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Building your own CLI Coding Agent with Pydantic-AI
This article details how to construct a custom CLI coding agent using open-source tools like Pydantic-AI and the Model Context Protocol (MCP). It emphasizes the advantages of tailored agents over general commercial tools, highlighting their ability to read code, run tests, and update codebases within specific project contexts. The author outlines the architectural components and capabilities added to evolve a basic agent into a powerful development partner. ✨
Article Points:
1
CLI coding agents are active development partners, not just chatbots or autocomplete tools.
2
Building custom agents provides context-specific solutions and insights into GenAI platforms.
3
Model Context Protocol (MCP) enables extensible, pluggable agent capabilities via a standardized interface.
4
Core agent features include unit testing, sandboxed Python execution, documentation access, and internet search.
5
Code reasoning and Desktop Commander allow agents to perform structured problem-solving and file/code operations.
6
CLI agents fundamentally shift AI from a writing assistant to a collaborative, intelligent development partner.
Building your own CLI Coding Agent with Pydantic-AI
Why Build Custom?
Context-specific solutions
Insights into GenAI Platform
Core Architecture
Pydantic-AI framework
Model Context Protocol (MCP)
AWS Bedrock & Claude Sonnet 4
Key Capabilities
Unit Testing
Sandboxed Python Execution
Up-to-date Docs & Internet Search
Code Reasoning
Desktop Commander (File/Code Ops)
Workflow Transformation
Agent runs tests & follows guidelines
Reliable calculations & current info
Structured problem solving & active editing
Future & Impact