모든 저장소에서 Semantic 코드 검색. 프로젝트를 한 번 색인하면 MCP를 통해 쿼리하십시오. AI 코딩 조수는 모든 패턴, 구현 및 컨벤션에 즉시 액세스 할 수 있습니다.

왜?

AI 코딩 조수는 쓰기 부호에 중대합니다, 그러나 그들은 your 부호를 모른다. 그들은 이미 하나가 있을 때 새로운 auth 흐름을 제안할 것입니다. 여러분의 위치가 맞을 때 로깅 패턴을 발명합니다. 그들은 당신이 이미 다른 프로젝트에 응답 한 질문을합니다.

srag 이 수정. 당신의 재조합을 색인하고, 당신의 조수는 당신이 아무것도 설명하지 않고 관련 구현, 패턴, 컨벤션을 찾을 수 있습니다.

사용 케이스

** 자신의 구현을 재사용 **

“OAuth 토큰 새로 고침 추가” → 에이전트는 프로젝트 전반에 걸쳐 기존 구현을 찾아 이미 처리 한 가장자리 케이스를 포함한 패턴을 복사

** 일관된 코드 스타일 **

Agent queries get_project_patterns 를 작성하기 전에 코드를 작성하기 전에 snake_case 를 사용하여 함수와 PascalCase 를 사용하여, 자동적으로 맞는

**당신이 썼는 것을 찾기 **

srag

Semantic code search across all your repositories. Index your projects once, then query them via MCP - your AI coding assistant gains instant access to every pattern, implementation, and convention you’ve ever written.

Why?

AI coding assistants are great at writing code, but they don’t know your code. They’ll suggest a new auth flow when you already have one. They’ll invent a logging pattern when yours is right there. They’ll ask questions you’ve already answered in another project.

srag fixes this. Index your repositories, and your assistant can search across all of them semantically - finding relevant implementations, patterns, and conventions without you having to explain anything.

Use Cases

Reuse your own implementations

“Add OAuth token refresh” → Agent finds your existing implementation across projects, copies the pattern including the edge cases you already handled

Consistent code style

Agent queries get_project_patterns before writing code, sees you use snake_case for functions and PascalCase for types, follows suit automatically

Find that thing you wrote

“How did I handle rate limiting?” → Semantic search finds the relevant code even if you don’t remember which project or what you called it

Cross-project knowledge

Working on project A, need to integrate with an API you’ve used before in project B → Agent finds your previous integration, reuses the error handling and retry logic

Debug with context

“Why is this failing?” → Agent searches for similar error handling patterns in your codebase, finds how you solved the same class of problem before

Onboard yourself

Returning to an old project? Query the index to understand the architecture, find where things are defined, see the patterns that were used

Code review context

“Is this how we usually do it?” → Search for similar implementations to check consistency with established patterns

Find duplication

Pass a code snippet to find_similar_code → Discover near-duplicates across your codebase that could be refactored

The real value compounds over time. The more projects you index, the more patterns your assistant can draw from. The quality scales with the model - better models make better use of the retrieved context.

Install

You’ll need Rust and Python 3.10+ installed first.