# describes the mcp servers to use
mcpServers:
  filesystem:
    type: stdio
    command: npx
    args:
      - -y
      - "@modelcontextprotocol/server-filesystem"
      - .
  playwright:
    type: stdio
    command: npx
    args:
      - -y
      - "@playwright/mcp@latest"
  # hf:
  #   type: stdio
  #   command: npx
  #   args:
  #     - -y
  #     - "@llmindset/mcp-hfspace"

# System prompt configuration - defines the agent's behavior and instructions
systemPrompt:
  contributors:
    - id: primary
      type: static
      priority: 0
      content: |
        You are a helpful AI assistant with access to tools.
        Use these tools when appropriate to answer user queries.
        You can use multiple tools in sequence to solve complex problems.
        After each tool result, determine if you need more information or can provide a final answer.

    - id: date
      type: dynamic
      priority: 10
      source: date
      enabled: true

# first start the ollama server
# ollama run gemma3n:e2b
# then run the following command to start the agent:
# dexto --agent <path_to_ollama.yml>
# dexto --agent <path_to_ollama.yml> for web ui
llm:
  provider: openai-compatible
  model: gemma3n:e2b
  baseURL: http://localhost:11434/v1
  apiKey: $OPENAI_API_KEY
  maxInputTokens: 32768

# Storage configuration - uses a two-tier architecture: cache (fast, ephemeral) and database (persistent, reliable)
# Memory cache with file-based database (good for development with persistence)
# storage:
#   cache:
#     type: in-memory
#   database:
#     type: sqlite
#     path: ./data/dexto.db

## To use Google Gemini, replace the LLM section with Google Gemini configuration below
## Similar for anthropic/groq/etc.
# llm:
#   provider: google
#   model: gemini-2.0-flash
#   apiKey: $GOOGLE_GENERATIVE_AI_API_KEY