Drop in any model. Get a harness.
Offline-first CLI + TUI that detects model type, runs the right harness, serves HTTP, and scaffolds plugins — for tabular, embeddings, LLMs, vision, diffusion, computer use, and more.
macOS and Linux only in v1. Windows is not supported.
- Drop-in models — local files, Hugging Face, Ollama, or Python callables
- Auto harness selection — tabular, embeddings, LLM, vision, speech, diffusion, computer, generic
- One CLI —
everyharness add→everyharness run→everyharness serve→everyharness train - Offline-first — works without the network;
EVERYHARNESS_OFFLINE=1hard-blocks outbound calls - Plugin system — publish
everyharness-*packages; scaffold witheveryharness plugin init - Agent UI bridge —
everyharness uiwrites a prompt pack for Cursor, Claude Code, Github Copilot, Pi, or Codex - Textual TUI — launch with bare
everyharness
pip install everyharness
# optional extras
pip install 'everyharness[tabular]'
pip install 'everyharness[llm]'
pip install 'everyharness[all]'# register models (kind auto-detected when possible)
everyharness add ./model.pkl --trust-pickle
everyharness add embeddings:demo --type embeddings
everyharness add python:callable_demo:echo
everyharness list
# same interface, different harnesses
everyharness run --trust-pickle <id> predict --input '[[1.5, 0.5]]'
everyharness run <id> similarity --input '{"a":"cat","b":"kitten"}'
everyharness run <id> call --input '{"hello":"world"}'
# scaffold a publishable harness plugin
everyharness plugin init weather --kind harnessLaunch the TUI:
everyharness| Kind | Examples | Typical commands |
|---|---|---|
tabular |
.pkl / .joblib sklearn |
predict, evaluate, explain |
embeddings |
sentence-transformers, embeddings: |
embed, similarity |
llm |
GGUF, Ollama, HF | complete, repl, serve |
vision |
image classifiers | harness-specific |
diffusion |
Diffusers pipelines | harness-specific |
generic |
Python callables | call, predict, info |
Generate a prompt pack so your coding agent can scaffold a local web/GUI wrapper:
everyharness ui <model-id> --agent cursor
# → ./harness-ui/<id>/{AGENT_PROMPT.md,model-card.json,manifest.json,README.md}Community packages publish as everyharness-* on PyPI:
everyharness plugin search tabular
everyharness plugin install everyharness-foo
everyharness plugin init mymodel --kind harness # harness | loader | detector | templatesCLI/TUI → plugin host → harnesses / loaders / detectors / templates. See docs/architecture.md.
everyharness update --check
everyharness update --yesSet EVERYHARNESS_OFFLINE=1 to block update checks, HF downloads, and everyharness plugin install.
uv sync --all-extras --dev
uv run pytest -q
uv run ruff check src testsApache-2.0 — see LICENSE.
