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@ibuildthecloud

Wow, could this be any easier. @pydantic AI https://t.co/2WaNbu4al4

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What\\u2019s happening here\",\"sender\":\"USER\",\"grokMode\":\"NORMAL\",\"mediaUrls\":[]},{\"message\":\"Let\\u2019s break down why Python code is being run from Deno in the context of the X post you\\u2019re interested in, and what\\u2019s happening in the broader technical picture.\\nWhy Python Code is Run from Deno?\\nDeno as a Secure Runtime Environment:\\n   - Deno is a modern JavaScript and TypeScript runtime, often seen as a more secure and streamlined alternative to Node.js. It has built-in security features like sandboxing, where scripts run with restricted permissions unless explicitly allowed (e.g., --allow-run, --allow-read). This is evident from the web search result [web:0], which mentions the need for the --allow-run flag to execute external processes like Python scripts.\\n   - In the X post, the script uses deno run to execute a Python script (mcp_sse_client.py). 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The web result [web:0] provides an example of how Deno can run a Python script using Deno.run or Deno.Command, capturing outputs like stdout and stderr. In the context of Pydantic AI, Deno acts as a orchestrator, running Python code to perform specific tasks (like interacting with an LLM) while managing the overall execution environment.\\n   - Why Python specifically? Python is the go-to language for AI and machine learning due to its extensive ecosystem of libraries (e.g., Pydantic, NumPy, TensorFlow). Pydantic AI leverages Python for its core logic, but uses Deno to provide a secure, lightweight runtime to manage the execution, as noted in [web:3]\\u2019s mention of moving away from npm to Deno for better sandboxing.\\nDeveloper Experience and Simplicity:\\n   - Darren Shepherd\\u2019s comment in the X post (\\u201cWow, could this be any easier\\u201d) reflects the seamless integration that Pydantic AI achieves by combining Deno and Python. 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The --node-modules-dir=auto flag ensures Deno manages dependencies locally, further simplifying the setup.\\nWhat\\u2019s Happening in the X Post?\\nLet\\u2019s dissect the specific script shown in the X post to understand the flow:\\nThe Command:\\n   - deno run --node-modules-dir=auto mcp_sse_client.py:\\n     - deno run: Executes the script using Deno.\\n     - --node-modules-dir=auto: Tells Deno to use a local node_modules directory for dependencies, as explained in [web:3].\\n     - mcp_sse_client.py: The Python script being run, which acts as an MCP client using the SSE transport to connect to a server.\\nThe Python Script (mcp_sse_client.py):\\n   - `python\\n     from pydantic_ai import Agent\\n     from pydantic_ai.sse import MCPServerHTTP\\n     server = MCPServerHTTP(url='http:\\/\\/localhost:3080\\/sse')\\n     agent = Agent('openai.gpt-40', mcp_servers=[server])\\n     `\\n     - from pydantic_ai import Agent: Imports the Agent class from Pydantic AI, which is a shim to interact with LLMs (as per [web:2]).\\n     - from pydantic_ai.sse import MCPServerHTTP: Imports the SSE-based MCP server implementation, which enables real-time communication with the agent.\\n     - server = MCPServerHTTP(url='http:\\/\\/localhost:3080\\/sse'): Sets up a connection to an MCP server running on localhost:3080 using SSE.\\n     - agent = Agent('openai.gpt-40', mcp_servers=[server]): Creates an agent that uses the openai.gpt-40 model (likely a fictional or future model name for illustrative purposes) and connects it to the MCP server.\\nSynchronous Definition and Execution:\\n   - `python\\n     async def main():\\n         conversation = await agent.run_mcp_servers()\\n         result = await agent.run(\\\"How many days between 2000-01-01 and 2025-03-18?\\\")\\n         print(result.output)\\n     `\\n     - async def main(): Defines an asynchronous function to handle the agent\\u2019s interaction.\\n     - conversation = await agent.run_mcp_servers(): Initializes the MCP server connection for the agent.\\n     - result = await agent.run(...): Sends a query to the LLM via the agent: \\u201cHow many days between 2000-01-01 and 2025-03-18?\\u201d The agent communicates with the LLM through the MCP server, which handles the request and streams the response back via SSE.\\n     - print(result.output): Outputs the result, which is There are 9,208 days between January 1, 2000, and March 18, 2025.\\nThe Result:\\n   - The LLM processes the query and calculates the number of days between the two dates. 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