mcp-run-python

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It focuses on providing a type-safe, model-agnostic, and extensible environment for developing AI agents. \n\n## Purpose and Core 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* **Tool Integration**: The framework provides a system for registering and executing tools, with automatic JSON schema generation and support for various output types. \n* **Graph-based Execution**: `pydantic-graph` manages agent workflows as finite state machines, allowing for detailed tracking of message history and usage. \n* **Observability**: Integration with OpenTelemetry and Logfire provides comprehensive observability for tracing agent runs, model requests, and tool executions. \n* **Durable Execution**: Integrations with systems like DBOS and Temporal enable agents to maintain state and resume execution after failures. \n\n## Repository Structure\n\nThe repository is organized as a monorepo using `uv` for package management. \nKey packages include:\n* `pydantic-ai-slim`: Contains the core framework components such as `Agent`, `Model`, and tools. \n* `pydantic-graph`: Provides the graph execution engine. \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:\n* `UserPromptNode`: Processes user input and creates initial `ModelRequest`. \n* `ModelRequestNode`: Calls `model.request()` or `model.request_stream()` and handles retries. \n* `CallToolsNode`: Executes tool functions via `RunContext[Deps]`. \n\nThe `Agent` provides methods like `run()`, `run_sync()`, and `run_stream()` for different execution scenarios. \n\n## Model Provider Support\n\nThe framework offers a unified `Model` abstract base class for various LLM providers, including native support for OpenAI, Anthropic, Google, Groq, Mistral,\n\nWiki pages you might want to explore:\n- [OpenAI, Anthropic, and Groq Models (pydantic/pydantic-ai)](/wiki/pydantic/pydantic-ai#3.2)\n\nView this search on DeepWiki: https://deepwiki.com/search/what-is-the-purpose-and-focus_a1b523d4-fbf7-4465-90d5-9d88141df70d\n"}]}} event: ping data: ping headers: access-control-allow-headers: - Content-Type, mcp-session-id, mcp-protocol-version access-control-allow-methods: - GET, POST, OPTIONS access-control-allow-origin: - '*' access-control-expose-headers: - mcp-session-id access-control-max-age: - '86400' alt-svc: - h3=":443"; 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It focuses on providing a type-safe, model-agnostic, and extensible environment for developing AI agents. \n\n## Purpose and Core 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* **Tool Integration**: The framework provides a system for registering and executing tools, with automatic JSON schema generation and support for various output types. \n* **Graph-based Execution**: `pydantic-graph` manages agent workflows as finite state machines, allowing for detailed tracking of message history and usage. \n* **Observability**: Integration with OpenTelemetry and Logfire provides comprehensive observability for tracing agent runs, model requests, and tool executions. \n* **Durable Execution**: Integrations with systems like DBOS and Temporal enable agents to maintain state and resume execution after failures. \n\n## Repository Structure\n\nThe repository is organized as a monorepo using `uv` for package management. \nKey packages include:\n* `pydantic-ai-slim`: Contains the core framework components such as `Agent`, `Model`, and tools. \n* `pydantic-graph`: Provides the graph execution engine. \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:\n* `UserPromptNode`: Processes user input and creates initial `ModelRequest`. \n* `ModelRequestNode`: Calls `model.request()` or `model.request_stream()` and handles retries. \n* `CallToolsNode`: Executes tool functions via `RunContext[Deps]`. \n\nThe `Agent` provides methods like `run()`, `run_sync()`, and `run_stream()` for different execution scenarios. \n\n## Model Provider Support\n\nThe framework offers a unified `Model` abstract base class for various LLM providers, including native support for OpenAI, Anthropic, Google, Groq, Mistral,\n\nWiki pages you might want to explore:\n- [OpenAI, Anthropic, and Groq Models (pydantic/pydantic-ai)](/wiki/pydantic/pydantic-ai#3.2)\n\nView this search on DeepWiki: https://deepwiki.com/search/what-is-the-purpose-and-focus_a1b523d4-fbf7-4465-90d5-9d88141df70d\n" role: tool tool_call_id: call_8s5wIpz38o0xHH9xcLBRJXAn model: gpt-4o stream: false tool_choice: auto tools: - function: description: Get a list of documentation topics for a GitHub repository name: read_wiki_structure parameters: additionalProperties: false properties: repoName: description: 'GitHub repository: owner/repo (e.g. "facebook/react")' type: string required: - repoName type: object strict: true type: function - function: description: View documentation about a GitHub repository name: read_wiki_contents parameters: additionalProperties: false properties: repoName: description: 'GitHub repository: owner/repo (e.g. "facebook/react")' type: string required: - repoName type: object strict: true type: function - function: description: Ask any question about a GitHub repository name: ask_question parameters: additionalProperties: false properties: question: description: The question to ask about the repository type: string repoName: description: 'GitHub repository: owner/repo (e.g. "facebook/react")' type: string required: - repoName - question type: object strict: true type: function uri: https://api.openai.com/v1/chat/completions response: headers: access-control-expose-headers: - X-Request-ID alt-svc: - h3=":443"; ma=86400 connection: - keep-alive content-length: - '1269' content-type: - application/json openai-organization: - pydantic-28gund openai-processing-ms: - '1857' openai-project: - proj_dKobscVY9YJxeEaDJen54e3d openai-version: - '2020-10-01' strict-transport-security: - max-age=31536000; includeSubDomains; preload transfer-encoding: - chunked parsed_body: choices: - finish_reason: stop index: 0 logprobs: null message: annotations: [] content: The `pydantic/pydantic-ai` repository is a Python agent framework crafted for developing production-grade Generative AI applications. It emphasizes type safety, model-agnostic design, and extensibility. The framework supports various LLM providers, manages agent workflows using graph-based execution, and ensures structured, reliable LLM outputs. Key packages include core framework components, graph execution engines, evaluation tools, and example applications. refusal: null role: assistant created: 1763053762 id: chatcmpl-CbV6wIVJebfetfZK4gljnVkk0AYZa model: gpt-4o-2024-08-06 object: chat.completion service_tier: default system_fingerprint: fp_b1442291a8 usage: completion_tokens: 85 completion_tokens_details: accepted_prediction_tokens: 0 audio_tokens: 0 reasoning_tokens: 0 rejected_prediction_tokens: 0 prompt_tokens: 925 prompt_tokens_details: audio_tokens: 0 cached_tokens: 0 total_tokens: 1010 status: code: 200 message: OK version: 1 ...