Graphify: A Claude Code Skill That Built a Knowledge Graph of Your Repo — 450k Downloads, 40k Stars in 26 Days
On April 5th, a developer shipped graphify — a Claude Code skill that indexes your entire codebase into a persistent knowledge graph. Type /graphify . and it reads every file, detects communities via the Leiden algorithm, and gives Claude long-term memory of the repo. The key metric: 71x fewer tokens per query vs. reading raw files.
Explosive Adoption
- 26 days after launch: 450k+ PyPI downloads, ~40k GitHub stars
- Peaked at GitHub global rank #2 in its first week
- Spawning Medium articles, YouTube tutorials, and third-party build-ons
Unexpected Use Cases
The creator reports people aren't just using it for code. They're feeding graphify:
- SQL schemas
- Obsidian vaults
- Research paper corpora
- Transcribed meeting recordings
- Whiteboard photos
And querying across all of them with /graphify query "..." — which became the primary command.
What's Missing?
The author is now asking the community: what's broken or missing in your workflow? The repo is actively evolving based on feedback.
Who It's For
Developers using Claude Code who want persistent, low-token-cost context for large codebases or mixed datasets.
📖 Read the full source: r/ClaudeAI
👀 See Also
OpenClaw memory loss fix using Mem0 plugin
OpenClaw agents experience memory loss due to context compaction rewriting files like MEMORY.md. The Mem0 plugin solves this by moving memory outside the context window with auto-recall and auto-capture features.
OpenClaw Memos Plugin Addresses Memory Handoff Issues in AI Coding Agents
A Reddit user shares how the Claude code leak highlighted problems with memory handoff in AI coding agents, where bloated transcripts cause issues during model switching. They implemented the memos plugin in OpenClaw with selective recall strategy to compress recent work and drop stale tool calls.
Stanford Researchers Release OpenJarvis: A Local-First Framework for On-Device AI Agents
Stanford researchers have released OpenJarvis, a local-first framework for building on-device personal AI agents with tools, memory, and learning capabilities. The project includes GitHub repository and website links for developers to explore.
Tacit: An LLM-First Programming Language Built with Claude Code and Opus 4.7
Tacit is an experimental LLM-first programming language designed and implemented using Claude Code and Opus 4.7. It strips away human conveniences to minimize token usage and ships with a primer that teaches mid-tier+ LLMs (Sonnet and above) how to write Tacit code.