OpenClaw Token Optimizer

通过智能模型路由、心跳优化、预算跟踪和本机 2026.2.15 功能（会话修剪、引导程序大小限制、缓存 TTL 对齐）减少 OpenClaw 令牌使用和 API 成本。当令牌成本很高、达到 API 速率限制或大规模托管多个代理时使用。 4 个可执行脚本（context_optimizer、model_router、heartbeat_optimizer、token_tracker）仅限本地 — 无网络请求、无子进程调用、无系统修改。参考文件（PROVIDERS.md、config-patches.json）记录了可选的多提供商策略，如果您选择使用它们，则需要外部 API 密钥和网络访问权限。请参阅 SECURITY.md 了解完整详细信息。

## 概述（中文）

通过智能模型路由、心跳优化、预算跟踪和本机 2026.2.15 功能（会话修剪、引导程序大小限制、缓存 TTL 对齐）减少 OpenClaw 令牌使用和 API 成本。当令牌成本很高、达到 API 速率限制或大规模托管多个代理时使用。 4 个可执行脚本（context_optimizer、model_router、heartbeat_optimizer、token_tracker）仅限本地 — 无网络请求、无子进程调用、无系统修改。参考文件（PROVIDERS.md、config-patches.json）记录了可选的多提供商策略，如果您选择使用它们，则需要外部 API 密钥和网络访问权限。请参阅 SECURITY.md 了解完整详细信息。

## 原文

# Token Optimizer

Comprehensive toolkit for reducing token usage and API costs in OpenClaw deployments. Combines smart model routing, optimized heartbeat intervals, usage tracking, and multi-provider strategies.

## Quick Start

**Immediate actions** (no config changes needed):

1. **Generate optimized AGENTS.md (BIGGEST WIN!):**
   ```bash
   python3 scripts/context_optimizer.py generate-agents
   # Creates AGENTS.md.optimized — review and replace your current AGENTS.md
   ```

2. **Check what context you ACTUALLY need:**
   ```bash
   python3 scripts/context_optimizer.py recommend "hi, how are you?"
   # Shows: Only 2 files needed (not 50+!)
   ```

3. **Install optimized heartbeat:**
   ```bash
   cp assets/HEARTBEAT.template.md ~/.openclaw/workspace/HEARTBEAT.md
   ```

4. **Enforce cheaper models for casual chat:**
   ```bash
   python3 scripts/model_router.py "thanks!"
   # Single-provider Anthropic setup: Use Sonnet, not Opus
   # Multi-provider setup (OpenRouter/Together): Use Haiku for max savings
   ```

5. **Check current token budget:**
   ```bash
   python3 scripts/token_tracker.py check
   ```

**Expected savings:** 50-80% reduction in token costs for typical workloads (context optimization is the biggest factor!).

## Core Capabilities

### 0. Lazy Skill Loading (NEW in v3.0 — BIGGEST WIN!)

**The single highest-impact optimization available.** Most agents burn 3,000–15,000 tokens per session loading skill files they never use. Stop that first.

**The pattern:**

1. Create a lightweight `SKILLS.md` catalog in your workspace (~300 tokens — list of skills + when to load them)
2. Only load individual SKILL.md files when a task actually needs them
3. Apply the same logic to memory files — load MEMORY.md at startup, daily logs only on demand

**Token savings:**

| Library size | Before (eager) | After (lazy) | Savings |
|---|---|---|---|
| 5 skills | ~3,000 tokens | ~600 tokens | **80%** |
| 10 skills | ~6,500 tokens | ~750 tokens | **88%** |
| 20 skills | ~13,000 tokens | ~900 tokens | **93%** |

**Quick implementation in AGENTS.md:**

```markdown
## Skills

At session start: Read SKILLS.md (the index only — ~300 tokens).
Load individual skill files ONLY when a task requires them.
Never load all skills upfront.
```

**Full implementation (with catalog template + optimizer script):**

```bash
clawhub install openclaw-skill-lazy-loader
```

The companion skill `openclaw-skill-lazy-loader` includes a `SKILLS.md.template`, an `AGENTS.md.template` lazy-loading section, and a `context_optimizer.py` CLI that recommends exactly which skills to load for any given task.

**Lazy loading handles context loading costs. The remaining capabilities below handle runtime costs.** Together they cover the full token lifecycle.

---

### 1. Context Optimization (NEW!)

**Biggest token saver** — Only load files you actually need, not everything upfront.

**Problem:** Default OpenClaw loads ALL context files every session:
- SOUL.md, AGENTS.md, USER.md, TOOLS.md, MEMORY.md
- docs/**/*.md (hundreds of files)
- memory/2026-*.md (daily logs)
- Total: Often 50K+ tokens before user even speaks!

**Solution:** Lazy loading based on prompt complexity.

**Usage:**
```bash
python3 scripts/context_optimizer.py recommend "<user prompt>"
```

**Examples:**
```bash
# Simple greeting → minimal context (2 files only!)
context_optimizer.py recommend "hi"
→ Load: SOUL.md, IDENTITY.md
→ Skip: Everything else
→ Savings: ~80% of context

# Standard work → selective loading
context_optimizer.py recommend "write a function"
→ Load: SOUL.md, IDENTITY.md, memory/TODAY.md
→ Skip: docs, old memory, knowledge base
→ Savings: ~50% of context

# Complex task → full context
context_optimizer.py recommend "analyze our entire architecture"
→ Load: SOUL.md, IDENTITY.md, MEMORY.md, memory/TODAY+YESTERDAY.md
→ Conditionally load: Relevant docs only
→ Savings: ~30% of context
```

**Output format:**
```json
{
  "complexity": "simple",
  "context_level": "minimal",
  "recommended_files": ["SOUL.md", "IDENTITY.md"],
  "file_count": 2,
  "savings_percent": 80,
  "skip_patterns": ["docs/**/*.md", "memory/20*.md"]
}
```

**Integration pattern:**
Before loading context for a new session:
```python
from context_optimizer import recommend_context_bundle

user_prompt = "thanks for your help"
recommendation = recommend_context_bundle(user_prompt)

if recommendation["context_level"] == "minimal":
    # Load only SOUL.md + IDENTITY.md
    # Skip everything else
    # Save ~80% tokens!
```

**Generate optimized AGENTS.md:**
```bash
context_optimizer.py generate-agents
# Creates AGENTS.md.optimized with lazy loading instructions
# Review and replace your current AGENTS.md
```

**Expected savings:** 50-80% reduction in context tokens.

### 2. Smart Model Routing (ENHANCED!)

Automatically classify tasks and route to appropriate model tiers.

**NEW: Communication pattern enforcement** — Never waste Opus tokens on "hi" or "thanks"!

**Usage:**
```bash
python3 scripts/model_router.py "<user prompt>" [current_model] [force_tier]
```

**Examples:**
```bash
# Communication (NEW!) → ALWAYS Haiku
python3 scripts/model_router.py "thanks!"
python3 scripts/model_router.py "hi"
python3 scripts/model_router.py "ok got it"
→ Enforced: Haiku (NEVER Sonnet/Opus for casual chat)

# Simple task → suggests Haiku
python3 scripts/model_router.py "read the log file"

# Medium task → suggests Sonnet
python3 scripts/model_router.py "write a function to parse JSON"

# Complex task → suggests Opus
python3 scripts/model_router.py "design a microservices architecture"
```

**Patterns enforced to Haiku (NEVER Sonnet/Opus):**

*Communication:*
- Greetings: hi, hey, hello, yo
- Thanks: thanks, thank you, thx
- Acknowledgments: ok, sure, got it, understood
- Short responses: yes, no, yep, nope
- Single words or very short phrases

*Background tasks:*
- Heartbeat checks: "check email", "monitor servers"
- Cronjobs: "scheduled task", "periodic check", "reminder"
- Document parsing: "parse CSV", "extract data from log", "read JSON"
- Log scanning: "scan error logs", "process logs"

**Integration pattern:**
```python
from model_router import route_task

user_prompt = "show me the config"
routing = route_task(user_prompt)

if routing["should_switch"]:
    # Use routing["recommended_model"]
    # Save routing["cost_savings_percent"]
```

**Customization:**
Edit `ROUTING_RULES` or `COMMUNICATION_PATTERNS` in `scripts/model_router.py` to adjust patterns and keywords.

### 3. Heartbeat Optimization

Reduce API calls from heartbeat polling with smart interval tracking:

**Setup:**
```bash
# Copy template to workspace
cp assets/HEARTBEAT.template.md ~/.openclaw/workspace/HEARTBEAT.md

# Plan which checks should run
python3 scripts/heartbeat_optimizer.py plan
```

**Commands:**
```bash
# Check if specific type should run now
heartbeat_optimizer.py check email
heartbeat_optimizer.py check calendar

# Record that a check was performed
heartbeat_optimizer.py record email

# Update check interval (seconds)
heartbeat_optimizer.py interval email 7200  # 2 hours

# Reset state
heartbeat_optimizer.py reset
```

**How it works:**
- Tracks last check time for each type (email, calendar, weather, etc.)
- Enforces minimum intervals before re-checking
- Respects quiet hours (23:00-08:00) — skips all checks
- Returns `HEARTBEAT_OK` when nothing needs attention (saves tokens)

**Default intervals:**
- Email: 60 minutes
- Calendar: 2 hours
- Weather: 4 hours
- Social: 2 hours
- Monitoring: 30 minutes

**Integration in HEARTBEAT.md:**
```markdown
## Email Check
Run only if: `heartbeat_optimizer.py check email` → `should_check: true