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Open-source All in One AI agent workspace. Run any agent — Claude Code, Codex — across your tools (100+ integrations + MCP), apps, browser, and files, with shared memory. Built-in models or BYOK.
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.
85
review score
Problem solved
It solves agent fragmentation and context loss by providing a single workspace where different agents share the same memory, tools, and skills, eliminating the need to rebuild setups when switching agents. It also addresses provider lock-in and API key management by offering built-in models with zero setup and BYOK flexibility, while keeping data local and user-controlled.
Deployment reality
The available setup signal starts with: npm install -g holaos. 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 NOASSERTION. 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 autonomous workflows that span coding, file management, web browsing, and third-party app interactions, all orchestrated by any supported agent. Real-world use cases include automated code generation and refactoring, multi-step research and data extraction, personal assistant tasks, and complex project management with persistent context. The ceiling is high: with MCP support and shared memory, agents can handle long-running, cross-tool projects that require continuity and adaptability.
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.
holaboss-ai/holaOS is tracked as a TypeScript project in the AI Agent, MCP Server, LLM Tool, UI Framework 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 7.3k total stars, with +769 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 9 days ago, and the open issue queue is 8, about 0.11% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 637 forks and 7.3k watchers suggest how often the project is reused or followed. License signal: NOASSERTION. 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 2 explicit source reference(s) for this report and a recent growth signal of 1.2k. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
LimitedLicense is unknown and should be checked before commercial use.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
7.3k Stars
Reuse
637 Forks
Attention
7.3k Watchers
Maintenance
active
License
NOASSERTION
Open issues
8
holaOS is a local-first desktop workspace that lets you run any AI agent (Claude Code, Codex, or its built-in agent) side by side, sharing one memory, tools, and apps. It provides a unified environment with built-in frontier models or bring-your-own keys, and supports HolaApps for interactive app-agent collaboration.
Key Features
- Run any agent (Claude Code, Codex, holaOS) in one workspace with shared memory, tools, and skills. - Built-in frontier models (Kimi K3, GLM 5.2, GPT 5.6, Claude Opus 5, Fable 5) or bring your own API keys. - HolaApps: install apps from a marketplace and have agents interact with them side-by-side in real-time. - Integrations with 50+ services (Gmail, Notion, Slack, GitHub, Linear) via one-click OAuth, plus MCP server support and reusable skills.
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
MCP Server
Model Context Protocol servers, clients, and integrations
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
UI Framework
Frontend frameworks, design systems, and interface libraries
npm install -g holaos73
Health Score
Active
Commit Activity
Mar 22, 2026
Created
Aug 14, 2026
Last push
+699
Today's growth
+1.2k
7-day growth
+1.2k
30-day growth
Forks
Open
Watchers
holaboss-ai
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✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
It solves agent fragmentation and context loss by providing a single workspace where different agents share the same memory, tools, and skills, eliminating the need to rebuild setups when switching agents. It also addresses provider lock-in and API key management by offering built-in models with zero setup and BYOK flexibility, while keeping data local and user-controlled.
Capabilities
Developers can build autonomous workflows that span coding, file management, web browsing, and third-party app interactions, all orchestrated by any supported agent. Real-world use cases include automated code generation and refactoring, multi-step research and data extraction, personal assistant tasks, and complex project management with persistent context. The ceiling is high: with MCP support and shared memory, agents can handle long-running, cross-tool projects that require continuity and adaptability.
Bottom Line
holaOS is ideal for developers and power users who want a flexible, local-first agent workspace without vendor lock-in, especially those who switch between agents or need persistent memory. It may be less suitable for teams requiring cloud collaboration or those who prefer a single, tightly integrated agent. The key trade-off is between the convenience of built-in models and the complexity of managing a multi-agent environment.