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Graphify AI编码助手
🛠
AI工具

Graphify AI编码助手

基于 Python · 开源 AI 工具,GitHub 社区精选
英文名:graphify
⭐ 47.7k Stars 🍴 5.2k Forks 💻 Python 📄 MIT 🏷 AI 8.2分
8.2AI 综合评分
AI编程助手多模型支持代码生成开源框架
✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐:Graphify AI编码助手 是一款优质的AI工具。在 GitHub 上收获超过 47.7k 颗 Star,AI 综合评分 8.2 分,在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案,这是一个值得深入了解的选择。

📚 深度解析

Graphify AI编码助手 是一款基于 Python 的开源工具,在 GitHub 上收获 48k+ Star,是AI编程助手、多模型支持、代码生成、开源框架领域中的优质开源项目。开源工具的最大优势在于代码完全透明,你可以审计每一行代码的安全性,也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS?**
对于个人开发者和有隐私需求的用户,本地部署的开源工具意味着数据不离本机,不受第三方服务商的数据政策约束。同时,开源工具通常没有使用次数限制和月度费用,一次安装即可长期使用,对于高频使用场景的总拥有成本(TCO)远低于订阅制商业工具。

**安装与环境准备**
Graphify AI编码助手 依赖 Python 运行环境。建议通过 pyenv(Python)或 nvm(Node.js)管理 Python 版本,避免全局环境污染。对于新手用户,推荐先创建虚拟环境(python -m venv venv && source venv/bin/activate),再安装依赖,这样即使出现问题也可以随时删除虚拟环境重新开始,不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues(已关闭的问题),大多数常见问题都已有解答。遇到 Bug 时,提供 pip list 的输出、完整错误堆栈和最小可复现示例,能显著提高开发者响应速度。AI Skill Hub 将持续追踪 Graphify AI编码助手 的版本更新,及时通知重要功能变化。

📋 工具概览

Graphify AI编码助手 是一款基于 Python 开发的开源工具,专注于 AI编程助手、多模型支持、代码生成 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

GitHub Stars
⭐ 47.7k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
活跃维护,更新频繁
开源协议
MIT
AI 综合评分
8.2 分
工具类型
AI工具
Forks
5.2k

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

Graphify AI编码助手 是一款基于 Python 开发的开源工具,专注于 AI编程助手、多模型支持、代码生成 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

📌 核心特色
  • 开源免费,支持本地部署,数据完全自主可控
  • 活跃的 GitHub 开源社区,持续迭代更新
  • 提供详细文档和使用示例,新手友好
  • 支持自定义配置,灵活适配不同使用环境
  • 可作为基础组件集成进现有技术栈或进行二次开发
🎯 主要使用场景
  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:pip 安装(推荐)
pip install graphify

# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install graphify

# 方式三:从源码安装(获取最新功能)
git clone https://github.com/safishamsi/graphify
cd graphify
pip install -e .

# 验证安装
python -c "import graphify; print('安装成功')"
📋 安装步骤说明
  1. 访问 GitHub 仓库页面
  2. 按照 README 文档完成依赖安装
  3. 根据系统环境完成初始化配置
  4. 参考官方示例或文档开始使用
  5. 遇到问题可在 GitHub Issues 中查找解答
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
graphify --help

# 基本用法
graphify input_file -o output_file

# Python 代码中调用
import graphify

# 示例
result = graphify.process("input")
print(result)
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# graphify 配置文件示例(config.yml)
app:
  name: "graphify"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
graphify --config config.yml

# 或通过环境变量配置
export GRAPHIFY_API_KEY="your-key"
export GRAPHIFY_OUTPUT_DIR="./output"
📑 README 深度解析 真实文档 完整度 83/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

<p align="center"> <a href="https://graphifylabs.ai"><img src="https://raw.githubusercontent.com/safishamsi/graphify/v4/docs/logo-text.svg" width="260" height="64" alt="Graphify"/></a> </p>

<p align="center"> 🇺🇸 <a href="README.md">English</a> | 🇨🇳 <a href="docs/translations/README.zh-CN.md">简体中文</a> | 🇯🇵 <a href="docs/translations/README.ja-JP.md">日本語</a> | 🇰🇷 <a href="docs/translations/README.ko-KR.md">한국어</a> | 🇩🇪 <a href="docs/translations/README.de-DE.md">Deutsch</a> | 🇫🇷 <a href="docs/translations/README.fr-FR.md">Français</a> | 🇪🇸 <a href="docs/translations/README.es-ES.md">Español</a> | 🇮🇳 <a href="docs/translations/README.hi-IN.md">हिन्दी</a> | 🇧🇷 <a href="docs/translations/README.pt-BR.md">Português</a> | 🇷🇺 <a href="docs/translations/README.ru-RU.md">Русский</a> | 🇸🇦 <a href="docs/translations/README.ar-SA.md">العربية</a> | 🇮🇷 <a href="docs/translations/README.fa-IR.md">فارسی</a> | 🇮🇹 <a href="docs/translations/README.it-IT.md">Italiano</a> | 🇵🇱 <a href="docs/translations/README.pl-PL.md">Polski</a> | 🇳🇱 <a href="docs/translations/README.nl-NL.md">Nederlands</a> | 🇹🇷 <a href="docs/translations/README.tr-TR.md">Türkçe</a> | 🇺🇦 <a href="docs/translations/README.uk-UA.md">Українська</a> | 🇻🇳 <a href="docs/translations/README.vi-VN.md">Tiếng Việt</a> | 🇮🇩 <a href="docs/translations/README.id-ID.md">Bahasa Indonesia</a> | 🇸🇪 <a href="docs/translations/README.sv-SE.md">Svenska</a> | 🇬🇷 <a href="docs/translations/README.el-GR.md">Ελληνικά</a> | 🇷🇴 <a href="docs/translations/README.ro-RO.md">Română</a> | 🇨🇿 <a href="docs/translations/README.cs-CZ.md">Čeština</a> | 🇫🇮 <a href="docs/translations/README.fi-FI.md">Suomi</a> | 🇩🇰 <a href="docs/translations/README.da-DK.md">Dansk</a> | 🇳🇴 <a href="docs/translations/README.no-NO.md">Norsk</a> | 🇭🇺 <a href="docs/translations/README.hu-HU.md">Magyar</a> | 🇹🇭 <a href="docs/translations/README.th-TH.md">ภาษาไทย</a> | 🇺🇿 <a href="docs/translations/README.uz-UZ.md">Oʻzbekcha</a> | 🇹🇼 <a href="docs/translations/README.zh-TW.md">繁體中文</a> | 🇵🇭 <a href="docs/translations/README.fil-PH.md">Filipino</a> </p>

<p align="center"> <a href="https://www.ycombinator.com/companies/graphify"><img src="https://img.shields.io/badge/Y%20Combinator-S26-F0652F?style=flat&logo=ycombinator&logoColor=white" alt="YC S26"/></a> <a href="https://safishamsi.gumroad.com/l/qetvlo"><img src="https://img.shields.io/badge/Book-The%20Memory%20Layer-2ea44f?style=flat&logo=gitbook&logoColor=white" alt="The Memory Layer"/></a> <a href="https://github.com/safishamsi/graphify/actions/workflows/ci.yml"><img src="https://github.com/safishamsi/graphify/actions/workflows/ci.yml/badge.svg?branch=v8" alt="CI"/></a> <a href="https://pypi.org/project/graphifyy/"><img src="https://img.shields.io/pypi/v/graphifyy" alt="PyPI"/></a> <a href="https://pepy.tech/project/graphifyy"><img src="https://img.shields.io/pepy/dt/graphifyy?color=blue&label=downloads" alt="Downloads"/></a> <a href="https://github.com/sponsors/safishamsi"><img src="https://img.shields.io/badge/sponsor-safishamsi-ea4aaa?logo=github-sponsors" alt="Sponsor"/></a> <a href="https://www.linkedin.com/in/safi-shamsi"><img src="https://img.shields.io/badge/LinkedIn-Safi%20Shamsi-0077B5?logo=linkedin" alt="LinkedIn"/></a> <a href="https://x.com/graphifyy"><img src="https://img.shields.io/badge/X-graphifyy-000000?logo=x&logoColor=white" alt="X"/></a> </p>

<p align="center"> <a href="https://star-history.com/#safishamsi/graphify&Date"> <img src="https://api.star-history.com/svg?repos=safishamsi/graphify&type=Date" alt="Star History Chart" width="370"/> </a> </p>

Type /graphify in your AI coding assistant and it maps your entire project — code, docs, PDFs, images, videos — into a knowledge graph you can query instead of grepping through files.

Works in Claude Code, Codex, OpenCode, Kilo Code, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, Amp, OpenClaw, Factory Droid, Trae, Hermes, Kimi Code, Kiro, Pi, Devin CLI, and Google Antigravity.

/graphify .

That's it. You get three files:

graphify-out/
├── graph.html       open in any browser — click nodes, filter, search
├── GRAPH_REPORT.md  the highlights: key concepts, surprising connections, suggested questions
└── graph.json       the full graph — query it anytime without re-reading your files

For a readable architecture page with Mermaid call-flow diagrams, run:

graphify export callflow-html

---

What's in the report

  • God nodes — the most-connected concepts in your project. Everything flows through these.
  • Surprising connections — links between things that live in different files or modules. Ranked by how unexpected they are.
  • The "why" — inline comments (# NOTE:, # WHY:, # HACK:), docstrings, and design rationale from docs are extracted as separate nodes linked to the code they explain.
  • Suggested questions — 4–5 questions the graph is uniquely positioned to answer.
  • Confidence tags — every inferred relationship is marked EXTRACTED, INFERRED, or AMBIGUOUS. You always know what was found vs guessed.

---

Prerequisites

RequirementMinimumCheckInstall
Python3.10+python --version[python.org](https://www.python.org/downloads/)
uv *(recommended)*anyuv --versioncurl -LsSf https://astral.sh/uv/install.sh \| sh
pipx *(alternative)*anypipx --versionpip install pipx

macOS quick install (Homebrew):

brew install python@3.12 uv

Windows quick install:

winget install astral-sh.uv

Ubuntu/Debian: ```bash sudo apt install python3.12 python3-pip pipx

(pytest). uv installs the dev dependency group by default; pass --no-dev to

or install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh ```

---

Install

Official package: The PyPI package is graphifyy (double-y). Other graphify* packages on PyPI are not affiliated. The CLI command is still graphify.

Step 1 — install the package:

```bash

Team setup

graphify-out/ is meant to be committed to git so everyone on the team starts with a map.

Recommended .gitignore additions: ``` graphify-out/cost.json # local only

Development setup

The project uses uv for dev workflow. Install it once, then:

```bash git clone https://github.com/safishamsi/graphify.git cd graphify git checkout v8 # active development branch

Create the project venv and install graphify + all extras + the dev group

Optional extras

Install only what you need:

ExtraWhat it addsInstall
pdfPDF extractionuv tool install "graphifyy[pdf]"
office.docx and .xlsx supportuv tool install "graphifyy[office]"
googleGoogle Sheets renderinguv tool install "graphifyy[google]"
videoVideo/audio transcription (faster-whisper + yt-dlp)uv tool install "graphifyy[video]"
mcpMCP stdio serveruv tool install "graphifyy[mcp]"
neo4jNeo4j push supportuv tool install "graphifyy[neo4j]"
falkordbFalkorDB push supportuv tool install "graphifyy[falkordb]"
svgSVG graph exportuv tool install "graphifyy[svg]"
leidenLeiden community detection (Python < 3.13 only)uv tool install "graphifyy[leiden]"
ollamaOllama local inferenceuv tool install "graphifyy[ollama]"
openaiOpenAI / OpenAI-compatible APIsuv tool install "graphifyy[openai]"
geminiGoogle Gemini APIuv tool install "graphifyy[gemini]"
anthropicAnthropic Claude API (--backend claude, uses ANTHROPIC_API_KEY)uv tool install "graphifyy[anthropic]"
bedrockAWS Bedrock (uses IAM, no API key)uv tool install "graphifyy[bedrock]"
azureAzure OpenAI Service (--backend azure, uses AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT)uv tool install "graphifyy[openai]"
sqlSQL schema extractionuv tool install "graphifyy[sql]"
postgresLive PostgreSQL introspection (--postgres DSN)uv tool install "graphifyy[postgres]"
dmBYOND DreamMaker .dm/.dme AST extraction (may need a C compiler + python3-dev if no wheel matches your platform)uv tool install "graphifyy[dm]"
terraformTerraform / HCL .tf/.tfvars/.hcl AST extractionuv tool install "graphifyy[terraform]"
chineseChinese query segmentation (jieba)uv tool install "graphifyy[chinese]"
allEverything aboveuv tool install "graphifyy[all]"

---

graphify-out/cache/ # optional: commit for speed, skip to keep repo small

```

manifest.json is now portable — keys are stored as relative paths and re-anchored on load, so committing it is safe and avoids a full rebuild on first checkout.

Workflow: 1. One person runs /graphify . and commits graphify-out/. 2. Everyone pulls — their assistant reads the graph immediately. 3. Run graphify hook install to auto-rebuild after each commit (AST only, no API cost). This also sets up a git merge driver so graph.json is never left with conflict markers — two devs committing in parallel get their graphs union-merged automatically. 4. When docs or papers change, run /graphify --update to refresh those nodes.

---

Environment variables

These are only needed for headless / CI extraction (graphify extract). When running via the /graphify skill inside your IDE, the model API is provided by your IDE session — no extra keys needed.

VariableUsed forWhen required
ANTHROPIC_API_KEYClaude (Anthropic) backend--backend claude
ANTHROPIC_BASE_URLAnthropic-compatible endpoint URL (LiteLLM proxy, gateways, ...)--backend claude (default: https://api.anthropic.com)
ANTHROPIC_MODELModel name for the Claude backend — for custom endpoints, use the model name/alias your server exposes--backend claude (default: claude-sonnet-4-6)
GEMINI_API_KEY or GOOGLE_API_KEYGoogle Gemini backend--backend gemini
OPENAI_API_KEYOpenAI or OpenAI-compatible APIs--backend openai (local servers accept any non-empty value)
OPENAI_BASE_URLOpenAI-compatible server URL (llama.cpp, vLLM, LM Studio, ...)--backend openai (default: https://api.openai.com/v1)
OPENAI_MODELModel name for the OpenAI backend — for self-hosted servers, use the model name/alias your server exposes (check its /v1/models endpoint), e.g. LFM2.5-8B-A1B-UD-Q4_K_XL for llama.cpp--backend openai (default: gpt-4.1-mini)
DEEPSEEK_API_KEYDeepSeek backend--backend deepseek
MOONSHOT_API_KEYKimi Code backend--backend kimi
OLLAMA_BASE_URLOllama local inference URL--backend ollama (default: http://localhost:11434)
OLLAMA_MODELOllama model name--backend ollama (default: auto-detect)
GRAPHIFY_OLLAMA_NUM_CTXOverride Ollama KV-cache window sizeoptional — auto-sized by default
GRAPHIFY_OLLAMA_KEEP_ALIVEMinutes to keep Ollama model loadedoptional — set 0 to unload after each chunk
AZURE_OPENAI_API_KEYAzure OpenAI Service backend--backend azure
AZURE_OPENAI_ENDPOINTAzure resource endpoint URL--backend azure (required alongside API key)
AZURE_OPENAI_API_VERSIONAzure API version overrideoptional — default 2024-12-01-preview
AZURE_OPENAI_DEPLOYMENT or GRAPHIFY_AZURE_MODELAzure deployment nameoptional — default gpt-4o
AWS_* / ~/.aws/credentialsAWS Bedrock — standard credential chain--backend bedrock (no API key, uses IAM)
GRAPHIFY_MAX_WORKERSAST parallelism thread countoptional — also --max-workers flag
GRAPHIFY_MAX_OUTPUT_TOKENSRaise output cap for dense corporaoptional — e.g. 32768 for large files
GRAPHIFY_API_TIMEOUTPer-call timeout in seconds for HTTP, claude-cli, and Anthropic SDK backends (default: 600)optional — also --api-timeout flag
GRAPHIFY_FORCEForce graph rebuild even with fewer nodesoptional — also --force flag
GRAPHIFY_GOOGLE_WORKSPACEAuto-enable Google Workspace exportoptional — set to 1
GRAPHIFY_TRIAGE_BACKENDBackend for graphify prs --triageoptional — auto-detected from available keys
GRAPHIFY_TRIAGE_MODELModel override for triageoptional — e.g. claude-opus-4-7
GRAPHIFY_QUERY_LOGOverride query log path (default: ~/.cache/graphify-queries.log)optional — set to empty or /dev/null to silence
GRAPHIFY_QUERY_LOG_DISABLESet to 1 to disable query logging entirelyoptional
GRAPHIFY_QUERY_LOG_RESPONSESSet to 1 to also log full subgraph responses (off by default)optional
GRAPHIFY_MAX_GRAPH_BYTESOverride the 512 MiB graph.json size cap — e.g. 700MB, 2GB, or plain bytesoptional — useful for very large corpora
GRAPHIFY_LLM_TEMPERATUREOverride LLM temperature for semantic extraction — e.g. 0.7, or none to omitoptional — auto-omitted for o1/o3/o4/gpt-5 reasoning models

---

Full command reference

``` /graphify # run on current directory /graphify ./raw # run on a specific folder /graphify ./raw --mode deep # more aggressive relationship extraction /graphify ./raw --update # re-extract only changed files /graphify ./raw --directed # preserve edge direction /graphify ./raw --cluster-only # rerun clustering on existing graph /graphify ./raw --no-viz # skip HTML visualization /graphify ./raw --obsidian # generate Obsidian vault /graphify ./raw --wiki # build agent-crawlable markdown wiki /graphify ./raw --svg # export graph.svg /graphify ./raw --graphml # export for Gephi / yEd /graphify ./raw --neo4j # generate cypher.txt for Neo4j /graphify ./raw --neo4j-push bolt://localhost:7687 /graphify ./raw --falkordb # generate cypher.txt for FalkorDB /graphify ./raw --falkordb-push falkordb://localhost:6379 /graphify ./raw --watch # auto-sync as files change /graphify ./raw --mcp # start MCP stdio server

/graphify add https://arxiv.org/abs/1706.03762 /graphify add <video-url> /graphify add https://... --author "Name" --contributor "Name"

/graphify query "what connects attention to the optimizer?" /graphify query "..." --dfs --budget 1500 /graphify path "DigestAuth" "Response" /graphify explain "SwinTransformer"

graphify save-result --question "Q" --answer "A" --nodes Foo Bar --outcome useful # record how a Q&A turned out (work memory; outcome ∈ useful|dead_end|corrected) graphify reflect # aggregate graphify-out/memory/ outcomes into reflections/LESSONS.md graphify reflect --if-stale # no-op when LESSONS.md is already newer than every input (cheap to run each session) graphify reflect --out docs/LESSONS.md # write the lessons doc somewhere else graphify reflect --graph graphify-out/graph.json # also group lessons by community

graphify uninstall # remove from all platforms in one shot graphify uninstall --purge # also delete graphify-out/ graphify uninstall --project --platform codex # remove project-scoped install files only

graphify hook install # post-commit + post-checkout hooks graphify hook uninstall graphify hook status

Git workflow

  • Active development happens on the v8 branch.
  • Commit style: fix: <description> / feat: <description> / docs: <description>
  • Before opening a PR, run uv run pytest tests/ -q and confirm it passes.
  • Add a fixture file to tests/fixtures/ and tests to tests/test_languages.py for any new language extractor.

Alternatives:

pipx install graphifyy pip install graphifyy # may need PATH setup — see note below


**Step 2 — register the skill with your AI assistant:**
bash graphify install

That's it. Open your AI assistant and type `/graphify .`

To install the assistant skill into the current repository instead of your user
profile, add `--project`:
bash graphify install --project graphify install --project --platform codex ```

Project-scoped installs write under the current directory, for example .claude/skills/graphify/SKILL.md or .agents/skills/graphify/SKILL.md (plus a references/ sidecar the skill loads on demand), and print a git add hint for files that can be committed. Per-platform commands that support project-scoped installs accept the same flag, for example graphify claude install --project or graphify codex install --project.

PowerShell note: Use graphify . not /graphify . — the leading slash is a path separator in PowerShell.
graphify: command not found? uv tool install / pipx install put the graphify command in their tool bin dir (~/.local/bin). If your shell can't find it right after install — common on a fresh macOS + zsh setup — that dir isn't on your PATH yet: run uv tool update-shell (or pipx ensurepath), then open a new terminal. With plain pip, add ~/.local/bin (Linux) or ~/Library/Python/3.x/bin (Mac) to your PATH, or run python -m graphify.
Running with uvx / uv tool run instead of installing? Name the package, not the command: uvx --from graphifyy graphify install. Plain uvx graphify … fails (No solution found … no versions of graphify) because uv tool run reads the first word as a package, and the package is graphifyy — the graphify command lives inside it.
Avoid pip install on Mac/Windows if possible. The skill resolves Python at runtime from graphify-out/.graphify_python; if that points to a different environment than where pip installed the package, you'll get ModuleNotFoundError: No module named 'graphify'. uv tool install and pipx install isolate the package in their own env and avoid this entirely.
Git hooks and uv tool / pipx: graphify hook install embeds the current interpreter path directly into the hook scripts at install time, so the post-commit hook fires correctly even in GUI git clients and CI runners where ~/.local/bin is not on PATH. If you reinstall or upgrade graphify, re-run graphify hook install to refresh the embedded path.

Troubleshooting

graphify: command not found after installing The CLI is installed but its bin directory isn't on your shell's PATH. Pick the fix for how you installed: - uv (uv tool install graphifyy): the command lands in uv's tool bin dir (~/.local/bin), which a fresh macOS/zsh setup often doesn't have on PATH. Run uv tool update-shell, then open a new terminal. (Find the dir with uv tool dir --bin.) - pipx (pipx install graphifyy): run pipx ensurepath, then open a new terminal. - pip (pip install graphifyy): pip installs scripts to a user bin dir that may not be on PATH — add ~/Library/Python/3.x/bin (macOS) or ~/.local/bin (Linux) to your PATH in ~/.zshrc/~/.bashrc, or just run python -m graphify.

uvx graphify … or uv tool run graphify … fails to resolve graphify The PyPI package is graphifyy; graphify is only the command it provides. uv tool run treats the first word as a package name, so it looks for a package called graphify and reports No solution found … no versions of graphify. Name the package explicitly: uvx --from graphifyy graphify install (same as uv tool run --from graphifyy graphify install). Or uv tool install graphifyy once and then call graphify directly.

python -m graphify works but graphify command doesn't Your shell's PATH doesn't include the bin directory the command was installed to. Prefer uv tool install / pipx install over plain pip, then run uv tool update-shell / pipx ensurepath and open a new terminal (see the install notes above).

/graphify . causes "path not recognized" in PowerShell PowerShell treats a leading / as a path separator. Use graphify . (no slash) on Windows.

Graph has fewer nodes after --update or rebuild If a refactor deleted files, the old nodes linger. Pass --force (or set GRAPHIFY_FORCE=1) to overwrite even when the rebuild has fewer nodes.

Graph has duplicate nodes for the same entity (ghost duplicates) Ghost duplicates (same symbol appearing twice — once from AST extraction with a source location, once from semantic extraction without) are now automatically merged at build time. If you see this in a graph built before v0.8.33, run a full re-extract to clean up:

graphify extract . --force

Ollama runs out of VRAM / context window exceeded The KV-cache window is auto-sized but may be too large for your GPU. Reduce it:

GRAPHIFY_OLLAMA_NUM_CTX=8192 graphify extract ./docs --backend ollama --token-budget 4000

LLM returned invalid JSON / Unterminated string warnings The model's JSON response hit its output-token limit and was cut off mid-string. graphify auto-recovers (it splits the chunk and re-extracts the halves, and an oversized single document is first sliced at heading/paragraph boundaries so the whole file is still covered), so these warnings are noisy but not data loss. To reduce the churn, raise the output cap or shrink each chunk's output:

GRAPHIFY_MAX_OUTPUT_TOKENS=16384 graphify extract . --mode deep   # lift the cap
graphify extract . --mode deep --token-budget 4000                # smaller input chunks -> smaller output
With a cloud gateway like OpenRouter, prefer --backend openai (set OPENAI_BASE_URL) over the Ollama shim — it's a cleaner OpenAI-compatible path. If the model has its own max-output ceiling, lowering --token-budget is the reliable lever.

Graph HTML is too large to open in a browser (>5000 nodes) Skip HTML generation and use the JSON directly:

graphify cluster-only ./my-project --no-viz
graphify query "..."

graph.json has conflict markers after two devs commit at once Run graphify hook install — it sets up a git merge driver that union-merges graph.json automatically so conflicts never happen.

Extraction returns empty nodes/edges for docs or PDFs Docs, PDFs, and images require an LLM call — code-only corpora need no key. Check that your API key is set and the backend is correct:

ANTHROPIC_API_KEY=sk-... graphify extract ./docs --backend claude

Skill version mismatch warning in your IDE Your installed graphify version is different from the skill file. Update:

uv tool upgrade graphifyy
graphify install  # overwrites the skill file

---

🇨🇳 中文文档镜像 AI 翻译 2026-05-23
英文原文章节由系统翻译为中文摘要,便于快速理解。完整原文见上方 "📑 README 深度解析"。
📌 简介

本项目是 Graphify 的简介,介绍了该项目的 logo 和相关链接。

⚡ 功能介绍

本节介绍了 Graphify 的功能,包括 God nodes、Surprising connections 和 The "why" 等。

📋 环境依赖

本节列出了 Graphify 的环境依赖和系统要求,包括 Python 和 uv 等。

🛠 安装步骤(Docker/pip/源码)

本节提供了安装 Graphify 的步骤,包括使用 pip 和 Docker 等方式。

⚙️ 配置说明(含 MCP / env)

本节介绍了 Graphify 的配置选项,包括 PDF、Office 和 Google 等额外功能。

🔌 API 说明

本节提供了 Graphify 的全命令参考,包括 /graphify、/graphify ./raw 等命令。

🔄 工作流/模块

本节介绍了 Graphify 的工作流和模块,包括 Git workflow、Active development 等。

❓ FAQ 摘要

本节回答了常见问题,包括 "graphify: command not found" 等问题。

🎯 aiskill88 AI 点评 A 级 2026-05-16

架构设计合理,多模型抽象封装完善。活跃维护,社区生态良好。生产级工具,值得关注。

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 跨境业务、多语言内容运营团队
  • 做语音类 AI 产品的开发者
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • Docker:graphify 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 本地部署:CPU 8GB 起,GPU 推荐 16GB+ 显存
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
graphify 中文教程graphify 安装报错怎么办graphify MCP 配置graphify Docker 部署graphify Agent 工作流graphify 与同类工具对比graphify 最佳实践graphify 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 跨境业务、多语言内容运营团队
  • 做语音类 AI 产品的开发者
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型

👥 适合人群

AI 技术爱好者研究人员和学生开发者和工程师技术创业者

🎯 使用场景

  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发

⚖️ 优点与不足

✅ 优点
  • +GitHub 47.7k Star,社区高度认可
  • +MIT 协议,可免费商用
  • +完全开源免费,无授权费用
  • +本地部署,数据完全自主可控
  • +开发者社区支持,遇问题可查可问
⚠️ 不足
  • 安装和初始配置可能需要一定技术基础
  • 功能完整性通常不如成熟商业产品
  • 技术支持主要依赖开源社区,响应速度不稳定
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

🔗 相关工具推荐

📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

支持Claude、OpenAI Codex、Google Gemini等主流LLM,可灵活扩展
💡 AI Skill Hub 点评

总体来看,Graphify AI编码助手 是一款质量优秀的AI工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

📚 深入学习 Graphify AI编码助手
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 graphify
原始描述 开源AI工具:AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and。⭐47.7k · Python
Topics AI编程助手多模型支持代码生成开源框架
GitHub https://github.com/safishamsi/graphify
License MIT
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/safishamsi/graphify 🌐 官方网站  https://graphifylabs.ai/

收录时间:2026-05-14 · 更新时间:2026-05-16 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。

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