Tag each agent’s requests so you can see exactly what each agent spends.
See which agent is driving LLM costs by tagging every request with that agent’s name.
1
Set X-Toolken-Metadata-Agent to your agent's name
Add X-Toolken-Metadata-Agent to every request the agent makes. Use the agent’s name as the value. The gateway records that value against every token used on the call.
from openai import OpenAI# Research agentresearch = OpenAI( base_url="https://gateway.toolken.ai/v1", api_key="sk-...", # your provider key, forwarded untouched default_headers={ "X-Toolken-Key": "tk_live_...", "X-Toolken-Metadata-Agent": "research-agent", },)research.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": "Find recent papers on transformer efficiency."}],)# Summarizer agentsummarizer = OpenAI( base_url="https://gateway.toolken.ai/v1", api_key="sk-...", default_headers={ "X-Toolken-Key": "tk_live_...", "X-Toolken-Metadata-Agent": "summarizer", },)summarizer.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": "Summarize the paper above in three sentences."}],)
import OpenAI from "openai";// Research agentconst research = new OpenAI({ baseURL: "https://gateway.toolken.ai/v1", apiKey: process.env.OPENAI_API_KEY, // forwarded untouched defaultHeaders: { "X-Toolken-Key": "tk_live_...", "X-Toolken-Metadata-Agent": "research-agent", },});await research.chat.completions.create({ model: "gpt-4o-mini", messages: [{ role: "user", content: "Find recent papers on transformer efficiency." }],});// Summarizer agentconst summarizer = new OpenAI({ baseURL: "https://gateway.toolken.ai/v1", apiKey: process.env.OPENAI_API_KEY, defaultHeaders: { "X-Toolken-Key": "tk_live_...", "X-Toolken-Metadata-Agent": "summarizer", },});await summarizer.chat.completions.create({ model: "gpt-4o-mini", messages: [{ role: "user", content: "Summarize the paper above in three sentences." }],});
# Research agentcurl https://gateway.toolken.ai/v1/chat/completions \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "X-Toolken-Key: tk_live_..." \ -H "X-Toolken-Metadata-Agent: research-agent" \ -H "Content-Type: application/json" \ -d '{"model":"gpt-4o-mini","messages":[{"role":"user","content":"Find recent papers on transformer efficiency."}]}'# Summarizer agentcurl https://gateway.toolken.ai/v1/chat/completions \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "X-Toolken-Key: tk_live_..." \ -H "X-Toolken-Metadata-Agent: summarizer" \ -H "Content-Type: application/json" \ -d '{"model":"gpt-4o-mini","messages":[{"role":"user","content":"Summarize the paper above in three sentences."}]}'
Pick a short, stable agent name and use it consistently so dashboard groupings stay readable over time.
2
Open the dashboard and group by Agent
Go to the cost view in the dashboard. Use the “Group by” control to select Agent. Each agent name appears as its own row with cost, token count, and request volume.
Example dashboard grouped by feature. Use the same Group by control to select Agent and get the equivalent per-agent breakdown.
Split one agent across providers
If the same agent uses different providers depending on the task, the dashboard already splits that for you. Group by both Agent and Provider (or Model) to see the breakdown. No extra headers needed.
Combine with customer attribution
Add X-Toolken-Metadata-Customer-Id alongside X-Toolken-Metadata-Agent on the same request. The dashboard can then show cost per agent per customer, or cost per customer per agent, depending on how you group.
