Advanced Features
Power features that differentiate insideLLMs from benchmark frameworks.
What’s Here
| Feature | What It Does |
|---|---|
| Pipeline Architecture | Composable middleware for caching, retry, cost tracking |
| Cost Management | Budget limits, usage tracking, cost forecasting |
| Structured Outputs | Extract Pydantic models from LLM responses |
| Agent Evaluation | Test tool-using agents with trace integration |
| Retry Strategies | Circuit breakers, exponential backoff, error handling |
Why These Matter
Traditional eval frameworks give you benchmark scores. insideLLMs gives you production-grade infrastructure:
Pipeline middleware - Wrap models with cross-cutting concerns without changing code.
Cost management - Set budgets, track spending, forecast costs. Don’t get surprised by API bills.
Structured outputs - Parse JSON from LLM responses reliably. No more manual string parsing.
Agent evaluation - Test tool-using agents systematically. Trace tool calls and decisions.
Retry strategies - Handle transient failures gracefully. Circuit breakers prevent cascade failures.
When to Use Advanced Features
| You Need… | Use This |
|---|---|
| Reduce API costs | Pipeline + Cost Management |
| Handle rate limits gracefully | Pipeline + Retry Strategies |
| Extract structured data | Structured Outputs |
| Test tool-using agents | Agent Evaluation |
| Production-grade reliability | All of the above |
These Are Differentiators
Eleuther, HELM, OpenAI Evals don’t have these. insideLLMs does.
If you’re shipping LLM products to production, you need more than benchmark scores. You need infrastructure.