hdl-kgraph 1.12.0

Knowledge graph extractor for SystemVerilog, Verilog, and VHDL

hdl-kgraph

A knowledge graph for your HDL design. hdl-kgraph parses SystemVerilog, Verilog, and VHDL sources and builds a queryable graph of modules, entities, instances, ports, parameters, signals, classes, packages, and the relationships between them — design hierarchy, port connectivity, package imports, class inheritance, clock domains, and more.

Status: v1.0 — stable. Milestones M1–M7 are in: SystemVerilog/Verilog and VHDL extraction with mixed-language linking, the SV preprocessor and real-world build inputs (.f filelists, defines, hdl-kgraph.toml), incremental rebuilds and watch mode, the clock/reset/CDC/lint/metrics analyses with an interactive visualization, and an MCP server so AI assistants can query the design. M7 adds opt-in semantic enrichment (build --enrich, pip install 'hdl-kgraph[enrich]'): the pyslang frontend elaborates the design — unrolling parameterized generates so instance counts match reality — and records a discrepancy report. hdl-kgraph enriched summarizes exactly what enrichment changed vs the default build. v0.9–v0.10 make it scale to large (10–100+ GB) designs: MCP/CLI queries answer from a bounded, index-backed subgraph instead of loading the whole graph (a localized query is sub-millisecond and tracks the answer size, not the design size), the whole-design clock/UVM reports are precomputed at build, and an incremental update reads and writes only the changed rows. See docs/scalability.md and the roadmap.

Why

HDL codebases are graphs — hierarchy, connectivity, clock domains — but the tools that understand them are locked inside simulators and synthesis flows. hdl-kgraph extracts that structure into a local-first SQLite database you can query from the CLI, scripts, or AI assistants via MCP. The architecture follows code-review-graph, adapted for hardware.

How this differs

The closest free, mature alternatives are full elaborators — Verible (SV linter/indexer with a Kythe knowledge-graph export), Surelog/UHDM (IEEE-1800 elaboration to a queryable model), Verilator (--xml-only elaborated AST). They're excellent, but they need a complete, compilable design. hdl-kgraph's default tier is syntactic (tree-sitter): it parses files with ERROR nodes and missing dependencies, so it builds a graph from incomplete or in-progress RTL that an elaborator would reject — then --enrich overlays an actual frontend (pyslang/GHDL) for elaborated precision where one is available.

	Verible	Surelog/UHDM	Verilator --xml-only	hdl-kgraph
Parses incomplete / broken / missing-dep sources	partial (error-recovering)	no (elaborates)	no (elaborates)	yes (tree-sitter, ERROR-tolerant)
Dependency footprint	C++ build	C++ build	C++ build	pip, pure-Python default
Output store	Kythe graph	UHDM model	XML AST	local SQLite, queryable
AI / MCP query surface	—	—	—	yes
Full elaboration (params, generates)	—	yes	yes	opt-in overlay (--enrich)

So hdl-kgraph sits alongside these tools rather than against them — the syntactic tier is the always-works floor for partial designs, and a native elaborator can plug in as an enrichment backend for the precision overlay.

Quickstart

pip install hdl-kgraph

hdl-kgraph build ./rtl            # parse sources -> ./rtl/.hdl-kgraph/graph.db
hdl-kgraph build -f sim/tb.f      # or drive the build from a vendor-style filelist
hdl-kgraph merge a.db b.db --db soc.db  # assemble per-block DBs into one SoC graph
hdl-kgraph status                 # files, parse errors, node/edge counts
hdl-kgraph tree soc_top           # print the design hierarchy from a top module
hdl-kgraph query instances-of fifo
hdl-kgraph query unresolved       # what couldn't be resolved (vendor IP, macros)

Most commands take --json for scripting. Unresolved targets render as [?] and ambiguous matches as [~0.6]: every cross-file edge carries a confidence score — the graph's honest contract about what was proven syntactically vs inferred by name matching (docs/extraction.md).

Features

Real-world build inputs. .f filelists (+incdir+/+define+, nested -f, $VAR expansion), preprocessor defines and include dirs, VHDL library mapping, --exclude/--max-file-size for vendored IP, and an hdl-kgraph.toml config. Per-file diagnostics (build -v, status --errors) tell you which files have parse errors or preprocessor warnings and why files were skipped. → docs/build-inputs.md

Incremental updates. update re-parses only changed files plus their include/macro dependents — one edit in a 2000-file design lands in about 1.5 s (budget < 1.8 s) — and watch does it on every save burst. As of v0.8 the pass-2 link is incremental too: for SystemVerilog/Verilog it re-resolves only the references whose target changed and mutates the prior resolved graph in place (byte-identical to a full re-link; VHDL, binds, and --enrich fall back). v0.10 scopes the database write to the dirty closure — a one-file edit reads and writes ~0.04 % of the rows, not the whole graph. detect-changes (exit codes: 0 clean, 1 dirty, 2 error; diffs against git, svn, or Perforce) and impact answer "what changed, and what does it affect?" in CI. → docs/incremental.md

Database merge & subtree caching. merge assembles independently-built block databases into one SoC graph by unioning their per-file IRs and re-linking once — byte-identical to a monolithic build of the same files. Keep each block's DB as a cache: when one block changes, rebuild only it and re-merge, reusing the unchanged blocks' cached IRs instead of re-parsing them (parse cost scales with the change; the link is paid once). → docs/merge-design.md

Mixed Verilog/VHDL designs link into one hierarchy. tree and query cross the language boundary in both directions; cross-language matches are scored ≤0.8, never 1.0. → docs/extraction.md

Analyses. Clock domains, reset trees, CDC suspects, signal drivers/readers, UVM topology, graph-level lint checks, fan-in/out and hub/bridge metrics, and a self-contained interactive HTML visualization (visualize: hierarchy + force-directed views, community filters, --kinds/--exclude-kinds to plot only the categories of interest, --collapse for subsystem supernodes, --layout tiers for large designs). → docs/analyses.md

AI assistants over MCP. hdl-kgraph setup detects installed assistants (Claude Code/Desktop, Cursor, Codex, Windsurf, Gemini CLI, VS Code), writes their MCP config, and seeds their instruction files (CLAUDE.md, AGENTS.md, …) with notes on querying the graph; hdl-kgraph serve exposes nine read-only, paginated tools (pip install 'hdl-kgraph[mcp]'). Each tool answers from the bounded subgraph it needs (v0.9), so queries stay fast on very large designs. Can't run MCP? The same nine tools are available as plain JSON-printing commands under hdl-kgraph tools … (no [mcp] extra needed). → docs/mcp.md

What gets extracted

• Design units: modules, interfaces, packages, programs; VHDL entities, architectures, packages, configurations
• Structure: instances with port connections and parameter overrides, include/define relationships, filelists
• Verification: SV classes (UVM hierarchies via inheritance chains), constraints, covergroups, assertions/properties/sequences, clocking blocks
• Dataflow: signal drivers/readers (process-, assign-, and instance-level), clock and reset trees, CDC-suspect crossings

Modports, checkers, UDPs, and generate blocks are not extracted yet. The full list, the confidence convention, and the schema pointers live in docs/extraction.md.

Roadmap at a glance

Milestone	Theme
M1 (v0.1)	SystemVerilog/Verilog structural graph + CLI
M2 (v0.2)	Preprocessor, .f filelists, includes
M3 (v0.3)	VHDL + mixed-language linking
M4 (v0.4)	Incremental updates, watch mode, impact analysis (v0.8: incremental pass-2 link)
M5 (v0.5)	Clock/reset/CDC analyses, lint checks, visualization
M6 (v0.6)	MCP server for AI assistants
M7 (v0.7)	Semantic enrichment via native frontends (pyslang + GHDL elaboration)
M8 (v1.x)	C/C++/Python boundary (DPI-C, cocotb)
M9 (v1.x)	Chisel/FIRRTL, Amaranth, SpinalHDL
M10 (v1.x)	Tcl/SDC/UPF constraints, Perl scripting, SLN portable stimulus

Cross-cutting (v0.9–v0.10): bounded index-backed reads, precomputed whole-design summaries, and a dirty-closure-scoped incremental write, so the tool scales to 10–100+ GB graphs — docs/scalability.md.

Details and acceptance criteria: ROADMAP.md.

Development

git clone https://github.com/chuanseng-ng/hdl-kgraph
cd hdl-kgraph
pip install -e .[dev]
ruff check . && ruff format --check . && mypy && pytest

See CONTRIBUTING.md. The single most useful contribution right now: the smallest HDL file that breaks extraction.

License

MIT