Hey everyone! I've been deep in my sandbox environment this week stress-testing a multi-agent workflow in SuperAGI, and I've hit a question that I think a lot of us optimizing for cost and performance will care about: **How are you all keeping track of token consumption across your different agents?**
We all know that with the power of these LLMs comes the variable cost of tokens, and when you have several agents working in sequence or parallel—say, a Researcher agent feeding a Content Writer agent, with a Reviewer agent checking the output—the bills can start to add up unpredictably. I'm coming from a background in marketing automation where every single email send, workflow execution, and API call is meticulously metered and reported on, so this feels like a bit of a blind spot for me right now.
I've been poking around the SuperAGI dashboard and the configuration files, and I'm trying to piece together a monitoring strategy. Here's what I'm considering, but I'd *love* to hear how you're handling it:
* **Is there a native, built-in method I'm missing?** Something like an "Agent Token Usage" report that breaks down consumption per agent instance over time? Or is it all aggregated at the project or LLM model level?
* **Are we expected to rely on the LLM provider's dashboard?** For example, if I'm using OpenAI, do I just have to correlate SuperAGI's agent runs with OpenAI's usage logs manually? That seems... messy, especially for attributing costs to specific agent types or workflows.
* **Custom logging via event listeners or the toolkit?** I'm wondering if using the `AfterEachStep` event or building a custom tool that logs token counts from the response metadata is the way to go. Has anyone built something like this?
* **Third-party or external monitoring tools?** Are there any lightweight dashboards or plugins that can sit between SuperAGI and the LLM API to track this?
My ultimate goal is to get to a point where I can say, "My 'Blog Outline Generator' agent costs an average of $0.12 per run, but my 'Detailed Draft Writer' is costing $1.45," so I can start optimizing prompts, setting step limits, or choosing different models for different tasks. Without clear visibility, it's hard to make those ROI decisions.
What's your experience? Have you found an elegant way to track this, or is it still a bit of a frontier? I'm really curious about both the high-level strategies and the nitty-gritty config details if you've got them!
— Emma
If it's not measurable, it's not marketing.
Hey there. I've been wrestling with the same problem on my team. We're a 25-person SaaS shop, and in production, we run a SuperAGI workflow with three specialized agents that handle customer support ticket categorization and initial response drafting. Monitoring token costs per agent is critical for us.
From what I've seen and tried, here's how the main approaches stack up:
1. **Native SuperAGI Dashboard**: It gives you aggregate usage per run, not per agent. In the "Agent Runs" view, you'll see a total token count for the entire workflow execution. To get per-agent costs, you'd need to run agents solo and do the math, which defeats the purpose of a multi-agent system. The detail just isn't there.
2. **LLM Provider Dashboards (OpenAI/Azure)**: You can filter logs by project or custom tags. If you set a unique `user` parameter for each agent in your SuperAGI config, you can tag costs. The granularity is good, but it's delayed by a few hours and you have to manually correlate agent names to tags. It also requires a dedicated subscription per project for the most detailed logs.
3. **Custom Logging Wrapper**: I built a simple middleware that intercepts calls to the LLM, logs agent name and token counts to a database, and forwards the request. It adds about 100ms of latency but gives real-time, per-agent figures. The initial setup took me a day, but now I have a Grafana dashboard. The hidden cost is maintaining this layer across SuperAGI updates.
4. **Third-party APM Tools (like Langfuse or Helicone)**: These are built for LLM observability. They can automatically capture token usage per agent if you integrate their SDK. Pricing is typically usage-based, starting around $20/month for small teams. The win is immediate, detailed charts; the limitation is they add another external dependency and can get pricey at high volume.
My pick for your described use case is starting with a third-party APM tool, especially if you're in a sandbox and want answers fast without building. If your workflow is stable and volume is high, I'd lean towards a custom logging wrapper for cost control and no data egress.
To make it cleaner, tell us your expected daily workflow runs and whether your team has any Python/dev hours to allocate to building internal tooling.
ian