agentic-fleet 0.4.73

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

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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AgenticFleet operates through a coordinated team of specialized agents:

System Architecture

AgenticFleet operates through a coordinated team of specialized agents:

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]

• 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

Installation Guide

Prerequisites

• Python Version: 3.10-3.12
• Operating Systems: macOS, Linux, Windows

1. Install uv Package Manager

macOS/Linux

# Using pip
pip install uv

# Using homebrew (macOS)
brew install uv

# Using curl
curl -LsSf https://astral.sh/uv/install.sh | sh

Windows

# Using pip
pip install uv

# Using winget
winget install uv

2. Create a Virtual Environment

# Create a new virtual environment
uv venv

# Activate the virtual environment
# On macOS/Linux
source .venv/bin/activate

# On Windows
.venv\Scripts\activate

3. Install AgenticFleet

# Install the latest stable version
uv pip install agentic-fleet

# Install with specific optional features
uv pip install 'agentic-fleet[telemetry]'
uv pip install 'agentic-fleet[tracing]'

# Install Playwright for web automation and scraping
uv pip install playwright
playwright install --with-deps chromium  # Install Chromium browser and dependencies

Playwright Installation Notes

• Installs Chromium browser for web automation
• Includes necessary browser dependencies
• Required for web scraping and browser-based agents
• Supports headless and headed browser modes

4. Verify Installation

# Check installed version
uv pip show agentic-fleet

# Run a quick test
python -c "import agentic_fleet; print(agentic_fleet.__version__)"

Troubleshooting

• Ensure you're using Python 3.10-3.12
• Update uv to the latest version: pip install -U uv
• If you encounter issues, check our GitHub Issues

Best Practices

• Always use a virtual environment
• Keep uv updated
• Use optional feature sets as needed
• Report any installation issues on GitHub

Optional Feature Sets

# Install with specific optional features
uv pip install 'agentic-fleet[telemetry]'
uv pip install 'agentic-fleet[tracing]'

⚠️ Warning About Editable Installations

DO NOT use -e unless you are a core contributor.

Editable installations:

• Are NOT supported in production
• May introduce unexpected behaviors
• Void package support and warranty
• Are intended ONLY for package development

If you modify the package locally:

1. Open a GitHub issue
2. Submit a pull request
3. Discuss proposed changes with maintainers

Unauthorized local modifications are strongly discouraged.

Quick Start with Docker

# Pull the latest image
docker pull qredence/agenticfleet:latest

# Run with minimum required configuration
docker run -d -p 8001:8001 qredence/agenticfleet:latest

# Or run with additional configuration
docker run -d -p 8001:8001 \
  -e AZURE_OPENAI_API_KEY=your_key \
  -e AZURE_OPENAI_ENDPOINT=your_endpoint \
  -e AZURE_OPENAI_DEPLOYMENT=your_deployment \
  -e AZURE_OPENAI_MODEL=your_model \
  -e USE_OAUTH=true \
  -e OAUTH_GITHUB_CLIENT_ID=your_client_id \
  -e OAUTH_GITHUB_CLIENT_SECRET=your_client_secret \
  qredence/agenticfleet:latest

# Run without OAuth
docker run -d -p 8001:8001 \
  -e AZURE_OPENAI_API_KEY=your_key \
  -e AZURE_OPENAI_ENDPOINT=your_endpoint \
  -e USE_OAUTH=false \
  qredence/agenticfleet:latest

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

Model Provider Installation

Install providers using pip:

# Install base package
pip install agentic-fleet

Supported Model Providers

AgenticFleet supports multiple LLM providers through a unified interface:

• OpenAI

  • GPT-4 and other OpenAI models
  • Function calling and vision capabilities
  • JSON mode support

• Azure OpenAI

  • Azure-hosted OpenAI models
  • Azure AD authentication support
  • Enterprise-grade security

• Google Gemini

  • Gemini Pro and Ultra models
  • OpenAI-compatible API
  • Multimodal capabilities

• DeepSeek

  • DeepSeek's language models
  • OpenAI-compatible API
  • Specialized model capabilities

• Ollama

  • Local model deployment
  • Various open-source models
  • Offline capabilities

• Azure AI Foundry

  • Azure-hosted models (e.g., Phi-4)
  • GitHub authentication
  • Enterprise integration

• CogCache

  • OpenAI-compatible API with caching
  • Improved response times
  • Cost optimization
  • Automatic retry handling

Key Features

• Advanced Capabilities

  • Multiple LLM provider support
  • 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

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 # Set to true to enable GitHub OAuth
OAUTH_GITHUB_CLIENT_ID=
OAUTH_GITHUB_CLIENT_SECRET=
OAUTH_REDIRECT_URI=http://localhost:8001/oauth/callback

# Optional: Other Model Provider Configurations
GEMINI_API_KEY=your_gemini_key
DEEPSEEK_API_KEY=your_deepseek_key
GITHUB_TOKEN=your_github_pat  # For Azure AI Foundry
COGCACHE_API_KEY=your_cogcache_key  # For CogCache proxy API

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

Community Contributions

🤝 We Welcome Your Contributions

AgenticFleet is an open-source project that thrives on community involvement. We actively encourage contributions from developers of all skill levels.

How to Contribute

1. Forking the Repository

   # Fork the repository on GitHub
   # Clone your forked repository
   git clone https://github.com/YOUR_USERNAME/AgenticFleet.git
   cd AgenticFleet
   

2. Reporting Issues

   • Use GitHub Issues to:
     • Report bugs
     • Suggest features
     • Ask questions
   • Create a New Issue

3. Pull Requests

   • Fork the repository
   • Create a new branch: git checkout -b feature/your-feature-name
   • Make your changes
   • Run pre-commit hooks: pre-commit run --all-files
   • Commit with a clear, descriptive message
   • Open a Pull Request

Contribution Guidelines

• Read our CONTRIBUTING.md for detailed guidelines
• Follow our Code of Conduct
• Ensure code quality with pre-commit hooks
• Write comprehensive tests for new features
• Update documentation accordingly

Ways to Contribute

• 🐛 Bug reports
• 📝 Documentation improvements
• 🚀 Feature suggestions
• 💻 Code contributions
• 📖 Tutorials and examples
• 🌐 Internationalization

Support the Project

• ⭐ Star the repository
• 🐦 Follow us on X (Twitter): @AgenticFleet
• 💬 Join our Discord Community: AgenticFleet Discord
• 💡 Share your use cases
• 🤝 Spread the word about AgenticFleet

We believe in the power of open-source and collaborative development. Your contributions, no matter how small, are valuable and appreciated!

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