Open sourceAI workflow design and CLI implementation

mini-cursor

A terminal AI coding assistant that experiments with agent workflows, command execution, and project scaffolding.

Source
Mini-Cursor - AI Coding Assistant

Problem

AI coding helpers are useful when they can reason over a task, produce files, and interact with the local environment. The goal was to explore that loop in a compact terminal-first tool.

Architecture

  • A CLI interface accepts developer prompts and project context.
  • LangGraph coordinates multi-step agent flows and tool execution.
  • LangChain integrations handle model calls and structured workflow steps.
  • Command execution is exposed through a controlled terminal layer.

Key decisions

  • Used a terminal interface because coding work already happens close to the shell.
  • Separated workflow orchestration from model calls so agent steps remain easier to debug.
  • Kept generated boilerplate explicit instead of hiding file changes behind automation.

Tradeoffs

  • Direct command execution makes the tool powerful, but it needs strong guardrails before broader use.
  • Agent graphs add structure, but they also add complexity compared with a single prompt-response loop.

What I learned

  • Agent tools become more useful when every action is inspectable.
  • Workflow orchestration is only valuable when it reduces uncertainty for the developer.

Stack

PythonLangGraphLangChainAITerminalWorkflow OrchestrationCode GenerationCLI
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