GibberLink 是一項創新的開源專案，讓 AI 助理之間以更高效的方式進行音頻對話。這項技術於 2025 年的 ElevenLabs 倫敦黑客馬拉松中脫穎而出，獲得了全球首獎。
🔍 GibberLink 是什麼？
GibberLink 是由 Boris Starkov 和 Anton Pidkuiko 兩位開發者在黑客馬拉松期間開發的開源專案。其核心理念是讓 AI 助理在識別到對方也是 AI 時，切換到一種更高效的通訊協議，使用聲波傳輸結構化數據，而非傳統的人類語言。這種方式不僅提高了通訊效率，還減少了計算資源的消耗。
git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus
安裝所需依賴：
pip install -r requirements.txt
修改設定檔（config.toml）：
cp config/config.example.toml config/config.toml
config.toml的內容如下，可以參考後修改
# Global LLM configuration
#[llm]
# model = "claude-3-7-sonnet-20250219" # The LLM model to use
# base_url = "https://api.anthropic.com/v1/" # API endpoint URL
# api_key = "YOUR_API_KEY" # Your API key
# max_tokens = 8192 # Maximum number of tokens in the response
# temperature = 0.0 # Controls randomness
# [llm] #AZURE OPENAI:
# api_type= 'azure'
# model = "YOUR_MODEL_NAME" #"gpt-4o-mini"
# base_url = "{YOUR_AZURE_ENDPOINT.rstrip('/')}/openai/deployments/{AZURE_DEPOLYMENT_ID}"
# api_key = "AZURE API KEY"
# max_tokens = 8096
# temperature = 0.0
# api_version="AZURE API VERSION" #"2024-08-01-preview"
[llm] #OLLAMA:
api_type = 'ollama'
model = "llama3.2"
base_url = "http://localhost:11434/v1"
api_key = "ollama"
max_tokens = 4096
temperature = 0.0
# Optional configuration for specific LLM models
#[llm.vision]
#model = "claude-3-7-sonnet-20250219" # The vision model to use
#base_url = "https://api.anthropic.com/v1/" # API endpoint URL for vision model
#api_key = "YOUR_API_KEY" # Your API key for vision model
#max_tokens = 8192 # Maximum number of tokens in the response
#temperature = 0.0 # Controls randomness for vision model
[llm.vision] #OLLAMA VISION:
api_type = 'ollama'
model = "llama3.2-vision"
base_url = "http://localhost:11434/v1"
api_key = "ollama"
max_tokens = 4096
temperature = 0.0
# Optional configuration for specific browser configuration
# [browser]
# Whether to run browser in headless mode (default: false)
#headless = false
# Disable browser security features (default: true)
#disable_security = true
# Extra arguments to pass to the browser
#extra_chromium_args = []
# Path to a Chrome instance to use to connect to your normal browser
# e.g. '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome'
#chrome_instance_path = ""
# Connect to a browser instance via WebSocket
#wss_url = ""
# Connect to a browser instance via CDP
#cdp_url = ""
# Optional configuration, Proxy settings for the browser
# [browser.proxy]
# server = "http://proxy-server:port"
# username = "proxy-username"
# password = "proxy-password"
# Optional configuration, Search settings.
# [search]
# Search engine for agent to use. Default is "Google", can be set to "Baidu" or "DuckDuckGo".
# engine = "Google"
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