CodeGPT has quickly become one of the most popular coding assistants, integrating seamlessly with Visual Studio Code and JetBrains IDEs. Its use of large language models (LLMs), like Llama, has transformed the way developers work, not only by generating code but also by helping them debug, navigate codebases, and onboard new developers to projects efficiently.
Since its launch in March 2023, CodeGPT has gained significant traction, with over 1.4 million downloads across 180 countries, and the user base continues to grow at a rapid pace. A major turning point for the platform came shortly after its release when Meta introduced Code Llama, an LLM based on Llama 2, designed specifically for code generation. This immediately caught the attention of CodeGPT’s development team.
“We were impressed by Llama’s performance and flexibility,” says Daniel Avila, CTO & Co-Founder of CodeGPT. The team quickly began experimenting with chat-based and fill-in-the-middle models to improve how developers interact with their code repositories. The experiments were so successful that they integrated Llama into the platform, creating a new level of AI-powered assistance. The company has since upgraded to Llama 3.2 (90B) and introduced specialized AI agents that focus on APIs and frameworks.
Impact on Developer Productivity
The integration of Llama has had a significant impact on the productivity of developers using CodeGPT. On average, users report a 30% boost in productivity, as the platform reduces the time spent on debugging, searching for solutions, and generating code. Additionally, companies using CodeGPT can onboard new developers much faster, reducing the process from months to just days.
Initially focused on code suggestions and autocompletion, CodeGPT has since expanded its use of Llama to offer more advanced features. The platform now autonomously generates entire project folders and files, and uses a codebase graph mechanism that allows Llama to understand the structure of the entire repository. This enables developers to “talk” to their codebase, making it easier for new team members to understand existing projects, and simplifying the debugging process.
Overcoming Challenges
Despite the success, integrating Llama into CodeGPT wasn’t without challenges. The biggest hurdle was getting the LLM to understand and navigate large, complex codebases. CodeGPT overcame this by developing the graph-based mechanism, which allows Llama to understand the codebase more holistically. The team also optimized Llama to handle multi-step tasks, such as generating code and calling external tools through API calls, while fine-tuning the LLM for specific use cases like code autocompletion, bug detection, and repository exploration.
Fine-tuning required extensive training on diverse codebases, programming languages, and real-world debugging scenarios. The team also integrated external knowledge sources, such as technical documentation and discussions from popular coding forums, to improve the models’ performance.
“Llama has transformed the way developers interact with their codebases, making coding more intuitive and efficient,” says Avila. “These models have enormous potential, not just in accelerating coding tasks, but in fundamentally reshaping software development workflows.”
Open Source and the Future
Open source has played a critical role in CodeGPT’s development, allowing the team to tap into the global developer community for feedback and problem-solving. This collaboration has sped up the iteration process and enabled faster development of new features. As Avila notes, many customers also prefer using open source LLMs, particularly for reasons related to data privacy.
For smaller companies like CodeGPT, open source models like Llama provide access to advanced AI technologies, allowing them to innovate quickly without the need for large-scale R&D budgets. Avila emphasizes, “We have seen a huge demand for open source models from our users.”
As CodeGPT continues to grow, the team has ambitious plans for the future. They intend to integrate real-time code collaboration and AI-powered refactoring tools powered by the latest Llama models. They are also exploring ways to scale Llama across larger projects, enhancing its repository comprehension and debugging capabilities.
According to Avila, “CodeGPT is one of the top players in the AI for developers space, and Llama models played a big role in that.”