Why Use NVIDIA AgentIQ?#

AgentIQ is a flexible library that allows for easy connection of existing enterprise agents, across any framework, to data sources and tools. The core principle of this library is every agent, tool, and agentic workflow exists as a function call - enabling composability between these agents, tools, and workflows that allow developers to build once and reuse in different scenarios. This makes AgentIQ able to work across any agentic framework, combining existing development work and reducing the need to replatform. This library is agentic framework agnostic, long term memory, and data source agnostic. It also allows development teams to move quickly if they already are developing with agents- focusing on what framework best meets their needs, while providing a holistic approach to evaluation and observability. A core component of AgentIQ is the profiler, which can be run to uncover hidden latencies and suboptimal models/tools for specific, granular parts of pipelines. An evaluation system is provided to help users verify and maintain the accuracy of the RAG and E2E system configurations.

Key Features#

  • Framework Agnostic: Works with any agentic framework, so you can use your current technology stack without replatforming.

  • Reusability: Every agent, tool, or workflow can be combined and repurposed, allowing developers to leverage existing work in new scenarios.

  • Rapid Development: Start with a pre-built agent, tool, or workflow, and customize it to your needs.

  • Profiling: Profile entire workflows down to the tool and agent level, track input/output tokens and timings, and identify bottlenecks.

  • Observability: Monitor and debug your workflows with any OpenTelemetry-compatible observability tool.

  • Evaluation System: Validate and maintain accuracy of agentic workflows with built-in evaluation tools.

  • User Interface: Use the AgentIQ UI chat interface to interact with your agents, visualize output, and debug workflows.

  • MCP Compatibility Compatible with Model Context Protocol (MCP), allowing tools served by MCP Servers to be used as AgentIQ functions.

Coming Soon#

AgentIQ is still under active development. Here are some of the features we are working on adding in the near future:

  • Additional end-to-end agentic use case for software engineering (Q2 2025)

  • AI-Q Blueprint customization guide (Q2 2025)

  • Optional integration of NeMo Guardrails (Q2 2025)

  • Agentic system level accelerations in partnership with Dynamo (Q3 2025)

  • Flexible deployment and workflow instantiation option (Q4 2025)

  • Addition of data feedback and improvement loop (flywheel) (Q4 2025)

What AgentIQ Is#

  • A lightweight, unifying library that makes every agent, tool, and workflow you already have work together, just as simple function calls work together in complex software applications.

  • An end-to-end agentic profiler, allowing you to track input/output tokens and timings at a granular level for every tool and agent, regardless of the amount of nesting.

  • A way to accomplish end-to-end evaluation and observability. With the potential to wrap and hook into every function call, AgentIQ can output observability data to your platform of choice. It also includes an end-to-end evaluation system, allowing you to consistently evaluate your complex, multi-framework workflows in the exact same way as you develop and deploy them.

  • A compliment to existing agentic frameworks and memory tools, not a replacement.

  • 100% opt in. While we encourage users to wrap (decorate) every tool and agent to get the most out of the profiler, you have the freedom to integrate to whatever level you want - tool level, agent level, or entire workflow level. You have the freedom to start small and where you believe you’ll see the most value and expand from there.

What AgentIQ Is Not#

  • An agentic framework. AgentIQ is built to work side-by-side and around existing agentic frameworks, including LangChain, Llama Index, Crew.ai, Microsoft Semantic Kernel, MCP, and many more - including customer enterprise frameworks and simple Python agents.

  • An attempt to solve agent-to-agent communication. Agent communication is best handled over existing protocols, such as HTTP, gRPC, and sockets.

  • An observability platform. While AgentIQ is able to collect and transmit fine-grained telemetry to help with optimization and evaluation, it does not replace your preferred observability platform and data collection application.