EvalFlow
AI Evaluation, Model Testing
& Thinking Analysis
Stop guessing if your AI outputs are good. Score, compare, and optimize model performance with structured evaluation frameworks and real-time cost-quality tradeoffs.
Evaluation Framework
Structured evaluation from creation to history — not ad-hoc testing.
Every evaluation has a name, a source, a score, and a status lifecycle. They persist, they accumulate, and they tell you whether your AI quality is improving over time.
Create evaluation
Define the evaluation with a name, source identifier, and report data. Optionally link it to a workflow execution to track which agent run produced the output being evaluated.
name · source · report · workflowExecutionId Score 0–100
Assign a normalized score from 0 to 100. Consistent scoring across all evaluations makes comparison meaningful — you're measuring apples to apples, not vibes to vibes.
score: number (0–100) · summary: text Track status lifecycle
Every evaluation moves through a defined state machine: pending while running, completed on success, failed on error. Status is always queryable — no silent failures.
pending → completed → failed Query evaluation history
List up to 200 evaluations ordered by creation time. Filter by status, source, or workflow execution. History accumulates over time — giving you the trend data that matters.
ORDER BY created_at DESC LIMIT 200 Thinking Analysis
Deep reasoning analysis.
Not just the answer — the chain.
Claude Opus 4.5's interleaved thinking exposes the reasoning process at each step. EvalFlow's ThinkingAnalysisService captures every thinking block, timestamps it, and exposes the full chain of reasoning alongside the final answer.
This means you can evaluate not just whether the output is correct, but whether the reasoning path is sound — catching hallucinations, reasoning gaps, and overconfident conclusions before they reach production.
Analyze this authentication flow for security vulnerabilities
Let me consider the JWT validation path first — there's no expiry check on line 42...
The refresh token rotation looks correct, but the revocation list isn't being checked on every request...
2 critical issues found. Confidence: 94%
Model Routing Optimization
Stop defaulting to the most expensive model. Route intelligently.
The routing engine scores every candidate model on quality fit, latency fit, and cost fit — then selects the best match for your workload type and quality tier. Every decision is logged.
claude-3.5-haiku Anthropic gpt-4o-mini OpenAI claude-3.5-sonnet Anthropic gpt-4.1 OpenAI Routing score formula
score = qualityFit × 0.5 + latencyFit × 0.25 + costFit × 0.25 Every routing decision is persisted to mie_model_routing_decisions with provider, model, rationale, and fitted scores — fully auditable.
Key Capabilities
Six capabilities. One evaluation surface.
Structured Scoring
Rate AI outputs on a consistent 0–100 scale with name, source, summary, and report data. Structured scoring makes comparison meaningful — every evaluation is a data point, not an opinion.
Model Comparison
A/B test any two models on identical inputs. Score both outputs with the same framework. See quality scores, latency, and cost side-by-side before committing to a model for production.
Thinking Analysis
Evaluate the reasoning chains of Claude Opus 4.5 outputs — not just the final answer. Capture every thinking block, timestamp it, and identify where the reasoning diverges from sound logic.
Cost-Quality Tradeoffs
Quantify the quality delta between models relative to their cost difference. Route to the cheapest model that passes your quality threshold — stop paying for premium when balanced is sufficient.
Evaluation History
Query up to 200 evaluations ordered by recency. Track score trends over time as you iterate prompts and swap models. History is the only thing that separates optimization from guessing.
Workflow Integration
Link any evaluation to a workflow execution ID. Every agent run that produces an output can be evaluated inline — closing the loop between generation and quality assurance in a single system.
Tool Replacement
What EvalFlow replaces — and saves.
| Tool You're Replacing | Typical Cost | What EvalFlow Does Instead |
|---|---|---|
| Manual AI testing | $0 + engineer hours | Structured evaluation framework with scoring, history, and workflow linkage — not ad-hoc prompting with no systematic record of what worked or didn't |
| Custom eval scripts | $0 + maintenance debt | Production-grade evaluation service with status lifecycle, score persistence, and report storage — no fragile one-off scripts that break when the model API changes |
| PromptLayer | $49–$199/mo | Native evaluation and thinking analysis built into your agent infrastructure — no third-party data egress, no per-seat pricing, no vendor lock-in |
| Braintrust | $0.05/eval | Flat-cost evaluation that runs on your infrastructure — score 10,000 outputs without a per-evaluation bill that grows with your usage |
| LangSmith | $39–$499/mo | Thinking analysis, model routing optimization, and evaluation history in one system — not a LangChain-specific observability layer that adds another dependency to your stack |
For a team running 5,000+ evaluations/month: significant per-eval cost avoidance plus the engineering time not spent maintaining custom eval scripts and fragile test harnesses.
Before / After
An AI engineer's evaluation workflow. Without and with EvalFlow.
- Engineer manually tests AI prompts by copy-pasting outputs into a spreadsheet — no scoring system, no history
- No model selection visibility: every task uses GPT-4 because "it's the safest option" — regardless of actual task complexity
- Thinking chains are invisible: you see the output but not the reasoning, so hallucinations are discovered in production
- Model comparison is a one-day project every time a new model releases — no persistent benchmark
- Prompt iterations are tracked in Notion comments; nobody knows if v7 was better than v4
- Structured evaluation framework scores every output 0–100 with name, source, and report data — history accumulates automatically
- Model routing selects the cheapest model that passes your quality threshold — economy for simple tasks, premium only when justified
- Thinking blocks are captured and evaluated: reasoning gaps and hallucinations are visible before they reach production
- A/B model comparison is a single API call — run both models, score both outputs, persist both results side-by-side
- Evaluation history shows score trends across 200 runs — you know exactly which prompt version improved quality and by how much
EvalFlow is running in production.
Structured scoring. Thinking analysis. Model routing optimization. Not a prototype — a production evaluation system.