MindsDB Agents

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How MindsDB Agents Work

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Recommended Usage

1. Data Preparation High-quality input significantly improves agent performance. Clean your data:
   • Remove irrelevant columns
   • Filter unnecessary rows
   • Normalize inconsistent formats
   Create views to simplify the agent’s reasoning:
   • Filtered views: Keep only relevant tables, columns, and rows
   • Aggregated views: Provide summary tables for frequent analytical queries
   • Joined views: Combine tables that are commonly queried together
   Well-designed views reduce cognitive load on the agent.
2. Agent Data Setup To maintain performance:
   • Limit connected objects (tables + knowledge bases) to 10 or fewer
   • Ensure objects are relevant to the use case
   • Provide descriptive context in the prompt
   Example of helpful context:
   • Describe data stored in connected tables or knowledge bases
   • Clarify relationships between available data objects
   • Specify expected output format
   The clearer the setup, the more accurate the agent’s reasoning.
3. Querying an Agent Agent outputs are non-deterministic, due to the nature of large language models. However, MindsDB provides mechanisms for limited output control. To receive a natural language answer:
   Copy

   Ask AI

   SELECT answer 
   FROM agent_name 
   WHERE question = "What is the capital of France?";
   
   This ensures the agent returns a human-readable response contained in the answer column. To enforce a specific output structure:
   Copy

   Ask AI

   SELECT product_name, monthly_sales_count
   FROM agent_name
   WHERE question = "How many sales were there per product last month?";
   
   The agent will format its response to match the defined columns. This enables:
   • Programmatic consumption
   • Dashboard integration
   • Downstream automation workflows

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Usage Example

CREATE AGENT my_agent
USING
    model = {
        "provider": "openai",
        "model_name" : "gpt-4o",
        "api_key": "sk-abc123"
    },
    data = {
         "knowledge_bases": ["mindsdb.sales_kb", "mindsdb.orders_kb"],
         "tables": ["postgres_conn.customers", "mysql_conn.products"]
    },
    prompt_template='
        mindsdb.sales_kb stores sales analytics data
        mindsdb.orders_kb stores order data
        postgres_conn.customers stores customers data
        mysql_conn.products stores products data
    ';

SELECT answer
FROM my_agent 
WHERE question = 'What is the average number of orders per customers?';

-- or

SELECT customer_name, avg_number_of_orders
FROM my_agent 
WHERE question = 'What is the average number of orders per customers?';

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MindsDB Agents vs Minds: Feature Comparison

• Prototyping
• Small-to-medium complexity use cases
• Controlled data environments

• You require full data catalog visibility
• You need persistent conversation memory
• You are connecting many tables or knowledge bases
• Your workflows involve complex multi-step reasoning
• You require production-level governance and control