Skip to main content

Knowledge graph extractor for SystemVerilog, Verilog, and VHDL

Project description

hdl-kgraph

CI

A knowledge graph for your HDL design. hdl-kgraph parses SystemVerilog, Verilog, and VHDL sources (and links SV DPI-C imports/exports to their C/C++ function definitions and cocotb Python testbenches to the DUTs they drive) 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: v2.4.0 — 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. v2.0 makes it scale to large (10–100+ GB) designs: the out-of-core, bounded-RAM architecture is fully delivered — every MCP/CLI query answers 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, and no query command full-loads the graph), the whole-design clock/reset/CDC/UVM reports are precomputed at build with an SQL-native out-of-core fallback, and an incremental update re-links and re-writes only the changed rows through the memory-bounded linker (now the default). See docs/scalability.md and the roadmap. M8's C/C++/Python boundary is in: SystemVerilog DPI-C imports/exports linked to their C/C++ function definitions (FOREIGN_BINDS, v2.3), and cocotb Python testbenches linked to the DUTs they drive (TEST_COVERS + dut.<signal> READS/DRIVES, v2.4).

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 elaboratorsVerible (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. The pass-2 link is incremental too, and as of v2.0 the memory-bounded linker is the default: for SystemVerilog/Verilog it re-resolves only the references whose target changed, reading just the dirty closure from SQLite (selective IR decode, out-of-core TEST_COVERS) instead of loading the prior graph — byte-identical to a full re-link; VHDL, binds, and --enrich fall back. The database write is scoped to the dirty closure too, so 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

Review digest & link metrics. review emits a content-free digest — parse health, graph shape, link quality, and analysis counts only, never identifiers — so it's safe to export from an air-gapped environment and diffs cleanly across builds. bench-link quantifies incremental-link locality (what fraction of references a typical edit re-resolves), the metric behind the bounded linker. → docs/review.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–v2.2, the v2.0 epic): bounded index-backed reads, precomputed whole-design summaries with an SQL-native out-of-core fallback, and a dirty-closure-scoped incremental link and write — delivered, so no query full-loads the graph and the tool scales to 10–100+ GB designs — 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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hdl_kgraph-2.4.0.tar.gz (708.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hdl_kgraph-2.4.0-py3-none-any.whl (360.6 kB view details)

Uploaded Python 3

File details

Details for the file hdl_kgraph-2.4.0.tar.gz.

File metadata

  • Download URL: hdl_kgraph-2.4.0.tar.gz
  • Upload date:
  • Size: 708.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for hdl_kgraph-2.4.0.tar.gz
Algorithm Hash digest
SHA256 a29a1e887bc2762a1718deebf68a73148d62569ece297ba260b1fea9f6ca8879
MD5 d3c55c78aacb204aecf6e22fd96c3fe8
BLAKE2b-256 26a4ff9538dd02f90076a49cc6e6c8b8b371595dc9cd17fd78c58aba16c65eea

See more details on using hashes here.

Provenance

The following attestation bundles were made for hdl_kgraph-2.4.0.tar.gz:

Publisher: release.yml on chuanseng-ng/hdl-kgraph

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file hdl_kgraph-2.4.0-py3-none-any.whl.

File metadata

  • Download URL: hdl_kgraph-2.4.0-py3-none-any.whl
  • Upload date:
  • Size: 360.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for hdl_kgraph-2.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 76451f60763b87b83b40458064725836bab00c08f642f87d9e7c68131a74fcc5
MD5 c8d91a231ec5d9042ffb96f4d333fa74
BLAKE2b-256 9e896699cface7d0ca2c1081ed9a9c249c7cd7f14ccb69442b6938a146c71121

See more details on using hashes here.

Provenance

The following attestation bundles were made for hdl_kgraph-2.4.0-py3-none-any.whl:

Publisher: release.yml on chuanseng-ng/hdl-kgraph

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page