Sql Support Bot
Description
Customer support chatbot built with DeepAgents that interacts with a SQL database (Chinook music store) to answer questions about music and customer accounts.
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
SQL Support Bot
Customer support chatbot built with DeepAgents that interacts with a SQL database (Chinook music store) to answer questions about music and customer accounts.
What This Bot Does
This bot can help customers:
- Find music - search for songs, albums, and artists in the catalog
- Access account info - look up customer account details
The bot uses DeepAgents to autonomously decide which tools to use based on the customer's query.
Setup
Install dependencies:
uv sync
Set your OpenAI API key:
export OPENAI_API_KEY="your-key-here"
Usage
Python Script
uv run python agent.py
Type your questions and the agent will respond. Type `quit` to exit.
Jupyter Notebook
uv run jupyter notebook
Then open `agent.ipynb` and run cells sequentially to interact with the agent.
Example Queries
- "Can you help me find songs by The Beatles?"
- "What albums does Pink Floyd have?"
- "What's the email for customer ID 5?"
How It Works
- Database: Uses the Chinook database (downloads automatically on first run)
- Tools: Agent has access to 4 tools for searching music and looking up customer info
- Routing: DeepAgents automatically decides which tool(s) to use based on the query
Writing Evals
The most valuable evals go beyond "did it return the right answer" — they test whether the agent behaves well under pressure, handles ambiguity gracefully, and fails safely. A few directions worth exploring:
**Robustness to ambiguous input** — What happens when a customer asks something that could mean two different things? For example, "find me something by Elvis" could mean Elvis Presley or Elvis Costello. Does the agent pick one silently, ask for clarification, or surface both? An eval here checks not just the final answer but *whether the agent handles the ambiguity in a reasonable way*.
**Tool use correctness under multi-step reasoning** — Some queries require chaining tools in
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