Rafael Silva

Posted on May 26

How I Built a Credit Optimizer That Saves 30-75% on AI Agent Costs (Open Architecture)

#opensource #ai #tutorial #productivity

The Problem: AI Agents Are Expensive By Default

If you're using AI agents like Manus AI, Claude, or ChatGPT with API access, you've probably noticed something frustrating: every task gets the same expensive model, regardless of complexity.

A simple "rename this variable" task burns the same credits as "analyze this 50-page legal document." That's like hiring a senior architect to hang a picture frame.

After burning through my monthly Manus credits in just 2 weeks, I decided to build a solution.

The Architecture: Intelligent Model Routing

The core idea is simple: analyze task complexity BEFORE execution, then route to the appropriate model tier.

Here's the decision tree:

Task Input → Complexity Analyzer → Score (1-10)
                                      ↓
Score >= 8  → Opus/GPT-4 (expensive, high quality)
Score 4-7   → Sonnet/GPT-4o (balanced)
Score <= 3  → Flash/GPT-4o-mini (cheap, fast)

The Complexity Scoring Algorithm

The scoring considers multiple factors:

Factor	Weight	Examples
Token count	20%	Long prompts = higher complexity
Domain keywords	25%	"analyze", "research", "compare" = high
Output requirements	25%	Code generation, multi-step = high
Context dependency	15%	References previous work = higher
Creativity demand	15%	"brainstorm", "innovate" = high

Implementation (Pseudocode)

def route_task(task_description: str) -> str:
    score = 0

    # Token analysis
    tokens = count_tokens(task_description)
    if tokens > 2000: score += 2
    elif tokens > 500: score += 1

    # Domain complexity
    high_complexity_keywords = [
        "analyze", "research", "compare", "synthesize",
        "architect", "design system", "debug complex"
    ]
    low_complexity_keywords = [
        "rename", "format", "list", "simple", "quick"
    ]

    for kw in high_complexity_keywords:
        if kw in task_description.lower():
            score += 2

    for kw in low_complexity_keywords:
        if kw in task_description.lower():
            score -= 1

    # Route based on score
    score = max(1, min(10, score))

    if score >= 8:
        return "opus"  # Most expensive, highest quality
    elif score >= 4:
        return "sonnet"  # Balanced
    else:
        return "flash"  # Cheapest, fastest

Real Results: 30-75% Cost Reduction

After implementing this system on my Manus AI workflow:

Metric	Before	After	Improvement
Monthly credit usage	100% in 14 days	100% in 30+ days	2x+ duration
Simple task cost	Same as complex	70% cheaper	-70%
Complex task quality	Baseline	Same or better	No degradation
Average response time	8-12s	3-8s (simple tasks faster)	-40%

The key insight: ~60% of daily tasks are simple enough for the cheapest model tier, but without routing, they all consume premium credits.

The Open Architecture

I packaged this into a skill called Credit Optimizer that works as a pre-processing layer:

Intercepts every task before execution
Scores complexity using the algorithm above
Routes to the optimal model tier
Logs decisions for continuous improvement
Learns from overrides (when you manually upgrade a task)

The architecture is model-agnostic — it works with any AI service that offers multiple model tiers:

OpenAI: GPT-4 → GPT-4o → GPT-4o-mini
Anthropic: Opus → Sonnet → Haiku
Manus AI: Max mode → Standard mode
Google: Ultra → Pro → Flash

Key Design Decisions

Why Not Just Always Use the Cheapest Model?

Because quality matters. Complex tasks genuinely need powerful models. The optimizer ensures you get the RIGHT model for each task — not always the cheapest, not always the most expensive.

Handling Edge Cases

Ambiguous tasks: Default to middle tier (safe choice)
Multi-step workflows: Score the overall workflow, not individual steps
User overrides: Always respected, and fed back into the learning system
Streaming tasks: Route based on initial prompt, don't re-route mid-stream

Try It Yourself

The Credit Optimizer is available at creditopt.ai — it includes:

The full routing algorithm
Pre-built configurations for Manus AI, OpenAI, and Anthropic
A dashboard showing your savings over time
Community-contributed routing rules

What's Next

I'm working on:

Adaptive scoring that learns from your specific usage patterns
Team-level optimization for organizations
API integration so you can plug it into any workflow
Cost prediction before task execution

Have you built something similar? I'd love to hear about different approaches to AI cost optimization. Drop a comment below or find me on creditopt.ai.

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