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Codebase Memory MCP

Codebase Memory MCP (github.com/DeusData/codebase-memory-mcp, ~32K stars) is a high-performance Model Context Protocol server that indexes a whole codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links, so an AI coding agent answers structural questions with roughly 120x fewer tokens than reading files one at a time. It parses 158 languages through tree-sitter AST analysis and adds Hybrid LSP semantic type resolution for Python, TypeScript, Go, Rust, C#, Java, and more, full-indexing an average repo in milliseconds and the 28M-LOC Linux kernel in 3 minutes. It ships as a single static C binary for macOS, Linux, and Windows with zero runtime dependencies, and works with Claude Code and any MCP-compatible client. A preprint (arXiv:2603.27277) documents 83% answer quality at 10x fewer tokens versus file-exploration baselines, making it a token-efficiency layer for serious agent coding workflows.

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codemap is an MIT-licensed project brain for AI coding tools that gives LLMs instant architectural context from your codebase without burning tokens. It generates a fast tree/context view, dependency flow, dependency blast-radius analysis, and a layered handoff format for cross-agent continuation, then exposes everything through a JSON context bundle and an MCP server compatible with Claude Code and Codex. A built-in Codex plugin and community skill registry make it easy to install and share. Developers use codemap to onboard agents to large repos in seconds, keep session continuity across handoffs, and scope the impact of a change before running it.

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Ollama is a local AI platform for running, managing, and sharing open models on your own machine or private infrastructure. It makes it easy to pull models, serve them through an API, and integrate local inference into developer workflows without relying on a fully managed cloud stack. Teams use Ollama for privacy-sensitive assistants, internal tools, offline experimentation, and rapid testing of open-weight models across laptops, workstations, and servers. It is especially useful for developers, operators, and AI builders who want quick setup with less operational overhead. What makes Ollama distinctive is how approachable it is: it packages model runtime, distribution, and deployment into a streamlined experience that helps people get productive with local AI in minutes instead of spending days on configuration.

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