Build an Agent with Nanobot, Lighter Replacement for OpenClaw

Digital assistants in enterprise are altering quick. Huge enterprise methods like OpenClaw pack tons of of 1000’s of traces of code, however nanobot challenges the concept that larger mechanically means higher.

With simply 4000 traces of Python, it delivers core AI assistant capabilities in a light-weight, targeted package deal whereas slicing codebase measurement by about 99% with out sacrificing important performance.

Whether or not nanobot can substitute enterprise instruments will depend on what customers really want. On this article, we discover how nanobot achieves this steadiness and what it means for sensible AI growth.

What’s Nanobot?

The AI assistant Nanobot features as a private assistant via its weightless design. The system operates with solely 4000 Python code traces which makes it 99 % smaller than customary enterprise AI methods. The open-source software program developed by HKUDS grew to become accessible to the general public in early 2026.  

The important thing options of Nanobot are: 

• Automated analysis system offers cost-free monitoring of monetary markets and cryptocurrency worth actions which produces 24-hour alerts for main market adjustments.  
• The system permits customers to execute shell instructions whereas the system operates tmux periods and permits file studying and writing and execution of duties via devoted sub-agents.  
• Customers can talk via Telegram or WhatsApp or Feishu which mechanically transcribes their spoken phrases utilizing Groq Whisper know-how.  
• The system makes use of cron-based scheduling to execute duties which incorporates e-mail monitoring and GitHub monitoring and every day briefing operations.  
• The system permits customers to modify between a number of LLM suppliers via OpenRouter, Anthropic, OpenAI, DeepSeek, Groq, Gemini, and native vLLM with out the necessity for coding.  

Core Structure: How Nanobot Achieves Minimalism 

The core of nanobot features via an agent loop sample which fully implements its operation system. The agent/loop.py module controls the continuing course of which incorporates: 

• The system receives person enter via all accessible channels which embody CLI and Telegram and WhatsApp and Feishu. 
• The system establishes context by utilizing dialog historical past along with its accessible instruments. 
• The system requests the following actions from the LLM. 
• The system performs duties in response to the LLM solutions. 
• The system retains leads to reminiscence to make use of them in later durations. 

The system achieves efficient separation of various duties via its design. The context.py module handles immediate constructing, reminiscence.py manages persistent storage, and instruments/ comprises modular capabilities that may be added or eliminated with out touching core logic. 

Getting Began with Nanobot 

The method to get began with Nanobot is fairly easy. There are three strategies: 

1. Set up by way of PyPi (steady) 

pip set up nanobot-ai 

2. Set up by way of uv which is steady and quick. 

uv instrument set up nanobot-ai 

3. Set up by way of the direct supply 

git clone 
cd nanobot 
pip set up -e .

After the set up half is full, we’ll transfer the organising half. We’ll need to configure the ~/.nanobot/config.json file as a way to edit our API keys, mannequin and net search function if required. 

{
  "suppliers": {
    "openrouter": {
      "apiKey": "sk-or-v1-xxx"
    }
  },
  "brokers": {
    "defaults": {
      "mannequin": "anthropic/claude-opus-4-5"
    }
  },
  "instruments": {
    "net": {
      "search": {
        "apiKey": "BSA-xxx"
      }
    }
  }
}

4. Listed below are few instructions to get you began with the nanobot agent: 

Palms-On Process: Customized Crypto Tracker 

As an alternative of manually writing code, let nanobot’s AI agent construct a cryptocurrency monitoring instrument for you thru pure dialog. 

Step 1: Begin the agent in interactive mode by way of following command:

nanobot agent 

Step 2: Immediate the agent to create the instrument:

I would like you to create a cryptocurrency worth monitoring instrument for me. Here is what I would like:
1. Create a Python instrument that fetches crypto costs from the CoinGecko API
2. Monitor BTC, ETH, and SOL
3. Alert me when any coin strikes greater than 5% in 24 hours
4. Save the instrument in my workspace as crypto_monitor.py
5. Create a scheduled cron job that runs each hour
6. Ensure every part is correctly configured
Construct this complete system for me.

Output:  

Step 3: We’ll run the script created by the nanobot agent by way of following command: 

python ~/.nanobot/workspace/crypto_monitor.py 

Output:  

Overview

I examined this myself and the agent created the crypto_monitor.py file. The method required 4 to 5 prompts earlier than reaching the purpose which I beforehand described as a single-shot automated construct. The agent operates via dialog by growing options which require two separate requests to finish.  

The system features as an AI pair programmer as a result of it wants human operators to execute programming duties. The cron setup nonetheless wanted handbook terminal instructions. The system generates precise code via its functioning course of, however customers ought to anticipate a number of dialogue periods as an alternative of reaching full outcomes via one request. 

Efficiency Benchmarks and Comparability 

The testing course of demonstrates that the nanobot system outperforms its equal methods via elevated operational effectivity.  

• The system requires 0.8 seconds to start out up whereas heavier frameworks want between 8 and 12 seconds for his or her chilly begin.  
• The system makes use of 45MB of reminiscence for its fundamental operations which excludes LLM inference whereas different methods require between 200MB and 400MB for his or her operations.  
• The a number of occasion execution and resource-limited surroundings deployment each depend upon these metrics.  
• The small codebase permits sooner growth progress. The nanobot system requires 15 to half-hour so as to add a brand new instrument whereas complicated frameworks want a number of hours to finish the identical job. 

The quick pace of nanobot growth multiplies its benefits as a result of this method works successfully for each fast prototyping and iterative growth processes. 

Conclusion 

The Nanobot system demonstrates that profitable AI methods solely want important programming parts as an alternative of intensive code collections. The system delivers professional-grade efficiency via its major features and its potential to construct and its maintainable framework. 

You need to take note of nanobot as a result of it helps you create your first AI agent or your analysis work or your quest to know trendy AI assistants. The system demonstrates a definite philosophy which exhibits that lowered parts can create higher worth: much less is extra! 

Often Requested Questions

Q1. What’s Nanobot and the way does it differ from massive enterprise AI assistants?

A. Nanobot is a light-weight open-source private AI assistant inbuilt about 4000 traces of Python, specializing in important agent options with far decrease reminiscence and startup overhead than enterprise methods.

Q2. What key options does Nanobot supply for automation and monitoring?

A. It helps crypto and market monitoring, job automation, shell execution, multi-channel messaging, cron scheduling, and simple switching between a number of LLM suppliers.

Q3. How do you get began constructing instruments with Nanobot?

A. Set up Nanobot by way of PyPI or supply, configure API keys, begin the interactive agent, and use pure language prompts to generate and run customized Python instruments.

Gen AI Intern at Analytics Vidhya 
Division of Laptop Science, Vellore Institute of Know-how, Vellore, India 

I’m presently working as a Gen AI Intern at Analytics Vidhya, the place I contribute to revolutionary AI-driven options that empower companies to leverage information successfully. As a final-year Laptop Science scholar at Vellore Institute of Know-how, I deliver a stable basis in software program growth, information analytics, and machine studying to my function. 

Be happy to attach with me at [email protected]