From Prototype to Production: Building AI Agents with Pydantic AI

From Prototype to Production: Building AI Agents with Pydantic AI

Building AI agents involves a series of steps, from initial planning to deployment. One effective approach is to begin with a no-code or low-code tool for rapid prototyping before transitioning to custom code for greater control and flexibility. This article explores how to transform an AI agent prototype built with a tool like N8N into a production-ready Python agent using Pydantic AI and OpenRouter.

Planning and Prototyping

The initial phase involves planning the agent's functionality. For example, an agent designed for GitHub code Q&A requires the ability to access and understand code repositories. Prototyping with no-code tools allows for quick experimentation and validation of the agent's core capabilities. Tools like N8N, Voiceflow, and Flowise offer visual interfaces to define the agent's workflow and logic.

Transitioning to Custom Code with Pydantic AI

While no-code tools offer speed and ease of use, custom coding provides unparalleled control and customization. Frameworks like Pydantic AI, Llama Index, Crew AI, and Langchain enable developers to build sophisticated agents with tailored functionality. Pydantic AI, in particular, simplifies the process of defining agents, managing conversation history, and integrating external tools.

Key Components of a Pydantic AI Agent

1. Dependencies: Define the necessary dependencies, such as HTTP clients and API keys, required for the agent to interact with external services.
2. Agent Definition: Specify the system prompt, which guides the agent's behavior, and select the desired language model (LLM), such as DeepSeq v3.
3. Tools: Define the tools the agent can use to perform specific tasks. Pydantic AI allows defining tools using standard Python functions with a simple decorator (@agent.tool). This decorator exposes the Python function to the LLM, enabling it to dynamically use the function when the user is asking a question, without the function being called directly in the code.

Leveraging OpenRouter for Cost-Effective LLM Access

OpenRouter provides a unified interface to access various LLMs, allowing developers to choose the most suitable model for their needs while optimizing costs. By overriding the base URL in Pydantic AI, developers can seamlessly integrate OpenRouter and utilize models like DeepSeq v3 without being tied to a single provider.

Building Tools for GitHub Code Q&A

To enable the agent to answer questions about GitHub repositories, several tools can be created:

• Get Repository Metadata: Retrieves information about a repository, such as its size, number of files, and number of stars.
• Get Repository Structure: Retrieves the file and folder structure of a repository, providing the agent with a map of the codebase.
• Get File Content: Retrieves the content of a specific file within a repository, allowing the agent to analyze and extract information.

AI coding assistants can be leveraged to expedite the creation of these tools. By providing the AI with a JSON representation of the N8N workflow and clear instructions, the AI can generate the necessary Python code.

Managing Conversation History

Maintaining conversation history is crucial for enabling context-aware interactions. Pydantic AI provides mechanisms to manage conversation history, allowing the agent to refer to previous interactions and provide more relevant responses. Including tool calls in the conversation history allows the LLM to refer to previous searches, optimizing responses and saving API costs.

Conclusion

Transitioning from a no-code prototype to a custom-coded AI agent with Pydantic AI offers a powerful and flexible approach to building intelligent applications. By leveraging OpenRouter, developers can access a wide range of LLMs while optimizing costs. As AI continues to evolve, the ability to quickly prototype and deploy custom agents will become increasingly valuable. What possibilities could this combination of tools and techniques unlock for your projects?

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