LangChain
Code Review Agent with LangChain
Build an AI code review agent that reads files, searches for patterns, analyzes code quality, and suggests improvements — with LangChain.
code reviewstatic analysisqualitydeveloper tools
Working Code
from langchain_openai import ChatOpenAIfrom langchain_core.tools import tool
@tooldef read_file(path: str) -> str: """Read a file from the project.""" return Path(path).read_text()
@tooldef find_files(pattern: str) -> str: """Find files matching a glob pattern.""" files = list(Path(".").rglob(pattern)) return "\n".join(str(f) for f in files[:20])
@tooldef search_code(pattern: str, path: str = ".") -> str: """Search for a regex pattern in source files.""" import subprocess result = subprocess.run( ["grep", "-rn", pattern, path, "--include=*.py"], capture_output=True, text=True, ) return result.stdout[:3000] or "No matches found."
model = ChatOpenAI(model="gpt-4o")model_with_tools = model.bind_tools([read_file, find_files, search_code])response = model_with_tools.invoke([ ("system", "You are a code reviewer. Find relevant files, read them, search for patterns, and provide a structured review covering security, performance, and best practices."), ("user", "Review the Python files in src/ for common security issues"),])Step by Step
1
Install dependencies
Install LangChain and the required tools for this use case.
2
Define your tools
Create the domain-specific tool functions your agent will use to interact with external services.
3
Create the agent and run
Initialize the LangChain agent with your tools, set the system prompt, and execute a query.
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