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Host a copy with our keys and logs.
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Chạy trên PlugMCP
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MởTừ nhà phát hành
Code indexing library for AI coding agents. Parses source code with tree-sitter, chunks it into semantic units (functions, structs, classes, impl blocks), embeds them locally, and stores everything in a single SQLite file with vector + full-text search.
No Docker, no server, no API keys. One .db file per codebase.
Codemogger is a code indexing library and MCP server for AI coding agents
Coding agents need to understand codebases. They need to find where things are defined, discover how concepts are implemented across files, and navigate unfamiliar code quickly. This requires both keyword search (precise identifier lookup) and semantic search (natural language queries when you don't know the exact names).
As AI coding tools become more composable - agents calling agents, MCP servers plugging into different hosts - this capability needs to exist as a library that runs locally. No external servers, no API keys, no Docker containers. Just a function call that returns results.
codemogger is that library. Embedded SQLite (via Turso) with FTS + vector search in a single .db file.
npm install -g codemogger
Or use npx to run without installing.
# Index a project
codemogger index ./my-project
# Search
codemogger search "authentication middleware"
Add to your coding agent's MCP config (Claude Code, OpenCode, etc.):
{
"mcpServers": {
"codemogger": {
"command": "npx",
"args": ["-y", "codemogger", "mcp"]
}
}
}
The MCP server exposes three tools:
codemogger_search - semantic and keyword search over indexed codecodemogger_index - index a codebase for the first timecodemogger_reindex - update the index after modifying filesAdd the local db to .gitignore:
# codemogger db
.codemogger/
codemogger is also usable as a library. The SDK has no model dependency - you provide your own embedding function:
import { CodeIndex } from "codemogger"
import { pipeline } from "@huggingface/transformers"
// Load embedding model (runs locally, no API keys)
const extractor = await pipeline("feature-extraction", "Xenova/all-MiniLM-L6-v2", { dtype: "q8" })
const embedder = async (texts: string[]): Promise<number[][]> => {
const output = await extractor(texts, { pooling: "mean", normalize: true })
return output.tolist() as number[][]
}
const db = new CodeIndex({
dbPath: "./my-project.db",
embedder,
embeddingModel: "all-MiniLM-L6-v2",
})
await db.index("/path/to/project")
// Semantic: "what does this codebase do?"
const results = await db.search("authentication middleware", { mode: "semantic" })
// Keyword: precise identifier lookup
const results = await db.search("BTreeCursor", { mode: "keyword" })
await db.close()
The MCP server and CLI ship with all-MiniLM-L6-v2 by default.
# Install globally
npm install -g codemogger
# Index a directory
codemogger index ./my-project
# Search
codemogger search "authentication middleware"
# List indexed codebases
codemogger list
.gitignore, detect language from extensionIncremental: only changed files (by SHA-256 hash) are re-processed on subsequent runs.
Rust, C, C++, Go, Python, Zig, Java, Scala, JavaScript, TypeScript, TSX, PHP, Ruby.
Benchmarked on 4 real-world codebases on an Apple M2 (8GB). Each project uses its own isolated database. Embeddings use vector8 (int8 quantized, 395 bytes/chunk vs 1,536 for float32). Embedding model: all-MiniLM-L6-v2 (q8 quantized, local CPU). Search times are p50 over 3 runs.
Keyword search is 25x-370x faster than ripgrep and returns precise definitions instead of thousands of file matches.
Indexing is a one-time cost dominated by embedding (~97% of time). Subsequent runs only re-embed changed files.
The real advantage isn't speed - it's finding the right code when you don't know the exact keywords.
"write-ahead log replication and synchronization" (Turso)
"SQL statement parsing and compilation" (Turso)
"HTTP request parsing and response writing" (Bun)
"scheduling pods to nodes based on resource requirements" (Kubernetes)
"container health check probes and restart policy" (Kubernetes)
ripgrep matches thousands of files on common keywords. codemogger returns the 5 most relevant definitions.
@tursodatabase/database is currently pinned to a pre-release version (0.5.0-pre.14). There is no stable 0.5.x release yet; once one is published this should be updated. The pre-release is stable enough for development use but may have breaking changes before it reaches a final release.
MIT
Codemogger is a code indexing library and MCP server for AI coding agents
Công cụ
Chưa có danh sách công cụ. Máy chủ từ xa sẽ có danh sách sau lần kiểm tra tình trạng đầu tiên.
Câu hỏi thường gặp
Sao chép cấu hình stdio cho Cursor, Claude Desktop, Claude Code hoặc VS Code trên trang này. Cấu hình dùng bản cài đặt của nhà phát hành, không phải proxy của PlugMCP.
Bản ghi này không liệt kê biến môi trường bắt buộc nào.
Codemogger chạy cục bộ qua stdio.
Kiểm tra lần cuối: 2026-11-05. Codemogger is a code indexing library and MCP server for AI coding agents
