Claude Code Multi-Agent Workflow Practice: Analysis of the Intelligent Restaurant Search Assistant DininingSpotClaw

DininingSpotClaw is a multi-agent workflow project built on Claude Code's sub-agent and skill system. Targeting team dining scenarios, it automatically completes the entire process of restaurant search, verification, and report generation via natural language input, solving the time-consuming and labor-intensive pain point of choosing a dining location and providing a reference case for intelligent solutions.

Project Background

The design of DininingSpotClaw originated from Korean developers' observations of team dining scenarios: In Korean workplace culture, 'hoesik' (company dinner) is an important social activity, but finding a suitable restaurant requires a lot of time browsing platforms and comparing options.

Core Requirements

Users describe their dining needs in natural language (e.g., "Jamsil 25 people company dinner 100,000 KRW"), and the system automatically completes the entire process of intent understanding, search query generation, information collection, key data verification, adaptability evaluation, and structured report output without manual intervention.

The project adopts a modular multi-agent design, decomposed into six professional agents:

1. Demand Analysis Agent: Understands natural language input, extracts key parameters (region, number of people, budget, etc.), and converts them into standardized query conditions.
2. Search Strategy Agent: Generates diverse search query combinations (e.g., variations like "Jamsil 25 people private room") to cover different platform indexing methods.
3. Information Collection Agent: Performs web searches, collects candidate restaurant information from local review platforms, and handles network requests and HTML parsing.
4. Information Verification Agent: Verifies key information such as candidate restaurants' capacity, price, private room availability, and BYOB policy to ensure accuracy.
5. Adaptability Evaluation Agent: Comprehensively scores (matching degree, cost-effectiveness, user reviews, etc.) based on verified information and ranks candidate restaurants.
6. Report Generation Agent: Integrates results into a structured Markdown report, including comparison tables, detailed information, and recommendation reasons.

The project implements functional modularity through the Claude Code skill system, with core skills including:

1. Search Query Planning Skill: Encapsulates the logic of converting demands into queries, supports strategies like exact matching and fuzzy expansion, and maximizes search coverage.
2. Report Format Standardization Skill: Defines report structure templates (restaurant information fields, comparison tables, score display, etc.) to ensure output consistency.
3. Verification Rule Skill: Encapsulates business rules for restaurant information verification (maximum number of people, price range, etc.) and can be flexibly configured to adapt to different scenarios.

Modular design makes the system scalable: Adding new platforms or evaluation dimensions only requires developing corresponding skill modules without modifying the core process.

When a user inputs a natural language request (e.g., "Find a restaurant in Jamsil or Songpa that can accommodate more than 25 people for a company dinner, with a budget of around 100,000 KRW per person. It doesn't matter if it's a hall, private room, or venue rental; if there's BYOB information, please include that too.")，the system automatically executes the following steps:

1. Parse key parameters: region (Jamsil/Songpa), number of people (25+), budget (100,000 KRW per person), no restriction on space type, and need for BYOB information.
2. Generate multiple search queries covering different platforms and keyword combinations.
3. Perform parallel searches to collect candidate restaurant information.
4. Verify candidate restaurants' capacity, price, policies, etc.
5. Evaluate adaptability and rank the candidates.
6. Generate a Markdown report with comparison tables and detailed information.

The entire process requires no manual intervention and completes work that originally took hours in a few minutes.

The project's technical highlights include:

1. Sub-agent Collaboration: Breaks down complex tasks into professional sub-agents, achieves collaboration through clear interfaces, and can be reused in other multi-step automation scenarios.
2. Natural Language to Structured Query: The demand analysis agent extracts structured parameters from ambiguous input, which is a key capability of AI applications.
3. Multi-source Integration and Verification: Collects information from multiple platforms and cross-validates it to ensure information accuracy.
4. Configurable Business Rules: The skill system encapsulates rules to adapt to demand changes in different regions/scenarios.

These designs provide a reusable reference model for multi-agent applications.

Limitations

The current project mainly targets the Korean local restaurant search scenario and relies on specific local platforms.

Future Outlook

• Support multi-language input and output;
• Integrate more data sources (real-time booking, traffic information, etc.);
• Add user preference learning (record historical choices);
• Integrate with enterprise internal systems (e.g., reimbursement processes).

The architecture design is universal and can be adapted to restaurant search needs in other regions.