Mastra Network Streams: Accessing Sub-Agent Reasoning

The Puzzle of Missing Reasoning in agent.network() Streams

Have you ever been deep in the development of a complex AI system using Mastra, perhaps orchestrating multiple agents to tackle a task? You set up your main agent, delegate parts of the job to sub-agents, and enable that exciting extended thinking feature. You're expecting a rich stream of information, including the detailed reasoning behind each step, just like you get when you use agent.stream() with sendReasoning: true. However, when you switch to agent.network(), a peculiar thing happens: the stream only shows agent-execution-event-text-delta, and the agent-execution-event-reasoning-* events seem to vanish. It's like the sub-agents are thinking, but their thought process is kept a secret from the main stream. This can be quite baffling, especially when the MastraAgentNetworkStream.usage object hints that reasoning tokens are indeed being tracked. You might find yourself wondering, "Is this a bug, or is there a specific way I'm supposed to access this valuable reasoning data when using agent.network()?"

This is precisely the scenario many developers encounter when building sophisticated multi-agent applications with Mastra. The expectation is a consistent and transparent flow of information across different agent interaction methods. When agent.stream() faithfully delivers reasoning deltas, but agent.network() doesn't, it creates a disconnect in how we monitor and debug our AI workflows. The ability to see the internal 'thought process' of sub-agents is crucial for understanding their decision-making, identifying potential biases, and fine-tuning their performance. Without it, troubleshooting complex interactions becomes significantly harder. The minimal reproduction code provided illustrates this clearly: a parent chatAgent configured with extended thinking and a strategistAgent as a sub-agent. When chatAgent.network() is called, the stream dutifully reports text deltas, but the expected reasoning deltas remain elusive. This behavior, coupled with the presence of reasoningTokens in the stream's usage data, strongly suggests that the reasoning information is being generated and consumed internally. The core question then becomes: why isn't it being propagated to the network stream, and if this is intentional, what's the recommended approach to retrieve it?

This article aims to demystify this behavior, explore potential reasons behind it, and guide you toward effective solutions for accessing sub-agent reasoning within your Mastra network streams. We'll delve into the nuances of Mastra's streaming mechanisms and provide practical insights to ensure you have full visibility into your AI's decision-making pipeline. Whether you're building a simple conversational agent or a complex multi-agent system, understanding how to access and leverage reasoning events is key to building robust, transparent, and high-performing AI applications. We'll break down the differences between agent.stream() and agent.network() in terms of event propagation and offer potential workarounds or configurations to achieve the desired outcome. Stay tuned as we unravel the mystery of the missing reasoning events!

Understanding Mastra's Streaming Mechanisms: stream() vs. network()

To truly grasp why reasoning events might not appear in agent.network() streams, it's essential to understand the fundamental differences in how agent.stream() and agent.network() operate within the Mastra framework. Both methods are designed to provide real-time feedback from your agents, but they serve slightly different purposes and, consequently, handle event propagation differently. agent.stream() is generally your go-to for direct, unfiltered interaction with a single agent or a closely coupled chain of agents where you want to observe every nuance of its output. When you enable sendReasoning: true with toAISdkStream(), you're explicitly telling Mastra, "Show me not just the final output, but also the intermediate steps, the 'thinking aloud' process of the agent." This is incredibly valuable for debugging and understanding how an agent arrives at its conclusions. It’s like having a direct line into the agent's internal monologue, providing agent-execution-event-reasoning-delta events that reveal its thought process, logical steps, and any internal considerations.

On the other hand, agent.network() is architected for a more sophisticated scenario: orchestrating multiple, potentially independent agents that form a network. Think of it as a communication bus or a router where agents can interact with each other, perhaps one agent calling another as a sub-agent. The primary goal of agent.network() is often to manage the flow of information between these agents and to provide a consolidated output to the end-user. Because it's designed to handle a network of agents, its default behavior might be to prioritize the final actionable output rather than the granular internal reasoning of each individual component. The agent-execution-event-text-delta events you do see represent the synthesized, user-facing output that has been processed and forwarded through the network. The reasoning tokens being consumed and tracked in MastraAgentNetworkStream.usage indicate that the sub-agents are performing their reasoning, but this information might be consumed internally by the network logic itself to decide what information to pass along, rather than being explicitly broadcast as distinct reasoning events in the primary stream.

This divergence in design philosophy leads to the observed behavior. agent.stream() is built for introspection and detailed observation of a single agent's cognitive process, hence its willingness to expose reasoning. agent.network(), conversely, is geared towards inter-agent communication and might abstract away some of the lower-level reasoning details to present a cleaner, more coherent output stream that represents the collective result of the network. Understanding this distinction is the first step in diagnosing why your reasoning events aren't showing up in network streams. It's not necessarily that the information is lost, but rather that it's being handled differently based on the intended use case of the streaming method. The presence of reasoningTokens in the usage object is a key clue; it confirms the reasoning is happening, but the delivery mechanism for that reasoning differs between stream and network calls.

Decoding the agent.network() Stream Behavior

When you observe that agent.network() streams primarily emit agent-execution-event-text-delta while omitting the agent-execution-event-reasoning-* events, it suggests a deliberate design choice within Mastra's architecture for managing complex agent networks. The network() function is optimized for orchestrating communication between agents and presenting a unified output, rather than exposing the fine-grained thought processes of every single sub-agent involved in that communication. The core idea behind network() is to facilitate the flow and synthesis of information across a network of agents. In this model, the reasoning of individual sub-agents might be considered an internal step in their execution, necessary for them to produce their part of the final output, but not necessarily intended for direct exposition in the main network stream.

Consider the minimal reproduction example: chatAgent acts as a router, possibly invoking strategistAgent. The strategistAgent performs its reasoning (consuming reasoning tokens as indicated by MastraAgentNetworkStream.usage), produces a result or an intermediate text output, and sends it back to chatAgent. chatAgent then processes this information, potentially synthesizing it with other inputs or outputs, and generates the text-delta that flows through the network stream. In this scenario, the reasoning of strategistAgent is crucial for its task completion, but chatAgent might be configured to only forward the outcome of that task, not the reasoning itself, to maintain a cleaner, more focused stream for the end-user or the next stage in the network. This is analogous to how a project manager might receive updates on task completion from team members but not necessarily the detailed minute-by-minute thought process of each team member while they worked.

Furthermore, the extended thinking configuration on the parent agent (chatAgent in the example) and potentially on sub-agents relates to their internal capabilities. When chatAgent.network() is called, it orchestrates the network interaction. If sub-agents have extended thinking enabled, they will use it to generate their responses, which are then fed back into the network. However, the network() stream's responsibility is to reflect the network's activity – the calls made, the results received, and the final output synthesized. It's less about broadcasting the internal mechanics of each node in the network and more about showing the overall progress and outcome of the network's operation. The fact that reasoningTokens are tracked in usage is a critical piece of evidence: the reasoning is happening and being accounted for, but it's not being surfaced as a distinct event type in the network stream by default. This suggests that the network stream prioritizes user-facing content and execution status over introspective reasoning details from sub-components. The focus is on what the network is producing, not necessarily how each agent within the network individually arrived at its contribution.

Accessing Sub-Agent Reasoning: Strategies and Workarounds

Given that agent.network() by default seems to prioritize synthesized output over granular sub-agent reasoning events in its stream, the question naturally arises: how can we access this valuable internal reasoning data when needed? If the behavior is indeed intentional, Mastra likely provides mechanisms or recommended patterns for achieving this. One primary approach involves rethinking how you structure your agent interactions and stream consumption. Instead of solely relying on the network stream for reasoning details, you might need to tap into the sub-agents more directly or employ alternative event handling.

1. Direct Streaming from Sub-Agents: If you have direct access to the sub-agent instances within your parent agent's configuration (as shown in the minimal reproduction with strategistAgent), you could potentially establish separate streams for those sub-agents if your architecture allows. However, this can quickly become complex and defeat the purpose of a unified network stream. A more practical approach might be to modify the sub-agent's configuration or its invocation logic. For instance, if a sub-agent is designed to return specific reasoning outputs, you could configure that sub-agent to use agent.stream() internally when called by the parent, and then somehow pipe those specific reasoning events back to the parent's network stream. This requires careful design of the sub-agent's return value and the parent's aggregation logic.

2. Custom Event Emission within Sub-Agents: A more robust solution might involve modifying the sub-agents themselves to emit custom events that the parent agent can then capture and forward. When a sub-agent performs its reasoning, it could emit a custom event (e.g., reasoning-step-completed) containing the reasoning details. The parent agent, orchestrating the network, would then listen for these custom events from its sub-agents and include them in its own network stream, perhaps under a new, custom event type (e.g., agent-execution-event-custom-reasoning). This gives you fine-grained control over what reasoning information is exposed and how it's categorized.

3. Leveraging MastraAgentNetworkStream.usage for Insights: While not providing real-time event data, the usage property of the MastraAgentNetworkStream does confirm that reasoning tokens are being consumed. If your primary goal is post-hoc analysis or understanding resource utilization, this property is invaluable. You can access it after the stream has completed to get a summary of reasoning token usage. However, this doesn't help with real-time debugging or observing the step-by-step reasoning process.

4. Revisiting agent.stream() with sendReasoning: true: If your use case critically depends on seeing reasoning events, and the network orchestration aspect of agent.network() isn't strictly necessary for that specific part of your debugging or monitoring, consider if agent.stream() with sendReasoning: true can be used in a targeted manner. You might invoke a critical sub-agent directly using stream() for detailed insight, even if the overall interaction is managed via network().

5. Configuration for network() Streams: It's possible that future versions of Mastra or specific configurations within the network() call itself might offer options to include or exclude reasoning events. Keep an eye on the official Mastra documentation and release notes for any updates regarding network stream event propagation. For instance, a hypothetical includeSubAgentReasoning: true option in the network() call's parameters could dramatically change this behavior.

Ultimately, accessing sub-agent reasoning in network streams often requires a blend of understanding Mastra's core design principles and potentially implementing custom logic. The absence of direct reasoning events in the network stream might be by design to keep the stream focused on the network's output, but the underlying data is generated and can likely be surfaced with the right strategies. Experimenting with custom events or carefully orchestrating direct streams from key sub-agents are promising avenues.

Conclusion: Navigating the Depths of Agent Reasoning

We've explored the intricacies of Mastra's agent.network() streams and the curious absence of sub-agent reasoning events, contrasting it with the more transparent agent.stream() behavior. It appears that the network() function, designed for orchestrating complex agent interactions, prioritizes the synthesized, user-facing output of the network over the granular, internal reasoning steps of individual sub-agents. This is not to say the reasoning isn't happening – the MastraAgentNetworkStream.usage object clearly indicates that reasoning tokens are being consumed. Instead, the network stream focuses on delivering the collective result, abstracting away the detailed 'thought process' of each component to maintain a cleaner, more coherent output.

While this default behavior might pose challenges for those seeking deep introspection into sub-agent decision-making during network operations, it's likely a design choice to keep the network stream focused on its primary purpose: managing inter-agent communication and delivering synthesized outcomes. The good news is that this doesn't mean sub-agent reasoning is inaccessible. As discussed, strategies such as implementing custom event emissions within sub-agents, carefully orchestrating direct streams from critical components, or leveraging the usage data for post-hoc analysis can help bridge this gap. The Mastra framework is powerful and flexible, and often, achieving specific visibility requires a tailored approach that aligns with the framework's design principles.

For further exploration into advanced AI orchestration and reasoning techniques, I highly recommend diving deeper into the official documentation of related frameworks and tools. Understanding how different systems handle agent communication and reasoning transparency can provide valuable insights. Consider exploring resources from organizations dedicated to advancing AI research and development. For instance, checking out the latest publications and best practices from Google AI or OpenAI can offer broader perspectives on building sophisticated AI systems and managing their internal states and reasoning processes.