Mastra AI Runtime Context Not Passing Data To Agent Solutions And Workarounds

Jul 18, 2025 by gitftunila 78 views

Runtime Context Not Passing Data to Agent in Mastra AI

Introduction

This article addresses an issue encountered in Mastra AI where the runtime context fails to pass data to an agent as expected. Specifically, the user attempted to use runtime context to provide the current date to an agent, but the agent responded with an incorrect date. This article explores the problem, examines the user's configuration, and suggests potential solutions and workarounds.

Problem Description

The user reported that the runtime context in Mastra AI is not effectively passing data to an agent. The user's intention was to inject the current date into the agent's instructions using the runtime context. However, when the agent was asked about the current date, it provided an outdated response, indicating that the runtime context data was not being utilized as anticipated.

User's Configuration

The user provided the following agent configuration:

export const defaultAgent = new Agent({
  name: 'Default',
  description:
    'General purpose AI assistant providing helpful responses and user support across various tasks.',
  instructions:
    'I am a helpful AI assistant. How can I help you today?\n\nALWAYS return well formatted md (markdown) text',
  model: provider('openai/gpt-4.1'),
  tools: { },
});

The agent, named 'Default,' is designed as a general-purpose AI assistant. The instructions specify that the agent should provide helpful responses and user support. The agent uses the openai/gpt-4.1 model. The user attempted to pass the current date as runtime context:

{
  "current-date": "Wed Jul 16 2025 13:35:22 GMT+0400"
}

When asked, "What is the current date?" the agent responded with "Today's date is June 13, 2024," which is incorrect.

Analysis

The issue arises from the fact that the runtime context, as implemented, may not be directly injected into the agent's instructions in the manner the user expected. The agent's instructions are set during its creation, and the runtime context might not automatically update these instructions for each interaction. This discrepancy leads to the agent relying on its pre-existing knowledge or internal clock, rather than the provided runtime context.

Key Challenges

Runtime Context Implementation: Understanding how Mastra AI's runtime context is designed to interact with agents is crucial. If the context is not meant to dynamically update instructions, the user's approach will not work.
Agent's Internal Knowledge: AI models like gpt-4.1 have internal knowledge and a sense of time based on their training data. This internal knowledge can override or conflict with the provided context if not handled correctly.
Dynamic Updates: The agent's instructions are static unless explicitly modified. Runtime context needs a mechanism to inject data into the instructions or the agent's query in real-time.

Potential Solutions and Workarounds

While the user mentioned that creating a dynamic agent might not be an elegant solution, it is indeed one of the viable approaches. Here are several strategies to address the issue, ranging from simple to more complex:

1. Dynamic Instructions with String Interpolation

One way to ensure the agent uses the provided date is to dynamically construct the instructions each time the agent is invoked. This involves embedding the runtime context data directly into the instructions string.

function createAgentWithDate(currentDate: string) {
  return new Agent({
    name: 'DynamicDateAgent',
    description: 'An agent that knows the current date.',
    instructions: `I am a helpful AI assistant. The current date is ${currentDate}. How can I help you today?\n\nALWAYS return well formatted md (markdown) text`,
    model: provider('openai/gpt-4.1'),
    tools: {},
  });
}

// Example Usage
const currentDate = new Date().toDateString();
const dynamicAgent = createAgentWithDate(currentDate);

In this solution, the createAgentWithDate function takes the current date as input and interpolates it into the instructions. Each time the function is called with a new date, a new agent instance is created with the updated instructions. This ensures the agent always has the correct date information.

2. Pre-Processing Queries

Instead of modifying the agent's instructions, the query itself can be pre-processed to include the current date. This approach involves adding the date information to the user's question before it is passed to the agent.

function preprocessQuery(query: string, currentDate: string) {
  return `The current date is ${currentDate}. ${query}`;
}

// Example Usage
const currentDate = new Date().toDateString();
const userQuery = "What is the current date?";
const processedQuery = preprocessQuery(userQuery, currentDate);
// Pass processedQuery to the agent

Here, the preprocessQuery function takes the user's query and the current date, combines them into a new query, and then passes the processed query to the agent. This method ensures the agent has the date context without modifying its core instructions.

3. Custom Tool for Date Retrieval

Another approach is to create a custom tool that the agent can use to retrieve the current date. This tool would encapsulate the date retrieval logic, and the agent can call it whenever it needs to know the date.

import { Tool } from 'mastra-ai';

const CurrentDateTool = new Tool({
  name: 'current_date',
  description: 'Retrieves the current date.',
  async call() {
    return new Date().toDateString();
  },
});

const agentWithDateTool = new Agent({
  name: 'DateToolAgent',
  description: 'An agent that can retrieve the current date using a tool.',
  instructions: 'I am a helpful AI assistant. I can use tools to get information. How can I help you today?\n\nALWAYS return well formatted md (markdown) text',
  model: provider('openai/gpt-4.1'),
  tools: { current_date: CurrentDateTool },
});

In this solution, a CurrentDateTool is created, which returns the current date when called. The agent is configured to use this tool, and when the agent needs the date, it can invoke the tool. This keeps the agent's instructions clean and modular.

4. Hybrid Approach: Dynamic Instructions and Query Pre-processing

A hybrid approach combines dynamic instructions and query pre-processing to provide even more context to the agent. This involves setting a general instruction about the date and then refining the query with the specific date.

function createAgentWithDateContext() {
  return new Agent({
    name: 'HybridDateAgent',
    description: 'An agent that knows the context of the current date.',
    instructions: 'I am a helpful AI assistant. I am aware of the current date provided in the context. How can I help you today?\n\nALWAYS return well formatted md (markdown) text',
    model: provider('openai/gpt-4.1'),
    tools: {},
  });
}

function preprocessQueryWithDate(query: string, currentDate: string) {
  return `The current date is ${currentDate}. ${query}`;
}

// Example Usage
const hybridAgent = createAgentWithDateContext();
const currentDate = new Date().toDateString();
const userQuery = "What is the current date?";
const processedQuery = preprocessQueryWithDate(userQuery, currentDate);
// Pass processedQuery to the agent

This method involves creating an agent that is aware of the date context and then pre-processing the query to include the specific date. This provides a balanced approach, ensuring the agent has both general awareness and specific information.

5. Mastra AI Configuration Review

It is essential to review the Mastra AI configuration to ensure that runtime context is correctly set up and passed to the agent. This involves checking the documentation and settings related to context management to ensure that the intended behavior is achieved.

Conclusion

The issue of runtime context not passing data to an agent in Mastra AI can be addressed through various strategies. The most effective solutions involve dynamically updating the agent's instructions, pre-processing queries, creating custom tools, or using a hybrid approach. By implementing one or more of these solutions, users can ensure that their agents have access to the necessary runtime information, leading to more accurate and context-aware responses. Additionally, reviewing the Mastra AI configuration and consulting the documentation can help identify any potential setup issues. Understanding the nuances of how runtime context interacts with agents is crucial for building robust and reliable AI applications.

By employing these strategies, developers can effectively manage the context within Mastra AI agents, ensuring they operate with the most current and relevant data. The flexibility to choose between dynamic instructions, query pre-processing, custom tools, or a hybrid approach allows for tailored solutions that best fit the specific needs of the application. Ultimately, this leads to more intelligent and context-aware AI interactions.