When building Data Analysis Agents, we often rely on CSV files and Python (Pandas/Numpy) for processing. While powerful, this approach has limitations: it lacks persistence, visualization, and the structured query power of SQL.
We integrated Teable, an open-source, high-performance low-code database, to bridge this gap.
Our choice wasn’t just about storage; it was about empowering the Agent with SQL and Automated AI Labeling.
The User Value: A Visible Data Space
For the end-user, the integration is seamless. We embed the Teable UI directly into the frontend. Users can see their data space, watch tables populate in real-time, and even manually correct data.
But the real magic happens in the backend.
Why Teable? (vs. Supabase/Airtable/Nocobase)
We evaluated several options, but Teable stood out for one specific feature: AI Fields.
If we wanted to “Classify these 1,000 feedback rows” using pure Python, the Agent would have to:
- Write a script to loop through rows.
- Call an LLM API for each row (managing tokens, rate limits, and retries).
- Handle exceptions and update the CSV.
This is essentially deploying a custom labeling program for every user request—expensive and fragile.
With Teable AI Fields, the Agent simply:
- Creates a column with a prompt:
"Classify the sentiment of {Review}". - Teable’s backend handles the batch processing, concurrency, and error handling.
This standardized service significantly reduces the complexity of our Agent’s code execution environment.
Empowering the Agent: SQL as a First-Class Citizen
By exposing Teable’s API as tools, we gave our Agent a new superpower: SQL.
The Toolset
list_tables: “What data do I have?”get_table_schema: “What columns are in the ‘Sales’ table?”query_sql: “ExecuteSELECT product, SUM(revenue) FROM sales GROUP BY product.”create_ai_field: “Add a column to extract email addresses.”
SQL Insight vs. Python Analysis
While Python is great for complex statistical modeling, SQL is often superior for quick insights and aggregations. It provides a robust, standardized way for the Agent to “understand” the data structure before diving into deep analysis.
Real-World Performance
We were initially skeptical: Can an LLM really handle the complexity of database management?
The answer is Yes. With well-crafted system prompts and tool descriptions, our Agent demonstrated impressive competence:
- It correctly queries schemas before writing SQL.
- It autonomously creates AI fields to “enrich” data when user intent requires extraction.
- It monitors the progress of background labeling jobs.
We didn’t need to write complex orchestration logic; we just provided the tools, and the Agent handled the rest.
Conclusion
Integrating a standardized Low-code platform like Teable proved to be more efficient than building custom data processing pipelines. It gave our Agent SQL capabilities for free and a robust AI Labeling engine out of the box.