Hello everyone,
I've been evaluating OpenClaw for a potential integration with our internal financial reporting workflows. The promise of a secure, structured output layer for our LLM calls is incredibly appealing from a compliance and data integrity standpoint—we're dealing with sensitive P&L figures and audit trails. However, before I can even consider a proposal to our tech lead, I need to quantify the performance cost. The vendor documentation discusses "minimal latency overhead," but as we all know, that's not a measurable benchmark.
My primary concern is understanding the *actual* added latency when guardrails are active, specifically for structured data extraction (think pulling invoice amounts, dates, and vendor names from unstructured text into a JSON schema). I want to move beyond "feels slower" and get to hard numbers I can present alongside the security benefits.
Here’s the evaluation framework I've drafted. I would deeply appreciate feedback on its methodology or suggestions for better tools:
**Goal:** Isolate and measure the latency introduced *specifically* by OpenClaw's guardrail processing, separate from the base LLM API call time.
**Proposed Test Setup:**
1. **Baseline Measurement:** Call the LLM (we're testing with GPT-4-Turbo and Claude 3 Haiku) directly with a well-structured prompt, requesting output in a specific JSON format. Time the call from request to complete response. Repeat with a diverse set of 100 financial text samples (earnings call snippets, invoice emails, etc.).
2. **OpenClaw Measurement:** Make the identical call, but routed through OpenClaw with a guardrail configured to validate and enforce the exact same JSON schema. Use the same 100 samples.
3. **Isolation Attempt:** To factor out network variance, both tests will run from the same Azure instance in the same region. The key metric will be: `(Median OpenClaw Call Time) - (Median Baseline LLM Call Time)`.
**Key Variables & My Control Plan:**
* **LLM Output Consistency:** I'll use a `max_tokens` limit and a low `temperature` (0.1) to keep outputs as consistent as possible between runs.
* **Guardrail Complexity:** I plan to test three tiers:
* **Simple:** A basic schema with 3-4 fields.
* **Moderate:** Nested objects with data type validation (e.g., date strings must match ISO 8601).
* **Complex:** Schema with field dependencies and custom validation rules (e.g., `total_tax` must equal `subtotal * tax_rate`).
* **Cache Effects:** I'll run two suites: one with caching disabled, and one with OpenClaw's caching enabled to see the potential benefit on repeated queries.
**My specific questions for this community are:**
* Has anyone here run a similar A/B test for guardrail latency? Did you discover any pitfalls in isolating the overhead?
* Are there other critical metrics I should be capturing besides median latency? (e.g., 95th percentile latency, which might matter more for user-facing applications).
* In your experience, does the overhead scale linearly with the complexity of the guardrail logic, or are there breaking points?
* I'm comfortable building this with Python's `asyncio` and `time.perf_counter()`. Would a different tool (like `locust` for load testing) provide more insightful data for a production-scale decision?
My next step is to build the test runner, but I wanted to get the methodology vetted first. The finance team will accept *some* overhead for guaranteed compliance, but I need to tell them exactly what "some" means in milliseconds. Any insights from your own testing would be invaluable