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https://gitee.com/wanwujie/deer-flow
synced 2026-04-18 20:14:44 +08:00
feat: add skills system for specialized agent workflows (#6)
Implement a skills framework that enables specialized workflows for specific tasks (e.g., PDF processing, web page generation). Skills are discovered from the skills/ directory and automatically mounted in sandboxes with path mapping support. - Add SkillsConfig for configuring skills path and container mount point - Implement dynamic skill loading from SKILL.md files with YAML frontmatter - Add path mapping in LocalSandbox to translate container paths to local paths - Mount skills directory in AIO Docker sandbox containers - Update lead agent prompt to dynamically inject available skills - Add setup documentation and expand config.example.yaml Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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backend/docs/CONFIGURATION.md
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backend/docs/CONFIGURATION.md
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# Configuration Guide
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This guide explains how to configure DeerFlow for your environment.
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## Quick Start
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1. **Copy the example configuration** (from project root):
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```bash
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# From project root directory (deer-flow/)
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cp config.example.yaml config.yaml
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```
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2. **Set your API keys**:
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Option A: Use environment variables (recommended):
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```bash
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export OPENAI_API_KEY="your-api-key-here"
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export ANTHROPIC_API_KEY="your-api-key-here"
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# Add other keys as needed
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```
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Option B: Edit `config.yaml` directly (not recommended for production):
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```yaml
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models:
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- name: gpt-4
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api_key: your-actual-api-key-here # Replace placeholder
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```
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3. **Start the application**:
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```bash
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make dev
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```
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## Configuration Sections
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### Models
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Configure the LLM models available to the agent:
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```yaml
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models:
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- name: gpt-4 # Internal identifier
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display_name: GPT-4 # Human-readable name
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use: langchain_openai:ChatOpenAI # LangChain class path
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model: gpt-4 # Model identifier for API
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api_key: $OPENAI_API_KEY # API key (use env var)
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max_tokens: 4096 # Max tokens per request
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temperature: 0.7 # Sampling temperature
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```
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**Supported Providers**:
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- OpenAI (`langchain_openai:ChatOpenAI`)
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- Anthropic (`langchain_anthropic:ChatAnthropic`)
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- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
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- Any LangChain-compatible provider
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**Thinking Models**:
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Some models support "thinking" mode for complex reasoning:
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```yaml
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models:
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- name: deepseek-v3
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supports_thinking: 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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```
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### Tool Groups
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Organize tools into logical groups:
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```yaml
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tool_groups:
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- name: web # Web browsing and search
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- name: file:read # Read-only file operations
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- name: file:write # Write file operations
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- name: bash # Shell command execution
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```
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### Tools
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Configure specific tools available to the agent:
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```yaml
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tools:
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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 # Optional
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```
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**Built-in Tools**:
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- `web_search` - Search the web (Tavily)
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- `web_fetch` - Fetch web pages (Jina AI)
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- `ls` - List directory contents
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- `read_file` - Read file contents
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- `write_file` - Write file contents
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- `str_replace` - String replacement in files
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- `bash` - Execute bash commands
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### Sandbox
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Choose between local execution or Docker-based isolation:
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**Option 1: Local Sandbox** (default, simpler setup):
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```yaml
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sandbox:
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use: src.sandbox.local:LocalSandboxProvider
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```
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**Option 2: Docker Sandbox** (isolated, more secure):
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```yaml
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sandbox:
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use: src.community.aio_sandbox:AioSandboxProvider
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port: 8080
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auto_start: true
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container_prefix: deer-flow-sandbox
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# Optional: Additional mounts
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mounts:
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- host_path: /path/on/host
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container_path: /path/in/container
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read_only: false
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```
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### Skills
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Configure the skills directory for specialized workflows:
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```yaml
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skills:
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# Host path (optional, default: ../skills)
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path: /custom/path/to/skills
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# Container mount path (default: /mnt/skills)
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container_path: /mnt/skills
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```
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**How Skills Work**:
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- Skills are stored in `deer-flow/skills/{public,custom}/`
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- Each skill has a `SKILL.md` file with metadata
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- Skills are automatically discovered and loaded
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- Available in both local and Docker sandbox via path mapping
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### Title Generation
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Automatic conversation title generation:
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```yaml
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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 first model in list
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```
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## Environment Variables
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DeerFlow supports environment variable substitution using the `$` prefix:
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```yaml
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models:
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- api_key: $OPENAI_API_KEY # Reads from environment
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```
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**Common Environment Variables**:
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- `OPENAI_API_KEY` - OpenAI API key
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- `ANTHROPIC_API_KEY` - Anthropic API key
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- `DEEPSEEK_API_KEY` - DeepSeek API key
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- `TAVILY_API_KEY` - Tavily search API key
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- `DEER_FLOW_CONFIG_PATH` - Custom config file path
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## Configuration Location
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The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
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## Configuration Priority
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DeerFlow searches for configuration in this order:
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1. Path specified in code via `config_path` argument
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2. Path from `DEER_FLOW_CONFIG_PATH` environment variable
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3. `config.yaml` in current working directory (typically `backend/` when running)
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4. `config.yaml` in parent directory (project root: `deer-flow/`)
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## Best Practices
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1. **Place `config.yaml` in project root** - Not in `backend/` directory
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2. **Never commit `config.yaml`** - It's already in `.gitignore`
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3. **Use environment variables for secrets** - Don't hardcode API keys
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4. **Keep `config.example.yaml` updated** - Document all new options
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5. **Test configuration changes locally** - Before deploying
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6. **Use Docker sandbox for production** - Better isolation and security
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## Troubleshooting
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### "Config file not found"
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- Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
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- The backend searches parent directory by default, so root location is preferred
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- Alternatively, set `DEER_FLOW_CONFIG_PATH` environment variable to custom location
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### "Invalid API key"
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- Verify environment variables are set correctly
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- Check that `$` prefix is used for env var references
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### "Skills not loading"
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- Check that `deer-flow/skills/` directory exists
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- Verify skills have valid `SKILL.md` files
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- Check `skills.path` configuration if using custom path
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### "Docker sandbox fails to start"
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- Ensure Docker is running
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- Check port 8080 (or configured port) is available
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- Verify Docker image is accessible
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## Examples
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See `config.example.yaml` for complete examples of all configuration options.
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