Installation Guide
This guide will walk you through the process of installing CleverBee on your system, including prerequisites, installation steps, and platform-specific considerations.
Table of Contents
Prerequisites
Before installing CleverBee, ensure your system meets the following requirements:
- Python 3.8+ - CleverBee requires Python 3.8 or newer
- pip - Python's package installer
- git - For cloning the repository
- Node.js/npm - Required for rebrowser patches (installed automatically if missing)
- CMake - Required for building llama-cpp-python if using local models
- jq - Used for JSON configuration during setup
- yq - Used for YAML configuration during setup (setup.sh will attempt to install if missing)
Note
The setup script will attempt to install some dependencies, but you should ensure your system has the core requirements (Python, git, jq, CMake if local models chosen) before proceeding.
Installing Required Packages
On macOS:
brew install python cmake git jq node
On Ubuntu/Debian:
sudo apt update
sudo apt install python3 python3-pip python3-venv cmake git jq nodejs
Installation Steps
Follow these steps to install CleverBee:
- Clone the repository:
git clone https://github.com/SureScaleAI/cleverbee cd cleverbee
- Run the setup script:
bash setup.sh
This script will guide you through:
- Checking prerequisites (
python3
,jq
,yq
,cmake
if local models chosen) - Creating a Python virtual environment (
venv/
) - Installing base Python dependencies from
requirements.txt
- Hardware Check & Model Preference: Asking if you want to enable local model setup (currently optimized for NVIDIA GPUs >= 24GB VRAM)
- Conditional
llama-cpp-python
Install: Installing with appropriate hardware acceleration (CUDA for NVIDIA) if local models are enabled - Hugging Face Login: Prompting login if using local models
- Model Configuration: Helping select primary reasoning and summarizer models, updating
config.yaml
- Model Download: Downloading selected local GGUF models if chosen
- GPU Layer Optimization: Recommending and setting
N_GPU_LAYERS
inconfig.yaml
for local models - Additional Config: Helping set memory limits and thinking mode in
config.yaml
- Installing necessary Playwright browsers and applying rebrowser patches
- API Key Setup: Checking for needed API keys (
ANTHROPIC_API_KEY
,GEMINI_API_KEY
) in.env
based on selected models and prompting if missing - Making
run.sh
executable
- Checking prerequisites (
Important
The setup process might take 15-30 minutes, especially when downloading models. GPU detection and model selection will be interactive, requiring your input during setup.
API Keys
CleverBee requires API keys for cloud models. The setup script will check if you need API keys based on your model selection and help you add them to the .env
file:
- Anthropic API Key (for Claude models): Get from Anthropic Console
- Google Gemini API Key (for Gemini models): Get from Google AI Studio
If you need to add or change keys later, edit the .env
file directly and restart the application.
Hardware Considerations
Important
We strongly discourage the use of local LLMs without a good NVIDIA GPU. Running research flows on systems without proper GPU acceleration can take hours or even days to complete. Cloud models like Gemini or Claude are strongly recommended for systems without NVIDIA GPUs.
RAM Requirements
- Cloud Models Only: 4GB RAM minimum
- Local Models (DeepSeek, Mistral, etc.): 8GB RAM minimum, 16GB+ recommended
- Large Local Models (Llama 3, etc.): 24GB+ RAM recommended
GPU Acceleration
Local models can benefit significantly from GPU acceleration:
- NVIDIA GPUs: CUDA support is enabled automatically during setup
- AMD GPUs: May require additional configuration (see platform-specific instructions)
The setup script will detect your hardware and recommend appropriate settings.
Platform-Specific Instructions
macOS
- Cloud models like Gemini or Claude are strongly recommended for best performance
- Install prerequisites with Homebrew:
brew install python cmake git jq node
Linux with NVIDIA GPUs
- Ensure you have NVIDIA drivers and CUDA installed for optimal performance
- The setup script will detect NVIDIA GPUs and enable CUDA acceleration
- Local models can provide good performance with CUDA acceleration
Windows
- Windows installation is possible but may require additional configuration
- WSL2 (Windows Subsystem for Linux) is recommended for the best experience
- Follow the Linux instructions if using WSL2
Model Selection
During setup, you'll be prompted to select models for two distinct purposes:
1. Primary Reasoning Model
This model handles the research planning, tool selection, and report writing. Options include:
- Cloud Models (recommended): Claude (3.7 Sonnet) or Gemini (2.5 Pro)
- Large Local Models (24GB+ RAM): Llama 3 70B (best reasoning quality)
- Medium Local Models (16GB+ RAM): Qwen 32B or DeepSeek 32B
- Small Local Models (4GB+ RAM): Mistral 7B
2. Summarizer Model
This model processes and summarizes content. Options include:
- Cloud Models (recommended for <8GB RAM): Gemini Flash
- Local Models (8GB+ RAM): DeepSeek 8B or Mistral 7B (64K context)
The setup script will recommend appropriate models based on your hardware.
Installation Troubleshooting
Common Issues
Issue: Model Download Failures
Solution: Ensure you've logged into Hugging Face using huggingface-cli login
and accepted model terms. You can re-run setup.sh
to retry downloads.
Issue: Missing Dependencies
Solution: Install missing system dependencies (like CMake, jq) and re-run the setup script.
Issue: GPU Acceleration Not Working
Solution: Ensure you have proper drivers installed. Check config.yaml
and verify N_GPU_LAYERS
is set appropriately.
Getting Help
If you encounter issues during installation:
- Check the FAQ & Troubleshooting page
- Review the error messages in the terminal
- Open an issue on the GitHub repository
Next Steps
Once installation is complete, proceed to the Configuration Guide to customize CleverBee for your needs, or jump to the Usage Guide to start using it right away.