Proxy Token Optimizer

Reduces LLM API costs for the openclaw-manager multi-tenant proxy platform through four strategies:

1. 1. Model-tier routing — Route prompts to the cheapest capable model
2. Heartbeat optimization — Cheapest model + longer intervals for heartbeat calls
3. Context lazy loading — Load only the context files each prompt actually needs
4. Platform usage analytics — Real data from PostgreSQL, not estimates

Why these strategies matter

The openclaw-manager platform proxies LLM requests for multiple OpenClaw instances through providers like zai-proxy, zai-coding-proxy, and kimi-coding-proxy. Each provider offers models at different price points (e.g., glm-4.7 vs glm-4.7-flashx). Without optimization, every request — including simple greetings and heartbeat pings — uses the default (expensive) model, and every session loads the full context regardless of need. These four strategies target the highest-impact cost drivers.

Quick start

All instance-side scripts run locally with no dependencies. Platform-side scripts need DB access.

CODEBLOCK0

Scripts reference

Instance-side (pure local, no network, no DB)

scripts/model_router.py

Routes prompts to the right model tier based on complexity analysis.

Tier logic:

• - cheap → glm-4.7-flashx: Greetings, acknowledgments, heartbeats, cron jobs, log parsing. Cost savings: 5-10x vs standard.
• standard → glm-4.7: Code writing, debugging, explanations. Default for unclear prompts.
• premium → glm-4.7 (or k2p5 for kimi): Architecture design, deep analysis, strategy planning.

Supports Chinese and English patterns. Provider-aware — works with zai-proxy, zai-coding-proxy, and kimi-coding-proxy.

CODEBLOCK1

scripts/context_optimizer.py

Analyzes prompt complexity to recommend which context files to load, reducing unnecessary token consumption.

Context levels:

Level	When	Files loaded	Token savings
minimal	"hi", "thanks", short msgs	SOUL.md + IDENTITY.md (2)	~80%
standard

"write a function", normal work | + memory/TODAY.md + conditional | ~50% |
| full | "design architecture", complex tasks | + MEMORY.md + all conditional | ~30% |

Also generates an optimized AGENTS.md template with lazy-loading rules baked in:

CODEBLOCK2

scripts/heartbeat_config.py

Generates openclaw.json configuration patches for heartbeat optimization:

• - Forces heartbeat model to glm-4.7-flashx (cheapest available)
• Sets interval to 55 minutes (keeps prompt cache warm within 1-hour TTL, avoids cache rebuild cost)

CODEBLOCK3

Platform-side (requires DB connection)

These scripts query the usage_records PostgreSQL table for real data. Run from the openclaw-manager project root with the virtualenv activated.

scripts/usage_report.py

Generates usage reports from actual database records — not estimates.

CODEBLOCK4

Overview includes: total calls/tokens, per-provider breakdown, per-model breakdown, top 10 instances by consumption, 7-day daily trend.

Instance report includes: per-model distribution, daily trend, lifetime totals.

scripts/quota_advisor.py

Compares actual 24-hour usage against quota plan limits to find mismatches:

• - Wasteful: Usage below 20% of plan limit → suggest downgrade
• Throttled: Usage above 80% of plan limit → suggest upgrade

CODEBLOCK5

Unified CLI

INLINECODE21 wraps all the above into a single entry point:

CODEBLOCK6

Project integration points

This skill works with existing openclaw-manager infrastructure:

Component	File	How this skill uses it
Provider config	INLINECODE22	Model names/endpoints for routing
Proxy routing

config_service.py | Where _inject_proxy_providers() registers models |
| Usage recording | proxy_common/usage_recorder.py | Source of real usage data |
| Quota plans | config/llm_proxy.yaml | Plan definitions for quota advisor |
| Instance model | app/models.py | Instance metadata for reports |

Expected savings

Strategy	Mechanism	Impact
Context lazy loading	Fewer tokens per request	50-80% context reduction
Model routing (flashx)

Lower per-token price | 5-10x on simple tasks | | Heartbeat → flashx | Lower heartbeat cost | Significant per-instance savings | | Heartbeat interval 55min | Fewer API calls | ~45% fewer heartbeat calls |