huly-coder-linux-x64/huly-coder.yaml

A lightweight autonomous AI coding agent with terminal-based UI, inspired by Codex and Cline.

# Huly-Coder Configuration
# -----------------------
# This configuration can be overridden in:
# - ~/huly-coder.yaml
# - huly-coder-local.yaml

#---------------------------------------
# AI Provider Configuration
#---------------------------------------
# Supported providers:
# - OpenRouter (default)
# - Anthropic
# - OpenAI
# - LMStudio
provider: OpenRouter

# Model Configuration
# Available models depend on the selected provider
# OpenRouter models:
# - anthropic/claude-3.5-sonnet (default)
# - gpt-4o
# Local models:
# - qwen3-8b
# - deepseek-r1-distill-llama-8b
model: anthropic/claude-3.5-sonnet

# Provider API Configuration
#provider_api_key: sk-xxxxxxxxxxxxxxxxxxxxx
#provider_base_url: http://127.0.0.1:1234/v1

# Advanced provider configuration (optional)
# For example, you can configure openrouter provider routing configuration(https://openrouter.ai/docs/features/provider-routing) as follows:
# provider_config:
#   provider:
#     order: [ "openai", "together"]
#     allow_fallbacks: false

#---------------------------------------
# Appearance Configuration
#---------------------------------------
# Supported appearance themes:
# - dark (default)
# - light
# - file path to yaml file
# to develop a new theme, you can run `cargo r -- --autoreload-theme` for autoreload theme on change and specify the theme path in the config file
appearance:
  theme: dark
  user_name: default_user

#---------------------------------------
# Permission mode
#---------------------------------------
# Supported permission modes:
# - full_autonomous
# - manual_approval (default)
# - deny_all
permission_mode: manual_approval

#---------------------------------------
# Workspace Configuration
#---------------------------------------
workspace: ./target/workspace

#---------------------------------------
# Memory Embedding Configuration
#---------------------------------------
# Supported embedding providers:
# - fastembed (default)
# - voyage_ai
#     type: voyage_ai
#     api_key: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
#     model: voyage-3.5-lite
#     dimensions: 256
memory_embedding:
  type: fastembed

#---------------------------------------
# Web Interaction Configuration
#---------------------------------------
# Web Fetch Options:
# - direct (default): Direct web access
# - chrome: Use Chrome browser
web_fetch: direct

# Web Search Configuration
# Web Search Options:
# - Brave Search (default)
#    type: brave
#    api_key: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# - SearX (alternative)
#    type: searx
#    url: http://localhost:8080/
web_search:
  type: brave
  api_key: BSAnIRlUpQfYjLAs4Dt23DvQyr24vL3

#---------------------------------------
# MCP (Model Context Protocol) Configuration
#---------------------------------------
# mcp:
#   servers:
#    # Fetch server configuration
#    fetch:
#      transport:
#        type: stdio
#        command: docker
#        args: [ "run", "-i", "--rm", "mcp/fetch", "--ignore-robots-txt" ]
#        protocol_version: 2024-11-05
#      system_prompt: Custom system prompt addon for MCP server
#    
#    # Weather server configuration
#    weather:
#      transport:
#        type: sse
#        url: http://127.0.0.1:8080/sse
#        protocol_version: 2024-11-05
#
#    # Web Browser automation (note works only with OpenRouter provider)
#     puppeteer:
#       transport:
#         type: stdio
#         command: docker.exe
#         args: ["run", "-i", "--rm", "--init", "-e", "DOCKER_CONTAINER=true", "mcp/puppeteer"]
#         protocol_version: 2024-11-05

#---------------------------------------
# AI Assistant Personality Configuration
#---------------------------------------
user_instructions: |
  You are dedicated software engineer working alone. You're free to choose any technology, approach, and solution. 
  If in doubt please choose the best way you think. Your goal is to build working software based on user request.