- Developers adding AI automation to existing tools and workflows without heavy framework dependencies
- Teams evaluating lightweight agent architectures for tool orchestration
- Builders who prefer minimal, focused agents over full-stack agent platforms
MIT · Tools
nanobot
Lightweight open-source AI agent that connects to your tools, chats, and workflows for automation.
44K stars 7.7K forks MIT license 2026-06-03 verified
$
# nanobot$
pip install nanobot$
npx nanobot --help Open source
Overview
What is nanobot?
nanobot is a lightweight open-source AI agent from the University of Hong Kong (HKU) designed for tool orchestration and workflow automation. It provides a minimal surface area for connecting AI to everyday tools, chats, and processes — with support for extensible tool integrations.
Automation
nanobot surfaces automation as a core capability in its published project metadata and source links.
This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs. Use cases
What teams use it for
Ecosystem
Tags & capabilities
Integrations
Comparison
How it stacks up
When to choose nanobot
Compare it with nearby tools by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.
FAQ
Questions
What makes nanobot different from other AI agents?
nanobot focuses on minimalism and extensibility — it is designed to be lightweight while providing a clean interface for tool integration.
Can I extend nanobot with custom tools?
Yes, nanobot supports extensible tool integrations through its plugin-like architecture.
Is nanobot open source?
Yes, it is open source under the MIT license with 43K+ GitHub stars.
Does nanobot require cloud infrastructure?
No, nanobot is designed to be lightweight and can run locally or on minimal infrastructure.
Decision brief
JSON
Should you use nanobot?
- Complex multi-agent workflows requiring conversation orchestration and role management
- Production systems requiring enterprise-grade monitoring and observability
- Verified 2026-06-03
- License: MIT
- Repo: HKUDS/nanobot
- Open-source signal
Deployment
self hosted, cloud
Permission surface
Low explicit permission surface in metadata
Decision signals
No extra signals recorded
Agent packet
Structured decision data for nanobot
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
automation
Constraints
open source
Deployment
self hosted, cloud
Permission surface
Low explicit permission surface in metadata
Recommended workflows
Browser automation, Evaluation and observability
Overview
What nanobot does
What it is
nanobot is a lightweight open-source AI agent from HKU for connecting AI to tools, chats, and workflows. It focuses on minimal architecture and extensible tool integrations.
Why it matters
nanobot demonstrates that effective AI agents don't need complex frameworks. Its lightweight design makes it approachable for developers new to agent development.
How to evaluate it
Evaluate nanobot by starting from the official sources, checking its repo interface surface, and running one narrow workflow before expanding scope. Recorded integrations include tools.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type tool
Category Tools
Maturity active
Difficulty Unknown
License MIT
Pricing open source
Verified 2026-06-03
Source confidence high
Risk level low
Fit matrix
Where nanobot fits in an agent stack
strong
Browser automation
nanobot has multiple signals for browser automation, including matching tags, capabilities, category, or positioning.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Evaluation and observability
nanobot has multiple signals for evaluation and observability, including matching tags, capabilities, category, or positioning.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Coding agent workflow
nanobot has at least one signal for coding agent workflow, but should be checked against a real task before adoption.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Connector or protocol layer
nanobot has at least one signal for connector or protocol layer, but should be checked against a real task before adoption.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Local or private AI stack
nanobot has at least one signal for local or private ai stack, but should be checked against a real task before adoption.
- Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
nanobot has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- Scores, traces, regression results, dashboards, or failure cases
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Source githubRepository source for code, license, issues, releases, and implementation details.
nanobot is listed as open source.
License metadata: MITnanobot has a recorded GitHub repository: HKUDS/nanobot.
Resource facts and GitHub source link.nanobot supports these recorded deployment modes: self hosted, cloud.
OpenAgent decision signal metadata.nanobot is tagged with automation capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating nanobot
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceInspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open source Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about nanobot
What makes nanobot different from other AI agents?
nanobot focuses on minimalism and extensibility — it is designed to be lightweight while providing a clean interface for tool integration.
Can I extend nanobot with custom tools?
Yes, nanobot supports extensible tool integrations through its plugin-like architecture.
Is nanobot open source?
Yes, it is open source under the MIT license with 43K+ GitHub stars.
Does nanobot require cloud infrastructure?
No, nanobot is designed to be lightweight and can run locally or on minimal infrastructure.