vibex 0.27.1

VibeX - An Opinionated Multi-Agent Framework

VibeX

An open-source framework for building autonomous AI agent teams.
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VibeX provides the backbone for creating, orchestrating, and observing sophisticated multi-agent systems. It moves beyond simple agent-to-agent communication to a robust, task-driven framework where teams of specialized agents collaborate to achieve complex goals.

✨ Key Features

Based on a refined and modular architecture, VibeX is built around a few core concepts:

• 🤖 Multi-Agent Teams: Define teams of specialized agents in simple YAML files. Each agent can have its own role, tools, and configuration.
• 🗣️ Natural Language Orchestration: Agents hand off tasks to each other using natural language. A central TaskExecutor interprets these handoffs and routes work to the appropriate agent, enabling complex, dynamic workflows.
• 🛠️ Secure & Extensible Tools: Tools are defined with Python decorators and their schemas are automatically generated. Shell commands are executed in a secure Docker sandbox, providing safety and isolation. A flexible ToolExecutor manages the entire lifecycle.
• 🧠 Stateful & Context-Aware Memory: Agents maintain long-term memory, enabling them to recall past interactions and context. The memory system supports semantic search, ensuring agents have the information they need, when they need it.
• 📡 Streamable Communication: The entire lifecycle of a task, from agent thoughts to tool calls and results, is available as a real-time stream of events. This allows you to build rich, observable UIs like the Vercel AI SDK.
• 🎯 Task-Centric API: Interact with the system through a simple, powerful API. Kick off complex workflows with execute_task() or manage interactive sessions with start_task().

🚀 Getting Started

The best way to get started is by following our Getting Started, which will walk you through building a simple chat application and a multi-agent writer/reviewer team.

1. Installation

Install VibeX from PyPI:

pip install vibex

Or for development, clone the repository:

git clone https://github.com/dustland/vibex.git
cd vibex
uv sync

2. Usage Examples

VibeX can be run directly from the command line or via its Python API. You can find complete, working examples in the examples/ directory.

Running an Example

This demonstrates a basic multi-agent collaboration:

# Navigate to an example directory
cd examples/simple_team

# Run the main script
python main.py

Using the CLI

The framework includes a powerful CLI for managing your agent system:

# Bootstrap a new project with interactive wizard
vibex init

# Monitor tasks and events in your terminal
vibex monitor

# Launch the web dashboard for rich observability
vibex monitor --web

# List available tools
vibex tools list

Using the Python API

Here is a simple example of an autonomous run:

import asyncio
from vibex import execute_task

async def main():
    # Execute a task with a simple prompt (completes when done)
    await execute_task(
        prompt="Write a brief report on renewable energy trends",
        config_path="config/team.yaml"
    )
    print("Task completed!")

asyncio.run(main())

The script above shows a simple autonomous run. For more advanced patterns like message streaming and interactive sessions, please see the complete scripts in the /examples directory.

Example Projects

• simple_writer - Single intelligent agent for both creative writing and market research
• simple_team - Multi-agent writer/reviewer collaboration
• simple_chat - Interactive chat with an AI assistant
• auto_writer - Advanced multi-agent writing system

🔧 Development Workflow

VibeX includes a comprehensive development setup with automated tooling to ensure code quality and documentation consistency.

Pre-commit Hooks

The project uses pre-commit hooks to automatically maintain code quality and keep documentation up-to-date:

# Set up pre-commit hooks (one-time setup)
uv run setup-hooks

# Now every commit will automatically:
# - Generate API documentation when Python files change
# - Format code and fix whitespace issues
# - Validate YAML and TOML files
# - Check for merge conflicts and large files

Development Commands

# Generate API documentation
uv run docs

# Build documentation site
uv run build-docs

# Run tests
uv run test

# Run development server with auto-reload
uv run dev

# Monitor tasks and events
uv run monitor

API Documentation

The API documentation is automatically generated from docstrings and kept in sync with the code:

• Source: Python docstrings in src/vibex/
• Output: Markdown files in docs/content/api/
• Automation: Generated on every commit via pre-commit hooks

To manually regenerate API docs:

uv run docs

📊 Observability & Monitoring

VibeX includes a comprehensive observability system for monitoring and debugging multi-agent workflows.

Launch a modern web dashboard built with FastAPI and Preline UI:

# Start web dashboard
vibex monitor --web

• Dashboard: System overview with metrics and recent activity.
• Tasks: Task conversation history viewer with export.
• Events: Real-time event monitoring with filtering.
• Memory: Memory browser with search and categories.
• Messages: Agent conversation history during execution.
• Configuration: System configuration and status viewer.

You can also use the observability features in CLI mode without the option --web.

🛠️ Tech Stack

VibeX is built on a robust foundation of modern Python technologies:

• LiteLLM - Unified interface for 100+ LLM providers
• Mem0 - Intelligent memory layer for long-term context
• SerpAPI - Web search capabilities for agents
• Crawl4AI - Open-source web content extraction with JavaScript support
• Browser-use - Browser automation

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

🙏 Acknowledgments

This project was initially inspired by and built upon concepts from AG2 (AutoGen), an excellent multi-agent conversation framework. While VibeX has evolved into its own distinct architecture and approach, we're grateful for the foundational ideas and patterns that AG2 provided to the multi-agent AI community.

Our message system and conversation architecture draws inspiration from Vercel AI SDK, particularly its elegant message format with role-based structure and parts array for handling complex content types. This design pattern has proven invaluable for building robust, extensible AI applications.

This project also referred to other open-source projects such as OpenManus, Suna and Magic etc.

📄 License

Licensed under the Apache License 2.0 - see LICENSE for details.

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Built with ❤️ by Dustland