Devforge/pyproject.toml at main · kelvinator07/Devforge · GitHub

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[project]
name = "devforge"
version = "0.1.0"
description = "Enterprise multi-agent engineering platform (capstone)"
requires-python = ">=3.12"

dependencies = [
    # Core framework
    "openai>=1.50.0",
    "openai-agents>=0.0.16",
    "pydantic>=2.9.0",
    "pyyaml>=6.0",
    "httpx>=0.27.0",
    "tenacity>=9.0.0",
    "python-dotenv>=1.0.0",
    # Control plane (FastAPI + GitHub App + Clerk)
    "fastapi>=0.115.0",
    "uvicorn>=0.30.0",
    "mangum>=0.19.0",
    "pyjwt[crypto]>=2.9.0",
    "cryptography>=43.0.0,<47.0.0",
    "fastapi-clerk-auth>=0.0.7",
    # MCP (Day 6)
    "mcp>=1.0.0",
    # Observability — kept in base so the AWS worker exports traces to
    # LangFuse. Pure Python, no glibc constraint. The exporter no-ops
    # cleanly when LANGFUSE_* env vars are unset.
    "langfuse>=2.45.0",
]

[project.optional-dependencies]
# For local development — embeds + vectors run on your laptop.
# torch is pinned explicitly here (not just transitively via sentence-transformers)
# so that [tool.uv.sources] below routes it through PyTorch's CPU-only index —
# avoids dragging in 1.5 GB of NVIDIA CUDA wheels we never use.
local = [
    "chromadb>=0.5.0",
    "sentence-transformers>=3.0.0",
    "torch>=2.4.0",
]

# Security gates (Day 8). Worker installs this; control plane doesn't.
# semgrep's wheel requires manylinux_2_35 which the Lambda base image
# (public.ecr.aws/lambda/python:3.12) doesn't satisfy — keeping it in a
# dedicated extra so `--extra aws` (used by both images) stays slim.
security = [
    "detect-secrets>=1.5.0",
    "semgrep>=1.85.0",
]

# For deploy-to-AWS — no ML deps, just boto3.
aws = [
    "boto3>=1.40.0",
]

# All — use this during development so you can flip DEVFORGE_BACKEND freely.
all = [
    "devforge[local,aws]",
]

# Optional: observability (Day 12). LangFuse moved to base deps so it
# ships in the AWS worker image; this group kept as an alias for
# `uv sync --extra obs` to remain a no-op (back-compat for older docs).
obs = []

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
addopts = "--tb=short -q"

# ---------------------------------------------------------------------------
# uv config — route torch through PyTorch's CPU-only wheel index so `uv sync`
# doesn't drag in 1.5 GB of NVIDIA CUDA wheels (cudnn, cublas, cuda-runtime,
# etc.) that sentence-transformers transitively requires. The local Docker
# image was running out of disk during build; CI runs slower with the GPU
# stack. We never use GPU — all-MiniLM-L6-v2 runs identically on CPU.
# ---------------------------------------------------------------------------
[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true

[tool.uv.sources]
torch = [{ index = "pytorch-cpu" }]