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uvx cgraphy==0.1.1Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add io.github.pmgarg-cgraphy -- uvx cgraphy==0.1.1รันในเทอร์มินัล
{ "mcpServers": { "io.github.pmgarg-cgraphy": { "command": "uvx", "args": [ "cgraphy==0.1.1" ] } }}claude_desktop_config.json
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รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
Paper (DOI): https://doi.org/10.5281/zenodo.21422935
Code knowledge graph over MCP: token-budgeted context for AI coding agents.
cgraphy is a Python code knowledge graph and Model Context Protocol (MCP) server for AI coding agents such as Claude Code, Codex CLI, Cursor, and Gemini CLI.
cgraphy indexes any codebase into a knowledge graph — functions, classes and files as nodes; calls, imports, inheritance and git co-change history as edges — and serves compact, token-budgeted slices of it to AI assistants through the Model Context Protocol. Instead of re-reading dozens of files to orient itself on every prompt, an agent asks the graph and gets the relevant subgraph in a couple of thousand tokens.
cgraphy_context expands the graph greedily around a
symbol and stops exactly at your token budget — cost scales with the
question, not the repo.--git-history mines commit history for files that change
together (logical coupling), an edge type static analysis can't see..cgraphy/. No services, no
daemons, no vector database.pip install cgraphy # or: uv tool install cgraphy
cd your-repo
cgraphy init # one command: MCP config + agent steering + index
cgraphy init does three things:
.mcp.json — picked up automatically by Claude
Code in all its forms: CLI, VSCode extension, and the desktop app.CLAUDE.md and AGENTS.md telling agents to
consult the graph (cgraphy_overview → cgraphy_search →
cgraphy_context) before reading files — this is what makes the graph
actually replace bulk file reading. (Agents can't be forced, only steered:
instruction files + persuasive tool descriptions + the tools being
genuinely faster is the mechanism, and it works.)Or register the MCP server manually with your assistant:
Claude Code
claude mcp add cgraphy -- uvx cgraphy serve /path/to/repo
Codex CLI (~/.codex/config.toml)
[mcp_servers.cgraphy]
command = "uvx"
args = ["cgraphy", "serve", "/path/to/repo"]
Gemini CLI (~/.gemini/settings.json) / Cursor (.cursor/mcp.json)
{"mcpServers": {"cgraphy": {"command": "uvx",
"args": ["cgraphy", "serve", "/path/to/repo"]}}}
Reading / orientation:
Editing / reviewing — the tools that make the graph part of the change loop:
Enrichment:
Retrieval is usage-aware: symbols an agent repeatedly asks about get a small, capped boost in future context expansion (telemetry stays in the local SQLite file; nothing leaves your machine).
pip install "cgraphy[semantic]"
Adds tiny static embeddings (model2vec, CPU-only, no torch) fused with FTS5
by reciprocal-rank fusion — closes the vocabulary gap between issue-style
prose ("login broken") and code identifiers (validate_jwt).
The graph self-heals: tools detect stale files and re-index incrementally (changed files only) before answering.
Structure is extracted automatically; meaning comes from summaries. Tell your agent once:
enrich the cgraphy graph
It will loop cgraphy_enrich → cgraphy_store_summaries until every symbol
has a one-line semantic summary. Summaries are keyed to a hash of each
symbol's source, so editing one function invalidates only that summary.
For CI, cgraphy index --summarize pre-bakes summaries with your own
Anthropic API key (pip install cgraphy[summarize], ANTHROPIC_API_KEY set).
cgraphy view . # http://localhost:8787
A dependency-free local page (bundled Cytoscape.js): search, color by kind, click for details, double-click to expand neighbors; co-change edges shown dashed.
python scripts/benchmark.py /path/to/repo "your question"
Prints the tokens an agent spends orienting via cgraphy (overview + search + context) versus reading every code file, and the reduction factor.
python scripts/eval_localization.py /path/to/repo 50
Mines fix-like commits from the repo's history (subject = query, touched files = ground truth, co-change mining excludes evaluated commits), then scores an ablation ladder — FTS-only, +PageRank, +graph expansion, ±co-change edges — on hit@5/hit@10/MRR and token cost. No LLM calls, no human grading, fully reproducible. Results and a paper draft live in paper/.
cgraphy index walks the repo (respecting .gitignore +
.cgraphyignore), parses each file with tree-sitter, and stores nodes and
edges in .cgraphy/graph.db (SQLite + FTS5). Re-indexing is incremental by
content hash.cgraphy serve exposes the five MCP tools over stdio.--git-history adds weighted co-change edges mined from
git log.Design details: docs/superpowers/specs/2026-07-08-cgraphy-design.md
If you use cgraphy in research or tooling, please cite the paper:
@misc{garg2026cgraphy,
author = {Garg, Prateek Mohan},
title = {Which Graph Signals Pay for Their Tokens? cgraphy: A Token-Budgeted
Code Knowledge Graph as a Portable Context Layer for AI Coding Agents},
year = {2026},
doi = {10.5281/zenodo.21422935},
url = {https://doi.org/10.5281/zenodo.21422935},
note = {Preprint}
}
MIT
mcp-name: io.github.pmgarg/cgraphy
Code knowledge graph over MCP: token-budgeted context for AI coding agents.
{ "mcpServers": { "io.github.pmgarg-cgraphy": { "command": "uvx", "args": [ "cgraphy==0.1.1" ] } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Cgraphy ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-10 Code knowledge graph over MCP: token-budgeted context for AI coding agents.