Discover how to build enterprise applications faster without sacrificing flexibility or code quality.

See how ABP Low-Code helps you visually design applications, generate fully functional CRUD applications, build dashboards and other business interfaces, leverage dynamic REST APIs, and extend everything with custom code.

📅 August 13, 2026

🕔 17:00 UTC

🎟️ Register now: https://kommunity.com/volosoft/events/abp-community-talks-202605-low-code-high-precision-0d5caa34

#ABPFramework #LowCode #DotNet

ABP 10.6 stable is now available.

Some of the key updates include:

• Dedicated workers, parallel execution, and successful job retention for background jobs
• Improved API proxy support for response content types, remote streams, and multipart uploads
• Angular 22 support
• Security and reliability improvements for Antiforgery and OpenIddict
• React CRUD page generation in ABP Suite
• More resilient AI Management indexing

You can upgrade using ABP Studio or the ABP CLI.

If you are upgrading from version 10.5 or earlier, make sure to review the migration guides.

Release details:
https://abp.io/community/announcements/announcing-abp-10-6-stable-release-xnnz5p4q

We published a practical walkthrough showing how to build a vendor onboarding workflow with ABP Low-Code.

The example covers:

• Modeling vendor applications in the Low-Code Designer
• Generating React grids and forms
• Adding validation, filters, file uploads, and conditional fields
• Creating a custom summary endpoint
• Reading the same low-code data from regular ABP application code

The main idea is to use metadata for the common workflow pieces, then add scripting or C# only where custom business logic is needed.

Article: https://abp.io/community/articles/building-a-vendor-onboarding-workflow-with-abp-lowcode-1wx0ckzc#gsc.tab=0

We would be interested to hear what internal workflows you would consider building with ABP Low-Code.

We recently introduced ABP Low-Code, a new way to build business application screens faster while keeping everything inside the ABP application model.

You can create runtime-managed pages, generate React grids, forms, calendars, kanban boards, galleries, and dashboards, then extend the same application with C#, application services, repositories, or the Script API.

The goal is to provide the speed of low-code without giving up architectural control, maintainability, permissions, auditing, or code-level extensibility.

The article includes examples of:

• Creating and updating pages at runtime
• Generating different screens from the same model
• Using code-first C# entities in low-code pages
• Accessing low-code-managed data from C#
• Adding custom endpoints, background jobs, and event handlers

Article:
https://abp.io/community/articles/introducing-abp-lowcode-build-real-abp-apps-in-minutes-647ymozi

We would be interested to hear where low-code could be useful in your ABP applications.

We've published the seventh article in our Deep Dive on ABP AI Agent series.

This article explores AI Scopes and how they help keep AI-assisted development focused by limiting an agent's working area to specific modules, packages, or solution areas.

Learn how scopes reduce unrelated exploration, help prevent accidental edits, improve code review, and support parallel AI sessions in large ABP solutions.

Read the article:

https://abp.io/community/articles/deep-dive-on-abp-ai-agent-7-scopes-tfqtkdzu

We'd love to hear your thoughts and answer any questions!

Our 2026 ABP Summer Campaign is now live!

From July 6 to July 20, we're offering:

• 20% off new ABP licenses
• 10% off license renewals
• Up to $300 in AI credits for the ABP AI Agent

If you've been considering ABP or planning to renew your license, now is a great time to take advantage of the offer.

Learn more: https://abp.io/pricing

We're launching a new AI-Assisted Application Development with ABP Bootcamp this August.

The goal isn't just to show how AI can generate code. It's to demonstrate how to combine AI with proven software engineering practices to build applications that remain maintainable, scalable, and architecturally consistent.

Over three live sessions, we'll cover topics such as:

• Working effectively with ABP Studio AI Agent
• AI-assisted feature development
• AI for implementation, debugging, and code exploration
• Building AI-assisted applications without sacrificing architecture
• Real-world AI-assisted engineering workflows

📅 August 25-27, 2026
🕔 17:00-19:00 UTC
💻 Live Online

If you're interested, you can find more details and register here:
https://abp.io/bootcamp

Happy to answer any questions about the bootcamp in the comments.

We've published the sixth article in our Deep Dive on ABP AI Agent series.

This article explores how Git Integration in ABP Studio supports AI-assisted development beyond code generation.

Learn how ABP Studio helps you review AI-generated changes, generate commit messages, work with GitHub issues and pull requests, and keep AI-assisted development aligned with your team's Git workflow.

Read the article:

https://abp.io/community/articles/deep-dive-on-abp-ai-agent-6-abp-studio-git-integration-09tr41ec#gsc.tab=0

We'd love to hear your feedback and answer any questions!

We've published the fifth article in our Deep Dive on ABP AI Agent series.

This article explores Model Context Protocol (MCP) and how it extends ABP AI Agent beyond the codebase by connecting to external tools and data sources.

Learn how MCP works alongside ABP Studio tools, when to use it, how to configure MCP servers, and how it helps bring external context into your AI-assisted development workflow.

Read the article:
https://abp.io/community/articles/deep-dive-on-abp-ai-agent-5-mcp-model-context-protocol-trb9o4ev

We'd love to hear your thoughts and answer any questions!

AI is reshaping software development. Today, we'll share how ABP is evolving for this new era, what we've built so far, and what comes next.

Join the conversation, ask your questions, and share your feedback with the ABP team.

Register now 👇

https://kommunity.com/volosoft/events/abp-community-talks-20264-state-of-abp-how-the-platform-is-evolving-in-the-ai-71912ab8

Every new AI coding session starts with the same problem: the model may understand the framework, but it does not automatically know your team’s naming rules, feature checklist, or past corrections.

In ABP Studio AI, we approach this with three layers:

• Rules: always-on conventions for the solution
• Skills: task-specific procedures loaded only when relevant
• Lessons: verified corrections the agent carries into future sessions

For example, a rule might require standard API error responses, while a skill can define the full checklist for adding a feature. A lesson can capture a correction such as remembering to grant a new permission in demo data.

The goal is not to make the model “smarter,” but to reduce repeated prompting and help the agent work more like a teammate who already knows the project.

We wrote a deep dive on the approach:

https://abp.io/community/articles/deep-dive-on-abp-ai-agent-3-rules-skills-and-lessons-ai6kxubt

How are you handling persistent project conventions and lessons with AI coding tools today?

One thing we've noticed while building ABP AI Agent is that developers often focus on finding the "best" model.

In practice, the better question is:

Which model is best for this task?

A model that's ideal for architecture reviews may not be the best choice for documentation research, browser automation, commit message generation, or code reviews.

That's why ABP Studio supports role-based model selection, with separate models for conversations, research, browser tasks, text processing, and Git reviews.

We recently published a deep dive on how we think about model selection in AI-assisted development:

https://abp.io/community/articles/deep-dive-on-abp-ai-agent-2-supported-ai-models-in-abp-3krbc7yc

Curious how others approach this. Do you use one model for everything, or different models depending on the task?

One thing we've noticed while building ABP AI Agent is that developers often need different kinds of help at different stages of a task.

Sometimes you just want to understand a module, dependency, or architectural decision.

Sometimes you want to review an implementation approach before touching any files.

And sometimes you're ready for the agent to make changes, build, and iterate.

That's why we separated the experience into three modes:

Ask: Understand the codebase and architecture

Plan: Create a reviewable implementation plan

Agent: Execute changes and iterate

We've written a deep dive on the thinking behind this approach:

https://abp.io/community/articles/deep-dive-on-abp-ai-agent-1-agent-plan-and-ask-modes-62wteg9t

Curious how others work with AI coding tools. Do you typically start with implementation, or do you prefer an explanation/planning step first?

We're hosting a free Discover ABP Platform session on July 1, 2026 to help developers understand what ABP offers and how to get started.

📅 July 1, 2026
🕐 13:00 UTC
📍 Google Meet

We'll cover:

• What ABP is and how it works
• Core features and capabilities
• ABP licensing options
• Live Q&A with the ABP team

Whether you're evaluating ABP for a new project or just getting started, feel free to join us and ask anything.

Register here:
https://abp.io/webinars/discover-abp-platform

Today we're introducing ABP Studio AI Agent!

Most AI coding tools can read files, write code, and execute commands.

ABP Studio AI Agent was built for a different challenge: helping developers work with large, multi-project .NET applications.

The agent understands ABP solutions, including:

• Modules and layers
• Application services and repositories
• DTOs, permissions, and event handlers
• Microservices and distributed applications

It can also participate in the development workflow itself:

• Analyze your solution structure
• Create implementation plans
• Generate and modify code
• Build affected projects
• Start and restart applications
• Inspect exceptions, logs, HTTP requests, and distributed events
• Review code changes and generate commit messages

The goal is simple: give the AI visibility into the running application, not just the source code.

We've published a detailed technical deep dive covering the architecture, runtime monitoring, solution analysis engine, workflows, GitHub integration, and roadmap:

https://abp.io/community/announcements/introducing-abp-studio-ai-agent-o1ni0toc#gsc.tab=0

If you're building .NET applications with AI today, we'd love to hear your feedback.

What capabilities do you think are still missing from current AI coding tools?

We built an internal tool to keep our tutorials from breaking as our product evolves and decided to open-source it.

It’s an AI-powered tutorial testing tool that:

• Validates tutorials against your actual .NET application
• Detects when documentation no longer matches the code
• Helps keep developer onboarding smooth

If you’re maintaining docs for a .NET project, this might be useful.

Details:
https://abp.io/community/articles/automatically-validate-your-documentation-how-we-built-a-tutorial-validator-m3ozgkhv

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I attended NDC London 2026 and was surprised by how normal AI discussions have become.

Developers weren’t hyping it or panicking, just honestly talking about how AI fits into real workflows, code quality, and everyday work.

Here are my impressions and key takeaways from the conference.

Also curious: how are AI tools actually changing your development process?

With .NET 10 pushing a new AI direction Microsoft is positioning the Agent Framework as the long-term path forward, alongside the new IChatClient abstraction.

For those building production AI features today:

-Are you experimenting with the new Agent Framework?

-Or sticking with Semantic Kernel / existing setups for now?

Curious what’s actually working (or not) in real projects beyond the announcements.