mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-03 06:12:14 +08:00
b6da3a219e
* feat: adds docker-based dev environment * docs: updates Docker command help * fix local dev * feat(sandbox): add Kubernetes-based sandbox provider for multi-instance support * fix: skills path in k8s * feat: add example config for k8s sandbox * fix: docker config * fix: load skills on docker dev * feat: support sandbox execution to Kubernetes Deployment model * chore: rename web service name
299 lines
11 KiB
YAML
299 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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# ============================================================================
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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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