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A powerful multi-agent system for adaptive AI reasoning and automation

Project description

AgenticFleet

A powerful multi-agent system for adaptive AI reasoning and automation. AgenticFleet combines Chainlit's interactive interface with AutoGen's multi-agent capabilities to create a flexible, powerful AI assistant platform.

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Core Components

AgenticFleet operates through a coordinated team of specialized agents:

  • WebSurfer: Expert web navigation agent

    • Extracts information from web pages
    • Captures and processes screenshots
    • Provides structured summaries of findings
  • FileSurfer: File system specialist

    • Searches and analyzes workspace files
    • Manages file operations efficiently
    • Extracts relevant information from documents
  • Coder: Development expert

    • Generates and reviews code
    • Implements solutions
    • Maintains code quality
  • Executor: Code execution specialist

    • Safely runs code in isolated workspace
    • Monitors execution and handles timeouts
    • Provides detailed execution feedback

Key Features

  • Multi-Agent System

    • Coordinated team of specialized AI agents
    • Real-time inter-agent communication
    • Task planning and execution tracking
  • Interactive Interface

    • Real-time streaming responses
    • Code syntax highlighting
    • Markdown rendering
    • File upload/download support
    • Progress visualization with task lists
  • Advanced Capabilities

    • GitHub OAuth authentication
    • Configurable agent behaviors
    • Comprehensive error handling and recovery
    • Multi-modal content processing (text, images)
    • Execution workspace isolation
  • Developer-Friendly

    • Easy-to-use CLI
    • Extensive documentation
    • Flexible configuration
    • Active community support

Quick Start

  1. Install AgenticFleet using uv (recommended):
uv pip install agentic-fleet
playwright install --with-deps chromium # Optional: Install Playwright Chromium dependencies
  1. Copy and configure environment variables:
# Copy the example environment file
cp .env.example .env

# Open .env and update with your values
# Required: Add your Azure OpenAI credentials
# Optional: Configure OAuth settings
  1. Start the server:
agenticfleet start   # Enable GitHub authentication
agenticfleet start --no-oauth # Default local mode

The web interface will be available at http://localhost:8001.

System Architecture

graph TD
    User[Chainlit UI] -->|HTTP| App[app.py]
    App --> AgentTeam[MagenticOneGroupChat]
    AgentTeam --> WebSurfer
    AgentTeam --> FileSurfer
    AgentTeam --> Coder
    AgentTeam --> Executor
    WebSurfer -->|Selenium| Web[External Websites]
    FileSurfer -->|OS| FileSystem[Local Files]
    Executor -->|Subprocess| Code[Python/Runtime]

Configuration

The .env.example file contains all required and recommended settings:

# Required: Azure OpenAI Configuration
AZURE_OPENAI_API_KEY=your_api_key
AZURE_OPENAI_ENDPOINT=your_endpoint
AZURE_OPENAI_DEPLOYMENT=your_deployment
AZURE_OPENAI_MODEL=your_model

# Optional: OAuth Configuration
USE_OAUTH=false
OAUTH_GITHUB_CLIENT_ID=
OAUTH_GITHUB_CLIENT_SECRET=
OAUTH_REDIRECT_URI=http://localhost:8001/oauth/callback

Error Handling

AgenticFleet implements comprehensive error handling:

  • Graceful degradation on service failures
  • Detailed error logging and reporting
  • Automatic cleanup of resources
  • Session state recovery
  • Execution timeout management

Development

Prerequisites

  • Python 3.10-3.12 (Python 3.13 is not yet supported)
  • uv package manager (recommended)
  • Azure OpenAI API access

Setup

  1. Clone and install:
git clone https://github.com/qredence/agenticfleet.git
cd agenticfleet
pip install uv
uv pip install -e .
uv pip install -e ".[dev]"
  1. Run tests:
pytest tests/

Documentation

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Security

For security concerns, please review our Security Policy.

License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.

Support

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