + 23' or 'What's sin(pi/2)?'") print("• Web Search: 'Search for Python tutorials' or 'Find information about AI'") print("• Weather: 'Weather in Tokyo' or 'What's the temperature in London?'") print("• Text Analysis: 'Analyze this text: [your text]'") print("• Current Time: 'What time is it?' or 'Current date'") print("• quit: Exit the chat\n") continue elif not user_input: continue response = agent.invoke( {"messages": [HumanMessage(content=user_input)]}, config=config ) last_message = response["messages"][-1] print(f"Agent: {last_message.content}\n") except KeyboardInterrupt: print("\nGoodbye!") break except Exception as e: print(f"Error: {str(e)}\n") The chat_with_agent function provides an interactive command-line interface for real-time conversations with the LangGraph multi-tool agent. It supports natural language queries and recognizes commands like “help” for usage guidance and “quit” to exit. Each user input is processed through the agent, which dynamically selects and invokes appropriate response tools. The function enhances user engagement by simulating a conversational experience and showcasing the agent’s capabilities in handling various queries, from math and web search to weather, text analysis, and time retrieval. if __name__ == "__main__": test_agent() print("=" * 60) print("🎉 LangGraph Multi-Tool Agent is ready!") print("=" * 60) chat_with_agent() def quick_demo(): """Quick demonstration of agent capabilities.""" config = {"configurable": {"thread_id": "demo"}} demos = [ ("Math", "Calculate the square root of 144 plus 5 times 3"), ("Search", "Find recent news about artificial intelligence"), ("Time", "What's the current date and time?") ] print("🚀 Quick Demo of Agent Capabilities\n") for category, query in demos: print(f"[{category}] Query: {query}") try: response = agent.invoke( {"messages": [HumanMessage(content=query)]}, config=config ) print(f"Response: {response['messages'][-1].content}\n") except Exception as e: print(f"Error: {str(e)}\n") print("\n" + "="*60) print("🔧 Usage Instructions:") print("1. Add your ANTHROPIC_API_KEY to use Claude model") print(" os.environ['ANTHROPIC_API_KEY'] = 'your-anthropic-api-key'") print("2. Run quick_demo() for a quick demonstration") print("3. Run chat_with_agent() for interactive chat") print("4. The agent supports: calculations, web search, weather, text analysis, and time") print("5. Example: 'Calculate 15*7+23' or 'Search for Python tutorials'") print("="*60) Finally, we orchestrate the execution of the LangGraph multi-tool agent. If the script is run directly, it initiates test_agent() to validate functionality with sample queries, followed by launching the interactive chat_with_agent() mode for real-time interaction. The quick_demo() function also briefly showcases the agent’s capabilities in math, search, and time queries. Clear usage instructions are printed at the end, guiding users on configuring the API key, running demonstrations, and interacting with the agent. This provides a smooth onboarding experience for users to explore and extend the agent’s functionality. In conclusion, this step-by-step tutorial gives valuable insights into building an effective multi-tool AI agent leveraging LangGraph and Claude’s generative capabilities. With straightforward explanations and hands-on demonstrations, the guide empowers users to integrate diverse utilities into a cohesive and interactive system. The agent’s flexibility in performing tasks, from complex calculations to dynamic information retrieval, showcases the versatility of modern AI development frameworks. Also, the inclusion of user-friendly functions for both testing and interactive chat enhances practical understanding, enabling immediate application in various contexts. Developers can confidently extend and customize their AI agents with this foundational knowledge. Check out the Notebook on GitHub. 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/A Comprehensive Coding Guide to Crafting Advanced Round-Robin Multi-Agent Workflows with Microsoft AutoGenAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Microsoft AI Introduces Magentic-UI: An Open-Source Agent Prototype that Works with People to Complete Complex Tasks that Require Multi-Step Planning and Browser UseAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Anthropic Releases Claude Opus 4 and Claude Sonnet 4: A Technical Leap in Reasoning, Coding, and AI Agent DesignAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Technology Innovation Institute TII Releases Falcon-H1: Hybrid Transformer-SSM Language Models for Scalable, Multilingual, and Long-Context Understanding
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  • Step-by-Step Guide to Dynamic Trails in UE5: Elevate Your Game VFX!

    Unlock the power of Unreal Engine 5 with our step-by-step guide to creating dynamic trail effects using Niagara! Perfect for game developers and VFX artists, this tutorial will teach you how to add stunning trails to your characters, enhancing your game's visual appeal. Learn setup, customization, and optimization techniques to elevate your game VFX effortlessly.

    #UnrealEngine #RealtimeVFX #trail
    #stepbystep #guide #dynamic #trails #ue5
    Step-by-Step Guide to Dynamic Trails in UE5: Elevate Your Game VFX!
    Unlock the power of Unreal Engine 5 with our step-by-step guide to creating dynamic trail effects using Niagara! 🎮✨ Perfect for game developers and VFX artists, this tutorial will teach you how to add stunning trails to your characters, enhancing your game's visual appeal. Learn setup, customization, and optimization techniques to elevate your game VFX effortlessly. #UnrealEngine #RealtimeVFX #trail #stepbystep #guide #dynamic #trails #ue5
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    Step-by-Step Guide to Dynamic Trails in UE5: Elevate Your Game VFX!
    Unlock the power of Unreal Engine 5 with our step-by-step guide to creating dynamic trail effects using Niagara! 🎮✨ Perfect for game developers and VFX artists, this tutorial will teach you how to add stunning trails to your characters, enhancing your game's visual appeal. Learn setup, customization, and optimization techniques to elevate your game VFX effortlessly. https://linktr.ee/cghow #UnrealEngine #RealtimeVFX #trail
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  • Step-by-Step Guide to Dynamic Trails in UE5: Elevate Your Game VFX!

    Unlock the power of Unreal Engine 5 with our step-by-step guide to creating dynamic trail effects using Niagara! Perfect for game developers and VFX artists, this tutorial will teach you how to add stunning trails to your characters, enhancing your game's visual appeal. Learn setup, customization, and optimization techniques to elevate your game VFX effortlessly.

    #UnrealEngine #RealtimeVFX #trail
    #stepbystep #guide #dynamic #trails #ue5
    Step-by-Step Guide to Dynamic Trails in UE5: Elevate Your Game VFX!
    Unlock the power of Unreal Engine 5 with our step-by-step guide to creating dynamic trail effects using Niagara! 🎮✨ Perfect for game developers and VFX artists, this tutorial will teach you how to add stunning trails to your characters, enhancing your game's visual appeal. Learn setup, customization, and optimization techniques to elevate your game VFX effortlessly. #UnrealEngine #RealtimeVFX #trail #stepbystep #guide #dynamic #trails #ue5
    WWW.YOUTUBE.COM
    Step-by-Step Guide to Dynamic Trails in UE5: Elevate Your Game VFX!
    Unlock the power of Unreal Engine 5 with our step-by-step guide to creating dynamic trail effects using Niagara! 🎮✨ Perfect for game developers and VFX artists, this tutorial will teach you how to add stunning trails to your characters, enhancing your game's visual appeal. Learn setup, customization, and optimization techniques to elevate your game VFX effortlessly. https://linktr.ee/cghow #UnrealEngine #RealtimeVFX #trail
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  • A Step-by-Step Implementation Tutorial for Building Modular AI Workflows Using Anthropic’s Claude Sonnet 3.7 through API and LangGraph

    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 = getpassos.environ= anthropic_key

    printWe 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 variableand 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, graph creation and visualization, interactive notebook display, and type annotationsfor clarity and maintainability.
    try:
    import anthropic
    except ImportError:
    print!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= fieldoutputs: List= fieldconfig: Dict= fieldThis 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 = api_key or os.environ.getif not self.api_key:
    from google.colab import userdata
    try:
    self.api_key = userdata.getif not self.api_key:
    raise ValueErrorexcept:
    printself.api_key = inputif not self.api_key:
    raise ValueErrorself.client = Anthropicself.graph = nx.DiGraphself.nodes = {}
    self.state = {}

    def add_node:
    self.nodes= node_config
    self.graph.add_nodefor input_node in node_config.inputs:
    if input_node in self.nodes:
    self.graph.add_edgereturn self

    def claude_node:
    """Convenience method to create a Claude API node"""
    inputs = inputs oroutputs = outputs ordef claude_fn:
    prompt = prompt_template
    for k, v in state.items:
    if isinstance:
    prompt = prompt.replacemessage_params = {
    "model": model,
    "max_tokens": 1000,
    "messages":}

    if system_prompt:
    message_params= system_prompt

    response = self.client.messages.createreturn response.content.text

    node_config = NodeConfigreturn self.add_nodedef transform_node:
    """Add a data transformation node"""
    inputs = inputs oroutputs = outputs ornode_config = NodeConfigreturn self.add_nodedef visualize:
    """Visualize the graph"""
    plt.figure)
    pos = nx.spring_layoutnx.drawplt.titleplt.tight_layoutplt.showprintfor node in self.graph.nodes:
    successors = list)
    if successors:
    print}")
    else:
    print")
    printdef _get_execution_order:
    """Determine execution order based on dependencies"""
    try:
    return list)
    except nx.NetworkXUnfeasible:
    raise ValueErrordef execute:
    """Execute the graph in topological order"""
    self.state = initial_state or {}
    execution_order = self._get_execution_orderprintfor node_name in execution_order:
    printnode = self.nodesinputs = {k: self.state.getfor k in node.inputs if k in self.state}

    result = node.functionif len== 1:
    self.state] = result
    elif isinstance) and len== len:
    for i, output_name in enumerate:
    self.state= resultprintreturn self.state

    def run_example:
    """Run an example LangGraph flow with a predefined question"""
    printgraph = LangGraphdef question_provider:
    return question

    graph.transform_nodegraph.claude_nodegraph.claude_nodegraph.visualizeresult = graph.executeprintprintprintprint}\n")
    print}\n")
    print}")
    printreturn 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 = LangGraphdef topic_selector:
    return "Graph-based AI systems"

    graph.transform_nodegraph.claude_nodegraph.claude_nodegraph.claude_nodedef assembler:
    return f"# {state}\n\n{introduction}\n\n## Outline\n{outline}\n\n## Conclusion\n{conclusion}"

    graph.transform_nodegraph.visualizeresult = graph.executeprintprintprintprint)
    printreturn 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.
    printquestion = "What are the three main advantages of using graph-based AI architectures?"
    simple_graph = run_exampleprintadvanced_graph = run_advanced_exampleFinally, 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_examplefunction. 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
    #stepbystep #implementation #tutorial #building #modular
    A Step-by-Step Implementation Tutorial for Building Modular AI Workflows Using Anthropic’s Claude Sonnet 3.7 through API and LangGraph
    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 = getpassos.environ= anthropic_key printWe 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 variableand 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, graph creation and visualization, interactive notebook display, and type annotationsfor clarity and maintainability. try: import anthropic except ImportError: print!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= fieldoutputs: List= fieldconfig: Dict= fieldThis 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 = api_key or os.environ.getif not self.api_key: from google.colab import userdata try: self.api_key = userdata.getif not self.api_key: raise ValueErrorexcept: printself.api_key = inputif not self.api_key: raise ValueErrorself.client = Anthropicself.graph = nx.DiGraphself.nodes = {} self.state = {} def add_node: self.nodes= node_config self.graph.add_nodefor input_node in node_config.inputs: if input_node in self.nodes: self.graph.add_edgereturn self def claude_node: """Convenience method to create a Claude API node""" inputs = inputs oroutputs = outputs ordef claude_fn: prompt = prompt_template for k, v in state.items: if isinstance: prompt = prompt.replacemessage_params = { "model": model, "max_tokens": 1000, "messages":} if system_prompt: message_params= system_prompt response = self.client.messages.createreturn response.content.text node_config = NodeConfigreturn self.add_nodedef transform_node: """Add a data transformation node""" inputs = inputs oroutputs = outputs ornode_config = NodeConfigreturn self.add_nodedef visualize: """Visualize the graph""" plt.figure) pos = nx.spring_layoutnx.drawplt.titleplt.tight_layoutplt.showprintfor node in self.graph.nodes: successors = list) if successors: print}") else: print") printdef _get_execution_order: """Determine execution order based on dependencies""" try: return list) except nx.NetworkXUnfeasible: raise ValueErrordef execute: """Execute the graph in topological order""" self.state = initial_state or {} execution_order = self._get_execution_orderprintfor node_name in execution_order: printnode = self.nodesinputs = {k: self.state.getfor k in node.inputs if k in self.state} result = node.functionif len== 1: self.state] = result elif isinstance) and len== len: for i, output_name in enumerate: self.state= resultprintreturn self.state def run_example: """Run an example LangGraph flow with a predefined question""" printgraph = LangGraphdef question_provider: return question graph.transform_nodegraph.claude_nodegraph.claude_nodegraph.visualizeresult = graph.executeprintprintprintprint}\n") print}\n") print}") printreturn 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 = LangGraphdef topic_selector: return "Graph-based AI systems" graph.transform_nodegraph.claude_nodegraph.claude_nodegraph.claude_nodedef assembler: return f"# {state}\n\n{introduction}\n\n## Outline\n{outline}\n\n## Conclusion\n{conclusion}" graph.transform_nodegraph.visualizeresult = graph.executeprintprintprintprint) printreturn 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. printquestion = "What are the three main advantages of using graph-based AI architectures?" simple_graph = run_exampleprintadvanced_graph = run_advanced_exampleFinally, 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_examplefunction. 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 #stepbystep #implementation #tutorial #building #modular
    WWW.MARKTECHPOST.COM
    A Step-by-Step Implementation Tutorial for Building Modular AI Workflows Using Anthropic’s Claude Sonnet 3.7 through API and LangGraph
    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
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  • Step-by-Step Guide to Create an AI agent with Google ADK

    Agent Development Kitis an open-source Python framework that helps developers build, manage, and deploy multi-agent systems. It’s designed to be modular and flexible, making it easy to use for both simple and complex agent-based applications.
    In this tutorial, we’ll create a simple AI agent using ADK. The agent will have access to two tools:

    get_company_overview
    get_earnings

    Step 1: Setting up the dependencies
    Google API Key
    To use Google’s AI services, you’ll need an API key:

    Visit
    Sign in and generate your API key
    Copy and store it securely — we’ll use it later in the tutorial.

    AlphaVantage API Key
    For accessing financial data, we’ll use the Alpha Vantage API:

    Go to /
    Click “Get your free API key” or visit this direct link
    Enter your email and follow the instructions
    Once you receive your API key, copy and save it securely. We’ll use it to authenticate requests to financial endpoints.

    Python Libraries
    We only need one package:
    pip install google-adk
    Step 2: Creating the Folder structure
    Set up your project folder with the following structure:
    parent_folder/

    └───multi_agent/
    ├── __init__.py
    ├── agent.py
    └── .env
    __init__.py
    Paste the following code into multi_agent/__init__.py:
    from . import agent
    .env
    Create a .env file inside the multi_agent folder and paste the following:
    GOOGLE_GENAI_USE_VERTEXAI=FALSE
    GOOGLE_API_KEY="<YOUR_GOOGLE_API_KEY>"
    ALPHA_VANTAGE_API_KEY="<YOUR_ALPHA_VANTAGE_KEY"
    Replace the placeholders with your actual API keys
    agent.py
    Paste the following code in the agent.py file:
    from google.adk.agents import Agent
    import requests
    import os
    from typing import Optional

    ALPHA_VANTAGE_API_KEY = os.getenvdef get_company_overview-> dict:
    """
    Get comprehensive company information and financial metrics

    Args:
    symbol: Stock ticker symbolReturns:
    dict: Company overview data or error
    """
    if not ALPHA_VANTAGE_API_KEY:
    return {"status": "error", "error": "Missing API key"}

    base_url = ";
    params = {
    "function": "OVERVIEW",
    "symbol": symbol,
    "apikey": ALPHA_VANTAGE_API_KEY
    }

    try:
    response = requests.getresponse.raise_for_statusdata = response.jsonif "Error Message" in data:
    return {"status": "error", "error": data}

    # Filter key metrics
    key_metrics = {
    "Description": data.get,
    "Sector": data.get,
    "MarketCap": data.get,
    "PERatio": data.get,
    "ProfitMargin": data.get,
    "52WeekHigh": data.get,
    "52WeekLow": data.get}

    return {
    "status": "success",
    "symbol": symbol,
    "overview": key_metrics
    }

    except Exception as e:
    return {"status": "error", "error": str}

    def get_earnings-> dict:
    """
    Get annual and quarterly earningsdata with analyst estimates and surprises

    Args:
    symbol: Stock ticker symbolReturns:
    dict: Earnings data with estimates or error message
    """
    if not ALPHA_VANTAGE_API_KEY:
    return {"status": "error", "error": "Missing API key"}

    base_url = ";
    params = {
    "function": "EARNINGS",
    "symbol": symbol,
    "apikey": ALPHA_VANTAGE_API_KEY
    }

    try:
    response = requests.getresponse.raise_for_statusdata = response.jsonif "Error Message" in data:
    return {"status": "error", "error": data}

    # Process annual and quarterly earnings
    annual_earnings = data.get# Last 5 years
    quarterly_earnings = data.get# Last 4 quarters

    # Format surprise percentages
    for q in quarterly_earnings:
    if "surprisePercentage" in q:
    q= f"{q}%"

    return {
    "status": "success",
    "symbol": symbol,
    "annual_earnings": annual_earnings,
    "quarterly_earnings": quarterly_earnings,
    "metrics": {
    "latest_eps": quarterly_earningsif quarterly_earnings else None
    }
    }

    except Exception as e:
    return {"status": "error", "error": str}

    root_agent = Agent,
    instruction=,
    tools=,
    )
    In this script, we define a financial analysis agent using the Google Agent Development Kit. The agent is designed to answer user queries by accessing real-time financial data through the Alpha Vantage API. Specifically, it exposes two tools: get_company_overview and get_earnings. The get_company_overview function retrieves key company details such as sector, market capitalization, P/E ratio, and 52-week high/low values. The get_earnings function provides both annual and quarterly earnings data, including reported EPS and surprise percentages.To create the agent, we use the Agent class from the google.adk.agents module, giving it a name, a model, a description, and an instruction prompt. The agent is then equipped with the two tools mentioned above, allowing it to respond to questions related to company financials.
    Step 3: Running the Agent
    To run the agent, navigate to the parent directory of your agent projectparent_folder/ ← Navigate to this directory in your terminal

    └───multi_agent/
    ├── __init__.py # Initializes the module
    ├── agent.py # Contains the agent logic and tools
    └── .env # Stores your API keys securely
    After navigating, run the following code:
    adk web
    Open the URL provideddirectly in your browser. You’ll see a simple chat interface where you can interact with your agent using the input textbox.

    Additionally, you can inspect each step of the agent’s reasoning by clicking on Actions. This allows you to view:

    The tools being called
    The inputs and outputs of each function
    The responses generated by the language model

    GitHub Link
    You can find the entire code along with folder structure at this link:
    Arham IslamI am a Civil Engineering Graduatefrom Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context ProtocolServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/How to Create a Custom Model Context ProtocolClient Using Gemini
    #stepbystep #guide #create #agent #with
    Step-by-Step Guide to Create an AI agent with Google ADK
    Agent Development Kitis an open-source Python framework that helps developers build, manage, and deploy multi-agent systems. It’s designed to be modular and flexible, making it easy to use for both simple and complex agent-based applications. In this tutorial, we’ll create a simple AI agent using ADK. The agent will have access to two tools: get_company_overview get_earnings Step 1: Setting up the dependencies Google API Key To use Google’s AI services, you’ll need an API key: Visit Sign in and generate your API key Copy and store it securely — we’ll use it later in the tutorial. AlphaVantage API Key For accessing financial data, we’ll use the Alpha Vantage API: Go to / Click “Get your free API key” or visit this direct link Enter your email and follow the instructions Once you receive your API key, copy and save it securely. We’ll use it to authenticate requests to financial endpoints. Python Libraries We only need one package: pip install google-adk Step 2: Creating the Folder structure Set up your project folder with the following structure: parent_folder/ │ └───multi_agent/ ├── __init__.py ├── agent.py └── .env __init__.py Paste the following code into multi_agent/__init__.py: from . import agent .env Create a .env file inside the multi_agent folder and paste the following: GOOGLE_GENAI_USE_VERTEXAI=FALSE GOOGLE_API_KEY="<YOUR_GOOGLE_API_KEY>" ALPHA_VANTAGE_API_KEY="<YOUR_ALPHA_VANTAGE_KEY" Replace the placeholders with your actual API keys agent.py Paste the following code in the agent.py file: from google.adk.agents import Agent import requests import os from typing import Optional ALPHA_VANTAGE_API_KEY = os.getenvdef get_company_overview-> dict: """ Get comprehensive company information and financial metrics Args: symbol: Stock ticker symbolReturns: dict: Company overview data or error """ if not ALPHA_VANTAGE_API_KEY: return {"status": "error", "error": "Missing API key"} base_url = "; params = { "function": "OVERVIEW", "symbol": symbol, "apikey": ALPHA_VANTAGE_API_KEY } try: response = requests.getresponse.raise_for_statusdata = response.jsonif "Error Message" in data: return {"status": "error", "error": data} # Filter key metrics key_metrics = { "Description": data.get, "Sector": data.get, "MarketCap": data.get, "PERatio": data.get, "ProfitMargin": data.get, "52WeekHigh": data.get, "52WeekLow": data.get} return { "status": "success", "symbol": symbol, "overview": key_metrics } except Exception as e: return {"status": "error", "error": str} def get_earnings-> dict: """ Get annual and quarterly earningsdata with analyst estimates and surprises Args: symbol: Stock ticker symbolReturns: dict: Earnings data with estimates or error message """ if not ALPHA_VANTAGE_API_KEY: return {"status": "error", "error": "Missing API key"} base_url = "; params = { "function": "EARNINGS", "symbol": symbol, "apikey": ALPHA_VANTAGE_API_KEY } try: response = requests.getresponse.raise_for_statusdata = response.jsonif "Error Message" in data: return {"status": "error", "error": data} # Process annual and quarterly earnings annual_earnings = data.get# Last 5 years quarterly_earnings = data.get# Last 4 quarters # Format surprise percentages for q in quarterly_earnings: if "surprisePercentage" in q: q= f"{q}%" return { "status": "success", "symbol": symbol, "annual_earnings": annual_earnings, "quarterly_earnings": quarterly_earnings, "metrics": { "latest_eps": quarterly_earningsif quarterly_earnings else None } } except Exception as e: return {"status": "error", "error": str} root_agent = Agent, instruction=, tools=, ) In this script, we define a financial analysis agent using the Google Agent Development Kit. The agent is designed to answer user queries by accessing real-time financial data through the Alpha Vantage API. Specifically, it exposes two tools: get_company_overview and get_earnings. The get_company_overview function retrieves key company details such as sector, market capitalization, P/E ratio, and 52-week high/low values. The get_earnings function provides both annual and quarterly earnings data, including reported EPS and surprise percentages.To create the agent, we use the Agent class from the google.adk.agents module, giving it a name, a model, a description, and an instruction prompt. The agent is then equipped with the two tools mentioned above, allowing it to respond to questions related to company financials. Step 3: Running the Agent To run the agent, navigate to the parent directory of your agent projectparent_folder/ ← Navigate to this directory in your terminal │ └───multi_agent/ ├── __init__.py # Initializes the module ├── agent.py # Contains the agent logic and tools └── .env # Stores your API keys securely After navigating, run the following code: adk web Open the URL provideddirectly in your browser. You’ll see a simple chat interface where you can interact with your agent using the input textbox. Additionally, you can inspect each step of the agent’s reasoning by clicking on Actions. This allows you to view: The tools being called The inputs and outputs of each function The responses generated by the language model GitHub Link You can find the entire code along with folder structure at this link: Arham IslamI am a Civil Engineering Graduatefrom Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context ProtocolServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/How to Create a Custom Model Context ProtocolClient Using Gemini #stepbystep #guide #create #agent #with
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    Step-by-Step Guide to Create an AI agent with Google ADK
    Agent Development Kit (ADK) is an open-source Python framework that helps developers build, manage, and deploy multi-agent systems. It’s designed to be modular and flexible, making it easy to use for both simple and complex agent-based applications. In this tutorial, we’ll create a simple AI agent using ADK. The agent will have access to two tools: get_company_overview get_earnings Step 1: Setting up the dependencies Google API Key To use Google’s AI services, you’ll need an API key: Visit https://aistudio.google.com/apikey Sign in and generate your API key Copy and store it securely — we’ll use it later in the tutorial. AlphaVantage API Key For accessing financial data, we’ll use the Alpha Vantage API: Go to https://www.alphavantage.co/ Click “Get your free API key” or visit this direct link Enter your email and follow the instructions Once you receive your API key, copy and save it securely. We’ll use it to authenticate requests to financial endpoints. Python Libraries We only need one package: pip install google-adk Step 2: Creating the Folder structure Set up your project folder with the following structure: parent_folder/ │ └───multi_agent/ ├── __init__.py ├── agent.py └── .env __init__.py Paste the following code into multi_agent/__init__.py: from . import agent .env Create a .env file inside the multi_agent folder and paste the following: GOOGLE_GENAI_USE_VERTEXAI=FALSE GOOGLE_API_KEY="<YOUR_GOOGLE_API_KEY>" ALPHA_VANTAGE_API_KEY="<YOUR_ALPHA_VANTAGE_KEY" Replace the placeholders with your actual API keys agent.py Paste the following code in the agent.py file: from google.adk.agents import Agent import requests import os from typing import Optional ALPHA_VANTAGE_API_KEY = os.getenv("ALPHA_VANTAGE_API_KEY") def get_company_overview(symbol: str) -> dict: """ Get comprehensive company information and financial metrics Args: symbol: Stock ticker symbol (e.g., IBM) Returns: dict: Company overview data or error """ if not ALPHA_VANTAGE_API_KEY: return {"status": "error", "error": "Missing API key"} base_url = "https://www.alphavantage.co/query" params = { "function": "OVERVIEW", "symbol": symbol, "apikey": ALPHA_VANTAGE_API_KEY } try: response = requests.get(base_url, params=params) response.raise_for_status() data = response.json() if "Error Message" in data: return {"status": "error", "error": data["Error Message"]} # Filter key metrics key_metrics = { "Description": data.get("Description"), "Sector": data.get("Sector"), "MarketCap": data.get("MarketCapitalization"), "PERatio": data.get("PERatio"), "ProfitMargin": data.get("ProfitMargin"), "52WeekHigh": data.get("52WeekHigh"), "52WeekLow": data.get("52WeekLow") } return { "status": "success", "symbol": symbol, "overview": key_metrics } except Exception as e: return {"status": "error", "error": str(e)} def get_earnings(symbol: str) -> dict: """ Get annual and quarterly earnings (EPS) data with analyst estimates and surprises Args: symbol: Stock ticker symbol (e.g., IBM) Returns: dict: Earnings data with estimates or error message """ if not ALPHA_VANTAGE_API_KEY: return {"status": "error", "error": "Missing API key"} base_url = "https://www.alphavantage.co/query" params = { "function": "EARNINGS", "symbol": symbol, "apikey": ALPHA_VANTAGE_API_KEY } try: response = requests.get(base_url, params=params) response.raise_for_status() data = response.json() if "Error Message" in data: return {"status": "error", "error": data["Error Message"]} # Process annual and quarterly earnings annual_earnings = data.get("annualEarnings", [])[:5] # Last 5 years quarterly_earnings = data.get("quarterlyEarnings", [])[:4] # Last 4 quarters # Format surprise percentages for q in quarterly_earnings: if "surprisePercentage" in q: q["surprise"] = f"{q['surprisePercentage']}%" return { "status": "success", "symbol": symbol, "annual_earnings": annual_earnings, "quarterly_earnings": quarterly_earnings, "metrics": { "latest_eps": quarterly_earnings[0]["reportedEPS"] if quarterly_earnings else None } } except Exception as e: return {"status": "error", "error": str(e)} root_agent = Agent( name="Financial_analyst_agent", model="gemini-2.0-flash", description=( "Agent to give company overviews with key financial metrics." ), instruction=( "You are a helpful AI agent that provides company overviews and earnings information" ), tools=[get_company_overview, get_earnings], ) In this script, we define a financial analysis agent using the Google Agent Development Kit (ADK). The agent is designed to answer user queries by accessing real-time financial data through the Alpha Vantage API. Specifically, it exposes two tools: get_company_overview and get_earnings. The get_company_overview function retrieves key company details such as sector, market capitalization, P/E ratio, and 52-week high/low values. The get_earnings function provides both annual and quarterly earnings data, including reported EPS and surprise percentages.To create the agent, we use the Agent class from the google.adk.agents module, giving it a name, a model (e.g., Gemini 2.0 Flash), a description, and an instruction prompt. The agent is then equipped with the two tools mentioned above, allowing it to respond to questions related to company financials. Step 3: Running the Agent To run the agent, navigate to the parent directory of your agent project (e.g. using cd ..) parent_folder/ ← Navigate to this directory in your terminal │ └───multi_agent/ ├── __init__.py # Initializes the module ├── agent.py # Contains the agent logic and tools └── .env # Stores your API keys securely After navigating, run the following code: adk web Open the URL provided (usually http://localhost:8000 or http://127.0.0.1:8000) directly in your browser. You’ll see a simple chat interface where you can interact with your agent using the input textbox. Additionally, you can inspect each step of the agent’s reasoning by clicking on Actions. This allows you to view: The tools being called The inputs and outputs of each function The responses generated by the language model GitHub Link You can find the entire code along with folder structure at this link: https://github.com/mohd-arham-islam/ADK-demo Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/How to Create a Custom Model Context Protocol (MCP) Client Using Gemini
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  • A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization

    Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as 4-bit quantization and LoRA. In this tutorial, we walk through a practical implementation on Google Colab to fine-tune Qwen3-14B using a combination of reasoning and instruction-following datasets, combining Unsloth’s FastLanguageModel utilities with trl.SFTTrainer users can achieve powerful fine-tuning performance with just consumer-grade hardware.
    %%capture
    import os
    if "COLAB_" not in "".join):
    !pip install unsloth
    else:
    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo
    !pip install sentencepiece protobuf "datasets>=3.4.1" huggingface_hub hf_transfer
    !pip install --no-deps unsloth
    We install all the essential libraries required for fine-tuning the Qwen3 model using Unsloth AI. It conditionally installs dependencies based on the environment, using a lightweight approach on Colab to ensure compatibility and reduce overhead. Key components like bitsandbytes, trl, xformers, and unsloth_zoo are included to enable 4-bit quantized training and LoRA-based optimization.
    from unsloth import FastLanguageModel
    import torch

    model, tokenizer = FastLanguageModel.from_pretrainedWe load the Qwen3-14B model using FastLanguageModel from the Unsloth library, which is optimized for efficient fine-tuning. It initializes the model with a context length of 2048 tokens and loads it in 4-bit precision, significantly reducing memory usage. Full fine-tuning is disabled, making it suitable for lightweight parameter-efficient techniques like LoRA.
    model = FastLanguageModel.get_peft_modelWe apply LoRAto the Qwen3 model using FastLanguageModel.get_peft_model. It injects trainable adapters into specific transformer layerswith a rank of 32, enabling efficient fine-tuning while keeping most model weights frozen. Using “unsloth” gradient checkpointing further optimizes memory usage, making it suitable for training large models on limited hardware.
    from datasets import load_dataset

    reasoning_dataset = load_datasetnon_reasoning_dataset = load_datasetWe load two pre-curated datasets from the Hugging Face Hub using the library. The reasoning_dataset contains chain-of-thoughtproblems from Unsloth’s OpenMathReasoning-mini, designed to enhance logical reasoning in the model. The non_reasoning_dataset pulls general instruction-following data from mlabonne’s FineTome-100k, which helps the model learn broader conversational and task-oriented skills. Together, these datasets support a well-rounded fine-tuning objective.
    def generate_conversation:
    problems = examplessolutions = examplesconversations =for problem, solution in zip:
    conversations.appendreturn {"conversations": conversations}
    This function, generate_conversation, transforms raw question–answer pairs from the reasoning dataset into a chat-style format suitable for fine-tuning. For each problem and its corresponding generated solution, a conversation is conducted in which the user asks a question and the assistant provides the answer. The output is a list of dictionaries following the structure expected by chat-based language models, preparing the data for tokenization with a chat template.
    reasoning_conversations = tokenizer.apply_chat_templatefrom unsloth.chat_templates import standardize_sharegpt
    dataset = standardize_sharegptnon_reasoning_conversations = tokenizer.apply_chat_templateimport pandas as pd

    chat_percentage = 0.75
    non_reasoning_subset = pd.Series.sample*),
    random_state=2407,
    )

    data = pd.concat,
    pd.Series])
    data.name = "text"
    We prepare the fine-tuning dataset by converting the reasoning and instruction datasets into a consistent chat format and then combining them. It first applies the tokenizer’s apply_chat_template to convert structured conversations into tokenizable strings. The standardize_sharegpt function normalizes the instruction dataset into a compatible structure. Then, a 75-25 mix is created by sampling 25% of the non-reasoningconversations and combining them with the reasoning data. This blend ensures the model is exposed to logical reasoning and general instruction-following tasks, improving its versatility during training. The final combined data is stored as a single-column Pandas Series named “text”.
    from datasets import Dataset

    combined_dataset = Dataset.from_pandas)
    combined_dataset = combined_dataset.shufflefrom trl import SFTTrainer, SFTConfig

    trainer = SFTTrainer)

    We take the preprocessed conversations, wrap them into a Hugging Face Dataset, and shuffle the dataset with a fixed seed for reproducibility. Then, the fine-tuning trainer is initialized using trl’s SFTTrainer and SFTConfig. The trainer is set up to use the combined datasetand defines training hyperparameters like batch size, gradient accumulation, number of warmup and training steps, learning rate, optimizer parameters, and a linear learning rate scheduler. This configuration is geared towards efficient fine-tuning while maintaining reproducibility and logging minimal details.
    trainer.traintrainer.trainstarts the fine-tuning process for the Qwen3-14B model using the SFTTrainer. It trains the model on the prepared mixed dataset of reasoning and instruction-following conversations, optimizing only the LoRA-adapted parameters thanks to the underlying Unsloth setup. Training will proceed according to the configuration specified earlier, and progress will be printed every logging step. This final command launches the actual model adaptation based on your custom data.
    model.save_pretrainedtokenizer.save_pretrainedWe save the fine-tuned model and tokenizer locally to the “qwen3-finetuned-colab” directory. By calling save_pretrained, the adapted weights and tokenizer configuration can be reloaded later for inference or further training, locally or for uploading to the Hugging Face Hub.
    In conclusion, with the help of Unsloth AI, fine-tuning massive LLMs like Qwen3-14B becomes feasible, using limited resources, and is highly efficient and accessible. This tutorial demonstrated how to load a 4-bit quantized version of the model, apply structured chat templates, mix multiple datasets for better generalization, and train using TRL’s SFTTrainer. Whether you’re building custom assistants or specialized domain models, Unsloth’s tools dramatically reduce the barrier to fine-tuning at scale. As open-source fine-tuning ecosystems evolve, Unsloth continues to lead the way in making LLM training faster, cheaper, and more practical for everyone.

    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/Chain-of-Thought May Not Be a Window into AI’s Reasoning: Anthropic’s New Study Reveals Hidden GapsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/How to Build a Powerful and Intelligent Question-Answering System by Using Tavily Search API, Chroma, Google Gemini LLMs, and the LangChain FrameworkAsif Razzaqhttps://www.marktechpost.com/author/6flvq/AWS Open-Sources Strands Agents SDK to Simplify AI Agent DevelopmentAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Windsurf Launches SWE-1: A Frontier AI Model Family for End-to-End Software Engineering

    Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub!
    #stepbystep #coding #guide #efficiently #finetune
    A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization
    Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as 4-bit quantization and LoRA. In this tutorial, we walk through a practical implementation on Google Colab to fine-tune Qwen3-14B using a combination of reasoning and instruction-following datasets, combining Unsloth’s FastLanguageModel utilities with trl.SFTTrainer users can achieve powerful fine-tuning performance with just consumer-grade hardware. %%capture import os if "COLAB_" not in "".join): !pip install unsloth else: !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo !pip install sentencepiece protobuf "datasets>=3.4.1" huggingface_hub hf_transfer !pip install --no-deps unsloth We install all the essential libraries required for fine-tuning the Qwen3 model using Unsloth AI. It conditionally installs dependencies based on the environment, using a lightweight approach on Colab to ensure compatibility and reduce overhead. Key components like bitsandbytes, trl, xformers, and unsloth_zoo are included to enable 4-bit quantized training and LoRA-based optimization. from unsloth import FastLanguageModel import torch model, tokenizer = FastLanguageModel.from_pretrainedWe load the Qwen3-14B model using FastLanguageModel from the Unsloth library, which is optimized for efficient fine-tuning. It initializes the model with a context length of 2048 tokens and loads it in 4-bit precision, significantly reducing memory usage. Full fine-tuning is disabled, making it suitable for lightweight parameter-efficient techniques like LoRA. model = FastLanguageModel.get_peft_modelWe apply LoRAto the Qwen3 model using FastLanguageModel.get_peft_model. It injects trainable adapters into specific transformer layerswith a rank of 32, enabling efficient fine-tuning while keeping most model weights frozen. Using “unsloth” gradient checkpointing further optimizes memory usage, making it suitable for training large models on limited hardware. from datasets import load_dataset reasoning_dataset = load_datasetnon_reasoning_dataset = load_datasetWe load two pre-curated datasets from the Hugging Face Hub using the library. The reasoning_dataset contains chain-of-thoughtproblems from Unsloth’s OpenMathReasoning-mini, designed to enhance logical reasoning in the model. The non_reasoning_dataset pulls general instruction-following data from mlabonne’s FineTome-100k, which helps the model learn broader conversational and task-oriented skills. Together, these datasets support a well-rounded fine-tuning objective. def generate_conversation: problems = examplessolutions = examplesconversations =for problem, solution in zip: conversations.appendreturn {"conversations": conversations} This function, generate_conversation, transforms raw question–answer pairs from the reasoning dataset into a chat-style format suitable for fine-tuning. For each problem and its corresponding generated solution, a conversation is conducted in which the user asks a question and the assistant provides the answer. The output is a list of dictionaries following the structure expected by chat-based language models, preparing the data for tokenization with a chat template. reasoning_conversations = tokenizer.apply_chat_templatefrom unsloth.chat_templates import standardize_sharegpt dataset = standardize_sharegptnon_reasoning_conversations = tokenizer.apply_chat_templateimport pandas as pd chat_percentage = 0.75 non_reasoning_subset = pd.Series.sample*), random_state=2407, ) data = pd.concat, pd.Series]) data.name = "text" We prepare the fine-tuning dataset by converting the reasoning and instruction datasets into a consistent chat format and then combining them. It first applies the tokenizer’s apply_chat_template to convert structured conversations into tokenizable strings. The standardize_sharegpt function normalizes the instruction dataset into a compatible structure. Then, a 75-25 mix is created by sampling 25% of the non-reasoningconversations and combining them with the reasoning data. This blend ensures the model is exposed to logical reasoning and general instruction-following tasks, improving its versatility during training. The final combined data is stored as a single-column Pandas Series named “text”. from datasets import Dataset combined_dataset = Dataset.from_pandas) combined_dataset = combined_dataset.shufflefrom trl import SFTTrainer, SFTConfig trainer = SFTTrainer) We take the preprocessed conversations, wrap them into a Hugging Face Dataset, and shuffle the dataset with a fixed seed for reproducibility. Then, the fine-tuning trainer is initialized using trl’s SFTTrainer and SFTConfig. The trainer is set up to use the combined datasetand defines training hyperparameters like batch size, gradient accumulation, number of warmup and training steps, learning rate, optimizer parameters, and a linear learning rate scheduler. This configuration is geared towards efficient fine-tuning while maintaining reproducibility and logging minimal details. trainer.traintrainer.trainstarts the fine-tuning process for the Qwen3-14B model using the SFTTrainer. It trains the model on the prepared mixed dataset of reasoning and instruction-following conversations, optimizing only the LoRA-adapted parameters thanks to the underlying Unsloth setup. Training will proceed according to the configuration specified earlier, and progress will be printed every logging step. This final command launches the actual model adaptation based on your custom data. model.save_pretrainedtokenizer.save_pretrainedWe save the fine-tuned model and tokenizer locally to the “qwen3-finetuned-colab” directory. By calling save_pretrained, the adapted weights and tokenizer configuration can be reloaded later for inference or further training, locally or for uploading to the Hugging Face Hub. In conclusion, with the help of Unsloth AI, fine-tuning massive LLMs like Qwen3-14B becomes feasible, using limited resources, and is highly efficient and accessible. This tutorial demonstrated how to load a 4-bit quantized version of the model, apply structured chat templates, mix multiple datasets for better generalization, and train using TRL’s SFTTrainer. Whether you’re building custom assistants or specialized domain models, Unsloth’s tools dramatically reduce the barrier to fine-tuning at scale. As open-source fine-tuning ecosystems evolve, Unsloth continues to lead the way in making LLM training faster, cheaper, and more practical for everyone. 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/Chain-of-Thought May Not Be a Window into AI’s Reasoning: Anthropic’s New Study Reveals Hidden GapsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/How to Build a Powerful and Intelligent Question-Answering System by Using Tavily Search API, Chroma, Google Gemini LLMs, and the LangChain FrameworkAsif Razzaqhttps://www.marktechpost.com/author/6flvq/AWS Open-Sources Strands Agents SDK to Simplify AI Agent DevelopmentAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Windsurf Launches SWE-1: A Frontier AI Model Family for End-to-End Software Engineering 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! #stepbystep #coding #guide #efficiently #finetune
    WWW.MARKTECHPOST.COM
    A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization
    Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as 4-bit quantization and LoRA (Low-Rank Adaptation). In this tutorial, we walk through a practical implementation on Google Colab to fine-tune Qwen3-14B using a combination of reasoning and instruction-following datasets, combining Unsloth’s FastLanguageModel utilities with trl.SFTTrainer users can achieve powerful fine-tuning performance with just consumer-grade hardware. %%capture import os if "COLAB_" not in "".join(os.environ.keys()): !pip install unsloth else: !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo !pip install sentencepiece protobuf "datasets>=3.4.1" huggingface_hub hf_transfer !pip install --no-deps unsloth We install all the essential libraries required for fine-tuning the Qwen3 model using Unsloth AI. It conditionally installs dependencies based on the environment, using a lightweight approach on Colab to ensure compatibility and reduce overhead. Key components like bitsandbytes, trl, xformers, and unsloth_zoo are included to enable 4-bit quantized training and LoRA-based optimization. from unsloth import FastLanguageModel import torch model, tokenizer = FastLanguageModel.from_pretrained( model_name = "unsloth/Qwen3-14B", max_seq_length = 2048, load_in_4bit = True, load_in_8bit = False, full_finetuning = False, ) We load the Qwen3-14B model using FastLanguageModel from the Unsloth library, which is optimized for efficient fine-tuning. It initializes the model with a context length of 2048 tokens and loads it in 4-bit precision, significantly reducing memory usage. Full fine-tuning is disabled, making it suitable for lightweight parameter-efficient techniques like LoRA. model = FastLanguageModel.get_peft_model( model, r = 32, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], lora_alpha = 32, lora_dropout = 0, bias = "none", use_gradient_checkpointing = "unsloth", random_state = 3407, use_rslora = False, loftq_config = None, ) We apply LoRA (Low-Rank Adaptation) to the Qwen3 model using FastLanguageModel.get_peft_model. It injects trainable adapters into specific transformer layers (like q_proj, v_proj, etc.) with a rank of 32, enabling efficient fine-tuning while keeping most model weights frozen. Using “unsloth” gradient checkpointing further optimizes memory usage, making it suitable for training large models on limited hardware. from datasets import load_dataset reasoning_dataset = load_dataset("unsloth/OpenMathReasoning-mini", split="cot") non_reasoning_dataset = load_dataset("mlabonne/FineTome-100k", split="train") We load two pre-curated datasets from the Hugging Face Hub using the library. The reasoning_dataset contains chain-of-thought (CoT) problems from Unsloth’s OpenMathReasoning-mini, designed to enhance logical reasoning in the model. The non_reasoning_dataset pulls general instruction-following data from mlabonne’s FineTome-100k, which helps the model learn broader conversational and task-oriented skills. Together, these datasets support a well-rounded fine-tuning objective. def generate_conversation(examples): problems = examples["problem"] solutions = examples["generated_solution"] conversations = [] for problem, solution in zip(problems, solutions): conversations.append([ {"role": "user", "content": problem}, {"role": "assistant", "content": solution}, ]) return {"conversations": conversations} This function, generate_conversation, transforms raw question–answer pairs from the reasoning dataset into a chat-style format suitable for fine-tuning. For each problem and its corresponding generated solution, a conversation is conducted in which the user asks a question and the assistant provides the answer. The output is a list of dictionaries following the structure expected by chat-based language models, preparing the data for tokenization with a chat template. reasoning_conversations = tokenizer.apply_chat_template( reasoning_dataset["conversations"], tokenize=False, ) from unsloth.chat_templates import standardize_sharegpt dataset = standardize_sharegpt(non_reasoning_dataset) non_reasoning_conversations = tokenizer.apply_chat_template( dataset["conversations"], tokenize=False, ) import pandas as pd chat_percentage = 0.75 non_reasoning_subset = pd.Series(non_reasoning_conversations).sample( int(len(reasoning_conversations) * (1.0 - chat_percentage)), random_state=2407, ) data = pd.concat([ pd.Series(reasoning_conversations), pd.Series(non_reasoning_subset) ]) data.name = "text" We prepare the fine-tuning dataset by converting the reasoning and instruction datasets into a consistent chat format and then combining them. It first applies the tokenizer’s apply_chat_template to convert structured conversations into tokenizable strings. The standardize_sharegpt function normalizes the instruction dataset into a compatible structure. Then, a 75-25 mix is created by sampling 25% of the non-reasoning (instruction) conversations and combining them with the reasoning data. This blend ensures the model is exposed to logical reasoning and general instruction-following tasks, improving its versatility during training. The final combined data is stored as a single-column Pandas Series named “text”. from datasets import Dataset combined_dataset = Dataset.from_pandas(pd.DataFrame(data)) combined_dataset = combined_dataset.shuffle(seed=3407) from trl import SFTTrainer, SFTConfig trainer = SFTTrainer( model=model, tokenizer=tokenizer, train_dataset=combined_dataset, eval_dataset=None, args=SFTConfig( dataset_text_field="text", per_device_train_batch_size=2, gradient_accumulation_steps=4, warmup_steps=5, max_steps=30, learning_rate=2e-4, logging_steps=1, optim="adamw_8bit", weight_decay=0.01, lr_scheduler_type="linear", seed=3407, report_to="none", ) ) We take the preprocessed conversations, wrap them into a Hugging Face Dataset (ensuring the data is in a consistent format), and shuffle the dataset with a fixed seed for reproducibility. Then, the fine-tuning trainer is initialized using trl’s SFTTrainer and SFTConfig. The trainer is set up to use the combined dataset (with the text column field named “text”) and defines training hyperparameters like batch size, gradient accumulation, number of warmup and training steps, learning rate, optimizer parameters, and a linear learning rate scheduler. This configuration is geared towards efficient fine-tuning while maintaining reproducibility and logging minimal details (with report_to=”none”). trainer.train() trainer.train() starts the fine-tuning process for the Qwen3-14B model using the SFTTrainer. It trains the model on the prepared mixed dataset of reasoning and instruction-following conversations, optimizing only the LoRA-adapted parameters thanks to the underlying Unsloth setup. Training will proceed according to the configuration specified earlier (e.g., max_steps=30, batch_size=2, lr=2e-4), and progress will be printed every logging step. This final command launches the actual model adaptation based on your custom data. model.save_pretrained("qwen3-finetuned-colab") tokenizer.save_pretrained("qwen3-finetuned-colab") We save the fine-tuned model and tokenizer locally to the “qwen3-finetuned-colab” directory. By calling save_pretrained(), the adapted weights and tokenizer configuration can be reloaded later for inference or further training, locally or for uploading to the Hugging Face Hub. In conclusion, with the help of Unsloth AI, fine-tuning massive LLMs like Qwen3-14B becomes feasible, using limited resources, and is highly efficient and accessible. This tutorial demonstrated how to load a 4-bit quantized version of the model, apply structured chat templates, mix multiple datasets for better generalization, and train using TRL’s SFTTrainer. Whether you’re building custom assistants or specialized domain models, Unsloth’s tools dramatically reduce the barrier to fine-tuning at scale. As open-source fine-tuning ecosystems evolve, Unsloth continues to lead the way in making LLM training faster, cheaper, and more practical for everyone. 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/Chain-of-Thought May Not Be a Window into AI’s Reasoning: Anthropic’s New Study Reveals Hidden GapsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/How to Build a Powerful and Intelligent Question-Answering System by Using Tavily Search API, Chroma, Google Gemini LLMs, and the LangChain FrameworkAsif Razzaqhttps://www.marktechpost.com/author/6flvq/AWS Open-Sources Strands Agents SDK to Simplify AI Agent DevelopmentAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Windsurf Launches SWE-1: A Frontier AI Model Family for End-to-End Software Engineering 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! (Promoted)
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  • A Step-by-Step Guide to Planting Your Summer Vegetables

    We may earn a commission from links on this page.Vegetable plants are expensive. To get the best yield out of them, you need to pay attention to more than just the soil, sun, and watering. You need to get your plants off on the right foot by planting them the right way. Here's what I mean. Choose the right plant at the nursery

    Credit: Amanda Blum

    When I first started gardening, I thought the best vegetable starts to buy were the ones that were the biggest, with flowers and fruit already on them. This would give the plant a head start, right? Sadly, no. Plants go through something called "transplant shock" when you move them. You’re disturbing the plant's roots, and moving it to a new environment. To survive, the plant needs to focus all its energy on the plant's roots, and if there's a lot of plant matter like leaves, flowers, and fruit to support, energy is wasted supporting them. Plants with established fruit, in particular, struggle during the transplant process. Choose plants that look healthy, with strong stems and leaves without damage, but that don't yet have flowers or fruit. Prepare your soil

    Credit: Amanda Blum

    There are legions of ways to handle your garden from season to season. Some people till the soil, while others employ a no-till method, and still others use something called "chop-and-drop." Regardless of the method, the soil you’re planting into has to be pliable enough that roots can flourish in them. For that reason, ensure that the soil is turned over and broken up—from a shovel to a shovel and a half’s depth. You can use a broadfork for this, if you don’t want to disturb the soil structure, but otherwise, just use a shovel. Breaking up the soil will help you see the texture—so you can add sand if the soil has too much clay in it, or compost if it isn't holding any moisture. You can use this time to add amendments such as vegetable fertilizer and lime. Fertilizer is obvious, but lime is used to turn your soil less acidic, which happens over time through watering and growing. Most vegetables don’t enjoy acidic environments. Turn the amendments into the soil. Choose an overcast dayYour plants will already be stressed by transplanting. Planting them into the blazing sun is even more stress. A stretch of overcast days is the perfect planting time. If that's not an option, plant at twilight to give your plants a night to adjust. Consider giving the plant some shade the next day to help it acclimate.Get the plant out of the pot without damaging the roots

    Credit: Amanda Blum

    By the time plants reach the nursery, they’re often root-bound in the plastic pots or six packs you buy them in. Roots are resilient, but you don’t want to disturb them more than necessary. The best way to break a plant free from a plastic pot is to use two fingers and squeeze the bottom of the pot. This should free the plant. Don’t turn the pot over or pound on it with your palm, and definitely don’t try to pull it loose by the plant’s stem. 

    on the left, the eggplant seedling just out of the pot, and on the right, after the roots have been broken up
    Credit: Amanda Blum

    Once the plant is out, you want to break up the roots by using your fingers like a comb on the bottom of the plant, so that roots are freed. That said, these plants do not enjoy their roots being disturbed: cucumbers, beans, pumpkins, luffa, beets, and most root vegetables. For these, I simply dig a hole, remove the plant from the plastic tray, carefully plop the plant in, and walk away.  Separate plants as necessary

    Credit: Amanda Blum

    Most pots have more than one seed in each cell. In some cases, like tomatoes, someone usually culls the seedlings so only one is left to flourish. However, in some cases, like herbs and lettuce, nurseries leave the seeds alone and let multiple seeds grow. In other cases like onions and carrots, the cells are purposely overseeded to be filled with lots of seedlings. Strawberries usually come in a pot of five to 10 starts. 

    If you take a cell of onion seedlings out, you can separate them by diving the block in half over and over again, until you have individual seedlings.
    Credit: Amanda Blum

    When there’s more than one seedling, you need to separate them. You shouldn’t try to plant them altogether. For lettuce or herbs, this is simple: Remove one cell, and with your fingers, gently pull the soil pod apart. Start by pulling the pod in half, and then keep dividing until all the seedlings are free. This works on larger plants like squash, and smaller plants like carrots where there can be 20 or more seedlings in a single cell. Once the individual seedlings are free, they can each be planted as if they’re a whole plant. This is how you get a whole row of carrots or onions. This is also a great way to save money, since you usually get far more than six lettuce heads from a six-pack of lettuce. Know the right depth

    Credit: Amanda Blum

    Plants need to go in the ground at the right depth, ensuring that the base of the plant is at soil level. In some cases, though, you canplant the stem deeper.Leeks and onions, for example, can be planted deeply. In particular, leeks can be planted as deeply as possible, with only an inch or two of seedling above the surface of the soil. This will help blanch the leek. Tomatoes, eggplant, and peppers can be planted deeply, as they’ll form roots along their entire stem. If your tomato is leggy, this is a spectacular way to fix the problem. When in doubt, follow the directions on the plant tag, or simply plant at a standard depth so the roots are covered, but the stem is exposed above the soil. Don’t mulch against your stemsWhile mulch is an important part of insulating your vegetable plants and keeping moisture in the ground, it’s also a way to spread pathogens. You want to ensure plants have a few inches of clearance between them and the mulch. Keep your labels or make new ones

    Keep those plant tags.
    Credit: Amanda Blum

    In the melee of planting, it’s common to lose your plant tags. After all, a tomato is a tomato. However, you’ll be sad at the end of the season when one tomato does spectacularly and another doesn’t, and you don’t know what variety each was. Label your plants! 
    #stepbystep #guide #planting #your #summer
    A Step-by-Step Guide to Planting Your Summer Vegetables
    We may earn a commission from links on this page.Vegetable plants are expensive. To get the best yield out of them, you need to pay attention to more than just the soil, sun, and watering. You need to get your plants off on the right foot by planting them the right way. Here's what I mean. Choose the right plant at the nursery Credit: Amanda Blum When I first started gardening, I thought the best vegetable starts to buy were the ones that were the biggest, with flowers and fruit already on them. This would give the plant a head start, right? Sadly, no. Plants go through something called "transplant shock" when you move them. You’re disturbing the plant's roots, and moving it to a new environment. To survive, the plant needs to focus all its energy on the plant's roots, and if there's a lot of plant matter like leaves, flowers, and fruit to support, energy is wasted supporting them. Plants with established fruit, in particular, struggle during the transplant process. Choose plants that look healthy, with strong stems and leaves without damage, but that don't yet have flowers or fruit. Prepare your soil Credit: Amanda Blum There are legions of ways to handle your garden from season to season. Some people till the soil, while others employ a no-till method, and still others use something called "chop-and-drop." Regardless of the method, the soil you’re planting into has to be pliable enough that roots can flourish in them. For that reason, ensure that the soil is turned over and broken up—from a shovel to a shovel and a half’s depth. You can use a broadfork for this, if you don’t want to disturb the soil structure, but otherwise, just use a shovel. Breaking up the soil will help you see the texture—so you can add sand if the soil has too much clay in it, or compost if it isn't holding any moisture. You can use this time to add amendments such as vegetable fertilizer and lime. Fertilizer is obvious, but lime is used to turn your soil less acidic, which happens over time through watering and growing. Most vegetables don’t enjoy acidic environments. Turn the amendments into the soil. Choose an overcast dayYour plants will already be stressed by transplanting. Planting them into the blazing sun is even more stress. A stretch of overcast days is the perfect planting time. If that's not an option, plant at twilight to give your plants a night to adjust. Consider giving the plant some shade the next day to help it acclimate.Get the plant out of the pot without damaging the roots Credit: Amanda Blum By the time plants reach the nursery, they’re often root-bound in the plastic pots or six packs you buy them in. Roots are resilient, but you don’t want to disturb them more than necessary. The best way to break a plant free from a plastic pot is to use two fingers and squeeze the bottom of the pot. This should free the plant. Don’t turn the pot over or pound on it with your palm, and definitely don’t try to pull it loose by the plant’s stem.  on the left, the eggplant seedling just out of the pot, and on the right, after the roots have been broken up Credit: Amanda Blum Once the plant is out, you want to break up the roots by using your fingers like a comb on the bottom of the plant, so that roots are freed. That said, these plants do not enjoy their roots being disturbed: cucumbers, beans, pumpkins, luffa, beets, and most root vegetables. For these, I simply dig a hole, remove the plant from the plastic tray, carefully plop the plant in, and walk away.  Separate plants as necessary Credit: Amanda Blum Most pots have more than one seed in each cell. In some cases, like tomatoes, someone usually culls the seedlings so only one is left to flourish. However, in some cases, like herbs and lettuce, nurseries leave the seeds alone and let multiple seeds grow. In other cases like onions and carrots, the cells are purposely overseeded to be filled with lots of seedlings. Strawberries usually come in a pot of five to 10 starts.  If you take a cell of onion seedlings out, you can separate them by diving the block in half over and over again, until you have individual seedlings. Credit: Amanda Blum When there’s more than one seedling, you need to separate them. You shouldn’t try to plant them altogether. For lettuce or herbs, this is simple: Remove one cell, and with your fingers, gently pull the soil pod apart. Start by pulling the pod in half, and then keep dividing until all the seedlings are free. This works on larger plants like squash, and smaller plants like carrots where there can be 20 or more seedlings in a single cell. Once the individual seedlings are free, they can each be planted as if they’re a whole plant. This is how you get a whole row of carrots or onions. This is also a great way to save money, since you usually get far more than six lettuce heads from a six-pack of lettuce. Know the right depth Credit: Amanda Blum Plants need to go in the ground at the right depth, ensuring that the base of the plant is at soil level. In some cases, though, you canplant the stem deeper.Leeks and onions, for example, can be planted deeply. In particular, leeks can be planted as deeply as possible, with only an inch or two of seedling above the surface of the soil. This will help blanch the leek. Tomatoes, eggplant, and peppers can be planted deeply, as they’ll form roots along their entire stem. If your tomato is leggy, this is a spectacular way to fix the problem. When in doubt, follow the directions on the plant tag, or simply plant at a standard depth so the roots are covered, but the stem is exposed above the soil. Don’t mulch against your stemsWhile mulch is an important part of insulating your vegetable plants and keeping moisture in the ground, it’s also a way to spread pathogens. You want to ensure plants have a few inches of clearance between them and the mulch. Keep your labels or make new ones Keep those plant tags. Credit: Amanda Blum In the melee of planting, it’s common to lose your plant tags. After all, a tomato is a tomato. However, you’ll be sad at the end of the season when one tomato does spectacularly and another doesn’t, and you don’t know what variety each was. Label your plants!  #stepbystep #guide #planting #your #summer
    LIFEHACKER.COM
    A Step-by-Step Guide to Planting Your Summer Vegetables
    We may earn a commission from links on this page.Vegetable plants are expensive. To get the best yield out of them, you need to pay attention to more than just the soil, sun, and watering. You need to get your plants off on the right foot by planting them the right way. Here's what I mean. Choose the right plant at the nursery Credit: Amanda Blum When I first started gardening, I thought the best vegetable starts to buy were the ones that were the biggest, with flowers and fruit already on them. This would give the plant a head start, right? Sadly, no. Plants go through something called "transplant shock" when you move them. You’re disturbing the plant's roots, and moving it to a new environment. To survive, the plant needs to focus all its energy on the plant's roots, and if there's a lot of plant matter like leaves, flowers, and fruit to support, energy is wasted supporting them. Plants with established fruit, in particular, struggle during the transplant process. Choose plants that look healthy, with strong stems and leaves without damage, but that don't yet have flowers or fruit. Prepare your soil Credit: Amanda Blum There are legions of ways to handle your garden from season to season. Some people till the soil, while others employ a no-till method, and still others use something called "chop-and-drop." Regardless of the method, the soil you’re planting into has to be pliable enough that roots can flourish in them. For that reason, ensure that the soil is turned over and broken up—from a shovel to a shovel and a half’s depth. You can use a broadfork for this, if you don’t want to disturb the soil structure, but otherwise, just use a shovel. Breaking up the soil will help you see the texture—so you can add sand if the soil has too much clay in it, or compost if it isn't holding any moisture. You can use this time to add amendments such as vegetable fertilizer and lime. Fertilizer is obvious, but lime is used to turn your soil less acidic, which happens over time through watering and growing. Most vegetables don’t enjoy acidic environments. Turn the amendments into the soil. Choose an overcast dayYour plants will already be stressed by transplanting. Planting them into the blazing sun is even more stress. A stretch of overcast days is the perfect planting time. If that's not an option, plant at twilight to give your plants a night to adjust. Consider giving the plant some shade the next day to help it acclimate.Get the plant out of the pot without damaging the roots Credit: Amanda Blum By the time plants reach the nursery, they’re often root-bound in the plastic pots or six packs you buy them in. Roots are resilient, but you don’t want to disturb them more than necessary. The best way to break a plant free from a plastic pot is to use two fingers and squeeze the bottom of the pot. This should free the plant. Don’t turn the pot over or pound on it with your palm, and definitely don’t try to pull it loose by the plant’s stem.  on the left, the eggplant seedling just out of the pot, and on the right, after the roots have been broken up Credit: Amanda Blum Once the plant is out, you want to break up the roots by using your fingers like a comb on the bottom of the plant, so that roots are freed. That said, these plants do not enjoy their roots being disturbed: cucumbers, beans, pumpkins, luffa, beets, and most root vegetables. For these, I simply dig a hole, remove the plant from the plastic tray, carefully plop the plant in, and walk away.  Separate plants as necessary Credit: Amanda Blum Most pots have more than one seed in each cell. In some cases, like tomatoes, someone usually culls the seedlings so only one is left to flourish. However, in some cases, like herbs and lettuce, nurseries leave the seeds alone and let multiple seeds grow. In other cases like onions and carrots, the cells are purposely overseeded to be filled with lots of seedlings. Strawberries usually come in a pot of five to 10 starts.  If you take a cell of onion seedlings out, you can separate them by diving the block in half over and over again, until you have individual seedlings. Credit: Amanda Blum When there’s more than one seedling, you need to separate them. You shouldn’t try to plant them altogether. For lettuce or herbs, this is simple: Remove one cell, and with your fingers, gently pull the soil pod apart. Start by pulling the pod in half, and then keep dividing until all the seedlings are free. This works on larger plants like squash, and smaller plants like carrots where there can be 20 or more seedlings in a single cell. Once the individual seedlings are free, they can each be planted as if they’re a whole plant. This is how you get a whole row of carrots or onions. This is also a great way to save money, since you usually get far more than six lettuce heads from a six-pack of lettuce. Know the right depth Credit: Amanda Blum Plants need to go in the ground at the right depth, ensuring that the base of the plant is at soil level. In some cases, though, you can (and should) plant the stem deeper.Leeks and onions, for example, can be planted deeply. In particular, leeks can be planted as deeply as possible, with only an inch or two of seedling above the surface of the soil. This will help blanch the leek (keep it white). Tomatoes, eggplant, and peppers can be planted deeply, as they’ll form roots along their entire stem. If your tomato is leggy (tall with little horizontal branching), this is a spectacular way to fix the problem. When in doubt, follow the directions on the plant tag, or simply plant at a standard depth so the roots are covered, but the stem is exposed above the soil. Don’t mulch against your stemsWhile mulch is an important part of insulating your vegetable plants and keeping moisture in the ground, it’s also a way to spread pathogens. You want to ensure plants have a few inches of clearance between them and the mulch. Keep your labels or make new ones Keep those plant tags. Credit: Amanda Blum In the melee of planting, it’s common to lose your plant tags. After all, a tomato is a tomato. However, you’ll be sad at the end of the season when one tomato does spectacularly and another doesn’t, and you don’t know what variety each was. Label your plants! 
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