NexusAGI — You State the Need, an AI Team Ships the Software #6142
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"NexusAGI You State the Need, an AI Team Ships the Software" is useful as a cost surface because it turns model spend into a workflow-level measurement problem, not just a monthly invoice. A useful shape would be one usage event per model call, with provider/model, feature name, token fields, retry count, latency, and billing bucket all attached. Then cost can be traced back to product behavior instead of guessed from invoices. I am working through similar issues while testing an OpenAI-compatible layer for official Chinese models, mainly around routing, cost visibility, and response-shape compatibility. In this case, is the main pain attribution by user/workspace, or just understanding which agent steps are consuming the most tokens? |
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NexusAGI — You State the Need, an AI Team Ships the Software
🌐 Website: https://agi.gpu.do
💬 In one line: You state the need, the AI team ships the software.
1. Overview Home: Command Your AI Team at a Glance
Launch straight into a clean overview dashboard: cumulative usage, call counts, number of agents, and total sessions are all visible at a glance. Combined with quick entries and recent sessions, it lets you jump back into work at any moment. This is the command center from which you direct your entire AI team.
2. Why a "Team," Not Just an "Assistant"
Most AI coding tools are, at their core, "one person" helping you write code.
But real software development is never a one-person job — it takes someone to understand the requirements, someone to design the architecture, someone to write the code, someone to review it, someone to design, someone to hunt bugs, and someone to manage delivery.
NexusAGI brings this complete engineering team right into your computer:
3. Agents + Skills: A Composable, Extensible Professional Lineup
The platform ships with multiple professional roles, each with a clear responsibility and capability boundary. It also comes with a rich, pluggable skill library covering front-end design, code review, test validation, document processing, security auditing, performance analysis, and more. Agents decide "who does it," skills decide "with what expertise it's done" — freely combine the two to build an AI team tailored to your project.
4. Virtual Office: Make the AI Team "Visible"
An original 2.5D isometric virtual office visualizes each agent as an anthropomorphic character at a workstation. Who's focused on work, who's resting, who's idle — see it all at a glance. Characters even walk around, grab coffee, and take breaks, with real-time bubbles above their heads showing current activity. It turns abstract multi-agent collaboration into a team you can "see and feel come alive."
5. Usage Statistics and Cost Insight
Precise accounting by real usage, with a consumption overview, daily trend curves, and distribution rankings by agent. Every call is crystal clear — costs and efficiency are visible at a glance, and team productivity becomes measurable and optimizable.
6. Desktop Companion: A Companion That Grows With You
A desktop-resident smart companion that grows as you use it — accumulating experience, leveling up, and unlocking new forms. It presents work status in a companionable way, adding a touch of fun and warmth to long development sessions.
7. Flexible Model Selection
Freely choose and switch among multiple models to match the best "brain" to each task. Whether you're after top-tier quality or maximum cost-efficiency, switch with one click and it takes effect instantly.
8. Multi-Agent Collaborative Invocation: Team Operations You Can See
Within a single conversation, you can directly watch how the Chief Orchestrator dispatches subtasks to different specialized members, along with each member's execution process and output. Who is invoked, what they're doing, what they produce — the entire collaboration chain is transparent and visible, like watching a real team get to work.
9. Task Decomposition and Execution: Complex Needs Broken Down Automatically
Faced with complex requirements, the platform automatically decomposes the big goal into ordered subtasks, sorts out their dependencies, drives forward in parallel what can be parallelized and delivers in sequence what must be serial, and tracks the execution status of every step in real time until all are complete. From a "one-sentence requirement" to a "structured execution plan" — fully automatic and traceable throughout.
10. Global Memory System: Remembers Your Habits
Long-term memory that works across projects and across sessions. It captures your general preferences and common conventions, so the AI carries its understanding of you into any project — getting to know you better the more you use it, with no need to repeat yourself.
11. Project Memory System: Understands Every One of Your Projects
An independent memory bank per project — isolated by workspace and shared across sessions. Each project's technical conventions, directory standards, and key decisions are remembered separately, so switching projects never mixes things up, keeping the AI always aligned with the current project's context.
12. Knowledge Graph: Let the AI Truly "Read" Your Codebase
Automatically builds a structured knowledge graph for your project, visually presenting the dependencies and relationships between code modules. The AI uses this to understand your project structure from a global perspective — locating more accurately and making changes more safely — while you can also use the graph to quickly grasp the project's structure. The graph is stored independently by workspace, decoupled from session lifecycle, ready to view or rebuild at any time.
13. Model and Service Configuration
Centrally manage model services, keys, and available models. Configuration is clean and intuitive, keys are encrypted, and onboarding, switching, and tuning are all done in one place — secure and worry-free.
14. What It Can Do for You
15. Who It's For
16. Get Started Now
🌐 Visit the website to learn more and download: https://agi.gpu.do
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