nanobot
★ 49.3k
vs
cockpit-agent
★ 26
nanobot vs cockpit-agent
nanobot: nanobot is an ultra-lightweight, self-hosted personal AI agent framework built in Python, offering a readable core for multi-agent delegation and automation. It supports various interfaces including WebUI, terminal, and chat apps, integrating tools, long-term memory, and OpenAI-compatible APIs.; cockpit-agent: Cockpit Agent is an edge-cloud collaborative AI agent system designed for smart cockpits, providing a single voice entry point for diverse tasks from millisecond-level vehicle control to multi-day trip planning and deep research. It emphasizes a separation of planning and execution, with LLMs handling understanding and planning while deterministic systems ensure safe and real-time execution of critical functions.
01
TL;DR
nanobotMulti-Agent · ★ 49.3k
When to choose:
- You want a lightweight, hackable Python personal AI agent framework with clean code, a WebUI, and built-in MCP client support.
- You prefer an easy-to-read, self-hosted agent core without the enterprise boilerplate of heavy agent frameworks.
When to skip:
- You require production-grade distributed state checkpointing, strict Pydantic type validation, or multi-role team SOPs; Pydantic AI or CrewAI offer more mature infrastructure.
- Your production stack is .NET or Go; Microsoft Agent Framework covers those runtimes.
cockpit-agentMulti-Agent · ★ 26
Context-aware Navigation: Planning complex routes with multiple waypoints, real-time ETA, and dynamic re-routing based on ongoing navigation (e.g., "add a gas station, don't be late").
02
Side-by-Side Comparison
Field
nanobotcockpit-agentCategory
Multi-Agent
Multi-Agent
Stars
★ 49.3k
★ 26
License
MIT
Apache-2.0
Install Command
curl -fsSL https://raw.githubusercontent.com/HKUDS/nanobot/main/scripts/install.sh | sh
cp .env.example .env && make proto && make up
Updated
1d ago
4d ago
Open Source
Yes
Yes
Tags
AI Agent Framework, Self-hosted, Python
Multi-Agent System, Smart Cockpit AI, Edge-Cloud Computing
03
Features
nanobot01Persistent workflows: goals, memory, tools, and chat context survive long-running work.
02Chat-native reach: WebUI, API, Telegram, Feishu, Slack, Discord, Teams, email, and Mattermost.
03Model freedom: OpenAI-compatible APIs, local LLMs, image generation, search, and fallbacks.
04Small core: readable internals with MCP, memory, deployment, and automation built in.
05Own your stack: inspect, customize, self-host, and extend without a giant platform.
cockpit-agent01Planning and Execution Separation: LLMs only produce intent/plans, while deterministic executors handle vehicle control with permission validation, ensuring safety.
02Fast/Slow Dual System Architecture: High-frequency, safety-critical commands execute on the edge with millisecond response times and offline availability; complex, cross-domain intents are orchestrated by the cloud LLM Planner.
03Agent Plug-and-Play: New domain agents can be added without modifying core orchestration code, leveraging a unified gRPC contract and Manifest declarations.
04Real Data Priority & Traceability: Integrates with real data sources (e.g., Gaode, Exa, Tushare), rejects mock data in strict mode, and provides source traceability for external data.
05Full-duplex Voice Interaction: Supports a complete streaming voice interaction loop, including KWS, VAD, real-time ASR/TTS, barge-in, and continuous dialogue.
04
Use Cases
nanobot↳Running long-horizon goals and scheduled automations.
↳Connecting to various chat applications like Telegram, Discord, Slack for AI interaction.
↳Exposing a Python SDK and OpenAI-compatible API for custom integrations and development.
↳Deploying as a long-running local or server-side agent gateway.
cockpit-agent↳Context-aware Navigation: Planning complex routes with multiple waypoints, real-time ETA, and dynamic re-routing based on ongoing navigation (e.g., "add a gas station, don't be late").
↳Personalized Long-term Memory: The system remembers user preferences (e.g., "wife likes Cantonese food") and applies them to future queries, directly influencing search results rather than just conversational fillers.
↳Mixed Multi-Intent Handling: Simultaneously executing local vehicle controls (e.g., "turn on AC, play music") and sending slower, complex intents to the cloud for parallel processing within a single request.
↳Asynchronous Deep Research: Initiating multi-perspective, iterative background research tasks (e.g., "research solid-state battery progress"), with progress tracking and proactive, referenced reports.
↳Dynamic Scene Creation & Automation: Creating custom car scenarios with natural language (e.g., "create fishing mode: recline seats, dim ambient light"), where LLM compiles during creation, and activation/execution are deterministic.
05
Best For
nanobotMost PopularEssential
cockpit-agent—
FAQ
FAQ
What is the difference between nanobot and cockpit-agent?
Both nanobot and cockpit-agent are in the Multi-Agent category. nanobot has 49.3k stars, while cockpit-agent has 26 stars.
Which is better, nanobot or cockpit-agent?
The best choice depends on your use case. Choose nanobot if Running long-horizon goals and scheduled automations., and cockpit-agent if Context-aware Navigation: Planning complex routes with multiple waypoints, real-time ETA, and dynamic re-routing based on ongoing navigation (e.g., "add a gas station, don't be late")..
Is nanobot free or open source?
Yes, nanobot is open source on GitHub (MIT).
Is cockpit-agent free or open source?
Yes, cockpit-agent is open source on GitHub (Apache-2.0).
Cloud Infrastructure
AdDeploy on DigitalOcean
Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.
→