@DataChaz: How do you index the entire Linux kernel (28M lines of code) for an AI agent in 3 minutes? You stop letting the agent r…

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open-source code-indexing knowledge-graph ast agent-tool mcp linux-kernel

Summary

A new open-source tool called codebase-memory-mcp indexes entire codebases like the Linux kernel in minutes using AST knowledge graphs, achieving massive efficiency gains for AI agents with 99% token reduction and 83% answer quality.

How do you index the entire Linux kernel (28M lines of code) for an AI agent in 3 minutes? You stop letting the agent read files one by one. There is a fascinating new open-source release called codebase-memory-mcp. It's a code intelligence engine that swaps traditional file-searching for high-speed AST knowledge graphs. What makes this project stand out is the research behind it. Evaluated across 31 real-world repositories (detailed in arXiv:2603.27277), the architectural shift yields massive efficiency gains: → 99% reduction in tokens for structural queries → 83% answer quality across complex tasks → 2.1x fewer tool calls required It maps functions, classes, HTTP routes, and cross-service links into a graph. When the agent needs context, it queries the graph directly. Security is prioritized too: everything happens 100% locally on your machine via a single static binary. It runs entirely locally. No Docker, no Ollama, no API keys. You download the binary, restart your agent, and it just works. Are we one good index away from cutting AI dev costs to zero? Paper and Repo links in the thread ↓
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Cached at: 06/15/26, 11:10 PM

How do you index the entire Linux kernel (28M lines of code) for an AI agent in 3 minutes?

You stop letting the agent read files one by one.

There is a fascinating new open-source release called codebase-memory-mcp.

It’s a code intelligence engine that swaps traditional file-searching for high-speed AST knowledge graphs.

What makes this project stand out is the research behind it.

Evaluated across 31 real-world repositories (detailed in arXiv:2603.27277), the architectural shift yields massive efficiency gains: → 99% reduction in tokens for structural queries → 83% answer quality across complex tasks → 2.1x fewer tool calls required

It maps functions, classes, HTTP routes, and cross-service links into a graph. When the agent needs context, it queries the graph directly.

Security is prioritized too: everything happens 100% locally on your machine via a single static binary.

It runs entirely locally.

No Docker, no Ollama, no API keys.

You download the binary, restart your agent, and it just works.

Are we one good index away from cutting AI dev costs to zero?

Paper and Repo links in the thread ↓

REPO:

PAPER: https://arxiv.org/abs/2603.27277

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@AriXZone: The Linux kernel, a very large project with 28 million lines of code and 75,000 files, takes only about 3 minutes to index, with sub-millisecond query responses. codebase-memory-mcp is an open-source MCP server from DeusData that provides AI coding assistants (Claude Cod…

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DeusData has open-sourced an MCP server called codebase-memory-mcp, which pre-parses code repositories into persistent knowledge graphs, providing AI coding assistants with sub-millisecond code structure query capabilities. It supports 158 languages, reduces token consumption by approximately 99% compared to traditional grep, and has extremely fast indexing speed.

DeusData/codebase-memory-mcp

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Codebase-memory-mcp is an ultra-fast code intelligence engine for AI coding agents that indexes entire repositories in milliseconds and answers structural queries in under 1ms using tree-sitter AST analysis and a persistent knowledge graph, with support for 158 languages and 14 MCP tools.