enzu 0.2.0

AI delegation orchestration. Tasks as budgeted jobs, not conversations.

Budgeted LLM tasks that scale beyond context.

enzu is a Python-first toolkit for AI engineers and builders who need reliable, budgeted LLM runs. It enforces hard limits (tokens, time, cost), switches to RLM when context is large, and works across OpenAI-compatible providers. Use it from Python, the CLI, or the HTTP API.

Quickstart (Python)

uv add enzu
# or: pip install enzu

export OPENAI_API_KEY=sk-...

from enzu import Enzu, ask

print(ask("What is 2+2?"))

client = Enzu()  # Auto-detects from env
answer = client.run(
    "Summarize the key points",
    data="...long document...",
    tokens=400,
)
print(answer)

Tip: Set OPENAI_API_KEY, OPENROUTER_API_KEY, or another provider key. You can always pass model= and provider= explicitly.

HTTP API (server)

uv pip install "enzu[server]"
uvicorn enzu.server:app --host 0.0.0.0 --port 8000

curl http://localhost:8000/v1/run \
  -H "Content-Type: application/json" \
  -d '{"task":"Say hello","model":"gpt-4o","provider":"openai"}'

If you set ENZU_API_KEY, pass X-API-Key on every request.

CLI worker

cat <<'JSON' | enzu
{
  "provider": "openai",
  "task": {
    "task_id": "hello-1",
    "input_text": "Say hello in one sentence.",
    "model": "gpt-4o"
  }
}
JSON

Docs

• docs/README.md - Start here
• docs/QUICKREF.md - Providers, env vars, model formats
• docs/DEPLOYMENT_QUICKSTART.md - CLI + integration patterns
• docs/SERVER.md - HTTP API
• docs/PYTHON_API_REFERENCE.md - Full Python API
• docs/COOKBOOK.md - Patterns and recipes

Contributing

See CONTRIBUTING.md.

Requirements

Python 3.9+