KohakuNodeIR

A text IR for node graphs: what an editor draws, a person reads, and a machine runs, as one program.

Specification  ·  Architecture  ·  Examples  ·  Discord

Is this for you?

You probably want KIR if you build node-based tools (an image pipeline, an agent workflow, a captioning graph) and want the graph to be a real program: diffable text, loops and branches as explicit control flow, typed nodes from signature files, a reference interpreter that defines the result, and an executor that is free to run whatever the wires allow in parallel.

You probably don't if a plain DAG of tasks covers everything you need (a workflow scheduler already does that well), or you want a general-purpose scripting language. KIR has no expressions: every computation is a node, and nodes are written in your host language.

What KIR is

KIR describes one job as a node graph. Nodes are joined by two kinds of wire: control wires ("then", top and bottom ports) and data wires (values, side ports). The text is that graph written line by line:

(inputs)node(outputs)          a node; data wires are the names
label:                         a namespace, entered only by a builtin
(cond)branch(`yes`, `no`)      control flow is explicit: branch, switch, jump, parallel, each
@dataflow:                     a block ordered by data alone
@def (x: int)double(y: int):   a subgraph, called like any node

A complete program (examples/tour/01_each.kir):

image = "photo"
boxes = [1, 2, 3, 4]
nodes = []
(boxes)each(box, i, `body`)
body:
    (box)is_new(fresh)
    (fresh)branch(`new_box`, `known_box`)
    new_box:
        (image, box)crop(c)
        (c)entity(node)
    known_box:
        node = None
    (nodes, node)append(nodes)
(nodes)summarize(summary)

A job means its sequential run. Statements run top to bottom, namespaces are entered only by builtins, and a name holds the value last written to it.

Parallel by default. Nodes talk only through their wires, so an executor may run any two nodes in either order or at once unless a data wire, the control path, or a node flag (mutates, ordered) orders them. Nothing in a program has to say what may run in parallel, the way a CPU issues several instructions of one thread at once while the program order still defines the result. parallel exists, but it states intent only.

One language, many forms. A program can be shown as a graph (for editors) or as text (for people and tools). Optimization passes rewrite it into simpler programs in the same language, down to a form an interpreter runs directly. Nothing in the text marks which form it is in; every form is KIR, and every form runs.

Where it fits

KIR sits between editors and executors. An editor (the example in apps/kir-editor, a ComfyUI import, a Python builder, or a person with a text editor) produces KIR; a machine receives KIR plus engines that implement its nodes and never needs to know how the KIR was made.

Key features

• Text and graph are the same program. kir graph / kir text convert both ways; canonical text survives the round trip byte for byte, and behaviour is always preserved.
• Explicit control flow as wires. branch, switch, jump, parallel, each and @try / @except each have a node shape, so loops and branches are visible in the graph, not hidden in node code.
• Optimization passes over one language. order_dataflow, inline, expand_each, version, group, strip_meta. Any prefix of the list is a valid program with the same behaviour; a tool picks the form it wants (kir lower --passes ..., compile_source(..., passes=[...])).
• Signatures and @use. Node libraries export .kirsig.json files (ports, types, defaults, flags), so the checker, editors and the language server never import node code. @use "gbc" as g makes a library callable.
• A static checker. Undefined reads on any path, wrong port counts, type mismatches, ambiguous names, bad labels, dataflow cycles: one code per rule (checks.md).
• Program hashes. A semantic hash (ignores @meta and @hint) to key caches and reproducibility, and a full hash that also covers hints.
• Hints that never change results. @hint lane="vlm" tells a scheduler something; deleting every hint leaves every result unchanged.
• A reference interpreter with a call hook, so a machine can replay its recorded node results and check that it computed what the program means.
• Editing support. A tree-sitter grammar, a VS Code extension, and a language server (kir-lsp: diagnostics, hover, completion, go-to-definition, formatting).
• ComfyUI import / export. Both the UI workflow and the API prompt.
• A Rust core (PyO3 module and WASM build) that reproduces the Python reference byte for byte on the shared golden corpus.

Quick start

Python 3.10 or newer. At run time kohakunode depends only on lark.

1. Install

pip install kohakunode                 # the Python reference, the Rust core (kohakunode._rs), `kir`
pip install "kohakunode[lsp]"          # plus the language server `kir-lsp`

From source (builds the Rust core, so a Rust toolchain is needed):

git clone https://github.com/KohakuBlueleaf/KohakuNodeIR.git
cd KohakuNodeIR
pip install -e ".[dev]"                # pytest, ruff, black, pygls, maturin

2. Write nodes

# my_nodes.py
from kohakunode import NodeLibrary

lib = NodeLibrary("my_nodes")

@lib.node(outputs=["sum"])
def add(a: int, b: int) -> int:
    return a + b

@lib.node(outputs=["graph"], mutates=["graph"])   # changes its input in place
def attach(graph: list, node: str) -> list:
    graph.append(node)
    return graph

@lib.node(ordered=True)                             # touches the outside world
def log(message: str) -> None:
    print(message)

A node may depend only on its inputs and affect the job only through its outputs; the two flags declare the exceptions once, in the registry (writing nodes).

3. Export signatures for tools that should not import your code

kir sig export my_nodes -o sigs/       # writes sigs/my_nodes.kirsig.json

Programs then say @use "my_nodes"; the tools find the file on --sig-path or KIR_SIG_PATH.

4. Use the toolchain

kir check prog.kir --lib my_nodes            # diagnostics with codes (checks.md)
kir fmt prog.kir -w                          # canonical text, in place
kir lower prog.kir --lib my_nodes            # after every pass
kir lower prog.kir --passes order_dataflow,inline --hash
kir graph prog.kir -o prog.kirgraph          # text -> graph view (--data variables: name -> node -> name)
kir text prog.kirgraph                       # graph view -> canonical text (--no-meta to drop ids/positions)
kir run prog.kir --lib my_nodes              # the reference run; prints final values
kir-lsp                                      # language server on stdio

--lib imports a Python module that defines a NodeLibrary; --sig-path adds directories of signature files.

Core mental model

Two kinds of wire

            control in
               |
      +--------v--------+
 a -->|                 |--> sum         data wires: values, by name
 b -->|   (a, b)add     |
      +--------+--------+
               |
            control out                  control wires: "then"

A call node has control ports on top and bottom and data ports on its sides. branch has one control output per label, merge joins control paths (a labelled merge is a loop entry), each has body / done outputs and a next input its body returns to. The full list of node kinds and ports is in graph.md.

Namespaces and continuation

(is_big)branch(`big`, `small`)
big:
    (x)shrink(x)
small:
    (x)grow(x)
(x)log()              # both paths continue here, after the namespace definitions

A namespace is skipped when reached in sequence and entered only by a builtin. When its body ends, execution continues after the namespace's definition; an each body returns to its each; parallel arms run one after another; @dataflow: and @try bodies continue after their statement. jump is the only way back, so a loop is a namespace entered by a jump from before it and re-entered by a jump from inside it.

The pass pipeline

 .kirgraph (editor) <-> canonical .kir text
                             |
                         check (E/W codes)
                             |
  order_dataflow -> inline -> expand_each -> version -> group -> strip_meta
       (every intermediate program is valid KIR and runs)
                             |
          +------------------+------------------+
          |                                     |
 reference interpreter                  machines (parallel,
 (sequential, defines the result)       as if sequential)

After every pass, each @dataflow: block is a straight run whose dependence graph a machine computes once: every write has its own version x__n, and a name written on several paths gets a home version x__0. Control statements between blocks choose what runs next; a job's state is its position plus its values (passes.md).

Toolchain

Rust core

maturin develop --release                   # from the repo root: kohakunode with kohakunode._rs, into the active venv
python rust/tests/parity/parity.py          # the Rust core against the Python reference
cd rust && cargo test                       # unit tests + golden corpus, byte for byte

# WASM for browsers
rustup target add wasm32-unknown-unknown
cargo install wasm-bindgen-cli --version 0.2.129   # must match wasm-bindgen in Cargo.lock
cargo build --release --target wasm32-unknown-unknown --features wasm
wasm-bindgen --target web --out-dir pkg target/wasm32-unknown-unknown/release/kohakunode_rs.wasm

kohakunode._rs takes and returns text: format, parse_to_json, check, lower(passes=None), hashes, to_graph, from_graph, std_signatures; signatures come as .kirsig.json texts and, for check / lower / hashes / to_graph, through base= and sig_path= the way @use resolves them. The WASM build exposes the same functions to browsers (signature texts only). \N{...} string escapes use the default unicode-names feature; --no-default-features --features wasm builds a smaller WASM file without it.

VS Code

pip install -e ".[lsp]"
cd editors/vscode && npm install
ln -s "$(pwd)" ~/.vscode/extensions/kir-language

Settings kir.server.path, kir.server.args and kir.sigPath choose the server command and the signature directories (editors/vscode).

Example node editor

pip install fastapi uvicorn
cd apps/kir-editor/backend && python -m uvicorn kir_editor.app:app --port 48888
cd apps/kir-editor/frontend && npm install && npm run dev     # http://localhost:5173

Five views of one program: nodes (ComfyUI / Blender / UE style, with data as variables or wires and control as exec wires or frames), a classic ROBO Pro flowchart, an EV3-style sequence, Scratch blocks, and text; any two side by side. A palette built from signature files, an inspector, undo / redo, and Problems / Run / Lowered panels with per-pass toggles (apps/kir-editor).

Python API

from kohakunode import (
    parse, write,                  # text <-> AST; parse(write(p)) == p
    check,                         # diagnostics
    compile_source, compile_file,  # env + check + passes + hashes in one call
    Interpreter,                   # the sequential reference run, with a call hook
    NodeLibrary,                   # the node registry
)
from kohakunode.graph import to_graph, from_graph          # graph view
from kohakunode.comfyui import from_comfyui, to_api        # ComfyUI
from kohakunode.lower import PASSES, run_passes            # individual passes

compiled = compile_source(text, [lib], passes=["order_dataflow", "expand_each"])
compiled.program         # the checked program as written
compiled.lowered         # after the chosen passes (all of them when passes=None)
compiled.diagnostics     # warnings; errors raise KirCheckError
compiled.semantic_hash, compiled.full_hash

graph = to_graph(compiled.program, compiled.env)   # .kirgraph dict
text = write(from_graph(graph, with_meta=False))   # canonical text

def replay(name, inputs, call):                    # a machine's recorded results
    return recorded[(name, key(inputs))]

Interpreter(compiled.lowered, compiled.env, hook=replay).run()

Codebase map

src/kohakunode/
  syntax/      kir.lark grammar, parser, AST, canonical writer
  sig/         NodeLibrary, .kirsig.json files, @use resolution, types, kir.std
  flow/        control-flow graph, reaching definitions, dataflow ordering
  check/       every rule of docs/spec/checks.md
  lower/       the optimization passes and program hashes
  run/         the sequential reference interpreter
  graph/       text <-> graph view
  comfyui/     ComfyUI workflow / API prompt import and export
  lsp/         the language server (kir-lsp)
  cli/         the kir command
  compiler.py  compile_source / compile_program / compile_file
rust/          the Rust core, PyO3 and WASM bindings
editors/       tree-sitter-kir, vscode
apps/          kir-editor (example node editor)
examples/      basics (v1 programs), tour (one file per feature), gbc, comfyui
tests/         unit tests, the v1 reference corpus, the golden corpus
docs/          spec/, guides/, architecture.md, plan/

Documentation map

Specification

Overview · Language · Semantics · Signatures and @use · Graph view · Optimization passes · Checks

Guides

Writing nodes · Running KIR on a machine · Examples

Internals

Architecture · v2 work plan

Testing

pytest                                   # the Python reference: unit tests, golden corpus, v1 parity, generated programs
cd rust && cargo test                    # the Rust core against the same golden corpus
cd apps/kir-editor/frontend && npm run test:e2e   # one browser smoke run of the example editor

The golden corpus (tests/corpus/golden) holds the canonical text, the fully optimized text, the graph view and the hashes of every case; the Python reference and the Rust core must both reproduce it byte for byte. The v1 corpus (tests/corpus/v1) records what the v1 implementation printed for every v1 program; v2 reproduces it except for listed, deliberate differences.

Releases

kohakunode is one wheel: the Python reference plus the Rust core as the extension module kohakunode._rs. The version is rust/Cargo.toml's (pyproject.toml reads it dynamically). Publishing a GitHub release, or pushing a v* tag, runs release.yml: wheels for CPython 3.10–3.14 on Linux x86_64 / aarch64 (manylinux 2_28), Windows x64 and macOS arm64, Android wheels (arm64, x86_64; Python 3.13) and the sdist, all uploaded to PyPI and to the GitHub release.

Coming from v1

v2 keeps v1's model (sequential meaning, namespaces entered by builtins, jump loops, @dataflow:, @def) and every program v1 parsed still parses. New: @use, each, @try / @except err, typed @def parameters and outputs, @hint, node flags, the graph view with control ports, and optimization passes in place of fixed levels. The v1 implementation is in the git history (commit 1abda17).

License

Apache-2.0

Copyright 2026 Shih-Ying Yeh (KohakuBlueLeaf) and contributors.

Community

• Discord: https://discord.gg/xWYrkyvJ2s
• Issues: https://github.com/KohakuBlueleaf/KohakuNodeIR/issues

FAQ

Does a program have to say what runs in parallel? No. A job means its sequential run, and an executor may overlap anything the wires, the control path and node flags allow. parallel states intent only.

Why is there no expression syntax? Every computation is a node, so the graph and the text stay the same thing. kir.std provides the small pure nodes most programs need (add, less, len, append, to_str, ...).

What does an executor need besides the text? Implementations of the nodes the program calls. Signature files describe them to tools; executors map node names to implementations themselves.

How do I know a machine computed the right thing? Run sample jobs on the machine and on the reference interpreter with a call hook that replays the machine's node results, then compare final values (machines.md).

Which pass output should a machine run? Its choice. The fully optimized program is the simplest to schedule; a machine that schedules each itself can stop before expand_each. Every form means the same thing.