Intelligent context injection system that reduces API token usage by 25-35% while maintaining code quality.
Four core strategies work together to minimize token usage without compromising quality.
Inject optimized context into agent files before execution
Implementation:
Temporary file replacement with optimized versions
Example: Agent receives shared context references instead of full duplication
Centralized context management reduces duplication
Implementation:
SharedContextServer on port 3003
Example: All agents reference the same project context
Each agent only receives relevant context
Implementation:
Context filtered by agent specialization
Example: Frontend agent only gets UI-related context
Original files preserved and restored after use
Implementation:
10-second injection window
Example: Agent files return to original state automatically
Strategic context window allocation maximizes information while minimizing token usage.
Full project requirements and specifications
Active code and immediate dependencies
Common information across all agents
Cached patterns and boilerplate
class TokenOptimizer {
private contextPool: Map<string, Context> = new Map();
private responseCache: LRUCache<string, Response>;
async optimizeContext(task: Task): Promise<OptimizedContext> {
// Share context across agents
const sharedContext = this.contextPool.get(task.projectId);
// Use incremental updates
const diff = this.calculateDiff(sharedContext, task.newContext);
// Apply caching
const cachedPatterns = this.responseCache.getRelevant(task.type);
return {
shared: sharedContext,
incremental: diff,
cached: cachedPatterns,
tokens: this.countTokens(optimizedContext)
};
}
}