Construct an Agent with Nanobot, Lighter Substitute for OpenClaw

Digital assistants in enterprise are altering quick. Large enterprise techniques like OpenClaw pack lots of of 1000’s of strains of code, however nanobot challenges the concept that greater robotically means higher.

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

Whether or not nanobot can substitute enterprise instruments depends upon what customers really need. On this article, we discover how nanobot achieves this stability and what it means for sensible AI growth.

What’s Nanobot?

The AI assistant Nanobot capabilities as a private assistant by means of its weightless design. The system operates with solely 4000 Python code strains which makes it 99 p.c smaller than normal enterprise AI techniques. The open-source software program developed by HKUDS turned out there to the general public in early 2026.  

The important thing options of Nanobot are: 

• Automated analysis system supplies cost-free monitoring of monetary markets and cryptocurrency value actions which produces 24-hour alerts for main market modifications.  
• The system permits customers to execute shell instructions whereas the system operates tmux classes and permits file studying and writing and execution of duties by means of devoted sub-agents.  
• Customers can talk by means of Telegram or WhatsApp or Feishu which robotically transcribes their spoken phrases utilizing Groq Whisper expertise.  
• 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 by means of 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 capabilities by means of an agent loop sample which utterly implements its operation system. The agent/loop.py module controls the continued course of which incorporates: 

• The system receives person enter by means of all out there channels which embrace CLI and Telegram and WhatsApp and Feishu. 
• The system establishes context by utilizing dialog historical past along with its out there instruments. 
• The system requests the subsequent actions from the LLM. 
• The system performs duties based on the LLM solutions. 
• The system retains ends in reminiscence to make use of them in later durations. 

The system achieves efficient separation of various duties by means of its design. The context.py module handles immediate constructing, reminiscence.py manages persistent storage, and instruments/ incorporates 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 device set up nanobot-ai 

3. Set up by way of the direct supply 

git clone https://github.com/HKUDS/nanobot.git 
cd nanobot 
pip set up -e .

After the set up half is full, we’ll transfer the organising half. We’ll should configure the ~/.nanobot/config.json file so as to edit our API keys, mannequin and net search characteristic 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 Activity: Customized Crypto Tracker 

As a substitute of manually writing code, let nanobot’s AI agent construct a cryptocurrency monitoring device 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 device:

I would like you to create a cryptocurrency value monitoring device for me. This is what I would like:
1. Create a Python device 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 device in my workspace as crypto_monitor.py
5. Create a scheduled cron job that runs each hour
6. Ensure the whole lot is correctly configured
Construct this whole 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:  

Assessment

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

The system capabilities as an AI pair programmer as a result of it wants human operators to execute programming duties. The cron setup nonetheless wanted guide terminal instructions. The system generates precise code by means of its functioning course of, however customers ought to anticipate a number of dialogue classes as a substitute of reaching full outcomes by means of one request. 

Efficiency Benchmarks and Comparability 

The testing course of demonstrates that the nanobot system outperforms its equal techniques by means of 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 primary operations which excludes LLM inference whereas different techniques require between 200MB and 400MB for his or her operations.  
• The a number of occasion execution and resource-limited setting deployment each rely on these metrics.  
• The small codebase permits quicker growth progress. The nanobot system requires 15 to half-hour so as to add a brand new device whereas complicated frameworks want a number of hours to finish the identical process. 

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

Conclusion 

The Nanobot system demonstrates that profitable AI techniques solely want important programming parts as a substitute of intensive code collections. The system delivers professional-grade efficiency by means of its main capabilities and its skill 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 fashionable AI assistants. The system demonstrates a definite philosophy which exhibits that diminished parts can create higher worth: much less is extra! 

Steadily Requested Questions

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

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

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

A. It helps crypto and market monitoring, process automation, shell execution, multi-channel messaging, cron scheduling, and straightforward 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 Pc Science, Vellore Institute of Know-how, Vellore, India 

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

Be happy to attach with me at [email protected]