2.1 KiB
2.1 KiB
PROJECT: Token Optimization (Universal Literate Note)
- Overview
- Phase A: Demand (PRD)
- Phase B: Blueprint (PROTOCOL)
- Phase D: Build (Implementation)
- Phase E: Chaos (Verification)
Overview
The Token Optimization project defines the strategy and implementation for cost-effective LLM usage. It implements a multi-tier, multi-provider approach to minimize inference costs while maximizing reasoning capability through smart routing and context compression.
Phase A: Demand (PRD)
1. Purpose
Minimize LLM operational expenses while maintaining high-fidelity agentic performance.
2. User Needs
- Multi-Tier Strategy: Resolve tasks using the cheapest model that meets the required intelligence threshold.
- Failover Resilience: Automated fallback chain (Gemini -> OpenRouter -> GPT-4o).
- Context Efficiency: Implement pruning and RAG to avoid token bloat.
- Usage Transparency: Real-time tracking and budget alerts.
3. Success Criteria
TODO 80% of queries handled by Tier 1 (Free/Fast) models
TODO Automated fallback triggered on rate limits
TODO Context compression reducing average prompt size by 30%
TODO Budget alerts active at 80% threshold
Phase B: Blueprint (PROTOCOL)
1. Architectural Intent
Interfaces for dynamic model selection and cost-aware request routing. Source of truth is the `openclaw.json` configuration and real-time provider telemetry.
2. Semantic Interfaces
(defun token-resolve-model (task-complexity)
"Selects the optimal model tier based on task metadata.")
(defun token-compress-context (raw-context)
"Applies pruning heuristics to reduce token count.")
Phase D: Build (Implementation)
Implementation consists of configuration and routing logic located in `projects/token-optimization/`.
Routing Logic
;; Logic for complexity-based routing stubs
Phase E: Chaos (Verification)
Verification involves A/B testing model choices and simulating rate limits to verify fallback integrity.