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Viberia

Viberia

AI

Desktop mission control to visually orchestrate and run multiple coding AI agents locally with provider-agnostic support.

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About Viberia

Viberia is a desktop application that provides a strategy game–style mission control for managing teams of AI agents focused on coding and automation tasks. It surfaces a visual map of agents, their statuses, conversations and tool usage, letting teams coordinate, split work, and chain actions automatically. Viberia is provider-agnostic (supports Claude, ChatGPT, Gemini and OpenAI-compatible providers), runs locally to keep code and conversations private, and lets users bring their own API keys or use existing subscriptions for model access. The app is available for macOS (Apple Silicon and Intel) and Windows (x64 and ARM64).

Screenshots

Viberia screenshot 1
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Key Features

Visual Agent Orchestration: Presents agents as units on a strategy-style map so you can see each agent's state, progress, and relationships at a glance, improving oversight and coordination.
Multi-Provider Support: Connects to Claude, ChatGPT, Gemini and any OpenAI-compatible provider, allowing you to bring your own API keys or reuse existing subscriptions for model execution.
Local-First Privacy: Runs entirely on the user's machine with no Viberia servers involved, ensuring code, logs and conversations remain private and do not leave the device.
Team Coordination & Automation: Enables agents to form teams, delegate subtasks, pass results between agents, and coordinate workflows automatically to complete complex development tasks.
Conversation & Tool Drilldown: Lets users open and inspect agent conversations, view tool usage and results, and trace how an agent reached a decision or produced code.
Cross-Platform Desktop Builds: Distributes native installers for macOS (Apple Silicon and Intel) and Windows (x64 and ARM64) for straightforward local installation.
Bring-Your-Keys Model Integration: Users configure provider credentials locally, so billing and usage remain tied to their model subscriptions rather than Viberia.
Resilient Tool Connections: Supports integrations and tool connections for agent capabilities (with compatibility notes for provider versions and known issues documented).
Visual mission-control UI for managing multiple agent teams and viewing agent status
Multi-provider support: Claude, ChatGPT, Gemini, and OpenAI-compatible providers
Bring-your-own-keys: use your own API keys or existing subscriptions; provider-agnostic
Local-first architecture: runs entirely on the user's machine; no Viberia servers
Agent coordination and automation: teams can coordinate and run workflows automatically
Conversation drill-down: inspect individual agent conversations and history
Tool connections support (note: Claude Code 2.1.74-2.1.117 have known HTTP MCP bug; update to 2.1.119+)
Official releases for Apple Silicon, Intel macOS, Windows x64, and Windows ARM64
Native installers: .dmg for macOS and .exe for Windows
Open-source presence and release artifacts hosted on GitHub (get-viberia/viberia-releases)

Use Cases

Coordinated Code Generation: Split a large feature into sub-tasks and assign specialized agent teams (e.g., frontend, backend, tests) to generate, integrate and validate code concurrently.
Automated Debugging Workflows: Launch agents to reproduce bugs, generate test cases, propose fixes, and validate patches, while inspecting agent conversations and tool outputs to audit changes.
Prototype Development: Rapidly prototype an application by orchestrating agents to scaffold project structure, implement core features, and produce runnable demos with minimal human bottlenecks.
Local, Private AI Workflows: Teams that require on-device privacy can run conversational and coding agents locally without sending source code or chat logs to third-party servers.
Multi-Model Experimentation: Evaluate and compare outputs from different providers (Claude, ChatGPT, Gemini) in parallel by assigning equivalent tasks to agents powered by each model.
Teaching Agent Coordination: Demonstrate multi-agent design patterns and workflows in workshops or internal training by visualizing agent roles, communication, and emergent behaviors.
Orchestrating multiple coding agents to collaborate on software development tasks
Prototyping and testing multi-agent workflows locally without sending data to external servers
Managing provider subscriptions and routing agents to different LLM providers
Debugging and inspecting agent conversations and tool usage during development
Running automated agent teams for code generation, testing, and CI-related tasks in a private environment

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