mcp-run-python

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It emphasizes type-safety, a model-agnostic design, and an ergonomic developer experience .\n\n## Core Purpose and Features\n\nThe primary purpose of `pydantic-ai` is to simplify the development of robust and reliable Generative AI applications by abstracting away complexities associated with different Large Language Model (LLM) providers and managing agent workflows .\n\nKey features include:\n* **Type-safe Agents**: Agents are defined using `Agent[Deps, Output]` for compile-time validation, leveraging Pydantic for output validation and dependency injection .\n* **Model-Agnostic Design**: It supports over 15 LLM providers through a unified `Model` interface, allowing for easy switching between different models and providers .\n* **Structured Outputs**: Automatic Pydantic validation and self-correction ensure structured and reliable outputs from LLMs .\n* **Comprehensive Observability**: Integration with OpenTelemetry and native Logfire provides real-time debugging, performance monitoring, and cost tracking .\n* **Production-ready Tooling**: This includes an evaluation framework (`pydantic-evals`), durable execution capabilities, and various protocol integrations like MCP, A2A, and AG-UI .\n* **Graph Support**: It provides a way to define graphs using type hints for complex applications .\n\n## Framework Architecture\n\nThe framework is structured as a UV workspace monorepo, containing several packages .\n\n### Core Packages\n* `pydantic-ai-slim`: Contains the core framework components such as `Agent`, `Model`, and tools .\n* `pydantic-ai`: A meta-package that includes all optional extras .\n\n### Supporting Packages\n* `pydantic-graph`: Provides the graph execution engine with `Graph` and `BaseNode` .\n* `pydantic-evals`: An evaluation framework for datasets and evaluators .\n* `examples`: Contains example applications .\n* `clai`: Provides a CLI interface .\n\n## Agent Execution Flow\n\nThe `Agent` class serves as the primary orchestrator . Agent execution is graph-based, utilizing a state machine from `pydantic_graph.Graph` . The execution involves three core node types: `UserPromptNode`, `ModelRequestNode`, and `CallToolsNode` .\n\n## Notes\nThe `CLAUDE.md` file provides guidance for Claude Code when working with the repository, outlining development commands and project architecture . 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Key features include: * **Type-safe Agents**: Agents are defined using `Agent[Deps, Output]` for compile-time validation, leveraging Pydantic for output validation and dependency injection . * **Model-Agnostic Design**: It supports over 15 LLM providers through a unified `Model` interface, allowing for easy switching between different models and providers . * **Structured Outputs**: Automatic Pydantic validation and self-correction ensure structured and reliable outputs from LLMs . * **Comprehensive Observability**: Integration with OpenTelemetry and native Logfire provides real-time debugging, performance monitoring, and cost tracking . * **Production-ready Tooling**: This includes an evaluation framework (`pydantic-evals`), durable execution capabilities, and various protocol integrations like MCP, A2A, and AG-UI . * **Graph Support**: It provides a way to define graphs using type hints for complex applications . ## Framework Architecture The framework is structured as a UV workspace monorepo, containing several packages . ### Core Packages * `pydantic-ai-slim`: Contains the core framework components such as `Agent`, `Model`, and tools . * `pydantic-ai`: A meta-package that includes all optional extras . ### Supporting Packages * `pydantic-graph`: Provides the graph execution engine with `Graph` and `BaseNode` . * `pydantic-evals`: An evaluation framework for datasets and evaluators . * `examples`: Contains example applications . * `clai`: Provides a CLI interface . ## Agent Execution Flow The `Agent` class serves as the primary orchestrator . 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