grida

The Python SDK for Grida FX. Import it as grida.fx.

It does three things: it authors node types and workflows (node, Workflow), it hosts node bodies when the engine runs them (python -m grida.fx.host, started by the engine), and it drives the grida-fx engine from Python. The engine is the only place with engine logic: planning, identities, the cache and budgets all happen in grida-fx, never in this package.

Install

pip install grida

Python releases use stable versions starting at 0.1.0 (uv pip install grida also works). Each wheel carries the engine for its platform: macOS on Apple silicon (11 or later) and on Intel (10.12 or later), and Linux with glibc 2.28 or later on x86_64 and aarch64. Windows is not supported: the engine's runner uses Unix process groups and signals. There is no source distribution, and the wheels install no console command: the engine runs as python -m grida.fx.

From a checkout of the repository, python3 tools/build_engine.py builds the engine and puts it where a wheel carries it: python/src/grida/fx/_bin/grida-fx in the checkout (ignored by git). An editable install of the checkout's python/ folder (uv sync --project python, or a path dependency in another project) then finds it. Run it again after every pull.

The engine from the command line

python -m grida.fx plan workflows/gallery.yaml --poster inputs/poster.png
python -m grida.fx run workflows/gallery.yaml --poster inputs/poster.png --live --max-usd 10

python -m grida.fx <verb> … takes the same arguments as grida-fx and exits with its status (on POSIX it becomes the grida-fx process), so a Python installation needs no Node. The binary is the first of:

1. GRIDA_FX_BIN, when set (a path; relative to the current directory);
2. the one inside the package (grida/fx/_bin/grida-fx): shipped in the wheel, or built into a checkout by tools/build_engine.py;
3. grida-fx on PATH.

With none of them, the command prints grida-fx: … naming GRIDA_FX_BIN and exits 2.

Planning and running from Python

from grida.fx import plan, run

planned = plan("workflows/gallery.yaml", inputs={"poster": "inputs/poster.png"}, max_usd=10)
print(planned.ok, planned.problems, planned.estimate())  # estimate: (low, high) in US dollars
for phase in planned.phases():
    print(phase["phase"], phase["steps"], phase["high_usd"])

result = run(planned, live=True)  # runs the plan with the target and options it was planned with
print(result.ok, result.cost, result.incomplete, result.failed)
for key, image in result.outputs["images"].items():  # a keyed collection
    verdict = result.steps[f"entity['{key}'].review"].facts["verdict"]
    print(key, image.path, verdict)
missing = result.deliver({"images": "out/{key}.png"})

• Targets. A workflow file, a workflow id, a builder file.py:function (its arguments as arguments={"level": "docks"}, the command line's --arg), or a Plan. A Workflow object is refused with TypeError: name the builder that returns it.
• Options. inputs (a mapping, written to a temporary inputs file in cwd, so relative paths in it mean what they mean on the command line), input_files (--inputs), routes (--routes), max_usd (--max-usd); run adds live, yes_up_to and run_dir. cwd is the engine's working directory (default: the current one); every relative path is relative to it. A Plan takes no planning options when it runs: plan again to change them.
• plan asks the engine for the plan (grida-fx plan --json) and its price (grida-fx price). Plan.document is the fx-graph-v1 document; ok, problems (where, message), estimate() and phases() read it. Planning never spends.
• run plans first, and raises PlanRefused (the plan is refused: and one line per problem) before any run folder exists. A failed step does not raise: check result.ok and result.failed. The result is read from the run folder's events.jsonl: ok, incomplete, cost (the folder's charge over every invocation, in US dollars), run_dir, failed, failures, stopped, stand_in, outputs and steps.
• Failures. result.failures holds a Failure for each failed instance id: its path, message, code (the protocol's name of the error, such as node_failure or call_failed; None for a timeout, an assertion or a skip), the node facts it reported, and skipped (it never ran, because something it reads failed). result.stopped says why a run stopped early.
• Files. An output file has path (its copy in the planning project's store, <cache>/files/…: the project above the workflow file for a .yaml target, else above cwd; read it, never write it), digest, kind, name, size, key, read_bytes() and copy_to(). A keyed collection is a dict, a list a list, an absent output None.
• Delivering. result.deliver({output: path}) copies output files out of the run; {key} in the path names each element of a collection or list (keys are made safe as run folders make them). A file that already holds the same bytes is left alone. It returns the outputs that hold no file.
• Errors. The engine's own errors (exit status 2: a workflow it cannot read, a bad option) raise FxError with its message; so does a run it refuses to start (refused: …).
• Async. plan_async and run_async do the same inside an event loop; plan and run cannot be called inside a running one. Cancelling run_async stops the run as Ctrl-C would.

Stand-ins: running without a provider

A test runs a workflow's paid calls offline by answering them itself:

from collections import Counter
from pathlib import Path

from grida.fx import DECLINE, Answer, CallRefused, run

asked = Counter()


def answer(call):  # a plain function, or an async def
    asked[call.capability] += 1
    if call.capability == "image.generate":
        return Answer(files={"image": Path(f"tests/pictures/{call.request['size']}.png")})
    if call.capability == "structured.generate":
        return Answer.json({"title": "The lighthouse"})
    if call.capability == "agent.turn":
        return Answer.submit(caption="a lighthouse at dusk")
    if call.capability == "image.edit":
        raise CallRefused("no edits in this test")
    return DECLINE


result = run("workflows/gallery.yaml", run_dir="runs/test", stand_in=answer)
assert asked["image.generate"] == 2
assert result.failures["touch_up#1"].code == "capability_refused"

• The call. answer gets a StandInCall for each paid call the cache cannot answer: capability, route.id and route.fingerprint, key, takes and take, instance.id, .path and .step, request (every file in it an InputFile: digest, path, read_bytes(), facts), files (the same files by digest) and params (the call as the engine sent it, files as {"file": digest}).
• The answer. Answer(files={name: …}, data=…): a file is bytes (it takes the kind the capability names), an Output of bytes or an InputFile (its own kind), or a path (the kind of its suffix). Answer.json(value), Answer.turn(text, tool_calls) and Answer.submit(**arguments) build the data of structured.generate and agent.turn. Return DECLINE to leave a call unanswered (it fails not_live); raise CallRefused or CallFailed to refuse or fail it. The engine checks every answer as it checks a provider's: an image of the wrong size fails the call.
• One at a time. The function runs in this process, so counters and scripted answers work. Calls are answered one at a time, in the order they come.
• Faults. Anything else it raises, or a return of None, stops the run; run then raises that exception, noting the run folder.
• Nothing is spent, and the real store is left alone. A stand-in run is never live and takes no yes_up_to; run does not plan it first. It keeps everything in the store's stand-in/ folder, where its result's files are read, so a later stand-in run, or a resumed one, replays its answers; delete that folder to forget them. A folder resumes only in the mode it was started in, and reroll and pick refuse it.
• From the command line: grida-fx run … --stand-in tests/stand_in.py#answer.

Standard bodies

grida.fx.std holds the Python bodies of FX's free standard node types. The engine declares them, and runs them in this package's node host when a workflow uses one (fx/image.resize@1, …); nobody imports them to use them.

The bodies are ported byte for byte from FX's predecessor, and every picture they write comes from Pillow's own PNG encoder, so pillow is pinned to one exact version: another build may write other bytes, which would change every identity and request downstream of them. grida.fx.std.body_of("fx/<name>@1") returns a body; grida.fx.std.pictures holds the Pillow reads and writes behind ctx.read.image and ctx.out.png.