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ToolWeaver: Secure Tool Orchestration for AI

Simple Explanation

Imagine two teammates: - Smart Planner (expensive): figures out the plan - Fast Worker (efficient): executes the steps safely

ToolWeaver helps the Planner find the right tools, then runs many small, cheap steps in parallel with safety rails. You get smart planning with fast, low-cost execution.

Technical Explanation

Two-model architecture: large models (GPT-4o/Claude) plan once; small models/tools (Phi-3/Llama, APIs) execute many cheap steps. ToolWeaver provides a registry, search, parallel dispatcher, caching, and sandboxing so complex workflows run safely and predictably.

The Problem

Integrating many tools and models into real workflows is hard: - Parallel fan-out without runaway cost or failures - Consistent schemas and discovery across heterogeneous tools - Safe execution (no secrets leakage, no dangerous code paths) - Performance (caching, idempotency) and observability

The Solution

ToolWeaver is a package-first library to register, discover, and run tools and agents with built-in guardrails: - Secure fan-out: parallel dispatch with concurrency, cost, duration, and failure-rate limits - Safe execution: template sanitization, PII redaction, secrets-safe logging, sandboxed code - Performance: dual-layer caching (Redis + file) and idempotency for instant retries - Useful outputs: aggregation (majority vote, rank by metric, best result)

Why It Matters

  • Ship faster: remove boilerplate for limits, logging, caching
  • Scale safely: guardrails prevent cost bombs and unstable fan-outs
  • Stay flexible: decorators, templates, or YAML—mix and match

Quickstart

Package Extras (what/when/why)

  • redis: distributed cache. Use when you need shared caching; faster than file cache.
  • vector: Qdrant + client deps. Use for semantic tool search at scale.
  • monitoring: Prometheus/OTLP/Grafana deps. Use for production observability.
  • all: everything above. Use for one-shot setup.

See install matrix: Get Started / Installation

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