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The problem: AI coding agents hallucinate library internals constantly. They confidently describe how a function works based on stale training data when the actual implementation does something different.
How it works: you ask a question about a repo, Instagit scans the source, and returns an answer with file paths and line numbers. You can target specific commits, branches, or tags. You can also swap "github" to "instagit" in any repo URL to get an instant wiki with Q&A (e.g. https://instagit.com/pandas-dev/pandas).
The real power though is giving your agent access via MCP rather than being the human in the loop. Point your agent at a large library, have it understand a specific feature, then rip out just the part you need into self-contained code. Drop the dependency entirely, sometimes even get better performance. The agent catches implementation details you'd miss reading the code yourself and that maintainers rarely document.
I get asked this a lot so might as well answer it now: how is this different from Context7, DeepWiki, CodeWiki, or GitHub MCP?
Context7, DeepWiki, and CodeWiki all pre-generate static summaries or guides. They're fast when they have what you need, but they don't cover every repo, they go stale, and there are hundreds of questions about any codebase that can't be pre-answered. GitHub MCP checks out files one at a time, which burns through context tokens fast and doesn't scale to large codebases.
Instagit reads source on demand for any public repo, returns just the answer, and keeps your context clean.
No API key or account needed to try it out: https://instagit.com/install
