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https://gitee.com/wanwujie/deer-flow
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8590249db4
Allow per-agent environment variables to be declared in config.yaml under acp_agents.<name>.env. Values prefixed with $ are resolved from the host environment at invocation time, consistent with other config fields. Passes None to spawn_agent_process when env is empty so the subprocess inherits the parent environment unchanged. Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
637 lines
24 KiB
YAML
637 lines
24 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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# Config Version (used to detect outdated config files)
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# ============================================================================
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# Bump this number when the config schema changes.
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# Run `make config-upgrade` to merge new fields into your local config.yaml.
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config_version: 3
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# ============================================================================
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# Logging
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# ============================================================================
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# Log level for deerflow modules (debug/info/warning/error)
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log_level: info
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# ============================================================================
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# Token Usage Tracking
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# ============================================================================
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# Track LLM token usage per model call (input/output/total tokens)
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# Logs at info level via TokenUsageMiddleware
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token_usage:
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enabled: false
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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: Volcengine (Doubao) model
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# - name: doubao-seed-1.8
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# display_name: Doubao-Seed-1.8
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# use: deerflow.models.patched_deepseek:PatchedChatDeepSeek
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# model: doubao-seed-1-8-251228
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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: true
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# supports_reasoning_effort: true
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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: 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: OpenAI Responses API model
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# - name: gpt-5-responses
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# display_name: GPT-5 (Responses API)
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# use: langchain_openai:ChatOpenAI
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# model: gpt-5
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# api_key: $OPENAI_API_KEY
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# use_responses_api: true
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# output_version: responses/v1
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# supports_vision: true
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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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# when_thinking_enabled:
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# thinking:
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# type: enabled
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# Example: Google Gemini model (native SDK, no thinking support)
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# - name: gemini-2.5-pro
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# display_name: Gemini 2.5 Pro
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# use: langchain_google_genai:ChatGoogleGenerativeAI
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# model: gemini-2.5-pro
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# gemini_api_key: $GEMINI_API_KEY
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# max_tokens: 8192
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# supports_vision: true
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# Example: Gemini model via OpenAI-compatible gateway (with thinking support)
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# Use PatchedChatOpenAI so that tool-call thought_signature values on tool_calls
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# are preserved across multi-turn tool-call conversations — required by the
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# Gemini API when thinking is enabled. See:
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# https://docs.cloud.google.com/vertex-ai/generative-ai/docs/thought-signatures
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# - name: gemini-2.5-pro-thinking
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# display_name: Gemini 2.5 Pro (Thinking)
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# use: deerflow.models.patched_openai:PatchedChatOpenAI
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# model: google/gemini-2.5-pro-preview # model name as expected by your gateway
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# api_key: $GEMINI_API_KEY
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# base_url: https://<your-openai-compat-gateway>/v1
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# max_tokens: 16384
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# supports_thinking: true
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# supports_vision: true
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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: 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: deerflow.models.patched_deepseek:PatchedChatDeepSeek
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# model: deepseek-reasoner
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# api_key: $DEEPSEEK_API_KEY
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# max_tokens: 8192
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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: 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: deerflow.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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# Example: Novita AI (OpenAI-compatible)
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# Novita provides an OpenAI-compatible API with competitive pricing
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# See: https://novita.ai
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# - name: novita-deepseek-v3.2
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# display_name: Novita DeepSeek V3.2
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# use: langchain_openai:ChatOpenAI
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# model: deepseek/deepseek-v3.2
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# api_key: $NOVITA_API_KEY
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# base_url: https://api.novita.ai/openai
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# max_tokens: 4096
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# temperature: 0.7
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# supports_thinking: true
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# supports_vision: true
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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: MiniMax (OpenAI-compatible) - International Edition
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# MiniMax provides high-performance models with 204K context window
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# Docs: https://platform.minimax.io/docs/api-reference/text-openai-api
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# - name: minimax-m2.5
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# display_name: MiniMax M2.5
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# use: langchain_openai:ChatOpenAI
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# model: MiniMax-M2.5
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# api_key: $MINIMAX_API_KEY
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# base_url: https://api.minimax.io/v1
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# max_tokens: 4096
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# temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
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# supports_vision: true
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# - name: minimax-m2.5-highspeed
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# display_name: MiniMax M2.5 Highspeed
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# use: langchain_openai:ChatOpenAI
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# model: MiniMax-M2.5-highspeed
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# api_key: $MINIMAX_API_KEY
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# base_url: https://api.minimax.io/v1
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# max_tokens: 4096
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# temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
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# supports_vision: true
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# Example: MiniMax (OpenAI-compatible) - CN 中国区用户
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# MiniMax provides high-performance models with 204K context window
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# Docs: https://platform.minimaxi.com/docs/api-reference/text-openai-api
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# - name: minimax-m2.7
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# display_name: MiniMax M2.7
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# use: langchain_openai:ChatOpenAI
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# model: MiniMax-M2.7
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# api_key: $MINIMAX_API_KEY
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# base_url: https://api.minimaxi.com/v1
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# max_tokens: 4096
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# temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
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# supports_vision: true
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# - name: minimax-m2.5-highspeed
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# display_name: MiniMax M2.5 Highspeed
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# use: langchain_openai:ChatOpenAI
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# model: MiniMax-M2.5-highspeed
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# api_key: $MINIMAX_API_KEY
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# base_url: https://api.minimaxi.com/v1
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# max_tokens: 4096
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# temperature: 1.0 # MiniMax requires temperature in (0.0, 1.0]
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# supports_vision: true
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# Example: OpenRouter (OpenAI-compatible)
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# OpenRouter models use the same ChatOpenAI + base_url pattern as other OpenAI-compatible gateways.
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# - name: openrouter-gemini-2.5-flash
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# display_name: Gemini 2.5 Flash (OpenRouter)
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# use: langchain_openai:ChatOpenAI
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# model: google/gemini-2.5-flash-preview
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# api_key: $OPENAI_API_KEY
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# base_url: https://openrouter.ai/api/v1
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# max_tokens: 8192
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# temperature: 0.7
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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 (uses DuckDuckGo, no API key required)
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- name: web_search
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group: web
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use: deerflow.community.ddg_search.tools:web_search_tool
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max_results: 5
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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: deerflow.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 search tool (uses InfoQuest, requires InfoQuest API key)
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# - name: web_search
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# group: web
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# use: deerflow.community.infoquest.tools:web_search_tool
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# # Used to limit the scope of search results, only returns content within the specified time range. Set to -1 to disable time filtering
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# search_time_range: 10
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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: deerflow.community.jina_ai.tools:web_fetch_tool
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timeout: 10
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# Web fetch tool (uses InfoQuest)
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# - name: web_fetch
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# group: web
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# use: deerflow.community.infoquest.tools:web_fetch_tool
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# # Overall timeout for the entire crawling process (in seconds). Set to positive value to enable, -1 to disable
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# timeout: 10
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# # Waiting time after page loading (in seconds). Set to positive value to enable, -1 to disable
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# fetch_time: 10
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# # Timeout for navigating to the page (in seconds). Set to positive value to enable, -1 to disable
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# navigation_timeout: 30
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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: deerflow.community.image_search.tools:image_search_tool
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max_results: 5
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# Image search tool (uses InfoQuest)
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# - name: image_search
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# group: web
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# use: deerflow.community.infoquest.tools:image_search_tool
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# # Used to limit the scope of image search results, only returns content within the specified time range. Set to -1 to disable time filtering
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# image_search_time_range: 10
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# # Image size filter. Options: "l" (large), "m" (medium), "i" (icon).
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# image_size: "i"
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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: deerflow.sandbox.tools:ls_tool
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- name: read_file
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group: file:read
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use: deerflow.sandbox.tools:read_file_tool
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- name: write_file
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group: file:write
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use: deerflow.sandbox.tools:write_file_tool
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- name: str_replace
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group: file:write
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use: deerflow.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: deerflow.sandbox.tools:bash_tool
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# ============================================================================
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# Tool Search Configuration (Deferred Tool Loading)
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# ============================================================================
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# When enabled, MCP tools are not loaded into the agent's context directly.
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# Instead, they are listed by name in the system prompt and discoverable
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# via the tool_search tool at runtime.
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# This reduces context usage and improves tool selection accuracy when
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# multiple MCP servers expose a large number of tools.
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tool_search:
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enabled: false
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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: deerflow.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: deerflow.community.aio_sandbox:AioSandboxProvider
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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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# # Optional: Maximum number of concurrent sandbox containers (default: 3)
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# # When the limit is reached the least-recently-used sandbox is evicted to
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# # make room for new ones. Use a positive integer here; omit this field to use the default.
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# # replicas: 3
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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: deerflow.community.aio_sandbox:AioSandboxProvider
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# provisioner_url: http://provisioner:8002
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# ============================================================================
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# Subagents Configuration
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# ============================================================================
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# Configure timeouts for subagent execution
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# Subagents are background workers delegated tasks by the lead agent
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# subagents:
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# # Default timeout in seconds for all subagents (default: 900 = 15 minutes)
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# timeout_seconds: 900
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#
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# # Optional per-agent timeout overrides
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# agents:
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# general-purpose:
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# timeout_seconds: 1800 # 30 minutes for complex multi-step tasks
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# bash:
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# timeout_seconds: 300 # 5 minutes for quick command execution
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# ============================================================================
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# ACP Agents Configuration
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# ============================================================================
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# Configure external ACP-compatible agents for the built-in `invoke_acp_agent` tool.
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# acp_agents:
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# claude_code:
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# # DeerFlow expects an ACP adapter here. The standard `claude` CLI does not
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# # speak ACP directly. Install `claude-agent-acp` separately or use:
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# command: npx
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# args: ["-y", "@zed-industries/claude-agent-acp"]
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# description: Claude Code for implementation, refactoring, and debugging
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# model: null
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# # auto_approve_permissions: false # Set to true to auto-approve ACP permission requests
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# # env: # Optional: inject environment variables into the agent subprocess
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# # ANTHROPIC_API_KEY: $ANTHROPIC_API_KEY # $VAR resolves from host environment
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#
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# codex:
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# # DeerFlow expects an ACP adapter here. The standard `codex` CLI does not
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# # speak ACP directly. Install `codex-acp` separately or use:
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# command: npx
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# args: ["-y", "@zed-industries/codex-acp"]
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# description: Codex CLI for repository tasks and code generation
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# model: null
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# # auto_approve_permissions: false # Set to true to auto-approve ACP permission requests
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# # env: # Optional: inject environment variables into the agent subprocess
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# # OPENAI_API_KEY: $OPENAI_API_KEY # $VAR resolves from host environment
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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
|
|
- type: tokens
|
|
value: 15564
|
|
# Uncomment to also trigger when message count reaches 50
|
|
# - type: messages
|
|
# value: 50
|
|
# Uncomment to trigger when 80% of model's max input tokens is reached
|
|
# - type: fraction
|
|
# value: 0.8
|
|
|
|
# Context retention policy after summarization
|
|
# Specifies how much recent history to preserve
|
|
keep:
|
|
# Keep the most recent 10 messages (recommended)
|
|
type: messages
|
|
value: 10
|
|
# Alternative: Keep specific token count
|
|
# type: tokens
|
|
# value: 3000
|
|
# Alternative: Keep percentage of model's max input tokens
|
|
# type: fraction
|
|
# value: 0.3
|
|
|
|
# Maximum tokens to keep when preparing messages for summarization
|
|
# Set to null to skip trimming (not recommended for very long conversations)
|
|
trim_tokens_to_summarize: 15564
|
|
|
|
# Custom summary prompt template (null = use default LangChain prompt)
|
|
# The prompt should guide the model to extract important context
|
|
summary_prompt: null
|
|
|
|
# ============================================================================
|
|
# Memory Configuration
|
|
# ============================================================================
|
|
# Global memory mechanism
|
|
# Stores user context and conversation history for personalized responses
|
|
memory:
|
|
enabled: true
|
|
storage_path: memory.json # Path relative to backend directory
|
|
debounce_seconds: 30 # Wait time before processing queued updates
|
|
model_name: null # Use default model
|
|
max_facts: 100 # Maximum number of facts to store
|
|
fact_confidence_threshold: 0.7 # Minimum confidence for storing facts
|
|
injection_enabled: true # Whether to inject memory into system prompt
|
|
max_injection_tokens: 2000 # Maximum tokens for memory injection
|
|
|
|
# ============================================================================
|
|
# Checkpointer Configuration
|
|
# ============================================================================
|
|
# Configure state persistence for the embedded DeerFlowClient.
|
|
# The LangGraph Server manages its own state persistence separately
|
|
# via the server infrastructure (this setting does not affect it).
|
|
#
|
|
# When configured, DeerFlowClient will automatically use this checkpointer,
|
|
# enabling multi-turn conversations to persist across process restarts.
|
|
#
|
|
# Supported types:
|
|
# memory - In-process only. State is lost when the process exits. (default)
|
|
# sqlite - File-based SQLite persistence. Survives restarts.
|
|
# Requires: uv add langgraph-checkpoint-sqlite
|
|
# postgres - PostgreSQL persistence. Suitable for multi-process deployments.
|
|
# Requires: uv add langgraph-checkpoint-postgres psycopg[binary] psycopg-pool
|
|
#
|
|
# Examples:
|
|
#
|
|
# In-memory (default when omitted — no persistence):
|
|
# checkpointer:
|
|
# type: memory
|
|
#
|
|
# SQLite (file-based, single-process):
|
|
checkpointer:
|
|
type: sqlite
|
|
connection_string: checkpoints.db
|
|
#
|
|
# PostgreSQL (multi-process, production):
|
|
# checkpointer:
|
|
# type: postgres
|
|
# connection_string: postgresql://user:password@localhost:5432/deerflow
|
|
|
|
# ============================================================================
|
|
# IM Channels Configuration
|
|
# ============================================================================
|
|
# Connect DeerFlow to external messaging platforms.
|
|
# All channels use outbound connections (WebSocket or polling) — no public IP required.
|
|
|
|
# channels:
|
|
# # LangGraph Server URL for thread/message management (default: http://localhost:2024)
|
|
# # For Docker deployments, use the Docker service name instead of localhost:
|
|
# # langgraph_url: http://langgraph:2024
|
|
# # gateway_url: http://gateway:8001
|
|
# langgraph_url: http://localhost:2024
|
|
# # Gateway API URL for auxiliary queries like /models, /memory (default: http://localhost:8001)
|
|
# gateway_url: http://localhost:8001
|
|
#
|
|
# # Optional: default mobile/session settings for all IM channels
|
|
# session:
|
|
# assistant_id: lead_agent
|
|
# config:
|
|
# recursion_limit: 100
|
|
# context:
|
|
# thinking_enabled: true
|
|
# is_plan_mode: false
|
|
# subagent_enabled: false
|
|
#
|
|
# feishu:
|
|
# enabled: false
|
|
# app_id: $FEISHU_APP_ID
|
|
# app_secret: $FEISHU_APP_SECRET
|
|
#
|
|
# slack:
|
|
# enabled: false
|
|
# bot_token: $SLACK_BOT_TOKEN # xoxb-...
|
|
# app_token: $SLACK_APP_TOKEN # xapp-... (Socket Mode)
|
|
# allowed_users: [] # empty = allow all
|
|
#
|
|
# telegram:
|
|
# enabled: false
|
|
# bot_token: $TELEGRAM_BOT_TOKEN
|
|
# allowed_users: [] # empty = allow all
|
|
#
|
|
# # Optional: channel-level session overrides
|
|
# session:
|
|
# assistant_id: mobile_agent
|
|
# context:
|
|
# thinking_enabled: false
|
|
#
|
|
# # Optional: per-user overrides by user_id
|
|
# users:
|
|
# "123456789":
|
|
# assistant_id: vip_agent
|
|
# config:
|
|
# recursion_limit: 150
|
|
# context:
|
|
# thinking_enabled: true
|
|
# subagent_enabled: true
|
|
|
|
# ============================================================================
|
|
# Guardrails Configuration
|
|
# ============================================================================
|
|
# Optional pre-execution authorization for tool calls.
|
|
# When enabled, every tool call passes through the configured provider
|
|
# before execution. Three options: built-in allowlist, OAP policy provider,
|
|
# or custom provider. See backend/docs/GUARDRAILS.md for full documentation.
|
|
#
|
|
# Providers are loaded by class path via resolve_variable (same as models/tools).
|
|
|
|
# --- Option 1: Built-in AllowlistProvider (zero external deps) ---
|
|
# guardrails:
|
|
# enabled: true
|
|
# provider:
|
|
# use: deerflow.guardrails.builtin:AllowlistProvider
|
|
# config:
|
|
# denied_tools: ["bash", "write_file"]
|
|
|
|
# --- Option 2: OAP passport provider (open standard, any implementation) ---
|
|
# The Open Agent Passport (OAP) spec defines passport format and decision codes.
|
|
# Any OAP-compliant provider works. Example using APort (reference implementation):
|
|
# pip install aport-agent-guardrails && aport setup --framework deerflow
|
|
# guardrails:
|
|
# enabled: true
|
|
# provider:
|
|
# use: aport_guardrails.providers.generic:OAPGuardrailProvider
|
|
|
|
# --- Option 3: Custom provider (any class with evaluate/aevaluate methods) ---
|
|
# guardrails:
|
|
# enabled: true
|
|
# provider:
|
|
# use: my_package:MyGuardrailProvider
|
|
# config:
|
|
# key: value
|