Gemini CLI Stats: See Role-Based Usage

Hey guys! Ever wondered where all those tokens are going in your Gemini CLI sessions? Up until now, the /stats command has given us a pretty neat, general overview of session statistics, model usage, and tool calls. But let's be real, sometimes we need to dive a bit deeper, right? We need to know who or what is actually using those tokens and making those tool calls. That's where the idea to categorize our statistics by specific 'roles' or 'initiators' comes in. Think of it like getting a detailed breakdown of your internet usage – you want to know if it's streaming, gaming, or work that's gobbling up your data. Similarly, with Gemini CLI, understanding the contribution of different components will seriously level up our insights and help us optimize our workflows. We're talking about making those stats super granular, showing you the exact impact of your main agent interactions, the magic behind subagents, the brains of the auto-router, and even how your tools and hooks are performing. This isn't just a minor tweak; it's a significant upgrade to how we can monitor and understand the powerful engine that is Gemini CLI. Get ready to unlock a whole new level of clarity and control over your AI interactions!

Why We Need Role-Based Statistics

So, why the big fuss about adding these specific roles to our /stats? Honestly, it's all about giving you guys more power and clarity. Imagine you're running a complex project with Gemini CLI, maybe delegating tasks to specialized subagents like a codebase_investigator or an automated_researcher. Right now, if you see a high token count, it's a bit of a mystery box. Was it the main chat you were having? Or was it that subagent you spun up to dig into your codebase? Or perhaps the auto-router working its magic behind the scenes to figure out the best model for a specific query? Without this breakdown, it's tough to pinpoint exactly where the resources are being spent, making optimization a bit of a shot in the dark. This new role-based categorization aims to shine a bright light into those dark corners. We'll be able to see, for instance, if your subagents are being particularly chatty and consuming more tokens than expected, or if the auto-router is doing a lot of heavy lifting in model selection. Understanding this granular usage is crucial for effective resource management, especially when dealing with large projects or when you're conscious of token costs. It also helps in debugging and performance tuning. If a specific part of your workflow, like a custom tool you're using, seems slow or expensive, you'll be able to see its exact contribution to the overall stats. This direct feedback loop empowers you to make informed decisions, tweak your prompts, adjust your subagent configurations, or even optimize the underlying logic of your tools. It's about moving from a general awareness to a precise understanding, ensuring you're getting the most bang for your buck (or rather, your tokens!) from Gemini CLI.

The Proposed Roles Explained

Let's break down the different 'roles' we're proposing to track in Gemini CLI's statistics. Think of these as the key players in your AI interaction orchestra, each with their own distinct job and potential resource consumption. Understanding each role is key to making sense of the new stats. First up, we have the Main Agent. This is your bread-and-butter interaction – the direct chat you're having with Gemini CLI, the primary conversation flow. When you ask a question and get an answer, that's typically the Main Agent at work, and its token usage will be logged here. Then, there are Subagents. These are like specialized workers you delegate tasks to. For example, if you use a command like delegate_to_agent to activate a codebase_investigator subagent, all the communication and processing happening within that subagent's context will be attributed to this 'Subagent' role. This is super important because subagents can often involve complex chains of thought or extensive tool usage, and we need to track that separately. Next, we have the Auto-Router. This guy is the unsung hero managing how your requests are handled. When Gemini CLI decides which model or strategy is best for a given query, especially with advanced classifier strategies, the auto-router is doing the heavy lifting. The tokens it uses for this decision-making process will fall under this 'Auto-Router' category. It’s vital to see how much processing power is dedicated just to smart routing. We're also looking at Tools. While tool calls are already tracked, we want to potentially track the overhead associated with direct tool execution separately if it becomes significant. This helps differentiate between the model's reasoning and the actual execution of an external function. Finally, we have Hooks. These are interesting because they can trigger actions at specific points in the interaction flow – maybe before or after a tool call, or at the start/end of a session. If these hooks involve token usage or model interactions, we want to capture that here. By separating these, we gain unprecedented insight into the architecture of our AI interactions within Gemini CLI, allowing for more targeted analysis and optimization.

Technical Deep Dive: How We'll Make It Happen

Alright, tech enthusiasts, let's get into the nitty-gritty of how we're planning to implement these awesome new role-based statistics in Gemini CLI. This is where the real engineering magic happens! Our primary focus is on enhancing our telemetry system. We'll be updating key data structures like ApiResponseEvent and ToolCallEvent, and their related classes found in packages/core/src/telemetry/types.ts. The core idea is to introduce a new field – let's call it initiator or role – into these events. This field will be a string or an enum that clearly identifies which of the proposed roles (Main Agent, Subagent, Auto-Router, Tool, Hook) was responsible for the event. This is the foundation upon which everything else will be built. Crucially, we need robust context propagation. Whenever the model is invoked, or a tool is called, the current 'role' needs to be seamlessly passed along. This means ensuring that the context object or state management within Gemini CLI correctly carries this role information from the point of initiation all the way down to the telemetry logging functions. This might involve modifying function signatures, updating context providers, or using state management libraries effectively. Next on the agenda is updating the Metrics Aggregation logic. The UiTelemetryService, located in packages/core/src/telemetry/uiTelemetry.ts, will be the central hub for processing these events. We'll modify its aggregation methods to group tokens, requests, and tool calls not just by type, but also by this new role field. This will allow us to calculate subtotals and overall statistics per role. The user-facing part is equally important. We'll update StatsDisplay.tsx and its associated components to visually represent this new data. We're considering a couple of options: perhaps a new subcommand, like /stats roles, that shows a dedicated breakdown, or integrating this information directly into the existing /stats session output, possibly as expandable sections. Finally, documentation is key. We'll update our user guides and any relevant developer documentation to clearly explain the new roles, what they mean, and how to interpret the updated statistics. This ensures everyone can leverage these new insights effectively. It’s a comprehensive plan, but one that promises a significant boost in the analytical power of Gemini CLI!

Visualizing the Stats: What You'll See

So, how will this exciting new role-based statistics feature actually look and feel to you, the user? We want to make it as intuitive and insightful as possible. Imagine running your /stats command and seeing a much richer, more detailed picture than before. Instead of just a total number of input tokens, you'll get a breakdown. For example, the output might start to look something like this (we're still refining the exact format, but this gives you the idea!):

Session Statistics:
  Total Input Tokens: 15,000
  Total Output Tokens: 12,000
  Total Tool Calls: 25

Breakdown by Role:

  Main Agent:
    Input Tokens: 7,000
    Output Tokens: 6,000
    Tool Calls: 10

  Subagents:
    'codebase_investigator' (delegated):
      Input Tokens: 5,000
      Output Tokens: 4,000
      Tool Calls: 8
    'researcher' (delegated):
      Input Tokens: 1,000
      Output Tokens: 1,000
      Tool Calls: 3

  Auto-Router:
    Input Tokens: 1,500
    Output Tokens: 500
    (Note: Auto-router typically uses input tokens for classification/routing decisions)

  Tools (Direct Overhead):
    Input Tokens: 500
    Output Tokens: 500
    (Example: Overhead for executing specific tool logic)

  Hooks:
    Input Tokens: 0
    Output Tokens: 0
    Tool Calls: 4
    (Example: Hooks triggered during session lifecycle)

See what I mean? This provides so much more actionable information. You can instantly see that while your main chat was active, the codebase_investigator subagent was also a significant consumer of tokens, perhaps indicating a deep dive into your project. You can also observe the token usage by the auto-router, giving you insight into how often sophisticated model selection strategies are being employed. If you notice a particular subagent or tool consuming an unexpectedly high number of tokens, you know exactly where to focus your optimization efforts. We might even offer a dedicated /stats roles subcommand for those who want to jump straight to this detailed view, or perhaps these details will be expandable within the main /stats session command. Whatever the final UI, the goal is to make these powerful new insights easily accessible and understandable for everyone. This level of detail will fundamentally change how you interact with and manage your Gemini CLI sessions, guys!

The Impact: Better Workflows and Optimization

So, what's the big payoff for all this technical effort? How will these new role-based statistics actually benefit you and your daily workflow with Gemini CLI? The impact is significant, and it boils down to enhanced control and smarter optimization. By having a clear breakdown of token usage and tool calls by role – Main Agent, Subagents, Auto-Router, Tools, and Hooks – you gain unprecedented visibility. Let's say you're working on a complex coding task and you've delegated parts of it to several subagents. Before, a high token count might have left you scratching your head. Now, you can pinpoint exactly which subagent is contributing the most, allowing you to ask targeted questions like, "Is the codebase_investigator performing redundant analyses?" or "Can the unit_test_generator be made more efficient?" This granular insight directly translates into better debugging capabilities. If you encounter unexpected behavior or performance bottlenecks, you can isolate the problematic component or role much faster. This isn't just about finding errors; it's about proactive performance tuning. You might discover that your auto-router is making many complex routing decisions, suggesting that perhaps a simpler strategy could be employed for certain types of queries, saving tokens and time. Or you might see that direct tool execution overhead is surprisingly high, prompting you to look for more efficient ways to integrate your tools. For developers and power users, this means optimizing your AI workflows like never before. You can fine-tune your prompts, adjust subagent configurations, and even refactor your tool integrations with data-driven decisions. Ultimately, this leads to more cost-effective usage of Gemini CLI, especially in token-sensitive scenarios. It empowers you to become a more efficient and effective AI practitioner. By understanding the mechanics of your AI interactions at this level, you can build more sophisticated, reliable, and resource-conscious applications. This is about moving beyond just using Gemini CLI to truly mastering it, and these statistics are your key to unlocking that mastery. Get ready to make your AI interactions smarter and leaner, guys!

Next Steps and Documentation

We've laid out the vision, the technical plan, and the benefits of introducing role-based statistics to Gemini CLI's /stats command. The next crucial phase involves putting this plan into action and ensuring everyone knows how to use these new features. The immediate next step is the implementation phase. Our engineering team will focus on updating the telemetry types (ApiResponseEvent, ToolCallEvent, etc.) to include the initiator/role field, ensuring proper context propagation throughout the application, and modifying the UiTelemetryService for accurate aggregation. This will be followed by the UI updates to StatsDisplay.tsx and related components, bringing these new metrics to life in a user-friendly format. We're aiming for a clear and intuitive presentation, whether that's through a dedicated /stats roles subcommand or integrated sections in the existing /stats session output. Alongside the technical implementation, documentation is paramount. We'll be updating the official Gemini CLI documentation to reflect these changes comprehensively. This will include:

• A clear explanation of each new role: What it represents and what kind of activities fall under its umbrella.
• Examples of the new statistics output: Demonstrating how to interpret the data and what insights can be gained.
• Guidance on optimization strategies: How users can leverage these new stats to improve their workflows, reduce token consumption, and enhance performance.
• Developer notes: If there are any implications for developers building custom tools or subagents, we'll ensure that information is readily available.

Our goal is to make this transition as smooth as possible for all users. We encourage you to keep an eye on our release notes and documentation updates. Once these features are rolled out, we'll be eager to hear your feedback! Let us know what you think of the new stats, how you're using them, and if there are any further improvements you'd like to see. Your input is invaluable as we continue to refine Gemini CLI and make it the most powerful and transparent AI tool available. Let's get these stats rolled out and start optimizing together!