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HKUDS/nanobot

AI Agent

Lightweight, open-source AI agent for your tools, chats, and workflows.

46k Stars8.1k Forks855 Open Issues46k WatchersPythonMIT
AI AgentLLM ToolDeveloper ToolAutomation
Review Readiness

This repository page is useful for visitors, but Git-Stars keeps it out of search indexing until more original evidence and comparison context are available.

75

review score

Needs README summaryNeeds sources
Decision Snapshot

Problem solved

Nanobot simplifies the creation of AI agents by providing a modular, lightweight framework that avoids the complexity of larger platforms, making it easy to connect to existing tools and workflows without heavy dependencies.

Deployment reality

The available setup signal starts with: pip install nanobot. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.

Commercial use

The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.

Capability ceiling

Developers can build custom AI assistants that interact with messaging platforms, automate workflows, and integrate with external APIs. Real-world use cases include chatbots for customer support, personal task automation, and workflow orchestration. The framework's modular design allows for scaling from simple scripts to complex multi-agent systems.

Source and compliance noteLast synced: Jul 23, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy
Editorial Evaluation

HKUDS/nanobot is tracked as a Python project in the AI Agent, LLM Tool, Developer Tool, Automation area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.

Momentum check: the repository has 46k total stars, with +0 today, +466 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.

Maintenance check: current activity is Moderate; the latest push was 84 days ago, and the open issue queue is 855, about 1.86% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.

Adoption check: 8.1k forks and 46k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.

Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.

Source check: Git-Stars currently has 1 explicit source reference(s) for this report and a recent growth signal of 466. Follow the original GitHub repository for final install, security, and release information.

Evidence Checklist

Analysis

Ready

Original problem, capability, and verdict guidance are available.

Sources

Limited

Needs clearer source references.

License

Ready

MIT is recorded for review.

Maintenance

Ready

Recent activity is visible in repository metadata.

Alternatives

Ready

Enough nearby projects exist for comparison.

Best For
  • AI prototypes, LLM workflows, and agent-style applications
  • Python teams evaluating ecosystem-native tooling
  • teams that prefer mature projects with broad adoption signals
Avoid When
  • you need a legal review, security audit, or production SLA
Adoption Signals

Momentum

46k Stars

Reuse

8.1k Forks

Attention

46k Watchers

Maintenance

moderate

License

MIT

Open issues

855

Overview

Nanobot is a lightweight, open-source AI agent framework designed to integrate with various tools, chats, and workflows, enabling users to automate tasks and build custom AI-powered assistants with minimal overhead.

Tool Positioning

AI Agent

Agent frameworks, autonomous workflows, and tool-use systems

LLM Tool

Libraries and tools for LLM apps, RAG, prompts, and evals

Developer Tool

Tools that improve coding, testing, build, and local workflow

Automation

Workflow automation, integration glue, and orchestration

Quick Start
pip install nanobot
View on GitHub Project Homepage
Project Activity

80

Health Score

Moderate

Commit Activity

Feb 1, 2026

Created

Jul 22, 2026

Last push

Source Trail

GitHub repository metadata

metadata

Star History

Star history will appear after daily syncs accumulate data.

Community Health
8.1k

Forks

855

Open

46k

Watchers

Owner
NA

HKUDS

GitHub profile
Topics & Language
Pythonaiai-agentai-agentsanthropicchatgptclaudeclaude-codecodexcodex-clillmnanobotopenaiopenclaw
Ecosystem & Usage
GitHub Repository Project Website
Alternatives & Comparison

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540k

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Interactive roadmaps, guides and other educational content to help developers grow in their careers.

365k

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Curated list of project-based tutorials

279k

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273k

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The library for web and native user interfaces.

247k
License
MIT
CreatedFeb 1, 2026
Last pushJul 22, 2026
Last syncedJul 23, 2026
Repository Standards

✓

License

✓

Forked

~ Moderate

Maintained

AI AnalysisAnalyzed by Git-Stars

Problem Solved

Nanobot simplifies the creation of AI agents by providing a modular, lightweight framework that avoids the complexity of larger platforms, making it easy to connect to existing tools and workflows without heavy dependencies.

Capabilities

Developers can build custom AI assistants that interact with messaging platforms, automate workflows, and integrate with external APIs. Real-world use cases include chatbots for customer support, personal task automation, and workflow orchestration. The framework's modular design allows for scaling from simple scripts to complex multi-agent systems.

Bottom Line

Nanobot is ideal for developers seeking a simple, lightweight AI agent framework for quick integration with existing tools and workflows. It may not suit those needing advanced features like built-in memory or complex multi-agent coordination. The key trade-off is simplicity versus out-of-the-box advanced capabilities.

Full AI Analysis