How does Nano Agent help compare different LLMs?

It utilizes a Higher Order Prompt (HOP) and Lower Order Prompt (LOP) pattern to systematically evaluate multiple models in parallel, benchmarking their performance, speed, and cost for agentic tasks.

What key functions can Nano Agent's AI agents perform?

Nano Agent's agents can perform file system operations like reading, writing, editing, and analyzing files. They are designed for autonomous task execution and robust engineering results.

Is Nano Agent suitable for experimentation and testing?

Absolutely. It's built for experimentation, offering robust testing, error handling, and detailed token usage tracking, making it ideal for comparing and optimizing LLM-powered agents.

Which LLM providers does Nano Agent support?

It seamlessly supports multiple LLM providers, including OpenAI (GPT-5), Anthropic (Claude), and Ollama (for local models). All providers are accessed through a unified OpenAI SDK interface.

Nano Agent is an MCP server designed to orchestrate experimental, small-scale engineering agents. It offers multi-provider LLM support for comprehensive comparative evaluation of AI agent capabilities.

Nano Agent: Multi-LLM Agent Orchestration & Evaluation Tool

Nano Agent serves as a Master Control Program (MCP) server designed to manage and orchestrate experimental, small-scale engineering agents. Its primary purpose is to facilitate the testing and comparison of agentic capabilities across various cloud-based and local Large Language Models (LLMs), evaluating them based on performance, speed, and cost. It moves beyond single prompt calls, focusing on how well agents can chain multiple tools to achieve real engineering results, providing crucial insights into the practical application and efficiency of different LLM providers and models.

Key Features

01Designed for experimentation with robust testing, error handling, and token usage tracking

02Utilizes a nested agent architecture where the MCP server spawns internal agents for task execution

03Provides a unified interface, allowing all LLM providers to be accessed through the same OpenAI SDK

04Enables agents to perform file system operations like reading, writing, editing, and analyzing files

0555 GitHub stars

06Supports multiple LLM providers including OpenAI (GPT-5), Anthropic (Claude), and Ollama (local models)

Use Cases

01Delegating complex engineering tasks to agents and scaling up compute through integration with MCP clients like Claude Code.

02Testing and comparing the agentic capabilities of various cloud and local LLMs (e.g., GPT-5 vs. Claude vs. Ollama) across performance, speed, and cost metrics.

03Systematically evaluating multiple LLM models in parallel using Higher Order Prompt (HOP) and Lower Order Prompt (LOP) patterns to benchmark results.