Multi-repository AI code search tool

How I'd rank the main options

The important distinction: Cursor's indexing is excellent, but its traditional model is centered around the codebases/workspaces you're working with. Cursor now supports multi-root workspaces, where multiple codebases can be indexed together. www.cursor.com Sourcegraph is architecturally much more natural when the question is “search our entire organization.”

Greptile is particularly interesting if your objective is AI understanding of related repositories. Its 2026 multi-repo context feature explicitly allows related repositories such as shared libraries and SDKs to be supplied during analysis.www.greptile.com

My recommendation by use case

“Find every implementation of X across 500 repos.”
→ Sourcegraph

“Explain how this feature works across our services and libraries.”
→ Sourcegraph Deep Search / Cody

“Have an AI agent understand our repo and make changes.”
→ Cursor

“Have AI review PRs while understanding dependent/shared repos.”
→ Greptile

“Everything is already on GitHub and we want minimal infrastructure.”
→ GitHub Code Search + Copilot

One caveat: there isn't a trustworthy public benchmark that establishes one of these as objectively the most accurate across arbitrary private codebases. The strongest evidence points to Sourcegraph for retrieval breadth + precision, while Cursor/Greptile can be excellent at the subsequent LLM reasoning over retrieved context.

If you tell me roughly how many repos / languages / whether they're all on GitHub / self-hosted requirements, I can give you a much more specific recommendation—including Sourcegraph vs Greptile vs Cursor vs GitHub, with pricing and architecture.

Best for: Engineering teams that need precise search and code understanding across many repositories.

• Searches across GitHub, GitLab, Bitbucket, and Perforce.
• Supports exact text, regex, symbol search, and repository-scoped queries.
• Offers cross-repository code navigation and AI-powered natural-language investigation.
• Supports web and IDE workflows.sourcegraph.com+3

Trade-off: Advanced organizational features are plan-dependent, and precise code navigation requires appropriate code indexing.

Official website

2. GitHub Copilot

Best for: Teams whose code is already hosted primarily on GitHub and who want search alongside their everyday coding workflow.

• Convenient integration with GitHub and supported development environments.
• Useful for explaining code, finding implementations, and answering programming questions.
• A simpler starting point if you don't need a separate, cross-host search platform.

Trade-off: GitHub-native search does not unify repositories hosted on other code platforms.6.3.sourcegraph.com+1

Official website

3. Greptile

Best for: Teams that want AI to understand relationships across repositories, particularly during code review and debugging.

• Builds a graph of code elements and their relationships.
• Provides AI-powered code review with broader codebase context.
• Supports several major code hosting platforms.www.greptile.com+1

Trade-off: Its core strength is AI code review and validation, rather than being a dedicated, general-purpose code search engine.

Official website

What I would choose for high accuracy

For a serious multi-repository engineering environment, I'd start by evaluating Sourcegraph's Deep Search + Code Search combination.

• For finding every occurrence of a function, API, or deprecated pattern: use exact, regex, and symbol-based search.
• For understanding how a feature works across services: use Deep Search to investigate the code and synthesize an explanation.
• For tracing callers and implementations across repositories: enable precise code navigation where supported. Its SCIP-based indexing can provide compiler-accurate navigation rather than relying solely on name matching.sourcegraph.com+2

One caveat: no AI search engine is automatically accurate for every natural-language question. I'd test each candidate against known answers in your own codebase, including cross-repository dependencies, stale code, and similarly named symbols.

One question to narrow this down: Are your repositories primarily on GitHub, spread across GitHub and GitLab/Bitbucket, or self-hosted? That will help determine whether Sourcegraph is the best fit or whether a lighter-weight alternative makes more sense.