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README.md

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### Architecture by Model Size
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| Model Size | Recommended Approach | FC Accuracy | Why Choose This |
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| ---------- | ---------------------------- | ----------- | ----------------------- |
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|------------|------------------------------|-------------|-------------------------|
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| **\<14B** | Pure SGR + Structured Output | 15-25% | FC practically unusable |
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| **14-32B** | SGR + FC hybrid | 45-65% | Best of both worlds |
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| **32B+** | Native FC with SGR fallback | 85%+ | FC works reliably |
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### When to Use SGR vs Function Calling
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| Use Case | Best Approach | Why |
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| ------------------------------- | ---------------- | ------------------------------------------------ |
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|---------------------------------|------------------|--------------------------------------------------|
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| **Data analysis & structuring** | SGR | Controlled reasoning with visibility |
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| **Document processing** | SGR | Step-by-step analysis with justification |
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| **Local models (\<32B)** | SGR | Forces reasoning regardless of model limitations |
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2. **Configure API keys:**
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```yaml
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# SGR Research Agent - Configuration Template
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# Production-ready configuration for Schema-Guided Reasoning
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# Copy this file to config.yaml and fill in your API keys
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# OpenAI API Configuration
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openai:
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api_key: "your-openai-api-key-here" # Required: Your OpenAI API key
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base_url: "" # Optional: Alternative URL (e.g., for proxy LiteLLM/vLLM)
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model: "gpt-4o-mini" # Model to use
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max_tokens: 8000 # Maximum number of tokens
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temperature: 0.4 # Generation temperature (0.0-1.0)
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proxy: "" # Example: "socks5://127.0.0.1:1081" or "http://127.0.0.1:8080" or leave empty for no proxy
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# Tavily Search Configuration
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tavily:
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api_key: "your-tavily-api-key-here" # Required: Your Tavily API key
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api_base_url: "https://api.tavily.com" # Tavily API base URL
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# Search Settings
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search:
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execution:
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max_steps: 6 # Maximum number of execution steps
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reports_dir: "reports" # Directory for saving reports
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logs_dir: "logs" # Directory for saving reports
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# Prompts Settings
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prompts:
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prompts_dir: "prompts" # Directory with prompts
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prompts_dir: "prompts" # Directory with prompts
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tool_function_prompt_file: "tool_function_prompt.txt" # Tool function prompt file
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system_prompt_file: "system_prompt.txt" # System prompt file
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system_prompt_file: "system_prompt.txt" # System prompt file
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```
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### Server Configuration
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python sgr_deep_research --host 127.0.0.1 --port 8080
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```
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## 🤖 Available Agent Models
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### Agent Types Overview
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| Agent Model | Description |
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|------------------------|------------------------------------|
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| `sgr-agent` | Pure SGR (Schema-Guided Reasoning) |
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| `sgr-tools-agent` | SGR + Function Calling hybrid |
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| `sgr-auto-tools-agent` | SGR + Auto Function Calling |
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| `sgr-so-tools-agent` | SGR + Structured Output |
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| `tools-agent` | Pure Function Calling |
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### Models Endpoint
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Get the list of available agent models:
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```bash
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curl http://localhost:8010/v1/models
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```
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## 📝 Reports
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Research reports are automatically saved to the `reports/` directory in Markdown format:

config.yaml.example

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execution:
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max_steps: 6 # Maximum number of execution steps
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reports_dir: "reports" # Directory for saving reports
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logs_dir: "logs" # Directory for saving reports
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logs_dir: "logs" # Directory for saving reports
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# Prompts Settings
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prompts:
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prompts_dir: "prompts" # Directory with prompts
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prompts_dir: "prompts" # Directory with prompts
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tool_function_prompt_file: "tool_function_prompt.txt" # Tool function prompt file
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system_prompt_file: "system_prompt.txt" # System prompt file
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system_prompt_file: "system_prompt.txt" # System prompt file

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