Everything your prompts need.
Three operations that take your prompts from draft to production-grade. Each one is powerful alone. Together, they're an operations layer.
Prompt Management
Version control for your AI instructions.
Every prompt in your multi-agent system lives in one place. Branch, diff, and merge prompts like source code. Full history, instant rollback, team collaboration built in.
Version History
Every edit tracked. Compare any two versions side by side. Roll back to any point in time.
Organize by Context
Tag prompts by agent, project, environment, or team. Filter and search instantly.
Access Control
Role-based permissions. Lock production prompts. Require reviews before merge.
// Register a prompt with full metadata
const prompt = await promptops.register({
name: "support-agent-v2",
model: "claude-sonnet",
prompt: systemPrompt,
tags: ["support", "production"],
team: "customer-success"
});
// Branch for experimentation
const branch = await prompt.branch("experiment/tone-shift");
// Compare versions
const diff = await prompt.diff("v1.2", "v1.3");Self-Optimization
AI-driven prompt refinement that learns.
Define your objective. PromptOps iteratively rewrites, tests, and scores your prompts using execution feedback. Every optimization cycle makes your prompts measurably better.
Objective-Driven
Set your goal: accuracy, conciseness, safety, or custom metrics. Optimization follows your intent.
Iterative Learning
Each iteration builds on the last. Watch accuracy curves climb from 0.73 to 0.91 over 50 runs.
Guardrails Built In
Set constraints on prompt length, tone, safety. Optimization respects your boundaries.
// Optimize with clear objectives
const result = await promptops.optimize({
prompt: "support-agent-v2",
dataset: "customer-tickets-q4",
objective: "accuracy",
constraints: {
maxTokens: 500,
tone: "professional",
safety: "strict"
},
iterations: 50
});
// Result: accuracy 0.73 → 0.91
console.log(result.improvement); // +24.6%LLM Benchmarking
Find the best model for every prompt.
Run your prompts against every major LLM using your own datasets. Get accuracy, latency, and cost metrics side by side. Make data-driven decisions about which model to deploy.
Multi-Model Comparison
Test against GPT-4o, Claude, Gemini, Llama, Mistral, and more. One command, all results.
Your Data, Your Metrics
Benchmark with your actual production data. Custom metrics that matter for your use case.
Cost-Performance Analysis
See accuracy vs. cost vs. latency. Find the sweet spot for your budget and requirements.
// Benchmark across models
const results = await promptops.benchmark({
prompt: "support-agent-v2",
models: [
"gpt-4o", "claude-sonnet",
"gemini-pro", "llama-3.1-70b"
],
dataset: "customer-tickets-q4",
metrics: ["accuracy", "latency", "cost"]
});
// Results ranked by objective
// 1. Claude Sonnet — 0.91 accuracy, 1.2s, $0.003
// 2. GPT-4o — 0.82 accuracy, 1.8s, $0.005
// 3. Gemini Pro — 0.78 accuracy, 0.9s, $0.002With vs. without PromptOps.
| Feature | Without | With PromptOps |
|---|---|---|
| Prompt versioning | Git commits, scattered files | Built-in version control with diff & merge |
| Optimization | Manual rewriting, trial and error | Automated self-optimization with objectives |
| LLM selection | Gut feeling, anecdotal testing | Data-driven benchmarking across models |
| Collaboration | Copy-paste in Slack, lost context | Team workspace with access control |
| Monitoring | No visibility into prompt performance | Real-time metrics and regression alerts |
| Deployment | Manual config updates, risky deploys | One-command deploy with instant rollback |