ModelsAgree

Head-to-head

Augment Code vs Sourcegraph

Sourcegraph leads: the AI models rank it above its rival on 1 of the 1 leaderboard they share. Based on how ChatGPT, Claude, Gemini & Grok rank both across the leaderboard they share — re-polled on demand, reasoning shown verbatim.

Augment Code0 wins
Sourcegraph1 win
LeaderboardAugment CodeSourcegraph
Best codebase chat tools for large monorepos#2 / 7#1 / 7

Why the models rank Augment Code — on best codebase chat tools for large monorepos

Best-in-class semantic codebase indexing and retrieval for very large repositories; its Context Engine maps relationships across hundreds of thousands of files, supports multi-repo/org-wide context, continuously syncs indexes, and can now supply that context to other MCP-compatible coding agents. For a genuinely large monorepo where retrieval quality is the bottleneck, it has the strongest overall fit.

Why the models rank Sourcegraph — on best codebase chat tools for large monorepos

Its entire foundation is indexing and searching giant, multi-repo codebases — precise cross-repo symbol/reference retrieval scales to millions of files where embedding-only tools degrade, and the same code-graph feeds its chat/agent context. Best-in-class for the "find where and why across the whole monorepo" question.

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Ranks from the merged 4-model leaderboards · re-polled on demand · methodology