mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-02 22:02:13 +08:00
300e5a519a
* Adds Kubernetes sandbox provisioner support * Improves Docker dev setup by standardizing host paths Replaces hardcoded host paths with a configurable root directory, making the development environment more portable and easier to use across different machines. Automatically sets the root path if not already defined, reducing manual setup steps.
306 lines
11 KiB
YAML
306 lines
11 KiB
YAML
# Configuration for the DeerFlow application
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#
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# Guidelines:
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# - Copy this file to `config.yaml` and customize it for your environment
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# - The default path of this configuration file is `config.yaml` in the current working directory.
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# However you can change it using the `DEER_FLOW_CONFIG_PATH` environment variable.
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# - Environment variables are available for all field values. Example: `api_key: $OPENAI_API_KEY`
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# - The `use` path is a string that looks like "package_name.sub_package_name.module_name:class_name/variable_name".
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# ============================================================================
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# Models Configuration
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# ============================================================================
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# Configure available LLM models for the agent to use
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models:
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# Example: OpenAI model
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- name: gpt-4
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display_name: GPT-4
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use: langchain_openai:ChatOpenAI
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model: gpt-4
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api_key: $OPENAI_API_KEY # Use environment variable
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max_tokens: 4096
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temperature: 0.7
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supports_vision: true # Enable vision support for view_image tool
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# Example: Anthropic Claude model
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# - name: claude-3-5-sonnet
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# display_name: Claude 3.5 Sonnet
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# use: langchain_anthropic:ChatAnthropic
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# model: claude-3-5-sonnet-20241022
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# api_key: $ANTHROPIC_API_KEY
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# max_tokens: 8192
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# supports_vision: true # Enable vision support for view_image tool
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# Example: DeepSeek model (with thinking support)
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# - name: deepseek-v3
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# display_name: DeepSeek V3 (Thinking)
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# use: langchain_deepseek:ChatDeepSeek
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# model: deepseek-chat
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# api_key: $DEEPSEEK_API_KEY
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# max_tokens: 16384
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# supports_thinking: true
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# supports_vision: false # DeepSeek V3 does not support vision
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# when_thinking_enabled:
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# extra_body:
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# thinking:
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# type: enabled
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# Example: Volcengine (Doubao) model
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# - name: doubao-seed-1.8
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# display_name: Doubao 1.8 (Thinking)
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# use: langchain_deepseek:ChatDeepSeek
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# model: ep-m-20260106111913-xxxxx
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# api_base: https://ark.cn-beijing.volces.com/api/v3
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# api_key: $VOLCENGINE_API_KEY
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# supports_thinking: true
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# supports_vision: false # Check your specific model's capabilities
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# when_thinking_enabled:
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# extra_body:
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# thinking:
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# type: enabled
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# Example: Kimi K2.5 model
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# - name: kimi-k2.5
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# display_name: Kimi K2.5
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# use: src.models.patched_deepseek:PatchedChatDeepSeek
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# model: kimi-k2.5
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# api_base: https://api.moonshot.cn/v1
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# api_key: $MOONSHOT_API_KEY
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# max_tokens: 32768
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# supports_thinking: true
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# supports_vision: true # Check your specific model's capabilities
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# when_thinking_enabled:
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# extra_body:
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# thinking:
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# type: enabled
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# ============================================================================
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# Tool Groups Configuration
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# ============================================================================
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# Define groups of tools for organization and access control
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tool_groups:
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- name: web
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- name: file:read
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- name: file:write
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- name: bash
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# ============================================================================
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# Tools Configuration
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# ============================================================================
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# Configure available tools for the agent to use
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tools:
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# Web search tool (requires Tavily API key)
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- name: web_search
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group: web
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use: src.community.tavily.tools:web_search_tool
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max_results: 5
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# api_key: $TAVILY_API_KEY # Set if needed
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# Web fetch tool (uses Jina AI reader)
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- name: web_fetch
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group: web
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use: src.community.jina_ai.tools:web_fetch_tool
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timeout: 10
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# Image search tool (uses DuckDuckGo)
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# Use this to find reference images before image generation
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- name: image_search
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group: web
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use: src.community.image_search.tools:image_search_tool
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max_results: 5
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# File operations tools
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- name: ls
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group: file:read
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use: src.sandbox.tools:ls_tool
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- name: read_file
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group: file:read
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use: src.sandbox.tools:read_file_tool
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- name: write_file
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group: file:write
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use: src.sandbox.tools:write_file_tool
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- name: str_replace
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group: file:write
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use: src.sandbox.tools:str_replace_tool
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# Bash execution tool
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- name: bash
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group: bash
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use: src.sandbox.tools:bash_tool
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# ============================================================================
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# Sandbox Configuration
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# ============================================================================
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# Choose between local sandbox (direct execution) or Docker-based AIO sandbox
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# Option 1: Local Sandbox (Default)
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# Executes commands directly on the host machine
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sandbox:
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use: src.sandbox.local:LocalSandboxProvider
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# Option 2: Container-based AIO Sandbox
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# Executes commands in isolated containers (Docker or Apple Container)
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# On macOS: Automatically prefers Apple Container if available, falls back to Docker
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# On other platforms: Uses Docker
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# Uncomment to use:
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# sandbox:
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# use: src.community.aio_sandbox:AioSandboxProvider
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#
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# # Optional: Use existing sandbox at this URL (no container will be started)
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# # base_url: http://localhost:8080
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#
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# # Optional: Container image to use (works with both Docker and Apple Container)
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# # Default: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
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# # Recommended: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest (works on both x86_64 and arm64)
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# # image: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
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#
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# # Optional: Base port for sandbox containers (default: 8080)
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# # port: 8080
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#
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# # Optional: Whether to automatically start Docker container (default: true)
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# # auto_start: true
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#
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# # Optional: Prefix for container names (default: deer-flow-sandbox)
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# # container_prefix: deer-flow-sandbox
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#
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# # Optional: Additional mount directories from host to container
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# # NOTE: Skills directory is automatically mounted from skills.path to skills.container_path
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# # mounts:
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# # # Other custom mounts
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# # - host_path: /path/on/host
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# # container_path: /home/user/shared
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# # read_only: false
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#
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# # Optional: Environment variables to inject into the sandbox container
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# # Values starting with $ will be resolved from host environment variables
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# # environment:
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# # NODE_ENV: production
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# # DEBUG: "false"
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# # API_KEY: $MY_API_KEY # Reads from host's MY_API_KEY env var
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# # DATABASE_URL: $DATABASE_URL # Reads from host's DATABASE_URL env var
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# Option 3: Provisioner-managed AIO Sandbox (docker-compose-dev)
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# Each sandbox_id gets a dedicated Pod in k3s, managed by the provisioner.
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# Recommended for production or advanced users who want better isolation and scalability.:
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# sandbox:
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# use: src.community.aio_sandbox:AioSandboxProvider
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# provisioner_url: http://provisioner:8002
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# ============================================================================
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# Skills Configuration
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# ============================================================================
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# Configure skills directory for specialized agent workflows
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skills:
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# Path to skills directory on the host (relative to project root or absolute)
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# Default: ../skills (relative to backend directory)
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# Uncomment to customize:
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# path: /absolute/path/to/custom/skills
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# Path where skills are mounted in the sandbox container
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# This is used by the agent to access skills in both local and Docker sandbox
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# Default: /mnt/skills
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container_path: /mnt/skills
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# ============================================================================
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# Title Generation Configuration
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# ============================================================================
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# Automatic conversation title generation settings
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title:
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enabled: true
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max_words: 6
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max_chars: 60
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model_name: null # Use default model (first model in models list)
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# ============================================================================
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# Summarization Configuration
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# ============================================================================
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# Automatically summarize conversation history when token limits are approached
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# This helps maintain context in long conversations without exceeding model limits
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summarization:
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enabled: true
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# Model to use for summarization (null = use default model)
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# Recommended: Use a lightweight, cost-effective model like "gpt-4o-mini" or similar
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model_name: null
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# Trigger conditions - at least one required
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# Summarization runs when ANY threshold is met (OR logic)
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# You can specify a single trigger or a list of triggers
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trigger:
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# Trigger when token count reaches 15564
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- type: tokens
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value: 15564
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# Uncomment to also trigger when message count reaches 50
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# - type: messages
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# value: 50
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# Uncomment to trigger when 80% of model's max input tokens is reached
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# - type: fraction
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# value: 0.8
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# Context retention policy after summarization
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# Specifies how much recent history to preserve
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keep:
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# Keep the most recent 10 messages (recommended)
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type: messages
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value: 10
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# Alternative: Keep specific token count
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# type: tokens
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# value: 3000
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# Alternative: Keep percentage of model's max input tokens
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# type: fraction
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# value: 0.3
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# Maximum tokens to keep when preparing messages for summarization
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# Set to null to skip trimming (not recommended for very long conversations)
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trim_tokens_to_summarize: 15564
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# Custom summary prompt template (null = use default LangChain prompt)
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# The prompt should guide the model to extract important context
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summary_prompt: null
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# ============================================================================
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# MCP (Model Context Protocol) Configuration
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# ============================================================================
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# Configure MCP servers to provide additional tools and capabilities
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# MCP configuration is loaded from a separate `mcp_config.json` file
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#
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# Setup:
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# 1. Copy `mcp_config.example.json` to `mcp_config.json` in the project root
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# 2. Enable desired MCP servers by setting `enabled: true`
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# 3. Configure server commands, arguments, and environment variables
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# 4. Restart the application to load MCP tools
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#
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# MCP servers provide tools that are automatically discovered and integrated
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# with DeerFlow's agent system. Examples include:
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# - File system access
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# - Database connections (PostgreSQL, etc.)
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# - External APIs (GitHub, Brave Search, etc.)
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# - Browser automation (Puppeteer)
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# - Custom MCP server implementations
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#
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# For more information, see: https://modelcontextprotocol.io
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# ============================================================================
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# Memory Configuration
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# ============================================================================
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# Global memory mechanism
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# Stores user context and conversation history for personalized responses
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memory:
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enabled: true
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storage_path: .deer-flow/memory.json # Path relative to backend directory
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debounce_seconds: 30 # Wait time before processing queued updates
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model_name: null # Use default model
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max_facts: 100 # Maximum number of facts to store
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fact_confidence_threshold: 0.7 # Minimum confidence for storing facts
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injection_enabled: true # Whether to inject memory into system prompt
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max_injection_tokens: 2000 # Maximum tokens for memory injection
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