GitMir vs Sourcegraph
Sourcegraph answers “where is this used” across very large codebases with authority. GitMir adds the answer to “which business rule and customer path does this change reach” — the two belong in one workflow.
Updated
Keep Sourcegraph. GitMir makes it stronger
Yes. Search stays where it is: gitmir.com/with/sourcegraph shows code search and a business-logic model in one workflow.
Sourcegraph assembles context for a request; GitMir holds the business logic between requests, current with every push, so the next question and the next agent start from it.
Before a change merges: the areas, customer-path steps and records it reaches — including those outside the diff that a search does not find.
A drawing and answers your team can review — the same understanding your agents get, not a slice that disappears with the prompt.
Every push read and compared with the product, and each agent change checked with GitMir Vibe before it is committed.
How to connect
Keep Sourcegraph for search and references; add GitMir's MCP server to the same agents, and connect the repositories in GitMir for the product model and change impact.
When GitMir is the context layer
GitMir does not replace code search across a monorepo. Where the question is what a change does to the product rather than where a symbol is used, GitMir answers it on its own — before the change merges, with the areas and records outside the diff that a search does not find.
When Sourcegraph on its own is enough
- The question is where something is: a symbol across many repositories, every caller of a function, a change across a monorepo.
- The codebase is small enough that a search finds everything a question needs.
Add GitMir when the question becomes what the product does and what a change reaches — the rules, customer paths and status flows behind the code, kept current between questions.
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 push.
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.
Asked before choosing
Yes. Search stays where it is: gitmir.com/with/sourcegraph shows code search and a business-logic model in one workflow.
GitMir does not replace code search across a monorepo. Where the question is what a change does to the product rather than where a symbol is used, GitMir answers it on its own — before the change merges, with the areas and records outside the diff that a search does not find.
Keep Sourcegraph for search and references; add GitMir's MCP server to the same agents, and connect the repositories in GitMir for the product model and change impact.
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 push, and gives the part a task touches to people and to AI.
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's answers lost 8% of the score to errors and omissions against 31% for Sourcegraph, on 8.4K tokens of context per question against 38.1K. The full method and every number are published.
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