pyproject.toml

[project]
name = "multi-agent-land"
version = "0.1.0"
description = "Small-model multi-agent Gradio experience for Thousand Token Wood."
requires-python = ">=3.10"
dependencies = [
  # The HF Spaces builder installs gradio[oauth,mcp]; carrying the same extras here makes
  # our lockfile resolve under the same constraints (notably the mcp extra's pydantic ceiling),
  # so requirements.txt can't drift into a version the Space rejects.
  "gradio[oauth,mcp]>=4.44.0",
  "pydantic>=2.8.0",
  "python-dotenv>=1.0.1",
  "openai>=1.40.0",
  "pyyaml>=6.0",
  # gradio's queueing layer (`_get_df`) is written against pandas 2.x APIs (the `copy=`
  # keyword on `infer_objects`, the `future.no_silent_downcasting` option). pandas 3.0
  # deprecates both, so running pandas 3 makes gradio emit Pandas4Warnings on every queue
  # poll. Cap at <3 to keep gradio on its supported pandas set until upstream updates it;
  # our own pandas use is a trivial version-agnostic DataFrame constructor.
  "pandas>=2.0,<3",
  "modal>=1.4.3",
  "opentelemetry-api>=1.20.0",
  "opentelemetry-sdk>=1.20.0",
  "opentelemetry-semantic-conventions>=0.41b0",
  # The app requires real infrastructure — a DB and live inference — and refuses to
  # run without it; there is no offline/stub product mode. Everything the live path
  # touches is therefore a core dependency rather than an opt-in extra:
  #   - sqlalchemy + psycopg : durable event store backend (ADR-0014); psycopg is the Neon driver.
  #   - litellm              : model gateway to the OpenAI-compatible endpoints (ADR-0015).
  #   - instructor           : validated, schema-constrained structured output (ADR-0016).
  #   - mcp                  : Model Context Protocol tool transport (ADR-0017).
  #   - mem0ai + sentence-transformers : semantic memory index over the ledger (ADR-0018).
  # These are still imported lazily at their call sites, but they always ship.
  "sqlalchemy>=2.0",
  "psycopg[binary]>=3",
  "litellm>=1.40",
  "instructor>=1.5",
  "mcp>=1.0",
  "mem0ai>=0.1",
  "sentence-transformers>=3.0",
  # torch ships transitively via sentence-transformers; pin it into the ZeroGPU-supported
  # range (HF ZeroGPU accepts 2.8.0–2.11.0) so the HF Space build doesn't try to install
  # an unsupported wheel. Upper bound, not exact, so the resolver can still pick per-Python.
  "torch>=2.8,<2.12",
  # HF ZeroGPU hardware refuses to start unless it detects at least one `@spaces.GPU`
  # function at launch ("No @spaces.GPU function detected during startup"). The `spaces`
  # package provides that decorator; it is effect-free off ZeroGPU, so it ships always
  # without touching the offline/local path. See src/ui/fishbowl/app.py (gpu_selftest).
  "spaces>=0.50.4",
  # transformers 5.x requires `accelerate` to load a model straight onto a device via
  # `device_map`. The local backend (ADR-0033) needs that supported path: a bare
  # `from_pretrained(...).to("cuda")` can leave meta tensors and crash ("Cannot copy out of
  # meta tensor"). Only used on the live local-GPU path; the offline stub never imports it.
  "accelerate>=1.14.0",
  "pillow>=12.2.0",
]

[project.optional-dependencies]
dev = [
  "pytest>=8.2.0",
  "ruff>=0.5.0",
]

[tool.ruff]
line-length = 120
target-version = "py310"

[tool.pytest.ini_options]
pythonpath = ["."]

[tool.uv.workspace]
members = [
    "modal",
]