CodeGPT is on a mission to give developers AI tools that understand their entire codebases, not just individual files. With its Deep Graph MCP technology, repositories are mapped into interconnected knowledge graphs, enabling AI assistants to deliver architecturally aware insights and recommendations that make programming faster, smarter, and more intuitive.
THEIR GOAL
Keep codebases private with local deployment
Context switching is a major pain point in software development. Developers may spend more than half of their time digging through existing code and struggling to understand dependencies across large codebases. This not only leads to painful onboarding for new team members and fragmented knowledge across teams, but also increases the risk of hidden dependency gaps — issues that often surface as costly defects once code reaches production.CodeGPT’s intelligent code graphs provide AI agents with a comprehensive understanding of the entire codebase, saving developers an estimated 30% or more of their time. However, enterprise customers need an option to deploy these AI agents in a self-hosted environment, ensuring sensitive codebases remain within their infrastructure. This approach allows organizations to maintain complete control over the security perimeter — an essential priority for CISOs and IT leaders who must safeguard intellectual property, enforce compliance and minimize external risks.
THEIR SOLUTION
Self-hosted solutions based on open-source Llama
The CodeGPT team selected Llama 4 Maverick for its self-hosted deployment option — the only model capable of meeting their stringent performance requirements in a local environment. Running on local hardware gives customers full control over their infrastructure and data, a cornerstone of CodeGPT’s privacy-first approach.Llama 4 Maverick delivered performance on par with GPT-4o-mini in code graph generation tasks while efficiently handling large-scale codebases. Its extended context window supports up to one million tokens. When deployed on Groq, Llama achieved sub-second inference speeds for background processing, further enhancing developer experience and responsiveness.
THEIR APPROACH
Protecting data with privacy and control
Enterprise codebases contain highly sensitive intellectual property. To protect that data, CodeGPT developed a self-hosted solution that can be deployed on any server infrastructure — including AWS, Google Cloud or Azure — to ensure complete privacy and control.
Deployment in 1–2 weeks:
Introductory sessions with the team
Testing with the company’s large repositories
THEIR SUCCESS
Seamless, private coding at scale
With CodeGPT’s intelligent tools for streamlining large software projects, developers can now spend more of their time creating new features. With the Llama integration, developers can access CodeGPT locally for privacy or via API key. Because there are no interaction limits with CodeGPT agents, developers can ask as many questions as they need and work uninterrupted.As Llama models improve, CodeGPT plans to adopt them for chat, tool calling agents and code completion. CodeGPT believes it’s important to have access to a variety of open-source models — both to support a broader range of features and to promote greater transparency across the software industry.
“Our enterprise customers require self-hosted solutions for sensitive codebases. Llama is the only high-performance model that enables complete local deployment without compromising quality.”
Alvaro Chavez, CEO & Co-Founder, CodeGPT
“The Llama and Groq combination delivers sub-second responses for background processes, giving developers a seamless user experience.”
Daniel Avila, CTO & Co-Founder, CodeGPT
“While we maintain flexibility with multiple models for different use cases, Llama became our backbone for the core Deep Graph technology because it uniquely balanced performance, privacy and operational efficiency in ways that proprietary alternatives couldn’t match.”
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As an open-source model, Llama combines high performance, transparency, and local deployability — perfectly aligned with CodeGPT’s mission to build privacy-centric development tools. Open source is essential to this vision: it enables rigorous security audits, satisfies compliance requirements, and eliminates reliance on proprietary vendor roadmaps. It also empowers CodeGPT to fully customize and optimize its systems without licensing restrictions or API limitations.
Optimizing AI agent prompts for querying knowledge graphs across different programming languages
Once this initial setup is complete, the company can distribute agents across their development teams. The only ongoing consideration is scaling the self-hosted solution as adoption grows throughout the organization.
Multi-agent workflow for generating knowledge graphs
Llama 4 analyzes codebase repositories to map function calls, data flows, component relationships and architectural patterns. It then creates deterministic knowledge graphs accessible via model context protocol (MCP) so that any AI model can understand the complete project context.
Rather than fine-tuning, the CodeGPT team developed sophisticated prompts to enable Llama to reason about code like an experienced software engineer. As part of a multi-agent workflow, different Llama instances handle specific tasks, such as analyzing code, validating changes and fixing issues.
CodeGPT worked with Groq to optimize its self-hosted solution, resulting in high inference performance and cost-efficient infrastructure. They also offer several other deployment options for Deep Graph MCP in the cloud or on-premises, including on AWS, Microsoft Azure, Google Cloud Platform, Groq or Cerebras infrastructure.
Multiple Llama instances handle specific tasks in this multi-agent approach to generating knowledge graphs.
80% lower cost with Llama vs. select proprietary models
8x faster inference with Llama on Groq Cloud compared to select proprietary models on cloud alternatives
30%+ time savings for developers
*All results are self-reported and not identifiably repeatable. Generally expected individual results will differ.
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