24 Best AI Tools for Developers in 2025 - Moz
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Introduction
Debugging endless code, racing against deadlines, and juggling clunky tools—sounds like just another day in the life of a developer. And then there’s your boss, who expects you to stay on top of new AI trends because the shinier, the better, right? There had to be a better way to hit those impossible deadlines and still deliver great work. So, I got to work testing AI tools that automate the mundane, speed up workflows, and make life a whole lot easier.
In this article, I’ll share 23 of the best AI tools for developers in 2025. From setting up large language models locally to exploring no-code platforms and AI coding assistants, these tools will save you time, simplify your workflows, and bring your ideas to life.
Local AI Tools
Relying on cloud services for AI tasks can be frustrating due to usage costs, internet dependency, and privacy concerns. I needed a solution that offered robust AI capabilities without these limitations.
With Ollama, I run large language models like Llama 3.2 on my machine to access advanced AI features without relying on cloud services or incurring ongoing costs.
Here's how I use Llama 3.2:
- Easy access to advanced AI features
- No reliance on cloud services
- No ongoing costs
It’s challenging to run AI models locally if you’re uncomfortable with the command line or dealing with code. I wanted a solution that made this process more accessible for those who prefer graphical interfaces over text-based commands.
Open WebUI is an open-source interface that builds upon Ollama. I use it to run open-source AI models like Llama 3 on my machine. Additionally, I can perform tasks I would typically do in ChatGPT, but locally without complex setups.
Here's how I use Open WebUI:
- User-friendly graphical interface
- No need for complex setups
- Run AI models locally
Hybrid AI Workflows
While local AI models offer control and privacy, sometimes I need the advanced capabilities of cloud services like ChatGPT. Msty combines open-source models running on my machine with cloud-based models for more flexibility and performance.
Here's how I use Msty:
- Run hybrid AI workflows
- Optimize workflow efficiency
- Blend cloud services with local models
AI Development Tools
When I need to run AI models or write code without setting up complex environments, I turn to Colab. I use this Google tool to run Python code in the cloud, making it perfect for tasks like Natural Language Processing (NLP) or data analysis.
One of the most efficient ways I’ve improved my workflows is by combining Gemini with Colab. I can tap into Gemini’s language model directly within the Colab environment, making coding and AI tasks faster and more intuitive.
Here's how I use Gemini with Colab:
- Run Python code with advanced AI assistance
- Keyword clustering for organizing SEO keywords
Programming Helper offers AI support for writing and debugging code, making it essential for website optimization, integrating APIs, or creating automation. It covers multiple programming languages and provides solutions where ChatGPT may not suffice.
Here's how I use Programming Helper:
- Code generation for various programming languages
- Learning support for unfamiliar programming challenges
Content Retrieval Tools
Finding the most relevant information quickly becomes a challenge when handling vast amounts of content. Llama Index creates RAG systems by indexing large document collections, allowing AI to find the most relevant text based on a query.
Here's how I use Llama Index:
- Generate accurate content retrieval
- Create RAG systems for automated tasks
LangChain is my go-to framework for building AI agents that can perform tasks based on real-time data. It enables me to integrate language models into workflows and automate processes beyond basic tasks.
Here's how I use LangChain:
- Connect to real-time data sources
- Automate technical SEO tasks based on live data
AI Workflow Optimization
When working with multiple AI models and complex workflows, it’s crucial to track how prompts perform and understand the inner workings of each interaction. LangFuse provides a comprehensive view of AI prompts, showing how they perform, when they’re used, and where adjustments are needed.
Here's how I use LangFuse:
- Track prompt performance across workflows
- Optimize prompt management for efficiency
- Manage complex workflows involving multiple AI models
Writing regular expressions (regex) can be challenging, especially if you’re not familiar with syntax—or, like me, you’d rather not write regex at all. Regexer solves this by generating regex patterns from natural language descriptions.
Here's how I use Regexer:
- Generate regex patterns from natural language descriptions
- Filter data effectively using regex patterns
- Create custom redirects for websites
