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!):

   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:

   python3 scripts/context_optimizer.py recommend "hi, how are you?"
   # Shows: Only 2 files needed (not 50+!)
   

3. Install optimized heartbeat:

   cp assets/HEARTBEAT.template.md ~/.openclaw/workspace/HEARTBEAT.md
   

4. Enforce cheap models for casual chat:

   python3 scripts/model_router.py "thanks!"
   # Shows: Use Quick, not Deep!
   

5. Check current token budget:

   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

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:

python3 scripts/context_optimizer.py recommend "<user prompt>"

Examples:

# 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:

{
  "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:

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!