LiteLLM & Agents SDK: Prompt ID Support For Agents

Nov 17, 2025 by Alex Johnson 51 views

Prompt ID Support for Agents Running LiteLLM Models

Introduction

This article delves into the possibility of using prompt IDs with the Agents SDK when employing LiteLLM models. Specifically, it addresses the question of whether the Agents SDK natively supports providing a prompt ID to an Agent that is running a LiteLLM model. This is particularly relevant when aiming for more control over the agent's responses by predefining specific prompts, rather than relying solely on dynamic text-based prompts.

The Agents SDK from OpenAI provides a framework for building agents that can interact with various models. LiteLLM, on the other hand, acts as a unified interface for accessing different language models, including those from OpenAI, Google, and others. Combining these two tools allows developers to create versatile agents capable of leveraging a wide range of models. However, the integration and specific functionalities, such as using prompt IDs, require careful consideration and understanding of both the Agents SDK and LiteLLM.

To effectively manage and customize agent behavior, developers often use prompts. These prompts guide the model's responses and ensure that the agent adheres to specific instructions or constraints. While text prompts are commonly used, prompt IDs offer a more structured and manageable approach, especially in complex applications. The ability to use prompt IDs can simplify prompt management, version control, and A/B testing of different prompts. Therefore, understanding whether the Agents SDK supports prompt IDs with LiteLLM models is crucial for developers aiming to build sophisticated and well-controlled agents.

Background on Agents SDK and LiteLLM

Before diving into the specifics of prompt ID support, let's briefly explore the Agents SDK and LiteLLM. The Agents SDK is designed to facilitate the creation of agents that can perform tasks, make decisions, and interact with users or other systems. It provides abstractions and tools for defining agent behavior, managing state, and integrating with models. The SDK supports various features, including defining tools, setting up memory, and specifying prompts.

LiteLLM, on the other hand, is a library that simplifies the process of working with multiple language models. It provides a unified API for accessing models from different providers, such as OpenAI, Google, and Azure. This abstraction allows developers to switch between models easily without modifying their code. LiteLLM also supports features like token management, logging, and retries, making it a valuable tool for building robust and scalable applications. By using LiteLLM, developers can focus on the logic of their agents rather than the specifics of each model provider.

The combination of Agents SDK and LiteLLM offers a powerful platform for building AI-powered applications. The Agents SDK provides the framework for creating intelligent agents, while LiteLLM simplifies the integration with various language models. This synergy enables developers to leverage the strengths of both tools to create versatile and adaptable agents. However, the integration requires careful consideration of the specific features and capabilities of each tool.

The Problem: Using Prompt IDs with LiteLLM Models

The core question revolves around whether the Agents SDK natively supports the use of prompt IDs when an Agent is running a LiteLLM model. In many applications, prompts are not just static text; they are managed resources with unique identifiers. These identifiers, or prompt IDs, can be used to retrieve specific prompts from a database or configuration file. This approach offers several advantages, including better organization, version control, and the ability to dynamically switch between prompts.

The issue arises when attempting to configure an Agent with a LiteLLM model to use a prompt ID instead of a direct text prompt. The provided script demonstrates an attempt to achieve this, where the prompt parameter of the Agent class is set to a dictionary containing an id field. However, the observed behavior indicates that the Agent does not correctly interpret the prompt ID, resulting in a general response rather than one tailored to the specified prompt.

This discrepancy highlights a potential gap in the integration between the Agents SDK and LiteLLM. While the Agents SDK may support prompt IDs in some contexts, it is not clear whether this support extends to LiteLLM models. The lack of native support for prompt IDs can complicate the development process, requiring developers to implement custom solutions for managing and retrieving prompts. Therefore, understanding the extent of prompt ID support is crucial for developers aiming to build sophisticated agents with LiteLLM models.

Analyzing the Script and Expected Behavior

Let's break down the provided script to understand the problem better. The script begins by importing the necessary modules from the agents package, including Agent, ModelSettings, Runner, set_tracing_export_api_key, and trace. It also imports the LitellmModel class from the agents.extensions.models.litellm_model module.

The script then defines the model name and API key for the LiteLLM model. In this case, the model is gemini/gemini-2.5-flash, and the API key is represented by the placeholder Gemini_API_KEY_HERE. The LitellmModel class is instantiated with the model name and API key, creating a model object that can be used by the Agent.

The set_tracing_export_api_key function is called to set the API key for tracing, which is used to monitor the Agent's behavior. The Agent class is then instantiated with the model, prompt, and model settings. The prompt is specified as a dictionary with an id field, representing the prompt ID. The ModelSettings class is used to enable usage tracking.

Finally, the Runner.run_sync function is called to run the Agent with the input "Hello, what can you do?". The result is then printed to the console. The expected behavior is that the Agent should use the prompt associated with the specified prompt ID to generate a response. However, the actual behavior is that the Agent generates a general response, indicating that the prompt ID is not being correctly interpreted.

This analysis suggests that the Agents SDK does not natively support prompt IDs with LiteLLM models. The Agent appears to be ignoring the id field in the prompt dictionary and instead using a default or generic prompt. This behavior highlights the need for a custom solution to manage and retrieve prompts when using prompt IDs with LiteLLM models.

Investigating Native Support in Agents SDK

To determine whether the Agents SDK natively supports prompt IDs with LiteLLM models, it is essential to consult the official documentation and source code. The documentation may provide insights into the supported prompt formats and how to configure agents with different types of prompts. The source code can reveal how the Agents SDK processes prompts and whether it includes any logic for handling prompt IDs.

If the documentation does not explicitly mention prompt ID support, it is possible that the feature is not yet implemented or is not intended for use with LiteLLM models. In this case, developers may need to rely on custom solutions or contribute to the Agents SDK to add support for prompt IDs.

Examining the source code can provide more detailed information about how the Agents SDK handles prompts. By tracing the execution path from the Agent class to the model invocation, developers can identify where the prompt is processed and whether any logic is applied to interpret prompt IDs. If the source code does not include any logic for handling prompt IDs, it is likely that the feature is not natively supported.

In the absence of native support, developers may need to implement custom solutions to manage and retrieve prompts. This could involve creating a custom prompt provider that retrieves prompts based on their IDs and then passes the retrieved prompts to the Agent. Alternatively, developers could modify the Agents SDK to add support for prompt IDs, but this would require a deeper understanding of the SDK's architecture and may not be feasible for all developers.

Potential Workarounds and Solutions

Given the apparent lack of native support for prompt IDs in the Agents SDK when using LiteLLM models, several workarounds and solutions can be considered:

Custom Prompt Provider:
- Implement a custom prompt provider that retrieves prompts based on their IDs. This provider would act as an intermediary between the Agent and the prompt storage (e.g., database, configuration file). The Agent would pass the prompt ID to the provider, which would then retrieve the corresponding prompt and pass it to the model. This approach requires implementing the logic for retrieving prompts and integrating it with the Agents SDK.
Dynamic Prompt Generation:
- Instead of using prompt IDs directly, construct the prompt dynamically within the application. This involves retrieving the prompt components or templates based on the prompt ID and then assembling them into a complete prompt. This approach requires defining a prompt template structure and implementing the logic for assembling the prompt. For example, you might store prompt templates in a JSON format and use a templating engine to fill in the dynamic parts.
Extending the LitellmModel Class:
- Create a subclass of LitellmModel that overrides the prompt processing logic. This subclass would intercept the prompt ID, retrieve the corresponding prompt, and then pass the retrieved prompt to the underlying LiteLLM model. This approach requires a good understanding of the LitellmModel class and how it interacts with the Agents SDK.
Contributing to Agents SDK:
- Contribute to the Agents SDK by adding support for prompt IDs. This involves modifying the SDK's source code to handle prompt IDs and integrate them with LiteLLM models. This approach requires a deep understanding of the Agents SDK's architecture and may involve significant development effort. However, it would provide a long-term solution that benefits the entire community.
Using Environment Variables or Configuration Files:
- Store prompts in environment variables or configuration files, using the prompt ID as the key. The agent can then retrieve the prompt based on the ID from the environment or configuration. This is a simpler approach for smaller projects where managing a database or external prompt storage is not necessary.

Each of these solutions has its own trade-offs in terms of complexity, maintainability, and performance. The best approach depends on the specific requirements of the application and the available resources.

Conclusion

In conclusion, while the Agents SDK offers a robust framework for building AI-powered agents and LiteLLM simplifies the integration with various language models, native support for prompt IDs when using LiteLLM models appears to be lacking. The provided script and analysis suggest that the Agents SDK does not automatically interpret prompt IDs, requiring developers to implement custom solutions.

Several workarounds and solutions can be considered, including implementing a custom prompt provider, dynamically generating prompts, extending the LitellmModel class, contributing to the Agents SDK, or using environment variables or configuration files. The choice of the best approach depends on the specific requirements of the application and the available resources.

As the Agents SDK and LiteLLM continue to evolve, it is possible that native support for prompt IDs will be added in the future. In the meantime, developers can leverage the available workarounds and solutions to build sophisticated agents that leverage the power of prompt IDs.

For more information about OpenAI Agents, visit the OpenAI Agents SDK documentation.