Overview
GREB by Cheetah AI is an intelligent, MCP-native code search engine designed for modern AI coding assistants. Instead of relying on brittle regex or slow full-text indexing, GREB exposes your repositories through the Model Context Protocol (MCP), giving AI agents a structured, semantic way to discover, navigate, and understand your codebase. Developers can ask natural-language questions like “Where is the payment retry logic implemented?” or “Show me how we validate JWTs,” and GREB will precisely locate the most relevant files, symbols, and usage patterns.
By integrating directly into AI copilots and command-line workflows, GREB eliminates context-window limitations and manual file hunting. It lets AI assistants pull only what they need, when they need it, while preserving your existing tools, IDEs, and Git workflows. GREB is language-agnostic and works across monoliths, microservices, and polyglot repositories, making it ideal for enterprise-scale codebases.
With GREB, teams ship faster by turning AI coding assistants into truly context-aware collaborators. Onboarding becomes smoother, refactors become safer, and incident response becomes more efficient because your AI tools finally understand how your code is actually organized and used.
Pricing
Detailed plans have not been confirmed in our catalog. Check the official website for current limits and billing terms.
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Use Cases
- AI-assisted onboarding for new developers by letting copilots instantly surface relevant modules, patterns, and ownership in large legacy repos.
- Faster refactoring and architecture changes by allowing AI agents to locate all usages, dependencies, and edge cases across services.
- Production incident triage where AI assistants can quickly find the related code paths, config, and safeguards behind failing endpoints.
- Test generation and coverage analysis by enabling AI tools to discover critical flows, boundary cases, and untested components.
- Developer productivity automation, such as bots that file targeted PRs after searching for deprecated APIs or insecure patterns.
Features
MCP-native code search engine
Semantic, context-aware retrieval
Natural language code queries
Cross-repo and monorepo support
Language-agnostic code indexing
Designed for AI coding assistants
Fast, precise symbol navigation
Works with existing dev workflows
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FAQ
What is GREB by Cheetah AI and how is it different from regular code search?
GREB is an MCP-native intelligent code search engine built for AI coding assistants. Unlike basic text or regex search, it exposes your codebase through the Model Context Protocol so AI tools can perform semantic, context-aware queries and retrieve only the most relevant files, symbols, and relationships.
How does GREB integrate with my existing AI coding assistants or agents?
GREB connects via the Model Context Protocol (MCP). Any MCP-compatible AI coding assistant or autonomous agent can call GREB as a tool to search, navigate, and retrieve code snippets on demand, without requiring changes to your IDE, Git hosting, or CI/CD setup.
Does GREB support multiple programming languages and large monorepos?
Yes. GREB is designed to be language-agnostic and to handle complex, large-scale repositories, including monorepos and distributed microservice architectures. It indexes code in a way that allows cross-repo and cross-service navigation for AI tools.
Is GREB suitable for enterprise environments and sensitive codebases?
GREB is built with enterprise-scale scenarios in mind and is intended to run in environments where you control access to your repositories. You can keep your code within your own infrastructure while still enabling powerful AI-driven search via MCP.
How is GREB priced and where can I learn more?
Pricing information has not been publicly disclosed yet. To get the latest details on plans, deployment options, and roadmap, visit https://grebmcp.com/ and contact the team directly.