Bot-With-Plan provides a modular Python template for building AI agents that first generate a detailed plan before execution. It uses OpenAI GPT to parse user instructions, decompose tasks into sequential steps, validate the plan, and then execute each step through external tools like web search or calculators. The framework includes prompt management, plan parsing, execution orchestration, and error handling. By separating planning and execution phases, it offers better oversight, easier debugging, and a clear structure for extending with new tools or capabilities.

LangGraph MCP leverages directed acyclic graphs to represent sequences of LLM calls, allowing developers to break down tasks into nodes with configurable prompts, inputs, and outputs. Each node corresponds to an LLM invocation or a data transformation, facilitating parameterized execution, conditional branching, and iterative loops. Users can serialize graphs in JSON/YAML format, version control workflows, and visualize execution paths. The framework supports integration with multiple LLM providers, custom prompt templates, and plugin hooks for preprocessing, postprocessing, and error handling. LangGraph MCP provides CLI tools and a Python SDK to load, execute, and monitor graph-based agent pipelines, ideal for automation, report generation, conversational flows, and decision support systems.