repokg 0.1.0

Generate an AI-ready knowledge graph (KNOWLEDGE_GRAPH.md + kg.json) of any codebase: modules, dependency edges, branches, PRs, ops surface.

repokg

Generate an AI-ready knowledge graph of any codebase — so an AI agent (or a new developer) can read one file and start building immediately.

repokg extracts everything that can be known deterministically about a repo — module inventory, internal import graph, every branch classified against every PR (merged / squash-merged / abandoned / stale), contributor stats, CI/Docker/Helm/Make surface — and renders it as:

• KNOWLEDGE_GRAPH.md — a single human/AI-readable document with a mermaid architecture graph, module tables, branch & PR catalog, timeline, and ops inventory.
• .repokg/kg.json — the same graph, machine-readable.

The semantic layer (module purposes, data-flow narratives, project eras, gotchas) can't be produced by static analysis without guessing — so repokg is agent-first: it emits .repokg/prompts/enrich.md, a rigorous prompt any AI coding agent (Claude Code, Cursor, Copilot Workspace…) executes to verify-and-fill the narrative sections, writing .repokg/narratives.json. Re-render and the knowledge graph is complete. No API keys, no LLM dependency in the tool itself.

Install

pipx install repokg        # or: pip install repokg
# from source:
pipx install git+https://github.com/NehharShah/repokg

Requirements: Python ≥ 3.9, git. Optional: gh (logged in) for the PR/branch cross-reference — without it the knowledge graph still builds, minus PR data.

Usage

cd your-repo
repokg                      # = generate: scan + prompts + render

Output:

.repokg/kg.json               # machine-readable knowledge graph
.repokg/prompts/enrich.md        # hand this to your AI agent
KNOWLEDGE_GRAPH.md                        # the knowledge graph document

Then, in your AI agent of choice:

Follow the instructions in .repokg/prompts/enrich.md

The agent explores the code, writes .repokg/narratives.json, and runs repokg render — KNOWLEDGE_GRAPH.md now carries verified purposes, data flows, timeline eras, and gotchas alongside the deterministic structure.

Commands

Command	Effect
repokg scan [path]	Extract structure → .repokg/kg.json
repokg prompts [path]	Write the enrichment prompt
repokg render [path]	kg.json (+ narratives.json) → KNOWLEDGE_GRAPH.md
repokg generate [path]	All three (default)
repokg inject [path]	Wire the knowledge graph into CLAUDE.md / AGENTS.md / Cursor rules
repokg check [path]	Exit 1 if the knowledge graph is stale vs HEAD (CI-friendly)

Flags: --out DIR (default <repo>/.repokg), --md FILE (default <repo>/KNOWLEDGE_GRAPH.md), --no-github, --pr-limit N.

Agent integration

repokg inject adds a managed block (delimited by <!-- repokg:begin/end -->, idempotent, never touches your hand-written content) pointing agents at KNOWLEDGE_GRAPH.md:

• CLAUDE.md (Claude Code) — updated if present
• AGENTS.md (the cross-tool agent standard) — updated if present, created if no agent file exists at all
• .github/copilot-instructions.md (Copilot) — updated if present
• .cursor/rules/repokg.mdc (Cursor, with alwaysApply: true) — created if .cursor/rules/ exists; falls back to legacy .cursorrules

Keep it fresh in CI:

- run: pipx run repokg check . || echo "::warning::KNOWLEDGE_GRAPH.md is stale"

KNOWLEDGE_GRAPH.md itself also lists any agent-context files it found, so an agent landing on the knowledge graph discovers your rules — and vice versa.

What gets extracted (all verified, never guessed)

Area	How
Branch classification	git for-each-ref + --merged ancestry vs the integration branch (auto-detects staging/develop), cross-referenced with every PR's head ref via gh — distinguishes true merges from squash-merges from abandoned work
PR catalog	gh pr list --state all — open / merged / closed-unmerged, full appendix table
Module inventory	Filesystem walk with LOC per directory, language detection, generated-code flagging
Import graph	Go: import blocks resolved against go.mod module paths · Python: stdlib ast incl. relative imports · JS/TS: relative import/require resolution. Directory→directory edges with counts
Ops surface	CI workflow names, Dockerfiles, compose files, Helm charts, Makefile targets, config/docs/test/migration dirs
Timeline	Merged PRs grouped by month with conventional-commit scope frequencies (replaced by agent-written eras after enrichment)

Why agent-first instead of calling an LLM API?

Because the enrichment quality depends on reading the code, and your coding agent already has the repo open, tools to search it, and your permission model. A prompt it can execute beats a second LLM integration with its own keys, costs, and context limits. The contract between tool and agent is one JSON file (narratives.json) with a fixed schema — everything else stays deterministic and reproducible.

Known limitations

• JS/TS: only relative imports are resolved; alias imports (@/…, tsconfig paths) are ignored.
• Fork PRs: a fork PR whose head branch name matches a local branch will be linked to it (GitHub's API reports bare head refs).
• Python: packages are discovered at the repo root and under src/; deeper monorepo layouts (packages/*/src/…) get file-level edges only.
• Branch ahead counts use one batched git call on git ≥ 2.41, with a per-branch fallback on older git.

Roadmap

• Rust / Java / Kotlin import graphs
• --exclude glob patterns
• llms.txt emission alongside KNOWLEDGE_GRAPH.md
• tsconfig paths alias resolution
• PyPI release + prebuilt GitHub Action

Development

pip install -e .
python -m unittest discover -s tests -v

No runtime dependencies — stdlib only.

License

MIT