# Agent Configuration Guide

mindroom uses a YAML-based configuration system that makes it easy to customize agents for your specific needs. You can modify existing agents or create entirely new ones by editing a simple configuration file.

## Configuration File

The default agent configuration file is `config.yaml` in the project root. You can also specify a custom configuration file when starting the bot.

## Configuration Structure

The configuration file has several main sections:

1. **memory** - Configuration for the embedding system used for agent memory
2. **models** - Define available AI models and their providers
3. **agents** - Configure individual agents and their capabilities
4. **defaults** - Default settings for all agents
5. **router** - Configuration for the agent routing system

## Model Configuration

Before configuring agents, you need to define which AI models are available. mindroom supports multiple model providers:

```yaml
models:
  default:  # Default model used when agent doesn't specify one
    provider: "ollama"
    id: "devstral:24b"

  anthropic:
    provider: "anthropic"
    id: "claude-3-5-haiku-latest"

  ollama:
    provider: "ollama"
    id: "devstral:24b"
    # For ollama, you can add:
    # host: "http://localhost:11434"

  openrouter:
    provider: "openrouter"
    id: "z-ai/glm-4.5-air:free"
```

### Supported Providers

- **ollama** - Local models via Ollama (requires Ollama installed)
- **anthropic** - Claude models (requires API key)
- **openai** - GPT models (requires API key)
- **openrouter** - Access multiple models through OpenRouter (requires API key)

## Memory Configuration

The memory system uses embeddings to help agents remember and retrieve relevant information:

```yaml
memory:
  embedder:
    provider: "ollama"  # Options: openai, ollama, huggingface, sentence-transformers
    config:
      model: "nomic-embed-text"  # Embedding model to use
      host: "http://localhost:11434"  # Ollama host URL
```

## Router Configuration

The router determines which agent should handle a user's request:

```yaml
router:
  model: "ollama"  # Which model to use for routing decisions (references models section)
```

## Available Agents

mindroom comes with several pre-configured agents, each specialized for different tasks:

### Default Agents

1. **general** - A friendly conversational assistant
   - No tools required
   - Provides helpful responses to general questions
   - Maintains conversation context

2. **calculator** - Mathematical problem solver
   - Uses calculator tool for accurate computations
   - Shows step-by-step solutions
   - Explains mathematical concepts

3. **code** - Programming assistant
   - Can read, write, and modify files
   - Executes shell commands
   - Follows coding best practices

4. **shell** - System operations specialist
   - Executes shell commands safely
   - Explains what commands do before running them
   - Suggests safer alternatives when needed

5. **summary** - Text summarization expert
   - Creates concise summaries of long texts
   - Extracts key points and main ideas
   - Preserves important details

6. **research** - Information gathering specialist
   - Searches the web with DuckDuckGo
   - Queries Wikipedia for encyclopedic information
   - Searches academic papers on arXiv
   - Cross-references multiple sources

7. **finance** - Financial data analyst
   - Retrieves real-time stock market data
   - Performs financial calculations
   - Provides market analysis

8. **news** - Current events reporter
   - Searches for recent news
   - Summarizes articles from multiple sources
   - Provides balanced coverage

9. **data_analyst** - Data analysis specialist
   - Processes CSV files
   - Performs statistical analysis
   - Creates insights from data

## Agent Configuration Structure

Each agent in the YAML file follows this structure:

```yaml
agents:
  agent_name:
    display_name: "Human-readable name"
    role: "What the agent does"
    tools:
      - tool_name_1
      - tool_name_2
    include_default_tools: true  # Optional: merge defaults.tools into this agent's tools
    skills:
      - skill_name_1
    instructions:
      - "Specific behavior instruction 1"
      - "Specific behavior instruction 2"
    rooms:
      - lobby
      - dev
    learning: true  # Optional: enable Agno Learning (defaults to true)
    learning_mode: "always"  # Optional: "always" or "agentic"
    knowledge_bases:
      - docs
    context_files:
      - ./workspace/SOUL.md
      - ./workspace/USER.md
    memory_dir: ./workspace/memory
    model: "anthropic"  # Optional: specific model for this agent (overrides default)
```

### Configuration Fields

- **agent_name**: The identifier used for the Matrix account (becomes `@mindroom_<agent_name>:localhost`)
- **display_name**: A friendly name shown in conversations
- **role**: A brief description of the agent's purpose
- **tools**: List of tools the agent can use (see Available Tools below)
- **include_default_tools**: Whether to merge `defaults.tools` into this agent's `tools` (default: true)
- **skills**: Skill names the agent can use
- **instructions**: Specific guidelines for the agent's behavior
- **rooms**: List of room aliases where this agent should be active
- **learning**: Enable Agno Learning for this agent (default: true)
- **learning_mode**: Learning mode (`always` or `agentic`, default: `always`)
- **knowledge_bases**: List of configured knowledge base IDs assigned to this agent
- **context_files**: File paths loaded into role context when the agent is created/reloaded
- **memory_dir**: Directory loaded into role context for `MEMORY.md` plus yesterday/today `YYYY-MM-DD.md` files
- **model**: (Optional) Specific model to use for this agent, overrides the default model
- **allow_self_config**: (Optional) When `true`, gives the agent a scoped tool to read and modify its own configuration at runtime (default: inherits from `defaults.allow_self_config`, which defaults to `false`)

### File-Based Context Loading

`context_files` and `memory_dir` are useful for OpenClaw-style file memory patterns:

- Paths are resolved relative to `config.yaml`
- `context_files` are injected in listed order
- `memory_dir` loads `MEMORY.md` (uppercase) and dated memory files for yesterday/today
- Content is refreshed when agents are created or reloaded

## Room Configuration

Agents can be assigned to specific rooms in your Matrix server. This allows you to:
- Create topic-specific rooms (e.g., "dev" for development, "research" for research tasks)
- Control which agents have access to which conversations
- Organize your AI assistants by domain or purpose

### How Rooms Work

1. **Room Aliases**: In `config.yaml`, you specify simple room aliases like `lobby`, `dev`, `research`
2. **Automatic Creation**: When you run `mindroom run`, it automatically creates any missing rooms
3. **Agent Assignment**: Agents are automatically invited to their configured rooms
4. **Room Persistence**: Room information is stored in `matrix_state.yaml` (auto-generated)

### Example Room Setup

```yaml
agents:
  code:
    display_name: "CodeAgent"
    role: "Programming assistant"
    tools: [file, shell]
    rooms:
      - lobby      # Available in main lobby
      - dev        # Available in development room
      - automation # Available in automation room

  research:
    display_name: "ResearchAgent"
    role: "Information gathering"
    tools: [duckduckgo, wikipedia, arxiv]
    rooms:
      - lobby      # Available in main lobby
      - research   # Available in research room
      - science    # Available in science room
```

### Common Room Patterns

- **lobby**: Main coordination room where all agents can interact
- **dev**: Development and programming discussions
- **research**: Research and information gathering
- **analysis**: Data analysis and insights
- **ops**: Operations and system management
- **help**: General assistance and support

## Available Tools

Tools give agents the ability to perform specific actions:

### Basic Tools
- **calculator** - Perform mathematical calculations
- **file** - Read, write, and manage files
- **shell** - Execute command line operations
- **python** - Run Python code snippets

### Data & Analysis Tools
- **csv** - Process and analyze CSV files
- **pandas** - Advanced data manipulation and analysis
- **yfinance** - Fetch financial market data

### Research & Information Tools
- **arxiv** - Search academic papers
- **duckduckgo** - Web search
- **googlesearch** - Google search (requires API key)
- **tavily** - AI-powered search (requires API key)
- **wikipedia** - Encyclopedia lookup
- **newspaper** - Parse and extract news articles
- **website** - Extract content from websites
- **jina** - Advanced document processing

### Development Tools
- **docker** - Manage Docker containers (requires Docker installed)
- **github** - Interact with GitHub repositories (requires token)

### Communication Tools
- **email** - Send emails (requires SMTP configuration)
- **telegram** - Send Telegram messages (requires bot token)

## Creating Custom Agents

### Example 1: Simple Helper Agent

```yaml
agents:
  helper:
    display_name: "HelpfulAssistant"
    role: "Provide friendly help and encouragement"
    tools: []
    instructions:
      - "Always be positive and encouraging"
      - "Offer specific, actionable advice"
      - "Ask clarifying questions when needed"
```

### Example 2: Project Manager Agent

```yaml
agents:
  project_manager:
    display_name: "ProjectManager"
    role: "Help manage software projects"
    tools:
      - file
      - shell
      - github
    instructions:
      - "Track project tasks and milestones"
      - "Generate status reports"
      - "Help with version control"
      - "Create and update documentation"
```

### Example 3: Data Science Agent

```yaml
agents:
  data_scientist:
    display_name: "DataScientist"
    role: "Analyze data and create insights"
    tools:
      - python
      - pandas
      - csv
      - calculator
    instructions:
      - "Perform statistical analysis"
      - "Create data visualizations"
      - "Clean and preprocess data"
      - "Explain findings clearly"
```

### Example 4: Research Assistant

```yaml
agents:
  researcher:
    display_name: "ResearchAssistant"
    role: "Comprehensive research and fact-checking"
    tools:
      - arxiv
      - wikipedia
      - duckduckgo
      - website
      - file
    instructions:
      - "Find credible sources"
      - "Cross-reference information"
      - "Create research summaries"
      - "Track sources and citations"
```

## Using Agents in the Multi-Agent System

Each agent now has its own Matrix account. To interact with an agent:

1. **Mention the agent by its full Matrix ID**: `@mindroom_agentname:localhost`
   - Example: `@mindroom_calculator:localhost what is 25 * 4?`
   - Example: `@mindroom_research:localhost tell me about quantum computing`

2. **In threads**: Agents automatically respond to all messages without needing mentions
   - Start a thread by replying to any message
   - The agent will see and respond to all subsequent messages in that thread

3. **Multiple agents**: You can mention multiple agents in one message
   - Example: `@mindroom_research:localhost @mindroom_analyst:localhost Compare renewable energy trends`

## Tool Requirements

Some tools need additional setup:

### Tools requiring API keys:
- **googlesearch** - Set up Google API credentials
- **tavily** - Get API key from Tavily
- **github** - Create a GitHub personal access token
- **telegram** - Create a Telegram bot and get token
- **email** - Configure SMTP server details

### Tools requiring software:
- **docker** - Install Docker on your system

### Tools that work immediately:
- **calculator**, **file**, **shell**, **python**, **csv**, **pandas**, **arxiv**, **duckduckgo**, **wikipedia**, **newspaper**, **website**, **jina**, **yfinance**

## Best Practices

1. **Clear Agent Roles**: Give each agent a specific, well-defined purpose
2. **Appropriate Tools**: Only include tools the agent actually needs
3. **Detailed Instructions**: Provide clear behavioral guidelines
4. **Test Your Agents**: Try different scenarios to ensure they behave as expected

## Tips for Writing Instructions

Good instructions are specific and actionable:

✅ Good: "Always cite your sources with author and publication date"
❌ Vague: "Be accurate"

✅ Good: "Explain technical concepts in simple terms"
❌ Vague: "Be helpful"

✅ Good: "Ask for clarification if the request is ambiguous"
❌ Vague: "Understand the user"

## Custom Configuration Files

You can create multiple configuration files for different purposes:

1. Create a new YAML file (e.g., `my_config.yaml`)
2. Define your models, memory, router, and agents using the structure above
3. Use the custom file when starting mindroom

### Complete Configuration Example

```yaml
# Memory configuration
memory:
  embedder:
    provider: "ollama"
    config:
      model: "nomic-embed-text"
      host: "http://localhost:11434"

# Model definitions
models:
  default:
    provider: "ollama"
    id: "devstral:24b"

  smart:
    provider: "anthropic"
    id: "claude-3-5-sonnet-latest"

# Agent configurations
agents:
  assistant:
    display_name: "SmartAssistant"
    role: "Advanced reasoning and analysis"
    model: "smart"  # Use the Claude model for this agent
    tools: []
    instructions:
      - "Provide thoughtful, detailed responses"
      - "Use advanced reasoning capabilities"
    rooms:
      - lobby

# Defaults
defaults:
  tools: [scheduler]
  markdown: true

# Router
router:
  model: "default"
```

## Troubleshooting

If an agent isn't working as expected:

1. Check that all required tools are properly configured
2. Verify the YAML syntax is correct (proper indentation)
3. Ensure tool names are spelled correctly
4. Test with simpler instructions first
5. Check logs for any error messages

Remember: agents are only as good as their configuration. Take time to craft clear roles and instructions for the best results!
