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How to turn your agent traces into a cheaper router with Experiential

Build a simulation from your traces, optimize a router against it and fine-tune a model you own.

Short answer: Collect OpenTelemetry traces from your current agent, or try the public terminal-tasks OTLP dataset on Hugging Face, which the README downloads with a curl command as traces.otel.jsonl; then run exp build support-agent and follow the prompts for providers, models, budget and your trace file; load the fitted router from Python with exp.load_router("my-project") as an official OpenAI client backed by its own private gateway. All steps.

Who it is forAn engineer running a support or coding agent in production who wants lower cost without losing quality.

The problemPicking models by gut feel leaves money on the table, and nobody has data on which cheaper model would have been good enough.

The outcomeA project router fitted to your own traffic, and optionally a fine-tuned open source model.

What you need first

  • Python with pip to install the package
  • Provider keys for the models you want to route to (hosted, BYOK or local)
  • For Capture: macOS, Python 3.13 or newer, and approval for its extension and certificate
  • Optional: an account on the hosted platform, and Tinker for fine-tuning an open source model

Steps

  1. Collect OpenTelemetry traces from your current agent, or try the public terminal-tasks OTLP dataset on Hugging Face, which the README downloads with a curl command as traces.otel.jsonl
  2. Run exp build support-agent and follow the prompts for providers, models, budget and your trace file
  3. Load the fitted router from Python with exp.load_router("my-project") as an official OpenAI client backed by its own private gateway
  4. After collecting traces from your router, run exp optimize model support-agent to fine-tune an open source model you own using Tinker

Prompt for your agent

Using experientiallabs/experiential, download the public terminal-tasks trace dataset from the README, run `exp build` on it for a project called <name>, and tell me which models and budget it chose and why. Do not run `exp optimize model` until I approve.

Caveat. The README does not state expected savings, so measure cost and quality on your own traces before you switch traffic.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for experientiallabs/experiential and "Turn your agent traces into a cheaper router"

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claude mcp add --transport http deduped https://dedupedai.com/mcp

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