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AIQToolkit

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Basic Information

AIQ toolkit (NVIDIA Agent Intelligence) is a lightweight, framework-agnostic library designed to connect existing enterprise agents to data sources, tools, and workflows without replatforming. It provides composable agents, tools, and workflows that can be used as simple function calls and integrated with popular agent frameworks and memory tools. The repository supplies a CLI, example workflows, and a UI chat for interacting with and debugging agents. It supports plugins for multiple integrations and can act as both an MCP client and MCP server to publish or consume remote tools. Installation instructions, prerequisites, and a Hello World example showing how to configure an LLM-backed workflow are included. The project aims to accelerate development, enable reuse of components, and make it easier to extend or customize agentic applications for enterprise contexts.

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App URL
https://github.com/NVIDIA/AIQToolkit
Github Repository
https://github.com/NVIDIA/AIQToolkit/blob/main/README.md

App Details

Features
The README highlights several core capabilities: framework agnostic operation that works alongside LangChain, LlamaIndex, CrewAI, Semantic Kernel and custom Python agents; reusable, composable agents, tools, and workflows; pre-built examples and workflow templates for rapid development; a profiler for measuring tool and agent-level performance including token and timing metrics; observability integration via OpenTelemetry-compatible tooling; a built-in evaluation system to validate workflow accuracy; a UI chat interface for visualizing agent output and debugging; and comprehensive MCP support to act as client or server. The repo also exposes a plugin architecture to install optional integrations, profiling extras, and example-driven dependency installation.
Use Cases
AIQ toolkit helps development teams integrate and orchestrate multiple agents and tools while preserving existing stacks, reducing the need to rewrite or replatform existing agent code. It speeds up prototyping by providing pre-built workflows and an extensible plugin model so teams can focus on domain logic rather than plumbing. The profiler and observability features help identify performance bottlenecks and track token usage, enabling optimization and cost control. The evaluation tools aid in maintaining correctness and quality of agent workflows. MCP support enables distributed tool sharing across services. The CLI and examples make it straightforward to run and test workflows, and the UI assists with debugging and interaction during development and demonstration.

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