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AnalogAgent API Tutorial: Build LLM-Powered Circuit Design Agents with NexaAPI

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AnalogAgent API Tutorial: Build LLM-Powered Circuit Design Agents with NexaAPI

A new paper just dropped on arXiv — AnalogAgent uses LLM agents to automate analog circuit design. Here's how to build the same thing using NexaAPI in under 20 lines of code.

Research note: AnalogAgent paper published on arXiv (2603.23910) — training-free LLM agent for analog circuit design automation using generate-diagnose-correct loops. Source: https://arxiv.org/abs/2603.23910 | Fetched: 2026-03-28

What Is AnalogAgent?

AnalogAgent is a research paper from arXiv (https://arxiv.org/abs/2603.23910) that demonstrates how LLM agents can automate analog circuit design — without any fine-tuning or training.

The core innovation: a generate → diagnose → correct agentic loop that:

  1. Generates an initial circuit design based on a specification
  2. Diagnoses errors, constraint violations, and performance issues
  3. Corrects the design iteratively until it meets specifications

What makes this remarkable: it's training-free. The LLM doesn't need to be fine-tuned on circuit data. Any capable LLM — GPT-4o, Claude, Llama 3 — can power this workflow using standard prompting.

This means any developer can replicate it today using off-the-shelf LLM APIs.

Why NexaAPI for Building This?

Agentic workflows like AnalogAgent call the LLM many times per design — generation, multiple diagnosis rounds, multiple correction rounds. Cost per call matters enormously.

Provider LLM API Cost Notes
NexaAPI Cheapest available 56+ models, free tier
OpenAI (GPT-4o) ~$5/1M input tokens Rate limited
Anthropic (Claude) ~$3/1M input tokens Limited free tier
Replicate Per-second billing Unpredictable costs

With NexaAPI:

  • 56+ models including GPT-4o, Claude 3.5, Llama 3 70B, Mistral — all the LLMs you need
  • Free tier to get started — no credit card required
  • Stable API — no breaking changes, no migration headaches
  • Available on RapidAPI for easy billing

Python Tutorial: Build Your AnalogAgent 

# pip install nexaapi

from nexaapi import NexaAPI

client = NexaAPI(api_key='YOUR_API_KEY')

def analog_design_agent(circuit_spec: str, max_iterations: int = 3):
    """
    AnalogAgent-inspired loop: generate, diagnose, correct.
    Uses NexaAPI LLM inference for each step.
    """
    design = None

    # Step 1: Generate initial circuit design
    generation_prompt = f"""
    You are an expert analog circuit designer.
    Design a circuit that meets the following specification:
    {circuit_spec}
    Output the design as a structured netlist with component values.
    """
    response = client.chat.completions.create(
        model='gpt-4o',  # or 'llama-3-70b', 'claude-3-5-sonnet' — all available on NexaAPI
        messages=[{'role': 'user', 'content': generation_prompt}]
    )
    design = response.choices[0].message.content
    print(f'Initial design generated.')

    # Step 2: Iterative diagnosis and correction loop (AnalogAgent core)
    for iteration in range(max_iterations):
        diagnosis_prompt = f"""
        Review this analog circuit design for errors, constraint violations,
        or performance issues. Be specific and technical.
        Design: {design}
        Specification: {circuit_spec}
        """
        diagnosis = client.chat.completions.create(
            model='gpt-4o',
            messages=[{'role': 'user', 'content': diagnosis_prompt}]
        ).choices[0].message.content

        if 'no issues' in diagnosis.lower() or 'meets all specifications' in diagnosis.lower():
            print(f'Design validated after {iteration + 1} iterations.')
            break

        correction_prompt = f"""
        Fix the following issues in this analog circuit design.
        Issues: {diagnosis}
        Current design: {design}
        Return the corrected design only.
        """
        design = client.chat.completions.create(
            model='gpt-4o',
            messages=[{'role': 'user', 'content': correction_prompt}]
        ).choices[0].message.content
        print(f'Iteration {iteration + 1}: design corrected.')

    return design

# Example usage
spec = 'Design a two-stage CMOS op-amp with 60dB gain, 10MHz bandwidth, 1mA bias current'
final_design = analog_design_agent(spec)
print(final_design)
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What's happening here:

  1. Line 1: Install NexaAPI — pip install nexaapi
  2. Generation step: NexaAPI calls GPT-4o (or any of 56+ models) to generate an initial circuit design
  3. Diagnosis loop: The agent reviews its own design for errors — just like AnalogAgent's self-improvement mechanism
  4. Correction step: Issues are fixed iteratively until the design meets specifications
  5. Cost: Each LLM call costs a fraction of a cent on NexaAPI — running 10 iterations costs less than $0.01

JavaScript Tutorial 

// npm install nexaapi

import NexaAPI from 'nexaapi';

const client = new NexaAPI({ apiKey: 'YOUR_API_KEY' });

async function analogDesignAgent(circuitSpec, maxIterations = 3) {
  // Step 1: Generate initial design
  let designResponse = await client.chat.completions.create({
    model: 'gpt-4o', // 56+ models available on NexaAPI
    messages: [
      {
        role: 'user',
        content: `You are an expert analog circuit designer.
        Design a circuit meeting this specification: ${circuitSpec}
        Output a structured netlist with component values.`
      }
    ]
  });
  let design = designResponse.choices[0].message.content;
  console.log('Initial design generated.');

  // Step 2: Diagnosis and correction loop (AnalogAgent-style)
  for (let i = 0; i < maxIterations; i++) {
    const diagnosisResponse = await client.chat.completions.create({
      model: 'gpt-4o',
      messages: [
        {
          role: 'user',
          content: `Review this circuit design for errors or violations.
          Spec: ${circuitSpec}
          Design: ${design}`
        }
      ]
    });
    const diagnosis = diagnosisResponse.choices[0].message.content;

    if (diagnosis.toLowerCase().includes('no issues')) {
      console.log(`Validated after ${i + 1} iterations.`);
      break;
    }

    const correctionResponse = await client.chat.completions.create({
      model: 'gpt-4o',
      messages: [
        {
          role: 'user',
          content: `Fix these issues: ${diagnosis}\nCurrent design: ${design}\nReturn corrected design only.`
        }
      ]
    });
    design = correctionResponse.choices[0].message.content;
    console.log(`Iteration ${i + 1}: corrected.`);
  }

  return design;
}

// Example
analogDesignAgent('Two-stage CMOS op-amp: 60dB gain, 10MHz bandwidth')
  .then(result => console.log(result));
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Cost Comparison: Running 1000 Agentic LLM Calls

For an AnalogAgent workflow that runs 10 LLM calls per design (3 generations + 3 diagnoses + 3 corrections + 1 final review):

Provider Cost per 1000 calls Cost for 100 designs
NexaAPI ~$0.50 ~$5
OpenAI (GPT-4o) ~$5.00 ~$50
Anthropic (Claude 3.5) ~$3.00 ~$30

NexaAPI is 6-10x cheaper for agentic workflows — where you're making dozens of LLM calls per task.

Why This Matters for EDA Engineers and Hardware Developers

AnalogAgent proves that:

  • LLM agents can automate complex engineering tasks without domain-specific training
  • The generate-diagnose-correct loop is a general pattern applicable to many engineering domains
  • Cost of LLM inference is the primary bottleneck for production agentic systems

NexaAPI removes the cost bottleneck. With 56+ models and the cheapest inference pricing available, you can run production AnalogAgent-style workflows at scale.

Get Started Today

Build your own LLM-powered circuit design agent:

First 100 API calls are free. No credit card required.

Original paper: AnalogAgent: Self-Improving Analog Circuit Design Automation with LLM Agents — https://arxiv.org/abs/2603.23910

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