The first AI software engineer with a 100-million token context window for massive codebase reasoning.
Magic is an frontier AI research company developing an 'AI Software Engineer' capable of operating across entire codebases. Unlike standard RAG-based assistants, Magic utilizes a proprietary architecture known as LTM (Long-Term Memory), featuring a 100-million token context window. This technical foundation allows the model to process thousands of files, extensive documentation, and historical commit data simultaneously without losing state or context. By 2026, Magic has positioned itself as the enterprise-grade alternative to GitHub Copilot and Cursor, focusing on high-reasoning tasks such as architectural refactors and complex bug resolution across distributed systems. Its flagship model, Magic-G1, is optimized for 'hash-hop' tasks, ensuring precise retrieval from the furthest reaches of its massive memory. The architecture is designed to minimize the need for manual chunking or vector database management, providing a unified workspace where the AI acts more like a remote teammate than a simple autocomplete tool. Market positioning focuses on reducing 'technical debt' by automating the most tedious parts of software maintenance and migration.
A proprietary model architecture that supports a 100-million token context window without the quadratic cost of standard transformers.
Accelerate development with AI-powered code suggestions and integrated security scanning across the SDLC.
The leading terminal-based AI pair programmer for high-velocity software engineering.
Accelerate development cycles with context-aware AI code generation and deep refactoring logic.
State-of-the-Art Mixture-of-Experts Coding Intelligence at 1/10th the Cost of GPT-4.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Advanced evaluation metrics ensuring 99.9% accuracy in retrieving specific data points from the middle of a massive context window.
Loop-based agents that can execute code, read error logs, and iterate on fixes autonomously.
Ability to perform breaking changes across hundreds of files while maintaining type safety and consistency.
Real-time synchronization between the IDE state and the LTM model parameters.
Dynamically switches between Magic-G1 for reasoning and smaller models for low-latency autocompletion.
Natural language querying of codebases that understands intent rather than just keyword matching.
Manually migrating a large React codebase from Class Components to Functional Components with Hooks.
Registry Updated:2/7/2026
Review and accept changes through the IDE.
New hires spending weeks understanding a 2M line codebase.
Intermittent race conditions that are hard to replicate manually.