SMELT compiler reduces OpenClaw workspace token usage by up to 95%

OpenClaw workspace token optimization tool

SMELT is a Python compiler that processes OpenClaw workspace markdown files to reduce token usage when sending content to AI models like Claude or GPT. The tool addresses a specific inefficiency: OpenClaw resends USER.md, SOUL.md, MEMORY.md, and AGENTS.md on every message, not just at startup.

Performance benchmarks

Testing on a Qwen 3.5 122B model on M3-Ultra hardware revealed:

• Startup bundle: 7,268 tokens reprocessed on every inference call
• 50-message session: Over 350,000 tokens of static workspace files reprocessed
• Query-specific token reductions:
• "Who is Sally?": 1,373 tokens raw → 73 tokens SMELT (94.7% savings)
• "When was John born?": 1,374 tokens raw → 62 tokens SMELT (95.5% savings)
• Broad "Tell me about Alex": 1,373 tokens raw → 328 tokens SMELT (76.1% savings)
• Startup TTFT: 14,121ms raw → 13,273ms SMELT (6% faster)

Technical implementation

SMELT uses a four-layer architecture:

1. Archive: Original files are never touched
2. Compile: Schema-aware structural compression
3. Compress: Dictionary replacement
4. Select: Query-conditioned retrieval that only sends relevant records with parent context

The fourth layer (Select) is where the 95% token reduction occurs. The compiler is schema-aware and built specifically for OpenClaw workspace file conventions.

Key findings from development

• Naive JSON conversion (a common optimization attempt) is 30% worse than raw markdown
• Heading stripping provides minimal benefit (7-8% improvement)
• Byte compression and token compression are different - measurements must use the actual tokenizer
• 11 of 13 test files achieved 100% fidelity, with two dense archival files having documented failures

Current limitations and availability

The schema is hand-built for OpenClaw workspace conventions. Support for arbitrary markdown requires schema learning (planned). The tool is free for personal use, with code available on GitHub under TooCas/SMELT and research published on Zenodo with DOI.

The project was built with GPT, Claude, and Codex as collaborators.