graphify vs Understand-Anything
/graphify skill for Claude Code, Codex, Cursor and 20+ assistants: maps code, docs, PDFs and media into a local knowledge graph — tree-sitter AST, EXTRACTED/INFERRED edges, no vector store. — versus — Plugin for Claude Code and 16 other hosts that turns any codebase into an interactive knowledge graph — multi-agent analysis, layered dashboard, guided tours, diff-impact view, domain mapping.
Same pitch — a Claude Code plugin that turns a codebase into an interactive knowledge graph. understand-anything leans on multi-agent LLM analysis and guided tours; graphify builds the code graph deterministically from tree-sitter with zero LLM credits.
| graphify | Understand-Anything | |
|---|---|---|
| Stars | 122k | 84k |
| Forks | 12k | 7.1k |
| Language | Python | TypeScript |
| License | Apache-2.0 | MIT |
| Last activity | yesterday | 16 days ago |
| Topics | code-intel, knowledge-graphs, skills | code-intel |
| Curated connections | 5 | 7 |
graphify — the curator's take
Reach for graphify when your agent burns tokens re-reading a repo to answer 'how does X connect to Y': one run writes graph.json plus a GRAPH_REPORT.md, then `graphify query`, `path` and `explain` answer from the graph, and strict mode in Claude Code redirects the session's first raw file read to it. Code extraction is free and deterministic (tree-sitter, ~40 languages, Leiden communities, zero LLM credits); docs, PDFs and video cost model tokens for the semantic pass, so a docs-heavy corpus is not free. Every edge is tagged EXTRACTED or INFERRED — trust the first, verify the second. Skip it if you want an always-on MCP code server with blast-radius tooling (code-review-graph, codebase-memory-mcp) or symbol-level edits (serena): graphify is on-demand, and the background-sync version is the paid graphify.com platform. Disclosure: StackMap's own discovery layer runs on graphify.
Understand-Anything — the curator's take
The onboarding killer app, and its motto is the right one: graphs that teach, not graphs that impress. /understand runs a multi-agent pipeline over the repo, then the dashboard gives you architecture layers, dependency-ordered guided tours, semantic search, diff blast-radius, and a business-domain view that maps code to real processes. The team trick is the sleeper: the graph is plain JSON — commit it once and every teammate skips the analysis. Incremental re-runs + a post-commit auto-update hook keep it fresh. Budget real tokens for the first full run on a large repo (their own warning), or point it at a local model. NOT an agent context server — this graph is for humans first; when you want a code graph served TO agents over MCP, that's codegraph-mcp or tokensave territory.