Grit
Automate technical debt management and massive code migrations with AI-driven refactoring.

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.
Magic is an frontier AI research company developing an 'AI Software Engineer' capable of operating across entire codebases.
Explore all tools that specialize in automated security patching. This domain focus ensures Magic delivers optimized results for this specific requirement.
A proprietary model architecture that supports a 100-million token context window without the quadratic cost of standard transformers.
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.
Request access via the Magic.dev official waitlist for LTM-2-100M models.
Install the Magic VS Code extension or the JetBrains plugin from the respective marketplace.
Authenticate using OAuth2 through GitHub or Enterprise SSO.
Select the root directory of your primary codebase for initial indexing.
Allow Magic to build a local context map and synchronize with the LTM cloud backend.
Configure .magicignore files to exclude sensitive data or non-relevant build artifacts.
Initiate a 'Codebase Audit' command to verify the AI's understanding of the architecture.
Connect your CI/CD pipeline via Magic's API for autonomous PR reviews.
Set resource limits and token spend thresholds in the developer console.
Run your first task by prompting for a cross-file feature implementation.
All Set
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Verified feedback from other users.
"Highly praised for its massive context window; developers note it is significantly more 'aware' of project-wide patterns than competitors."
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