GitMir vs Sourcegraph
Sourcegraph answers “where is this used” across very large codebases with authority. GitMir answers “which business rule and customer path does this change reach”.
The same repository, questions
and AI model for both.
Fifty questions in the words work is assigned in, the same model and settings for every system, answers graded blind.
Sourcegraph works with the code
and its sources. GitMir works
with the business logic.
Sourcegraph
Code intelligence at scale: search, precise references and history across monorepos, with agentic tooling on top.
The reference implementation of code intelligence at scale: search, precise references and history across very large monorepos, now with agentic tooling on top. Its graph is symbols and their relationships, so it answers “where is this used” with authority — and leaves “which customer path breaks if this changes” to the reader.
GitMir
Features, rules, customer paths and how they connect, built from your code and kept current with every commit.
GitMir establishes what a product does once, from the repository, and serves each task only what it needs — with every statement traceable to the source it came from.
A code graph connects symbols. The behaviour a change alters, and the customer path that meets it, are not symbols — they are the product, and they live above the graph.
| Dimension | Sourcegraph | GitMir |
|---|---|---|
| Primary layer | Code Intelligence | Meaning / Product |
| What it understands | Repos, files, symbols, references, history | What the product does and how it behaves |
| Relationships | Code graph | Business + technical |
| AI agent context | Strong | Established product knowledge |
| Change impact | Code-level | Product-logic level |
| Primary buyer | Enterprise engineering | CTO / VP Eng / Product / AI |
Positioning per each vendor’s own public description. Nothing here says a tool is bad at what it is built for.
Keep Sourcegraph. Add the business logic.
Search stays where it is: /with/sourcegraph shows code search and a business-logic model in one workflow.
Asked before choosing.
Search stays where it is: /with/sourcegraph shows code search and a business-logic model in one workflow.
Benchmark 001: one repository, fifty questions in the words work is actually assigned in, the same AI model and settings for every system, answers graded blind. GitMir answered 92% correctly against 69% for Sourcegraph, on 8.4K tokens of context per question against 38.1K. The full method and every number are published.
A code graph connects symbols. The behaviour a change alters, and the customer path that meets it, are not symbols — they are the product, and they live above the graph. GitMir builds the business logic of the product once — features, rules, customer paths and how they connect — keeps it current with every commit, and gives the part a task touches to people and to AI.
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