framesdkpy 0.3.0

Python SDK for FRAME -- typed project-context architecture for AI-assisted development

FrameSDK

The Python SDK for FRAME -- a typed project-context architecture for AI-assisted development.

When you switch coding agents, the project forgets itself. Not its code -- the code is fine. But the understanding. The rules you agreed on. The decisions you made and why. The checks that matter. Things previous agents touched or broke.

FRAME gives the project a typed shape that agents and tools read consistently. framesdkpy is how Python tools read that shape.

What it does

Takes a .haxaml/ directory with 5 YAML files and returns a typed FRAME object:

from framesdkpy import load_frame

frame = load_frame(".haxaml/")
frame.facts.profile.name          # "Pharmax"
frame.rules.governance_level      # "strict"
frame.map.entrypoints[0].path     # "Backend/main.py"
frame.expect.checks["backend_tests"].pass_condition  # "exit_code == 0"

Every downstream tool -- Haxaml, a CLI, a CI pipeline -- gets the same shaped answer. Cross-language SDKs return the same JSON shape.

Install

uv add framesdkpy
# or
pip install framesdkpy

Requires Python 3.11+. Three dependencies: PyYAML, jsonschema, referencing. That's it. No Pydantic, no heavy framework.

What's in the box

• loaders -- load_frame() builds a typed FRAME from 5 YAML files. Strict single-directory discovery. Schema and character limit validation at load time.
• models -- 27 typed dataclasses across 7 files. One import: from framesdkpy.models import FRAME.
• validators -- Schema, character limits, cross-file consistency. Callable independently or through the loader.
• translators -- YAML to JSON with full normalization. Handles yes/True, ~/None, on/off rejection.

Usage patterns

from framesdkpy import load_frame, translate_directory, validate_file

# Full pipeline -- load all 5 files, validate, assemble
frame = load_frame(".haxaml/")

# Translate YAML to clean dict (normalized, but no validation)
data = translate_directory(".haxaml/")

# Validate a single file without loading the full model
result = validate_file(".haxaml/facts.yaml")
print(result.summary())  # "valid" or "2 error(s), 1 warning(s)"

# Serialize for cross-language use
json_string = frame.to_json()

How it's built

Spec-first. Every module has a design doc (docs/models.md, docs/loaders.md, etc.) with locked decisions before any code was written. 106 tests cover construction, serialization, YAML normalization, schema enforcement, character limits, cross-file checks, and integration against a real Pharmax fixture.

No graph building, no cross-referencing, no governance. That's Haxaml's job. framesdkpy is pure ingestion -- load, validate, assemble, return.