Technical Overview
Built for the Era of Abundant Intelligence
ObjectWeaver is designed for a world where inference is cheap but structure is expensive. By decoupling structure from generation, we allow you to leverage the declining cost of inference to build more robust systems.
How ObjectWeaver Optimizes Performance and Cost
ObjectWeaver guarantees JSON through compositional validation, not inference-time constraint. This eliminates reasoning degradation seen when structural constraints interfere with model thinking—letting each field focus on its task without juggling format requirements.
Parallel Generation & Speed
Go's concurrency primitives process independent fields simultaneously, achieving n× speedups for production schemas with many independent fields.
Model Specialization for Cost Optimization
Field-level model routing transforms cost economics. Here's a 100-field object breakdown:
No Context Pollution = Focused Intelligence
Each field evaluates independently with isolated context:
- Simple classifications use efficient models with decisive prompts—no wasted tokens on complex reasoning infrastructure
- Pattern detection uses moderate models focused solely on pattern matching—no context bleeding from unrelated fields
- Deep reasoning uses powerful models with methodical guidance—dedicated compute for complex analysis
- Validation operates with specific criteria—clean evaluation without prior generation artifacts
When CodeLeft.ai grades code on SOLID principles, the evaluation isn't polluted by tokens from the complexity analysis. Each metric gets fresh, focused assessment.
Breaking Through Output Limitations
Field-level architecture means total output = n × model window. Generate structures that dwarf single-pass limits (typically 4K-16K tokens).
Real-world example: CodeLeft.ai generates comprehensive analysis across 20+ metrics for entire repositories—architectural patterns, security assessments, maintainability scores, SOLID compliance, complexity analysis—far exceeding what any single model call could produce.
Production-Grade Features
Field Dependencies
Create workflows where one field's output feeds the next: classification → routing → response generation → quality validation.
processingOrder
Decision Points
Embed adaptive intelligence in schemas:
- Auto-generate simplified versions when readability is low
- Trigger mitigation plans when risk exceeds thresholds
- Dynamic conditional logic impossible with static constraints
Pick the best suited model
Production schemas have varying complexity:
- Simple → gpt-3.5-turbo + decisive prompts
- Complex → gpt-4 + analytical prompts
- Creative → gpt-4 + specialized prompts
- Validation → gpt-3.5-turbo + criteria
Alternatives force a choice: expensive models for everything (wasteful) or cheap models for everything (inadequate).
Technical Trade-offs
Token Complexity vs. Cost Savings
ObjectWeaver's orchestration introduces higher token complexity: O(p·t) vs single-pass O(t), where p = fields and t = tokens per field.
However, intelligent model routing can enable 10-20× cost reduction by routing 80% of fields to efficient models while reserving expensive models for complex reasoning.
This trade-off improves as LLM prices decline—costs dropped 60% in 2024 alone. As inference costs approach zero, reliability and developer velocity become the bottleneck. ObjectWeaver optimizes for both.