tool-prune

Calibrated tool selection and schema pruning for AI agents. Dual-engine: zero-dependency offline TurboQuant or TypeSafe System One.

pip install tool-prune

# Optional SIMD acceleration
pip install turbovec

# Optional: TypeSafe API key for cloud reasoning
export TYPESAFE_API_KEY="apikey_..."

Quick start

from tool_prune import prune

tools = {
    "read_file": "Read raw text from local filesystem path",
    "run_query": "Execute SQL queries against database",
    "web_search": "Search public web for documentation or articles"
}

# Works offline out of the box with TurboQuant:
match = prune("what tables exist in the db?", tools)
print(match.tool)   # 'run_query'
print(match.engine) # 'turboquant'

prune() narrows schemas offline via TurboQuant by default, or routes to TypeSafe System One when TYPESAFE_API_KEY is present. That's the whole API.

Schema pruning for LLMs

from tool_prune import ToolPrune

router = ToolPrune(tools)
top_tools = router.filter(user_prompt, k=5)

response = llm.chat(
    tools=top_tools,
    messages=[{"role": "user", "content": user_prompt}]
)

Cuts prompt tokens by up to 92% and eliminates context confusion without losing tools.

Fast-path direct dispatch

result = router.dispatch("read ./pyproject.toml", {
    "read_file": lambda q, m: open("pyproject.toml").read(),
    "run_query": lambda q, m: db.query(q),
    "fallback": lambda q, m: call_llm(q)
})

Runs deterministic handlers in under 160ms with zero token cost.

Dual engine

local_match = prune(query, tools, engine="turboquant")
cloud_match = prune(query, tools, engine="typesafe", api_key="...")

• turboquant: 100% offline, zero network, zero dependencies. Uses turbovec (Rust SIMD) if installed, with built-in FWHT fallback.
• typesafe: Cloud System One reasoning (Jev). 100% Top-1 accuracy on subtle distractors with calibrated probabilities.

Demo

python examples/quickstart.py

License

MIT © Hemanth.HM