LangChain·Software Engineer·Take-home Assignment·Intermediate

Intermediate

May 2026

Summary

LangChain SWE interview with a single take-home style coding problem: build a SQL parser that turns raw SQL strings into programming objects or ORM primitives. You could use any tools or AI help, which felt almost too open-ended.

Questions Asked (1)

Build a SQL parser that takes raw SQL strings as input and converts them into structured programming objects or ORM primitives.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

The open-ended nature of this threw me more than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and requirements: what SQL dialect, what output format (AST, ORM objects), and how it integrates with LangChain's ecosystem. Then outline a layered architecture: tokenizer, parser, AST builder, and ORM mapper, discussing trade-offs between using existing libraries (e.g., sqlglot) versus building from scratch. Finally, highlight key design decisions around extensibility, error handling, and security.

Pro tip: Emphasize that for LangChain, the parser should be designed as a tool that can be invoked by LLM agents, so focus on producing a clean, serializable intermediate representation (like JSON AST) that can be easily consumed by other components. Also, mention the importance of handling SQL injection and validating queries before execution.

1. Clarify Requirements and Scope

Ask about the target SQL dialect(s), expected output (e.g., AST, ORM objects, query builder), performance needs, and integration points with LangChain (e.g., as a tool for agents).

2. Choose Parsing Strategy

Decide between using an existing parser library (e.g., sqlglot, sqlparse) or writing a custom parser. Discuss trade-offs: speed of development, dialect support, maintainability, and control.

3. Design the Architecture

Outline components: lexer/tokenizer, parser (recursive descent or PEG), AST builder, and ORM mapper. Consider using a visitor pattern for extensibility and separating concerns.

4. Define the Output Representation

Specify the structured output: a JSON-serializable AST or ORM primitives (e.g., SQLAlchemy Core objects). Ensure it's easily consumable by other LangChain components.

5. Address Edge Cases and Integration

Discuss error handling (syntax errors, unsupported features), security (SQL injection prevention), and how the parser will be exposed (e.g., as a LangChain tool with a clear API).

Key Points to Mention

Choice of parsing technique: recursive descent vs. parser combinators vs. using existing libraries like sqlglot.
Output format: AST vs. ORM objects, and why a serializable intermediate representation is useful for LLM integration.
Extensibility: supporting multiple SQL dialects and custom functions via a plugin architecture.
Security: validating and sanitizing input to prevent SQL injection, especially when executing generated queries.
Performance: handling large queries, caching parsed results, and optimizing for common cases.
Integration with LangChain: exposing the parser as a tool, handling LLM-generated SQL, and providing feedback for errors.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.